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Sample records for generate neural stem-like

  1. Neural stem cells induce bone-marrow-derived mesenchymal stem cells to generate neural stem-like cells via juxtacrine and paracrine interactions

    International Nuclear Information System (INIS)

    Alexanian, Arshak R.

    2005-01-01

    Several recent reports suggest that there is far more plasticity that previously believed in the developmental potential of bone-marrow-derived cells (BMCs) that can be induced by extracellular developmental signals of other lineages whose nature is still largely unknown. In this study, we demonstrate that bone-marrow-derived mesenchymal stem cells (MSCs) co-cultured with mouse proliferating or fixed (by paraformaldehyde or methanol) neural stem cells (NSCs) generate neural stem cell-like cells with a higher expression of Sox-2 and nestin when grown in NS-A medium supplemented with N2, NSC conditioned medium (NSCcm) and bFGF. These neurally induced MSCs eventually differentiate into β-III-tubulin and GFAP expressing cells with neuronal and glial morphology when grown an additional week in Neurobasal/B27 without bFGF. We conclude that juxtacrine interaction between NSCs and MSCs combined with soluble factors released from NSCs are important for generation of neural-like cells from bone-marrow-derived adherent MSCs

  2. Transcriptional profiling of adult neural stem-like cells from the human brain.

    Directory of Open Access Journals (Sweden)

    Cecilie Jonsgar Sandberg

    Full Text Available There is a great potential for the development of new cell replacement strategies based on adult human neural stem-like cells. However, little is known about the hierarchy of cells and the unique molecular properties of stem- and progenitor cells of the nervous system. Stem cells from the adult human brain can be propagated and expanded in vitro as free floating neurospheres that are capable of self-renewal and differentiation into all three cell types of the central nervous system. Here we report the first global gene expression study of adult human neural stem-like cells originating from five human subventricular zone biopsies (mean age 42, range 33-60. Compared to adult human brain tissue, we identified 1,189 genes that were significantly up- and down-regulated in adult human neural stem-like cells (1% false discovery rate. We found that adult human neural stem-like cells express stem cell markers and have reduced levels of markers that are typical of the mature cells in the nervous system. We report that the genes being highly expressed in adult human neural stem-like cells are associated with developmental processes and the extracellular region of the cell. The calcium signaling pathway and neuroactive ligand-receptor interactions are enriched among the most differentially regulated genes between adult human neural stem-like cells and adult human brain tissue. We confirmed the expression of 10 of the most up-regulated genes in adult human neural stem-like cells in an additional sample set that included adult human neural stem-like cells (n = 6, foetal human neural stem cells (n = 1 and human brain tissues (n = 12. The NGFR, SLITRK6 and KCNS3 receptors were further investigated by immunofluorescence and shown to be heterogeneously expressed in spheres. These receptors could potentially serve as new markers for the identification and characterisation of neural stem- and progenitor cells or as targets for manipulation of cellular

  3. A stem cell medium containing neural stimulating factor induces a pancreatic cancer stem-like cell-enriched population

    Science.gov (United States)

    WATANABE, YUSAKU; YOSHIMURA, KIYOSHI; YOSHIKAWA, KOICHI; TSUNEDOMI, RYOICHI; SHINDO, YOSHITARO; MATSUKUMA, SOU; MAEDA, NORIKO; KANEKIYO, SHINSUKE; SUZUKI, NOBUAKI; KURAMASU, ATSUO; SONODA, KOUHEI; TAMADA, KOJI; KOBAYASHI, SEI; SAYA, HIDEYUKI; HAZAMA, SHOICHI; OKA, MASAAKI

    2014-01-01

    Cancer stem cells (CSCs) have been studied for their self-renewal capacity and pluripotency, as well as their resistance to anticancer therapy and their ability to metastasize to distant organs. CSCs are difficult to study because their population is quite low in tumor specimens. To overcome this problem, we established a culture method to induce a pancreatic cancer stem-like cell (P-CSLC)-enriched population from human pancreatic cancer cell lines. Human pancreatic cancer cell lines established at our department were cultured in CSC-inducing media containing epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), leukemia inhibitory factor (LIF), neural cell survivor factor-1 (NSF-1), and N-acetylcysteine. Sphere cells were obtained and then transferred to a laminin-coated dish and cultured for approximately two months. The surface markers, gene expression, aldehyde dehydrogenase (ALDH) activity, cell cycle, and tumorigenicity of these induced cells were examined for their stem cell-like characteristics. The population of these induced cells expanded within a few months. The ratio of CD24high, CD44high, epithelial specific antigen (ESA) high, and CD44variant (CD44v) high cells in the induced cells was greatly enriched. The induced cells stayed in the G0/G1 phase and demonstrated mesenchymal and stemness properties. The induced cells had high tumorigenic potential. Thus, we established a culture method to induce a P-CSLCenriched population from human pancreatic cancer cell lines. The CSLC population was enriched approximately 100-fold with this method. Our culture method may contribute to the precise analysis of CSCs and thus support the establishment of CSC-targeting therapy. PMID:25118635

  4. Generation and Characterisation of Cisplatin-Resistant Non-Small Cell Lung Cancer Cell Lines Displaying a Stem-Like Signature

    Science.gov (United States)

    Barr, Martin P.; Gray, Steven G.; Hoffmann, Andreas C.; Hilger, Ralf A.; Thomale, Juergen; O’Flaherty, John D.; Fennell, Dean A.; Richard, Derek; O’Leary, John J.; O’Byrne, Kenneth J.

    2013-01-01

    Introduction Inherent and acquired cisplatin resistance reduces the effectiveness of this agent in the management of non-small cell lung cancer (NSCLC). Understanding the molecular mechanisms underlying this process may result in the development of novel agents to enhance the sensitivity of cisplatin. Methods An isogenic model of cisplatin resistance was generated in a panel of NSCLC cell lines (A549, SKMES-1, MOR, H460). Over a period of twelve months, cisplatin resistant (CisR) cell lines were derived from original, age-matched parent cells (PT) and subsequently characterized. Proliferation (MTT) and clonogenic survival assays (crystal violet) were carried out between PT and CisR cells. Cellular response to cisplatin-induced apoptosis and cell cycle distribution were examined by FACS analysis. A panel of cancer stem cell and pluripotent markers was examined in addition to the EMT proteins, c-Met and β-catenin. Cisplatin-DNA adduct formation, DNA damage (γH2AX) and cellular platinum uptake (ICP-MS) was also assessed. Results Characterisation studies demonstrated a decreased proliferative capacity of lung tumour cells in response to cisplatin, increased resistance to cisplatin-induced cell death, accumulation of resistant cells in the G0/G1 phase of the cell cycle and enhanced clonogenic survival ability. Moreover, resistant cells displayed a putative stem-like signature with increased expression of CD133+/CD44+cells and increased ALDH activity relative to their corresponding parental cells. The stem cell markers, Nanog, Oct-4 and SOX-2, were significantly upregulated as were the EMT markers, c-Met and β-catenin. While resistant sublines demonstrated decreased uptake of cisplatin in response to treatment, reduced cisplatin-GpG DNA adduct formation and significantly decreased γH2AX foci were observed compared to parental cell lines. Conclusion Our results identified cisplatin resistant subpopulations of NSCLC cells with a putative stem-like signature, providing

  5. Generation and characterisation of cisplatin-resistant non-small cell lung cancer cell lines displaying a stem-like signature.

    Directory of Open Access Journals (Sweden)

    Martin P Barr

    Full Text Available Inherent and acquired cisplatin resistance reduces the effectiveness of this agent in the management of non-small cell lung cancer (NSCLC. Understanding the molecular mechanisms underlying this process may result in the development of novel agents to enhance the sensitivity of cisplatin.An isogenic model of cisplatin resistance was generated in a panel of NSCLC cell lines (A549, SKMES-1, MOR, H460. Over a period of twelve months, cisplatin resistant (CisR cell lines were derived from original, age-matched parent cells (PT and subsequently characterized. Proliferation (MTT and clonogenic survival assays (crystal violet were carried out between PT and CisR cells. Cellular response to cisplatin-induced apoptosis and cell cycle distribution were examined by FACS analysis. A panel of cancer stem cell and pluripotent markers was examined in addition to the EMT proteins, c-Met and β-catenin. Cisplatin-DNA adduct formation, DNA damage (γH2AX and cellular platinum uptake (ICP-MS was also assessed.Characterisation studies demonstrated a decreased proliferative capacity of lung tumour cells in response to cisplatin, increased resistance to cisplatin-induced cell death, accumulation of resistant cells in the G0/G1 phase of the cell cycle and enhanced clonogenic survival ability. Moreover, resistant cells displayed a putative stem-like signature with increased expression of CD133+/CD44+cells and increased ALDH activity relative to their corresponding parental cells. The stem cell markers, Nanog, Oct-4 and SOX-2, were significantly upregulated as were the EMT markers, c-Met and β-catenin. While resistant sublines demonstrated decreased uptake of cisplatin in response to treatment, reduced cisplatin-GpG DNA adduct formation and significantly decreased γH2AX foci were observed compared to parental cell lines.Our results identified cisplatin resistant subpopulations of NSCLC cells with a putative stem-like signature, providing a further understanding of the

  6. Target irradiation induced bystander effects between stem-like and non stem-like cancer cells

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    Liu, Yu [State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871 (China); Space Radiation Research Unit, International Open Laboratory, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Kobayashi, Alisa [Space Radiation Research Unit, International Open Laboratory, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Department of Technical Support and Development, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Maeda, Takeshi [Department of Technical Support and Development, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Fu, Qibin [State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871 (China); Oikawa, Masakazu [Department of Technical Support and Development, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Yang, Gen, E-mail: gen.yang@pku.edu.cn [State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871 (China); Space Radiation Research Unit, International Open Laboratory, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Konishi, Teruaki, E-mail: tkonishi@nirs.go.jp [Space Radiation Research Unit, International Open Laboratory, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Department of Technical Support and Development, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Uchihori, Yukio [Space Radiation Research Unit, International Open Laboratory, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); Department of Technical Support and Development, National Institute of Radiological Sciences, 4-9-1 Inage-ku, Chiba 263-8555 (Japan); and others

    2015-03-15

    Highlights: • Existence of radiation induced bystander effects (RIBE) between cancer stem-like cells (CSCs) and non stem-like cancer cells (NSCCs) in human fibrosarcoma HT1080 cells. • Existence of significant difference in generation and response of bystander signals between CSCs and NSCCs. • CSCs are significantly less sensitive to NO scavenger than that of NSCCs in terms of DNA double strand breaks induced by RIBE. - Abstract: Tumors are heterogeneous in nature and consist of multiple cell types. Among them, cancer stem-like cells (CSCs) are suggested to be the principal cause of tumor metastasis, resistance and recurrence. Therefore, understanding the behavior of CSCs in direct and indirect irradiations is crucial for clinical radiotherapy. Here, the CSCs and their counterpart non stem-like cancer cells (NSCCs) in human HT1080 fibrosarcoma cell line were sorted and labeled, then the two cell subtypes were mixed together and chosen separately to be irradiated via a proton microbeam. The radiation-induced bystander effect (RIBE) between the CSCs and NSCCs was measured by imaging 53BP1 foci, a widely used indicator for DNA double strand break (DSB). CSCs were found to be less active than NSCCs in both the generation and the response of bystander signals. Moreover, the nitric oxide (NO) scavenger c-PTIO can effectively alleviate the bystander effect in bystander NSCCs but not in bystander CSCs, indicating a difference of the two cell subtypes in NO signal response. To our knowledge, this is the first report shedding light on the RIBE between CSCs and NSCCs, which might contribute to a further understanding of the out-of-field effect in cancer radiotherapy.

  7. Target irradiation induced bystander effects between stem-like and non stem-like cancer cells

    International Nuclear Information System (INIS)

    Liu, Yu; Kobayashi, Alisa; Maeda, Takeshi; Fu, Qibin; Oikawa, Masakazu; Yang, Gen; Konishi, Teruaki; Uchihori, Yukio

    2015-01-01

    Highlights: • Existence of radiation induced bystander effects (RIBE) between cancer stem-like cells (CSCs) and non stem-like cancer cells (NSCCs) in human fibrosarcoma HT1080 cells. • Existence of significant difference in generation and response of bystander signals between CSCs and NSCCs. • CSCs are significantly less sensitive to NO scavenger than that of NSCCs in terms of DNA double strand breaks induced by RIBE. - Abstract: Tumors are heterogeneous in nature and consist of multiple cell types. Among them, cancer stem-like cells (CSCs) are suggested to be the principal cause of tumor metastasis, resistance and recurrence. Therefore, understanding the behavior of CSCs in direct and indirect irradiations is crucial for clinical radiotherapy. Here, the CSCs and their counterpart non stem-like cancer cells (NSCCs) in human HT1080 fibrosarcoma cell line were sorted and labeled, then the two cell subtypes were mixed together and chosen separately to be irradiated via a proton microbeam. The radiation-induced bystander effect (RIBE) between the CSCs and NSCCs was measured by imaging 53BP1 foci, a widely used indicator for DNA double strand break (DSB). CSCs were found to be less active than NSCCs in both the generation and the response of bystander signals. Moreover, the nitric oxide (NO) scavenger c-PTIO can effectively alleviate the bystander effect in bystander NSCCs but not in bystander CSCs, indicating a difference of the two cell subtypes in NO signal response. To our knowledge, this is the first report shedding light on the RIBE between CSCs and NSCCs, which might contribute to a further understanding of the out-of-field effect in cancer radiotherapy

  8. Differentiation of glioblastoma multiforme stem-like cells leads to downregulation of EGFR and EGFRvIII and decreased tumorigenic and stem-like cell potential

    DEFF Research Database (Denmark)

    Stockhausen, Marie-Thérése; Kristoffersen, Karina; Stobbe, Louise

    2014-01-01

    cancer stem-like cells (bCSC), to play a pivotal role in GBM malignancy. bCSC are identified by their resemblance to normal neural stem cells (NSC), and it is speculated that the bCSC have to be targeted in order to improve treatment outcome for GBM patients. One hallmark of GBM is aberrant expression...

  9. Generating Seismograms with Deep Neural Networks

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    Krischer, L.; Fichtner, A.

    2017-12-01

    The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

  10. Neural mechanisms of sequence generation in songbirds

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    Langford, Bruce

    Animal models in research are useful for studying more complex behavior. For example, motor sequence generation of actions requiring good muscle coordination such as writing with a pen, playing an instrument, or speaking, may involve the interaction of many areas in the brain, each a complex system in itself; thus it can be difficult to determine causal relationships between neural behavior and the behavior being studied. Birdsong, however, provides an excellent model behavior for motor sequence learning, memory, and generation. The song consists of learned sequences of notes that are spectrographically stereotyped over multiple renditions of the song, similar to syllables in human speech. The main areas of the songbird brain involve in singing are known, however, the mechanisms by which these systems store and produce song are not well understood. We used a custom built, head-mounted, miniature motorized microdrive to chronically record the neural firing patterns of identified neurons in HVC, a pre-motor cortical nucleus which has been shown to be important in song timing. These were done in Bengalese finch which generate a song made up of stereotyped notes but variable note sequences. We observed song related bursting in neurons projecting to Area X, a homologue to basal ganglia, and tonic firing in HVC interneurons. Interneuron had firing rate patterns that were consistent over multiple renditions of the same note sequence. We also designed and built a light-weight, low-powered wireless programmable neural stimulator using Bluetooth Low Energy Protocol. It was able to generate perturbations in the song when current pulses were administered to RA, which projects to the brainstem nucleus responsible for syringeal muscle control.

  11. Neural network application to diesel generator diagnostics

    International Nuclear Information System (INIS)

    Logan, K.P.

    1990-01-01

    Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

  12. Neural correlates of continuous causal word generation.

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    Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

    2012-09-01

    Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

  13. CD271+ osteosarcoma cells display stem-like properties.

    Directory of Open Access Journals (Sweden)

    Jiguang Tian

    Full Text Available Cancer stem cell (CSC theory has been proposed and verified in many cancers. The existence of osteosarcoma CSCs has been confirmed for many years and multiple surface markers have been employed to identify them. In this study, we identified CD271(+ subpopulation of osteosarcoma displaying stem-like properties. CD271, known as the neural crest nerve growth factor receptor, is the marker of bone marrow mesenchymal stem cells (MSCs and human melanoma-initiating cells. We discovered that CD271 was expressed differentially in diverse types of human osteosarcoma and stabilized cell lines. CD271(+ osteosarcoma cells displayed most of the properties of CSC, such as self-renewal, differentiation, drug resistance and tumorigenicity in vivo. Nanog, Oct3/4, STAT3, DNA-PKcs, Bcl-2 and ABCG2 were more expressed in CD271(+ cells compared with CD271- cells. Our study supported the osteosarcoma CSC hypothesis and, to a certain extent, revealed one of the possible mechanisms involved in maintaining CSCs properties.

  14. Characterization of stem-like cells in a new astroblastoma cell line

    Energy Technology Data Exchange (ETDEWEB)

    Coban, Esra Aydemir; Kasikci, Ezgi [Department of Genetics and Bioengineering, Faculty of Engineering and Architecture, Yeditepe University, Istanbul (Turkey); Karatas, Omer Faruk [Molecular Biology and Genetics Department, Erzurum Technical University, Erzurum (Turkey); Suakar, Oznur; Kuskucu, Aysegul [Department of Medical Genetics, Yeditepe University Medical School and Yeditepe University Hospital, Istanbul (Turkey); Altunbek, Mine [Department of Genetics and Bioengineering, Faculty of Engineering and Architecture, Yeditepe University, Istanbul (Turkey); Türe, Uğur [Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul (Turkey); Sahin, Fikrettin [Department of Genetics and Bioengineering, Faculty of Engineering and Architecture, Yeditepe University, Istanbul (Turkey); Bayrak, Omer Faruk, E-mail: ofbayrak@yeditepe.edu.tr [Department of Medical Genetics, Yeditepe University Medical School and Yeditepe University Hospital, Istanbul (Turkey)

    2017-03-15

    Cell lines established from tumors are the most commonly used models in cancer research, and their use in recent years has enabled a greater understanding of the biology of cancer and the means to develop effective treatment strategies. Astroblastomas are uncommon neuroepithelial tumors of glial origin, predominantly affecting young people, mainly teenagers and children, predominantly females. To date, only a single study has reported that astroblastomas contain a large number of neural stem-like cells, which had only a partial proliferation capacity and differentiation. Our objective was to establish an astroblastoma cell line to investigate the presence of astroblastic cells and cancer stem-like cells. The migratory and invasion abilities of the cells were quantified with invasion and migration assays and compared to a glioblastoma cell line. The presence of stem cells was detected with surface-marker analysis by using flow cytometry, and measuring the differentiation ability with a differentiation assay and the self-renewal capacity with a sphere-forming assay. These characteristics may determine whether this novel cell line is a model for astroblastomas that may have stem-cell characteristics. With this novel cell line, scientists can investigate the molecular pathways underlying astroblastomas and develop new therapeutic strategies for patients with these tumors. - Highlights: • An establishment of a novel astroblastoma cell line was proposed. • The presence of astroblastic cells and cancer stem-like cells was investigated. • The molecular pathways underlying astroblastomas may be investigated. • New therapeutic strategies for patients with astroblastoma may be developed.

  15. Application of genetic neural network in steam generator fault diagnosing

    International Nuclear Information System (INIS)

    Lin Xiaogong; Jiang Xingwei; Liu Tao; Shi Xiaocheng

    2005-01-01

    In the paper, a new algorithm which neural network and genetic algorithm are mixed is adopted, aiming at the problems of slow convergence rate and easily falling into part minimums in network studying of traditional BP neural network, and used in the fault diagnosis of steam generator. The result shows that this algorithm can solve the convergence problem in the network trains effectively. (author)

  16. Impact of Microenvironment and Stem-Like Plasticity in Cholangiocarcinoma

    DEFF Research Database (Denmark)

    Raggi, Chiara; Invernizzi, Pietro; Andersen, Jesper Bøje

    2014-01-01

    or tumor microenvironment (TME) likely promotes initiation and progression of this malignancy contributing to its heterogeneity. This review will emphasize the dynamic interplay between stem-like intrinsic and TME-extrinsic pathways, which may represent novel options for multi-targeted therapies in CCA....

  17. Dysregulation of Iron Metabolism in Cholangiocarcinoma Stem-like Cells

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    Raggi, Chiara; Gammella, Elena; Correnti, Margherita

    2017-01-01

    Cholangiocarcinoma (CCA) is a devastating liver tumour arising from malignant transformation of bile duct epithelial cells. Cancer stem cells (CSC) are a subset of tumour cells endowed with stem-like properties, which play a role in tumour initiation, recurrence and metastasis. In appropriate con...... compartment as a novel metabolic factor involved in CCA growth, may have implications for a better therapeutic approach....

  18. The Expression of Connexins and SOX2 Reflects the Plasticity of Glioma Stem-Like Cells

    Directory of Open Access Journals (Sweden)

    Joana Balça-Silva

    2017-08-01

    Full Text Available Glioblastoma (GBM is the most malignant primary brain tumor, with an average survival rate of 15 months. GBM is highly refractory to therapy, and such unresponsiveness is due, primarily, but not exclusively, to the glioma stem-like cells (GSCs. This subpopulation express stem-like cell markers and is responsible for the heterogeneity of GBM, generating multiple differentiated cell phenotypes. However, how GBMs maintain the balance between stem and non-stem populations is still poorly understood. We investigated the GBM ability to interconvert between stem and non-stem states through the evaluation of the expression of specific stem cell markers as well as cell communication proteins. We evaluated the molecular and phenotypic characteristics of GSCs derived from differentiated GBM cell lines by comparing their stem-like cell properties and expression of connexins. We showed that non-GSCs as well as GSCs can undergo successive cycles of gain and loss of stem properties, demonstrating a bidirectional cellular plasticity model that is accompanied by changes on connexins expression. Our findings indicate that the interconversion between non-GSCs and GSCs can be modulated by extracellular factors culminating on differential expression of stem-like cell markers and cell-cell communication proteins. Ultimately, we observed that stem markers are mostly expressed on GBMs rather than on low-grade astrocytomas, suggesting that the presence of GSCs is a feature of high-grade gliomas. Together, our data demonstrate the utmost importance of the understanding of stem cell plasticity properties in a way to a step closer to new strategic approaches to potentially eliminate GSCs and, hopefully, prevent tumor recurrence.

  19. Automated Item Generation with Recurrent Neural Networks.

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    von Davier, Matthias

    2018-03-12

    Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven's progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.

  20. Glioblastoma Stem-Like Cells—Biology and Therapeutic Implications

    International Nuclear Information System (INIS)

    Gürsel, Demirkan B.; Shin, Benjamin J.; Burkhardt, Jan-Karl; Kesavabhotla, Kartik; Schlaff, Cody D.; Boockvar, John A.

    2011-01-01

    The cancer stem-cell hypothesis proposes that malignant tumors are likely to encompass a cellular hierarchy that parallels normal tissue and may be responsible for the maintenance and recurrence of glioblastoma multiforme (GBM) in patients. The purpose of this manuscript is to review methods for optimizing the derivation and culturing of stem-like cells also known as tumor stem cells (TSCs) from patient-derived GBM tissue samples. The hallmarks of TSCs are that they must be able to self-renew and retain tumorigenicity. The isolation, optimization and derivation of TSCs as outlined in this review, will be important in understanding biology and therapeutic applications related to these cells

  1. Preferential Iron Trafficking Characterizes Glioblastoma Stem-like Cells

    DEFF Research Database (Denmark)

    Schonberg, David L; Miller, Tyler E; Wu, Qiulian

    2015-01-01

    Glioblastomas display hierarchies with self-renewing cancer stem-like cells (CSCs). RNA sequencing and enhancer mapping revealed regulatory programs unique to CSCs causing upregulation of the iron transporter transferrin, the top differentially expressed gene compared with tissue......, to propagate and form tumors in vivo. Depleting ferritin disrupted CSC mitotic progression, through the STAT3-FoxM1 regulatory axis, revealing an iron-regulated CSC pathway. Iron is a unique, primordial metal fundamental for earliest life forms, on which CSCs have an epigenetically programmed, targetable...

  2. Discovery of a stem-like multipotent cell fate.

    Science.gov (United States)

    Paffhausen, Emily S; Alowais, Yasir; Chao, Cara W; Callihan, Evan C; Creswell, Karen; Bracht, John R

    2018-01-01

    Adipose derived stem cells (ASCs) can be obtained from lipoaspirates and induced in vitro to differentiate into bone, cartilage, and fat. Using this powerful model system we show that after in vitro adipose differentiation a population of cells retain stem-like qualities including multipotency. They are lipid (-), retain the ability to propagate, express two known stem cell markers, and maintain the capacity for trilineage differentiation into chondrocytes, adipocytes, and osteoblasts. However, these cells are not traditional stem cells because gene expression analysis showed an overall expression profile similar to that of adipocytes. In addition to broadening our understanding of cellular multipotency, our work may be particularly relevant to obesity-associated metabolic disorders. The adipose expandability hypothesis proposes that inability to differentiate new adipocytes is a primary cause of metabolic syndrome in obesity, including diabetes and cardiovascular disease. Here we have defined a differentiation-resistant stem-like multipotent cell population that may be involved in regulation of adipose expandability in vivo and may therefore play key roles in the comorbidities of obesity.

  3. Isolation of stem-like cells from spontaneous feline mammary carcinomas: Phenotypic characterization and tumorigenic potential

    Energy Technology Data Exchange (ETDEWEB)

    Barbieri, Federica; Wurth, Roberto [Section of Pharmacology, Dept. of Internal Medicine Di.M.I., and Center of Excellence for Biomedical Research - University of Genova, Viale Benedetto XV, 2, 16132 Genova (Italy); Ratto, Alessandra; Campanella, Chiara; Vito, Guendalina [Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle D' Aosta, National Reference Center of Veterinary and Comparative Oncology (CEROVEC), Piazza Borgo Pila, 16129, Genova (Italy); Thellung, Stefano [Section of Pharmacology, Dept. of Internal Medicine Di.M.I., and Center of Excellence for Biomedical Research - University of Genova, Viale Benedetto XV, 2, 16132 Genova (Italy); Daga, Antonio [Laboratory of Translational Oncology, IRCCS Azienda Ospedaliera Universitaria San Martino - IST- Istituto Nazionale Ricerca sul Cancro, L.go R. Benzi, 10, 16132 Genova Italy (Italy); Cilli, Michele [Animal Facility, IRCCS Azienda Ospedaliera Universitaria San Martino - IST- Istituto Nazionale Ricerca sul Cancro, L.go R. Benzi, 10, 16132 Genova Italy (Italy); Ferrari, Angelo [Istituto Zooprofilattico Sperimentale del Piemonte, Liguria e Valle D' Aosta, National Reference Center of Veterinary and Comparative Oncology (CEROVEC), Piazza Borgo Pila, 16129, Genova (Italy); Florio, Tullio, E-mail: tullio.florio@unige.it [Section of Pharmacology, Dept. of Internal Medicine Di.M.I., and Center of Excellence for Biomedical Research - University of Genova, Viale Benedetto XV, 2, 16132 Genova (Italy)

    2012-04-15

    Current carcinogenesis theory states that only a small subset of tumor cells, the cancer stem cells or tumor initiating cells (TICs), are responsible for tumor formation and progression. Human breast cancer-initiating cells have been identified as CD44-expressing cells, which retain tumorigenic activity and display stem cell-like properties. Spontaneous feline mammary carcinoma (FMC) is an aggressive cancer, which shows biological similarities to the human tumor counterpart. We report the isolation and phenotypic characterization of FMC-derived stem/progenitor cells, showing in vitro self-renewal, long-lasting proliferation and in vivo tumorigenicity. Twenty-one FMC samples were collected, histologically classified and characterized for the expression of Ki67, EGFR, ER-{alpha} and CD44, by immunohistochemistry. By culture in stem cell permissive conditions, we isolated, from 13 FMCs, a CD44-positive subpopulation able to survive and proliferate in vitro as mammospheres of different sizes and morphologies. When injected in NOD/SCID mice, FMC stem-like cells initiate tumors, generating cell heterogeneity and recapitulating the original histotype. In serum-containing medium, spheroid cells showed differentiation properties as shown by morphological changes, the loss of CD44 expression and tumorigenic potential. These data show that stem-defined culture of FMC enriches for TICs and validate the use of these cells as a suitable model for comparative oncology studies of mammary biology and testing therapeutic strategies aimed at eradicating TICs. -- Highlights: Black-Right-Pointing-Pointer Feline mammary carcinoma contain a sub-population of stem-like cells expressing CD44 Black-Right-Pointing-Pointer These grow as spheres in serum-free medium and self-renew Black-Right-Pointing-Pointer Isolated stem-like cancer cells initiate tumor in immunodeficient mice Black-Right-Pointing-Pointer Xenografted tumors are phenotypically similar to the original tumor Black

  4. Isolation of stem-like cells from spontaneous feline mammary carcinomas: Phenotypic characterization and tumorigenic potential

    International Nuclear Information System (INIS)

    Barbieri, Federica; Wurth, Roberto; Ratto, Alessandra; Campanella, Chiara; Vito, Guendalina; Thellung, Stefano; Daga, Antonio; Cilli, Michele; Ferrari, Angelo; Florio, Tullio

    2012-01-01

    Current carcinogenesis theory states that only a small subset of tumor cells, the cancer stem cells or tumor initiating cells (TICs), are responsible for tumor formation and progression. Human breast cancer-initiating cells have been identified as CD44-expressing cells, which retain tumorigenic activity and display stem cell–like properties. Spontaneous feline mammary carcinoma (FMC) is an aggressive cancer, which shows biological similarities to the human tumor counterpart. We report the isolation and phenotypic characterization of FMC-derived stem/progenitor cells, showing in vitro self-renewal, long-lasting proliferation and in vivo tumorigenicity. Twenty-one FMC samples were collected, histologically classified and characterized for the expression of Ki67, EGFR, ER-α and CD44, by immunohistochemistry. By culture in stem cell permissive conditions, we isolated, from 13 FMCs, a CD44-positive subpopulation able to survive and proliferate in vitro as mammospheres of different sizes and morphologies. When injected in NOD/SCID mice, FMC stem-like cells initiate tumors, generating cell heterogeneity and recapitulating the original histotype. In serum-containing medium, spheroid cells showed differentiation properties as shown by morphological changes, the loss of CD44 expression and tumorigenic potential. These data show that stem-defined culture of FMC enriches for TICs and validate the use of these cells as a suitable model for comparative oncology studies of mammary biology and testing therapeutic strategies aimed at eradicating TICs. -- Highlights: ► Feline mammary carcinoma contain a sub-population of stem-like cells expressing CD44 ► These grow as spheres in serum-free medium and self-renew ► Isolated stem-like cancer cells initiate tumor in immunodeficient mice ► Xenografted tumors are phenotypically similar to the original tumor ► Upon differentiation, cells grow as monolayers, loosing the tumorigenic potential

  5. User-generated content curation with deep convolutional neural networks

    OpenAIRE

    Tous Liesa, Rubén; Wust, Otto; Gómez, Mauro; Poveda, Jonatan; Elena, Marc; Torres Viñals, Jordi; Makni, Mouna; Ayguadé Parra, Eduard

    2016-01-01

    In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call v...

  6. Modeling of steam generator in nuclear power plant using neural network ensemble

    International Nuclear Information System (INIS)

    Lee, S. K.; Lee, E. C.; Jang, J. W.

    2003-01-01

    Neural network is now being used in modeling the steam generator is known to be difficult due to the reverse dynamics. However, Neural network is prone to the problem of overfitting. This paper investigates the use of neural network combining methods to model steam generator water level and compares with single neural network. The results show that neural network ensemble is effective tool which can offer improved generalization, lower dependence of the training set and reduced training time

  7. Asymmetric Variate Generation via a Parameterless Dual Neural Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Simone Fiori

    2008-01-01

    Full Text Available In a previous work (S. Fiori, 2006, we proposed a random number generator based on a tunable non-linear neural system, whose learning rule is designed on the basis of a cardinal equation from statistics and whose implementation is based on look-up tables (LUTs. The aim of the present manuscript is to improve the above-mentioned random number generation method by changing the learning principle, while retaining the efficient LUT-based implementation. The new method proposed here proves easier to implement and relaxes some previous limitations.

  8. The neural circuits that generate tics in Tourette's syndrome.

    Science.gov (United States)

    Wang, Zhishun; Maia, Tiago V; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S

    2011-12-01

    The purpose of this study was to examine neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette's syndrome. Functional magnetic resonance imaging data were acquired from 13 individuals with Tourette's syndrome and 21 healthy comparison subjects during spontaneous or simulated tics. Independent component analysis with hierarchical partner matching was used to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. Granger causality was used to investigate causal interactions among these regions. The Tourette's syndrome group exhibited stronger neural activity and interregional causality than healthy comparison subjects throughout all portions of the motor pathway, including the sensorimotor cortex, putamen, pallidum, and substantia nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette's syndrome group was stronger during spontaneous tics than during voluntary tics in the somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette's syndrome group than in the healthy comparison group within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (the caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may result in their failure to control tic behaviors or the premonitory urges that generate them. Our findings, taken together, suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico

  9. Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

    Directory of Open Access Journals (Sweden)

    Jonathan Cannon

    2015-11-01

    Full Text Available Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

  10. Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

    Science.gov (United States)

    Cannon, Jonathan; Kopell, Nancy; Gardner, Timothy; Markowitz, Jeffrey

    2015-11-01

    Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

  11. Neural net generated seismic facies map and attribute facies map

    International Nuclear Information System (INIS)

    Addy, S.K.; Neri, P.

    1998-01-01

    The usefulness of 'seismic facies maps' in the analysis of an Upper Wilcox channel system in a 3-D survey shot by CGG in 1995 in Lavaca county in south Texas was discussed. A neural net-generated seismic facies map is a quick hydrocarbon exploration tool that can be applied regionally as well as on a prospect scale. The new technology is used to classify a constant interval parallel to a horizon in a 3-D seismic volume based on the shape of the wiggle traces using a neural network technology. The tool makes it possible to interpret sedimentary features of a petroleum deposit. The same technology can be used in regional mapping by making 'attribute facies maps' in which various forms of amplitude attributes, phase attributes or frequency attributes can be used

  12. Physiologic oxygen concentration enhances the stem-like properties of CD133+ human glioblastoma cells in vitro.

    Science.gov (United States)

    McCord, Amy M; Jamal, Muhammad; Shankavaram, Uma T; Shankavarum, Uma T; Lang, Frederick F; Camphausen, Kevin; Tofilon, Philip J

    2009-04-01

    In vitro investigations of tumor stem-like cells (TSC) isolated from human glioblastoma (GB) surgical specimens have been done primarily at an atmospheric oxygen level of 20%. To determine whether an oxygen level more consistent with in situ conditions affects their stem cell-like characteristics, we compared GB TSCs grown under conditions of 20% and 7% oxygen. Growing CD133(+) cells sorted from three GB neurosphere cultures at 7% O(2) reduced their doubling time and increased the self-renewal potential as reflected by clonogenicity. Furthermore, at 7% oxygen, the cultures exhibited an enhanced capacity to differentiate along both the glial and neuronal pathways. As compared with 20%, growth at 7% oxygen resulted in an increase in the expression levels of the neural stem cell markers CD133 and nestin as well as the stem cell markers Oct4 and Sox2. In addition, whereas hypoxia inducible factor 1alpha was not affected in CD133(+) TSCs grown at 7% O(2), hypoxia-inducible factor 2alpha was expressed at higher levels as compared with 20% oxygen. Gene expression profiles generated by microarray analysis revealed that reducing oxygen level to 7% resulted in the up-regulation and down-regulation of a significant number of genes, with more than 140 being commonly affected among the three CD133(+) cultures. Furthermore, Gene Ontology categories up-regulated at 7% oxygen included those associated with stem cells or GB TSCs. Thus, the data presented indicate that growth at the more physiologically relevant oxygen level of 7% enhances the stem cell-like phenotype of CD133(+) GB cells.

  13. Artificial earthquake record generation using cascade neural network

    Directory of Open Access Journals (Sweden)

    Bani-Hani Khaldoon A.

    2017-01-01

    Full Text Available This paper presents the results of using artificial neural networks (ANN in an inverse mapping problem for earthquake accelerograms generation. This study comprises of two parts: 1-D site response analysis; performed for Dubai Emirate at UAE, where eight earthquakes records are selected and spectral matching are performed to match Dubai response spectrum using SeismoMatch software. Site classification of Dubai soil is being considered for two classes C and D based on shear wave velocity of soil profiles. Amplifications factors are estimated to quantify Dubai soil effect. Dubai’s design response spectra are developed for site classes C & D according to International Buildings Code (IBC -2012. In the second part, ANN is employed to solve inverse mapping problem to generate time history earthquake record. Thirty earthquakes records and their design response spectrum with 5% damping are used to train two cascade forward backward neural networks (ANN1, ANN2. ANN1 is trained to map the design response spectrum to time history and ANN2 is trained to map time history records to the design response spectrum. Generalized time history earthquake records are generated using ANN1 for Dubai’s site classes C and D, and ANN2 is used to evaluate the performance of ANN1.

  14. Neural network based daily precipitation generator (NNGEN-P)

    Energy Technology Data Exchange (ETDEWEB)

    Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

    2007-02-15

    Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

  15. Neural network based control of Doubly Fed Induction Generator in wind power generation

    Science.gov (United States)

    Barbade, Swati A.; Kasliwal, Prabha

    2012-07-01

    To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

  16. Vascular regulation of glioma stem-like cells: a balancing act.

    Science.gov (United States)

    Brooks, Lucy J; Parrinello, Simona

    2017-12-01

    Glioblastoma (GBM) are aggressive and therapy-resistant brain tumours driven by glioma stem-like cells (GSCs). GSC behaviour is controlled by the microenvironment, or niche, in which the cells reside. It is well-established that the vasculature is a key component of the GSC niche, which drives maintenance in the tumour bulk and invasion at the margin. Emerging evidence now indicates that the specific properties of the vasculature within these two regions impose different functional states on resident GSCs, generating distinct subpopulations. Here, we review these recent findings, focusing on the mechanisms that underlie GSC/vascular communication. We further discuss how plasticity enables GSCs to respond to vascular changes by interconverting bidirectionally between states, and address the therapeutic implications of this dynamic response. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Eckol suppresses maintenance of stemness and malignancies in glioma stem-like cells

    International Nuclear Information System (INIS)

    Hyun, Kyung-Hwan; Yoon, Chang-Hwan; Kim, Rae-Kwon; Lim, Eun-Jung; An, Sungkwan; Park, Myung-Jin; Hyun, Jin-Won; Suh, Yongjoon; Kim, Min-Jung; Lee, Su-Jae

    2011-01-01

    A subpopulation of cancer cells with stem cell properties is responsible for tumor maintenance and progression, and may contribute to resistance to anticancer treatments. Thus, compounds that target cancer stem-like cells could be usefully applied to destroy cancer. In this study, we investigated the effect of Eckol, a phlorotannin compound, on stemness and malignancies in glioma stem-like cells. To determine whether Eckol targets glioma stem-like cells, we examined whether Eckol treatment could change the expression levels of glioma stem-like cell markers and self-renewal-related proteins as well as the sphere forming ability, and the sensitivity to anticancer treatments. Alterations in the malignant properties of sphere-derived cells by Eckol were also investigated by soft-agar colony forming assay, by xenograft assay in nude mice, and by cell invasion assay. Treatment of sphere-forming glioma cells with Eckol effectively decreased the sphere formation as well as the CD133 + cell population. Eckol treatment suppressed expression of the glioma stem-like cell markers and the self-renewal-related proteins without cell death. Moreover, treatment of glioma stem-like cells with Eckol significantly attenuated anchorage-independent growth on soft agar and tumor formation in xenograft mice. Importantly, Eckol treatment effectively reduced the resistance of glioma stem-like cells to ionizing radiation and temozolomide. Treatment of glioma stem-like cells with Eckol markedly blocked both phosphoinositide 3-kinase-Akt and Ras-Raf-1-Erk signaling pathways. These results indicate that the natural phlorotannin Eckol suppresses stemness and malignancies in glioma stem-like cells, and thereby makes glioma stem-like cells more sensitive to anticancer treatments, providing novel therapeutic strategies targeting specifically cancer stem-like cells.

  18. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

  19. Dynamic simulation of a steam generator by neural networks

    International Nuclear Information System (INIS)

    Masini, R.; Padovani, E.; Ricotti, M.E.; Zio, E.

    1999-01-01

    Numerical simulation by computers of the dynamic evolution of complex systems and components is a fundamental phase of any modern engineering design activity. This is of particular importance for risk-based design projects which require that the system behavior be analyzed under several and often extreme conditions. The traditional methods of simulation typically entail long, iterative, processes which lead to large simulation times, often exceeding the transients real time. Artificial neural networks (ANNs) may be exploited in this context, their advantages residing mainly in the speed of computation, in the capability of generalizing from few examples, in the robustness to noisy and partially incomplete data and in the capability of performing empirical input-output mapping without complete knowledge of the underlying physics. In this paper we present a novel approach to dynamic simulation by ANNs based on a superposition scheme in which a set of networks are individually trained, each one to respond to a different input forcing function. The dynamic simulation of a steam generator is considered as an example to show the potentialities of this tool and to point out the difficulties and crucial issues which typically arise when attempting to establish an efficient neural network simulator. The structure of the networks system is such to feedback, at each time step, a portion of the past evolution of the transient and this allows a good reproduction of also non-linear dynamic behaviors. A nice characteristic of the approach is that the modularization of the training reduces substantially its burden and gives this neural simulation tool a nice feature of transportability. (orig.)

  20. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    Directory of Open Access Journals (Sweden)

    Zhihuai Xiao

    2015-01-01

    Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

  1. mir-300 promotes self-renewal and inhibits the differentiation of glioma stem-like cells

    KAUST Repository

    Zhang, Daming

    2014-01-28

    MicroRNAs (miRNAs) are small noncoding RNAs that have been critically implicated in several human cancers. miRNAs are thought to participate in various biological processes, including proliferation, cell cycle, apoptosis, and even the regulation of the stemness properties of cancer stem cells. In this study, we explore the potential role of miR-300 in glioma stem-like cells (GSLCs). We isolated GSLCs from glioma biopsy specimens and identified the stemness properties of the cells through neurosphere formation assays, multilineage differentiation ability analysis, and immunofluorescence analysis of glioma stem cell markers. We found that miR-300 is commonly upregulated in glioma tissues, and the expression of miR-300 was higher in GSLCs. The results of functional experiments demonstrated that miR-300 can enhance the self-renewal of GSLCs and reduce differentiation toward both astrocyte and neural fates. In addition, LZTS2 is a direct target of miR-300. In conclusion, our results demonstrate the critical role of miR-300 in GSLCs and its functions in LZTS2 inhibition and describe a new approach for the molecular regulation of tumor stem cells. © 2014 Springer Science+Business Media.

  2. An artificial neural network model for periodic trajectory generation

    Science.gov (United States)

    Shankar, S.; Gander, R. E.; Wood, H. C.

    A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

  3. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  4. ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

    OpenAIRE

    Xiao, Zhihuai; He, Xinying; Fu, Xiangqian; Malik, O. P.

    2015-01-01

    Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO-) initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the fr...

  5. Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

    International Nuclear Information System (INIS)

    Zhou Gang; Yang Li

    2009-01-01

    A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

  6. Metabolic Heterogeneity Evidenced by MRS among Patient-Derived Glioblastoma Multiforme Stem-Like Cells Accounts for Cell Clustering and Different Responses to Drugs

    Directory of Open Access Journals (Sweden)

    Sveva Grande

    2018-01-01

    Full Text Available Clustering of patient-derived glioma stem-like cells (GSCs through unsupervised analysis of metabolites detected by magnetic resonance spectroscopy (MRS evidenced three subgroups, namely clusters 1a and 1b, with high intergroup similarity and neural fingerprints, and cluster 2, with a metabolism typical of commercial tumor lines. In addition, subclones generated by the same GSC line showed different metabolic phenotypes. Aerobic glycolysis prevailed in cluster 2 cells as demonstrated by higher lactate production compared to cluster 1 cells. Oligomycin, a mitochondrial ATPase inhibitor, induced high lactate extrusion only in cluster 1 cells, where it produced neutral lipid accumulation detected as mobile lipid signals by MRS and lipid droplets by confocal microscopy. These results indicate a relevant role of mitochondrial fatty acid oxidation for energy production in GSCs. On the other hand, further metabolic differences, likely accounting for different therapy responsiveness observed after etomoxir treatment, suggest that caution must be used in considering patient treatment with mitochondria FAO blockers. Metabolomics and metabolic profiling may contribute to discover new diagnostic or prognostic biomarkers to be used for personalized therapies.

  7. Generating original ideas: The neural underpinning of originality.

    Science.gov (United States)

    Mayseless, Naama; Eran, Ayelet; Shamay-Tsoory, Simone G

    2015-08-01

    One of the key aspects of creativity is the ability to produce original ideas. Originality is defined in terms of the novelty and rarity of an idea and is measured by the infrequency of the idea compared to other ideas. In the current study we focused on divergent thinking (DT) - the ability to produce many alternate ideas - and assessed the neural pathways associated with originality. Considering that generation of original ideas involves both the ability to generate new associations and the ability to overcome automatic common responses, we hypothesized that originality would be associated with activations in regions related to associative thinking, including areas of the default mode network (DMN) such as medial prefrontal areas, as well as with areas involved in cognitive control and inhibition. Thirty participants were scanned while performing a DT task that required the generation of original uses for common objects. The results indicate that the ability to produce original ideas is mediated by activity in several regions that are part of the DMN including the medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC). Furthermore, individuals who are more original exhibited enhanced activation in the ventral anterior cingulate cortex (vACC), which was also positively coupled with activity in the left occipital-temporal area. These results are in line with the dual model of creativity, according to which original ideas are a product of the interaction between a system that generates ideas and a control system that evaluates these ideas. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  9. Microenvironmental Modulation of Decorin and Lumican in Temozolomide-Resistant Glioblastoma and Neuroblastoma Cancer Stem-Like Cells.

    Directory of Open Access Journals (Sweden)

    Cristiano Farace

    Full Text Available The presence of cancer stem cells (CSCs or tumor-initiating cells can lead to cancer recurrence in a permissive cell-microenvironment interplay, promoting invasion in glioblastoma (GBM and neuroblastoma (NB. Extracellular matrix (ECM small leucine-rich proteoglycans (SLRPs play multiple roles in tissue homeostasis by remodeling the extracellular matrix (ECM components and modulating intracellular signaling pathways. Due to their pan-inhibitory properties against receptor tyrosine kinases (RTKs, SLRPs are reported to exert anticancer effects in vitro and in vivo. However, their roles seem to be tissue-specific and they are also involved in cancer cell migration and drug resistance, paving the way to complex different scenarios. The aim of this study was to determine whether the SLRPs decorin (DCN and lumican (LUM are recruited in cell plasticity and microenvironmental adaptation of differentiated cancer cells induced towards stem-like phenotype. Floating neurospheres were generated by applying CSC enrichment medium (neural stem cell serum-free medium, NSC SFM to the established SF-268 and SK-N-SH cancer cell lines, cellular models of GBM and NB, respectively. In both models, the time-dependent synergistic activation of DCN and LUM was observed. The highest DCN and LUM mRNA/protein expression was detected after cell exposure to NSC SFM for 8/12 days, considering these cells as SLRP-expressing (SLRP+ CSC-like. Ultrastructural imaging showed the cellular heterogeneity of both the GBM and NB neurospheres and identified the inner living cells. Parental cell lines of both GBM and NB grew only in soft agar + NSC SFM, whereas the secondary neurospheres (originated from SLRP+ t8 CSC-like showed lower proliferation rates than primary neurospheres. Interestingly, the SLRP+ CSC-like from the GBM and NB neurospheres were resistant to temozolomide (TMZ at concentrations >750 μM. Our results suggest that GBM and NB CSC-like promote the activation of huge

  10. Microenvironmental Modulation of Decorin and Lumican in Temozolomide-Resistant Glioblastoma and Neuroblastoma Cancer Stem-Like Cells.

    Science.gov (United States)

    Farace, Cristiano; Oliver, Jaime Antonio; Melguizo, Consolacion; Alvarez, Pablo; Bandiera, Pasquale; Rama, Ana Rosa; Malaguarnera, Giulia; Ortiz, Raul; Madeddu, Roberto; Prados, Jose

    2015-01-01

    The presence of cancer stem cells (CSCs) or tumor-initiating cells can lead to cancer recurrence in a permissive cell-microenvironment interplay, promoting invasion in glioblastoma (GBM) and neuroblastoma (NB). Extracellular matrix (ECM) small leucine-rich proteoglycans (SLRPs) play multiple roles in tissue homeostasis by remodeling the extracellular matrix (ECM) components and modulating intracellular signaling pathways. Due to their pan-inhibitory properties against receptor tyrosine kinases (RTKs), SLRPs are reported to exert anticancer effects in vitro and in vivo. However, their roles seem to be tissue-specific and they are also involved in cancer cell migration and drug resistance, paving the way to complex different scenarios. The aim of this study was to determine whether the SLRPs decorin (DCN) and lumican (LUM) are recruited in cell plasticity and microenvironmental adaptation of differentiated cancer cells induced towards stem-like phenotype. Floating neurospheres were generated by applying CSC enrichment medium (neural stem cell serum-free medium, NSC SFM) to the established SF-268 and SK-N-SH cancer cell lines, cellular models of GBM and NB, respectively. In both models, the time-dependent synergistic activation of DCN and LUM was observed. The highest DCN and LUM mRNA/protein expression was detected after cell exposure to NSC SFM for 8/12 days, considering these cells as SLRP-expressing (SLRP+) CSC-like. Ultrastructural imaging showed the cellular heterogeneity of both the GBM and NB neurospheres and identified the inner living cells. Parental cell lines of both GBM and NB grew only in soft agar + NSC SFM, whereas the secondary neurospheres (originated from SLRP+ t8 CSC-like) showed lower proliferation rates than primary neurospheres. Interestingly, the SLRP+ CSC-like from the GBM and NB neurospheres were resistant to temozolomide (TMZ) at concentrations >750 μM. Our results suggest that GBM and NB CSC-like promote the activation of huge quantities

  11. Cancer Stem-Like Cells Accumulated in Nickel-Induced Malignant Transformation

    Science.gov (United States)

    Wang, Lei; Fan, Jia; Hitron, John Andrew; Son, Young-Ok; Wise, James T.F.; Roy, Ram Vinod; Kim, Donghern; Dai, Jin; Pratheeshkumar, Poyil; Zhang, Zhuo; Shi, Xianglin

    2016-01-01

    Nickel compounds are known as human carcinogens. Chronic environmental exposure to nickel is a worldwide health concern. Although the mechanisms of nickel-induced carcinogenesis are not well understood, recent studies suggest that stem cells/cancer stem cells are likely important targets. This study examines the role of cancer stem cells in nickel-induced cell transformation. The nontransformed human bronchial epithelial cell line (Beas-2B) was chronically exposed to nickel chloride for 12 months to induce cell transformation. Nickel induced Beas-2B cell transformation, and cancer stem-like cells were enriched in nickel-transformed cell (BNiT) population. The BNiT cancer stem-like cells demonstrated enhanced self-renewal and distinctive differentiation properties. In vivo tumorigenesis studies show that BNiT cancer stem-like cells possess a high tumor-initiating capability. It was also demonstrated that superoxide dismutase 1 was involved in the accumulation of cancer stem-like cells; the regulation of superoxide dismutase 1 expression was different in transformed stem-like cells and nontransformed. Overall, the accumulation of stem-like cells and their enhanced stemness functions contribute to nickel-induced tumorigenesis. Our study provides additional insight into the mechanisms by which metals or other chemicals can induce carcinogenesis. PMID:26962057

  12. Ursodeoxycholic acid inhibits the proliferation of colon cancer cells by regulating oxidative stress and cancer stem-like cell growth

    Science.gov (United States)

    Kim, EuiJoo

    2017-01-01

    Introduction The regulation of reactive oxygen species (ROS) exists as a therapeutic target for cancer treatments. Previous studies have shown that ursodeoxycholic acid (UDCA) suppresses the proliferation of colon cancer cells. The aim of this study was to evaluate the effect of UDCA upon the proliferation of colon cancer cells as a direct result of the regulation of ROS. Method Colon cancer cell lines (HT29 and HCT116) were treated with UDCA. The total number of cells and the number of dead cells were determined using cell counters. A fluorescein isothiocyanate-bromodeoxyuridine flow kit was used to analyze cell cycle variations. Upon exposure to UDCA, the protein levels of p27, p21, CDK2, CDK4 and CDK6 were determined using western blotting, and qRT-PCR was used to determine levels of mRNA. We preformed dichlorofluorescindiacetate (DCF-DA) staining to detect alteration of intracellular ROS using fluorescence activated cell sorting (FACS). Colon cancer stem-like cell lines were generated by tumorsphere culture and treated with UDCA for seven days. The total number of tumorspheres was determined using microscopy. Results We found that UDCA reduced the total number of colon cancer cells, but did not increase the number of dead cells. UDCA inhibited the G1/S and G2/M transition phases in colon cancer cells. UDCA induced expression of cell cycle inhibitors such as p27 and p21. However, it was determined that UDCA suppressed levels of CDK2, CDK4, and CDK6. UDCA regulated intracellular ROS generation in colon cancer cells, and induced activation of Erk1/2. Finally, UDCA inhibited formation of colon cancer stem-like cells. Conclusion Our results indicate that UDCA suppresses proliferation through regulation of oxidative stress in colon cancer cells, as well as colon cancer stem-like cells. PMID:28708871

  13. Sequentially firing neurons confer flexible timing in neural pattern generators

    International Nuclear Information System (INIS)

    Urban, Alexander; Ermentrout, Bard

    2011-01-01

    Neuronal networks exhibit a variety of complex spatiotemporal patterns that include sequential activity, synchrony, and wavelike dynamics. Inhibition is the primary means through which such patterns are implemented. This behavior is dependent on both the intrinsic dynamics of the individual neurons as well as the connectivity patterns. Many neural circuits consist of networks of smaller subcircuits (motifs) that are coupled together to form the larger system. In this paper, we consider a particularly simple motif, comprising purely inhibitory interactions, which generates sequential periodic dynamics. We first describe the dynamics of the single motif both for general balanced coupling (all cells receive the same number and strength of inputs) and then for a specific class of balanced networks: circulant systems. We couple these motifs together to form larger networks. We use the theory of weak coupling to derive phase models which, themselves, have a certain structure and symmetry. We show that this structure endows the coupled system with the ability to produce arbitrary timing relationships between symmetrically coupled motifs and that the phase relationships are robust over a wide range of frequencies. The theory is applicable to many other systems in biology and physics.

  14. Prostate Cancer Stem-like Cells Contribute to the Development of Castration-Resistant Prostate Cancer

    Directory of Open Access Journals (Sweden)

    Diane Ojo

    2015-11-01

    Full Text Available Androgen deprivation therapy (ADT has been the standard care for patients with advanced prostate cancer (PC since the 1940s. Although ADT shows clear benefits for many patients, castration-resistant prostate cancer (CRPC inevitably occurs. In fact, with the two recent FDA-approved second-generation anti-androgens abiraterone and enzalutamide, resistance develops rapidly in patients with CRPC, despite their initial effectiveness. The lack of effective therapeutic solutions towards CRPC largely reflects our limited understanding of the underlying mechanisms responsible for CRPC development. While persistent androgen receptor (AR signaling under castration levels of serum testosterone (<50 ng/mL contributes to resistance to ADT, it is also clear that CRPC evolves via complex mechanisms. Nevertheless, the physiological impact of individual mechanisms and whether these mechanisms function in a cohesive manner in promoting CRPC are elusive. In spite of these uncertainties, emerging evidence supports a critical role of prostate cancer stem-like cells (PCSLCs in stimulating CRPC evolution and resistance to abiraterone and enzalutamide. In this review, we will discuss the recent evidence supporting the involvement of PCSLC in CRPC acquisition as well as the pathways and factors contributing to PCSLC expansion in response to ADT.

  15. Therapeutic potential of the metabolic modulator Metformin on osteosarcoma cancer stem-like cells.

    Science.gov (United States)

    Paiva-Oliveira, Daniela I; Martins-Neves, Sara R; Abrunhosa, Antero J; Fontes-Ribeiro, Carlos; Gomes, Célia M F

    2018-01-01

    Osteosarcoma is the most common primary bone tumour appearing in children and adolescents. Recent studies demonstrate that osteosarcoma possesses a stem-like cell subset, so-called cancer stem-like cells, refractory to conventional chemotherapeutics and pointed out as responsible for relapses frequently observed in osteosarcoma patients. Here, we explored the therapeutic potential of Metformin on osteosarcoma stem-like cells, alone and as a chemosensitizer of doxorubicin. Stem-like cells were isolated from human osteosarcoma cell lines, MNNG/HOS and MG-63, using the sphere-forming assay. Metformin cytotoxicity alone and combined with doxorubicin were evaluated using MTT/BrdU assays. Protein levels of AMPK and AKT were evaluated by Western Blot. Cellular metabolic status was assessed based on [ 18 F]-FDG uptake and lactate production measurements. Sphere-forming efficiency and expression of pluripotency transcription factors analysed by qRT-PCR were tested as readout of Metformin effects on stemness features. Metformin induced a concentration-dependent decrease in the metabolic activity and proliferation of sphere-forming cells and improved doxorubicin-induced cytotoxicity. This drug also down-regulated the expression of master regulators of pluripotency (OCT4, SOX2, NANOG), and decreased spheres' self-renewal ability. Metformin effects on mitochondria led to the activation and phosphorylation of the energetic sensor AMPK along with an upregulation of the pro-survival AKT pathway in both cell populations. Furthermore, Metformin-induced mitochondrial stress increased [ 18 F]-FDG uptake and lactate production in parental cells but not in the quiescent stem-like cells, suggesting the inability of the latter to cope with the energy crisis induced by metformin. This preclinical study suggests that Metformin may be a potentially useful therapeutic agent and chemosensitizer of osteosarcoma stem-like cells to doxorubicin.

  16. Metabolic enzymes: key modulators of functionality in cancer stem-like cells.

    Science.gov (United States)

    Dong, Bo-Wen; Qin, Guang-Ming; Luo, Yan; Mao, Jian-Shan

    2017-02-21

    Cancer Stem-like Cells (CSCs) are a subpopulation of cancer cells with self-renewal capacity and are important for the initiation, progression and recurrence of cancer diseases. The metabolic profile of CSCs is consistent with their stem-like properties. Studies have indicated that enzymes, the main regulators of cellular metabolism, dictate functionalities of CSCs in both catalysis-dependent and catalysis-independent manners. This paper reviews diverse studies of metabolic enzymes, and describes the effects of these enzymes on metabolic adaptation, gene transcription and signal transduction, in CSCs.

  17. Non-Viral Generation of Neural Precursor-like Cells from Adult Human Fibroblasts

    Directory of Open Access Journals (Sweden)

    Maucksch C

    2012-01-01

    Full Text Available Recent studies have reported direct reprogramming of human fibroblasts to mature neurons by the introduction of defined neural genes. This technology has potential use in the areas of neurological disease modeling and drug development. However, use of induced neurons for large-scale drug screening and cell-based replacement strategies is limited due to their inability to expand once reprogrammed. We propose it would be more desirable to induce expandable neural precursor cells directly from human fibroblasts. To date several pluripotent and neural transcription factors have been shown to be capable of converting mouse fibroblasts to neural stem/precursor-like cells when delivered by viral vectors. Here we extend these findings and demonstrate that transient ectopic insertion of the transcription factors SOX2 and PAX6 to adult human fibroblasts through use of non-viral plasmid transfection or protein transduction allows the generation of induced neural precursor (iNP colonies expressing a range of neural stem and pro-neural genes. Upon differentiation, iNP cells give rise to neurons exhibiting typical neuronal morphologies and expressing multiple neuronal markers including tyrosine hydroxylase and GAD65/67. Importantly, iNP-derived neurons demonstrate electrophysiological properties of functionally mature neurons with the capacity to generate action potentials. In addition, iNP cells are capable of differentiating into glial fibrillary acidic protein (GFAP-expressing astrocytes. This study represents a novel virus-free approach for direct reprogramming of human fibroblasts to a neural precursor fate.

  18. Metabolic analysis of radioresistant medulloblastoma stem-like clones and potential therapeutic targets.

    Directory of Open Access Journals (Sweden)

    Lue Sun

    Full Text Available Medulloblastoma is a fatal brain tumor in children, primarily due to the presence of treatment-resistant medulloblastoma stem cells. The energy metabolic pathway is a potential target of cancer therapy because it is often different between cancer cells and normal cells. However, the metabolic properties of medulloblastoma stem cells, and whether specific metabolic pathways are essential for sustaining their stem cell-like phenotype and radioresistance, remain unclear. We have established radioresistant medulloblastoma stem-like clones (rMSLCs by irradiation of the human medulloblastoma cell line ONS-76. Here, we assessed reactive oxygen species (ROS production, mitochondria function, oxygen consumption rate (OCR, energy state, and metabolites of glycolysis and tricarboxylic acid cycle in rMSLCs and parental cells. rMSLCs showed higher lactate production and lower oxygen consumption rate than parental cells. Additionally, rMSLCs had low mitochondria mass, low endogenous ROS production, and existed in a low-energy state. Treatment with the metabolic modifier dichloroacetate (DCA resulted in mitochondria dysfunction, glycolysis inhibition, elongated mitochondria morphology, and increased ROS production. DCA also increased radiosensitivity by suppression of the DNA repair capacity through nuclear oxidization and accelerated the generation of acetyl CoA to compensate for the lack of ATP. Moreover, treatment with DCA decreased cancer stem cell-like characters (e.g., CD133 positivity and sphere-forming ability in rMSLCs. Together, our findings provide insights into the specific metabolism of rMSLCs and illuminate potential metabolic targets that might be exploited for therapeutic benefit in medulloblastoma.

  19. The hypoxic microenvironment upgrades stem-like properties of ovarian cancer cells

    OpenAIRE

    Liang Dongming; Ma Yuanyuan; Liu Jian; Trope Claes; Holm Ruth; Nesland Jahn M; Suo Zhenhe

    2012-01-01

    Background To study whether hypoxia influences the stem-like properties of ovarian cancer cells and their biological behavior under hypoxia. Method Ovarian cancer cell lines ES-2 and OVCAR-3 were cultivated in different oxygen tensions for proliferation, cell cycling and invasion analyses. The clonogenic potential of cells was examined by colony formation and sphere formation assays. Stem cell surface...

  20. ANNarchy: a code generation approach to neural simulations on parallel hardware

    Science.gov (United States)

    Vitay, Julien; Dinkelbach, Helge Ü.; Hamker, Fred H.

    2015-01-01

    Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions. PMID:26283957

  1. Resveratrol suppresses growth of cancer stem-like cells by inhibiting fatty acid synthase.

    Science.gov (United States)

    Pandey, Puspa R; Okuda, Hiroshi; Watabe, Misako; Pai, Sudha K; Liu, Wen; Kobayashi, Aya; Xing, Fei; Fukuda, Koji; Hirota, Shigeru; Sugai, Tamotsu; Wakabayashi, Go; Koeda, Keisuke; Kashiwaba, Masahiro; Suzuki, Kazuyuki; Chiba, Toshimi; Endo, Masaki; Fujioka, Tomoaki; Tanji, Susumu; Mo, Yin-Yuan; Cao, Deliang; Wilber, Andrew C; Watabe, Kounosuke

    2011-11-01

    Resveratrol is a natural polyphenolic compound and has been shown to exhibit cardio-protective as well as anti-neoplastic effects on various types of cancers. However, the exact mechanism of its anti-tumor effect is not clearly defined. Resveratrol has been shown to have strong hypolipidemic effect on normal adipocytes and as hyper-lipogenesis is a hallmark of cancer cell physiology, the effect of resveratrol on lipid synthesis in cancer stem-like cells (CD24(-)/CD44(+)/ESA(+)) that were isolated from both ER+ and ER- breast cancer cell lines was examined. The authors found that resveratrol significantly reduced the cell viability and mammosphere formation followed by inducing apoptosis in cancer stem-like cells. This inhibitory effect of resveratrol is accompanied by a significant reduction in lipid synthesis which is caused by the down-regulation of the fatty acid synthase (FAS) gene followed by up-regulation of pro-apoptotic genes, DAPK2 and BNIP3. The activation of apoptotic pathway in the cancer stem-like cells was suppressed by TOFA and by Fumonisin B1, suggesting that resveratrol-induced apoptosis is indeed through the modulation of FAS-mediated cell survival signaling. Importantly, resveratrol was able to significantly suppress the growth of cancer stem-like cells in an animal model of xenograft without showing apparental toxicity. Taken together, the results of this study indicate that resveratrol is capable of inducing apoptosis in the cancer stem-like cells through suppression of lipogenesis by modulating FAS expression, which highlights a novel mechanism of anti-tumor effect of resveratrol.

  2. Relationship between neural rhythm generation disorders and physical disabilities in Parkinson's disease patients' walking.

    Science.gov (United States)

    Ota, Leo; Uchitomi, Hirotaka; Ogawa, Ken-ichiro; Orimo, Satoshi; Miyake, Yoshihiro

    2014-01-01

    Walking is generated by the interaction between neural rhythmic and physical activities. In fact, Parkinson's disease (PD), which is an example of disease, causes not only neural rhythm generation disorders but also physical disabilities. However, the relationship between neural rhythm generation disorders and physical disabilities has not been determined. The aim of this study was to identify the mechanism of gait rhythm generation. In former research, neural rhythm generation disorders in PD patients' walking were characterized by stride intervals, which are more variable and fluctuate randomly. The variability and fluctuation property were quantified using the coefficient of variation (CV) and scaling exponent α. Conversely, because walking is a dynamic process, postural reflex disorder (PRD) is considered the best way to estimate physical disabilities in walking. Therefore, we classified the severity of PRD using CV and α. Specifically, PD patients and healthy elderly were classified into three groups: no-PRD, mild-PRD, and obvious-PRD. We compared the contributions of CV and α to the accuracy of this classification. In this study, 45 PD patients and 17 healthy elderly people walked 200 m. The severity of PRD was determined using the modified Hoehn-Yahr scale (mH-Y). People with mH-Y scores of 2.5 and 3 had mild-PRD and obvious-PRD, respectively. As a result, CV differentiated no-PRD from PRD, indicating the correlation between CV and PRD. Considering that PRD is independent of neural rhythm generation, this result suggests the existence of feedback process from physical activities to neural rhythmic activities. Moreover, α differentiated obvious-PRD from mild-PRD. Considering α reflects the intensity of interaction between factors, this result suggests the change of the interaction. Therefore, the interaction between neural rhythmic and physical activities is thought to plays an important role for gait rhythm generation. These characteristics have

  3. Disorder generated by interacting neural networks: application to econophysics and cryptography

    International Nuclear Information System (INIS)

    Kinzel, Wolfgang; Kanter, Ido

    2003-01-01

    When neural networks are trained on their own output signals they generate disordered time series. In particular, when two neural networks are trained on their mutual output they can synchronize; they relax to a time-dependent state with identical synaptic weights. Two applications of this phenomenon are discussed for (a) econophysics and (b) cryptography. (a) When agents competing in a closed market (minority game) are using neural networks to make their decisions, the total system relaxes to a state of good performance. (b) Two partners communicating over a public channel can find a common secret key

  4. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  5. The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator

    Science.gov (United States)

    2017-09-01

    REPORT TYPE Technical Report 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE The Effect of Training Data Set Composition on the Performance of a...ARL-TR-8124 ● SEP 2017 US Army Research Laboratory The Effect of Training Data Set Composition on the Performance of a Neural...Laboratory The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator by Abigail Wilson Montgomery Blair

  6. Activation of c-MET induces a stem-like phenotype in human prostate cancer.

    Directory of Open Access Journals (Sweden)

    Geert J L H van Leenders

    Full Text Available Prostate cancer consists of secretory cells and a population of immature cells. The function of immature cells and their mutual relation with secretory cells are still poorly understood. Immature cells either have a hierarchical relation to secretory cells (stem cell model or represent an inducible population emerging upon appropriate stimulation of differentiated cells. Hepatocyte Growth Factor (HGF receptor c-MET is specifically expressed in immature prostate cells. Our objective is to determine the role of immature cells in prostate cancer by analysis of the HGF/c-MET pathway.Gene-expression profiling of DU145 prostate cancer cells stimulated with HGF revealed induction of a molecular signature associated with stem cells, characterized by up-regulation of CD49b, CD49f, CD44 and SOX9, and down-regulation of CD24 ('stem-like signature'. We confirmed the acquisition of a stem-like phenotype by quantitative PCR, FACS analysis and Western blotting. Further, HGF led to activation of the stem cell related Notch pathway by up-regulation of its ligands Jagged-1 and Delta-like 4. Small molecules SU11274 and PHA665752 targeting c-MET activity were both able to block the molecular and biologic effects of HGF. Knock-down of c-MET by shRNA infection resulted in significant reduction and delay of orthotopic tumour-formation in male NMRI mice. Immunohistochemical analysis in prostatectomies revealed significant enrichment of c-MET positive cells at the invasive front, and demonstrated co-expression of c-MET with stem-like markers CD49b and CD49f.In conclusion, activation of c-MET in prostate cancer cells induced a stem-like phenotype, indicating a dynamic relation between differentiated and stem-like cells in this malignancy. Its mediation of efficient tumour-formation in vivo and predominant receptor expression at the invasive front implicate that c-MET regulates tumour infiltration in surrounding tissues putatively by acquisition of a stem-like phenotype.

  7. ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

    Directory of Open Access Journals (Sweden)

    Ulises A Aregueta-Robles

    2014-05-01

    Full Text Available Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes.

  8. Epigallocatechin-3-gallate inhibits stem-like inflammatory breast cancer cells.

    Directory of Open Access Journals (Sweden)

    Nora D Mineva

    Full Text Available Inflammatory Breast Cancer (IBC is a highly aggressive form of cancer characterized by high rates of proliferation, lymphangiogenesis and metastasis, and an overall poor survival. As regular green tea consumption has been associated with improved prognosis of breast cancer patients, including decreased risk of recurrence, here the effects of the green tea polyphenol epigallocatechin-3-gallate (EGCG were tested on two IBC lines: SUM-149 and SUM-190. EGCG decreased expression of genes that promote proliferation, migration, invasion, and survival. Consistently, growth, invasive properties, and survival of IBC cells were reduced by EGCG treatment. EGCG also reduced lymphangiogenesis-promoting genes, in particular VEGF-D. Conditioned media from EGCG-treated IBC cells displayed decreased VEGF-D secretion and reduced ability to promote lymphangiogenesis in vitro as measured by hTERT-HDLEC lymphatic endothelial cell migration and tube formation. Tumorsphere formation by SUM-149 cells was robustly inhibited by EGCG, suggesting effects on self-renewal ability. Stem-like SUM-149 cells with high aldehyde dehydrogenase (ALDH activity, previously implicated in poor patient prognosis, were isolated. EGCG treatment reduced growth and induced apoptosis of the stem-like SUM-149 cells in culture. In an orthotopic mouse model, EGCG decreased growth of pre-existing tumors derived from ALDH-positive stem-like SUM-149 cells and their expression of VEGF-D, which correlated with a significant decrease in peritumoral lymphatic vessel density. Thus, EGCG inhibits the overall aggressive IBC phenotype. Reduction of the stem-like cell compartment by EGCG may explain the decreased risk of breast cancer recurrence among green tea drinkers. Recent clinical trials demonstrate the efficacy of green tea polyphenol extracts in treatment of prostate cancer and lymphocytic leukemia with low toxicity. Given the poor prognosis of IBC patients, our findings suggest further exploration

  9. Generation of artificial accelerograms using neural networks for data of Iran

    International Nuclear Information System (INIS)

    Bargi, Kh.; Loux, C.; Rohani, H.

    2002-01-01

    A new method for generation of artificial earthquake accelerograms from response spectra is proposed by Ghaboussi and Lin in 1997 using neural networks. In this paper the methodology has been extended and enhanced for data of Iran. For this purpose, first 40 records of Iran acceleration is chosen, then an RBF neural network which called generalized regression neural network learn the inverse mapping directly from the response spectrum to the Discrete Cosine Transform of accelerograms. Discrete Cosine Transform has been used as an assisting device to extract the content of frequency domain. Learning of network is reasonable and a generalized regression neural network learns it in a few second. Outputs are presented to demonstrate the performance of this method and show its capabilities

  10. Novel anticancer activity of phloroglucinol against breast cancer stem-like cells

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Rae-Kwon; Uddin, Nizam [Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul 133-791 (Korea, Republic of); Hyun, Jin-Won [College of Medicine and Applied Radiological Science Research Institute, Jeju National University, Jeju-si 690-756 (Korea, Republic of); Kim, Changil [Department of Biotechnology, Konkuk University, Chungju 380-701 (Korea, Republic of); Suh, Yongjoon, E-mail: hiswork@hanmail.net [Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul 133-791 (Korea, Republic of); Lee, Su-Jae, E-mail: sj0420@hanyang.ac.kr [Department of Life Science, Research Institute for Natural Sciences, Hanyang University, Seoul 133-791 (Korea, Republic of)

    2015-08-01

    Poor prognosis of breast cancer patients is closely associated with metastasis and relapse. There is substantial evidence supporting that cancer stem-like cells (CSCs) are primarily responsible for relapse in breast cancer after anticancer treatment. However, there is a lack of suitable drugs that target breast cancer stem-like cells (BCSCs). Here, we report that phloroglucinol (PG), a natural phlorotannin component of brown algae, suppresses sphere formation, anchorage-independent colony formation and in vivo tumorigenicity. In line with these observations, treatment with PG also decreased CD44{sup +} cancer cell population as well as expression of CSC regulators such as Sox2, CD44, Oct4, Notch2 and β-catenin. Also, treatment with PG sensitized breast cancer cells to anticancer drugs such as cisplatin, etoposide, and taxol as well as to ionizing radiation. Importantly, PG inhibited KRAS and its downstream PI3K/AKT and RAF-1/ERK signaling pathways that regulate the maintenance of CSCs. Taken together, our findings implicate PG as a good candidate to target BCSCs and to prevent the disease relapse. - Highlights: • Phloroglucinol suppresses in vivo tumor formation. • Phloroglucinol sensitizes breast cancer cells to anticancer agents. • Phloroglucinol inhibits breast cancer stem-like cells. • Phloroglucinol inhibits PI3K/AKT and KRAS/RAF/ERK signaling pathways.

  11. Generation and prediction of time series by a neural network

    International Nuclear Information System (INIS)

    Eisenstein, E.; Kanter, I.; Kessler, D.A.; Kinzel, W.

    1995-01-01

    Generation and prediction of time series are analyzed for the case of a bit generator: a perceptron where in each time step the input units are shifted one bit to the right with the state of the leftmost input unit set equal to the output unit in the previous time step. The long-time dynamical behavior of the bit generator consists of cycles whose typical period scales polynomially with the size of the network and whose spatial structure is periodic with a typical finite wavelength. The generalization error on a cycle is zero for a finite training set, and global dynamical behaviors can also be learned in a finite time. Hence, a projection of a rule can be learned in a finite time

  12. A neural model of rule generation in inductive reasoning.

    Science.gov (United States)

    Rasmussen, Daniel; Eliasmith, Chris

    2011-01-01

    Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects. Copyright © 2011 Cognitive Science Society, Inc.

  13. Learning Orthographic Structure with Sequential Generative Neural Networks

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-01-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

  14. Neural net based determination of generator-shedding requirements in electric power systems

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M [Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Applied Physics Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Computer Engineering and Science AI WARE Inc., Cleveland, OH (United States)

    1992-09-01

    This paper presents an application of artificial neural networks (ANN) in support of a decision-making process by power system operators directed towards the fast stabilisation of multi-machine systems. The proposed approach considers generator shedding as the most effective discrete supplementary control for improving the dynamic performance of faulted power systems and preventing instabilities. The sensitivity of the transient energy function (TEF) with respect to changes in the amount of dropped generation is used during the training phase of ANNs to assess the critical amount of generator shedding required to prevent the loss of synchronism. The learning capabilities of neural nets are used to establish complex mappings between fault information and the amount of generation to be shed, suggesting it as the control signal to the power system operator. (author)

  15. Generation of Neural Progenitor Spheres from Human Pluripotent Stem Cells in a Suspension Bioreactor.

    Science.gov (United States)

    Yan, Yuanwei; Song, Liqing; Tsai, Ang-Chen; Ma, Teng; Li, Yan

    2016-01-01

    Conventional two-dimensional (2-D) culture systems cannot provide large numbers of human pluripotent stem cells (hPSCs) and their derivatives that are demanded for commercial and clinical applications in in vitro drug screening, disease modeling, and potentially cell therapy. The technologies that support three-dimensional (3-D) suspension culture, such as a stirred bioreactor, are generally considered as promising approaches to produce the required cells. Recently, suspension bioreactors have also been used to generate mini-brain-like structure from hPSCs for disease modeling, showing the important role of bioreactor in stem cell culture. This chapter describes a detailed culture protocol for neural commitment of hPSCs into neural progenitor cell (NPC) spheres using a spinner bioreactor. The basic steps to prepare hPSCs for bioreactor inoculation are illustrated from cell thawing to cell propagation. The method for generating NPCs from hPSCs in the spinner bioreactor along with the static control is then described. The protocol in this study can be applied to the generation of NPCs from hPSCs for further neural subtype specification, 3-D neural tissue development, or potential preclinical studies or clinical applications in neurological diseases.

  16. Evodiamine selectively targets cancer stem-like cells through the p53-p21-Rb pathway

    International Nuclear Information System (INIS)

    Han, Seula; Woo, Jong Kyu; Jung, Yuchae; Jeong, Dawoon; Kang, Minsook; Yoo, Young-Ji; Lee, Hani; Oh, Seung Hyun; Ryu, Jae-Ha; Kim, Woo-Young

    2016-01-01

    In spite of the recent improvements, the resistance to chemotherapy/radiotherapy followed by relapse is the main hurdle for the successful treatment of breast cancer, a leading cause of death in women. A small population of breast cancer cells that have stem-like characteristics (cancer stem-like cells; CSLC) may contribute to this resistance and relapse. Here, we report on a component of a traditional Chinese medicine, evodiamine, which selectively targets CSLC of breast cancer cell lines MCF7 and MDAMB 231 at a concentration that does show a little or no cytotoxic effect on bulk cancer cells. While evodiamine caused the accumulation of bulk cancer cells at the G2/M phase, it did not hold CSLC in a specific cell cycle phase but instead, selectively killed CSLC. This was not due to the culture of CSLC in suspension or without FBS. A proteomic analysis and western blotting revealed that evodiamine changed the expression of cell cycle regulating molecules more efficiently in CSLC cells than in bulk cancer cells. Surprisingly, evodiamine selectively activated p53 and p21 and decreased inactive Rb, the master molecules in G1/S checkpoint. These data collectively suggest a novel mechanism involving CSLC-specific targeting by evodiamine and its possible use to the therapy of breast cancer. - Highlights: • Evodiamine selectively kills breast cancer stem like cells at G1 phase. • Evodiamine utilizes different mechanism of cell cycle modulation in CSLC and in bulk cancer cells. • Evodiamine activate the p53, p21 and Rb pathway.

  17. Evodiamine selectively targets cancer stem-like cells through the p53-p21-Rb pathway

    Energy Technology Data Exchange (ETDEWEB)

    Han, Seula [The Research Center for Cell Fate Control, College of Pharmacy, Sookmyung Women' s University, Seoul (Korea, Republic of); Woo, Jong Kyu [College of Pharmacy, Gachon University, Incheon (Korea, Republic of); Jung, Yuchae; Jeong, Dawoon; Kang, Minsook; Yoo, Young-Ji; Lee, Hani [The Research Center for Cell Fate Control, College of Pharmacy, Sookmyung Women' s University, Seoul (Korea, Republic of); Oh, Seung Hyun [College of Pharmacy, Gachon University, Incheon (Korea, Republic of); Ryu, Jae-Ha [The Research Center for Cell Fate Control, College of Pharmacy, Sookmyung Women' s University, Seoul (Korea, Republic of); Kim, Woo-Young, E-mail: wykim@sookmyung.ac.kr [The Research Center for Cell Fate Control, College of Pharmacy, Sookmyung Women' s University, Seoul (Korea, Republic of)

    2016-01-22

    In spite of the recent improvements, the resistance to chemotherapy/radiotherapy followed by relapse is the main hurdle for the successful treatment of breast cancer, a leading cause of death in women. A small population of breast cancer cells that have stem-like characteristics (cancer stem-like cells; CSLC) may contribute to this resistance and relapse. Here, we report on a component of a traditional Chinese medicine, evodiamine, which selectively targets CSLC of breast cancer cell lines MCF7 and MDAMB 231 at a concentration that does show a little or no cytotoxic effect on bulk cancer cells. While evodiamine caused the accumulation of bulk cancer cells at the G2/M phase, it did not hold CSLC in a specific cell cycle phase but instead, selectively killed CSLC. This was not due to the culture of CSLC in suspension or without FBS. A proteomic analysis and western blotting revealed that evodiamine changed the expression of cell cycle regulating molecules more efficiently in CSLC cells than in bulk cancer cells. Surprisingly, evodiamine selectively activated p53 and p21 and decreased inactive Rb, the master molecules in G1/S checkpoint. These data collectively suggest a novel mechanism involving CSLC-specific targeting by evodiamine and its possible use to the therapy of breast cancer. - Highlights: • Evodiamine selectively kills breast cancer stem like cells at G1 phase. • Evodiamine utilizes different mechanism of cell cycle modulation in CSLC and in bulk cancer cells. • Evodiamine activate the p53, p21 and Rb pathway.

  18. Delayed cell death associated with mitotic catastrophe in γ-irradiated stem-like glioma cells

    International Nuclear Information System (INIS)

    Firat, Elke; Gaedicke, Simone; Tsurumi, Chizuko; Esser, Norbert; Weyerbrock, Astrid; Niedermann, Gabriele

    2011-01-01

    Stem-like tumor cells are regarded as highly resistant to ionizing radiation (IR). Previous studies have focused on apoptosis early after irradiation, and the apoptosis resistance observed has been attributed to reduced DNA damage or enhanced DNA repair compared to non-stem tumor cells. Here, early and late radioresponse of patient-derived stem-like glioma cells (SLGCs) and differentiated cells directly derived from them were examined for cell death mode and the influence of stem cell-specific growth factors. Primary SLGCs were propagated in serum-free medium with the stem-cell mitogens epidermal growth factor (EGF) and fibroblast growth factor-2 (FGF-2). Differentiation was induced by serum-containing medium without EGF and FGF. Radiation sensitivity was evaluated by assessing proliferation, clonogenic survival, apoptosis, and mitotic catastrophe. DNA damage-associated γH2AX as well as p53 and p21 expression were determined by Western blots. SLGCs failed to apoptose in the first 4 days after irradiation even at high single doses up to 10 Gy, but we observed substantial cell death later than 4 days postirradiation in 3 of 6 SLGC lines treated with 5 or 10 Gy. This delayed cell death was observed in 3 of the 4 SLGC lines with nonfunctional p53, was associated with mitotic catastrophe and occurred via apoptosis. The early apoptosis resistance of the SLGCs was associated with lower γH2AX compared to differentiated cells, but we found that the stem-cell culture cytokines EGF plus FGF-2 strongly reduce γH2AX levels. Nonetheless, in two p53-deficient SLGC lines examined γIR-induced apoptosis even correlated with EGF/FGF-induced proliferation and mitotic catastrophe. In a line containing CD133-positive and -negative stem-like cells, the CD133-positive cells proliferated faster and underwent more γIR-induced mitotic catastrophe. Our results suggest the importance of delayed apoptosis, associated mitotic catastrophe, and cellular proliferation for γIR-induced death of

  19. Natural compounds' activity against cancer stem-like or fast-cycling melanoma cells.

    Directory of Open Access Journals (Sweden)

    Malgorzata Sztiller-Sikorska

    Full Text Available BACKGROUND: Accumulating evidence supports the concept that melanoma is highly heterogeneous and sustained by a small subpopulation of melanoma stem-like cells. Those cells are considered as responsible for tumor resistance to therapies. Moreover, melanoma cells are characterized by their high phenotypic plasticity. Consequently, both melanoma stem-like cells and their more differentiated progeny must be eradicated to achieve durable cure. By reevaluating compounds in heterogeneous melanoma populations, it might be possible to select compounds with activity not only against fast-cycling cells but also against cancer stem-like cells. Natural compounds were the focus of the present study. METHODS: We analyzed 120 compounds from The Natural Products Set II to identify compounds active against melanoma populations grown in an anchorage-independent manner and enriched with cells exerting self-renewing capacity. Cell viability, cell cycle arrest, apoptosis, gene expression, clonogenic survival and label-retention were analyzed. FINDINGS: Several compounds efficiently eradicated cells with clonogenic capacity and nanaomycin A, streptonigrin and toyocamycin were effective at 0.1 µM. Other anti-clonogenic but not highly cytotoxic compounds such as bryostatin 1, siomycin A, illudin M, michellamine B and pentoxifylline markedly reduced the frequency of ABCB5 (ATP-binding cassette, sub-family B, member 5-positive cells. On the contrary, treatment with maytansine and colchicine selected for cells expressing this transporter. Maytansine, streptonigrin, toyocamycin and colchicine, even if highly cytotoxic, left a small subpopulation of slow-dividing cells unaffected. Compounds selected in the present study differentially altered the expression of melanocyte/melanoma specific microphthalmia-associated transcription factor (MITF and proto-oncogene c-MYC. CONCLUSION: Selected anti-clonogenic compounds might be further investigated as potential adjuvants

  20. Generation and properties of a new human ventral mesencephalic neural stem cell line

    DEFF Research Database (Denmark)

    Villa, Ana; Liste, Isabel; Courtois, Elise T

    2009-01-01

    . Here we report the generation of a new stable cell line of human neural stem cells derived from ventral mesencephalon (hVM1) based on v-myc immortalization. The cells expressed neural stem cell and radial glia markers like nestin, vimentin and 3CB2 under proliferation conditions. After withdrawal......Neural stem cells (NSCs) are powerful research tools for the design and discovery of new approaches to cell therapy in neurodegenerative diseases like Parkinson's disease. Several epigenetic and genetic strategies have been tested for long-term maintenance and expansion of these cells in vitro...... derivatives may constitute good candidates for the study of development and physiology of human dopaminergic neurons in vitro, and to develop tools for Parkinson's disease cell replacement preclinical research and drug testing....

  1. Establishment and Analysis of Cancer Stem-Like and Non-Cancer Stem-Like Clone Cells from the Human Colon Cancer Cell Line SW480.

    Directory of Open Access Journals (Sweden)

    Akari Takaya

    Full Text Available Human cancer stem-like cells (CSCs/cancer-initiating cells (CICs can be isolated as side population (SP cells, aldehyde dehydrogenase high (ALDHhigh cells or cell surface marker-positive cells including CD44+ cells and CD133+ cells. CSCs/CICs and non-CSCs/CICs are unstable in in vitro culture, and CSCs/CICs can differentiate into non-CSCs/CICs and some non-CSCs/CICs can dedifferentiate into CSCs/CICs. Therefore, experiments using a large amount of CSCs/CICs are technically very difficult. In this study, we isolated single cell clones from SP cells and main population (MP cells derived from the human colon cancer cell line SW480. SP analysis revealed that SP clone cells had relatively high percentages of SP cells, whereas MP clone cells showed very few SP cells, and the phenotypes were sustainable for more than 2 months of in vitro culture. Xenograft transplantation revealed that SP clone cells have higher tumor-initiating ability than that of MP clone cells and SP clone cell showed higher chemo-resistance compared with MP clone cells. These results indicate that SP clone cells derived from SW480 cells are enriched with CSCs/CICs, whereas MP clone cells are pure non-CSCs/CICs. SP clone cells and MP clone cells are a very stable in vitro CSC/CIC-enriched and non-CSC/CIC model for further analysis.

  2. Establishment and Analysis of Cancer Stem-Like and Non-Cancer Stem-Like Clone Cells from the Human Colon Cancer Cell Line SW480.

    Science.gov (United States)

    Takaya, Akari; Hirohashi, Yoshihiko; Murai, Aiko; Morita, Rena; Saijo, Hiroshi; Yamamoto, Eri; Kubo, Terufumi; Nakatsugawa, Munehide; Kanaseki, Takayuki; Tsukahara, Tomohide; Tamura, Yasuaki; Takemasa, Ichiro; Kondo, Toru; Sato, Noriyuki; Torigoe, Toshihiko

    2016-01-01

    Human cancer stem-like cells (CSCs)/cancer-initiating cells (CICs) can be isolated as side population (SP) cells, aldehyde dehydrogenase high (ALDHhigh) cells or cell surface marker-positive cells including CD44+ cells and CD133+ cells. CSCs/CICs and non-CSCs/CICs are unstable in in vitro culture, and CSCs/CICs can differentiate into non-CSCs/CICs and some non-CSCs/CICs can dedifferentiate into CSCs/CICs. Therefore, experiments using a large amount of CSCs/CICs are technically very difficult. In this study, we isolated single cell clones from SP cells and main population (MP) cells derived from the human colon cancer cell line SW480. SP analysis revealed that SP clone cells had relatively high percentages of SP cells, whereas MP clone cells showed very few SP cells, and the phenotypes were sustainable for more than 2 months of in vitro culture. Xenograft transplantation revealed that SP clone cells have higher tumor-initiating ability than that of MP clone cells and SP clone cell showed higher chemo-resistance compared with MP clone cells. These results indicate that SP clone cells derived from SW480 cells are enriched with CSCs/CICs, whereas MP clone cells are pure non-CSCs/CICs. SP clone cells and MP clone cells are a very stable in vitro CSC/CIC-enriched and non-CSC/CIC model for further analysis.

  3. Curcumin targets breast cancer stem-like cells with microtentacles that persist in mammospheres and promote reattachment

    OpenAIRE

    Charpentier, Monica S.; Whipple, Rebecca A.; Vitolo, Michele I.; Boggs, Amanda E.; Slovic, Jana; Thompson, Keyata N.; Bhandary, Lekhana; Martin, Stuart S.

    2013-01-01

    Cancer stem-like cells (CSC) and circulating tumor cells (CTCs) have related properties associated with distant metastasis, but the mechanisms through which CSCs promote metastasis are unclear. In this study, we report that breast cancer cell lines with more stem-like properties display higher levels of microtentacles (McTNs), a type of tubulin-based protrusion of the plasma cell membrane which forms on detached or suspended cells and aid in cell reattachment. We hypothesized that CSCs with l...

  4. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron

    Energy Technology Data Exchange (ETDEWEB)

    Hontoria, L.; Aguilera, J. [Universidad de Jaen, Linares-Jaen (Spain). Dpto. de Electronica; Zufiria, P. [Ciudad Universitaria, Madrid (Spain). Grupo de Redes Neuronales

    2002-05-01

    In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed. (author)

  5. Research of PV Power Generation MPPT based on GABP Neural Network

    Science.gov (United States)

    Su, Yu; Lin, Xianfu

    2018-05-01

    Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

  6. Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

    Directory of Open Access Journals (Sweden)

    Christian Nowke

    2018-06-01

    Full Text Available Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

  7. A functional study of EGFR and Notch signaling in brain cancer stem-like cells from glioblastoma multiforme (Ph.d.)

    DEFF Research Database (Denmark)

    Kristoffersen, Karina

    2013-01-01

    Glioblastoma Multiforme (GBM) is the most common and aggressive brain tumor in adults with a median survival for newly diagnosed GBM patients at less than 1.5 year. Despite intense treatment efforts the vast majority of patients will experience relapse and much research today is therefore searching...... for new molecular and cellular targets that can improve the prognosis for GBM patients. One such target is the brain cancer stem-like cells (bCSC) that are believed to be responsible for tumor initiation, progression, treatment resistance and ultimately relapse. bCSC are identified based...... on their resemblance to normal neural stem cells (NSC) and their tumorigenic potential. Like for NSC, the epidermal growth factor receptor (EGFR) and Notch receptor signaling pathways are believed to be important for the maintenance of bCSC. These pathways as such present promising targets in a future anti-bCSC GBM...

  8. Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells

    Directory of Open Access Journals (Sweden)

    Chad M. Toledo

    2015-12-01

    Full Text Available To identify therapeutic targets for glioblastoma (GBM, we performed genome-wide CRISPR-Cas9 knockout (KO screens in patient-derived GBM stem-like cells (GSCs and human neural stem/progenitors (NSCs, non-neoplastic stem cell controls, for genes required for their in vitro growth. Surprisingly, the vast majority GSC-lethal hits were found outside of molecular networks commonly altered in GBM and GSCs (e.g., oncogenic drivers. In vitro and in vivo validation of GSC-specific targets revealed several strong hits, including the wee1-like kinase, PKMYT1/Myt1. Mechanistic studies demonstrated that PKMYT1 acts redundantly with WEE1 to inhibit cyclin B-CDK1 activity via CDK1-Y15 phosphorylation and to promote timely completion of mitosis in NSCs. However, in GSCs, this redundancy is lost, most likely as a result of oncogenic signaling, causing GBM-specific lethality.

  9. Molecular and Microenvironmental Determinants of Glioma Stem-Like Cell Survival and Invasion

    Directory of Open Access Journals (Sweden)

    Alison Roos

    2017-06-01

    Full Text Available Glioblastoma multiforme (GBM is the most frequent primary brain tumor in adults with a 5-year survival rate of 5% despite intensive research efforts. The poor prognosis is due, in part, to aggressive invasion into the surrounding brain parenchyma. Invasion is a complex process mediated by cell-intrinsic pathways, extrinsic microenvironmental cues, and biophysical cues from the peritumoral stromal matrix. Recent data have attributed GBM invasion to the glioma stem-like cell (GSC subpopulation. GSCs are slowly dividing, highly invasive, therapy resistant, and are considered to give rise to tumor recurrence. GSCs are localized in a heterogeneous cellular niche, and cross talk between stromal cells and GSCs cultivates a fertile environment that promotes GSC invasion. Pro-migratory soluble factors from endothelial cells, astrocytes, macrophages, microglia, and non-stem-like tumor cells can stimulate peritumoral invasion of GSCs. Therefore, therapeutic efforts designed to target the invasive GSCs may enhance patient survival. In this review, we summarize the current understanding of extrinsic pathways and major stromal and immune players facilitating GSC maintenance and survival.

  10. Canine osteosarcoma cell lines contain stem-like cancer cells: biological and pharmacological characterization.

    Science.gov (United States)

    Gatti, Monica; Wurth, Roberto; Vito, Guendalina; Pattarozzi, Alessandra; Campanella, Chiara; Thellung, Stefano; Maniscalco, Lorella; De Maria, Raffaella; Villa, Valentina; Corsaro, Alessandro; Nizzari, Mario; Bajetto, Adriana; Ratto, Alessandra; Ferrari, Angelo; Barbieri, Federica; Florio, Tullio

    2016-05-01

    Cancer stem cells (CSCs) represent a small subpopulation of cells responsible for tumor formation and progression, drug resistance, tumor recurrence and metastasization. CSCs have been identified in many human tumors including osteosarcoma (OSA). CSC distinctive properties are the expression of stem cell markers, sustained growth, self-renewal and tumorigenicity. Here we report the isolation of stem-like cells from two canine OSA cultures, characterized by self-renewal, evaluated by sphere formation ability, differential marker expression, and in vitro proliferation when cultured in a medium containing EGF and bFGF. Current therapies for OSA increased survival time, but prognosis remains poor, due to the development of drug resistance and metastases. Chemotherapy shrinks the tumor mass but CSCs remain unaffected, leading to tumor recurrence. Metformin, a drug for type 2 diabetes, has been shown to possess antitumor properties affecting CSC survival in different human and animal cancers. Here we show that metformin has a significant antiproliferative effect on canine OSA stem-like cells, validating this in vitro model for further pre-clinical drug evaluations. In conclusion, our results demonstrate the feasibility of obtaining CSC-enriched cultures from primary canine OSA cells as a promising model for biological and pharmacological studies of canine and human OSAs.

  11. Tryptophan derivatives regulate the transcription of Oct4 in stem-like cancer cells.

    Science.gov (United States)

    Cheng, Jie; Li, Wenxin; Kang, Bo; Zhou, Yanwen; Song, Jiasheng; Dan, Songsong; Yang, Ying; Zhang, Xiaoqian; Li, Jingchao; Yin, Shengyong; Cao, Hongcui; Yao, Hangping; Zhu, Chenggang; Yi, Wen; Zhao, Qingwei; Xu, Xiaowei; Zheng, Min; Zheng, Shusen; Li, Lanjuan; Shen, Binghui; Wang, Ying-Jie

    2015-06-10

    The aryl hydrocarbon receptor (AhR), a ligand-activated transcription factor that responds to environmental toxicants, is increasingly recognized as a key player in embryogenesis and tumorigenesis. Here we show that a variety of tryptophan derivatives that act as endogenous AhR ligands can affect the transcription level of the master pluripotency factor Oct4. Among them, ITE enhances the binding of the AhR to the promoter of Oct4 and suppresses its transcription. Reduction of endogenous ITE levels in cancer cells by tryptophan deprivation or hypoxia leads to Oct4 elevation, which can be reverted by administration with synthetic ITE. Consequently, synthetic ITE induces the differentiation of stem-like cancer cells and reduces their tumorigenic potential in both subcutaneous and orthotopic xenograft tumour models. Thus, our results reveal a role of tryptophan derivatives and the AhR signalling pathway in regulating cancer cell stemness and open a new therapeutic avenue to target stem-like cancer cells.

  12. Reconstruction of three-dimensional porous media using generative adversarial neural networks

    Science.gov (United States)

    Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

    2017-10-01

    To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

  13. Efficient and Rapid Derivation of Primitive Neural Stem Cells and Generation of Brain Subtype Neurons From Human Pluripotent Stem Cells

    OpenAIRE

    Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C.

    2013-01-01

    This study developed a highly efficient serum-free pluripotent stem cell (PSC) neural induction medium that can induce human PSCs into primitive neural stem cells (NSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. This method of primitive NSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

  14. Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation

    International Nuclear Information System (INIS)

    Arantes Monteiro, Raul Vitor; Caixeta Guimarães, Geraldo; Rocio Castillo, Madeleine; Matheus Moura, Fabrício Augusto; Tamashiro, Márcio Augusto

    2016-01-01

    Current energy policies are encouraging the connection of power generation based on low-polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt.

  15. Routes to the past: Neural substrates of direct and generative autobiographical memory retrieval

    OpenAIRE

    Addis, Donna Rose; Knapp, Katie; Roberts, Reece P.; Schacter, Daniel L.

    2011-01-01

    Models of autobiographical memory propose two routes to retrieval depending on cue specificity. When available cues are specific and personally-relevant, a memory can be directly accessed. However, when available cues are generic, one must engage a generative retrieval process to produce more specific cues to successfully access a relevant memory. The current study sought to characterize the neural bases of these retrieval processes. During functional magnetic resonance imaging (fMRI), partic...

  16. Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

    Directory of Open Access Journals (Sweden)

    J. C. Ochoa-Rivera

    2002-01-01

    Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..

  17. EGFR/Src/Akt signaling modulates Sox2 expression and self-renewal of stem-like side-population cells in non-small cell lung cancer.

    Science.gov (United States)

    Singh, Sandeep; Trevino, Jose; Bora-Singhal, Namrata; Coppola, Domenico; Haura, Eric; Altiok, Soner; Chellappan, Srikumar P

    2012-09-25

    Cancer stem cells are thought to be responsible for the initiation and progression of cancers. In non-small cell lung cancers (NSCLCs), Hoechst 33342 dye effluxing side population (SP) cells are shown to have stem cell like properties. The oncogenic capacity of cancer stem-like cells is in part due to their ability to self-renew; however the mechanistic correlation between oncogenic pathways and self-renewal of cancer stem-like cells has remained elusive. Here we characterized the SP cells at the molecular level and evaluated its ability to generate tumors at the orthotopic site in the lung microenvironment. Further, we investigated if the self-renewal of SP cells is dependent on EGFR mediated signaling. SP cells were detected and isolated from multiple NSCLC cell lines (H1650, H1975, A549), as well as primary human tumor explants grown in nude mice. SP cells demonstrated stem-like properties including ability to self-renew and grow as spheres; they were able to generate primary and metastatic tumors upon orthotopic implantation into the lung of SCID mice. In vitro study revealed elevated expression of stem cell associated markers like Oct4, Sox2 and Nanog as well as demonstrated intrinsic epithelial to mesenchymal transition features in SP cells. Further, we show that abrogation of EGFR, Src and Akt signaling through pharmacological or genetic inhibitors suppresses the self-renewal growth and expansion of SP-cells and resulted in specific downregulation of Sox2 protein expression. siRNA mediated depletion of Sox2 significantly blocked the SP phenotype as well as its self-renewal capacity; whereas other transcription factors like Oct4 and Nanog played a relatively lesser role in regulating self-renewal. Interestingly, Sox2 was elevated in metastatic foci of human NSCLC samples. Our findings suggest that Sox2 is a novel target of EGFR-Src-Akt signaling in NSCLCs that modulates self-renewal and expansion of stem-like cells from NSCLC. Therefore, the outcome of the

  18. EGFR/Src/Akt signaling modulates Sox2 expression and self-renewal of stem-like side-population cells in non-small cell lung cancer

    Directory of Open Access Journals (Sweden)

    Singh Sandeep

    2012-09-01

    Full Text Available Abstract Background Cancer stem cells are thought to be responsible for the initiation and progression of cancers. In non-small cell lung cancers (NSCLCs, Hoechst 33342 dye effluxing side population (SP cells are shown to have stem cell like properties. The oncogenic capacity of cancer stem-like cells is in part due to their ability to self-renew; however the mechanistic correlation between oncogenic pathways and self-renewal of cancer stem-like cells has remained elusive. Here we characterized the SP cells at the molecular level and evaluated its ability to generate tumors at the orthotopic site in the lung microenvironment. Further, we investigated if the self-renewal of SP cells is dependent on EGFR mediated signaling. Results SP cells were detected and isolated from multiple NSCLC cell lines (H1650, H1975, A549, as well as primary human tumor explants grown in nude mice. SP cells demonstrated stem-like properties including ability to self-renew and grow as spheres; they were able to generate primary and metastatic tumors upon orthotopic implantation into the lung of SCID mice. In vitro study revealed elevated expression of stem cell associated markers like Oct4, Sox2 and Nanog as well as demonstrated intrinsic epithelial to mesenchymal transition features in SP cells. Further, we show that abrogation of EGFR, Src and Akt signaling through pharmacological or genetic inhibitors suppresses the self-renewal growth and expansion of SP-cells and resulted in specific downregulation of Sox2 protein expression. siRNA mediated depletion of Sox2 significantly blocked the SP phenotype as well as its self-renewal capacity; whereas other transcription factors like Oct4 and Nanog played a relatively lesser role in regulating self-renewal. Interestingly, Sox2 was elevated in metastatic foci of human NSCLC samples. Conclusions Our findings suggest that Sox2 is a novel target of EGFR-Src-Akt signaling in NSCLCs that modulates self-renewal and expansion of

  19. Routes to the past: neural substrates of direct and generative autobiographical memory retrieval.

    Science.gov (United States)

    Addis, Donna Rose; Knapp, Katie; Roberts, Reece P; Schacter, Daniel L

    2012-02-01

    Models of autobiographical memory propose two routes to retrieval depending on cue specificity. When available cues are specific and personally-relevant, a memory can be directly accessed. However, when available cues are generic, one must engage a generative retrieval process to produce more specific cues to successfully access a relevant memory. The current study sought to characterize the neural bases of these retrieval processes. During functional magnetic resonance imaging (fMRI), participants were shown personally-relevant cues to elicit direct retrieval, or generic cues (nouns) to elicit generative retrieval. We used spatiotemporal partial least squares to characterize the spatial and temporal characteristics of the networks associated with direct and generative retrieval. Both retrieval tasks engaged regions comprising the autobiographical retrieval network, including hippocampus, and medial prefrontal and parietal cortices. However, some key neural differences emerged. Generative retrieval differentially recruited lateral prefrontal and temporal regions early on during the retrieval process, likely supporting the strategic search operations and initial recovery of generic autobiographical information. However, many regions were activated more strongly during direct versus generative retrieval, even when we time-locked the analysis to the successful recovery of events in both conditions. This result suggests that there may be fundamental differences between memories that are accessed directly and those that are recovered via the iterative search and retrieval process that characterizes generative retrieval. Copyright © 2011 Elsevier Inc. All rights reserved.

  20. Epimorphin regulates bile duct formation via effects on mitosis orientation in rat liver epithelial stem-like cells.

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    Junnian Zhou

    Full Text Available Understanding how hepatic precursor cells can generate differentiated bile ducts is crucial for studies on epithelial morphogenesis and for development of cell therapies for hepatobiliary diseases. Epimorphin (EPM is a key morphogen for duct morphogenesis in various epithelial organs. The role of EPM in bile duct formation (DF from hepatic precursor cells, however, is not known. To address this issue, we used WB-F344 rat epithelial stem-like cells as model for bile duct formation. A micropattern and a uniaxial static stretch device was used to investigate the effects of EPM and stress fiber bundles on the mitosis orientation (MO of WB cells. Immunohistochemistry of liver tissue sections demonstrated high EPM expression around bile ducts in vivo. In vitro, recombinant EPM selectively induced DF through upregulation of CK19 expression and suppression of HNF3alpha and HNF6, with no effects on other hepatocytic genes investigated. Our data provide evidence that EPM guides MO of WB-F344 cells via effects on stress fiber bundles and focal adhesion assembly, as supported by blockade EPM, beta1 integrin, and F-actin assembly. These blockers can also inhibit EPM-induced DF. These results demonstrate a new biophysical action of EPM in bile duct formation, during which determination of MO plays a crucial role.

  1. The Neural Circuits that Generate Tics in Gilles de la Tourette Syndrome

    Science.gov (United States)

    Wang, Zhishun; Maia, Tiago V.; Marsh, Rachel; Colibazzi, Tiziano; Gerber, Andrew; Peterson, Bradley S.

    2014-01-01

    Objective To study neural activity and connectivity within cortico-striato-thalamo-cortical circuits and to reveal circuit-based neural mechanisms that govern tic generation in Tourette syndrome. Method We acquired fMRI data from 13 participants with Tourette syndrome and 21 controls during spontaneous or simulated tics. We used independent component analysis with hierarchical partner matching to isolate neural activity within functionally distinct regions of cortico-striato-thalamo-cortical circuits. We used Granger causality to investigate causal interactions among these regions. Results We found that the Tourette group exhibited stronger neural activity and interregional causality than controls throughout all portions of the motor pathway including sensorimotor cortex, putamen, pallidum, and substania nigra. Activity in these areas correlated positively with the severity of tic symptoms. Activity within the Tourette group was stronger during spontaneous tics than during voluntary tics in somatosensory and posterior parietal cortices, putamen, and amygdala/hippocampus complex, suggesting that activity in these regions may represent features of the premonitory urges that generate spontaneous tic behaviors. In contrast, activity was weaker in the Tourette group than in controls within portions of cortico-striato-thalamo-cortical circuits that exert top-down control over motor pathways (caudate and anterior cingulate cortex), and progressively less activity in these regions accompanied more severe tic symptoms, suggesting that faulty activity in these circuits may fail to control tic behaviors or the premonitory urges that generate them. Conclusions Our findings taken together suggest that tics are caused by the combined effects of excessive activity in motor pathways and reduced activation in control portions of cortico-striato-thalamo-cortical circuits. PMID:21955933

  2. Kinomic and phospho-proteomic analysis of breast cancer stem-like cells

    DEFF Research Database (Denmark)

    Leth-Larsen, Rikke; Christensen, Anne Geske Lindhard; Ehmsen, Sidse

    Kinomic and phospho-proteomic analysis of breast cancer stem-like cells Rikke Leth-Larsen1, Anne G Christensen1, Sidse Ehmsen1, Mark Møller1, Giuseppe Palmisano2, Martin R Larsen2, Henrik J Ditzel1,3 1Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark 2Institute...... of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark 3Dept. of Oncology, Odense University Hospital, Odense, Denmark Cancer stem cells are thought to be responsible for tumorigenic potential and to possess resistance mechanisms against chemotherapy- and radiation-induced cancer...... cell death, while the bulk of a tumor lacks these capacities. The resistance mechanisms may cause these cells to survive and become the source of later tumor recurrence, highlighting the need for therapeutic strategies that specifically target pathways central to these cancer stem cells. The CD44hi...

  3. Biology and immunology of cancer stem(-like) cells in head and neck cancer.

    Science.gov (United States)

    Qian, Xu; Ma, Chenming; Nie, Xiaobo; Lu, Jianxin; Lenarz, Minoo; Kaufmann, Andreas M; Albers, Andreas E

    2015-09-01

    Immunological approaches against tumors including head and neck squamous cell carcinoma (HNSCC) have been investigated for about 50 years. Such immunotherapeutic treatments are still not sufficiently effective for therapy of HNSCC. Despite the existence of immunosurveillance tumor cells may escape from the host immune system by a variety of mechanisms. Recent findings have indicated that cancer stem(-like) cells (CSCs) in HNSCC have the ability to reconstitute the heterogeneity of the bulk tumor and contribute to immunosuppression and resistance to current therapies. With regard to the CSC model, future immunotherapy possibly in combination with other modes of treatment should target this subpopulation specifically to reduce local recurrence and metastasis. In this review, we will summarize recent research findings on immunological features of CSCs and the potential of immune targeting of CSCs. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  4. Prostate cancer cell lines under hypoxia exhibit greater stem-like properties.

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    Yuanyuan Ma

    Full Text Available Hypoxia is an important environmental change in many cancers. Hypoxic niches can be occupied by cancer stem/progenitor-like cells that are associated with tumor progression and resistance to radiotherapy and chemotherapy. However, it has not yet been fully elucidated how hypoxia influences the stem-like properties of prostate cancer cells. In this report, we investigated the effects of hypoxia on human prostate cancer cell lines, PC-3 and DU145. In comparison to normoxia (20% O(2, 7% O(2 induced higher expressions of HIF-1α and HIF-2α, which were associated with upregulation of Oct3/4 and Nanog; 1% O(2 induced even greater levels of these factors. The upregulated NANOG mRNA expression in hypoxia was confirmed to be predominantly retrogene NANOGP8. Similar growth rates were observed for cells cultivated under hypoxic and normoxic conditions for 48 hours; however, the colony formation assay revealed that 48 hours of hypoxic pretreatment resulted in the formation of more colonies. Treatment with 1% O(2 also extended the G(0/G(1 stage, resulting in more side population cells, and induced CD44 and ABCG2 expressions. Hypoxia also increased the number of cells positive for ABCG2 expression, which were predominantly found to be CD44(bright cells. Correspondingly, the sorted CD44(bright cells expressed higher levels of ABCG2, Oct3/4, and Nanog than CD44(dim cells, and hypoxic pretreatment significantly increased the expressions of these factors. CD44(bright cells under normoxia formed significantly more colonies and spheres compared with the CD44(dim cells, and hypoxic pretreatment even increased this effect. Our data indicate that prostate cancer cells under hypoxia possess greater stem-like properties.

  5. The hypoxic microenvironment upgrades stem-like properties of ovarian cancer cells

    Directory of Open Access Journals (Sweden)

    Liang Dongming

    2012-05-01

    Full Text Available Abstract Background To study whether hypoxia influences the stem-like properties of ovarian cancer cells and their biological behavior under hypoxia. Method Ovarian cancer cell lines ES-2 and OVCAR-3 were cultivated in different oxygen tensions for proliferation, cell cycling and invasion analyses. The clonogenic potential of cells was examined by colony formation and sphere formation assays. Stem cell surface markers, SP and CD44bright and CD44dim cells were analyzed by flow cytometry. Protein expression of HIF-1α, HIF-2α, Ot3/4 and Sox2 were investigated by Western blotting. Results Both cell lines cultivated at hypoxic condition grew relatively slowly with extended G0/G1 phase. However, if the cells were pre-treated under 1% O2 for 48 hrs before brought back to normoxia, the cells showed significantly higher proliferation rate with higher infiltration capability, and significant more colonies and spheres, in comparison to the cells always cultivated under normoxia. CD44bright cells expressed significantly higher levels of Oct3/4 and Sox2 than the CD44dim cells and formed significantly more clones and spheres examined in vitro. Hypoxic treatment of the cells resulted in stronger CD44 expression in both cell lines, and stronger CD133 expression in the OVCAR-3 cell line. In parallel with these findings, significantly increased number of side population (SP cells and up-regulated expression of Oct3/4 and Sox2 in both ES-2 and OVCAR-3 cell lines were observed. Conclusion We conclude that ovarian cancer cells survive hypoxia by upgrading their stem-like properties through up-regulation of stemness-related factors and behave more aggressively when brought back to higher oxygen environment.

  6. A neural network driving curve generation method for the heavy-haul train

    Directory of Open Access Journals (Sweden)

    Youneng Huang

    2016-05-01

    Full Text Available The heavy-haul train has a series of characteristics, such as the locomotive traction properties, the longer length of train, and the nonlinear train pipe pressure during train braking. When the train is running on a continuous long and steep downgrade railway line, the safety of the train is ensured by cycle braking, which puts high demands on the driving skills of the driver. In this article, a driving curve generation method for the heavy-haul train based on a neural network is proposed. First, in order to describe the nonlinear characteristics of train braking, the neural network model is constructed and trained by practical driving data. In the neural network model, various nonlinear neurons are interconnected to work for information processing and transmission. The target value of train braking pressure reduction and release time is achieved by modeling the braking process. The equation of train motion is computed to obtain the driving curve. Finally, in four typical operation scenarios, comparing the curve data generated by the method with corresponding practical data of the Shuohuang heavy-haul railway line, the results show that the method is effective.

  7. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    Science.gov (United States)

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  8. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  9. Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells.

    Science.gov (United States)

    Chandrasekaran, Abinaya; Avci, Hasan X; Ochalek, Anna; Rösingh, Lone N; Molnár, Kinga; László, Lajos; Bellák, Tamás; Téglási, Annamária; Pesti, Krisztina; Mike, Arpad; Phanthong, Phetcharat; Bíró, Orsolya; Hall, Vanessa; Kitiyanant, Narisorn; Krause, Karl-Heinz; Kobolák, Julianna; Dinnyés, András

    2017-12-01

    Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency of 2D induction with 3D induction method in their ability to generate NPCs, and subsequently neurons and astrocytes. Neural differentiation was analysed at the protein level qualitatively by immunocytochemistry and quantitatively by flow cytometry for NPC (SOX1, PAX6, NESTIN), neuronal (MAP2, TUBB3), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells and the derived neurons exhibited longer neurites. In contrast, 2D neural induction resulted in more SOX1 positive cells. While 2D monolayer induction resulted in slightly less mature neurons, at an early stage of differentiation, the patch clamp analysis failed to reveal any significant differences between the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6 + /NESTIN + cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural induction, independent of iPSCs' genetic background. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  10. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  11. Generation of Oligodendrogenic Spinal Neural Progenitor Cells From Human Induced Pluripotent Stem Cells.

    Science.gov (United States)

    Khazaei, Mohamad; Ahuja, Christopher S; Fehlings, Michael G

    2017-08-14

    This unit describes protocols for the efficient generation of oligodendrogenic neural progenitor cells (o-NPCs) from human induced pluripotent stem cells (hiPSCs). Specifically, detailed methods are provided for the maintenance and differentiation of hiPSCs, human induced pluripotent stem cell-derived neural progenitor cells (hiPS-NPCs), and human induced pluripotent stem cell-oligodendrogenic neural progenitor cells (hiPSC-o-NPCs) with the final products being suitable for in vitro experimentation or in vivo transplantation. Throughout, cell exposure to growth factors and patterning morphogens has been optimized for both concentration and timing, based on the literature and empirical experience, resulting in a robust and highly efficient protocol. Using this derivation procedure, it is possible to obtain millions of oligodendrogenic-NPCs within 40 days of initial cell plating which is substantially shorter than other protocols for similar cell types. This protocol has also been optimized to use translationally relevant human iPSCs as the parent cell line. The resultant cells have been extensively characterized both in vitro and in vivo and express key markers of an oligodendrogenic lineage. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley and Sons, Inc.

  12. Imidacloprid Exposure Suppresses Neural Crest Cells Generation during Early Chick Embryo Development.

    Science.gov (United States)

    Wang, Chao-Jie; Wang, Guang; Wang, Xiao-Yu; Liu, Meng; Chuai, Manli; Lee, Kenneth Ka Ho; He, Xiao-Song; Lu, Da-Xiang; Yang, Xuesong

    2016-06-15

    Imidacloprid is a neonicotinoid pesticide that is widely used in the control pests found on crops and fleas on pets. However, it is still unclear whether imidacloprid exposure could affect early embryo development-despite some studies having been conducted on the gametes. In this study, we demonstrated that imidacloprid exposure could lead to abnormal craniofacial osteogenesis in the developing chick embryo. Cranial neural crest cells (NCCs) are the progenitor cells of the chick cranial skull. We found that the imidacloprid exposure retards the development of gastrulating chick embryos. HNK-1, PAX7, and Ap-2α immunohistological stainings indicated that cranial NCCs generation was inhibited after imidacloprid exposure. Double immunofluorescent staining (Ap-2α and PHIS3 or PAX7 and c-Caspase3) revealed that imidacloprid exposure inhibited both NCC proliferation and apoptosis. In addition, it inhibited NCCs production by repressing Msx1 and BMP4 expression in the developing neural tube and by altering expression of EMT-related adhesion molecules (Cad6B, E-Cadherin, and N-cadherin) in the developing neural crests. We also determined that imidacloprid exposure suppressed cranial NCCs migration and their ability to differentiate. In sum, we have provided experimental evidence that imidacloprid exposure during embryogenesis disrupts NCCs development, which in turn causes defective cranial bone development.

  13. Generation and properties of a new human ventral mesencephalic neural stem cell line

    Energy Technology Data Exchange (ETDEWEB)

    Villa, Ana; Liste, Isabel; Courtois, Elise T.; Seiz, Emma G.; Ramos, Milagros [Center of Molecular Biology ' Severo Ochoa' , Autonomous University of Madrid-C.S.I.C., Campus Cantoblanco 28049-Madrid (Spain); Meyer, Morten [Department of Anatomy and Neurobiology, Institute of Medical Biology, University of Southern Denmark, Winslowparken 21,st, DK-500, Odense C (Denmark); Juliusson, Bengt; Kusk, Philip [NsGene A/S, Ballerup (Denmark); Martinez-Serrano, Alberto, E-mail: amserrano@cbm.uam.es [Center of Molecular Biology ' Severo Ochoa' , Autonomous University of Madrid-C.S.I.C., Campus Cantoblanco 28049-Madrid (Spain)

    2009-07-01

    Neural stem cells (NSCs) are powerful research tools for the design and discovery of new approaches to cell therapy in neurodegenerative diseases like Parkinson's disease. Several epigenetic and genetic strategies have been tested for long-term maintenance and expansion of these cells in vitro. Here we report the generation of a new stable cell line of human neural stem cells derived from ventral mesencephalon (hVM1) based on v-myc immortalization. The cells expressed neural stem cell and radial glia markers like nestin, vimentin and 3CB2 under proliferation conditions. After withdrawal of growth factors, proliferation and expression of v-myc were dramatically reduced and the cells differentiated into astrocytes, oligodendrocytes and neurons. hVM1 cells yield a large number of dopaminergic neurons (about 12% of total cells are TH{sup +}) after differentiation, which also produce dopamine. In addition to proneural genes (NGN2, MASH1), differentiated cells show expression of several genuine mesencephalic dopaminergic markers such as: LMX1A, LMX1B, GIRK2, ADH2, NURR1, PITX3, VMAT2 and DAT, indicating that they retain their regional identity. Our data indicate that this cell line and its clonal derivatives may constitute good candidates for the study of development and physiology of human dopaminergic neurons in vitro, and to develop tools for Parkinson's disease cell replacement preclinical research and drug testing.

  14. The neural coding of creative idea generation across adolescence and early adulthood

    Directory of Open Access Journals (Sweden)

    Sietske eKleibeuker

    2013-12-01

    Full Text Available Creativity is considered key to human prosperity, yet the neurocognitive principles underlying creative performance, and their development, are still poorly understood. To fill this void, we examined the neural correlates of divergent thinking in adults (25-30 yrs and adolescents (15-17 yrs. Participants generated alternative uses (AU or ordinary characteristics (OC for common objects while brain activity was assessed using fMRI. Adults outperformed adolescents on the number of solutions for AU and OC trials. Contrasting neural activity for AU with OC trials revealed increased recruitment of left angular gyrus, left supramarginal gyrus, and bilateral middle temporal gyrus in both adults and adolescents. When only trials with multiple alternative uses were included in the analysis, participants showed additional left inferior frontal gyrus (IFG/middle frontal gyrus (MFG activation for AU compared to OC trials. Correspondingly, individual difference analyses showed a positive correlation between activations for AU relative to OC trials in left IFG/MFG and divergent thinking performance and activations were more pronounced in adults than in adolescents. Taken together, the results of this study demonstrated that creative idea generation involves recruitment of mainly left lateralized parietal and temporal brain regions. Generating multiple creative ideas, a hallmark of divergent thinking, shows additional lateral PFC activation that is not yet optimized in adolescence.

  15. Hedgehog Signaling Regulates Epithelial-Mesenchymal Transition in Pancreatic Cancer Stem-Like Cells

    Science.gov (United States)

    Wang, Feng; Ma, Ling; Zhang, Zhengkui; Liu, Xiaoran; Gao, Hongqiao; Zhuang, Yan; Yang, Pei; Kornmann, Marko; Tian, Xiaodong; Yang, Yinmo

    2016-01-01

    Hedgehog (Hh) signaling is crucially involved in tumorigenesis. This study aimed to assess the role of Hh signaling in the regulation of epithelial-mesenchymal transition (EMT), stemness properties and chemoresistance of human pancreatic Panc-1 cancer stem cells (CSCs). Panc-1 cells were transfected with recombinant lentiviral vectors to silence SMO and serum-free floating-culture system was used to isolate Panc-1 tumorspheres. The expression of CSC and EMT markers was detected by flow cytometry, real-time RT-PCR and Western blot analysis. Malignant behaviors of Panc-1 CSC were evaluated by tumorigenicity assays and nude mouse lung metastasis model. We found that tumorspheres derived from pancreatic cancer cell line Panc-1 possessed self-renewal, differentiation and stemness properties. Hh pathway and EMT were active in Panc-1 tumorspheres. Inhibition of Hh signaling by SMO knockdown inhibited self-renewal, EMT, invasion, chemoresistance, pulmonary metastasis, tumorigenesis of pancreatic CSCs. In conclusion, Hh signaling contributes to the maintenance of stem-like properties and chemoresistance of pancreatic CSC and promotes the tumorigenesis and metastasis of pancreatic cancer. Hh pathway is a potential molecular target for the development of therapeutic strategies for pancreatic CSCs. PMID:26918054

  16. Spatial and temporal characterization of endometrial mesenchymal stem-like cells activity during the menstrual cycle

    International Nuclear Information System (INIS)

    Shan, Xu; Chan, Rachel W.S.; Ng, Ernest H.Y.; Yeung, William S.B.

    2017-01-01

    The human endometrium is a highly dynamic tissue with the ability to cyclically regenerate during the reproductive life. Endometrial mesenchymal stem-like cells (eMSCs) located throughout the endometrium have shown to functionally contribute to endometrial regeneration. In this study we examine whether the menstrual cycle stage and the location in the endometrial bilayer (superficial and deep portions of the endometrium) has an effect on stem cell activities of eMSCs (CD140b"+CD146"+ cells). Here we show the percentage and clonogenic ability of eMSCs were constant in the various stages of the menstrual cycle (menstrual, proliferative and secretory). However, eMSCs from the menstrual endometrium underwent significantly more rounds of self-renewal and enabled a greater total cell output than those from the secretory phase. Significantly more eMSCs were detected in the deeper portion of the endometrium compared to the superficial layer but their clonogenic and self-renewal activities remained similar. Our findings suggest that eMSCs are activated in the menstrual phase for the cyclical regeneration of the endometrium. - Highlights: • The percentages of endometrial mesenchymal-like stem cells (eMSCs) were constant across the menstrual cycle. • Menstruation eMSCs display superior self-renewal and long-term proliferative activities. • More eMSCs reside in the deeper portion of the endometrium than the superficial layer.

  17. Embryonic stem-like cells derived from in vitro produced bovine blastocysts

    Directory of Open Access Journals (Sweden)

    Erika Regina Leal de Freitas

    2011-06-01

    Full Text Available The aim of this work was to study the derivation of bovine embryonic stem-like (ES-like cells from the inner cell mass (ICM of in vitro produced blastocysts. The ICMs were mechanically isolated and six out of seventeen (35% ICMs could attach to a monolayer of murine embryonic fibroblasts (MEF. Ten days after, primary outgrowths were mechanically dissected into several small clumps and transferred to a new MEF layer. Cells were further propagated and passaged by physical dissociation over a 60 days period. The pluripotency of the bovine ES-like cells was confirmed by RT-PCR of Oct-4 and STAT-3 gene markers. The colonies were weakly stained for alkaline phosphatase and the mesoderm and endoderm differentiation gene markers such as GATA-4 and Flk-1, respectively, were not expressed. Embryoid bodies were spontaneously formed at the seventh passage. Results showed that bovine ES-like cells could be obtained and passaged by mechanical procedures from the fresh in vitro produced blastocysts.

  18. Genetically engineered oncolytic Newcastle disease virus mediates cytolysis of prostate cancer stem like cells.

    Science.gov (United States)

    Raghunath, Shobana; Pudupakam, Raghavendra Sumanth; Allen, Adria; Biswas, Moanaro; Sriranganathan, Nammalwar

    2017-10-20

    Oncolytic virotherapy is a promising novel approach that overcomes the limitations posed by radiation and chemotherapy. In this study, the oncolytic efficacy of a recombinant Newcastle disease virus (rNDV) BC-KLQL-GFP, against prostate cancer stem-like/tumor initiating cells was evaluated. Xenograft derived prostaspheres (XPS) induced tumor more efficiently than monolayer cell derived prostaspheres (MCPS) in nude mice. Primary and secondary XPS show enhanced self-renewal and clonogenic potential compared to MCPS. XPS also expressed embryonic stem cell markers, such as Nanog, CD44 and Nestin. Further, prostate specific antigen (PSA) activated recombinant Newcastle Disease Virus (rNDV) was selectively cytotoxic to tumor derived DU145 prostaspheres. An effective concentration (EC 50 ) of 0.11-0.14 multiplicity of infection was sufficient to cause prostasphere cell death in serum free culture. DU145 tumor xenograft derived prostaspheres were used as tumor surrogates as they were enriched for a putative tumor initiating cell population. PSA activated rNDV was efficient in inducing cell death of cells and prostaspheres derived from primary xenografts ex-vivo, thus signifying a potential in vivo efficacy. The EC 50 (∼0.1 MOI) for cytolysis of tumor initiating cells was slightly higher than that was required for the parental cell line, but within the therapeutic margin for safety and efficacy. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Spatial and temporal characterization of endometrial mesenchymal stem-like cells activity during the menstrual cycle

    Energy Technology Data Exchange (ETDEWEB)

    Shan, Xu [Department of Obstetrics and Gynaecology, The University of Hong Kong, Pokfulam, Hong Kong, SAR (China); Chan, Rachel W.S., E-mail: rwschan@hku.hk [Department of Obstetrics and Gynaecology, The University of Hong Kong, Pokfulam, Hong Kong, SAR (China); Centre of Reproduction, Development of Growth, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR (China); Ng, Ernest H.Y.; Yeung, William S.B. [Department of Obstetrics and Gynaecology, The University of Hong Kong, Pokfulam, Hong Kong, SAR (China); Centre of Reproduction, Development of Growth, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR (China)

    2017-01-01

    The human endometrium is a highly dynamic tissue with the ability to cyclically regenerate during the reproductive life. Endometrial mesenchymal stem-like cells (eMSCs) located throughout the endometrium have shown to functionally contribute to endometrial regeneration. In this study we examine whether the menstrual cycle stage and the location in the endometrial bilayer (superficial and deep portions of the endometrium) has an effect on stem cell activities of eMSCs (CD140b{sup +}CD146{sup +} cells). Here we show the percentage and clonogenic ability of eMSCs were constant in the various stages of the menstrual cycle (menstrual, proliferative and secretory). However, eMSCs from the menstrual endometrium underwent significantly more rounds of self-renewal and enabled a greater total cell output than those from the secretory phase. Significantly more eMSCs were detected in the deeper portion of the endometrium compared to the superficial layer but their clonogenic and self-renewal activities remained similar. Our findings suggest that eMSCs are activated in the menstrual phase for the cyclical regeneration of the endometrium. - Highlights: • The percentages of endometrial mesenchymal-like stem cells (eMSCs) were constant across the menstrual cycle. • Menstruation eMSCs display superior self-renewal and long-term proliferative activities. • More eMSCs reside in the deeper portion of the endometrium than the superficial layer.

  20. The senescent microenvironment promotes the emergence of heterogeneous cancer stem-like cells.

    Science.gov (United States)

    Castro-Vega, Luis Jaime; Jouravleva, Karina; Ortiz-Montero, Paola; Liu, Win-Yan; Galeano, Jorge Luis; Romero, Martha; Popova, Tatiana; Bacchetti, Silvia; Vernot, Jean Paul; Londoño-Vallejo, Arturo

    2015-10-01

    There is a well-established association between aging and the onset of metastasis. Although the mechanisms through which age impinges upon the malignant phenotype remain uncharacterized, the role of a senescent microenvironment has been emphasized. We reported previously that human epithelial cells that undergo telomere-driven chromosome instability (T-CIN) display global microRNA (miR) deregulation and develop migration and invasion capacities. Here, we show that post-crisis cells are not able to form tumors unless a senescent microenvironment is provided. The characterization of cell lines established from such tumors revealed that these cells have acquired cell autonomous tumorigenicity, giving rise to heterogeneous tumors. Further experiments demonstrate that explanted cells, while displaying differences in cell differentiation markers, are all endowed of enhanced stem cell properties including self-renewal and multilineage differentiation capacity. Treatments of T-CIN+ cells with senescence-conditioned media induce sphere formation exclusively in cells with senescence-associated tumorigenicity, a capacity that depends on miR-145 repression. These results indicate that the senescent microenvironment, while promoting further transdifferentiations in cells with genome instability, is able to propel the progression of premalignant cells towards a malignant, cell stem-like state. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  1. Tumor Mesenchymal Stem-Like Cell as a Prognostic Marker in Primary Glioblastoma

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    Seon-Jin Yoon

    2016-01-01

    Full Text Available The isolation from brain tumors of tumor mesenchymal stem-like cells (tMSLCs suggests that these cells play a role in creating a microenvironment for tumor initiation and progression. The clinical characteristics of patients with primary glioblastoma (pGBM positive for tMSLCs have not been determined. This study analyzed samples from 82 patients with pGBM who had undergone tumor removal, pathological diagnosis, and isolation of tMSLC from April 2009 to October 2014. Survival, extent of resection, molecular markers, and tMSLC culture results were statistically evaluated. Median overall survival was 18.6 months, 15.0 months in tMSLC-positive patients and 29.5 months in tMSLC-negative patients (P=0.014. Multivariate cox regression model showed isolation of tMSLC (OR = 2.5, 95% CI = 1.1~5.6, P=0.021 showed poor outcome while larger extent of resection (OR = 0.5, 95% CI = 0.2~0.8, P=0.011 has association with better outcome. The presence of tMSLCs isolated from the specimen of pGBM is associated with the survival of patient.

  2. Combined Gemcitabine and Metronidazole Is a Promising Therapeutic Strategy for Cancer Stem-like Cholangiocarcinoma.

    Science.gov (United States)

    Kawamoto, Makoto; Umebayashi, Masayo; Tanaka, Hiroto; Koya, Norihiro; Nakagawa, Sinichiro; Kawabe, Ken; Onishi, Hideya; Nakamura, Masafumi; Morisaki, Takashi

    2018-05-01

    Metronidazole (MNZ) is a common antibiotic that exerts disulfiram-like effects when taken together with alcohol. However, the relationship between MNZ and aldehyde dehydrogenase (ALDH) activity remains unclear. This study investigated whether MNZ reduces cancer stemness by suppressing ALDH activity and accordingly reducing the malignancy of cholangiocarcinoma (CCA). We developed gemcitabine (GEM)-resistant TFK-1 cells and originally established CCA cell line from a patient with GEM-resistant CCA. Using these cell lines, we analyzed the impacts of MNZ for cancer stem cell markers, invasiveness, and chemosensitivity. MNZ reduced ALDH activity in GEM-resistant CCA cells, leading to decreased invasiveness and enhanced chemosensitivity. MNZ diminished the invasiveness by inducing mesenchymal-epithelial transition and enhancing chemosensitivity by increasing ENT1 (equilibrative nucleoside transporter 1) and reducing RRM1 (ribonucleotide reductase M1). MNZ reduced cancer stemness in GEM-resistant CCA cells. Combined GEM and MNZ would be a promising therapeutic strategy for cancer stem-like CAA. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

  3. MicroRNA-Mediated Dynamic Bidirectional Shift between the Subclasses of Glioblastoma Stem-like Cells

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    Arun K. Rooj

    2017-06-01

    Full Text Available Large-scale transcriptomic profiling of glioblastoma (GBM into subtypes has provided remarkable insight into the pathobiology and heterogeneous nature of this disease. The mechanisms of speciation and inter-subtype transitions of these molecular subtypes require better characterization to facilitate the development of subtype-specific targeting strategies. The deregulation of microRNA expression among GBM subtypes and their subtype-specific targeting mechanisms are poorly understood. To reveal the underlying basis of microRNA-driven complex subpopulation dynamics within the heterogeneous intra-tumoral ecosystem, we characterized the expression of the subtype-enriched microRNA-128 (miR-128 in transcriptionally and phenotypically diverse subpopulations of patient-derived glioblastoma stem-like cells. Because microRNAs are capable of re-arranging the molecular landscape in a cell-type-specific manner, we argue that alterations in miR-128 levels are a potent mechanism of bidirectional transitions between GBM subpopulations, resulting in intermediate hybrid stages and emphasizing highly intricate intra-tumoral networking.

  4. Evolutionary mechanisms that generate morphology and neural-circuit diversity of the cerebellum.

    Science.gov (United States)

    Hibi, Masahiko; Matsuda, Koji; Takeuchi, Miki; Shimizu, Takashi; Murakami, Yasunori

    2017-05-01

    The cerebellum is derived from the dorsal part of the anterior-most hindbrain. The vertebrate cerebellum contains glutamatergic granule cells (GCs) and gamma-aminobutyric acid (GABA)ergic Purkinje cells (PCs). These cerebellar neurons are generated from neuronal progenitors or neural stem cells by mechanisms that are conserved among vertebrates. However, vertebrate cerebella are widely diverse with respect to their gross morphology and neural circuits. The cerebellum of cyclostomes, the basal vertebrates, has a negligible structure. Cartilaginous fishes have a cerebellum containing GCs, PCs, and deep cerebellar nuclei (DCNs), which include projection neurons. Ray-finned fish lack DCNs but have projection neurons termed eurydendroid cells (ECs) in the vicinity of the PCs. Among ray-finned fishes, the cerebellum of teleost zebrafish has a simple lobular structure, whereas that of weakly electric mormyrid fish is large and foliated. Amniotes, which include mammals, independently evolved a large, foliated cerebellum, which contains massive numbers of GCs and has functional connections with the dorsal telencephalon (neocortex). Recent studies of cyclostomes and cartilaginous fish suggest that the genetic program for cerebellum development was already encoded in the genome of ancestral vertebrates. In this review, we discuss how alterations of the genetic and cellular programs generated diversity of the cerebellum during evolution. © 2017 Japanese Society of Developmental Biologists.

  5. Curcumin targets breast cancer stem-like cells with microtentacles that persist in mammospheres and promote reattachment.

    Science.gov (United States)

    Charpentier, Monica S; Whipple, Rebecca A; Vitolo, Michele I; Boggs, Amanda E; Slovic, Jana; Thompson, Keyata N; Bhandary, Lekhana; Martin, Stuart S

    2014-02-15

    Cancer stem-like cells (CSC) and circulating tumor cells (CTC) have related properties associated with distant metastasis, but the mechanisms through which CSCs promote metastasis are unclear. In this study, we report that breast cancer cell lines with more stem-like properties display higher levels of microtentacles (McTN), a type of tubulin-based protrusion of the plasma cell membrane that forms on detached or suspended cells and aid in cell reattachment. We hypothesized that CSCs with large numbers of McTNs would more efficiently attach to distant tissues, promoting metastatic efficiency. The naturally occurring stem-like subpopulation of the human mammary epithelial (HMLE) cell line presents increased McTNs compared with its isogenic non-stem-like subpopulation. This increase was supported by elevated α-tubulin detyrosination and vimentin protein levels and organization. Increased McTNs in stem-like HMLEs promoted a faster initial reattachment of suspended cells that was inhibited by the tubulin-directed drug, colchicine, confirming a functional role for McTNs in stem cell reattachment. Moreover, live-cell confocal microscopy showed that McTNs persist in breast stem cell mammospheres as flexible, motile protrusions on the surface of the mammosphere. Although exposed to the environment, they also function as extensions between adjacent cells along cell-cell junctions. We found that treatment with the breast CSC-targeting compound curcumin rapidly extinguished McTN in breast CSC, preventing reattachment from suspension. Together, our results support a model in which breast CSCs with cytoskeletal alterations that promote McTNs can mediate attachment and metastasis but might be targeted by curcumin as an antimetastatic strategy. ©2013 AACR.

  6. CD44 staining of cancer stem-like cells is influenced by down-regulation of CD44 variant isoforms and up-regulation of the standard CD44 isoform in the population of cells that have undergone epithelial-to-mesenchymal transition.

    Directory of Open Access Journals (Sweden)

    Adrian Biddle

    Full Text Available CD44 is commonly used as a cell surface marker of cancer stem-like cells in epithelial tumours, and we have previously demonstrated the existence of two different CD44(high cancer stem-like cell populations in squamous cell carcinoma, one having undergone epithelial-to-mesenchymal transition and the other maintaining an epithelial phenotype. Alternative splicing of CD44 variant exons generates a great many isoforms, and it is not known which isoforms are expressed on the surface of the two different cancer stem-like cell phenotypes. Here, we demonstrate that cancer stem-like cells with an epithelial phenotype predominantly express isoforms containing the variant exons, whereas the cancer stem-like cells that have undergone an epithelial-to-mesenchymal transition down-regulate these variant isoforms and up-regulate expression of the standard CD44 isoform that contains no variant exons. In addition, we find that enzymatic treatments used to dissociate cells from tissue culture or fresh tumour specimens cause destruction of variant CD44 isoforms at the cell surface whereas expression of the standard CD44 isoform is preserved. This results in enrichment within the CD44(high population of cancer stem-like cells that have undergone an epithelial-to-mesenchymal transition and depletion from the CD44(high population of cancer stem-like cells that maintain an epithelial phenotype, and therefore greatly effects the characteristics of any cancer stem-like cell population isolated based on expression of CD44. As well as effecting the CD44(high population, enzymatic treatment also reduces the percentage of the total epithelial cancer cell population staining CD44-positive, with potential implications for studies that aim to use CD44-positive staining as a prognostic indicator. Analyses of the properties of cancer stem-like cells are largely dependent on the ability to accurately identify and assay these populations. It is therefore critical that

  7. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  8. A Phox2b BAC Transgenic Rat Line Useful for Understanding Respiratory Rhythm Generator Neural Circuitry.

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    Keiko Ikeda

    Full Text Available The key role of the respiratory neural center is respiratory rhythm generation to maintain homeostasis through the control of arterial blood pCO2/pH and pO2 levels. The neuronal network responsible for respiratory rhythm generation in neonatal rat resides in the ventral side of the medulla and is composed of two groups; the parafacial respiratory group (pFRG and the pre-Bötzinger complex group (preBötC. The pFRG partially overlaps in the retrotrapezoid nucleus (RTN, which was originally identified in adult cats and rats. Part of the pre-inspiratory (Pre-I neurons in the RTN/pFRG serves as central chemoreceptor neurons and the CO2 sensitive Pre-I neurons express homeobox gene Phox2b. Phox2b encodes a transcription factor and is essential for the development of the sensory-motor visceral circuits. Mutations in human PHOX2B cause congenital hypoventilation syndrome, which is characterized by blunted ventilatory response to hypercapnia. Here we describe the generation of a novel transgenic (Tg rat harboring fluorescently labeled Pre-I neurons in the RTN/pFRG. In addition, the Tg rat showed fluorescent signals in autonomic enteric neurons and carotid bodies. Because the Tg rat expresses inducible Cre recombinase in PHOX2B-positive cells during development, it is a potentially powerful tool for dissecting the entire picture of the respiratory neural network during development and for identifying the CO2/O2 sensor molecules in the adult central and peripheral nervous systems.

  9. Identification of quiescent, stem-like cells in the distal female reproductive tract.

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    Yongyi Wang

    Full Text Available In fertile women, the endometrium undergoes regular cycles of tissue build-up and regression. It is likely that uterine stem cells are involved in this remarkable turn over. The main goal of our current investigations was to identify slow-cycling (quiescent endometrial stem cells by means of a pulse-chase approach to selectively earmark, prospectively isolate, and characterize label-retaining cells (LRCs. To this aim, transgenic mice expressing histone2B-GFP (H2B-GFP in a Tet-inducible fashion were administered doxycycline (pulse which was thereafter withdrawn from the drinking water (chase. Over time, dividing cells progressively loose GFP signal whereas infrequently dividing cells retain H2B-GFP expression. We evaluated H2B-GFP retaining cells at different chase time points and identified long-term (LT; >12 weeks LRCs. The LT-LRCs are negative for estrogen receptor-α and express low levels of progesterone receptors. LRCs sorted by FACS are able to form spheroids capable of self-renewal and differentiation. Upon serum stimulation spheroid cells are induced to differentiate and form glandular structures which express markers of mature műllerian epithelial cells. Overall, the results indicate that quiescent cells located in the distal oviduct have stem-like properties and can differentiate into distinct cell lineages specific of endometrium, proximal and distal oviduct. Future lineage-tracing studies will elucidate the role played by these cells in homeostasis, tissue injury and cancer of the female reproductive tract in the mouse and eventually in man.

  10. Lunatic Fringe and p53 Cooperatively Suppress Mesenchymal Stem-Like Breast Cancer

    Directory of Open Access Journals (Sweden)

    Wen-Cheng Chung

    2017-11-01

    Full Text Available Claudin-low breast cancer (CLBC is a poor prognosis molecular subtype showing stemness and mesenchymal features. We previously discovered that deletion of a Notch signaling modulator, Lunatic Fringe (Lfng, in the mouse mammary gland induced a subset of tumors resembling CLBC. Here we report that deletion of one copy of p53 on this background not only accelerated mammary tumor development but also led to a complete penetrance of the mesenchymal stem-like phenotype. All mammary tumors examined in the Lfng/p53 compound mutant mice displayed a mesenchymal/spindloid pathology. These tumors showed high level expressions of epithelial-to-mesenchymal transition (EMT markers including Vimentin, Twist, and PDGFRα, a gene known to be enriched in CLBC. Prior to tumor onset, Lfng/p53 mutant mammary glands exhibited increased levels of Vimentin and E-cadherin, but decreased expressions of cytokeratin 14 and cytokeratin 8, accompanied by elevated basal cell proliferation and an expanded mammary stem cell-enriched population. Lfng/p53 mutant glands displayed increased accumulation of Notch3 intracellular fragment, up-regulation of Hes5 and down-regulation of Hes1. Analysis in human breast cancer datasets found the lowest HES1 and second lowest LFNG expressions in CLBC among molecular subtypes, and low level of LFNG is associated with poor survival. Immunostaining of human breast cancer tissue array found correlation between survival and LFNG immunoreactivity. Finally, patients carrying TP53 mutations express lower LFNG than patients with wild type TP53. Taken together, these data revealed genetic interaction between Lfng and p53 in mammary tumorigenesis, established a new mouse model resembling CLBC, and may suggest targeting strategy for this disease.

  11. CD44+ cancer stem-like cells in EBV-associated nasopharyngeal carcinoma.

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    Samantha Wei-Man Lun

    Full Text Available Nasopharyngeal carcinoma (NPC is a unique EBV-associated epithelial malignancy, showing highly invasive and metastatic phenotype. Despite increasing evidence demonstrating the critical role of cancer stem-like cells (CSCs in the maintenance and progression of tumors in a variety of malignancies, the existence and properties of CSC in EBV-associated NPC are largely unknown. Our study aims to elucidate the presence and role of CSCs in the pathogenesis of this malignant disease. Sphere-forming cells were isolated from an EBV-positive NPC cell line C666-1 and its tumor-initiating properties were confirmed by in vitro and in vivo assays. In these spheroids, up-regulation of multiple stem cell markers were found. By flow cytometry, we demonstrated that both CD44 and SOX2 were overexpressed in a majority of sphere-forming C666-1 cells. The CD44+SOX2+ cells was detected in a minor population in EBV-positive xenografts and primary tumors and considered as potential CSC in NPC. Notably, the isolated CD44+ NPC cells were resistant to chemotherapeutic agents and with higher spheroid formation efficiency, showing CSC properties. On the other hand, microarray analysis has revealed a number of differentially expressed genes involved in transcription regulation (e.g. FOXN4, GLI1, immune response (CCR7, IL8 and transmembrane transport (e.g. ABCC3, ABCC11 in the spheroids. Among these genes, increased expression of CCR7 in CD44+ CSCs was confirmed in NPC xenografts and primary tumors. Importantly, blocking of CCR7 abolished the sphere-forming ability of C666-1 in vitro. Expression of CCR7 was associated with recurrent disease and distant metastasis. The current study defined the specific properties of a CSC subpopulation in EBV-associated NPC. Our findings provided new insights into developing effective therapies targeting on CSCs, thereby potentiating treatment efficacy for NPC patients.

  12. Possible association between stem-like hallmark and radioresistance in human cervical carcinoma cells.

    Science.gov (United States)

    Kumazawa, Shoko; Kajiyama, Hiroaki; Umezu, Tomokazu; Mizuno, Mika; Suzuki, Shiro; Yamamoto, Eiko; Mitsui, Hiroko; Sekiya, Ryuichiro; Shibata, Kiyosumi; Kikkawa, Fumitaka

    2014-05-01

    We aimed to investigate the possibility of an association between a stem-like hallmark and radiotherapeutic sensitivity in human cervical carcinoma cells. Side-population (SP) cells and non-SP (NSP) cells in HeLa cells were isolated using flow cytometry and Hoechst 33342 efflux. We performed Western blot analysis to evaluate the expression of stem cell markers (CXCR4, Oct3/4, CD133, and SOX2) and apoptosis markers after irradiation. In addition, SP and NSP cells were injected into nude mice and we assessed subcutaneous tumor formation. To examine tolerance of irradiation, colony formation and apoptosis change were confirmed in the SP and NSP cells. SP cells showed a higher expression of CXCR4, Oct3/4, CD133, and SOX2 than NSP cells. The colony size of SP cells cultured on non-coated dishes was larger than that of NSP cells, and NSP cells were easily induced to undergo apoptosis. SP cells tended to form spheroids and showed a higher level of tumorigenicity compared with NSP cells. In addition, nude mice inoculated with SP cells showed greater tumor growth compared with NSP cells. SP cells showed a higher tumorigenicity and lower apoptotic potential, leading to enhanced radiotolerance. Tumor SP cells showed higher-level stem-cell-like characters and radioresistance than NSP cells. SP cells may be useful for new therapeutic approaches for radiation-resistant cervical cancer. © 2014 The Authors. Journal of Obstetrics and Gynaecology Research © 2014 Japan Society of Obstetrics and Gynecology.

  13. CD44+ Cancer Stem-Like Cells in EBV-Associated Nasopharyngeal Carcinoma

    Science.gov (United States)

    Lun, Samantha Wei-Man; Cheung, Siu Tim; Cheung, Phyllis Fung Yi; To, Ka-Fai; Woo, John Kong-Sang; Choy, Kwong-Wai; Chow, Chit; Cheung, Chartia Ching-Mei; Chung, Grace Tin-Yun; Cheng, Alice Suk-Hang; Ko, Chun-Wai; Tsao, Sai-Wah; Busson, Pierre; Ng, Margaret Heung-Ling; Lo, Kwok-Wai

    2012-01-01

    Nasopharyngeal carcinoma (NPC) is a unique EBV-associated epithelial malignancy, showing highly invasive and metastatic phenotype. Despite increasing evidence demonstrating the critical role of cancer stem-like cells (CSCs) in the maintenance and progression of tumors in a variety of malignancies, the existence and properties of CSC in EBV-associated NPC are largely unknown. Our study aims to elucidate the presence and role of CSCs in the pathogenesis of this malignant disease. Sphere-forming cells were isolated from an EBV-positive NPC cell line C666-1 and its tumor-initiating properties were confirmed by in vitro and in vivo assays. In these spheroids, up-regulation of multiple stem cell markers were found. By flow cytometry, we demonstrated that both CD44 and SOX2 were overexpressed in a majority of sphere-forming C666-1 cells. The CD44+SOX2+ cells was detected in a minor population in EBV-positive xenografts and primary tumors and considered as potential CSC in NPC. Notably, the isolated CD44+ NPC cells were resistant to chemotherapeutic agents and with higher spheroid formation efficiency, showing CSC properties. On the other hand, microarray analysis has revealed a number of differentially expressed genes involved in transcription regulation (e.g. FOXN4, GLI1), immune response (CCR7, IL8) and transmembrane transport (e.g. ABCC3, ABCC11) in the spheroids. Among these genes, increased expression of CCR7 in CD44+ CSCs was confirmed in NPC xenografts and primary tumors. Importantly, blocking of CCR7 abolished the sphere-forming ability of C666-1 in vitro. Expression of CCR7 was associated with recurrent disease and distant metastasis. The current study defined the specific properties of a CSC subpopulation in EBV-associated NPC. Our findings provided new insights into developing effective therapies targeting on CSCs, thereby potentiating treatment efficacy for NPC patients. PMID:23285037

  14. Colorectal adenoma stem-like cell populations: associations with adenoma characteristics and metachronous colorectal neoplasia.

    Science.gov (United States)

    Bartley, Angela N; Parikh, Nila; Hsu, Chiu-Hsieh; Roe, Denise J; Buckmeier, Julie A; Corley, Lynda; Phipps, Ron A; Gallick, Gary; Lance, Peter; Thompson, Patricia A; Hamilton, Stanley R

    2013-11-01

    Cancer stem cells have tumor-initiation and tumor-maintenance capabilities. Stem-like cells are present in colorectal adenomas, but their relationship to adenoma pathology and patient characteristics, including metachronous development of an additional adenoma ("recurrence"), has not been studied extensively. We evaluated the expression of aldehyde dehydrogenase isoform 1A1 (ALDH1A1), a putative stem cell marker, in baseline adenomas from the placebo arm of chemoprevention trial participants with colonoscopic follow-up. An exploratory set of 20 baseline adenomas was analyzed by ALDH1A1 immunohistochemistry with morphometry, and a replication set of 89 adenomas from 76 high-risk participants was evaluated by computerized image analysis. ALDH1A1-labeling indices (ALI) were similar across patient characteristics and in advanced and nonadvanced adenomas. There was a trend toward higher ALIs in adenomas occurring in the right than left colon (P = 0.09). ALIs of synchronous adenomas were correlated (intraclass correlation coefficient 0.67). Participants in both sample sets who developed a metachronous adenoma had significantly higher ALIs in their baseline adenoma than participants who remained adenoma free. In the replication set, the adjusted odds for metachronous adenoma increased 1.46 for each 10% increase in ALIs (P = 0.03). A best-fit algorithm-based cutoff point of 22.4% had specificity of 75.0% and positive predictive value of 70.0% for metachronous adenoma development. A larger population of ALDH1A1-expressing cells in an adenoma is associated with a higher risk for metachronous adenoma, independent of adenoma size or histopathology. If confirmed, ALDH1A1 has potential as a novel biomarker in risk assessment and as a potential stem cell target for chemoprevention. ©2013 AACR

  15. Functional overlap of top-down emotion regulation and generation: an fMRI study identifying common neural substrates between cognitive reappraisal and cognitively generated emotions.

    Science.gov (United States)

    Otto, Benjamin; Misra, Supriya; Prasad, Aditya; McRae, Kateri

    2014-09-01

    One factor that influences the success of emotion regulation is the manner in which the regulated emotion was generated. Recent research has suggested that reappraisal, a top-down emotion regulation strategy, is more effective in decreasing self-reported negative affect when emotions were generated from the top-down, versus the bottom-up. On the basis of a process overlap framework, we hypothesized that the neural regions active during reappraisal would overlap more with emotions that were generated from the top-down, rather than from the bottom-up. In addition, we hypothesized that increased neural overlap between reappraisal and the history effects of top-down emotion generation would be associated with increased reappraisal success. The results of several analyses suggested that reappraisal and emotions that were generated from the top-down share a core network of prefrontal, temporal, and cingulate regions. This overlap is specific; no such overlap was observed between reappraisal and emotions that were generated in a bottom-up fashion. This network consists of regions previously implicated in linguistic processing, cognitive control, and self-relevant appraisals, which are processes thought to be crucial to both reappraisal and top-down emotion generation. Furthermore, individuals with high reappraisal success demonstrated greater neural overlap between reappraisal and the history of top-down emotion generation than did those with low reappraisal success. The overlap of these key regions, reflecting overlapping processes, provides an initial insight into the mechanism by which generation history may facilitate emotion regulation.

  16. Neural Stem Cell Differentiation Using Microfluidic Device-Generated Growth Factor Gradient.

    Science.gov (United States)

    Kim, Ji Hyeon; Sim, Jiyeon; Kim, Hyun-Jung

    2018-04-11

    Neural stem cells (NSCs) have the ability to self-renew and differentiate into multiple nervous system cell types. During embryonic development, the concentrations of soluble biological molecules have a critical role in controlling cell proliferation, migration, differentiation and apoptosis. In an effort to find optimal culture conditions for the generation of desired cell types in vitro , we used a microfluidic chip-generated growth factor gradient system. In the current study, NSCs in the microfluidic device remained healthy during the entire period of cell culture, and proliferated and differentiated in response to the concentration gradient of growth factors (epithermal growth factor and basic fibroblast growth factor). We also showed that overexpression of ASCL1 in NSCs increased neuronal differentiation depending on the concentration gradient of growth factors generated in the microfluidic gradient chip. The microfluidic system allowed us to study concentration-dependent effects of growth factors within a single device, while a traditional system requires multiple independent cultures using fixed growth factor concentrations. Our study suggests that the microfluidic gradient-generating chip is a powerful tool for determining the optimal culture conditions.

  17. Performance assessment of electric power generations using an adaptive neural network algorithm

    International Nuclear Information System (INIS)

    Azadeh, A.; Ghaderi, S.F.; Anvari, M.; Saberi, M.

    2007-01-01

    Efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes a non-parametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed computational method is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used. Moreover, the effect of the return to scale of decision-making units (DMUs) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). An example using real data is presented for illustrative purposes. In the application to the power generation sector of Iran, we find that the neural network provide more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored. Moreover, principle component analysis (PCA) is used to verify the findings of the proposed algorithm

  18. Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

    International Nuclear Information System (INIS)

    Uzlu, Ergun; Akpınar, Adem; Özturk, Hasan Tahsin; Nacar, Sinan; Kankal, Murat

    2014-01-01

    The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower. - Highlights: • This study is associated with predicting hydropower generation in Turkey. • Sensitivity analysis was performed to determine predictor variables. • GEED, population, energy consumption and AYT were used as predictor variables. • ANN-ABC predicted the hydropower generation more accurately

  19. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

    Science.gov (United States)

    Adamović, Vladimir M; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2017-01-01

    This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

  20. Applying a Cerebellar Model Articulation Controller Neural Network to a Photovoltaic Power Generation System Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Kuei-Hsiang Chao

    2013-01-01

    Full Text Available This study employed a cerebellar model articulation controller (CMAC neural network to conduct fault diagnoses on photovoltaic power generation systems. We composed a module array using 9 series and 2 parallel connections of SHARP NT-R5E3E 175 W photovoltaic modules. In addition, we used data that were outputted under various fault conditions as the training samples for the CMAC and used this model to conduct the module array fault diagnosis after completing the training. The results of the training process and simulations indicate that the method proposed in this study requires fewer number of training times compared to other methods. In addition to significantly increasing the accuracy rate of the fault diagnosis, this model features a short training duration because the training process only tunes the weights of the exited memory addresses. Therefore, the fault diagnosis is rapid, and the detection tolerance of the diagnosis system is enhanced.

  1. Automatic modulation format recognition for the next generation optical communication networks using artificial neural networks

    Science.gov (United States)

    Guesmi, Latifa; Hraghi, Abir; Menif, Mourad

    2015-03-01

    A new technique for Automatic Modulation Format Recognition (AMFR) in next generation optical communication networks is presented. This technique uses the Artificial Neural Network (ANN) in conjunction with the features of Linear Optical Sampling (LOS) of the detected signal at high bit rates using direct detection or coherent detection. The use of LOS method for this purpose mainly driven by the increase of bit rates which enables the measurement of eye diagrams. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of -500 to 500 ps/nm, differential group delay (DGD) in the range of 0-15 ps and the optical signal tonoise ratio (OSNR) in the range of 10-30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.8%.

  2. Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model.

    Science.gov (United States)

    Bi, Size; Liang, Xiao; Huang, Ting-Lei

    2016-01-01

    Word embedding, a lexical vector representation generated via the neural linguistic model (NLM), is empirically demonstrated to be appropriate for improvement of the performance of traditional language model. However, the supreme dimensionality that is inherent in NLM contributes to the problems of hyperparameters and long-time training in modeling. Here, we propose a force-directed method to improve such problems for simplifying the generation of word embedding. In this framework, each word is assumed as a point in the real world; thus it can approximately simulate the physical movement following certain mechanics. To simulate the variation of meaning in phrases, we use the fracture mechanics to do the formation and breakdown of meaning combined by a 2-gram word group. With the experiments on the natural linguistic tasks of part-of-speech tagging, named entity recognition and semantic role labeling, the result demonstrated that the 2-dimensional word embedding can rival the word embeddings generated by classic NLMs, in terms of accuracy, recall, and text visualization.

  3. Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model

    Directory of Open Access Journals (Sweden)

    Size Bi

    2016-01-01

    Full Text Available Word embedding, a lexical vector representation generated via the neural linguistic model (NLM, is empirically demonstrated to be appropriate for improvement of the performance of traditional language model. However, the supreme dimensionality that is inherent in NLM contributes to the problems of hyperparameters and long-time training in modeling. Here, we propose a force-directed method to improve such problems for simplifying the generation of word embedding. In this framework, each word is assumed as a point in the real world; thus it can approximately simulate the physical movement following certain mechanics. To simulate the variation of meaning in phrases, we use the fracture mechanics to do the formation and breakdown of meaning combined by a 2-gram word group. With the experiments on the natural linguistic tasks of part-of-speech tagging, named entity recognition and semantic role labeling, the result demonstrated that the 2-dimensional word embedding can rival the word embeddings generated by classic NLMs, in terms of accuracy, recall, and text visualization.

  4. Generation of novel motor sequences: the neural correlates of musical improvisation.

    Science.gov (United States)

    Berkowitz, Aaron L; Ansari, Daniel

    2008-06-01

    While some motor behavior is instinctive and stereotyped or learned and re-executed, much action is a spontaneous response to a novel set of environmental conditions. The neural correlates of both pre-learned and cued motor sequences have been previously studied, but novel motor behavior has thus far not been examined through brain imaging. In this paper, we report a study of musical improvisation in trained pianists with functional magnetic resonance imaging (fMRI), using improvisation as a case study of novel action generation. We demonstrate that both rhythmic (temporal) and melodic (ordinal) motor sequence creation modulate activity in a network of brain regions comprised of the dorsal premotor cortex, the rostral cingulate zone of the anterior cingulate cortex, and the inferior frontal gyrus. These findings are consistent with a role for the dorsal premotor cortex in movement coordination, the rostral cingulate zone in voluntary selection, and the inferior frontal gyrus in sequence generation. Thus, the invention of novel motor sequences in musical improvisation recruits a network of brain regions coordinated to generate possible sequences, select among them, and execute the decided-upon sequence.

  5. CD133(+)/CD44(+)/Oct4(+)/Nestin(+) stem-like cells isolated from Panc-1 cell line may contribute to multi-resistance and metastasis of pancreatic cancer.

    Science.gov (United States)

    Wang, Dongqing; Zhu, Haitao; Zhu, Ying; Liu, Yanfang; Shen, Huiling; Yin, Ruigen; Zhang, Zhijian; Su, Zhaoliang

    2013-05-01

    Pancreatic cancer is an aggressive malignant disease. Owing to the lack of early symptoms, accompanied by extensive metastasis and high resistance to chemotherapy, pancreatic adenocarcinoma becomes the fourth leading cause of cancer-related deaths. In this study, we identified a subpopulation of cells isolated from the Panc-1 cell line and named pancreatic cancer stem-like cells. These Panc-1 stem-like cells expressed high levels of CD133/CD44/Oct4/Nestin. Compared to Panc-1 cells, Panc-1 stem-like cells were resistant to gemcitabine and expressed high levels of MDR1; furthermore, Panc-1 stem-like cells have high anti-apoptotic, but weak proliferative potential. These results indicated that Panc-1 stem-like cells, as a novel group, may be a potential major cause of pancreatic cancer multidrug resistance and extensive metastasis. Copyright © 2012 Elsevier GmbH. All rights reserved.

  6. ΔNp63α is an oncogene that induces Lsh expression and promotes stem-like proliferation

    Science.gov (United States)

    Keyes, William M.; Pecoraro, Matteo; Aranda, Victoria; Vernersson-Lindahl, Emma; Li, Wangzhi; Vogel, Hannes; Guo, Xuecui; Garcia, Elvin L.; Michurina, Tatyana V.; Enikolopov, Grigori; Muthuswamy, Senthil K.; Mills, Alea A.

    2014-01-01

    SUMMARY The p53 homolog p63 is essential for development, yet its role in cancer is not clear. We discovered that p63 deficiency evokes the tumor suppressive mechanism of cellular senescence, causing a striking absence of stratified epithelia such as the skin. Here we identify the predominant p63 isoform, ΔNp63α, as a protein that bypasses oncogene induced senescence to drive tumorigenesis in vivo. Interestingly, bypass of senescence promotes stem-like proliferation and maintains survival of the keratin 15-positive stem cell population. Furthermore, we identify the chromatin remodeling protein Lsh as a new target of ΔNp63α that is an essential mediator of senescence bypass. These findings indicate that ΔNp63α is an oncogene that cooperates with Ras to promote tumor-initiating stem-like proliferation, and suggest that Lsh-mediated chromatin remodeling events are critical to this process. PMID:21295273

  7. Gambogic Acid Efficiently Kills Stem-Like Colorectal Cancer Cells by Upregulating ZFP36 Expression

    Directory of Open Access Journals (Sweden)

    Fang Wei

    2018-03-01

    Full Text Available Background/Aims: Gambogic acid (GA, the main active compound of Gamboge hanburyi, has been reported to be a potential novel antitumor drug. Whether GA inhibits putative cancer stem cells (CSCs, which are considered to be the major cause of cancer treatment failure, remains largely unknown. This study investigated whether GA inhibits the CSCs of colorectal cancer (CRC and its possible mechanisms. Methods: We performed CCK8 and tumor sphere formation assays, percentage analysis of both side population and CD133+CD44+ cells, and the detection of stem cells markers, in order to assess the role of GA in inhibiting the stem celllike features of CRC. An mRNA microarray was performed to identify the downstream gene affected by GA and rescue assays were performed to further clarify whether the downstream gene is involved in the GA induced decrease of the stem cell-like CRC population. CRC cells were engineered with a CSC detector vector encoding GFP and luciferase (Luc under the control of the Nanog promoter, which were utilized to investigate the effect of GA on putative CSC in human tumor xenograft-bearing mice using in vivo bioluminescence imaging. Results: Our results showed that GA significantly reduced tumor sphere formation and the percentages of side population and CD133+CD44+ cells, while also decreasing the expression of stemness and EMT-associated markers in CRC cells in vitro. GA killed stem-like CRC cells by upregulating the expression of ZFP36, which is dependent on the inactivation of the EGFR/ ERK signaling pathway. GFP+ cells harboring the PNanog-GFP-T2A-Luc transgene exhibited CSC characteristics. The in vivo results showed that GA significantly inhibited tumor growth in nude mice, accompanied by a remarkable reduction in the putative CSC number, based on whole-body bioluminescence imaging. Conclusion: These findings suggest that GA significantly inhibits putative CSCs of CRC both in vitro and in vivo by inhibiting the activation of the

  8. Ovarian cancer plasticity and epigenomics in the acquisition of a stem-like phenotype

    Directory of Open Access Journals (Sweden)

    Berry Nicholas B

    2008-11-01

    Full Text Available Abstract Aggressive epithelial ovarian cancer (EOC is genetically and epigenetically distinct from normal ovarian surface epithelial cells (OSE and early neoplasia. Co-expression of epithelial and mesenchymal markers in EOC suggests an involvement of epithelial-mesenchymal transition (EMT in cancer initiation and progression. This phenomenon is often associated with acquisition of a stem cell-like phenotype and chemoresistance that correlate with the specific gene expression patterns accompanying transformation, revealing a plasticity of the ovarian cancer cell genome during disease progression. Differential gene expressions between normal and transformed cells reflect the varying mechanisms of regulation including genetic changes like rearrangements within the genome, as well as epigenetic changes such as global genomic hypomethylation with localized promoter CpG island hypermethylation. The similarity of gene expression between ovarian cancer cells and the stem-like ovarian cancer initiating cells (OCIC are surprisingly also correlated with epigenetic mechanisms of gene regulation in normal stem cells. Both normal and cancer stem cells maintain genetic flexibility by co-placement of activating and/or repressive epigenetic modifications on histone H3. The co-occupancy of such opposing histone marks is believed to maintain gene flexibility and such bivalent histones have been described as being poised for transcriptional activation or epigenetic silencing. The involvement of both-microRNA (miRNA mediated epigenetic regulation, as well as epigenetic-induced changes in miRNA expression further highlight an additional complexity in cancer stem cell epigenomics. Recent advances in array-based whole-genome/epigenome analyses will continue to further unravel the genomes and epigenomes of cancer and cancer stem cells. In order to illuminate phenotypic signatures that delineate ovarian cancer from their associated cancer stem cells, a priority must lie

  9. Stability and bifurcation in a model for the dynamics of stem-like cells in leukemia under treatment

    Science.gov (United States)

    Rǎdulescu, I. R.; Cândea, D.; Halanay, A.

    2012-11-01

    A mathematical model for the dynamics of leukemic cells during treatment is introduced. Delay differential equations are used to model cells' evolution and are based on the Mackey-Glass approach, incorporating Goldie-Coldman law. Since resistance is propagated by cells that have the capacity of self-renewal, a population of stem-like cells is studied. Equilibrium points are calculated and their stability properties are investigated.

  10. Autocrine Semaphorin3A signaling is essential for the maintenance of stem-like cells in lung cancer

    International Nuclear Information System (INIS)

    Yamada, Daisuke; Takahashi, Kensuke; Kawahara, Kohichi; Maeda, Takehiko

    2016-01-01

    Cancer stem-like cells (CSCs) exist in tumor tissues composed of heterogeneous cell population and are characterized by their self-renewal capacity and tumorigenicity. Many studies demonstrate that eradication of CSCs prevents development and recurrences of tumor; yet, molecules critical for the maintenance of CSCs have not been completely understood. We previously reported that Semaphorin3A (Sema3a) knockdown suppressed the tumorigenicity and proliferative capacity of Lewis lung carcinoma (LLC) cells. Therefore, we identified Sema3a as an essential factor for the establishment or maintenance of CSCs derived from LLC (LLC-stem cell). shRNA against Sema3a was introduced into LLC cells to establish a LLC-stem cell line and its effects on tumorigenesis, sphere formation, and mTORC1 activity were tested. Sema3a knockdown completely abolished tumorigenicity and the sphere-formation and self-renewal ability of LLC-stem cells. The Sema3a knockdown was also associated with decreased expression of mRNA for stem cell markers. The self-renewal ability abolished by Sema3a knockdown could not be recovered by exogenous addition of recombinant SEMA3A. In addition, the activity of mammalian target of rapamycin complex 1 (mTORC1) and the expression of its substrate p70S6K1 were also decreased. These results demonstrate that Sema3a is a potential therapeutic target in eradication of CSCs. - Highlights: • Sema3a enhances tumorigenic capacity of cancer stem-like cells. • Sema3a is essential for the maintenance of cancer stem-like cells. • Sema3a can be a therapeutic target to eradicate cancer stem-like cells.

  11. Xenografts in zebrafish embryos as a rapid functional assay for breast cancer stem-like cell identification.

    Science.gov (United States)

    Eguiara, Arrate; Holgado, Olaia; Beloqui, Izaskun; Abalde, Leire; Sanchez, Yolanda; Callol, Carles; Martin, Angel G

    2011-11-01

    The cancer stem cell is defined by its capacity to self-renew, the potential to differentiate into all cells of the tumor and the ability to proliferate and drive the expansion of the tumor. Thus, targeting these cells may provide novel anti-cancer treatment strategies. Breast cancer stem cells have been isolated according to surface marker expression, ability to efflux fluorescent dyes, increased activity of aldehyde dehydrogenase or the capacity to form spheres in non-adherent culture conditions. In order to test novel drugs directed towards modulating self-renewal of cancer stem cells, rapid, easy and inexpensive assays must be developed. Using 2 days-post-fertilization (dpf) zebrafish embryos as transplant recipients, we show that cells grown in mammospheres from breast carcinoma cell lines migrate to the tail of the embryo and form masses with a significantly higher frequency than parental monolayer populations. When stem-like self-renewal was targeted in the parental population by the use of the dietary supplement curcumin, cell migration and mass formation were reduced, indicating that these effects were associated with stem-like cell content. This is a proof of principle report that proposes a rapid and inexpensive assay to target in vivo cancer stem-like cells, which may be used to unravel basic cancer stem cell biology and for drug screening.

  12. Generation of daily solar irradiation by means of artificial neural net works

    Energy Technology Data Exchange (ETDEWEB)

    Siqueira, Adalberto N.; Tiba, Chigueru; Fraidenraich, Naum [Departamento de Energia Nuclear, da Universidade Federal de Pernambuco, Av. Prof. Luiz Freire, 1000 - CDU, CEP 50.740-540 Recife, Pernambuco (Brazil)

    2010-11-15

    The present study proposes the utilization of Artificial Neural Networks (ANN) as an alternative for generating synthetic series of daily solar irradiation. The sequences were generated from the use of daily temporal series of a group of meteorological variables that were measured simultaneously. The data used were measured between the years of 1998 and 2006 in two temperate climate localities of Brazil, Ilha Solteira (Sao Paulo) and Pelotas (Rio Grande do Sul). The estimates were taken for the months of January, April, July and October, through two models which are distinguished regarding the use or nonuse of measured bright sunshine hours as an input variable. An evaluation of the performance of the 56 months of solar irradiation generated by way of ANN showed that by using the measured bright sunshine hours as an input variable (model 1), the RMSE obtained were less or equal to 23.2% being that of those, although 43 of those months presented RMSE less or equal to 12.3%. In the case of the model that did not use the measured bright sunshine hours but used a daylight length (model 2), RMSE were obtained that varied from 8.5% to 37.5%, although 38 of those months presented RMSE less or equal to 20.0%. A comparison of the monthly series for all of the years, achieved by means of the Kolmogorov-Smirnov test (to a confidence level of 99%), demonstrated that of the 16 series generated by ANN model only two, obtained by model 2 for the months of April and July in Pelotas, presented significant difference in relation to the distributions of the measured series and that all mean deviations obtained were inferior to 0.39 MJ/m{sup 2}. It was also verified that the two ANN models were able to reproduce the principal statistical characteristics of the frequency distributions of the measured series such as: mean, mode, asymmetry and Kurtosis. (author)

  13. Embodied learning of a generative neural model for biological motion perception and inference.

    Science.gov (United States)

    Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

    2015-01-01

    Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  14. Schema generation in recurrent neural nets for intercepting a moving target.

    Science.gov (United States)

    Fleischer, Andreas G

    2010-06-01

    The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load. Subjects were asked to hit a target that was moving along a circular path by means of a cursor. Randomized initial target positions and velocities were detected in the periphery of the eyes, resulting in a saccade toward the target. Even when the target disappeared, the eyes followed the target's anticipated course. The Gestalt of the trajectories was dependent on target velocity. The prediction capability of the motor schema was investigated by varying the visibility range of cursor and target. Motor schemata were determined to be of limited precision, and therefore visual feedback was continuously required to intercept the moving target. To intercept a target, the motor schema caused the hand to aim ahead and to adapt to the target trajectory. The control of cursor velocity determined the point of interception. From a modeling point of view, a neural network was developed that allowed the implementation of a motor schema interacting with feedback control in an iterative manner. The neural net of the Wilson type consists of an excitation-diffusion layer allowing the generation of a moving bubble. This activation bubble runs down an eye-centered motor schema and causes a planar arm model to move toward the target. A bubble provides local integration and straightening of the trajectory during repetitive moves. The schema adapts to task demands by learning and serves as forward controller. On the basis of these model considerations the principal problem of embedding motor schemata in generalized control strategies is discussed.

  15. Generation of human cortical neurons from a new immortal fetal neural stem cell line

    International Nuclear Information System (INIS)

    Cacci, E.; Villa, A.; Parmar, M.; Cavallaro, M.; Mandahl, N.; Lindvall, O.; Martinez-Serrano, A.; Kokaia, Z.

    2007-01-01

    Isolation and expansion of neural stem cells (NSCs) of human origin are crucial for successful development of cell therapy approaches in neurodegenerative diseases. Different epigenetic and genetic immortalization strategies have been established for long-term maintenance and expansion of these cells in vitro. Here we report the generation of a new, clonal NSC (hc-NSC) line, derived from human fetal cortical tissue, based on v-myc immortalization. Using immunocytochemistry, we show that these cells retain the characteristics of NSCs after more than 50 passages. Under proliferation conditions, when supplemented with epidermal and basic fibroblast growth factors, the hc-NSCs expressed neural stem/progenitor cell markers like nestin, vimentin and Sox2. When growth factors were withdrawn, proliferation and expression of v-myc and telomerase were dramatically reduced, and the hc-NSCs differentiated into glia and neurons (mostly glutamatergic and GABAergic, as well as tyrosine hydroxylase-positive, presumably dopaminergic neurons). RT-PCR analysis showed that the hc-NSCs retained expression of Pax6, Emx2 and Neurogenin2, which are genes associated with regionalization and cell commitment in cortical precursors during brain development. Our data indicate that this hc-NSC line could be useful for exploring the potential of human NSCs to replace dead or damaged cortical cells in animal models of acute and chronic neurodegenerative diseases. Taking advantage of its clonality and homogeneity, this cell line will also be a valuable experimental tool to study the regulatory role of intrinsic and extrinsic factors in human NSC biology

  16. Embodied Learning of a Generative Neural Model for Biological Motion Perception and Inference

    Directory of Open Access Journals (Sweden)

    Fabian eSchrodt

    2015-07-01

    Full Text Available Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

  17. Widespread neural oscillations in the delta band dissociate rule convergence from rule divergence during creative idea generation

    NARCIS (Netherlands)

    Boot, N.; Baas, M.; Mühlfeld, E.; de Dreu, C.K.W.; van Gaal, S.

    Critical to creative cognition and performance is both the generation of multiple alternative solutions in response to open-ended problems (divergent thinking) and a series of cognitive operations that converges on the correct or best possible answer (convergent thinking). Although the neural

  18. Recurrent glioblastomas exhibit higher expression of biomarkers with stem-like properties

    Directory of Open Access Journals (Sweden)

    B N Nandeesh

    2018-01-01

    observed in recurrent tumors but was not statistically significant. Conclusion: A significant increase in the expression of SOX2 in recurrent tumors probably indicates the presence of undifferentiated cells with stem-like properties in these tumors. EGFR is known to mediate SOX2 expression thereby resulting in stemness of the glioma cancer cells, which could further explain its overexpression in recurrent GBMs. Furthermore, a decreased expression of TOP2A observed in the recurrent tumors could probably be due to reduction in chemosensitivity to temozolomide, which has been shown in earlier studies. We also noted that p53 expression remained unaltered in the recurrent tumors when compared to the primary, suggesting the absence of preferential clonal expansion of p53 mutant cells following exposure to radiochemotherapy. Our study reiterates the fact that GBM recurrences are associated with molecular alterations that probably contribute to radiochemoresistance, increased invasiveness, therapeutic efficacy, and stemness.

  19. Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

    Directory of Open Access Journals (Sweden)

    Atsuhito Toyomaki

    Full Text Available Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

  20. Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

    Science.gov (United States)

    Toyomaki, Atsuhito; Hashimoto, Naoki; Kako, Yuki; Murohashi, Harumitsu; Kusumi, Ichiro

    2017-01-01

    Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

  1. Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions.

    Science.gov (United States)

    Testolin, Alberto; Zorzi, Marco

    2016-01-01

    Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.

  2. Efficient and rapid derivation of primitive neural stem cells and generation of brain subtype neurons from human pluripotent stem cells.

    Science.gov (United States)

    Yan, Yiping; Shin, Soojung; Jha, Balendu Shekhar; Liu, Qiuyue; Sheng, Jianting; Li, Fuhai; Zhan, Ming; Davis, Janine; Bharti, Kapil; Zeng, Xianmin; Rao, Mahendra; Malik, Nasir; Vemuri, Mohan C

    2013-11-01

    Human pluripotent stem cells (hPSCs), including human embryonic stem cells and human induced pluripotent stem cells, are unique cell sources for disease modeling, drug discovery screens, and cell therapy applications. The first step in producing neural lineages from hPSCs is the generation of neural stem cells (NSCs). Current methods of NSC derivation involve the time-consuming, labor-intensive steps of an embryoid body generation or coculture with stromal cell lines that result in low-efficiency derivation of NSCs. In this study, we report a highly efficient serum-free pluripotent stem cell neural induction medium that can induce hPSCs into primitive NSCs (pNSCs) in 7 days, obviating the need for time-consuming, laborious embryoid body generation or rosette picking. The pNSCs expressed the neural stem cell markers Pax6, Sox1, Sox2, and Nestin; were negative for Oct4; could be expanded for multiple passages; and could be differentiated into neurons, astrocytes, and oligodendrocytes, in addition to the brain region-specific neuronal subtypes GABAergic, dopaminergic, and motor neurons. Global gene expression of the transcripts of pNSCs was comparable to that of rosette-derived and human fetal-derived NSCs. This work demonstrates an efficient method to generate expandable pNSCs, which can be further differentiated into central nervous system neurons and glia with temporal, spatial, and positional cues of brain regional heterogeneity. This method of pNSC derivation sets the stage for the scalable production of clinically relevant neural cells for cell therapy applications in good manufacturing practice conditions.

  3. A Combination of Central Pattern Generator-based and Reflex-based Neural Networks for Dynamic, Adaptive, Robust Bipedal Locomotion

    DEFF Research Database (Denmark)

    Di Canio, Giuliano; Larsen, Jørgen Christian; Wörgötter, Florentin

    2016-01-01

    Robotic systems inspired from humans have always been lightening up the curiosity of engineers and scientists. Of many challenges, human locomotion is a very difficult one where a number of different systems needs to interact in order to generate a correct and balanced pattern. To simulate...... the interaction of these systems, implementations with reflexbased or central pattern generator (CPG)-based controllers have been tested on bipedal robot systems. In this paper we will combine the two controller types, into a controller that works with both reflex and CPG signals. We use a reflex-based neural...... network to generate basic walking patterns of a dynamic bipedal walking robot (DACBOT) and then a CPG-based neural network to ensure robust walking behavior...

  4. Beyond GLMs: a generative mixture modeling approach to neural system identification.

    Directory of Open Access Journals (Sweden)

    Lucas Theis

    Full Text Available Generalized linear models (GLMs represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.

  5. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

    Directory of Open Access Journals (Sweden)

    Alireza Taravat

    2015-02-01

    Full Text Available A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

  6. Neural networks for the generation of sea bed models using airborne lidar bathymetry data

    Science.gov (United States)

    Kogut, Tomasz; Niemeyer, Joachim; Bujakiewicz, Aleksandra

    2016-06-01

    Various sectors of the economy such as transport and renewable energy have shown great interest in sea bed models. The required measurements are usually carried out by ship-based echo sounding, but this method is quite expensive. A relatively new alternative is data obtained by airborne lidar bathymetry. This study investigates the accuracy of these data, which was obtained in the context of the project `Investigation on the use of airborne laser bathymetry in hydrographic surveying'. A comparison to multi-beam echo sounding data shows only small differences in the depths values of the data sets. The IHO requirements of the total horizontal and vertical uncertainty for laser data are met. The second goal of this paper is to compare three spatial interpolation methods, namely Inverse Distance Weighting (IDW), Delaunay Triangulation (TIN), and supervised Artificial Neural Networks (ANN), for the generation of sea bed models. The focus of our investigation is on the amount of required sampling points. This is analyzed by manually reducing the data sets. We found that the three techniques have a similar performance almost independently of the amount of sampling data in our test area. However, ANN are more stable when using a very small subset of points.

  7. Neural networks for the generation of sea bed models using airborne lidar bathymetry data

    Directory of Open Access Journals (Sweden)

    Kogut Tomasz

    2016-06-01

    Full Text Available Various sectors of the economy such as transport and renewable energy have shown great interest in sea bed models. The required measurements are usually carried out by ship-based echo sounding, but this method is quite expensive. A relatively new alternative is data obtained by airborne lidar bathymetry. This study investigates the accuracy of these data, which was obtained in the context of the project ‘Investigation on the use of airborne laser bathymetry in hydrographic surveying’. A comparison to multi-beam echo sounding data shows only small differences in the depths values of the data sets. The IHO requirements of the total horizontal and vertical uncertainty for laser data are met. The second goal of this paper is to compare three spatial interpolation methods, namely Inverse Distance Weighting (IDW, Delaunay Triangulation (TIN, and supervised Artificial Neural Networks (ANN, for the generation of sea bed models. The focus of our investigation is on the amount of required sampling points. This is analyzed by manually reducing the data sets. We found that the three techniques have a similar performance almost independently of the amount of sampling data in our test area. However, ANN are more stable when using a very small subset of points.

  8. Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks.

    Science.gov (United States)

    Eunsuk Chong; Taejin Choi; Hyungmin Kim; Seung-Jong Kim; Yoha Hwang; Jong Min Lee

    2017-07-01

    We propose a novel approach of selecting useful input sensors as well as learning a mathematical model for predicting lower limb joint kinematics. We applied a feature selection method based on the mutual information called the variational information maximization, which has been reported as the state-of-the-art work among information based feature selection methods. The main difficulty in applying the method is estimating reliable probability density of input and output data, especially when the data are high dimensional and real-valued. We addressed this problem by applying a generative stochastic neural network called the restricted Boltzmann machine, through which we could perform sampling based probability estimation. The mutual informations between inputs and outputs are evaluated in each backward sensor elimination step, and the least informative sensor is removed with its network connections. The entire network is fine-tuned by maximizing conditional likelihood in each step. Experimental results are shown for 4 healthy subjects walking with various speeds, recording 64 sensor measurements including electromyogram, acceleration, and foot-pressure sensors attached on both lower limbs for predicting hip and knee joint angles. For test set of walking with arbitrary speed, our results show that our suggested method can select informative sensors while maintaining a good prediction accuracy.

  9. U-tube steam generator empirical model development and validation using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Chong, K.T.; Atiya, A.

    1992-01-01

    Empirical modeling techniques that use model structures motivated from neural networks research have proven effective in identifying complex process dynamics. A recurrent multilayer perception (RMLP) network was developed as a nonlinear state-space model structure along with a static learning algorithm for estimating the parameter associated with it. The methods developed were demonstrated by identifying two submodels of a U-tube steam generator (UTSG), each valid around an operating power level. A significant drawback of this approach is the long off-line training times required for the development of even a simplified model of a UTSG. Subsequently, a dynamic gradient descent-based learning algorithm was developed as an accelerated alternative to train an RMLP network for use in empirical modeling of power plants. The two main advantages of this learning algorithm are its ability to consider past error gradient information for future use and the two forward passes associated with its implementation. The enhanced learning capabilities provided by the dynamic gradient descent-based learning algorithm were demonstrated via the case study of a simple steam boiler power plant. In this paper, the dynamic gradient descent-based learning algorithm is used for the development and validation of a complete UTSG empirical model

  10. Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

    Science.gov (United States)

    Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

    2018-01-01

    Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

  11. Vitamin D compounds inhibit cancer stem-like cells and induce differentiation in triple negative breast cancer.

    Science.gov (United States)

    Shan, Naing Lin; Wahler, Joseph; Lee, Hong Jin; Bak, Min Ji; Gupta, Soumyasri Das; Maehr, Hubert; Suh, Nanjoo

    2017-10-01

    Triple-negative breast cancer is one of the least responsive breast cancer subtypes to available targeted therapies due to the absence of hormonal receptors, aggressive phenotypes, and the high rate of relapse. Early breast cancer prevention may therefore play an important role in delaying the progression of triple-negative breast cancer. Cancer stem cells are a subset of cancer cells that are thought to be responsible for tumor progression, treatment resistance, and metastasis. We have previously shown that vitamin D compounds, including a Gemini vitamin D analog BXL0124, suppress progression of ductal carcinoma in situ in vivo and inhibit cancer stem-like cells in MCF10DCIS mammosphere cultures. In the present study, the effects of vitamin D compounds in regulating breast cancer stem-like cells and differentiation in triple-negative breast cancer were assessed. Mammosphere cultures, which enriches for breast cancer cells with stem-like properties, were used to assess the effects of 1α,25(OH) 2 D 3 and BXL0124 on cancer stem cell markers in the triple-negative breast cancer cell line, SUM159. Vitamin D compounds significantly reduced the mammosphere forming efficiency in primary, secondary and tertiary passages of mammospheres compared to control groups. Key markers of cancer stem-like phenotype and pluripotency were analyzed in mammospheres treated with 1α,25(OH) 2 D 3 and BXL0124. As a result, OCT4, CD44 and LAMA5 levels were decreased. The vitamin D compounds also down-regulated the Notch signaling molecules, Notch1, Notch2, Notch3, JAG1, JAG2, HES1 and NFκB, which are involved in breast cancer stem cell maintenance. In addition, the vitamin D compounds up-regulated myoepithelial differentiating markers, cytokeratin 14 and smooth muscle actin, and down-regulated the luminal marker, cytokeratin 18. Cytokeratin 5, a biomarker associated with basal-like breast cancer, was found to be significantly down-regulated by the vitamin D compounds. These results suggest

  12. MR-based synthetic CT generation using a deep convolutional neural network method.

    Science.gov (United States)

    Han, Xiao

    2017-04-01

    Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion. The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN

  13. Involvement of miRNAs in the differentiation of human glioblastoma multiforme stem-like cells.

    Directory of Open Access Journals (Sweden)

    Beatriz Aldaz

    Full Text Available Glioblastoma multiforme (GBM-initiating cells (GICs represent a tumor subpopulation with neural stem cell-like properties that is responsible for the development, progression and therapeutic resistance of human GBM. We have recently shown that blockade of NFκB pathway promotes terminal differentiation and senescence of GICs both in vitro and in vivo, indicating that induction of differentiation may be a potential therapeutic strategy for GBM. MicroRNAs have been implicated in the pathogenesis of GBM, but a high-throughput analysis of their role in GIC differentiation has not been reported. We have established human GIC cell lines that can be efficiently differentiated into cells expressing astrocytic and neuronal lineage markers. Using this in vitro system, a microarray-based high-throughput analysis to determine global expression changes of microRNAs during differentiation of GICs was performed. A number of changes in the levels of microRNAs were detected in differentiating GICs, including over-expression of hsa-miR-21, hsa-miR-29a, hsa-miR-29b, hsa-miR-221 and hsa-miR-222, and down-regulation of hsa-miR-93 and hsa-miR-106a. Functional studies showed that miR-21 over-expression in GICs induced comparable cell differentiation features and targeted SPRY1 mRNA, which encodes for a negative regulator of neural stem-cell differentiation. In addition, miR-221 and miR-222 inhibition in differentiated cells restored the expression of stem cell markers while reducing differentiation markers. Finally, miR-29a and miR-29b targeted MCL1 mRNA in GICs and increased apoptosis. Our study uncovers the microRNA dynamic expression changes occurring during differentiation of GICs, and identifies miR-21 and miR-221/222 as key regulators of this process.

  14. Automatic cell cloning assay for determining the clonogenic capacity of cancer and cancer stem-like cells.

    Science.gov (United States)

    Fedr, Radek; Pernicová, Zuzana; Slabáková, Eva; Straková, Nicol; Bouchal, Jan; Grepl, Michal; Kozubík, Alois; Souček, Karel

    2013-05-01

    The clonogenic assay is a well-established in vitro method for testing the survival and proliferative capability of cells. It can be used to determine the cytotoxic effects of various treatments including chemotherapeutics and ionizing radiation. However, this approach can also characterize cells with different phenotypes and biological properties, such as stem cells or cancer stem cells. In this study, we implemented a faster and more precise method for assessing the cloning efficiency of cancer stem-like cells that were characterized and separated using a high-speed cell sorter. Cell plating onto a microplate using an automatic cell deposition unit was performed in a single-cell or dilution rank mode by the fluorescence-activated cell sorting method. We tested the new automatic cell-cloning assay (ACCA) on selected cancer cell lines and compared it with the manual approach. The obtained results were also compared with the results of the limiting dilution assay for different cell lines. We applied the ACCA to analyze the cloning capacity of different subpopulations of prostate and colon cancer cells based on the expression of the characteristic markers of stem (CD44 and CD133) and cancer stem cells (TROP-2, CD49f, and CD44). Our results revealed that the novel ACCA is a straightforward approach for determining the clonogenic capacity of cancer stem-like cells identified in both cell lines and patient samples. Copyright © 2013 International Society for Advancement of Cytometry.

  15. ATM kinase sustains breast cancer stem-like cells by promoting ATG4C expression and autophagy.

    Science.gov (United States)

    Antonelli, Martina; Strappazzon, Flavie; Arisi, Ivan; Brandi, Rossella; D'Onofrio, Mara; Sambucci, Manolo; Manic, Gwenola; Vitale, Ilio; Barilà, Daniela; Stagni, Venturina

    2017-03-28

    The efficacy of Ataxia-Telangiectasia Mutated (ATM) kinase signalling inhibition in cancer therapy is tempered by the identification of new emerging functions of ATM, which suggests that the role of this protein in cancer progression is complex. We recently demonstrated that this tumor suppressor gene could act as tumor promoting factor in HER2 (Human Epidermal Growth Factor Receptor 2) positive breast cancer. Herein we put in evidence that ATM expression sustains the proportion of cells with a stem-like phenotype, measured as the capability to form mammospheres, independently of HER2 expression levels. Transcriptomic analyses revealed that, in mammospheres, ATM modulates the expression of cell cycle-, DNA repair- and autophagy-related genes. Among these, the silencing of the autophagic gene, autophagy related 4C cysteine peptidase (ATG4C), impairs mammosphere formation similarly to ATM depletion. Conversely, ATG4C ectopic expression in cells silenced for ATM expression, rescues mammospheres growth. Finally, tumor array analyses, performed using public data, identify a significant correlation between ATM and ATG4C expression levels in all human breast cancer subtypes, except for the basal-like one.Overall, we uncover a new connection between ATM kinase and autophagy regulation in breast cancer. We demonstrate that, in breast cancer cells, ATM and ATG4C are essential drivers of mammosphere formation, suggesting that their targeting may improve current approaches to eradicate breast cancer cells with a stem-like phenotype.

  16. Cellular stress induces cancer stem-like cells through expression of DNAJB8 by activation of heat shock factor 1.

    Science.gov (United States)

    Kusumoto, Hiroki; Hirohashi, Yoshihiko; Nishizawa, Satoshi; Yamashita, Masamichi; Yasuda, Kazuyo; Murai, Aiko; Takaya, Akari; Mori, Takashi; Kubo, Terufumi; Nakatsugawa, Munehide; Kanaseki, Takayuki; Tsukahara, Tomohide; Kondo, Toru; Sato, Noriyuki; Hara, Isao; Torigoe, Toshihiko

    2018-03-01

    In a previous study, we found that DNAJB8, a heat shock protein (HSP) 40 family member is expressed in kidney cancer stem-like cells (CSC)/cancer-initiating cells (CIC) and that it has a role in the maintenance of kidney CSC/CIC. Heat shock factor (HSF) 1 is a key transcription factor for responses to stress including heat shock, and it induces HSP family expression through activation by phosphorylation. In the present study, we therefore examined whether heat shock (HS) induces CSC/CIC. We treated the human kidney cancer cell line ACHN with HS, and found that HS increased side population (SP) cells. Western blot analysis and qRT-PCR showed that HS increased the expression of DNAJB8 and SOX2. Gene knockdown experiments using siRNAs showed that the increase in SOX2 expression and SP cell ratio depends on DNAJB8 and that the increase in DNAJB8 and SOX2 depend on HSF1. Furthermore, treatment with a mammalian target of rapamycin (mTOR) inhibitor, temsirolimus, decreased the expression of DNAJB8 and SOX2 and the ratio of SP cells. Taken together, the results indicate that heat shock induces DNAJB8 by activation of HSF1 and induces cancer stem-like cells. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

  17. Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network.

    Science.gov (United States)

    Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana M; Dan, Bernard; McIntyre, Joseph; Cheron, Guy

    2014-01-01

    In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

  18. An Exploratory Application of Neural Networks to the Sortie Generation Forecasting Problem

    Science.gov (United States)

    1991-09-01

    research of Dr. David A. Diener, Major, USAF. As the initial research increment to be improved upon by future researchers, this study (1) provides a... David A. Diener, Major, USAF, who virtually transformed my dream of exploring neural network techniques into concrete reality. His talents in...New York: John Wiley & Sons, 1978. Barron R. L., Gilstrap, L. 0., and Shrier , S. "Polynomial al and Neural Networks: Analogies and Engineering

  19. High linear-energy-transfer radiation can overcome radioresistance of glioma stem-like cells to low linear-energy-transfer radiation.

    Science.gov (United States)

    Hirota, Yuki; Masunaga, Shin-Ichiro; Kondo, Natsuko; Kawabata, Shinji; Hirakawa, Hirokazu; Yajima, Hirohiko; Fujimori, Akira; Ono, Koji; Kuroiwa, Toshihiko; Miyatake, Shin-Ichi

    2014-01-01

    Ionizing radiation is applied as the standard treatment for glioblastoma multiforme (GBM). However, radiotherapy remains merely palliative, not curative, because of the existence of glioma stem cells (GSCs), which are regarded as highly radioresistant to low linear-energy-transfer (LET) photons. Here we analyzed whether or not high-LET particles can overcome the radioresistance of GSCs. Glioma stem-like cells (GSLCs) were induced from the GBM cell line A172 in stem cell culture medium. The phenotypes of GSLCs and wild-type cells were confirmed using stem cell markers. These cells were irradiated with (60)Co gamma rays or reactor neutron beams. Under neutron-beam irradiation, high-LET proton particles can be produced through elastic scattering or nitrogen capture reaction. Radiosensitivity was assessed by a colony-forming assay, and the DNA double-strand breaks (DSBs) were assessed by a histone gamma-H2AX focus detection assay. In stem cell culture medium, GSLCs could form neurosphere-like cells and express neural stem cell markers (Sox2 and Musashi) abundantly in comparison with their parental cells. GSLCs were significantly more radioresistant to gamma rays than their parental cells, but neutron beams overcame this resistance. There were significantly fewer gamma-H2AX foci in the A172 GSLCs 24 h after irradiation with gamma rays than in their parental cultured cells, while there was no apparent difference following neutron-beam irradiation. High-LET radiation can overcome the radioresistance of GSLCs by producing unrepairable DNA DSBs. High-LET radiation therapy might have the potential to overcome GBM's resistance to X-rays in a clinical setting.

  20. High linear-energy-transfer radiation can overcome radioresistance of glioma stem-like cells to low linear-energy-transfer radiation

    International Nuclear Information System (INIS)

    Hirota, Yuki; Kawabata, Shinji; Kuroiwa, Toshihiko; Miyatake, Shin-ichi; Masunaga, Shin-ichiro; Kondo, Natsuko; Ono, Koji; Hirakawa, Hirokazu; Yajima, Hirohiko; Fujimori, Akira

    2014-01-01

    Ionizing radiation is applied as the standard treatment for glioblastoma multiforme (GBM). However, radiotherapy remains merely palliative, not curative, because of the existence of glioma stem cells (GSCs), which are regarded as highly radioresistant to low linear-energy-transfer (LET) photons. Here we analyzed whether or not high-LET particles can overcome the radioresistance of GSCs. Glioma stem-like cells (GSLCs) were induced from the GBM cell line A172 in stem cell culture medium. The phenotypes of GSLCs and wild-type cells were confirmed using stem cell markers. These cells were irradiated with 60 Co gamma rays or reactor neutron beams. Under neutron-beam irradiation, high-LET proton particles can be produced through elastic scattering or nitrogen capture reaction. Radiosensitivity was assessed by a colony-forming assay, and the DNA double-strand breaks (DSBs) were assessed by a histone gamma-H2AX focus detection assay. In stem cell culture medium, GSLCs could form neurosphere-like cells and express neural stem cell markers (Sox2 and Musashi) abundantly in comparison with their parental cells. GSLCs were significantly more radioresistant to gamma rays than their parental cells, but neutron beams overcame this resistance. There were significantly fewer gamma-H2AX foci in the A172 GSLCs 24 h after irradiation with gamma rays than in their parental cultured cells, while there was no apparent difference following neutron-beam irradiation. High-LET radiation can overcome the radioresistance of GSLCs by producing unrepairable DNA DSBs. High-LET radiation therapy might have the potential to overcome GBM's resistance to X-rays in a clinical setting. (author)

  1. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    Directory of Open Access Journals (Sweden)

    Paul Tonelli

    Full Text Available A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1 the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2 synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT. Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1 in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2 whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  2. Differentiation of neurons from neural precursors generated in floating spheres from embryonic stem cells

    Directory of Open Access Journals (Sweden)

    Forrester Jeff

    2009-09-01

    Full Text Available Abstract Background Neural differentiation of embryonic stem (ES cells is usually achieved by induction of ectoderm in embryoid bodies followed by the enrichment of neuronal progenitors using a variety of factors. Obtaining reproducible percentages of neural cells is difficult and the methods are time consuming. Results Neural progenitors were produced from murine ES cells by a combination of nonadherent conditions and serum starvation. Conversion to neural progenitors was accompanied by downregulation of Oct4 and NANOG and increased expression of nestin. ES cells containing a GFP gene under the control of the Sox1 regulatory regions became fluorescent upon differentiation to neural progenitors, and ES cells with a tau-GFP fusion protein became fluorescent upon further differentiation to neurons. Neurons produced from these cells upregulated mature neuronal markers, or differentiated to glial and oligodendrocyte fates. The neurons gave rise to action potentials that could be recorded after application of fixed currents. Conclusion Neural progenitors were produced from murine ES cells by a novel method that induced neuroectoderm cells by a combination of nonadherent conditions and serum starvation, in contrast to the embryoid body method in which neuroectoderm cells must be selected after formation of all three germ layers.

  3. Establishment of a novel human medulloblastoma cell line characterized by highly aggressive stem-like cells.

    Science.gov (United States)

    Silva, Patrícia Benites Gonçalves da; Rodini, Carolina Oliveira; Kaid, Carolini; Nakahata, Adriana Miti; Pereira, Márcia Cristina Leite; Matushita, Hamilton; Costa, Silvia Souza da; Okamoto, Oswaldo Keith

    2016-08-01

    Medulloblastoma is a highly aggressive brain tumor and one of the leading causes of morbidity and mortality related to childhood cancer. These tumors display differential ability to metastasize and respond to treatment, which reflects their high degree of heterogeneity at the genetic and molecular levels. Such heterogeneity of medulloblastoma brings an additional challenge to the understanding of its physiopathology and impacts the development of new therapeutic strategies. This translational effort has been the focus of most pre-clinical studies which invariably employ experimental models using human tumor cell lines. Nonetheless, compared to other cancers, relatively few cell lines of human medulloblastoma are available in central repositories, partly due to the rarity of these tumors and to the intrinsic difficulties in establishing continuous cell lines from pediatric brain tumors. Here, we report the establishment of a new human medulloblastoma cell line which, in comparison with the commonly used and well-established cell line Daoy, is characterized by enhanced proliferation and invasion capabilities, stem cell properties, increased chemoresistance, tumorigenicity in an orthotopic metastatic model, replication of original medulloblastoma behavior in vivo, strong chromosome structural instability and deregulation of genes involved in neural development. These features are advantageous for designing biologically relevant experimental models in clinically oriented studies, making this novel cell line, named USP-13-Med, instrumental for the study of medulloblastoma biology and treatment.

  4. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks

    International Nuclear Information System (INIS)

    Almonacid, F.; Rus, C.; Perez-Higueras, P.; Hontoria, L.

    2011-01-01

    The use of photovoltaics for electricity generation purposes has recorded one of the largest increases in the field of renewable energies. The energy production of a grid-connected PV system depends on various factors. In a wide sense, it is considered that the annual energy provided by a generator is directly proportional to the annual radiation incident on the plane of the generator and to the installed nominal power. However, a range of factors is influencing the expected outcome by reducing the generation of energy. The aim of this study is to compare the results of four different methods for estimating the annual energy produced by a PV generator: three of them are classical methods and the fourth one is based on an artificial neural network developed by the R and D Group for Solar and Automatic Energy at the University of Jaen. The results obtained shown that the method based on an artificial neural network provides better results than the alternative classical methods in study, mainly due to the fact that this method takes also into account some second order effects, such as low irradiance, angular and spectral effects. -- Research highlights: → It is considered that the annual energy provided by a PV generator is directly proportional to the annual radiation incident on the plane of the generator and to the installed nominal power. → A range of factors are influencing the expected outcome by reducing the generation of energy (mismatch losses, dirt and dust, Ohmic losses,.). → The aim of this study is to compare the results of four different methods for estimating the annual energy produced by a PV generator: three of them are classical methods and the fourth one is based on an artificial neural network. → The results obtained shown that the method based on an artificial neural network provides better results than the alternative classical methods in study. While classical methods have only taken into account temperature losses, the method based in

  5. Cancer cell-soluble factors reprogram mesenchymal stromal cells to slow cycling, chemoresistant cells with a more stem-like state

    Directory of Open Access Journals (Sweden)

    Ahmed El-Badawy

    2017-11-01

    Full Text Available Abstract Background Mesenchymal stem cells (MSCs play different roles in modulating tumor progression, growth, and metastasis. MSCs are recruited to the tumor site in large numbers and subsequently have an important microenvironmental role in modulating tumor progression and drug sensitivity. However, the effect of the tumor microenvironment on MSC plasticity remains poorly understood. Herein, we report a paracrine effect of cancer cells, in which they secrete soluble factors that promote a more stem-like state in bone marrow mesenchymal stem cells (BM-MSCs. Methods The effect of soluble factors secreted from MCF7, Hela, and HepG2 cancer cell lines on BM-MSCs was assessed using a Transwell indirect coculture system. After 5 days of coculture, BM-MSCs were characterized by flow cytometry for surface marker expression, by qPCR for gene expression profile, and by confocal immunofluorescence for marker expression. We then measured the sensitivity of cocultured BM-MSCs to chemotherapeutic agents, their cell cycle profile, and their response to DNA damage. The sphere formation, invasive properties, and in-vivo performance of BM-MSCs after coculture with cancer cells were also measured. Results Indirect coculture of cancer cells and BM-MSCs, without direct cell contact, generated slow cycling, chemoresistant spheroid stem cells that highly expressed markers of pluripotency, cancer cells, and cancer stem cells (CSCs. They also displayed properties of a side population and enhanced sphere formation in culture. Accordingly, these cells were termed cancer-induced stem cells (CiSCs. CiSCs showed a more mesenchymal phenotype that was further augmented upon TGF-β stimulation and demonstrated a high expression of the β-catenin pathway and ALDH1A1. Conclusions These findings demonstrate that MSCs, recruited to the tumor microenvironment in large numbers, may display cellular plasticity, acquire a more stem-like state, and acquire some properties of CSCs upon

  6. Photovoltaic generator. Estimate of the energy produced by neural networks; Generador fotovoltaico. Estimacion de la energia producida mediante redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Almonacid, F.; Rus, C.; Perez-Higueras, P.; Hontoria, L.

    2010-07-01

    Despite the great technological advances in photovoltaic and in particular in network-connected systems, efforts are still required in research, technological development and innovation (i + d + i) must be aimed primarily at addressing the different system parts. one aspect that can help achieve this goal is majorette estimation methods of energy produced by photovoltaic generators. There are a number of cases resulting in a decrease of the expected energy. In this paper we will compare a standard method widely used in the estimation of the power of the photovoltaic generator with another novel method, developed at the University of Jaen, based on artificial neural networks (ANN). (Author) 9 refs.

  7. Identifying Stem-like Cells Using Mitochondrial Membrane Potential | Center for Cancer Research

    Science.gov (United States)

    Therapies that are based on living cells promise to improve treatments for metastatic cancer and for many degenerative diseases. Lasting treatment of these maladies may require the durable persistence of cells. Long-term engraftment of cells – for months or years – and the generation of large numbers of progeny are characteristics of stem cells. Most approaches to isolate

  8. Large-scale generation of human iPSC-derived neural stem cells/early neural progenitor cells and their neuronal differentiation.

    Science.gov (United States)

    D'Aiuto, Leonardo; Zhi, Yun; Kumar Das, Dhanjit; Wilcox, Madeleine R; Johnson, Jon W; McClain, Lora; MacDonald, Matthew L; Di Maio, Roberto; Schurdak, Mark E; Piazza, Paolo; Viggiano, Luigi; Sweet, Robert; Kinchington, Paul R; Bhattacharjee, Ayantika G; Yolken, Robert; Nimgaonka, Vishwajit L; Nimgaonkar, Vishwajit L

    2014-01-01

    Induced pluripotent stem cell (iPSC)-based technologies offer an unprecedented opportunity to perform high-throughput screening of novel drugs for neurological and neurodegenerative diseases. Such screenings require a robust and scalable method for generating large numbers of mature, differentiated neuronal cells. Currently available methods based on differentiation of embryoid bodies (EBs) or directed differentiation of adherent culture systems are either expensive or are not scalable. We developed a protocol for large-scale generation of neuronal stem cells (NSCs)/early neural progenitor cells (eNPCs) and their differentiation into neurons. Our scalable protocol allows robust and cost-effective generation of NSCs/eNPCs from iPSCs. Following culture in neurobasal medium supplemented with B27 and BDNF, NSCs/eNPCs differentiate predominantly into vesicular glutamate transporter 1 (VGLUT1) positive neurons. Targeted mass spectrometry analysis demonstrates that iPSC-derived neurons express ligand-gated channels and other synaptic proteins and whole-cell patch-clamp experiments indicate that these channels are functional. The robust and cost-effective differentiation protocol described here for large-scale generation of NSCs/eNPCs and their differentiation into neurons paves the way for automated high-throughput screening of drugs for neurological and neurodegenerative diseases.

  9. Maintenance and Neuronal Differentiation of Chicken Induced Pluripotent Stem-Like Cells

    OpenAIRE

    Dai, Rui; Rossello, Ricardo; Chen, Chun-chun; Kessler, Joeran; Davison, Ian; Hochgeschwender, Ute; Jarvis, Erich D.

    2014-01-01

    Pluripotent stem cells have the potential to become any cell in the adult body, including neurons and glia. Avian stem cells could be used to study questions, like vocal learning, that would be difficult to examine with traditional mouse models. Induced pluripotent stem cells (iPSCs) are differentiated cells that have been reprogrammed to a pluripotent stem cell state, usually using inducing genes or other molecules. We recently succeeded in generating avian iPSC-like cells using mammalian ge...

  10. HA117 endows HL60 cells with a stem-like signature by inhibiting the degradation of DNMT1 via its ability to down-regulate expression of the GGL domain of RGS6.

    Directory of Open Access Journals (Sweden)

    Shuangshuang Li

    Full Text Available All-trans retinoic acid (ATRA induces complete remission in almost all patients with acute promyelocytic leukemia (APL via its ability to induce the in vivo differentiation of APL blasts. However, prolonged ATRA treatment can result in drug resistance. In previous studies, we generated a multi-drug-resistant HL60/ATRA cell line and found it to contain a new drug resistance-related gene segment, HA117. In this study, we demonstrate that ATRA induces multi-drug-resistant subpopulations of HL60 cells with a putative stem-like signature by up-regulating the expression of the new gene segment HA117. Western blot analysis and quantitative real-time PCR demonstrated that HA117 causes alternative splicing of regulator of G-protein signaling 6 (RGS6 and down-regulation of the expression of the GGL domain of RGS6, which plays an important role in DNA methyltransferase 1 (DNMT1 degradation. Moreover, DNMT1 expression was increased in multi-drug resistance HL60/ATRA cells. Knockdown of HA117 restored expression of the GGL domain and blocked DNMT1 expression. Moreover, resistant cells displayed a putative stem-like signature with increased expression of cancer steam cell markers CD133 and CD123. The stem cell marker, Nanog, was significantly up-regulated. In conclusion, our study shows that HA117 potentially promotes the stem-like signature of the HL60/ATRA cell line by inhibiting by the ubiquitination and degradation of DNMT1 and by down-regulating the expression of the GGL domain of RGS6. These results throw light on the cellular events associated with the ATRA-induced multi-drug resistance phenotype in acute leukemia.

  11. Generation of Regionally Specific Neural Progenitor Cells (NPCs) and Neurons from Human Pluripotent Stem Cells (hPSCs).

    Science.gov (United States)

    Cutts, Josh; Brookhouser, Nicholas; Brafman, David A

    2016-01-01

    Neural progenitor cells (NPCs) derived from human pluripotent stem cells (hPSCs) are a multipotent cell population capable of long-term expansion and differentiation into a variety of neuronal subtypes. As such, NPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine. Current methods for the generation of NPCs results in cell populations homogenous for pan-neural markers such as SOX1 and SOX2 but heterogeneous with respect to regional identity. In order to use NPCs and their neuronal derivatives to investigate mechanisms of neurological disorders and develop more physiologically relevant disease models, methods for generation of regionally specific NPCs and neurons are needed. Here, we describe a protocol in which exogenous manipulation of WNT signaling, through either activation or inhibition, during neural differentiation of hPSCs, promotes the formation of regionally homogenous NPCs and neuronal cultures. In addition, we provide methods to monitor and characterize the efficiency of hPSC differentiation to these regionally specific cell identities.

  12. Generation of Regionally Specified Neural Progenitors and Functional Neurons from Human Embryonic Stem Cells under Defined Conditions

    Directory of Open Access Journals (Sweden)

    Agnete Kirkeby

    2012-06-01

    Full Text Available To model human neural-cell-fate specification and to provide cells for regenerative therapies, we have developed a method to generate human neural progenitors and neurons from human embryonic stem cells, which recapitulates human fetal brain development. Through the addition of a small molecule that activates canonical WNT signaling, we induced rapid and efficient dose-dependent specification of regionally defined neural progenitors ranging from telencephalic forebrain to posterior hindbrain fates. Ten days after initiation of differentiation, the progenitors could be transplanted to the adult rat striatum, where they formed neuron-rich and tumor-free grafts with maintained regional specification. Cells patterned toward a ventral midbrain (VM identity generated a high proportion of authentic dopaminergic neurons after transplantation. The dopamine neurons showed morphology, projection pattern, and protein expression identical to that of human fetal VM cells grafted in parallel. VM-patterned but not forebrain-patterned neurons released dopamine and reversed motor deficits in an animal model of Parkinson's disease.

  13. Stator current harmonics evolution by neural network method based on CFE/SS algorithm for ACEC generator of Rey Power Plant

    International Nuclear Information System (INIS)

    Soleymani, S.; Ranjbar, A.M.; Mirabedini, H.

    2001-01-01

    One method for on-line fault diagnosis in synchronous generator is stator current harmonics analysis. Then artificial neural network is considered in this paper in order to evaluate stator current harmonics in different loads. Training set of artificial neural network is made ready by generator modeling, finite element method and state space model. Many points from generator capability curve are used in order to complete this set. Artificial neural network which is used in this paper is a percept ron network with a single hidden layer, Eight hidden neurons and back propagation algorithm. Results are indicated that the trained artificial neural network can identify stator current harmonics for arbitrary load from the capability curve. The error is less than 10% in comparison with values obtained directly from the CFE-SS algorithm. The rating parameters of modeled generator are 43950 (kV A), 11(KV), 3000 (rpm), 50 (H Z), (P F=0.8)

  14. Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator

    Directory of Open Access Journals (Sweden)

    Khaoula Ghefiri

    2018-04-01

    Full Text Available Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

  15. Generation of Induced Pluripotent Stem Cells and Neural Stem/Progenitor Cells from Newborns with Spina Bifida Aperta.

    Science.gov (United States)

    Bamba, Yohei; Nonaka, Masahiro; Sasaki, Natsu; Shofuda, Tomoko; Kanematsu, Daisuke; Suemizu, Hiroshi; Higuchi, Yuichiro; Pooh, Ritsuko K; Kanemura, Yonehiro; Okano, Hideyuki; Yamasaki, Mami

    2017-12-01

    We established induced pluripotent stem cells (iPSCs) and neural stem/progenitor cells (NSPCs) from three newborns with spina bifida aperta (SBa) using clinically practical methods. We aimed to develop stem cell lines derived from newborns with SBa for future therapeutic use. SBa is a common congenital spinal cord abnormality that causes defects in neurological and urological functions. Stem cell transplantation therapies are predicted to provide beneficial effects for patients with SBa. However, the availability of appropriate cell sources is inadequate for clinical use because of their limited accessibility and expandability, as well as ethical issues. Fibroblast cultures were established from small fragments of skin obtained from newborns with SBa during SBa repair surgery. The cultured cells were transfected with episomal plasmid vectors encoding reprogramming factors necessary for generating iPSCs. These cells were then differentiated into NSPCs by chemical compound treatment, and NSPCs were expanded using neurosphere technology. We successfully generated iPSC lines from the neonatal dermal fibroblasts of three newborns with SBa. We confirmed that these lines exhibited the characteristics of human pluripotent stem cells. We successfully generated NSPCs from all SBa newborn-derived iPSCs with a combination of neural induction and neurosphere technology. We successfully generated iPSCs and iPSC-NSPCs from surgical samples obtained from newborns with SBa with the goal of future clinical use in patients with SBa.

  16. Gefitinib Radiosensitizes Stem-Like Glioma Cells: Inhibition of Epidermal Growth Factor Receptor-Akt-DNA-PK Signaling, Accompanied by Inhibition of DNA Double-Strand Break Repair

    International Nuclear Information System (INIS)

    Kang, Khong Bee; Zhu Congju; Wong Yinling; Gao Qiuhan; Ty, Albert; Wong, Meng Cheong

    2012-01-01

    Purpose: We compared radiosensitivity of brain tumor stem cells (BTSCs) with matched nonstem glioma cells, and determined whether gefitinib enhanced BTSC radiosensitivity by inhibiting epidermal growth factor receptor (EGFR)–Akt-DNA–dependent protein kinase (DNA-PK) signaling, followed by enhanced DNA double-stand breaks (DSBs) and inhibition of DSB repair. Methods and Materials: Radiosensitivity of stem-like gliomaspheres and nonstem glioma cells (obtained at patient neurosurgical resection) were evaluated by clonogenic assays, γ-H 2 AX immunostaining and cell cycle distribution. Survival of irradiated and nonirradiated NOD-SCID mice intracranially implanted with stem-like gliomaspheres were monitored. Glioma cells treated with gefitinib, irradiation, or both were assayed for clonogenic survival, γ-H 2 AX immunostaining, DNA-PKcs expression, and phosphorylation of EGFR and Akt. Results: Stem-like gliomaspheres displayed BTSC characteristics of self-renewal; differentiation into lineages of neurons, oligodendrocytes, and astrocytes; and initiation of glioma growth in NOD-SCID mice. Irradiation dose-dependently reduced clonogenic survival, induced G 2 /M arrest and increased γ-H 2 AX immunostaining of nonstem glioma cells, but not stem-like gliomaspheres. There was no difference in survival of irradiated and nonirradiated mice implanted with stem-like gliomaspheres. The addition of gefitinib significantly inhibited clonogenic survival, increased γ-H 2 AX immunostaining, and reduced DNA-PKcs expression of irradiated stem-like gliomaspheres, without affecting irradiated-nonstem glioma cells. Gefitinib alone, and when combined with irradiation, inhibited phosphorylation of EGFR (Y1068 and Y1045) and Akt (S473) in stem-like gliomaspheres. In nonstem glioma cells, gefitinib alone inhibited EGFR Y1068 phosphorylation, with further inhibition by combined gefitinib and irradiation. Conclusions: Stem-like gliomaspheres are resistant to irradiation

  17. Gefitinib radiosensitizes stem-like glioma cells: inhibition of epidermal growth factor receptor-Akt-DNA-PK signaling, accompanied by inhibition of DNA double-strand break repair.

    Science.gov (United States)

    Kang, Khong Bee; Zhu, Congju; Wong, Yin Ling; Gao, Qiuhan; Ty, Albert; Wong, Meng Cheong

    2012-05-01

    We compared radiosensitivity of brain tumor stem cells (BTSCs) with matched nonstem glioma cells, and determined whether gefitinib enhanced BTSC radiosensitivity by inhibiting epidermal growth factor receptor (EGFR)-Akt-DNA-dependent protein kinase (DNA-PK) signaling, followed by enhanced DNA double-stand breaks (DSBs) and inhibition of DSB repair. Radiosensitivity of stem-like gliomaspheres and nonstem glioma cells (obtained at patient neurosurgical resection) were evaluated by clonogenic assays, γ-H(2)AX immunostaining and cell cycle distribution. Survival of irradiated and nonirradiated NOD-SCID mice intracranially implanted with stem-like gliomaspheres were monitored. Glioma cells treated with gefitinib, irradiation, or both were assayed for clonogenic survival, γ-H(2)AX immunostaining, DNA-PKcs expression, and phosphorylation of EGFR and Akt. Stem-like gliomaspheres displayed BTSC characteristics of self-renewal; differentiation into lineages of neurons, oligodendrocytes, and astrocytes; and initiation of glioma growth in NOD-SCID mice. Irradiation dose-dependently reduced clonogenic survival, induced G(2)/M arrest and increased γ-H(2)AX immunostaining of nonstem glioma cells, but not stem-like gliomaspheres. There was no difference in survival of irradiated and nonirradiated mice implanted with stem-like gliomaspheres. The addition of gefitinib significantly inhibited clonogenic survival, increased γ-H(2)AX immunostaining, and reduced DNA-PKcs expression of irradiated stem-like gliomaspheres, without affecting irradiated-nonstem glioma cells. Gefitinib alone, and when combined with irradiation, inhibited phosphorylation of EGFR (Y1068 and Y1045) and Akt (S473) in stem-like gliomaspheres. In nonstem glioma cells, gefitinib alone inhibited EGFR Y1068 phosphorylation, with further inhibition by combined gefitinib and irradiation. Stem-like gliomaspheres are resistant to irradiation-induced cytotoxicity, G(2)/M arrest, and DNA DSBs, compared with nonstem

  18. Gefitinib Radiosensitizes Stem-Like Glioma Cells: Inhibition of Epidermal Growth Factor Receptor-Akt-DNA-PK Signaling, Accompanied by Inhibition of DNA Double-Strand Break Repair

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Khong Bee, E-mail: dmskkb@nccs.com.sg [Brain Tumour Research Laboratory, Division of Medical Sciences, National Cancer Centre Singapore (Singapore); Zhu Congju; Wong Yinling; Gao Qiuhan; Ty, Albert; Wong, Meng Cheong [Brain Tumour Research Laboratory, Division of Medical Sciences, National Cancer Centre Singapore (Singapore)

    2012-05-01

    Purpose: We compared radiosensitivity of brain tumor stem cells (BTSCs) with matched nonstem glioma cells, and determined whether gefitinib enhanced BTSC radiosensitivity by inhibiting epidermal growth factor receptor (EGFR)-Akt-DNA-dependent protein kinase (DNA-PK) signaling, followed by enhanced DNA double-stand breaks (DSBs) and inhibition of DSB repair. Methods and Materials: Radiosensitivity of stem-like gliomaspheres and nonstem glioma cells (obtained at patient neurosurgical resection) were evaluated by clonogenic assays, {gamma}-H{sub 2}AX immunostaining and cell cycle distribution. Survival of irradiated and nonirradiated NOD-SCID mice intracranially implanted with stem-like gliomaspheres were monitored. Glioma cells treated with gefitinib, irradiation, or both were assayed for clonogenic survival, {gamma}-H{sub 2}AX immunostaining, DNA-PKcs expression, and phosphorylation of EGFR and Akt. Results: Stem-like gliomaspheres displayed BTSC characteristics of self-renewal; differentiation into lineages of neurons, oligodendrocytes, and astrocytes; and initiation of glioma growth in NOD-SCID mice. Irradiation dose-dependently reduced clonogenic survival, induced G{sub 2}/M arrest and increased {gamma}-H{sub 2}AX immunostaining of nonstem glioma cells, but not stem-like gliomaspheres. There was no difference in survival of irradiated and nonirradiated mice implanted with stem-like gliomaspheres. The addition of gefitinib significantly inhibited clonogenic survival, increased {gamma}-H{sub 2}AX immunostaining, and reduced DNA-PKcs expression of irradiated stem-like gliomaspheres, without affecting irradiated-nonstem glioma cells. Gefitinib alone, and when combined with irradiation, inhibited phosphorylation of EGFR (Y1068 and Y1045) and Akt (S473) in stem-like gliomaspheres. In nonstem glioma cells, gefitinib alone inhibited EGFR Y1068 phosphorylation, with further inhibition by combined gefitinib and irradiation. Conclusions: Stem-like gliomaspheres are

  19. Identification of cancer stem-like side population cells in purified primary cultured human laryngeal squamous cell carcinoma epithelia.

    Directory of Open Access Journals (Sweden)

    Chun-Ping Wu

    Full Text Available Cancer stem-like side population (SP cells have been identified in many solid tumors; however, most of these investigations are performed using established cancer cell lines. Cancer cells in tumor tissue containing fibroblasts and many other types of cells are much more complex than any cancer cell line. Although SP cells were identified in the laryngeal squamous cell carcinoma (LSCC cell line Hep-2 in our pilot study, it is unknown whether the LSCC tissue contains SP cells. In this study, LSCC cells (LSCCs were primary cultured and purified from a surgically resected LSCC specimen derived from a well-differentiated epiglottic neoplasm of a Chinese male. This was followed by the verification of epithelium-specific characteristics, such as ultrastructure and biomarkers. A distinct SP subpopulation (4.45±1.07% was isolated by Hoechst 33342 efflux analysis from cultured LSCCs by using a flow cytometer. Cancer stem cell (CSC-associated assays, including expression of self-renewal and CSC marker genes, proliferation, differentiation, spheroid formation, chemotherapy resistance, and tumorigenicity were then conducted between SP and non-SP (NSP LSCCs. In vitro and in vivo assays revealed that SP cells manifested preferential expression of self-renewal and CSC marker genes, higher capacity for proliferation, differentiation, and spheroid formation; enhanced resistance to chemotherapy; and greater xenograft tumorigenicity in immunodeficient mice compared with NSP cells. These findings suggest that the primary cultured and purified LSCCs contain cancer stem-like SP cells, which may serve as a valuable model for CSC research in LSCC.

  20. Therapeutic implications of an enriched cancer stem-like cell population in a human osteosarcoma cell line

    International Nuclear Information System (INIS)

    Martins-Neves, Sara R; Lopes, Áurio O; Carmo, Anália do; Paiva, Artur A; Simões, Paulo C; Abrunhosa, Antero J; Gomes, Célia MF

    2012-01-01

    Osteosarcoma is a bone-forming tumor of mesenchymal origin that presents a clinical pattern that is consistent with the cancer stem cell model. Cells with stem-like properties (CSCs) have been identified in several tumors and hypothesized as the responsible for the relative resistance to therapy and tumor relapses. In this study, we aimed to identify and characterize CSCs populations in a human osteosarcoma cell line and to explore their role in the responsiveness to conventional therapies. CSCs were isolated from the human MNNG/HOS cell line using the sphere formation assay and characterized in terms of self-renewal, mesenchymal stem cell properties, expression of pluripotency markers and ABC transporters, metabolic activity and tumorigenicity. Cell's sensitivity to conventional chemotherapeutic agents and to irradiation was analyzed and related with cell cycle-induced alterations and apoptosis. The isolated CSCs were found to possess self-renewal and multipotential differentiation capabilities, express markers of pluripotent embryonic stem cells Oct4 and Nanog and the ABC transporters P-glycoprotein and BCRP, exhibit low metabolic activity and induce tumors in athymic mice. Compared with parental MNNG/HOS cells, CSCs were relatively more resistant to both chemotherapy and irradiation. None of the treatments have induced significant cell-cycle alterations and apoptosis in CSCs. MNNG/HOS osteosarcoma cells contain a stem-like cell population relatively resistant to conventional chemotherapeutic agents and irradiation. This resistant phenotype appears to be related with some stem features, namely the high expression of the drug efflux transporters P-glycoprotein and BCRP and their quiescent nature, which may provide a biological basis for resistance to therapy and recurrence commonly observed in osteosarcoma

  1. Therapeutic implications of an enriched cancer stem-like cell population in a human osteosarcoma cell line

    Directory of Open Access Journals (Sweden)

    Martins-Neves Sara R

    2012-04-01

    Full Text Available Abstract Background Osteosarcoma is a bone-forming tumor of mesenchymal origin that presents a clinical pattern that is consistent with the cancer stem cell model. Cells with stem-like properties (CSCs have been identified in several tumors and hypothesized as the responsible for the relative resistance to therapy and tumor relapses. In this study, we aimed to identify and characterize CSCs populations in a human osteosarcoma cell line and to explore their role in the responsiveness to conventional therapies. Methods CSCs were isolated from the human MNNG/HOS cell line using the sphere formation assay and characterized in terms of self-renewal, mesenchymal stem cell properties, expression of pluripotency markers and ABC transporters, metabolic activity and tumorigenicity. Cell's sensitivity to conventional chemotherapeutic agents and to irradiation was analyzed and related with cell cycle-induced alterations and apoptosis. Results The isolated CSCs were found to possess self-renewal and multipotential differentiation capabilities, express markers of pluripotent embryonic stem cells Oct4 and Nanog and the ABC transporters P-glycoprotein and BCRP, exhibit low metabolic activity and induce tumors in athymic mice. Compared with parental MNNG/HOS cells, CSCs were relatively more resistant to both chemotherapy and irradiation. None of the treatments have induced significant cell-cycle alterations and apoptosis in CSCs. Conclusions MNNG/HOS osteosarcoma cells contain a stem-like cell population relatively resistant to conventional chemotherapeutic agents and irradiation. This resistant phenotype appears to be related with some stem features, namely the high expression of the drug efflux transporters P-glycoprotein and BCRP and their quiescent nature, which may provide a biological basis for resistance to therapy and recurrence commonly observed in osteosarcoma.

  2. DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing.

    Science.gov (United States)

    Vidaki, Athina; Ballard, David; Aliferi, Anastasia; Miller, Thomas H; Barron, Leon P; Syndercombe Court, Denise

    2017-05-01

    The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R 2 =0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R 2 =0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R 2 =0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next

  3. δ-Tocotrienol, a natural form of vitamin E, inhibits pancreatic cancer stem-like cells and prevents pancreatic cancer metastasis.

    Science.gov (United States)

    Husain, Kazim; Centeno, Barbara A; Coppola, Domenico; Trevino, Jose; Sebti, Said M; Malafa, Mokenge P

    2017-05-09

    The growth, metastasis, and chemotherapy resistance of pancreatic ductal adenocarcinoma (PDAC) is characterized by the activation and growth of tumor-initiating cells in distant organs that have stem-like properties. Thus, inhibiting growth of these cells may prevent PDAC growth and metastases. We have demonstrated that δ-tocotrienol, a natural form of vitamin E (VEDT), is bioactive against cancer, delays progression, and prevents metastases in transgenic mouse models of PDAC. In this report, we provide the first evidence that VEDT selectively inhibits PDAC stem-like cells. VEDT inhibited the viability, survival, self-renewal, and expression of Oct4 and Sox2 transcription factors in 3 models of PDAC stem-like cells. In addition, VEDT inhibited the migration, invasion, and several biomarkers of epithelial-to-mesenchymal transition and angiogenesis in PDAC cells and tumors. These processes are critical for tumor metastases. Furthermore, in the L3.6pl orthotopic model of PDAC metastases, VEDT significantly inhibited growth and metastases of these cells. Finally, in an orthotopic xenograft model of human PDAC stem-like cells, we showed that VEDT significantly retarded the growth and metastases of gemcitabine-resistant PDAC human stem-like cells. Because VEDT has been shown to be safe and to reach bioactive levels in humans, this work supports investigating VEDT for chemoprevention of PDAC metastases.

  4. Bladder Cancer Stem-Like Cells: Their Origin and Therapeutic Perspectives

    Directory of Open Access Journals (Sweden)

    Tomokazu Ohishi

    2015-12-01

    Full Text Available Bladder cancer (BC, the most common cancer arising from the human urinary tract, consists of two major clinicopathological phenotypes: muscle-invasive bladder cancer (MIBC and non-muscle-invasive bladder cancer (NMIBC. MIBC frequently metastasizes and is associated with an unfavorable prognosis. A certain proportion of patients with metastatic BC can achieve a remission with systemic chemotherapy; however, the disease relapses in most cases. Evidence suggests that MIBC comprises a small population of cancer stem cells (CSCs, which may be resistant to these treatments and may be able to form new tumors in the bladder or other organs. Therefore, the unambiguous identification of bladder CSCs and the development of targeted therapies are urgently needed. Nevertheless, it remains unclear where bladder CSCs originate and how they are generated. We review recent studies on bladder CSCs, specifically focusing on their proposed origin and the possible therapeutic options based on the CSC theory.

  5. The nutritional phenome of EMT-induced cancer stem-like cells.

    Science.gov (United States)

    Cuyàs, Elisabet; Corominas-Faja, Bruna; Menendez, Javier A

    2014-06-30

    The metabolic features of cancer stem (CS) cells and the effects of specific nutrients or metabolites on CS cells remain mostly unexplored. A preliminary study to delineate the nutritional phenome of CS cells exploited the landmark observation that upon experimental induction into an epithelial-to-mesenchymal (EMT) transition, the proportion of CS-like cells drastically increases within a breast cancer cell population. EMT-induced CS-like cells (HMLERshEcad) and isogenic parental cells (HMLERshCntrol) were simultaneously screened for their ability to generate energy-rich NADH when cultured in a standardized high-throughput metabolic phenotyping platform comprising >350 wells that were pre-loaded with different carbohydrates/starches, alcohols, fatty acids, ketones, carboxylic acids, amino acids, and bi-amino acids. The generation of "phenetic maps" of the carbon and nitrogen utilization patterns revealed that the acquisition of a CS-like cellular state provided an enhanced ability to utilize additional catabolic fuels, especially under starvation conditions. Crucially, the acquisition of cancer stemness activated a metabolic infrastructure that enabled the vectorial transfer of high-energy nutrients such as glycolysis end products (pyruvate, lactate) and bona fide ketone bodies (β-hydroxybutyrate) from the extracellular microenvironment to support mitochondrial energy production in CS-like cells. Metabolic reprogramming may thus constitute an efficient adaptive strategy through which CS-like cells would rapidly obtain an advantage in hostile conditions such as nutrient starvation following the inhibition of tumor angiogenesis. By understanding how specific nutrients could bioenergetically boost EMT-CS-like phenotypes, "smart foods" or systemic "metabolic nichotherapies" may be tailored to specific nutritional CSC phenomes, whereas high-resolution heavy isotope-labeled nutrient tracking may be developed to monitor the spatiotemporal distribution and functionality

  6. Plasmid-based generation of induced neural stem cells from adult human fibroblasts

    Directory of Open Access Journals (Sweden)

    Philipp Capetian

    2016-10-01

    Full Text Available Direct reprogramming from somatic to neural cell types has become an alternative to induced pluripotent stem cells. Most protocols employ viral expression systems, posing the risk of random genomic integration. Recent developments led to plasmid-based protocols, lowering this risk. However, these protocols either relied on continuous presence of a variety of small molecules or were only able to reprogram murine cells. We therefore established a reprogramming protocol based on vectors containing the Epstein-Barr virus (EBV-derived oriP/EBNA1 as well as the defined expression factors Oct3/4, Sox2, Klf4, L-myc, Lin28, and a small hairpin directed against p53. We employed a defined neural medium in combination with the neurotrophins bFGF, EGF and FGF4 for cultivation without the addition of small molecules. After reprogramming, cells demonstrated a temporary increase in the expression of endogenous Oct3/4. We obtained induced neural stem cells (iNSC 30 days after transfection. In contrast to previous results, plasmid vectors as well as a residual expression of reprogramming factors remained detectable in all cell lines. Cells showed a robust differentiation into neuronal (72% and glial cells (9% astrocytes, 6% oligodendrocytes. Despite the temporary increase of pluripotency-associated Oct3/4 expression during reprogramming, we did not detect pluripotent stem cells or non-neural cells in culture (except occasional residual fibroblasts. Neurons showed electrical activity and functional glutamatergic synapses. Our results demonstrate that reprogramming adult human fibroblasts to iNSC by plasmid vectors and basic neural medium without small molecules is possible and feasible. However, a full set of pluripotency-associated transcription factors may indeed result in the acquisition of a transient (at least partial pluripotent intermediate during reprogramming. In contrast to previous reports, the EBV-based plasmid system remained present and active inside

  7. Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells

    DEFF Research Database (Denmark)

    Chandrasekaran, Abinaya; Avci, Hasan; Ochalek, Anna

    2017-01-01

    Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency......), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells...... the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6+/NESTIN+ cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural...

  8. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  9. Molecular and ultra-structural insight into the enrichment of Glioblastoma and Neuroblastoma stem-like cells

    OpenAIRE

    Farace, Cristiano

    2014-01-01

    Cancer stem cells (CSC) and tumor micro-environments play a significant role in malignant cancer initiation and progression. Metastasis in vivo involves a stem-like, epithelial-mesenchymal transition (EMT). Serum-free cultures of 3-D neurospheres represent the gold standard in CSC-like enrichment. The aim of the thesis was to explore the induction of stem-like phenotypes in Glioblastoma (GBM) and Neuroblastoma (NBL) cell lines, in order to assess common stem/oncogenic related marks. CSC chara...

  10. Neural network-based voltage regulator for an isolated asynchronous generator supplying three-phase four-wire loads

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Bhim; Kasal, Gaurav Kumar [Department of Electrical Engineering, Indian Institute of Technology, Delhi, Hauz-Khas, New Delhi 110016 (India)

    2008-06-15

    This paper deals with a neural network-based solid state voltage controller for an isolated asynchronous generator (IAG) driven by constant speed prime mover like diesel engine, bio-gas or gasoline engine and supplying three-phase four-wire loads. The proposed control scheme uses an indirect current control and a fast adaptive linear element (adaline) based neural network reference current extractor, which extracts the real positive sequence current component without any phase shift. The neutral current of the source is also compensated by using three single-phase bridge configuration of IGBT (insulated gate bipolar junction transistor) based voltage source converter (VSC) along-with single-phase transformer having self-supported dc bus. The proposed controller provides the functions as a voltage regulator, a harmonic eliminator, a neutral current compensator, and a load balancer. The proposed isolated electrical system with its controller is modeled and simulated in MATLAB along with Simulink and PSB (Power System Block set) toolboxes. The simulated results are presented to demonstrate the capability of an isolated asynchronous generating system driven by a constant speed prime mover for feeding three-phase four-wire loads. (author)

  11. Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Panda, Priyadarshini; Roy, Kaushik

    2017-01-01

    Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.

  12. Performance assessment of electric power generations using an adaptive neural network algorithm and fuzzy DEA

    Energy Technology Data Exchange (ETDEWEB)

    Javaheri, Zahra

    2010-09-15

    Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.

  13. Acquisition of epithelial-mesenchymal transition and cancer stem-like phenotypes within chitosan-hyaluronan membrane-derived 3D tumor spheroids.

    Science.gov (United States)

    Huang, Yen-Jang; Hsu, Shan-Hui

    2014-12-01

    Cancer drug development has to go through rigorous testing and evaluation processes during pre-clinical in vitro studies. However, the conventional two-dimensional (2D) in vitro culture is often discounted by the insufficiency to present a more typical tumor microenvironment. The multicellular tumor spheroids have been a valuable model to provide more comprehensive assessment of tumor in response to therapeutic strategies. Here, we applied chitosan-hyaluronan (HA) membranes as a platform to promote three-dimensional (3D) tumor spheroid formation. The biological features of tumor spheroids of human non-small cell lung cancer (NSCLC) cells on chitosan-HA membranes were compared to those of 2D cultured cells in vitro. The cells in tumor spheroids cultured on chitosan-HA membranes showed higher levels of stem-like properties and epithelial-mesenchymal transition (EMT) markers, such as NANOG, SOX2, CD44, CD133, N-cadherin, and vimentin, than 2D cultured cells. Moreover, they exhibited enhanced invasive activities and multidrug resistance by the upregulation of MMP2, MMP9, BCRC5, BCL2, MDR1, and ABCG2 as compared with 2D cultured cells. The grafting densities of HA affected the tumor sphere size and mRNA levels of genes on the substrates. These evidences suggest that chitosan-HA membranes may offer a simple and valuable biomaterial platform for rapid generation of tumor spheroids in vitro as well as for further applications in cancer stem cell research and cancer drug screening. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Gemcitabine treatment causes resistance and malignancy of pancreatic cancer stem-like cells via induction of lncRNA HOTAIR

    Science.gov (United States)

    Wang, Li; Dong, Ping; Wang, Weiguo; Huang, Mingquan; Tian, Bole

    2017-01-01

    Gemcitabine is the first-line chemotherapeutic agent for advanced adenocarcinoma of the pancreas, despite the high risk of chemoresistance as a major disadvantage. In the past few years, significant advances have been made in the field of pancreatic cancer stem-like cells (CSCs) and their critical roles in drug resistance, invasion and metastasis, which are tightly regulated by long non-coding RNAs (lncRNAs). The present study demonstrated that HOX antisense intergenic RNA (HOTAIR) is not different between the pancreatic cancer cell line PANC-1 and its enriched CSC sub-population. However, after gemcitabine treatment, the expression levels of HOTAIR in CSCs were induced, but not in PANC-1 cells. HOTAIR induced by gemcitabine failed to cause chemoresistance, but promoted the clonogenicity, proliferation and migration of the cells. By introducing HOTAIR using lentivirus, chemoresistance was induced and the self-renewal capacity, proliferation and migration were significantly promoted. By contrast, HOTAIR knockdown in PANC-1 CSCs treated with or without gemcitabine decreased the cell proliferation, altered the cell cycle progression and induced apoptosis, demonstrating its critical roles in regulating the malignant character of PANC-1 CSCs. In conclusion, the present study demonstrated that HOTAIR may be induced by gemcitabine and acts as a tumor promoter by inhibiting the chemosensitivity, and promoting the self-renewal capacity, proliferation and migration of PANC-1 CSCs, which supports its potential application as a novel therapeutic approach for pancreatic cancer. PMID:29201179

  15. ER-mitochondria contacts control surface glycan expression and sensitivity to killer lymphocytes in glioma stem-like cells.

    Science.gov (United States)

    Bassoy, Esen Yonca; Kasahara, Atsuko; Chiusolo, Valentina; Jacquemin, Guillaume; Boydell, Emma; Zamorano, Sebastian; Riccadonna, Cristina; Pellegatta, Serena; Hulo, Nicolas; Dutoit, Valérie; Derouazi, Madiha; Dietrich, Pierre Yves; Walker, Paul R; Martinvalet, Denis

    2017-06-01

    Glioblastoma is a highly heterogeneous aggressive primary brain tumor, with the glioma stem-like cells (GSC) being more sensitive to cytotoxic lymphocyte-mediated killing than glioma differentiated cells (GDC). However, the mechanism behind this higher sensitivity is unclear. Here, we found that the mitochondrial morphology of GSCs modulates the ER-mitochondria contacts that regulate the surface expression of sialylated glycans and their recognition by cytotoxic T lymphocytes and natural killer cells. GSCs displayed diminished ER-mitochondria contacts compared to GDCs. Forced ER-mitochondria contacts in GSCs increased their cell surface expression of sialylated glycans and reduced their susceptibility to cytotoxic lymphocytes. Therefore, mitochondrial morphology and dynamism dictate the ER-mitochondria contacts in order to regulate the surface expression of certain glycans and thus play a role in GSC recognition and elimination by immune effector cells. Targeting the mitochondrial morphology, dynamism, and contacts with the ER could be an innovative strategy to deplete the cancer stem cell compartment to successfully treat glioblastoma. © 2017 The Authors.

  16. Ovarian cancer stem-like cells differentiate into endothelial cells and participate in tumor angiogenesis through autocrine CCL5 signaling.

    Science.gov (United States)

    Tang, Shu; Xiang, Tong; Huang, Shuo; Zhou, Jie; Wang, Zhongyu; Xie, Rongkai; Long, Haixia; Zhu, Bo

    2016-06-28

    Cancer stem cells (CSCs) are well known for their self-regeneration and tumorigenesis potential. In addition, the multi-differentiation potential of CSCs has become a popular issue and continues to attract increased research attention. Recent studies demonstrated that CSCs are able to differentiate into functional endothelial cells and participate in tumor angiogenesis. In this study, we found that ovarian cancer stem-like cells (CSLCs) activate the NF-κB and STAT3 signal pathways through autocrine CCL5 signaling and mediate their own differentiation into endothelial cells (ECs). Our data demonstrate that CSLCs differentiate into ECs morphologically and functionally. Anti-CCL5 antibodies and CCL5-shRNA lead to markedly inhibit EC differentiation and the tube formation of CSLCs, both in vitro and in vivo. Recombinant human-CCL5 significantly promotes ovarian CSLCs that differentiate into ECs and form microtube network. The CCL5-mediated EC differentiation of CSLCs depends on binding to receptors, such as CCR1, CCR3, and CCR5. The results demonstrated that CCL5-CCR1/CCR3/CCR5 activates the NF-κB and STAT3 signal pathways, subsequently mediating the differentiation of CSLCs into ECs. Therefore, this study was conducted based on the theory that CSCs improve tumor angiogenesis and provides a novel strategy for anti-angiogenesis in ovarian cancer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. EGFR regulation of colon cancer stem-like cells during aging and in response to the colonic carcinogen dimethylhydrazine.

    Science.gov (United States)

    Nautiyal, Jyoti; Du, Jianhua; Yu, Yingjie; Kanwar, Shailender S; Levi, Edi; Majumdar, Adhip P N

    2012-04-01

    One of the most consistent pathological conditions in the gastrointestinal tract with advancing age is malignancy, particularly gastrointestinal cancers, the incidence of which increases sharply with aging. Although the reasons for the age-related rise in colorectal cancer are not fully understood, we hypothesize that aging increases susceptibility of the colon to carcinogen(s)/toxicant(s), leading to an increase in cancer stem-like cells (CSLCs) that express cancer stem cell markers, in the colonic mucosa. The current study demonstrates that aging is associated with increased expression of several colon CSLC markers [CD44, CD166, and aldehyde dehydrogenase 1 (ALDH-1)] and a higher proportion of cells expressing these markers. Aging is also accompanied by increased expression of miR-21 in colon. These increases are further increased in response to the colonic carcinogen dimethylhydrazine (DMH). Aging is also associated with increased tyrosine-phosphorylated epidermal growth factor receptor (EGFR). Inhibition of EGFR using the EGFR inhibitor cetuximab abrogated the age-related increase in CD166 and ALDH-1 as well as miRNA (miR)-21. Our results provide new evidence that aging and DMH are associated with increases in CSLC biomarkers and miR21, each of which have been linked to colorectal cancer. EGFR inhibition attenuates these changes, indicating a role for EGFR in age- and mutagen-associated changes in CSLCs.

  18. Stem-Like Cell Characteristics from Breast Milk of Mothers with Preterm Infants as Compared to Mothers with Term Infants.

    Science.gov (United States)

    Briere, Carrie-Ellen; Jensen, Todd; McGrath, Jacqueline M; Young, Erin E; Finck, Christine

    2017-04-01

    Breast milk stem cells are hypothesized to be involved in infant health and development. Our research team is the first known team to enroll mothers of hospitalized preterm infants during the first few weeks of lactation and compare stem cell phenotypes and gene expression to mothers of healthy full-term infants. Participants were recruited from a Level IV Neonatal Intensive Care Unit (preterm dyads) and the community (full-term dyads) in the northeastern United States. Mothers of hospitalized preterm infants (mothers of healthy full-term infants (>39 weeks gestational age at birth). Breast milk stem-like cell populations were identified in both preterm and full-term breast milk samples. The data suggest variability in the proportion of stem cell phenotypes present, as well as statistically significant differential expression (both over- and underexpression) of stem cell-specific genetic markers when comparing mothers' milk for preterm and full-term births. Our findings indicate that (1) stem cells are present in preterm breast milk; (2) differential expression of stem cell-specific markers can be detected in preterm and full-term breast milk samples; and (3) the percentage of cells expressing the various stem cell-specific markers differs when preterm and full-term breast milk samples are compared.

  19. β-Elemene-Attenuated Tumor Angiogenesis by Targeting Notch-1 in Gastric Cancer Stem-Like Cells

    Directory of Open Access Journals (Sweden)

    Bing Yan

    2013-01-01

    Full Text Available Emerging evidence suggests that cancer stem cells are involved in tumor angiogenesis. The Notch signaling pathway is one of the most important regulators of these processes. β-Elemene, a naturally occurring compound extracted from Curcumae Radix, has been used as an antitumor drug for various cancers in China. However, its underlying mechanism in the treatment of gastric cancer remains largely unknown. Here, we report that CD44+ gastric cancer stem-like cells (GCSCs showed enhanced proliferation capacity compared to their CD44− counterparts, and this proliferation was accompanied by the high expression of Notch-1 (in vitro. These cells were also more superior in spheroid colony formation (in vitro and tumorigenicity (in vivo and positively associated with microvessel density (in vivo. β-Elemene was demonstrated to effectively inhibit the viability of GCSCs in a dose-dependent manner, most likely by suppressing Notch-1 (in vitro. β-Elemene also contributed to growth suppression and attenuated the angiogenesis capacity of these cells (in vivo most likely by interfering with the expression of Notch-1 but not with Dll4. Our findings indicated that GCSCs play an important role in tumor angiogenesis, and Notch-1 is one of the most likely mediators involved in these processes. β-Elemene was effective at attenuating angiogenesis by targeting the GCSCs, which could be regarded as a potential mechanism for its efficacy in gastric cancer management in the future.

  20. Semaphorin 3 C drives epithelial-to-mesenchymal transition, invasiveness, and stem-like characteristics in prostate cells.

    Science.gov (United States)

    Tam, Kevin J; Hui, Daniel H F; Lee, Wilson W; Dong, Mingshu; Tombe, Tabitha; Jiao, Ivy Z F; Khosravi, Shahram; Takeuchi, Ario; Peacock, James W; Ivanova, Larissa; Moskalev, Igor; Gleave, Martin E; Buttyan, Ralph; Cox, Michael E; Ong, Christopher J

    2017-09-13

    Prostate cancer (PCa) is among the most commonly-occurring cancers worldwide and a leader in cancer-related deaths. Local non-invasive PCa is highly treatable but limited treatment options exist for those with locally-advanced and metastatic forms of the disease underscoring the need to identify mechanisms mediating PCa progression. The semaphorins are a large grouping of membrane-associated or secreted signalling proteins whose normal roles reside in embryogenesis and neuronal development. In this context, semaphorins help establish chemotactic gradients and direct cell movement. Various semaphorin family members have been found to be up- and down-regulated in a number of cancers. One family member, Semaphorin 3 C (SEMA3C), has been implicated in prostate, breast, ovarian, gastric, lung, and pancreatic cancer as well as glioblastoma. Given SEMA3C's roles in development and its augmented expression in PCa, we hypothesized that SEMA3C promotes epithelial-to-mesenchymal transition (EMT) and stem-like phenotypes in prostate cells. In the present study we show that ectopic expression of SEMA3C in RWPE-1 promotes the upregulation of EMT and stem markers, heightened sphere-formation, and cell plasticity. In addition, we show that SEMA3C promotes migration and invasion in vitro and cell dissemination in vivo.

  1. Coculture with astrocytes reduces the radiosensitivity of glioblastoma stem-like cells and identifies additional targets for radiosensitization

    International Nuclear Information System (INIS)

    Rath, Barbara H; Wahba, Amy; Camphausen, Kevin; Tofilon, Philip J

    2015-01-01

    Toward developing a model system for investigating the role of the microenvironment in the radioresistance of glioblastoma (GBM), human glioblastoma stem-like cells (GSCs) were grown in coculture with human astrocytes. Using a trans-well assay, survival analyses showed that astrocytes significantly decreased the radiosensitivity of GSCs compared to standard culture conditions. In addition, when irradiated in coculture, the initial level of radiation-induced γH2AX foci in GSCs was reduced and foci dispersal was enhanced suggesting that the presence of astrocytes influenced the induction and repair of DNA double-strand breaks. These data indicate that astrocytes can decrease the radiosensitivity of GSCs in vitro via a paracrine-based mechanism and further support a role for the microenvironment as a determinant of GBM radioresponse. Chemokine profiling of coculture media identified a number of bioactive molecules not present under standard culture conditions. The gene expression profiles of GSCs grown in coculture were significantly different as compared to GSCs grown alone. These analyses were consistent with an astrocyte-mediated modification in GSC phenotype and, moreover, suggested a number of potential targets for GSC radiosensitization that were unique to coculture conditions. Along these lines, STAT3 was activated in GSCs grown with astrocytes; the JAK/STAT3 inhibitor WP1066 enhanced the radiosensitivity of GSCs under coculture conditions and when grown as orthotopic xenografts. Further, this coculture system may also provide an approach for identifying additional targets for GBM radiosensitization

  2. An activated form of ADAM10 is tumor selective and regulates cancer stem-like cells and tumor growth

    Science.gov (United States)

    Saha, Nayanendu; Eissman, Moritz F.; Xu, Kai; Llerena, Carmen; Kusebauch, Ulrike; Ding, Bi-Sen; Cao, Zhongwei; Rafii, Shahin; Ernst, Matthias; Scott, Andrew M.; Nikolov, Dimitar B.; Lackmann, Martin

    2016-01-01

    The transmembrane metalloprotease ADAM10 sheds a range of cell surface proteins, including ligands and receptors of the Notch, Eph, and erbB families, thereby activating signaling pathways critical for tumor initiation and maintenance. ADAM10 is thus a promising therapeutic target. Although widely expressed, its activity is normally tightly regulated. We now report prevalence of an active form of ADAM10 in tumors compared with normal tissues, in mouse models and humans, identified by our conformation-specific antibody mAb 8C7. Structure/function experiments indicate mAb 8C7 binds an active conformation dependent on disulfide isomerization and oxidative conditions, common in tumors. Moreover, this active ADAM10 form marks cancer stem-like cells with active Notch signaling, known to mediate chemoresistance. Importantly, specific targeting of active ADAM10 with 8C7 inhibits Notch activity and tumor growth in mouse models, particularly regrowth after chemotherapy. Our results indicate targeted inhibition of active ADAM10 as a potential therapy for ADAM10-dependent tumor development and drug resistance. PMID:27503072

  3. Survivin Modulates Squamous Cell Carcinoma-Derived Stem-Like Cell Proliferation, Viability and Tumor Formation in Vivo

    Directory of Open Access Journals (Sweden)

    Roberta Lotti

    2016-01-01

    Full Text Available Squamous Cell Carcinoma-derived Stem-like Cells (SCC-SC originate from alterations in keratinocyte stem cells (KSC gene expression and sustain tumor development, invasion and recurrence. Since survivin, a KSC marker, is highly expressed in SCC-SC, we evaluate its role in SCC-SC cell growth and SCC models. Survivin silencing by siRNA decreases clonal growth of SCC keratinocytes and viability of total, rapidly adhering (RAD and non-RAD (NRAD cells from primary SCC. Similarly, survivin silencing reduces the expression of stem cell markers (OCT4, NOTCH1, CD133, β1-integrin, while it increases the level of differentiation markers (K10, involucrin. Moreover, survivin silencing improves the malignant phenotype of SCC 3D-reconstruct, as demonstrated by reduced epidermal thickness, lower Ki-67 positive cell number, and decreased expression of MMP9 and psoriasin. Furthermore, survivin depletion by siRNA in RasG12V-IκBα-derived tumors leads to smaller tumor formation characterized by lower mitotic index and reduced expression of the tumor-associated marker HIF1α, VEGF and CD51. Therefore, our results indicate survivin as a key gene in regulating SCC cancer stem cell formation and cSCC development.

  4. CD133+ and Nestin+ Glioma Stem-Like Cells Reside Around CD31+ Arterioles in Niches that Express SDF-1α, CXCR4, Osteopontin and Cathepsin K

    NARCIS (Netherlands)

    Hira, Vashendriya V. V.; Ploegmakers, Kimberley J.; Grevers, Frederieke; Verbovšek, Urška; Silvestre-Roig, Carlos; Aronica, Eleonora; Tigchelaar, Wikky; Turnšek, Tamara Lah; Molenaar, Remco J.; van Noorden, Cornelis J. F.

    2015-01-01

    Poor survival of high-grade glioma is at least partly caused by glioma stem-like cells (GSLCs) that are resistant to therapy. GSLCs reside in niches in close vicinity of endothelium. The aim of the present study was to characterize proteins that may be functional in the GSLC niche by performing

  5. Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

    Science.gov (United States)

    Radziszewski, Kacper

    2017-10-01

    The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

  6. Neural network radiative transfer solvers for the generation of high resolution solar irradiance spectra parameterized by cloud and aerosol parameters

    International Nuclear Information System (INIS)

    Taylor, M.; Kosmopoulos, P.G.; Kazadzis, S.; Keramitsoglou, I.; Kiranoudis, C.T.

    2016-01-01

    This paper reports on the development of a neural network (NN) model for instantaneous and accurate estimation of solar radiation spectra and budgets geared toward satellite cloud data using a ≈2.4 M record, high-spectral resolution look up table (LUT) generated with the radiative transfer model libRadtran. Two NN solvers, one for clear sky conditions dominated by aerosol and one for cloudy skies, were trained on a normally-distributed and multiparametric subset of the LUT that spans a very broad class of atmospheric and meteorological conditions as inputs with corresponding high resolution solar irradiance target spectra as outputs. The NN solvers were tested by feeding them with a large (10 K record) “off-grid” random subset of the LUT spanning the training data space, and then comparing simulated outputs with target values provided by the LUT. The NN solvers demonstrated a capability to interpolate accurately over the entire multiparametric space. Once trained, the NN solvers allow for high-speed estimation of solar radiation spectra with high spectral resolution (1 nm) and for a quantification of the effect of aerosol and cloud optical parameters on the solar radiation budget without the need for a massive database. The cloudy sky NN solver was applied to high spatial resolution (54 K pixel) cloud data extracted from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary Meteosat Second Generation 3 (MSG3) satellite and demonstrated that coherent maps of spectrally-integrated global horizontal irradiance at this resolution can be produced on the order of 1 min. - Highlights: • Neural network radiative transfer solvers for generation of solar irradiance spectra. • Sensitivity analysis of irradiance spectra with respect to aerosol and cloud parameters. • Regional maps of total global horizontal irradiance for cloudy sky conditions. • Regional solar radiation maps produced directly from MSG3/SEVIRI satellite inputs.

  7. YAP1 Regulates OCT4 Activity and SOX2 Expression to Facilitate Self-Renewal and Vascular Mimicry of Stem-Like Cells.

    Science.gov (United States)

    Bora-Singhal, Namrata; Nguyen, Jonathan; Schaal, Courtney; Perumal, Deepak; Singh, Sandeep; Coppola, Domenico; Chellappan, Srikumar

    2015-06-01

    Non-small cell lung cancer (NSCLC) is highly correlated with smoking and has very low survival rates. Multiple studies have shown that stem-like cells contribute to the genesis and progression of NSCLC. Our results show that the transcriptional coactivator yes-associated protein 1 (YAP1), which is the oncogenic component of the Hippo signaling pathway, is elevated in the stem-like cells from NSCLC and contributes to their self-renewal and ability to form angiogenic tubules. Inhibition of YAP1 by a small molecule or depletion of YAP1 by siRNAs suppressed self-renewal and vascular mimicry of stem-like cells. These effects of YAP1 were mediated through the embryonic stem cell transcription factor, Sox2. YAP1 could transcriptionally induce Sox2 through a physical interaction with Oct4; Sox2 induction occurred independent of TEAD2 transcription factor, which is the predominant mediator of YAP1 functions. The binding of Oct4 to YAP1 could be detected in cell lines as well as tumor tissues; the interaction was elevated in NSCLC samples compared to normal tissue as seen by proximity ligation assays. YAP1 bound to Oct4 through the WW domain, and a peptide corresponding to this region could disrupt the interaction. Delivery of the WW domain peptide to stem-like cells disrupted the interaction and abrogated Sox2 expression, self-renewal, and vascular mimicry. Depleting YAP1 reduced the expression of multiple epithelial-mesenchymal transition genes and prevented the growth and metastasis of tumor xenografts in mice; overexpression of Sox2 in YAP1 null cells rescued these functions. These results demonstrate a novel regulation of stem-like functions by YAP1, through the modulation of Sox2 expression. © 2015 AlphaMed Press.

  8. Artificial neural Network-Based modeling and monitoring of photovoltaic generator

    Directory of Open Access Journals (Sweden)

    H. MEKKI

    2015-03-01

    Full Text Available In this paper, an artificial neural network based-model (ANNBM is introduced for partial shading detection losses in photovoltaic (PV panel. A Multilayer Perceptron (MLP is used to estimate the electrical outputs (current and voltage of the photovoltaic module using the external meteorological data: solar irradiation G (W/m2 and the module temperature T (°C. Firstly, a database of the BP150SX photovoltaic module operating without any defect has been used to train the considered MLP. Subsequently, in the first case of this study, the developed model is used to estimate the output current and voltage of the PV module considering the partial shading effect. Results confirm the good ability of the ANNBM to detect the partial shading effect in the photovoltaic module with logical accuracy. The proposed strategy could also be used for the online monitoring and supervision of PV modules.

  9. Self-generation of controller of an underwater robot with neural network

    International Nuclear Information System (INIS)

    Suto, T.; Ura, T.

    1994-01-01

    A self-organizing controller system is constructed based on artificial neural networks and applied to constant altitude swimming of the autonomous underwater robot PTEROA 150. The system consists of a controller and a forward model which calculates the values for evaluation as a result of control. Some methods are introduced for quick and appropriate adjustment of the controller network. Modification of the controller network is executed based on error-back-propagation method utilizing the forward model network. The forward model is divided into three sub-networks which represent dynamics of the vehicle, estimation of relative position to the seabed and calculation of the altitude. The proposed adaptive system is demonstrated in computer simulations where objective of a vehicle is keeping a constant altitude from seabed which is constituted of triangular ridges

  10. Convolutional neural network using generated data for SAR ATR with limited samples

    Science.gov (United States)

    Cong, Longjian; Gao, Lei; Zhang, Hui; Sun, Peng

    2018-03-01

    Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

  11. Generation of highly purified neural stem cells from human adipose-derived mesenchymal stem cells by Sox1 activation.

    Science.gov (United States)

    Feng, Nianhua; Han, Qin; Li, Jing; Wang, Shihua; Li, Hongling; Yao, Xinglei; Zhao, Robert Chunhua

    2014-03-01

    Neural stem cells (NSCs) are ideal candidates in stem cell-based therapy for neurodegenerative diseases. However, it is unfeasible to get enough quantity of NSCs for clinical application. Generation of NSCs from human adipose-derived mesenchymal stem cells (hAD-MSCs) will provide a solution to this problem. Currently, the differentiation of hAD-MSCs into highly purified NSCs with biological functions is rarely reported. In our study, we established a three-step NSC-inducing protocol, in which hAD-MSCs were induced to generate NSCs with high purity after sequentially cultured in the pre-inducing medium (Step1), the N2B27 medium (Step2), and the N2B27 medium supplement with basic fibroblast growth factor and epidermal growth factor (Step3). These hAD-MSC-derived NSCs (adNSCs) can form neurospheres and highly express Sox1, Pax6, Nestin, and Vimentin; the proportion was 96.1% ± 1.3%, 96.8% ± 1.7%, 96.2% ± 1.3%, and 97.2% ± 2.5%, respectively, as detected by flow cytometry. These adNSCs can further differentiate into astrocytes, oligodendrocytes, and functional neurons, which were able to generate tetrodotoxin-sensitive sodium current. Additionally, we found that the neural differentiation of hAD-MSCs were significantly suppressed by Sox1 interference, and what's more, Step1 was a key step for the following induction, probably because it was associated with the initiation and nuclear translocation of Sox1, an important transcriptional factor for neural development. Finally, we observed that bone morphogenetic protein signal was inhibited, and Wnt/β-catenin signal was activated during inducing process, and both signals were related with Sox1 expression. In conclusion, we successfully established a three-step inducing protocol to derive NSCs from hAD-MSCs with high purity by Sox1 activation. These findings might enable to acquire enough autologous transplantable NSCs for the therapy of neurodegenerative diseases in clinic.

  12. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  13. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    International Nuclear Information System (INIS)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-01-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing

  14. Multivoxel magnetic resonance spectroscopy identifies enriched foci of cancer stem-like cells in high-grade gliomas

    Directory of Open Access Journals (Sweden)

    He T

    2017-01-01

    Full Text Available Tao He,1–3,* Tianming Qiu,4,* Xiaodong Wang,5 Hongxing Gui,6 Xilong Wang,2 Qikuan Hu,3,7 Hechun Xia,2 Gaoyang Qi,1,2 Jinsong Wu,4 Hui Ma2 1Clinical Medicine College, Ningxia Medical University, 2Department of Neurosurgery, General Hospital of Ningxia Medical University, 3Ningxia Key Laboratory of Cerebrocranial Diseases, The National Key Laboratory Incubation Base, Yinchuan, 4Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, 5Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China; 6Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School of Rutgers University, Piscataway, NJ, USA; 7Department of Physiology, Ningxia Medical University, Yinchuan, People’s Republic of China *These authors contributed equally to this work Objective: This study investigated the correlation between choline/creatine (Cho/Cr ratios determined by multivoxel proton magnetic resonance spectroscopy (1H-MRS and the distribution of cancer stem-like cells (CSLCs in high-grade gliomas. Patients and methods: Sixteen patients with high-grade gliomas were recruited and underwent 1H-MRS examination before surgery to identify distinct tumor regions with variable Cho/Cr ratios. Using intraoperative neuronavigation, tumor tissues were accurately sampled from regions with high and low Cho/Cr ratios within each tumor. The distribution of CSLCs in samples from glioma tissue regions with different Cho/Cr ratios was quantified by neurosphere culture, immunohistochemistry, and Western blot. Results: The mean neurosphere formation rate in tissues with high Cho/Cr ratios was significantly increased compared with that in low Cho/Cr ratio tissues (13.94±5.94 per 100 cells vs 8.04±3.99 per 100 cells, P<0.001. Immunohistochemistry indicated that tissues with high Cho/Cr ratios had elevated expression of CD133, nestin, and CD15, relative to low Cho/Cr ratio tissue

  15. Combinatorial Effects of VEGFR Kinase Inhibitor Axitinib and Oncolytic Virotherapy in Mouse and Human Glioblastoma Stem-Like Cell Models.

    Science.gov (United States)

    Saha, Dipongkor; Wakimoto, Hiroaki; Peters, Cole W; Antoszczyk, Slawomir J; Rabkin, Samuel D; Martuza, Robert L

    2018-03-29

    Purpose: Glioblastoma (GBM), a fatal brain cancer, contains a subpopulation of GBM stem-like cells (GSCs) that contribute to resistance to current therapy. Angiogenesis also plays a key role in GBM progression. Therefore, we developed a strategy to target the complex GBM microenvironment, including GSCs and tumor vasculature. Experimental Design: We evaluated the cytotoxic effects of VEFGR tyrosine kinase inhibitor (TKI) axitinib in vitro and then tested antitumor efficacy of axitinib in combination with oncolytic herpes simplex virus (oHSV) expressing antiangiogenic cytokine murine IL12 (G47Δ-mIL12) in two orthotopic GSC-derived GBM models: patient-derived recurrent MGG123 GSCs, forming vascular xenografts in immunodeficient mice; and mouse 005 GSCs, forming syngeneic tumors in immunocompetent mice. Results: GSCs form endothelial-like tubes and were sensitive to axitinib. G47Δ-mIL12 significantly improved survival, as did axitinib, while dual combinations further extended survival significantly compared with single therapies alone in both models. In MGG123 tumors, axitinib was effective only at high doses (50 mg/kg), alone and in combination with G47Δ-mIL12, and this was associated with greatly decreased vascularity, increased macrophage infiltration, extensive tumor necrosis, and PDGFR/ERK pathway inhibition. In the mouse 005 model, antiglioma activity, after single and combination therapy, was only observed in immunocompetent mice and not the T-cell-deficient athymic mice. Interestingly, immune checkpoint inhibition did not improve efficacy. Conclusions: Systemic TKI (axitinib) beneficially combines with G47Δ-mIL12 to enhance antitumor efficacy in both immunodeficient and immunocompetent orthotopic GBM models. Our results support further investigation of TKIs in combination with oHSV for GBM treatment. Clin Cancer Res; 1-14. ©2018 AACR. ©2018 American Association for Cancer Research.

  16. Radiation Response of Cancer Stem-Like Cells From Established Human Cell Lines After Sorting for Surface Markers

    International Nuclear Information System (INIS)

    Al-Assar, Osama; Muschel, Ruth J.; Mantoni, Tine S.; McKenna, W. Gillies; Brunner, Thomas B.

    2009-01-01

    Purpose: A subpopulation of cancer stem-like cells (CSLC) is hypothesized to exist in different cancer cell lines and to mediate radioresistance in solid tumors. Methods and Materials: Cells were stained for CSLC markers and sorted (fluorescence-activated cell sorter/magnetic beads) to compare foci and radiosensitivity of phosphorylated histone H2AX at Ser 139 (γ-H2AX) in sorted vs. unsorted populations in eight cell lines from different organs. CSLC properties were examined using anchorage-independent growth and levels of activated Notch1. Validation consisted of testing tumorigenicity and postirradiation enrichment of CSLC in xenograft tumors. Results: The quantity of CSLC was generally in good agreement with primary tumors. CSLC from MDA-MB-231 (breast) and Panc-1 and PSN-1 (both pancreatic) cells had fewer residual γ-H2AX foci than unsorted cells, pointing to radioresistance of CSLC. However, only MDA-MB-231 CSLC were more radioresistant than unsorted cells. Furthermore, MDA-MB-231 CSLC showed enhanced anchorage-independent growth and overexpression of activated Notch1 protein. The expression of cancer stem cell surface markers in the MDA-MB-231 xenograft model was increased after exposure to fractionated radiation. In contrast to PSN-1 cells, a growth advantage for MDA-MB-231 CSLC xenograft tumors was found compared to tumors arising from unsorted cells. Conclusions: CSLC subpopulations showed no general radioresistant phenotype, despite the quantities of CSLC subpopulations shown to correspond relatively well in other reports. Likewise, CSLC characteristics were found in some but not all of the tested cell lines. The reported problems in testing for CSLC in cell lines may be overcome by additional techniques, beyond sorting for markers.

  17. N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) promote growth and inhibit differentiation of glioma stem-like cells.

    Science.gov (United States)

    Long, Patrick M; Moffett, John R; Namboodiri, Aryan M A; Viapiano, Mariano S; Lawler, Sean E; Jaworski, Diane M

    2013-09-06

    Metabolic reprogramming is a pathological feature of cancer and a driver of tumor cell transformation. N-Acetylaspartate (NAA) is one of the most abundant amino acid derivatives in the brain and serves as a source of metabolic acetate for oligodendrocyte myelination and protein/histone acetylation or a precursor for the synthesis of the neurotransmitter N-acetylaspartylglutamate (NAAG). NAA and NAAG as well as aspartoacylase (ASPA), the enzyme responsible for NAA degradation, are significantly reduced in glioma tumors, suggesting a possible role for decreased acetate metabolism in tumorigenesis. This study sought to examine the effects of NAA and NAAG on primary tumor-derived glioma stem-like cells (GSCs) from oligodendroglioma as well as proneural and mesenchymal glioblastoma, relative to oligodendrocyte progenitor cells (Oli-Neu). Although the NAA dicarboxylate transporter NaDC3 is primarily thought to be expressed by astrocytes, all cell lines expressed NaDC3 and, thus, are capable of NAA up-take. Treatment with NAA or NAAG significantly increased GSC growth and suppressed differentiation of Oli-Neu cells and proneural GSCs. Interestingly, ASPA was expressed in both the cytosol and nuclei of GSCs and exhibited greatest nuclear immunoreactivity in differentiation-resistant GSCs. Both NAA and NAAG elicited the expression of a novel immunoreactive ASPA species in select GSC nuclei, suggesting differential ASPA regulation in response to these metabolites. Therefore, this study highlights a potential role for nuclear ASPA expression in GSC malignancy and suggests that the use of NAA or NAAG is not an appropriate therapeutic approach to increase acetate bioavailability in glioma. Thus, an alternative acetate source is required.

  18. N-Acetylaspartate (NAA) and N-Acetylaspartylglutamate (NAAG) Promote Growth and Inhibit Differentiation of Glioma Stem-like Cells*

    Science.gov (United States)

    Long, Patrick M.; Moffett, John R.; Namboodiri, Aryan M. A.; Viapiano, Mariano S.; Lawler, Sean E.; Jaworski, Diane M.

    2013-01-01

    Metabolic reprogramming is a pathological feature of cancer and a driver of tumor cell transformation. N-Acetylaspartate (NAA) is one of the most abundant amino acid derivatives in the brain and serves as a source of metabolic acetate for oligodendrocyte myelination and protein/histone acetylation or a precursor for the synthesis of the neurotransmitter N-acetylaspartylglutamate (NAAG). NAA and NAAG as well as aspartoacylase (ASPA), the enzyme responsible for NAA degradation, are significantly reduced in glioma tumors, suggesting a possible role for decreased acetate metabolism in tumorigenesis. This study sought to examine the effects of NAA and NAAG on primary tumor-derived glioma stem-like cells (GSCs) from oligodendroglioma as well as proneural and mesenchymal glioblastoma, relative to oligodendrocyte progenitor cells (Oli-Neu). Although the NAA dicarboxylate transporter NaDC3 is primarily thought to be expressed by astrocytes, all cell lines expressed NaDC3 and, thus, are capable of NAA up-take. Treatment with NAA or NAAG significantly increased GSC growth and suppressed differentiation of Oli-Neu cells and proneural GSCs. Interestingly, ASPA was expressed in both the cytosol and nuclei of GSCs and exhibited greatest nuclear immunoreactivity in differentiation-resistant GSCs. Both NAA and NAAG elicited the expression of a novel immunoreactive ASPA species in select GSC nuclei, suggesting differential ASPA regulation in response to these metabolites. Therefore, this study highlights a potential role for nuclear ASPA expression in GSC malignancy and suggests that the use of NAA or NAAG is not an appropriate therapeutic approach to increase acetate bioavailability in glioma. Thus, an alternative acetate source is required. PMID:23884408

  19. β-Elemene Selectively Inhibits the Proliferation of Glioma Stem-Like Cells Through the Downregulation of Notch1.

    Science.gov (United States)

    Feng, Hai-Bin; Wang, Jing; Jiang, Hao-Ran; Mei, Xin; Zhao, Yi-Ying; Chen, Fu-Rong; Qu, Yue; Sai, Ke; Guo, Cheng-Cheng; Yang, Qun-Ying; Zhang, Zong-Ping; Chen, Zhong-Ping

    2017-03-01

    Glioma is the most frequent primary central nervous system tumor. Although the current first-line medicine, temozolomide (TMZ), promotes patient survival, drug resistance develops easily. Thus, it is important to investigate novel therapeutic reagents to solidify the treatment effect. β-Elemene (bELE) is a compound from a Chinese herb whose anticancer effect has been shown in various types of cancer. However, its role in the inhibition of glioma stem-like cells (GSLCs) has not yet been reported. We studied both the in vitro and the in vivo inhibitory effect of bELE and TMZ in GSLCs and parental cells and their combined effects. The molecular mechanisms were also investigated. We also optimized the delivery methods of bELE. We found that bELE selectively inhibits the proliferation and sphere formation of GSLCs, other than parental glioma cells, and TMZ exerts its effects on parental cells instead of GSLCs. The in vivo data confirmed that the combination of bELE and TMZ worked better in the xenografts of GSLCs, mimicking the situation of tumorigenesis of human cancer. Notch1 was downregulated with bELE treatment. Our data also demonstrated that the continuous administration of bELE produces an ideal effect to control tumor progression. Our findings have demonstrated, for the first time, that bELE could compensate for TMZ to kill both GSLCs and nonstem-like cancer cells, probably improving the prognosis of glioma patients tremendously. Notch1 might be a downstream target of bELE. Therefore, our data shed light on improving the outcomes of glioma patients by combining bELE and TMZ. Stem Cells Translational Medicine 2017;6:830-839. © 2016 The Authors Stem Cells Translational Medicine published by Wiley Periodicals, Inc. on behalf of AlphaMed Press.

  20. Chemo-predictive assay for targeting cancer stem-like cells in patients affected by brain tumors.

    Directory of Open Access Journals (Sweden)

    Sarah E Mathis

    Full Text Available Administration of ineffective anticancer therapy is associated with unnecessary toxicity and development of resistant clones. Cancer stem-like cells (CSLCs resist chemotherapy, thereby causing relapse of the disease. Thus, development of a test that identifies the most effective chemotherapy management offers great promise for individualized anticancer treatments. We have developed an ex vivo chemotherapy sensitivity assay (ChemoID, which measures the sensitivity of CSLCs as well as the bulk of tumor cells to a variety of chemotherapy agents. Two patients, a 21-year old male (patient 1 and a 5-month female (patient 2, affected by anaplastic WHO grade-III ependymoma were screened using the ChemoID assay. Patient 1 was found sensitive to the combination of irinotecan and bevacizumab, which resulted in a prolonged disease progression free period of 18 months. Following recurrence, the combination of various chemotherapy drugs was tested again with the ChemoID assay. We found that benzyl isothiocyanate (BITC greatly increased the chemosensitivity of the ependymoma cells to the combination of irinotecan and bevacizumab. After patient 1 was treated for two months with irinotecan, bevacizumab and supplements of cruciferous vegetable extracts containing BITC, we observed over 50% tumoral regression in comparison with pre-ChemoID scan as evidenced by MRI. Patient 2 was found resistant to all treatments tested and following 6 cycles of vincristine, carboplatin, cyclophosphamide, etoposide, and cisplatin in various combinations, the tumor of this patient rapidly progressed and proton beam therapy was recommended. As expected animal studies conducted with patient derived xenografts treated with ChemoID screened drugs recapitulated the clinical observation. This assay demonstrates that patients with the same histological stage and grade of cancer may vary considerably in their clinical response, suggesting that ChemoID testing which measures the sensitivity

  1. Neural Network based Control of SG based Standalone Generating System with Energy Storage for Power Quality Enhancement

    Science.gov (United States)

    Nayar, Priya; Singh, Bhim; Mishra, Sukumar

    2017-08-01

    An artificial intelligence based control algorithm is used in solving power quality problems of a diesel engine driven synchronous generator with automatic voltage regulator and governor based standalone system. A voltage source converter integrated with a battery energy storage system is employed to mitigate the power quality problems. An adaptive neural network based signed regressor control algorithm is used for the estimation of the fundamental component of load currents for control of a standalone system with load leveling as an integral feature. The developed model of the system performs accurately under varying load conditions and provides good dynamic response to the step changes in loads. The real time performance is achieved using MATLAB along with simulink/simpower system toolboxes and results adhere to an IEEE-519 standard for power quality enhancement.

  2. Neural Plasticity and Proliferation in the Generation of Antidepressant Effects: Hippocampal Implication

    Directory of Open Access Journals (Sweden)

    Fuencisla Pilar-Cuéllar

    2013-01-01

    Full Text Available It is widely accepted that changes underlying depression and antidepressant-like effects involve not only alterations in the levels of neurotransmitters as monoamines and their receptors in the brain, but also structural and functional changes far beyond. During the last two decades, emerging theories are providing new explanations about the neurobiology of depression and the mechanism of action of antidepressant strategies based on cellular changes at the CNS level. The neurotrophic/plasticity hypothesis of depression, proposed more than a decade ago, is now supported by multiple basic and clinical studies focused on the role of intracellular-signalling cascades that govern neural proliferation and plasticity. Herein, we review the state-of-the-art of the changes in these signalling pathways which appear to underlie both depressive disorders and antidepressant actions. We will especially focus on the hippocampal cellularity and plasticity modulation by serotonin, trophic factors as brain-derived neurotrophic factor (BDNF, and vascular endothelial growth factor (VEGF through intracellular signalling pathways—cAMP, Wnt/β-catenin, and mTOR. Connecting the classic monoaminergic hypothesis with proliferation/neuroplasticity-related evidence is an appealing and comprehensive attempt for improving our knowledge about the neurobiological events leading to depression and associated to antidepressant therapies.

  3. Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

    Directory of Open Access Journals (Sweden)

    Chih-Hong Lin

    2016-06-01

    Full Text Available A permanent magnet (PM synchronous generator system driven by wind turbine (WT, connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN and amended particle swarm optimization (PSO to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.

  4. Prediction of power system frequency response after generator outages using neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M B; Popovic, D P [Electrotechnicki Inst. ' Nikola Tesla' , Belgrade (Yugoslavia); Sobajic, D J; Pao, Y -H [Case Western Reserve Univ., Cleveland, OH (United States)

    1993-09-01

    A new methodology is presented for estimating the frequency behaviour of power systems necessary for an indication of under-frequency load shedding in steady-state security assessment. It is well known that large structural disturbances such as generator tripping or load outages can initiate cascading outages, system separation into islands, and even the complete breakup. The approach provides a fairly accurate method of estimating the system average frequency response without making simplifications or neglecting non-linearities and small time constants in the equations of generating units, voltage regulators and turbines. The efficiency of the new procedure is demonstrated using the New England power system model for a series of characteristic perturbations. The validity of the proposed approach is verified by comparison with the simulation of short-term dynamics including effects of control and automatic devices. (author)

  5. Eddy Current Signature Classification of Steam Generator Tube Defects Using A Learning Vector Quantization Neural Network

    International Nuclear Information System (INIS)

    Garcia, Gabe V.

    2005-01-01

    A major cause of failure in nuclear steam generators is degradation of their tubes. Although seven primary defect categories exist, one of the principal causes of tube failure is intergranular attack/stress corrosion cracking (IGA/SCC). This type of defect usually begins on the secondary side surface of the tubes and propagates both inwards and laterally. In many cases this defect is found at or near the tube support plates

  6. Decreased expression of cell adhesion genes in cancer stem-like cells isolated from primary oral squamous cell carcinomas.

    Science.gov (United States)

    Mishra, Amrendra; Sriram, Harshini; Chandarana, Pinal; Tanavde, Vivek; Kumar, Rekha V; Gopinath, Ashok; Govindarajan, Raman; Ramaswamy, S; Sadasivam, Subhashini

    2018-05-01

    The goal of this study was to isolate cancer stem-like cells marked by high expression of CD44, a putative cancer stem cell marker, from primary oral squamous cell carcinomas and identify distinctive gene expression patterns in these cells. From 1 October 2013 to 4 September 2015, 76 stage III-IV primary oral squamous cell carcinoma of the gingivobuccal sulcus were resected. In all, 13 tumours were analysed by immunohistochemistry to visualise CD44-expressing cells. Expression of CD44 within The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma RNA-sequencing data was also assessed. Seventy resected tumours were dissociated into single cells and stained with antibodies to CD44 as well as CD45 and CD31 (together referred as Lineage/Lin). From 45 of these, CD44 + Lin - and CD44 - Lin - subpopulations were successfully isolated using fluorescence-activated cell sorting, and good-quality RNA was obtained from 14 such sorted pairs. Libraries from five pairs were sequenced and the results analysed using bioinformatics tools. Reverse transcription quantitative polymerase chain reaction was performed to experimentally validate the differential expression of selected candidate genes identified from the transcriptome sequencing in the same 5 and an additional 9 tumours. CD44 was expressed on the surface of poorly differentiated tumour cells, and within the The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma samples, its messenger RNA levels were higher in tumours compared to normal. Transcriptomics revealed that 102 genes were upregulated and 85 genes were downregulated in CD44 + Lin - compared to CD44 - Lin - cells in at least 3 of the 5 tumours sequenced. The upregulated genes included those involved in immune regulation, while the downregulated genes were enriched for genes involved in cell adhesion. Decreased expression of PCDH18, MGP, SPARCL1 and KRTDAP was confirmed by reverse transcription quantitative polymerase chain reaction. Lower expression of

  7. In vitro identification and characterization of CD133(pos cancer stem-like cells in anaplastic thyroid carcinoma cell lines.

    Directory of Open Access Journals (Sweden)

    Giovanni Zito

    Full Text Available Recent publications suggest that neoplastic initiation and growth are dependent on a small subset of cells, termed cancer stem cells (CSCs. Anaplastic Thyroid Carcinoma (ATC is a very aggressive solid tumor with poor prognosis, characterized by high dedifferentiation. The existence of CSCs might account for the heterogeneity of ATC lesions. CD133 has been identified as a stem cell marker for normal and cancerous tissues, although its biological function remains unknown.ATC cell lines ARO, KAT-4, KAT-18 and FRO were analyzed for CD133 expression. Flow cytometry showed CD133(pos cells only in ARO and KAT-4 (64+/-9% and 57+/-12%, respectively. These data were confirmed by qRT-PCR and immunocytochemistry. ARO and KAT-4 were also positive for fetal marker oncofetal fibronectin and negative for thyrocyte-specific differentiating markers thyroglobulin, thyroperoxidase and sodium/iodide symporter. Sorted ARO/CD133(pos cells exhibited higher proliferation, self-renewal, colony-forming ability in comparison with ARO/CD133(neg. Furthermore, ARO/CD133(pos showed levels of thyroid transcription factor TTF-1 similar to the fetal thyroid cell line TAD-2, while the expression in ARO/CD133(neg was negligible. The expression of the stem cell marker OCT-4 detected by RT-PCR and flow cytometry was markedly higher in ARO/CD133(pos in comparison to ARO/CD133(neg cells. The stem cell markers c-KIT and THY-1 were negative. Sensitivity to chemotherapy agents was investigated, showing remarkable resistance to chemotherapy-induced apoptosis in ARO/CD133(pos when compared with ARO/CD133(neg cells.We describe CD133(pos cells in ATC cell lines. ARO/CD133(pos cells exhibit stem cell-like features--such as high proliferation, self-renewal ability, expression of OCT-4--and are characterized by higher resistance to chemotherapy. The simultaneous positivity for thyroid specific factor TTF-1 and onfFN suggest they might represent putative thyroid cancer stem-like cells. Our in

  8. Cell reprogramming by 3D bioprinting of human fibroblasts in polyurethane hydrogel for fabrication of neural-like constructs.

    Science.gov (United States)

    Ho, Lin; Hsu, Shan-Hui

    2018-04-01

    3D bioprinting is a technique which enables the direct printing of biodegradable materials with cells into 3D tissue. So far there is no cell reprogramming in situ performed with the 3D bioprinting process. Forkhead box D3 (FoxD3) is a transcription factor and neural crest marker, which was reported to reprogram human fibroblasts into neural crest stem-like cells. In this study, we synthesized a new biodegradable thermo-responsive waterborne polyurethane (PU) gel as a bioink. FoxD3 plasmids and human fibroblasts were co-extruded with the PU hydrogel through the syringe needle tip for cell reprogramming. The rheological properties of the PU hydrogel including the modulus, gelation time, and shear thinning were optimized for the transfection effect of FoxD3 in situ. The corresponding shear rate and shear stress were examined. Results showed that human fibroblasts could be reprogrammed into neural crest stem-like cells with high cell viability during the extrusion process under an average shear stress ∼190 Pa. We further translated the method to the extrusion-based 3D bioprinting, and demonstrated that human fibroblasts co-printed with FoxD3 in the thermo-responsive PU hydrogel could be reprogrammed and differentiated into a neural-tissue like construct at 14 days after induction. The neural-like tissue construct produced by 3D bioprinting from human fibroblasts may be applied to personalized drug screening or neuroregeneration. There is no study so far on cell reprogramming in situ with 3D bioprinting. In this manuscript, a new thermoresponsive polyurethane bioink was developed and employed to deliver FoxD3 plasmid into human fibroblasts by the extrusion-based bioprinting. When the polyurethane gel was extruded through the syringe tip, the shear stress generated may have caused the transient membrane permeability for transfection. The shear stress was optimized for transfection in situ by 3D bioprinting. We demonstrated that human fibroblasts could be

  9. Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor

    Directory of Open Access Journals (Sweden)

    Hong-en Qu

    2017-01-01

    Full Text Available Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

  10. Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor.

    Science.gov (United States)

    Qu, Hong-En; Niu, Chuanxin M; Li, Si; Hao, Man-Zhao; Hu, Zi-Xiang; Xie, Qing; Lan, Ning

    2017-12-01

    Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

  11. The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

    International Nuclear Information System (INIS)

    Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

    2009-01-01

    Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R 2 were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R 2 confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

  12. Garcinol downregulates Notch1 signaling via modulating miR-200c and suppresses oncogenic properties of PANC-1 cancer stem-like cells.

    Science.gov (United States)

    Huang, Chi-Cheng; Lin, Chien-Min; Huang, Yan-Jiun; Wei, Li; Ting, Lei-Li; Kuo, Chia-Chun; Hsu, Cheyu; Chiou, Jeng-Fong; Wu, Alexander T H; Lee, Wei-Hwa

    2017-03-01

    Pancreatic cancer represents one of the most aggressive types of malignancy due to its high resistance toward most clinically available treatments. The presence of pancreatic cancer stem-like cells (CSCs) has been attributed to the intrinsically high resistance and highly metastatic potential of this disease. Here, we identified and isolated pancreatic CSCs using the side population (SP) method from human pancreatic cancer cell line, PANC-1. We then compared the SP and non-SP PANC-1 cells genetically. PANC-1 SP cells exhibited CSC properties including enhanced self-renewal ability, increased metastatic potential, and resistance toward gemcitabine treatment. These cancer stem-like phenotypes were supported by their enhanced expression of ABCG2, Oct4, and CD44. A traditional plant-derived antioxidant, garcinol, has been implicated for its anticancer properties. Here, we found that garcinol treatment to PANC-1 SP cells significantly suppressed the stem-like properties of PANC-1 SP cells and metastatic potential by downregulating the expression of Mcl-1, EZH2, ABCG2, Gli-1, and Notch1. More importantly, garcinol treatment led to the upregulation of several tumor suppressor microRNAs, and miR-200c increased by garcinol treatment was found to target and downregulate Notch1. Thus, PANC-1 SP cells may serve as a model for studying drug-resistant pancreatic CSCs, and garcinol has the potential as an antagonist against pancreatic CSCs. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

  13. A Computational Model of Torque Generation: Neural, Contractile, Metabolic and Musculoskeletal Components

    Science.gov (United States)

    Callahan, Damien M.; Umberger, Brian R.; Kent-Braun, Jane A.

    2013-01-01

    The pathway of voluntary joint torque production includes motor neuron recruitment and rate-coding, sarcolemmal depolarization and calcium release by the sarcoplasmic reticulum, force generation by motor proteins within skeletal muscle, and force transmission by tendon across the joint. The direct source of energetic support for this process is ATP hydrolysis. It is possible to examine portions of this physiologic pathway using various in vivo and in vitro techniques, but an integrated view of the multiple processes that ultimately impact joint torque remains elusive. To address this gap, we present a comprehensive computational model of the combined neuromuscular and musculoskeletal systems that includes novel components related to intracellular bioenergetics function. Components representing excitatory drive, muscle activation, force generation, metabolic perturbations, and torque production during voluntary human ankle dorsiflexion were constructed, using a combination of experimentally-derived data and literature values. Simulation results were validated by comparison with torque and metabolic data obtained in vivo. The model successfully predicted peak and submaximal voluntary and electrically-elicited torque output, and accurately simulated the metabolic perturbations associated with voluntary contractions. This novel, comprehensive model could be used to better understand impact of global effectors such as age and disease on various components of the neuromuscular system, and ultimately, voluntary torque output. PMID:23405245

  14. Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

    Science.gov (United States)

    Tang, Guoyu; Ni, Yuan; Wang, Keqiang; Yong, Qin

    2018-01-01

    The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the workload on the doctors, a better method is to automatically retrieve the semantically equivalent question from the archive. We present a Generative Adversarial Networks (GAN) based approach to automatically retrieve patient question. We apply supervised deep learning based approaches to determine the similarity between patient questions. Then a GAN framework is used to fine-tune the pre-trained deep learning models. The experiment results show that fine-tuning by GAN can improve the performance.

  15. Voltage regulation in MV networks with dispersed generations by a neural-based multiobjective methodology

    Energy Technology Data Exchange (ETDEWEB)

    Galdi, Vincenzo [Dipartimento di Ingegneria dell' Informazione e Ingegneria Elettrica, Universita degli studi di Salerno, Via Ponte Don Melillo 1, 84084 Fisciano (Italy); Vaccaro, Alfredo; Villacci, Domenico [Dipartimento di Ingegneria, Universita degli Studi del Sannio, Piazza Roma 21, 82100 Benevento (Italy)

    2008-05-15

    This paper puts forward the role of learning techniques in addressing the problem of an efficient and optimal centralized voltage control in distribution networks equipped with dispersed generation systems (DGSs). The proposed methodology employs a radial basis function network (RBFN) to identify the multidimensional nonlinear mapping between a vector of observable variables describing the network operating point and the optimal set points of the voltage regulating devices. The RBFN is trained by numerical data generated by solving the voltage regulation problem for a set of network operating points by a rigorous multiobjective solution methodology. The RBFN performance is continuously monitored by a supervisor process that notifies when there is the need of a more accurate solution of the voltage regulation problem if nonoptimal network operating conditions (ex post monitoring) or excessive distances between the actual network state and the neuron's centres (ex ante monitoring) are detected. A more rigorous problem solution, if required, can be obtained by solving the voltage regulation problem by a conventional multiobjective optimization technique. This new solution, in conjunction with the corresponding input vector, is then adopted as a new train data sample to adapt the RBFN. This online training process allows RBFN to (i) adaptively learn the more representative domain space regions of the input/output mapping without needing a prior knowledge of a complete and representative training set, and (ii) manage effectively any time varying phenomena affecting this mapping. The results obtained by simulating the regulation policy in the case of a medium-voltage network are very promising. (author)

  16. Rationale and Methodology of Reprogramming for Generation of Induced Pluripotent Stem Cells and Induced Neural Progenitor Cells

    Directory of Open Access Journals (Sweden)

    Zuojun Tian

    2016-04-01

    Full Text Available Great progress has been made regarding the capabilities to modify somatic cell fate ever since the technology for generation of induced pluripotent stem cells (iPSCs was discovered in 2006. Later, induced neural progenitor cells (iNPCs were generated from mouse and human cells, bypassing some of the concerns and risks of using iPSCs in neuroscience applications. To overcome the limitation of viral vector induced reprogramming, bioactive small molecules (SM have been explored to enhance the efficiency of reprogramming or even replace transcription factors (TFs, making the reprogrammed cells more amenable to clinical application. The chemical induced reprogramming process is a simple process from a technical perspective, but the choice of SM at each step is vital during the procedure. The mechanisms underlying cell transdifferentiation are still poorly understood, although, several experimental data and insights have indicated the rationale of cell reprogramming. The process begins with the forced expression of specific TFs or activation/inhibition of cell signaling pathways by bioactive chemicals in defined culture condition, which initiates the further reactivation of endogenous gene program and an optimal stoichiometric expression of the endogenous pluri- or multi-potency genes, and finally leads to the birth of reprogrammed cells such as iPSCs and iNPCs. In this review, we first outline the rationale and discuss the methodology of iPSCs and iNPCs in a stepwise manner; and then we also discuss the chemical-based reprogramming of iPSCs and iNPCs.

  17. Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application

    Directory of Open Access Journals (Sweden)

    Si Le Van

    2016-12-01

    Full Text Available Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30% corresponding to the number of samples used for training-validation-testing was selected for estimation with the total coefficient of determination of 0.986 and a root mean square error of 1.63%. In terms of model application, the chemical concentration and injection strategy were optimized to maximize the net present value (NPV of the project at a specific oil price from the just created ANN model. To evaluate the feasibility of the project comprehensively in terms of market variations, a range of oil prices from 30 $/bbl to 60 $/bbl referenced from a real market situation was considered in conjunction with its probability following a statistical distribution on the NPV computation. Feasibility analysis of the optimal chemical injection scheme revealed a variation of profit from 0.42 $MM to 1.0 $MM, corresponding to the changes in oil price. In particular, at the highest possible oil prices, the project can earn approximately 0.61 $MM to 0.87 $MM for a quarter

  18. Transforming growth factor-beta1 promotes the migration and invasion of sphere-forming stem-like cell subpopulations in esophageal cancer

    International Nuclear Information System (INIS)

    Yue, Dongli; Zhang, Zhen; Li, Jieyao; Chen, Xinfeng; Ping, Yu; Liu, Shasha; Shi, Xiaojuan; Li, Lifeng; Wang, Liping; Huang, Lan; Zhang, Bin; Sun, Yan

    2015-01-01

    Esophageal cancer is one of the most lethal solid malignancies. Mounting evidence demonstrates that cancer stem cells (CSCs) are able to cause tumor initiation, metastasis and responsible for chemotherapy and radiotherapy failures. As CSCs are thought to be the main reason of therapeutic failure, these cells must be effectively targeted to elicit long-lasting therapeutic responses. We aimed to enrich and identify the esophageal cancer cell subpopulation with stem-like properties and help to develop new target therapy strategies for CSCs. Here, we found esophageal cancer cells KYSE70 and TE1 could form spheres in ultra low attachment surface culture and be serially passaged. Sphere-forming cells could redifferentiate and acquire morphology comparable to parental cells, when return to adherent culture. The sphere-forming cells possessed the key criteria that define CSCs: persistent self-renewal, overexpression of stemness genes (SOX2, ALDH1A1 and KLF4), reduced expression of differentiation marker CK4, chemoresistance, strong invasion and enhanced tumorigenic potential. SB525334, transforming growth factor-beta 1(TGF-β1) inhibitor, significantly inhibited migration and invasion of sphere-forming stem-like cells and had no effect on sphere-forming ability. In conclusion, esophageal cancer sphere-forming cells from KYSE70 and TE1 cultured in ultra low attachment surface possess cancer stem cell properties, providing a model for CSCs targeted therapy. TGF-β1 promotes the migration and invasion of sphere-forming stem-like cells, which may guide future studies on therapeutic strategies targeting these cells. - Highlights: • Esophageal cancer sphere-forming cells possess cancer stem cell properties. • Sphere-forming cells enhance TGF-β1 pathway activity. • TGF-β 1 inhibitor suppresses the migration and invasion of sphere-forming cells

  19. Tight regulation between cell survival and programmed cell death in GBM stem-like cells by EGFR/GSK3b/PP2A signaling.

    Science.gov (United States)

    Gürsel, Demirkan B; Banu, Matei A; Berry, Nicholas; Marongiu, Roberta; Burkhardt, Jan-Karl; Kobylarz, Keith; Kaplitt, Michael G; Rafii, Shahin; Boockvar, John A

    2015-01-01

    Malignant gliomas represent one of the most aggressive forms of cancer, displaying high mortality rates and limited treatment options. Specific subpopulations of cells residing in the tumor niche with stem-like characteristics have been postulated to initiate and maintain neoplasticity while resisting conventional therapies. The study presented here aims to define the role of glycogen synthase kinase 3 beta (GSK3b) in patient-derived glioblastoma (GBM) stem-like cell (GSC) proliferation, apoptosis and invasion. To evaluate the potential role of GSK3b in GBM, protein profiles from 68 GBM patients and 20 normal brain samples were analyzed for EGFR-mediated PI3kinase/Akt and GSK3b signaling molecules including protein phosphatase 2A (PP2A). To better understand the function of GSK3b in GBM, GSCs were isolated from GBM patient samples. Blocking GSK3b phosphorylation at Serine 9 attenuated cell proliferation while concomitantly stimulating apoptosis through activation of Caspase-3 in patient-derived GSCs. Increasing GSK3b protein content resulted in the inhibition of cell proliferation, colony formation and stimulated programmed cell death. Depleting GSK3b in GSCs down regulated PP2A. Furthermore, knocking down PP2A or blocking its activity by okadaic acid inactivated GSK3b by increasing GSK3b phosphorylation at Serine 9. Our data suggests that GSK3b may function as a regulator of apoptosis and tumorigenesis in GSCs. Therapeutic approaches targeting GSK3b in glioblastoma stem-like cells may be a useful addition to our current therapeutic armamentarium.

  20. Transforming growth factor-beta1 promotes the migration and invasion of sphere-forming stem-like cell subpopulations in esophageal cancer

    Energy Technology Data Exchange (ETDEWEB)

    Yue, Dongli; Zhang, Zhen; Li, Jieyao; Chen, Xinfeng [Biotherapy Center, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Road, Zhengzhou 450052, Henan, PR China (China); Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052 (China); Ping, Yu; Liu, Shasha [Biotherapy Center, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Road, Zhengzhou 450052, Henan, PR China (China); School of Life Sciences, Zhengzhou University, Zhengzhou 450000 (China); Shi, Xiaojuan; Li, Lifeng [Biotherapy Center, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Road, Zhengzhou 450052, Henan, PR China (China); Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052 (China); Wang, Liping [Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052 (China); Huang, Lan [Biotherapy Center, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Road, Zhengzhou 450052, Henan, PR China (China); Zhang, Bin [Biotherapy Center, the First Affiliated Hospital of Zhengzhou University, No. 1 Jianshe Road, Zhengzhou 450052, Henan, PR China (China); Robert H. Lurie Comprehensive Cancer Center, Department of Medicine-Division of Hematology/Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611 (United States); Sun, Yan [Department of Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052 (China); Department of Medical Oncology, Cancer Hospital, Chinese Academy of Medical Sciences (China); and others

    2015-08-01

    Esophageal cancer is one of the most lethal solid malignancies. Mounting evidence demonstrates that cancer stem cells (CSCs) are able to cause tumor initiation, metastasis and responsible for chemotherapy and radiotherapy failures. As CSCs are thought to be the main reason of therapeutic failure, these cells must be effectively targeted to elicit long-lasting therapeutic responses. We aimed to enrich and identify the esophageal cancer cell subpopulation with stem-like properties and help to develop new target therapy strategies for CSCs. Here, we found esophageal cancer cells KYSE70 and TE1 could form spheres in ultra low attachment surface culture and be serially passaged. Sphere-forming cells could redifferentiate and acquire morphology comparable to parental cells, when return to adherent culture. The sphere-forming cells possessed the key criteria that define CSCs: persistent self-renewal, overexpression of stemness genes (SOX2, ALDH1A1 and KLF4), reduced expression of differentiation marker CK4, chemoresistance, strong invasion and enhanced tumorigenic potential. SB525334, transforming growth factor-beta 1(TGF-β1) inhibitor, significantly inhibited migration and invasion of sphere-forming stem-like cells and had no effect on sphere-forming ability. In conclusion, esophageal cancer sphere-forming cells from KYSE70 and TE1 cultured in ultra low attachment surface possess cancer stem cell properties, providing a model for CSCs targeted therapy. TGF-β1 promotes the migration and invasion of sphere-forming stem-like cells, which may guide future studies on therapeutic strategies targeting these cells. - Highlights: • Esophageal cancer sphere-forming cells possess cancer stem cell properties. • Sphere-forming cells enhance TGF-β1 pathway activity. • TGF-β 1 inhibitor suppresses the migration and invasion of sphere-forming cells.

  1. Normal and malignant epithelial cells with stem-like properties have an extended G2 cell cycle phase that is associated with apoptotic resistance

    Directory of Open Access Journals (Sweden)

    Biddle Adrian

    2010-04-01

    Full Text Available Abstract Background Subsets of cells with stem-like properties have been previously isolated from human epithelial cancers and their resistance to apoptosis-inducing stimuli has been related to carcinoma recurrence and treatment failure. The aim of this study was to investigate the mechanisms of resistance to apoptosis-inducing agents of cells with stem-like properties in both normal and malignant human epithelia. Methods Cells isolated from fresh human head and neck carcinomas (n = 11, cell lines derived from head and neck, prostate and breast human carcinomas (n = 7, and from normal human oral mucosa (n = 5, were exposed to various apoptosis-inducing stimuli (UV, Tumour Necrosis Factor, Cisplatin, Etoposide, and Neocarzinostatin. Flow cytometry for CD44 and epithelial-specific antigen (ESA expression, colony morphology, tumour sphere formation and rapid adherence assays were used to identify the subset of cells with stem-like properties. Apoptosis, cell cycle and expression of various cell cycle checkpoint proteins were assessed (Western Blot, qPCR. The role of G2-checkpoint regulators Chk1 and Chk2 was investigated by use of debromohymenialdisine (DBH and siRNA. Results In both cancer biopsies and carcinoma cell lines a subset of CD44high cells showed increased clonogenicity, a significantly lower rate of apoptosis, and a significantly higher proportion of cells in the G2-phase of the cell cycle. An inverse correlation between the percentage of cells in G2-phase and the rate of apoptosis was found. Pulse-chase with iododeoxyuridine (IdU demonstrated that CD44high carcinoma cells spent longer time in G2, even in un-treated controls. These cells expressed higher levels of G2 checkpoint proteins, and their release from G2 with BDH or Chk1 siRNA increased their rate of apoptosis. Low passage cultures of normal keratinocytes were also found to contain a subset of CD44high cells showing increased clonogenicity, and a similar pattern of G2-block

  2. Normal and malignant epithelial cells with stem-like properties have an extended G2 cell cycle phase that is associated with apoptotic resistance

    International Nuclear Information System (INIS)

    Harper, Lisa J; Costea, Daniela Elena; Gammon, Luke; Fazil, Bilal; Biddle, Adrian; Mackenzie, Ian C

    2010-01-01

    Subsets of cells with stem-like properties have been previously isolated from human epithelial cancers and their resistance to apoptosis-inducing stimuli has been related to carcinoma recurrence and treatment failure. The aim of this study was to investigate the mechanisms of resistance to apoptosis-inducing agents of cells with stem-like properties in both normal and malignant human epithelia. Cells isolated from fresh human head and neck carcinomas (n = 11), cell lines derived from head and neck, prostate and breast human carcinomas (n = 7), and from normal human oral mucosa (n = 5), were exposed to various apoptosis-inducing stimuli (UV, Tumour Necrosis Factor, Cisplatin, Etoposide, and Neocarzinostatin). Flow cytometry for CD44 and epithelial-specific antigen (ESA) expression, colony morphology, tumour sphere formation and rapid adherence assays were used to identify the subset of cells with stem-like properties. Apoptosis, cell cycle and expression of various cell cycle checkpoint proteins were assessed (Western Blot, qPCR). The role of G2-checkpoint regulators Chk1 and Chk2 was investigated by use of debromohymenialdisine (DBH) and siRNA. In both cancer biopsies and carcinoma cell lines a subset of CD44 high cells showed increased clonogenicity, a significantly lower rate of apoptosis, and a significantly higher proportion of cells in the G2-phase of the cell cycle. An inverse correlation between the percentage of cells in G2-phase and the rate of apoptosis was found. Pulse-chase with iododeoxyuridine (IdU) demonstrated that CD44 high carcinoma cells spent longer time in G2, even in un-treated controls. These cells expressed higher levels of G2 checkpoint proteins, and their release from G2 with BDH or Chk1 siRNA increased their rate of apoptosis. Low passage cultures of normal keratinocytes were also found to contain a subset of CD44 high cells showing increased clonogenicity, and a similar pattern of G2-block associated with apoptotic resistance. These data

  3. Estimation of Entropy Generation for Ag-MgO/Water Hybrid Nanofluid Flow through Rectangular Minichannel by Using Artificial Neural Network

    OpenAIRE

    Uysal, Cuneyt; Korkmaz, Mehmet Erdi

    2018-01-01

    The convective heat transfer andentropy generation characteristics of Ag-MgO/water hybrid nanofluid flowthrough rectangular minichannel were numerically investigated. The Reynoldsnumber was in the range of 200 to 2000 and different nanoparticle volume fractionswere varied between = 0.005 and 0.02. In addition, ArtificialNeural Network was used to create a model for estimating of entropy generationof Ag-MgO/water hybrid nanofluid flow. As a result, it was found th...

  4. On the estimation of stellar parameters with uncertainty prediction from Generative Artificial Neural Networks: application to Gaia RVS simulated spectra

    Science.gov (United States)

    Dafonte, C.; Fustes, D.; Manteiga, M.; Garabato, D.; Álvarez, M. A.; Ulla, A.; Allende Prieto, C.

    2016-10-01

    Aims: We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case) to the given inputs (spectra). Such a model can be integrated in a Bayesian framework to estimate the posterior distribution of the outputs. Methods: The architecture of the GANN follows the same scheme as a normal ANN, but with the inputs and outputs inverted. We train the network with the set of atmospheric parameters (Teff, log g, [Fe/H] and [α/ Fe]), obtaining the stellar spectra for such inputs. The residuals between the spectra in the grid and the estimated spectra are minimized using a validation dataset to keep solutions as general as possible. Results: The performance of both conventional ANNs and GANNs to estimate the stellar parameters as a function of the star brightness is presented and compared for different Galactic populations. GANNs provide significantly improved parameterizations for early and intermediate spectral types with rich and intermediate metallicities. The behaviour of both algorithms is very similar for our sample of late-type stars, obtaining residuals in the derivation of [Fe/H] and [α/ Fe] below 0.1 dex for stars with Gaia magnitude Grvs satellite. Conclusions: Uncertainty estimation of computed astrophysical parameters is crucial for the validation of the parameterization itself and for the subsequent exploitation by the astronomical community. GANNs produce not only the parameters for a given spectrum, but a goodness-of-fit between the observed spectrum and the predicted one for a given set of parameters. Moreover, they allow us to obtain the full posterior distribution over the astrophysical parameters space once a noise model is assumed. This can be used for novelty detection and quality assessment.

  5. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

    International Nuclear Information System (INIS)

    Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

    2014-01-01

    Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

  6. Application of mitochondrial pyruvate carrier blocker UK5099 creates metabolic reprogram and greater stem-like properties in LnCap prostate cancer cells in vitro

    Science.gov (United States)

    Zhong, Yali; Li, Xiaoran; Yu, Dandan; Li, Xiaoli; Li, Yaqing; Long, Yuan; Yuan, Yuan; Ji, Zhenyu; Zhang, Mingzhi; Wen, Jian-Guo; Nesland, Jahn M.; Suo, Zhenhe

    2015-01-01

    Aerobic glycolysis is one of the important hallmarks of cancer cells and eukaryotic cells. In this study, we have investigated the relationship between blocking mitochondrial pyruvate carrier (MPC) with UK5099 and the metabolic alteration as well as stemness phenotype of prostatic cancer cells. It was found that blocking pyruvate transportation into mitochondrial attenuated mitochondrial oxidative phosphorylation (OXPHOS) and increased glycolysis. The UK5099 treated cells showed significantly higher proportion of side population (SP) fraction and expressed higher levels of stemness markers Oct3/4 and Nanog. Chemosensitivity examinations revealed that the UK5099 treated cells became more resistant to chemotherapy compared to the non-treated cells. These results demonstrate probably an intimate connection between metabolic reprogram and stem-like phenotype of LnCap cells in vitro. We propose that MPC blocker (UK5099) application may be an ideal model for Warburg effect studies, since it attenuates mitochondrial OXPHOS and increases aerobic glycolysis, a phenomenon typically reflected in the Warburg effect. We conclude that impaired mitochondrial OXPHOS and upregulated glycolysis are related with stem-like phenotype shift in prostatic cancer cells. PMID:26413751

  7. CD44 and SSEA-4 positive cells in an oral cancer cell line HSC-4 possess cancer stem-like cell characteristics.

    Science.gov (United States)

    Noto, Zenko; Yoshida, Toshiko; Okabe, Motonori; Koike, Chika; Fathy, Moustafa; Tsuno, Hiroaki; Tomihara, Kei; Arai, Naoya; Noguchi, Makoto; Nikaido, Toshio

    2013-08-01

    Cancer may be derived from cancer stem-like cells (CSCs), which are tumor-initiating cells that have properties similar to those of stem cells. Identification and isolation of CSCs needs to be improved further. CSCs markers were examined in human oral cancer cell lines by flow cytometry. The stem cell properties of subpopulations expressing different markers were assessed further by in vitro sphere formation assays, expression of stemness genes, drug resistance assays, and the ability to form tumors in nude mice. We demonstrated that CSCs could be isolated by the cell surface markers CD44 and stage-specific embryonic antigen-4 (SSEA-4). CD44+SSEA-4+ cells exhibited cancer stem-like properties, including extensive self-renewal into the bulk of cancer cells. In vivo xenograft experiments indicated that CD44+SSEA-4+ cells exhibit the highest tumorigenic capacity compared with the remaining subpopulations and parental cells. Double-positive cells for CD44 and SSEA-4 exhibited preferential expression of some stemness genes and were more resistant to the anticancer drugs, cisplatin and 5-fluorouracil (5-FU). In addition, cells expressing CD44 and SSEA-4 were detected in all tumor specimens analyzed, while coexpression of CD44 and SSEA-4 was not detectable in normal oral mucosa. Our findings suggest that CD44+SSEA-4+ cells exhibit the characteristics of CSCs in oral squamous cell carcinoma and provide a target for the development of more effective therapies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  8. Cellular Components, Including Stem-Like Cells, of Preterm Mother's Mature Milk as Compared with Those in Her Colostrum: A Pilot Study.

    Science.gov (United States)

    Kaingade, Pankaj; Somasundaram, Indumathi; Sharma, Akshita; Patel, Darshan; Marappagounder, Dhanasekaran

    2017-09-01

    Whether the preterm mothers' mature milk retains the same cellular components as those in colostrum including stem-like cell, cell adhesion molecules, and immune cells. A total of five preterm mothers were recruited for the study having an average age of 30.2 years and gestational age of 29.8 weeks from the Pristine Women's Hospital, Kolhapur. Colostrum milk was collected within 2-5 days and matured milk was collected 20-30 days after delivery from the same mothers. Integral cellular components of 22 markers including stem cells, immune cells, and cell adhesion molecules were measured using flowcytometry. Preterm mature milk was found to possess higher expressions of hematopoietic stem cells, mesenchymal stem-like cells, immune cells, few cell adhesion molecules, and side population cells than colostrum. The increased level of these different cell components in mature milk may be important in the long-term preterm baby's health growth. Further similar research in a larger population of various gestational ages and lactation stages of preterm mothers is warranted to support these pilot findings.

  9. Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach

    International Nuclear Information System (INIS)

    Meo, Santolo; Zohoori, Alireza; Vahedi, Abolfazl

    2016-01-01

    Highlights: • A new optimal design of flux switching permanent magnet generator is developed. • A prototype is employed to validate numerical data used for optimization. • A novel hybrid multi-objective particle swarm optimization approach is proposed. • Optimization targets are weight, cost, voltage and its total harmonic distortion. • The hybrid approach preference is proved compared with other optimization methods. - Abstract: In this paper a new hybrid approach obtained combining a multi-objective particle swarm optimization and artificial neural network is proposed for the design optimization of a direct-drive permanent magnet flux switching generators for low power wind applications. The targets of the proposed multi-objective optimization are to reduce the costs and weight of the machine while maximizing the amplitude of the induced voltage as well as minimizing its total harmonic distortion. The permanent magnet width, the stator and rotor tooth width, the rotor teeth number and stator pole number of the machine define the search space for the optimization problem. Four supervised artificial neural networks are designed for modeling the complex relationships among the weight, the cost, the amplitude and the total harmonic distortion of the output voltage respect to the quantities of the search space. Finite element analysis is adopted to generate training dataset for the artificial neural networks. Finite element analysis based model is verified by experimental results with a 1.5 kW permanent magnet flux switching generator prototype suitable for renewable energy applications, having 6/19 stator poles/rotor teeth. Finally the effectiveness of the proposed hybrid procedure is compared with the results given by conventional multi-objective optimization algorithms. The obtained results show the soundness of the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology for optimal design of permanent

  10. Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

    Energy Technology Data Exchange (ETDEWEB)

    Assareh, Ehsanolah; Poultangari, Iman [Dezful Branch, Islamic Azad University, Dezful (Iran, Islamic Republic of); Tandis, Emad [Mechanical Engineering Department, University of Jundi Shapor, Dezful (Iran, Islamic Republic of); Nedael, Mojtaba [Dept. of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)

    2016-10-15

    Enhancing the energy production from wind power in low-wind areas has always been a fundamental subject of research in the field of wind energy industry. In the first phase of this research, an initial investigation was performed to evaluate the potential of wind in south west of Iran. The initial results indicate that the wind potential in the studied location is not sufficient enough and therefore the investigated region is identified as a low wind speed area. In the second part of this study, an advanced optimization model was presented to regulate the torque in the wind generators. For this primary purpose, the torque of wind turbine is adjusted using a Proportional and integral (PI) control system so that at lower speeds of the wind, the power generated by generator is enhanced significantly. The proposed model uses the RBF neural network to adjust the net obtained gains of the PI controller for the purpose of acquiring the utmost electricity which is produced through the generator. Furthermore, in order to edify and instruct the neural network, the optimal data set is obtained by a Hybrid genetic algorithm along with a gravitational search algorithm (HGA-GSA). The proposed method is evaluated by using a 5MW wind turbine manufactured by National Renewable Energy Laboratory (NREL). Final results of this study are indicative of the satisfactory and successful performance of the proposed investigated model.

  11. Mesenchymal Stromal Cell-Derived Interleukin-6 Promotes Epithelial-Mesenchymal Transition and Acquisition of Epithelial Stem-Like Cell Properties in Ameloblastoma Epithelial Cells.

    Science.gov (United States)

    Jiang, Chunmiao; Zhang, Qunzhou; Shanti, Rabie M; Shi, Shihong; Chang, Ting-Han; Carrasco, Lee; Alawi, Faizan; Le, Anh D

    2017-09-01

    Epithelial-mesenchymal transition (EMT), a biological process associated with cancer stem-like or cancer-initiating cell formation, contributes to the invasiveness, metastasis, drug resistance, and recurrence of the malignant tumors; it remains to be determined whether similar processes contribute to the pathogenesis and progression of ameloblastoma (AM), a benign but locally invasive odontogenic neoplasm. Here, we demonstrated that EMT- and stem cell-related genes were expressed in the epithelial islands of the most common histologic variant subtype, the follicular AM. Our results revealed elevated interleukin (IL)-6 signals that were differentially expressed in the stromal compartment of the follicular AM. To explore the stromal effect on tumor pathogenesis, we isolated and characterized both mesenchymal stromal cells (AM-MSCs) and epithelial cells (AM-EpiCs) from follicular AM and demonstrated that, in in vitro culture, AM-MSCs secreted a significantly higher level of IL-6 as compared to the counterpart AM-EpiCs. Furthermore, both in vitro and in vivo studies revealed that exogenous and AM-MSC-derived IL-6 induced the expression of EMT- and stem cell-related genes in AM-EpiCs, whereas such effects were significantly abrogated either by a specific inhibitor of STAT3 or ERK1/2, or by knockdown of Slug gene expression. These findings suggest that AM-MSC-derived IL-6 promotes tumor-stem like cell formation by inducing EMT process in AM-EpiCs through STAT3 and ERK1/2-mediated signaling pathways, implying a role in the etiology and progression of the benign but locally invasive neoplasm. Stem Cells 2017;35:2083-2094. © 2017 AlphaMed Press.

  12. A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network

    OpenAIRE

    Khatib, Tamer; Elmenreich, Wilfried

    2015-01-01

    This paper presents a model for predicting hourly solar radiation data using daily solar radiation averages. The proposed model is a generalized regression artificial neural network. This model has three inputs, namely, mean daily solar radiation, hour angle, and sunset hour angle. The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. The results show that...

  13. Breast cancer stem-like cells are inhibited by a non-toxic aryl hydrocarbon receptor agonist.

    Directory of Open Access Journals (Sweden)

    Gérald J Prud'homme

    2010-11-01

    Full Text Available Cancer stem cells (CSCs have increased resistance to cancer chemotherapy. They can be enriched as drug-surviving CSCs (D-CSCs by growth with chemotherapeutic drugs, and/or by sorting of cells expressing CSC markers such as aldehyde dehydrogenase-1 (ALDH. CSCs form colonies in agar, mammospheres in low-adherence cultures, and tumors following xenotransplantation in Scid mice. We hypothesized that tranilast, a non-toxic orally active drug with anti-cancer activities, would inhibit breast CSCs.We examined breast cancer cell lines or D-CSCs generated by growth of these cells with mitoxantrone. Tranilast inhibited colony formation, mammosphere formation and stem cell marker expression. Mitoxantrone-selected cells were enriched for CSCs expressing stem cell markers ALDH, c-kit, Oct-4, and ABCG2, and efficient at forming mammospheres. Tranilast markedly inhibited mammosphere formation by D-CSCs and dissociated formed mammospheres, at pharmacologically relevant concentrations. It was effective against D-CSCs of both HER-2+ and triple-negative cell lines. Tranilast was also effective in vivo, since it prevented lung metastasis in mice injected i.v. with triple-negative (MDA-MB-231 mitoxantrone-selected cells. The molecular targets of tranilast in cancer have been unknown, but here we demonstrate it is an aryl hydrocarbon receptor (AHR agonist and this plays a key role. AHR is a transcription factor activated by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, polycyclic aromatic hydrocarbons and other ligands. Tranilast induced translocation of the AHR to the nucleus and stimulated CYP1A1 expression (a marker of AHR activation. It inhibited binding of the AHR to CDK4, which has been linked to cell-cycle arrest. D-CSCs expressed higher levels of the AHR than other cells. Knockdown of the AHR with siRNA, or blockade with an AHR antagonist, entirely abrogated the anti-proliferative and anti-mammosphere activity of tranilast. Thus, the anti-cancer effects of

  14. I think therefore I am: Rest-related prefrontal cortex neural activity is involved in generating the sense of self.

    Science.gov (United States)

    Gruberger, M; Levkovitz, Y; Hendler, T; Harel, E V; Harari, H; Ben Simon, E; Sharon, H; Zangen, A

    2015-05-01

    The sense of self has always been a major focus in the psychophysical debate. It has been argued that this complex ongoing internal sense cannot be explained by any physical measure and therefore substantiates a mind-body differentiation. Recently, however, neuro-imaging studies have associated self-referential spontaneous thought, a core-element of the ongoing sense of self, with synchronous neural activations during rest in the medial prefrontal cortex (PFC), as well as the medial and lateral parietal cortices. By applying deep transcranial magnetic stimulation (TMS) over human PFC before rest, we disrupted activity in this neural circuitry thereby inducing reports of lowered self-awareness and strong feelings of dissociation. This effect was not found with standard or sham TMS, or when stimulation was followed by a task instead of rest. These findings demonstrate for the first time a critical, causal role of intact rest-related PFC activity patterns in enabling integrated, enduring, self-referential mental processing. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. The Long Non-coding RNA HIF1A-AS2 Facilitates the Maintenance of Mesenchymal Glioblastoma Stem-like Cells in Hypoxic Niches

    Directory of Open Access Journals (Sweden)

    Marco Mineo

    2016-06-01

    Full Text Available Long non-coding RNAs (lncRNAs have an undefined role in the pathobiology of glioblastoma multiforme (GBM. These tumors are genetically and phenotypically heterogeneous with transcriptome subtype-specific GBM stem-like cells (GSCs that adapt to the brain tumor microenvironment, including hypoxic niches. We identified hypoxia-inducible factor 1 alpha-antisense RNA 2 (HIF1A-AS2 as a subtype-specific hypoxia-inducible lncRNA, upregulated in mesenchymal GSCs. Its deregulation affects GSC growth, self-renewal, and hypoxia-dependent molecular reprogramming. Among the HIF1A-AS2 interactome, IGF2BP2 and DHX9 were identified as direct partners. This association was needed for maintenance of expression of their target gene, HMGA1. Downregulation of HIF1A-AS2 led to delayed growth of mesenchymal GSC tumors, survival benefits, and impaired expression of HMGA1 in vivo. Our data demonstrate that HIF1A-AS2 contributes to GSCs’ speciation and adaptation to hypoxia within the tumor microenvironment, acting directly through its interactome and targets and indirectly by modulating responses to hypoxic stress depending on the subtype-specific genetic context.

  16. Kinome-wide shRNA Screen Identifies the Receptor Tyrosine Kinase AXL as a Key Regulator for Mesenchymal Glioblastoma Stem-like Cells

    Directory of Open Access Journals (Sweden)

    Peng Cheng

    2015-05-01

    Full Text Available Glioblastoma is a highly lethal cancer for which novel therapeutics are urgently needed. Two distinct subtypes of glioblastoma stem-like cells (GSCs were recently identified: mesenchymal (MES and proneural (PN. To identify mechanisms to target the more aggressive MES GSCs, we combined transcriptomic expression analysis and kinome-wide short hairpin RNA screening of MES and PN GSCs. In comparison to PN GSCs, we found significant upregulation and phosphorylation of the receptor tyrosine kinase AXL in MES GSCs. Knockdown of AXL significantly decreased MES GSC self-renewal capacity in vitro and inhibited the growth of glioblastoma patient-derived xenografts. Moreover, inhibition of AXL with shRNA or pharmacologic inhibitors also increased cell death significantly more in MES GSCs. Clinically, AXL expression was elevated in the MES GBM subtype and significantly correlated with poor prognosis in multiple cancers. In conclusion, we identified AXL as a potential molecular target for novel approaches to treat glioblastoma and other solid cancers.

  17. Elimination of Cancer Stem-Like “Side Population” Cells in Hepatoma Cell Lines by Chinese Herbal Mixture “Tien-Hsien Liquid”

    Directory of Open Access Journals (Sweden)

    Chih-Jung Yao

    2012-01-01

    Full Text Available There are increasing pieces of evidence suggesting that the recurrence of cancer may result from a small subpopulation of cancer stem cells, which are resistant to the conventional chemotherapy and radiotherapy. We investigated the effects of Chinese herbal mixture Tien-Hsien Liquid (THL on the cancer stem-like side population (SP cells isolated from human hepatoma cells. After sorting and subsequent culture, the SP cells from Huh7 hepatoma cells appear to have higher clonogenicity and mRNA expressions of stemness genes such as SMO, ABCG2, CD133, β-catenin, and Oct-4 than those of non-SP cells. At dose of 2 mg/mL, THL reduced the proportion of SP cells in HepG2, Hep3B, and Huh7 cells from 1.33% to 0.49%, 1.55% to 0.43%, and 1.69% to 0.27%, respectively. The viability and colony formation of Huh7 SP cells were effectively suppressed by THL dose-dependently, accompanied with the inhibition of stemness genes, e.g., ABCG2, CD133, and SMO. The tumorigenicity of THL-treated Huh7 SP cells in NOD/SCID mice was also diminished. Moreover, combination with THL could synergize the effect of doxorubicin against Huh7 SP cells. Our data indicate that THL may act as a cancer stem cell targeting therapeutics and be regarded as complementary and integrative medicine in the treatment of hepatoma.

  18. Combination Treatment with PPARγ Ligand and Its Specific Inhibitor GW9662 Downregulates BIS and 14-3-3 Gamma, Inhibiting Stem-Like Properties in Glioblastoma Cells.

    Science.gov (United States)

    Im, Chang-Nim

    2017-01-01

    PPAR γ is a nuclear receptor that regulates differentiation and proliferation and is highly expressed in many cancer cells. Its synthetic ligands, such as rosiglitazone and ciglitazone, and its inhibitor GW9662, were shown to induce cellular differentiation, inhibit proliferation, and lead to apoptosis. Glioblastoma is a common brain tumor with poor survival prospects. Recently, glioblastoma stem cells (GSCs) have been examined as a potential target for anticancer therapy; however, little is known about the combined effect of various agents on GSCs. In this study, we found that cotreatment with PPAR γ ligands and GW9662 inhibited stem-like properties in GSC-like spheres, which significantly express SOX2. In addition, this treatment decreased the activation of STAT3 and AKT and decreased the amounts of 14-3-3 gamma and BIS proteins. Moreover, combined administration of small-interfering RNA (siRNA) transfection with PPAR γ ligands induced downregulation of SOX2 and MMP2 activity together with inhibition of sphere-forming activity regardless of poly(ADP-ribose) polymerase (PARP) cleavage. Taken together, our findings suggest that a combination therapy using PPAR γ ligands and its inhibitor could be a potential therapeutic strategy targeting GSCs.

  19. Combination Treatment with PPARγ Ligand and Its Specific Inhibitor GW9662 Downregulates BIS and 14-3-3 Gamma, Inhibiting Stem-Like Properties in Glioblastoma Cells

    Directory of Open Access Journals (Sweden)

    Chang-Nim Im

    2017-01-01

    Full Text Available PPARγ is a nuclear receptor that regulates differentiation and proliferation and is highly expressed in many cancer cells. Its synthetic ligands, such as rosiglitazone and ciglitazone, and its inhibitor GW9662, were shown to induce cellular differentiation, inhibit proliferation, and lead to apoptosis. Glioblastoma is a common brain tumor with poor survival prospects. Recently, glioblastoma stem cells (GSCs have been examined as a potential target for anticancer therapy; however, little is known about the combined effect of various agents on GSCs. In this study, we found that cotreatment with PPARγ ligands and GW9662 inhibited stem-like properties in GSC-like spheres, which significantly express SOX2. In addition, this treatment decreased the activation of STAT3 and AKT and decreased the amounts of 14-3-3 gamma and BIS proteins. Moreover, combined administration of small-interfering RNA (siRNA transfection with PPARγ ligands induced downregulation of SOX2 and MMP2 activity together with inhibition of sphere-forming activity regardless of poly(ADP-ribose polymerase (PARP cleavage. Taken together, our findings suggest that a combination therapy using PPARγ ligands and its inhibitor could be a potential therapeutic strategy targeting GSCs.

  20. HDAC4 and HDAC6 sustain DNA double strand break repair and stem-like phenotype by promoting radioresistance in glioblastoma cells.

    Science.gov (United States)

    Marampon, Francesco; Megiorni, Francesca; Camero, Simona; Crescioli, Clara; McDowell, Heather P; Sferra, Roberta; Vetuschi, Antonella; Pompili, Simona; Ventura, Luca; De Felice, Francesca; Tombolini, Vincenzo; Dominici, Carlo; Maggio, Roberto; Festuccia, Claudio; Gravina, Giovanni Luca

    2017-07-01

    The role of histone deacetylase (HDAC) 4 and 6 in glioblastoma (GBM) radioresistance was investigated. We found that tumor samples from 31 GBM patients, who underwent temozolomide and radiotherapy combined treatment, showed HDAC4 and HDAC6 expression in 93.5% and 96.7% of cases, respectively. Retrospective clinical data analysis demonstrated that high-intensity HDAC4 and/or HDAC6 immunostaining was predictive of poor clinical outcome. In vitro experiments revealed that short hairpin RNA-mediated silencing of HDAC4 or HDAC6 radiosensitized U87MG and U251MG GBM cell lines by promoting DNA double-strand break (DSBs) accumulation and by affecting DSBs repair molecular machinery. We found that HDAC6 knock-down predisposes to radiation therapy-induced U251MG apoptosis- and U87MG autophagy-mediated cell death. HDAC4 silencing promoted radiation therapy-induced senescence, independently by the cellular context. Finally, we showed that p53 WT expression contributed to the radiotherapy lethal effects and that HDAC4 or HDAC6 sustained GBM stem-like radioresistant phenotype. Altogether, these observations suggest that HDAC4 and HDAC6 are guardians of irradiation-induced DNA damages and stemness, thus promoting radioresistance, and may represent potential prognostic markers and therapeutic targets in GBM. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. A simple, xeno-free method for oligodendrocyte generation from human neural stem cells derived from umbilical cord: engagement of gelatinases in cell commitment and differentiation.

    Science.gov (United States)

    Sypecka, Joanna; Ziemka-Nalecz, Małgorzata; Dragun-Szymczak, Patrycja; Zalewska, Teresa

    2017-05-01

    Oligodendrocyte progenitors (OPCs) are ranked among the most likely candidates for cell-based strategies aimed at treating neurodegenerative diseases accompanied by dys/demyelination of the central nervous system (CNS). In this regard, different sources of stem cells are being tested to elaborate xeno-free protocols for efficient generation of OPCs for clinical applications. In the present study, neural stem cells of human umbilical cord blood (HUCB-NSCs) have been used to derive OPCs and subsequently to differentiate them into mature, GalC-expressing oligodendrocytes. Applied components of the extracellular matrix (ECM) and the analogues of physiological substances known to increase glial commitment of neural stem cells have been shown to significantly increase the yield of the resulting OPC fraction. The efficiency of ECM components in promoting oligodendrocyte commitment and differentiation prompted us to investigate the potential role of gelatinases in those processes. Subsequently, endogenous and ECM metalloproteinases (MMPs) activity has been compared with that detected in primary cultures of rat oligodendrocytes in vitro, as well as in rat brains in vivo. The data indicate that gelatinases are engaged in gliogenesis both in vitro and in vivo, although differently, which presumably results from distinct extracellular conditions. In conclusion, the study presents an efficient xeno-free method of deriving oligodendrocyte from HUCB-NSCs and analyses the engagement of MMP-2/MMP-9 in the processes of cell commitment and maturation. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  2. Pre-procambial cells are niches for pluripotent and totipotent stem-like cells for organogenesis and somatic embryogenesis in the peach palm: a histological study.

    Science.gov (United States)

    de Almeida, Marcilio; de Almeida, Cristina Vieira; Mendes Graner, Erika; Ebling Brondani, Gilvano; Fiori de Abreu-Tarazi, Monita

    2012-08-01

    The direct induction of adventitious buds and somatic embryos from explants is a morphogenetic process that is under the influence of exogenous plant growth regulators and its interactions with endogenous phytohormones. We performed an in vitro histological analysis in peach palm (Bactris gasipaes Kunth) shoot apexes and determined that the positioning of competent cells and their interaction with neighboring cells, under the influence of combinations of exogenously applied growth regulators (NAA/BAP and NAA/TDZ), allows the pre-procambial cells (PPCs) to act in different morphogenic pathways to establish niche competent cells. It is likely that there has been a habituation phenomenon during the regeneration and development of the microplants. This includes promoting the tillering of primary or secondary buds due to culturing in the absence of NAA/BAP or NAA/TDZ after a period in the presence of these growth regulators. Histological analyses determined that the adventitious roots were derived from the dedifferentiation of the parenchymal cells located in the basal region of the adventitious buds, with the establishment of rooting pole, due to an auxin gradient. Furthermore, histological and histochemical analyses allowed us to characterize how the PPCs provide niches for multipotent, pluripotent and totipotent stem-like cells for vascular differentiation, organogenesis and somatic embryogenesis in the peach palm. The histological and histochemical analyses also allowed us to detect the unicellular or multicellular origin of somatic embryogenesis. Therefore, our results indicate that the use of growth regulators in microplants can lead to habituation and to different morphogenic pathways leading to potential niche establishment, depending on the positioning of the competent cells and their interaction with neighboring cells. Our results indicate that the use of growth regulators in microplants can lead to habituation and to different morphogenic pathways leading to

  3. Stem-like tumor-initiating cells isolated from IL13Rα2 expressing gliomas are targeted and killed by IL13-zetakine-redirected T Cells.

    Science.gov (United States)

    Brown, Christine E; Starr, Renate; Aguilar, Brenda; Shami, Andrew F; Martinez, Catalina; D'Apuzzo, Massimo; Barish, Michael E; Forman, Stephen J; Jensen, Michael C

    2012-04-15

    To evaluate IL13Rα2 as an immunotherapeutic target for eliminating glioma stem-like cancer initiating cells (GSC) of high-grade gliomas, with particular focus on the potential of genetically engineered IL13Rα2-specific primary human CD8(+) CTLs (IL13-zetakine(+) CTL) to target this therapeutically resistant glioma subpopulation. A panel of low-passage GSC tumor sphere (TS) and serum-differentiated glioma lines were expanded from patient glioblastoma specimens. These glioblastoma lines were evaluated for expression of IL13Rα2 and for susceptibility to IL13-zetakine(+) CTL-mediated killing in vitro and in vivo. We observed that although glioma IL13Rα2 expression varies between patients, for IL13Rα2(pos) cases this antigen was detected on both GSCs and more differentiated tumor cell populations. IL13-zetakine(+) CTL were capable of efficient recognition and killing of both IL13Rα2(pos) GSCs and IL13Rα2(pos) differentiated cells in vitro, as well as eliminating glioma-initiating activity in an orthotopic mouse tumor model. Furthermore, intracranial administration of IL13-zetakine(+) CTL displayed robust antitumor activity against established IL13Rα2(pos) GSC TS-initiated orthotopic tumors in mice. Within IL13Rα2 expressing high-grade gliomas, this receptor is expressed by GSCs and differentiated tumor populations, rendering both targetable by IL13-zetakine(+) CTLs. Thus, our results support the potential usefullness of IL13Rα2-directed immunotherapeutic approaches for eradicating therapeutically resistant GSC populations. ©2012 AACR.

  4. Role of epimorphin in bile duct formation of rat liver epithelial stem-like cells: involvement of small G protein RhoA and C/EBPβ.

    Science.gov (United States)

    Jia, Yali; Yao, Hailei; Zhou, Junnian; Chen, Lin; Zeng, Quan; Yuan, Hongfeng; Shi, Lei; Nan, Xue; Wang, Yunfang; Yue, Wen; Pei, Xuetao

    2011-11-01

    Epimorphin/syntaxin 2 is a high conserved and very abundant protein involved in epithelial morphogenesis in various organs. We have shown recently that epimorphin (EPM), a protein exclusively expressed on the surface of hepatic stellate cells and myofibroblasts of the liver, induces bile duct formation of hepatic stem-like cells (WB-F344 cells) in a putative biophysical way. Therefore, the aim of this study was to present some of the molecular mechanisms by which EPM mediates bile duct formation. We established a biliary differentiation model by co-culture of EPM-overexpressed mesenchymal cells (PT67(EPM)) with WB-F344 cells. Here, we showed that EPM could promote WB-F344 cells differentiation into bile duct-like structures. Biliary differentiation markers were also elevated by EPM including Yp, Cx43, aquaporin-1, CK19, and gamma glutamyl transpeptidase (GGT). Moreover, the signaling pathway of EPM was analyzed by focal adhesion kinase (FAK), extracellular regulated kinase 1/2 (ERK1/2), and RhoA Western blot. Also, a dominant negative (DN) RhoA-WB-F344 cell line (WB(RhoA-DN)) was constructed. We found that the levels of phosphorylation (p) of FAK and ERK1/2 were up-regulated by EPM. Most importantly, we also showed that RhoA is necessary for EPM-induced activation of FAK and ERK1/2 and bile duct formation. In addition, a dual luciferase-reporter assay and CHIP assay was performed to reveal that EPM regulates GGT IV and GGT V expression differentially, possibly mediated by C/EBPβ. Taken together, these data demonstrated that EPM regulates bile duct formation of WB-F344 cells through effects on RhoA and C/EBPβ, implicating a dual aspect of this morphoregulator in bile duct epithelial morphogenesis. Copyright © 2011 Wiley-Liss, Inc.

  5. Cancer stem-like cells of ovarian clear cell carcinoma are enriched in the ALDH-high population associated with an accelerated scavenging system in reactive oxygen species.

    Science.gov (United States)

    Mizuno, T; Suzuki, N; Makino, H; Furui, T; Morii, E; Aoki, H; Kunisada, T; Yano, M; Kuji, S; Hirashima, Y; Arakawa, A; Nishio, S; Ushijima, K; Ito, K; Itani, Y; Morishige, K

    2015-05-01

    In ovarian cancer cases, recurrence after chemotherapy is frequently observed, suggesting the involvement of ovarian cancer stem-like cells (CSCs). The chemoresistance of ovarian clear cell carcinomas is particularly strong in comparison to other epithelial ovarian cancer subtypes. We investigated the relationship between a CSC marker, aldehyde dehydrogenase 1 (ALDH1), and clinical prognosis using ovarian clear cell carcinoma tissue samples. Furthermore, we investigated the antioxidant mechanism by which CSCs maintain a lower reactive oxygen species (ROS) level, which provides protection from chemotherapeutic agents. Immunohistochemical staining was performed to examine the CSC markers (CD133, CD44, ALDH1) using ovarian clear cell carcinoma tissue samples (n=81). Clear cell carcinoma cell lines (KOC-7C, OVTOKO) are separated into the ALDH-high and ALDH-low populations by ALDEFLUOR assay and fluorescence-activated cell sorting (FACS). We compared the intracellular ROS level, mRNA level of the antioxidant enzymes and Nrf2 expression of the two populations. High ALDH1 expression levels are related to advanced stage in clear cell carcinoma cases. ALDH1 expression significantly reduced progression free survival. Other markers are not related to clinical stage and prognosis. ALDH-high cells contained a lower ROS level than ALDH-low cells. Antioxidant enzymes were upregulated in ALDH-high cells. ALDH-high cells showed increased expression of Nrf2, a key transcriptional factor of the antioxidant system. ALDH-positive CSCs might have increased Nrf2-induced antioxidant scavengers, which lower ROS level relevant to chemoresistance in ovarian clear cell carcinoma. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. Neural substrates of semantic relationships: common and distinct left-frontal activities for generation of synonyms vs. antonyms.

    Science.gov (United States)

    Jeon, Hyeon-Ae; Lee, Kyoung-Min; Kim, Young-Bo; Cho, Zang-Hee

    2009-11-01

    Synonymous and antonymous relationships among words may reflect the organization and/or processing in the mental lexicon and its implementation in the brain. In this study, functional magnetic resonance imaging (fMRI) is employed to compare brain activities during generation of synonyms (SYN) and antonyms (ANT) prompted by the same words. Both SYN and ANT, when compared with reading nonwords (NW), activated a region in the left middle frontal gyrus (BA 46). Neighboring this region, there was a dissociation observed in that the ANT activation extended more anteriorly and laterally to the SYN activation. The activations in the left middle frontal gyrus may be related to mental processes that are shared in the SYN and ANT generations, such as engaging semantically related parts of mental lexicon for the word search, whereas the distinct activations unique for either SYN or ANT generation may reflect the additional component of antonym retrieval, namely, reversing the polarity of semantic relationship in one crucial dimension. These findings suggest that specific components in the semantic processing, such as the polarity reversal for antonym generation and the similarity assessment for synonyms, are separately and systematically laid out in the left-frontal cortex.

  7. A program for assisting automatic generation control of the ELETRONORTE using artificial neural network; Um programa para assistencia ao controle automatico de geracao da Eletronorte usando rede neuronal artificial

    Energy Technology Data Exchange (ETDEWEB)

    Brito Filho, Pedro Rodrigues de; Nascimento Garcez, Jurandyr do [Para Univ., Belem, PA (Brazil). Centro Tecnologico; Charone, Junior, Wady [Centrais Eletricas do Nordeste do Brasil S.A. (ELETRONORTE), Belem, PA (Brazil)

    1994-12-31

    This work presents an application of artificial neural network as a support to decision making in the automatic generation control (AGC) of the ELETRONORTE. It uses a software to auxiliary in the decisions in real time of the AGC. (author) 2 refs., 6 figs., 1 tab.

  8. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

    Science.gov (United States)

    Azadi, Sama; Karimi-Jashni, Ayoub

    2016-02-01

    Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

    Science.gov (United States)

    Judd, Nicolas; Smith, Jason; Jain, Manu; Mukherjee, Sushmita; Icaza, Michael; Gallagher, Ryan; Szeligowski, Richard; Wu, Binlin

    2018-02-01

    A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification.

  10. Epigallocathechin gallate, polyphenol present in green tea, inhibits stem-like characteristics and epithelial-mesenchymal transition in nasopharyngeal cancer cell lines

    Directory of Open Access Journals (Sweden)

    Lin Chien-Hung

    2012-10-01

    Full Text Available Abstract Background Previous studies have demonstrated that the consumption of green tea inhibits the growth of various cancers. Most cancers are believed to be initiated from and maintained by a small population of cancer stem-like cells (CSC or tumor-initiating cells (TIC that are responsible for tumor relapse and chemotherapeutic resistance. Although epigallocathechin gallate (EGCG, the most abundant catechin in green tea, has been reported to induce growth inhibition and apoptosis in some cancer cells, its effect on CSC is undefined. In this study, we enriched CSC by the sphere formation, and provided an efficient model for further experiments. Using this method, we examined the effects of EGCG regulating the nasopharyngeal carcinoma (NPC CSC and attempted to elucidate the possible mechanisms. Methods NPC TW01 and TW06 cell lines were enriched by sphere formation and characterized their phenotypical properties, such as invasion capacity, epithelial-mesenchymal transition (EMT and gene expression were analyzed by quantitative real-time reverse transcription polymerase chain reaction (q-RT-PCR. EGCG-induced growth inhibition in the parental and sphere-derived cells was determined by MTT and bromodeoxyuridine (BrdU assay. EGCG-induced apoptosis was analyzed by flow cytometry with Annexin V and PI staining. The effects of EGCG on sphere-derived cell tumorigenicity, migration and invasion were determined by soft agar assay, wound healing, and cell invasion assay. The alternation of protein expression regulated by EGCG on these sphere-derived cells was assessed by immunofluorescence staining and western blot. Results NPC sphere-derived cells grown in serum-free non-adherent culture showed increased expression of stem cell markers and EMT markers compared to parental cells grown in conventional culture. Although EGCG induced growth inhibition and apoptosis in the parental cells in a dose-dependent manner, it was not as effective against spheres

  11. Enhancement of cancer stem-like and epithelial−mesenchymal transdifferentiation property in oral epithelial cells with long-term nicotine exposure: Reversal by targeting SNAIL

    International Nuclear Information System (INIS)

    Yu, Cheng-Chia; Chang, Yu-Chao

    2013-01-01

    Cigarette smoking is one of the major risk factors in the development and further progression of tumorigenesis, including oral squamous cell carcinoma (OSCC). Recent studies suggest that interplay cancer stem-like cells (CSCs) and epithelial−mesenchymal transdifferentiation (EMT) properties are responsible for the tumor maintenance and metastasis in OSCC. The aim of the present study was to investigate the effects of long-term exposure with nicotine, a major component in cigarette, on CSCs and EMT characteristics. The possible reversal regulators were further explored in nicotine-induced CSCs and EMT properties in human oral epithelial (OE) cells. Long-term exposure with nicotine was demonstrated to up-regulate ALDH1 population in normal gingival and primary OSCC OE cells dose-dependently. Moreover, long-term nicotine treatment was found to enhance the self-renewal sphere-forming ability and stemness gene signatures expression and EMT regulators in OE cells. The migration/cell invasiveness/anchorage independent growth and in vivo tumor growth by nude mice xenotransplantation assay was enhanced in long-term nicotine-stimulated OE cells. Knockdown of Snail in long-term nicotine-treated OE cells was found to reduce their CSCs properties. Therapeutic delivery of Si-Snail significantly blocked the xenograft tumorigenesis of long-term nicotine-treated OSCC cells and largely significantly improved the recipient survival. The present study demonstrated that the enrichment of CSCs coupled EMT property in oral epithelial cells induced by nicotine is critical for the development of OSCC tumorigenesis. Targeting Snail might offer a new strategy for the treatment of OSCC patients with smoking habit. -- Highlights: ► Sustained nicotine treatment induced CSCs properties of oral epithelial cells. ► Long-term nicotine treatment enhance EMT properties of oral epithelial cells. ► Long-term nicotine exposure increased tumorigenicity of oral epithelial cells. ► Si

  12. Enhancement of cancer stem-like and epithelial−mesenchymal transdifferentiation property in oral epithelial cells with long-term nicotine exposure: Reversal by targeting SNAIL

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Cheng-Chia [Institute of Oral Science, Chung Shan Medical University, Taichung, Taiwan (China); School of Dentistry, Chung Shan Medical University, Taichung, Taiwan (China); Department of Dentistry, Chung Shan Medical University Hospital, Taichung, Taiwan (China); Chang, Yu-Chao, E-mail: cyc@csmu.edu.tw [School of Dentistry, Chung Shan Medical University, Taichung, Taiwan (China); Department of Dentistry, Chung Shan Medical University Hospital, Taichung, Taiwan (China)

    2013-02-01

    Cigarette smoking is one of the major risk factors in the development and further progression of tumorigenesis, including oral squamous cell carcinoma (OSCC). Recent studies suggest that interplay cancer stem-like cells (CSCs) and epithelial−mesenchymal transdifferentiation (EMT) properties are responsible for the tumor maintenance and metastasis in OSCC. The aim of the present study was to investigate the effects of long-term exposure with nicotine, a major component in cigarette, on CSCs and EMT characteristics. The possible reversal regulators were further explored in nicotine-induced CSCs and EMT properties in human oral epithelial (OE) cells. Long-term exposure with nicotine was demonstrated to up-regulate ALDH1 population in normal gingival and primary OSCC OE cells dose-dependently. Moreover, long-term nicotine treatment was found to enhance the self-renewal sphere-forming ability and stemness gene signatures expression and EMT regulators in OE cells. The migration/cell invasiveness/anchorage independent growth and in vivo tumor growth by nude mice xenotransplantation assay was enhanced in long-term nicotine-stimulated OE cells. Knockdown of Snail in long-term nicotine-treated OE cells was found to reduce their CSCs properties. Therapeutic delivery of Si-Snail significantly blocked the xenograft tumorigenesis of long-term nicotine-treated OSCC cells and largely significantly improved the recipient survival. The present study demonstrated that the enrichment of CSCs coupled EMT property in oral epithelial cells induced by nicotine is critical for the development of OSCC tumorigenesis. Targeting Snail might offer a new strategy for the treatment of OSCC patients with smoking habit. -- Highlights: ► Sustained nicotine treatment induced CSCs properties of oral epithelial cells. ► Long-term nicotine treatment enhance EMT properties of oral epithelial cells. ► Long-term nicotine exposure increased tumorigenicity of oral epithelial cells. ► Si

  13. Role of Rac1 Pathway in Epithelial-to-Mesenchymal Transition and Cancer Stem-like Cell Phenotypes in Gastric Adenocarcinoma.

    Science.gov (United States)

    Yoon, Changhwan; Cho, Soo-Jeong; Chang, Kevin K; Park, Do Joong; Ryeom, Sandra W; Yoon, Sam S

    2017-08-01

    Rac1, a Rho GTPase family member, is dysregulated in a variety of tumor types including gastric adenocarcinoma, but little is known about its role in cancer stem-like cells (CSCs). Therefore, Rac1 activity and inhibition were examined in gastric adenocarcinoma cells and mouse xenograft models for epithelial-to-mesenchymal transition (EMT) and CSC phenotypes. Rac1 activity was significantly higher in spheroid-forming or CD44 + gastric adenocarcinoma CSCs compared with unselected cells. Rac1 inhibition using Rac1 shRNA or a Rac1 inhibitor (NSC23766) decreased expression of the self-renewal transcription factor, Sox-2, decreased spheroid formation by 78%-81%, and prevented tumor initiation in immunodeficient mice. Gastric adenocarcinoma CSCs had increased expression of the EMT transcription factor Slug, 4.4- to 8.3-fold greater migration, and 4.2- to 12.6-fold greater invasion than unselected cells, and these increases could be blocked completely with Rac1 inhibition. Gastric adenocarcinoma spheroid cells were resistant to 5-fluorouracil and cisplatin chemotherapy, and this chemotherapy resistance could be reversed with Rac1 shRNA or NSC23766. The PI3K/Akt pathway may be upstream of Rac1, and JNK may be downstream of Rac1. In the MKN-45 xenograft model, cisplatin inhibited tumor growth by 50%, Rac1 inhibition by 35%, and the combination by 77%. Higher Rac1 activity, in clinical specimens from gastric adenocarcinoma patients who underwent potentially curative surgery, correlated with significantly worse survival ( P = 0.017). In conclusion, Rac1 promotes the EMT program in gastric adenocarcinoma and the acquisition of a CSC state. Rac1 inhibition in gastric adenocarcinoma cells blocks EMT and CSC phenotypes, and thus may prevent metastasis and augment chemotherapy. Implications: In gastric adenocarcinoma, therapeutic targeting of the Rac1 pathway may prevent or reverse EMT and CSC phenotypes that drive tumor progression, metastasis, and chemotherapy resistance. Mol

  14. Rapid generation of sub-type, region-specific neurons and neural networks from human pluripotent stem cell-derived neurospheres

    Directory of Open Access Journals (Sweden)

    Aynun N. Begum

    2015-11-01

    Full Text Available Stem cell-based neuronal differentiation has provided a unique opportunity for disease modeling and regenerative medicine. Neurospheres are the most commonly used neuroprogenitors for neuronal differentiation, but they often clump in culture, which has always represented a challenge for neurodifferentiation. In this study, we report a novel method and defined culture conditions for generating sub-type or region-specific neurons from human embryonic and induced pluripotent stem cells derived neurosphere without any genetic manipulation. Round and bright-edged neurospheres were generated in a supplemented knockout serum replacement medium (SKSRM with 10% CO2, which doubled the expression of the NESTIN, PAX6 and FOXG1 genes compared with those cultured with 5% CO2. Furthermore, an additional step (AdSTEP was introduced to fragment the neurospheres and facilitate the formation of a neuroepithelial-type monolayer that we termed the “neurosphederm”. The large neural tube-type rosette (NTTR structure formed from the neurosphederm, and the NTTR expressed higher levels of the PAX6, SOX2 and NESTIN genes compared with the neuroectoderm-derived neuroprogenitors. Different layers of cortical, pyramidal, GABAergic, glutamatergic, cholinergic neurons appeared within 27 days using the neurosphederm, which is a shorter period than in traditional neurodifferentiation-protocols (42–60 days. With additional supplements and timeline dopaminergic and Purkinje neurons were also generated in culture too. Furthermore, our in vivo results indicated that the fragmented neurospheres facilitated significantly better neurogenesis in severe combined immunodeficiency (SCID mouse brains compared with the non-fragmented neurospheres. Therefore, this neurosphere-based neurodifferentiation protocol is a valuable tool for studies of neurodifferentiation, neuronal transplantation and high throughput screening assays.

  15. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

    Directory of Open Access Journals (Sweden)

    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

  16. Generation of brain tumours in mice by Cre-mediated recombination of neural progenitors in situ with the tamoxifen metabolite endoxifen.

    Science.gov (United States)

    Benedykcinska, Anna; Ferreira, Andreia; Lau, Joanne; Broni, Jessica; Richard-Loendt, Angela; Henriquez, Nico V; Brandner, Sebastian

    2016-02-01

    Targeted cell- or region-specific gene recombination is widely used in the functional analysis of genes implicated in development and disease. In the brain, targeted gene recombination has become a mainstream approach to study neurodegeneration or tumorigenesis. The use of the Cre-loxP system to study tumorigenesis in the adult central nervous system (CNS) can be limited, when the promoter (such as GFAP) is also transiently expressed during development, which can result in the recombination of progenies of different lineages. Engineering of transgenic mice expressing Cre recombinase fused to a mutant of the human oestrogen receptor (ER) allows the circumvention of transient developmental Cre expression by inducing recombination in the adult organism. The recombination of loxP sequences occurs only in the presence of tamoxifen. Systemic administration of tamoxifen can, however, exhibit toxicity and might also recombine unwanted cell populations if the promoter driving Cre expression is active at the time of tamoxifen administration. Here, we report that a single site-specific injection of an active derivative of tamoxifen successfully activates Cre recombinase and selectively recombines tumour suppressor genes in neural progenitor cells of the subventricular zone in mice, and we demonstrate its application in a model for the generation of intrinsic brain tumours. © 2016. Published by The Company of Biologists Ltd.

  17. Towards modeling of combined cooling, heating and power system with artificial neural network for exergy destruction and exergy efficiency prognostication of tri-generation components

    International Nuclear Information System (INIS)

    Taghavifar, Hadi; Anvari, Simin; Saray, Rahim Khoshbakhti; Khalilarya, Shahram; Jafarmadar, Samad; Taghavifar, Hamid

    2015-01-01

    The current study is an attempt to address the investigation of the CCHP (combined cooling, heating and power) system when 10 input variables were chosen to analyze 10 most important objective output parameters. Moreover, ANN (artificial neural network) was successfully applied on the tri-generation system on account of its capability to predict responses with great confidence. The results of sensitivity analysis were considered as foundation for selecting the most suitable and potent input parameters of the supposed cycle. Furthermore, the best ANN topology was attained based on the least amount of MSE and number of iterations. Consequently, the trainlm (Levenberg–Marquardt) training approach with 10-9-10 configuration has been exploited for ANN modeling in order to give the best output correspondence. The maximum MRE = 1.75% (mean relative error) and minimum R 2  = 0.984 represents the reliability and outperformance of the developed ANN over common conventional thermodynamic analysis carried out by EES (engineering equation solver) software. - Highlights: • Exergy analysis is undertaken for CCHP components based on operative factors. • ANN tool is applied to obtained database from thermodynamic analyses session. • The best ANN topology is detected at 10-9-10 with trainlm learning algorithm. • The input and output layer parameters were selected based on sensitivity analysis.

  18. Generation of brain tumours in mice by Cre-mediated recombination of neural progenitors in situ with the tamoxifen metabolite endoxifen

    Directory of Open Access Journals (Sweden)

    Anna Benedykcinska

    2016-02-01

    Full Text Available Targeted cell- or region-specific gene recombination is widely used in the functional analysis of genes implicated in development and disease. In the brain, targeted gene recombination has become a mainstream approach to study neurodegeneration or tumorigenesis. The use of the Cre-loxP system to study tumorigenesis in the adult central nervous system (CNS can be limited, when the promoter (such as GFAP is also transiently expressed during development, which can result in the recombination of progenies of different lineages. Engineering of transgenic mice expressing Cre recombinase fused to a mutant of the human oestrogen receptor (ER allows the circumvention of transient developmental Cre expression by inducing recombination in the adult organism. The recombination of loxP sequences occurs only in the presence of tamoxifen. Systemic administration of tamoxifen can, however, exhibit toxicity and might also recombine unwanted cell populations if the promoter driving Cre expression is active at the time of tamoxifen administration. Here, we report that a single site-specific injection of an active derivative of tamoxifen successfully activates Cre recombinase and selectively recombines tumour suppressor genes in neural progenitor cells of the subventricular zone in mice, and we demonstrate its application in a model for the generation of intrinsic brain tumours.

  19. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  20. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  1. An application of neural networks in microeconomics: input-output mapping in a power generation subsector of the US electricity industry

    NARCIS (Netherlands)

    Erbas, B.C.; Stefanou, S.E.

    2009-01-01

    The use of the artificial neural networks in economics and business goes back to 1950s, while the major bulk of the applications have been developed in more recent years. Reviewing this literature indicates that the field of business benefits from the neural networks in a wide spectrum from

  2. Generation of H1 PAX6WT/EGFP reporter cells to purify PAX6 positive neural stem/progenitor cells.

    Science.gov (United States)

    Wu, Wei; Liu, Juli; Su, Zhenghui; Li, Zhonghao; Ma, Ning; Huang, Ke; Zhou, Tiancheng; Wang, Linli

    2018-08-25

    Neural conversion from human pluripotent cells (hPSCs) is a potential therapy to neurological disease in the future. However, this is still limited by efficiency and stability of existed protocols used for neural induction from hPSCs. To overcome this obstacle, we developed a reporter system to screen PAX6 + neural progenitor/stem cells using transcription activator like effector nuclease (TALEN). We found that knock-in 2 A-EGFP cassette into PAX6 exon of human embryonic stem cells H1 with TALEN-based homology recombination could establish PAX6 WT/EGFP H1 reporter cell line fast and efficiently. This reporter cell line could differentiate into PAX6 and EGFP double positive neural progenitor/stem cells (NPCs/NSCs) after neural induction. Those PAX6 WT/EGFP NPCs could be purified, expanded and specified to post-mitotic neurons in vitro efficiently. With this reporter cell line, we also screened out 1 NPC-specific microRNA, hsa-miR-99a-5p, and 3 ESCs-enriched miRNAs, hsa-miR-302c-5p, hsa-miR-512-3p and hsa-miR-518 b. In conclusion, the TALEN-based neural stem cell screening system is safe and efficient and could help researcher to acquire adequate and pure neural progenitor cells for further application. Copyright © 2018 Elsevier Inc. All rights reserved.

  3. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  4. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  5. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  6. Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

    Science.gov (United States)

    Li, Man; Ling, Cheng; Xu, Qi; Gao, Jingyang

    2018-02-01

    Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

  7. Gli1-Mediated Regulation of Sox2 Facilitates Self-Renewal of Stem-Like Cells and Confers Resistance to EGFR Inhibitors in Non-Small Cell Lung Cancer.

    Science.gov (United States)

    Bora-Singhal, Namrata; Perumal, Deepak; Nguyen, Jonathan; Chellappan, Srikumar

    2015-07-01

    Non-small cell lung cancer (NSCLC) patients have very low survival rates because the current therapeutic strategies are not fully effective. Although EGFR tyrosine kinase inhibitors are effective for NSCLC patients harboring EGFR mutations, patients invariably develop resistance to these agents. Alterations in multiple signaling cascades have been associated with the development of resistance to EGFR inhibitors. Sonic Hedgehog and associated Gli transcription factors play a major role in embryonic development and have recently been found to be reactivated in NSCLC, and elevated Gli1 levels correlate with poor prognosis. The Hedgehog pathway has been implicated in the functions of cancer stem cells, although the underlying molecular mechanisms are not clear. In this context, we demonstrate that Gli1 is a strong regulator of embryonic stem cell transcription factor Sox2. Depletion of Gli1 or inhibition of the Hedgehog signaling significantly abrogated the self-renewal of stem-like side-population cells from NSCLCs as well as vascular mimicry of such cells. Gli1 was found to transcriptionally regulate Sox2 through its promoter region, and Gli1 could be detected on the Sox2 promoter. Inhibition of Hedgehog signaling appeared to work cooperatively with EGFR inhibitors in markedly reducing the viability of NSCLC cells as well as the self-renewal of stem-like cells. Thus, our study demonstrates a cooperative functioning of the EGFR signaling and Hedgehog pathways in governing the stem-like functions of NSCLC cancer stem cells and presents a novel therapeutic strategy to combat NSCLC harboring EGFR mutations. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  8. In vitro generation of three-dimensional substrate-adherent embryonic stem cell-derived neural aggregates for application in animal models of neurological disorders.

    Science.gov (United States)

    Hargus, Gunnar; Cui, Yi-Fang; Dihné, Marcel; Bernreuther, Christian; Schachner, Melitta

    2012-05-01

    In vitro-differentiated embryonic stem (ES) cells comprise a useful source for cell replacement therapy, but the efficiency and safety of a translational approach are highly dependent on optimized protocols for directed differentiation of ES cells into the desired cell types in vitro. Furthermore, the transplantation of three-dimensional ES cell-derived structures instead of a single-cell suspension may improve graft survival and function by providing a beneficial microenvironment for implanted cells. To this end, we have developed a new method to efficiently differentiate mouse ES cells into neural aggregates that consist predominantly (>90%) of postmitotic neurons, neural progenitor cells, and radial glia-like cells. When transplanted into the excitotoxically lesioned striatum of adult mice, these substrate-adherent embryonic stem cell-derived neural aggregates (SENAs) showed significant advantages over transplanted single-cell suspensions of ES cell-derived neural cells, including improved survival of GABAergic neurons, increased cell migration, and significantly decreased risk of teratoma formation. Furthermore, SENAs mediated functional improvement after transplantation into animal models of Parkinson's disease and spinal cord injury. This unit describes in detail how SENAs are efficiently derived from mouse ES cells in vitro and how SENAs are isolated for transplantation. Furthermore, methods are presented for successful implantation of SENAs into animal models of Huntington's disease, Parkinson's disease, and spinal cord injury to study the effects of stem cell-derived neural aggregates in a disease context in vivo.

  9. Metformin inhibits TGF-β1-induced epithelial-to-mesenchymal transition-like process and stem-like properties in GBM via AKT/mTOR/ZEB1 pathway.

    Science.gov (United States)

    Song, Yang; Chen, Yong; Li, Yunqian; Lyu, Xiaoyan; Cui, Jiayue; Cheng, Ye; Zhao, Liyan; Zhao, Gang

    2018-01-23

    Glioblastoma (GBM) is the most frequent and aggressive brain tumor in adults. In spite of advances in diagnosis and therapy, the prognosis is still relatively poor. The invasive property of GBM is the major cause of death in patients. Epithelial-to-mesenchymal transition-like process (EMT-like process) is considered to play an important role in the invasive property. Metformin has been reported as a regulator of EMT-like process. In this study, we confirmed that metformin inhibited TGF-β1-induced EMT-like process and EMT-associated migration and invasion in LN18 and U87 GBM cells. Our results also showed that metformin significantly suppressed self-renewal capacity of glioblastoma stem cells (GSCs), and expression of stem cell markers Bmi1, Sox2 and Musashi1, indicating that metformin can inhibit cancer stem-like properties of GBM cells. We further clarified that metformin specifically inhibited TGF-β1 activated AKT, the downstream molecular mTOR and the leading transcription factor ZEB1. Taken together, our data demonstrate that metformin inhibits TGF-β1-induced EMT-like process and cancer stem-like properties in GBM cells via AKT/mTOR/ZEB1 pathway and provide evidence of metformin for further clinical investigation targeted GBM.

  10. Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

    Science.gov (United States)

    Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

    2010-03-01

    Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians

  11. able utilizando redes neuronales artificiales; UTILIZATION OF ARTIFICIAL NEURAL NETWORK IN THE SIMULATION AND CONTROL OF WIND TURBINE GENERATORS WITH VARIABLE SPEED AND VARIABLE PITCH.

    Directory of Open Access Journals (Sweden)

    Osley López González

    2011-02-01

    , considered as a whole, must be able of respond with anadequate precision and speed in response to the randomness and variability of the wind.The relationship between the wind speed, the blade pitch and the generator speed in order to produce themaximum power and also be able to limit the output power for large wind speeds is a very complicated oneand it is very difficult to find its mathematical function.In this paper, the authors, utilizing the MATLABSIMULINK toolboxes, propose representing this functional relation by means of an Artificial Neural Network(ANN. The parameters and characteristics of an existing wind turbine generator are utilized and it isdemonstrated that it is possible to use an ANN in the simulation and control of a variable speed, variablepitch wind turbine that capture the maximum power from the wind.

  12. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  13. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  14. SOX2 plays a critical role in EGFR-mediated self-renewal of human prostate cancer stem-like cells.

    Science.gov (United States)

    Rybak, Adrian P; Tang, Damu

    2013-12-01

    SOX2 is an essential transcription factor for stem cells and plays a role in tumorigenesis, however its role in prostate cancer stem cells (PCSCs) remains unclear. We report here a significant upregulation of SOX2 at both mRNA and protein levels in DU145 PCSCs propagated as suspension spheres in vitro. The expression of SOX2 in DU145 PCSCs is positively regulated by epidermal growth factor receptor (EGFR) signaling. Activation of EGFR signaling, following the addition of epidermal growth factor (EGF) or ectopic expression of a constitutively-active EGFR mutant (EGFRvIII), increased SOX2 expression and the self-renewal of DU145 PCSCs. Conversely, a small molecule EGFR inhibitor (AG1478) blocked EGFR activation, reduced SOX2 expression and inhibited PCSC self-renewal activity, implicating SOX2 in mediating EGFR-dependent self-renewal of PCSCs. In line with this notion, ectopic SOX2 expression enhanced EGF-induced self-renewal of DU145 PCSCs, while SOX2 knockdown reduced PCSC self-renewal with EGF treatment no longer capable of enhancing their propagation. Furthermore, SOX2 knockdown reduced the capacity of DU145 PCSCs to grow under anchorage-independent conditions. Finally, DU145 PCSCs generated xenograft tumors more aggressively with elevated levels of SOX2 expression compared to xenograft tumors derived from non-PCSCs. Collectively, we provide evidence that SOX2 plays a critical role in EGFR-mediated self-renewal of DU145 PCSCs. © 2013.

  15. New approaches of PARP-1 inhibitors in human lung cancer cells and cancer stem-like cells by some selected anthraquinone-derived small molecules.

    Directory of Open Access Journals (Sweden)

    Yu-Ru Lee

    Full Text Available Poly (ADP-ribose polymerase-1 (PARP-1 and telomerase, as well as DNA damage response pathways are targets for anticancer drug development, and specific inhibitors are currently under clinical investigation. The purpose of this work is to evaluate anticancer activities of anthraquinone-derived tricyclic and tetracyclic small molecules and their structure-activity relationships with PARP-1 inhibition in non-small cell lung cancer (NSCLC and NSCLC-overexpressing Oct4 and Nanog clone, which show high-expression of PARP-1 and more resistance to anticancer drug. We applied our library selected compounds to NCI's 60 human cancer cell-lines (NCI-60 in order to generate systematic profiling data. Based on our analysis, it is hypothesized that these drugs might be, directly and indirectly, target components to induce mitochondrial permeability transition and the release of pro-apoptotic factors as potential anti-NSCLC or PARP inhibitor candidates. Altogether, the most active NSC747854 showed its cytotoxicity and dose-dependent PARP inhibitory manner, thus it emerges as a promising structure for anti-cancer therapy with no significant negative influence on normal cells. Our studies present evidence that telomere maintenance should be taken into consideration in efforts not only to overcome drug resistance, but also to optimize the use of telomere-based therapeutics. These findings will be of great value to facilitate structure-based design of selective PARP inhibitors, in general, and telomerase inhibitors, in particular. Together, the data presented here expand our insight into the PARP inhibitors and support the resource-demanding lead optimization of structurally related small molecules for human cancer therapy.

  16. New Approaches of PARP-1 Inhibitors in Human Lung Cancer Cells and Cancer Stem-Like Cells by Some Selected Anthraquinone-Derived Small Molecules

    Science.gov (United States)

    Yu, Dah-Shyong; Huang, Kuo-Feng; Chou, Shih-Jie; Chen, Tsung-Chih; Lee, Chia-Chung; Chen, Chun-Liang; Chiou, Shih-Hwa; Huang, Hsu-Shan

    2013-01-01

    Poly (ADP-ribose) polymerase-1 (PARP-1) and telomerase, as well as DNA damage response pathways are targets for anticancer drug development, and specific inhibitors are currently under clinical investigation. The purpose of this work is to evaluate anticancer activities of anthraquinone-derived tricyclic and tetracyclic small molecules and their structure-activity relationships with PARP-1 inhibition in non-small cell lung cancer (NSCLC) and NSCLC-overexpressing Oct4 and Nanog clone, which show high-expression of PARP-1 and more resistance to anticancer drug. We applied our library selected compounds to NCI's 60 human cancer cell-lines (NCI-60) in order to generate systematic profiling data. Based on our analysis, it is hypothesized that these drugs might be, directly and indirectly, target components to induce mitochondrial permeability transition and the release of pro-apoptotic factors as potential anti-NSCLC or PARP inhibitor candidates. Altogether, the most active NSC747854 showed its cytotoxicity and dose-dependent PARP inhibitory manner, thus it emerges as a promising structure for anti-cancer therapy with no significant negative influence on normal cells. Our studies present evidence that telomere maintenance should be taken into consideration in efforts not only to overcome drug resistance, but also to optimize the use of telomere-based therapeutics. These findings will be of great value to facilitate structure-based design of selective PARP inhibitors, in general, and telomerase inhibitors, in particular. Together, the data presented here expand our insight into the PARP inhibitors and support the resource-demanding lead optimization of structurally related small molecules for human cancer therapy. PMID:23451039

  17. Efficient enrichment of hepatic cancer stem-like cells from a primary rat HCC model via a density gradient centrifugation-centered method.

    Directory of Open Access Journals (Sweden)

    Wei-hui Liu

    Full Text Available BACKGROUND: Because few definitive markers are available for hepatic cancer stem cells (HCSCs, based on physical rather than immunochemical properties, we applied a novel method to enrich HCSCs. METHODOLOGY: After hepatic tumor cells (HTCs were first isolated from diethylinitrosamine-induced F344 rat HCC model using percoll discontinuous gradient centrifugation (PDGC and purified via differential trypsinization and differential attachment (DTDA, they were separated into four fractions using percoll continuous gradient centrifugation (PCGC and sequentially designated as fractions I-IV (FI-IV. Morphological characteristics, mRNA and protein levels of stem cell markers, proliferative abilities, induced differentiation, in vitro migratory capacities, in vitro chemo-resistant capacities, and in vivo malignant capacities were determined for the cells of each fraction. FINDINGS: As the density of cells increased, 22.18%, 11.62%, 4.73% and 61.47% of primary cultured HTCs were segregated in FI-FIV, respectively. The cells from FIII (density between 1.041 and 1.062 g/ml displayed a higher nuclear-cytoplasmic ratio and fewer organelles and expressed higher levels of stem cell markers (AFP, EpCAM and CD133 than cells from other fractions (P<0.01. Additionally, in vitro, the cells from FIII showed a greater capacity to self-renew, differentiate into mature HTCs, transit across membranes, close scratches, and carry resistance to chemotherapy than did cells from any other fraction; in vivo, injection of only 1×10(4 cells from FIII could generate tumors not only in subcutaneous tissue but also in the livers of nude mice. CONCLUSIONS: Through our novel method, HCSC-like cells were successfully enriched in FIII. This study will greatly contribute to two important areas of biological interest: CSC isolation and HCC therapy.

  18. Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

    Directory of Open Access Journals (Sweden)

    Weibo Zhao

    2017-12-01

    Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.

  19. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  20. A novel berbamine derivative inhibits cell viability and induces apoptosis in cancer stem-like cells of human glioblastoma, via up-regulation of miRNA-4284 and JNK/AP-1 signaling.

    Directory of Open Access Journals (Sweden)

    Fan Yang

    Full Text Available Glioblastoma (GBM is the most common primary brain tumor, accounting for approximately 40% of all central nervous system malignancies. Despite standard treatment consisting of surgical resection, radiotherapy and/or chemotherapy, the prognosis for GBM is poor; with a median survival of 14.6 months. The cancer stem cell or cancer-initiating cell model has provided a new paradigm for understanding development and recurrence of GBM following treatment. Berbamine (BBM is a natural compound derived from the Berberis amurensis plant, and along with its derivatives, has been shown to exhibit antitumor activity in several cancers. Here, we reported that a novel synthetic Berbamine derivative, BBMD3, inhibits cell viability and induces apoptosis of cancer stem-like cells (CSCs in a time- and dose-dependent manner when the CSCs from four GBM patients (PBT003, PBT008, PBT022, and PBT030 were cultured. These CSCs grew in neurospheres and expressed CD133 and nestin as markers. Treatment with BBMD3 destroyed the neurosphere morphology, and led to the induction of apoptosis in the CSCs. Induction of apoptosis in these CSCs is dependent upon activation of caspase-3 and cleavage of poly (ADP-ribose polymerase (PARP. MicroRNA-4284 (miR-4284 was shown to be over-expressed about 4-fold in the CSCs following BBMD3 treatment. Furthermore, transfection of synthetic anti-sense oligonucleotide against human miR-4284 partially blocked the anticancer effects of BBMD3 on the GBM derived CSCs. BBMD3 also increased phosphorylation of the c-Jun N-terminal kinase (JNK/stress-activated protein kinase (SAPK, resulting in an increase expression of phosphorylated c-Jun and total c-Fos; the major components of transcriptional factor AP-1. The JNK-c-Jun/AP-1 signaling pathway plays an important role in the induction of apoptosis in response to UV irradiation and some drug treatments. Targeting glioblastoma stem-like cells with BBMD3 is therefore novel, and may have promise as an

  1. Inhibition of heat-shock protein 90 sensitizes liver cancer stem-like cells to magnetic hyperthermia and enhances anti-tumor effect on hepatocellular carcinoma-burdened nude mice

    Science.gov (United States)

    Yang, Rui; Tang, Qiusha; Miao, Fengqin; An, Yanli; Li, Mengfei; Han, Yong; Wang, Xihui; Wang, Juan; Liu, Peidang; Chen, Rong

    2015-01-01

    Purpose To explore the thermoresistance and expression of heat-shock protein 90 (HSP90) in magnetic hyperthermia-treated human liver cancer stem-like cells (LCSCs) and the effects of a heat-shock protein HSP90 inhibitor 17-allylamino-17-demethoxgeldanamycin (17-AAG) on hepatocellular carcinoma-burdened nude mice. Methods CD90+ LCSCs were isolated by magnetic-activated cell sorting from BEL-7404. Spheroid formation, proliferation, differentiation, drug resistance, and tumor formation assays were performed to identify stem cell characteristics. CD90-targeted thermosensitive magnetoliposomes (TMs)-encapsulated 17-AAG (CD90@17-AAG/TMs) was prepared by reverse-phase evaporation and its characteristics were studied. Heat tolerance in CD90+ LCSCs and the effect of CD90@17-AAG/TMs-mediated heat sensitivity were examined in vitro and in vivo. Results CD90+ LCSCs showed significant stem cell-like properties. The 17-AAG/TMs were successfully prepared and were spherical in shape with an average size of 128.9±7.7 nm. When exposed to magnetic hyperthermia, HSP90 was up-regulated in CD90+ LCSCs. CD90@17-AAG/TMs inhibited the activity of HSP90 and increased the sensitivity of CD90+ LCSCs to magnetic hyperthermia. Conclusion The inhibition of HSP90 could sensitize CD90+ LCSCs to magnetic hyperthermia and enhance its anti-tumor effects in vitro and in vivo. PMID:26677324

  2. High expression of CD109 antigen regulates the phenotype of cancer stem-like cells/cancer-initiating cells in the novel epithelioid sarcoma cell line ESX and is related to poor prognosis of soft tissue sarcoma.

    Directory of Open Access Journals (Sweden)

    Makoto Emori

    Full Text Available Epithelioid sarcoma (ES is a relatively rare, highly malignant soft tissue sarcoma. The mainstay of treatment is resection or amputation. Currently other therapeutic options available for this disease are limited. Therefore, a novel therapeutic option needs to be developed. In the present study, we established a new human ES cell line (ESX and analyzed the characteristics of its cancer stem-like cells/cancer-initiating cells (CSCs/CICs based on ALDH1 activity. We demonstrated that a subpopulation of ESX cells with high ALDH1 activity (ALDH(high cells correlated with enhanced clonogenic ability, sphere-formation ability, and invasiveness in vitro and showed higher tumorigenicity in vivo. Next, using gene expression profiling, we identified CD109, a GPI-anchored protein upregulated in the ALDH(high cells. CD109 mRNA was highly expressed in various sarcoma cell lines, but weakly expressed in normal adult tissues. CD109-positive cells in ESX predominantly formed spheres in culture, whereas siCD109 reduced ALDH1 expression and inhibited the cell proliferation in vitro. Subsequently, we evaluated the expression of CD109 protein in 80 clinical specimens of soft tissue sarcoma. We found a strong correlation between CD109 protein expression and the prognosis (P = 0.009. In conclusion, CD109 might be a CSC/CIC marker in epithelioid sarcoma. Moreover, CD109 is a promising prognostic biomarker and a molecular target of cancer therapy for sarcomas including ES.

  3. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  4. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  5. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  6. The hominoid-specific gene TBC1D3 promotes generation of basal neural progenitors and induces cortical folding in mice

    Science.gov (United States)

    Ju, Xiang-Chun; Hou, Qiong-Qiong; Sheng, Ai-Li; Wu, Kong-Yan; Zhou, Yang; Jin, Ying; Wen, Tieqiao; Yang, Zhengang; Wang, Xiaoqun; Luo, Zhen-Ge

    2016-01-01

    Cortical expansion and folding are often linked to the evolution of higher intelligence, but molecular and cellular mechanisms underlying cortical folding remain poorly understood. The hominoid-specific gene TBC1D3 undergoes segmental duplications during hominoid evolution, but its role in brain development has not been explored. Here, we found that expression of TBC1D3 in ventricular cortical progenitors of mice via in utero electroporation caused delamination of ventricular radial glia cells (vRGs) and promoted generation of self-renewing basal progenitors with typical morphology of outer radial glia (oRG), which are most abundant in primates. Furthermore, down-regulation of TBC1D3 in cultured human brain slices decreased generation of oRGs. Interestingly, localized oRG proliferation resulting from either in utero electroporation or transgenic expression of TBC1D3, was often found to underlie cortical regions exhibiting folding. Thus, we have identified a hominoid gene that is required for oRG generation in regulating the cortical expansion and folding. DOI: http://dx.doi.org/10.7554/eLife.18197.001 PMID:27504805

  7. ALDH1-high ovarian cancer stem-like cells can be isolated from serous and clear cell adenocarcinoma cells, and ALDH1 high expression is associated with poor prognosis.

    Directory of Open Access Journals (Sweden)

    Takafumi Kuroda

    Full Text Available Cancer stem-like cells (CSCs/cancer-initiating cells (CICs are defined as a small population of cancer cells that have high tumorigenicity. Furthermore, CSCs/CICs are resistant to several cancer therapies, and CSCs/CICs are therefore thought to be responsible for cancer recurrence after treatment and distant metastasis. In epithelial ovarian cancer (EOC cases, disease recurrence after chemotherapy is frequently observed, suggesting ovarian CSCs/CICs are involved. There are four major histological subtypes in EOC, and serous adenocarcinoma and clear cell adenocarcinoma are high-grade malignancies. We therefore analyzed ovarian CSCs/CICs from ovarian carcinoma cell lines (serous adenocarcinoma and clear cell adenocarcinoma and primary ovarian cancer cells in this study. We isolated ovarian CSCs/CICs as an aldehyde dehydrogenase 1 high (ALDH1(high population from 6 EOC cell lines (3 serous adenocarcinomas and 3 clear cell adenocarcinomas by the ALDEFLUOR assay. ALDH1(high cells showed greater sphere-forming ability, higher tumorigenicity and greater invasive capability, indicating that ovarian CSCs/CICs are enriched in ALDH1(high cells. ALDH1(high cells could also be isolated from 8 of 11 primary ovarian carcinoma samples. Immunohistochemical staining revealed that higher ALDH1 expression levels in ovary cancer cases are related to poorer prognosis in both serous adenocarcinoma cases and clear cell adenocarcinoma cases. Taken together, the results indicate that ALDH1 is a marker for ovarian CSCs/CICs and that the expression level of ALDH1 might be a novel biomarker for prediction of poor prognosis.

  8. IWR-1, a tankyrase inhibitor, attenuates Wnt/β-catenin signaling in cancer stem-like cells and inhibits in vivo the growth of a subcutaneous human osteosarcoma xenograft.

    Science.gov (United States)

    Martins-Neves, Sara R; Paiva-Oliveira, Daniela I; Fontes-Ribeiro, Carlos; Bovée, Judith V M G; Cleton-Jansen, Anne-Marie; Gomes, Célia M F

    2018-02-01

    Wnt/β-catenin or canonical Wnt signaling pathway regulates the self-renewal of cancer stem-like cells (CSCs) and is involved in tumor progression and chemotherapy resistance. Previously, we reported that this pathway is activated in a subset of osteosarcoma CSCs and that doxorubicin induced stemness properties in differentiated cells through Wnt/β-catenin activation. Here, we investigated whether pharmacological Wnt/β-catenin inhibition, using a tankyrase inhibitor (IWR-1), might constitute a strategy to target CSCs and improve chemotherapy efficacy in osteosarcoma. IWR-1 was specifically cytotoxic for osteosarcoma CSCs. IWR-1 impaired spheres' self-renewal capacity by compromising landmark steps of the canonical Wnt signaling, namely translocation of β-catenin to the nucleus and subsequent TCF/LEF activation and expression of Wnt/β-catenin downstream targets. IWR-1 also hampered the activity and expression of key stemness-related markers. In vitro, IWR-1 induced apoptosis of osteosarcoma spheres and combined with doxorubicin elicited synergistic cytotoxicity, reversing spheres' resistance to this drug. In vivo, IWR-1 co-administration with doxorubicin substantially decreased tumor progression, associated with specific down-regulation of TCF/LEF transcriptional activity, nuclear β-catenin and expression of the putative CSC marker Sox2. We suggest that targeting the Wnt/β-catenin pathway can eliminate CSCs populations in osteosarcoma. Combining conventional chemotherapy with Wnt/β-catenin inhibition may ameliorate therapeutic outcomes, by eradicating the aggressive osteosarcoma CSCs and reducing drug resistance. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning

    Energy Technology Data Exchange (ETDEWEB)

    Su, K; Kuo, J [Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio (United States); Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio (United States); Hu, L; Traughber, M [Philips Healthcare, Cleveland, Ohio (United States); Pereira, G; Traughber, B [Department of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve University, Cleveland, Ohio (United States); Herrmann, K [Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio (United States); Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio (United States); Muzic, R [Case Center for Imaging Research, Case Western Reserve University, Cleveland, Ohio (United States); Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio (United States); Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH (United States)

    2015-06-15

    Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation and R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the

  10. WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning

    International Nuclear Information System (INIS)

    Su, K; Kuo, J; Hu, L; Traughber, M; Pereira, G; Traughber, B; Herrmann, K; Muzic, R

    2015-01-01

    Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation and R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the

  11. Neural network error correction for solving coupled ordinary differential equations

    Science.gov (United States)

    Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

    1992-01-01

    A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

  12. Gemcitabine treatment induces endoplasmic reticular (ER) stress and subsequently upregulates urokinase plasminogen activator (uPA) to block mitochondrial-dependent apoptosis in Panc-1 cancer stem-like cells (CSCs).

    Science.gov (United States)

    Wang, Li; Zhang, Yi; Wang, Weiguo; Zhu, Yunjie; Chen, Yang; Tian, Bole

    2017-01-01

    Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with poor survival rates. The presence of cancer stem-like cells (CSCs) is believed to be among the underlying reasons for the aggressiveness of PDAC, which contributes to chemoresistance and recurrence. However, the mechanisms that induce chemoresistance and inhibit apoptosis remain largely unknown. We used serum-free medium to enrich CSCs from panc-1 human pancreatic cancer cells and performed sphere formation testing, flow cytometry, quantitative reverse transcription polymerase chain reaction (RT-qPCR) and semi-quantitative western blotting to confirm the stemness of panc-1 CSCs. Hallmarks of endoplasmic reticulum (ER) stress, including IRE1, PERK, ATF4, ATF6α, GRP78 and uPA expression, were detected after gemcitabine treatment. Effects of gemcitabine-induced uPA expression on cell invasion, sphere formation, colony formation and gemcitabine sensitivity were detected. Electrophoretic mobility shift assays (EMSAs) and RNA-immunoprecipitation (RIP) were performed to detect interaction between the uPA mRNA 3'-UTR and mutant p53-R273H expressed by panc-1 CSCs. The effects of upregulated uPA by gemcitabine on apoptosis were detected by Annexin V-FITC/PI staining, and the impact of uPA on small molecule CP-31398-restored mutant p53 transcriptional activity was measured by a luciferase reporter assay. Enriched panc-1 CSCs expressing high levels of CD44 and CD133 also produced significantly higher amounts of Oct4 and Nanog. Compared with panc-1 cells, panc-1 CSCs presented chemoresistance to gemcitabine. ER stress gene detections demonstrated effects of gemcitabine-induced ER stress on both the pro-apoptotic and pro-survival branches. ER stress-induced ATF6α upregulated level of uPA by transcriptionally activating GRP78. Gemcitabine-induced uPA promoted invasion, sphere formation and colony formation and attenuated apoptosis induced by gemcitabine in panc-1 CSCs, depending on interaction with mutant p53

  13. The gefitinib long-term responder (LTR)--a cancer stem-like cell story? Insights from molecular analyses of German long-term responders treated in the IRESSA expanded access program (EAP).

    Science.gov (United States)

    Gottschling, Sandra; Herpel, Esther; Eberhardt, Wilfried E E; Heigener, David F; Fischer, Jürgen R; Köhne, Claus-Henning; Kortsik, Cornelius; Kuhnt, Thomas; Muley, Thomas; Meister, Michael; Bischoff, Helge G; Klein, Peter; Moldenhauer, Ines; Schnabel, Philipp A; Thomas, Michael; Penzel, Roland

    2012-07-01

    In selected patients with advanced non-small cell lung cancer (NSCLC) the EGFR (epidermal growth factor receptor) tyrosine kinase inhibitor (TKI) gefitinib (IRESSA) shows response rates of ≥ 70% and a significant prolongation of progression free survival (PFS). However, cogent biomarkers predicting long-term response to EGFR-TKIs are yet lacking. Cancer stem-like cells (CSC) are thought to play a pivotal role in tumor regeneration and appear to be influenced by the EGFR-pathway. This makes them a promising candidate for predicting long-term response to EGFR-TKIs. We analyzed pre-therapeutic tissue specimens of a rare and specific subset of previously treated German patients with advanced NSCLC who experienced ≥ 3 year response to gefitinib within the International IRESSA EAP. 11/20 identified long-term responders (LTRs) had appropriate tissue specimens available. Those were analyzed for EGFR and k-ras (Kirsten rat sarcoma) mutations, EGFR and c-met (met proto-oncogene) amplifications and protein expression of EGFR, E-cadherin/vimentin and the CSC antigens CD133 and BCRP1 (breast cancer resistance protein 1). The results were compared to primary resistant patients (RPs) and intermediate responders (IRs) showing a median response of 8.6 months. Each group consisted of 6 women and 5 men, with 1 squamous cell carcinoma (SCC) and 10 adenocarcinoma (AC). Along the LTRs, all but the SCC had EGFR mutations, whereas the RPs had no EGFR, but k-ras mutations in 5/11 cases. 8/11 IRs had EGFR and 3/11 k-ras mutations, of which 2 occurred concomitantly. One patient of each group had an EGFR and/or c-met amplification. EGFR and E-cadherin/vimentin expression was not different between the groups, whereas CD133 was expressed only in 4/10 LTRs and BCRP1 predominantly in responders. The LTRs showed a substantially longer mean PFS to previous therapies, a substantially lower number of metastatic sites and almost exclusively pulmonary or pleural metastasis. LTRs display established

  14. Neural differentiation of choroid plexus epithelial cells: role of human traumatic cerebrospinal fluid

    Directory of Open Access Journals (Sweden)

    Elham Hashemi

    2017-01-01

    Full Text Available As the key producer of cerebrospinal fluid (CSF, the choroid plexus (CP provides a unique protective system in the central nervous system. CSF components are not invariable and they can change based on the pathological conditions of the central nervous system. The purpose of the present study was to assess the effects of non-traumatic and traumatic CSF on the differentiation of multipotent stem-like cells of CP into the neural and/or glial cells. CP epithelial cells were isolated from adult male rats and treated with human non-traumatic and traumatic CSF. Alterations in mRNA expression of Nestin and microtubule-associated protein (MAP2, as the specific markers of neurogenesis, and astrocyte marker glial fibrillary acidic protein (GFAP in cultured CP epithelial cells were evaluated using quantitative real-time PCR. The data revealed that treatment with CSF (non-traumatic and traumatic led to increase in mRNA expression levels of MAP2 and GFAP. Moreover, the expression of Nestin decreased in CP epithelial cells treated with non-traumatic CSF, while treatment with traumatic CSF significantly increased its mRNA level compared to the cells cultured only in DMEM/F12 as control. It seems that CP epithelial cells contain multipotent stem-like cells which are inducible under pathological conditions including exposure to traumatic CSF because of its compositions.

  15. Neural Network Models for Time Series Forecasts

    OpenAIRE

    Tim Hill; Marcus O'Connor; William Remus

    1996-01-01

    Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

  16. Deep Gate Recurrent Neural Network

    Science.gov (United States)

    2016-11-22

    and Fred Cummins. Learning to forget: Continual prediction with lstm . Neural computation, 12(10):2451–2471, 2000. Alex Graves. Generating sequences...DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory ( LSTM ) and...Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM

  17. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  18. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  19. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  20. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  1. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  2. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Classification of behavior using unsupervised temporal neural networks

    International Nuclear Information System (INIS)

    Adair, K.L.

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem

  4. Neural Networks in Mobile Robot Motion

    Directory of Open Access Journals (Sweden)

    Danica Janglová

    2004-03-01

    Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

  5. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  6. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  7. Control of autonomous robot using neural networks

    Science.gov (United States)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  8. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  9. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  11. Eddy Current Flaw Characterization Using Neural Networks

    International Nuclear Information System (INIS)

    Song, S. J.; Park, H. J.; Shin, Y. K.

    1998-01-01

    Determination of location, shape and size of a flaw from its eddy current testing signal is one of the fundamental issues in eddy current nondestructive evaluation of steam generator tubes. Here, we propose an approach to this problem; an inversion of eddy current flaw signal using neural networks trained by finite element model-based synthetic signatures. Total 216 eddy current signals from four different types of axisymmetric flaws in tubes are generated by finite element models of which the accuracy is experimentally validated. From each simulated signature, total 24 eddy current features are extracted and among them 13 features are finally selected for flaw characterization. Based on these features, probabilistic neural networks discriminate flaws into four different types according to the location and the shape, and successively back propagation neural networks determine the size parameters of the discriminated flaw

  12. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  13. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  14. A Possible Neural Representation of Mathematical Group Structures.

    Science.gov (United States)

    Pomi, Andrés

    2016-09-01

    Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation. A formal neurocognitive theory must account for all the activities developed by our brain and provide a possible neural representation for them. Associative memories are neural network models that have a good chance of achieving a universal representation of cognitive phenomena. In this work, we present a possible neural representation of mathematical group structures based on associative memory models that store finite groups through their Cayley graphs. A context-dependent associative memory stores the transitions between elements of the group when multiplied by each generator of a given presentation of the group. Under a convenient election of the vector basis mapping the elements of the group in the neural activity, the input of a vector corresponding to a generator of the group collapses the context-dependent rectangular matrix into a virtual square permutation matrix that is the matrix representation of the generator. This neural representation corresponds to the regular representation of the group, in which to each element is assigned a permutation matrix. This action of the generator on the memory matrix can also be seen as the dissection of the corresponding monochromatic subgraph of the Cayley graph of the group, and the adjacency matrix of this subgraph is the permutation matrix corresponding to the generator.

  15. Modeling and control of magnetorheological fluid dampers using neural networks

    Science.gov (United States)

    Wang, D. H.; Liao, W. H.

    2005-02-01

    Due to the inherent nonlinear nature of magnetorheological (MR) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the direct identification and inverse dynamic modeling for MR fluid dampers using feedforward and recurrent neural networks are studied. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line, on the basis of the dynamic responses across the MR fluid damper and the command voltage, and the inverse dynamic neural network model can be used to generate the command voltage according to the desired damping force through supervised learning. The architectures and the learning methods of the dynamic neural network models and inverse neural network models for MR fluid dampers are presented, and some simulation results are discussed. Finally, the trained neural network models are applied to predict and control the damping force of the MR fluid damper. Moreover, validation methods for the neural network models developed are proposed and used to evaluate their performance. Validation results with different data sets indicate that the proposed direct identification dynamic model using the recurrent neural network can be used to predict the damping force accurately and the inverse identification dynamic model using the recurrent neural network can act as a damper controller to generate the command voltage when the MR fluid damper is used in a semi-active mode.

  16. A review of organic and inorganic biomaterials for neural interfaces.

    Science.gov (United States)

    Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

    2014-03-26

    Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

  17. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  18. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  19. Photon spectrometry utilizing neural networks

    International Nuclear Information System (INIS)

    Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.

    2015-01-01

    Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)

  20. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  1. Tinnitus and neural plasticity of the brain

    NARCIS (Netherlands)

    Bartels, Hilke; Staal, Michiel J.; Albers, Frans W. J.

    Objective: To describe the current ideas about the manifestations of neural plasticity in generating tinnitus. Data Sources: Recently published source articles were identified using MEDLINE, PubMed, and Cochrane Library according to the key words mentioned below. Study Selection: Review articles and

  2. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  3. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  4. Associative memory model with spontaneous neural activity

    Science.gov (United States)

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2012-05-01

    We propose a novel associative memory model wherein the neural activity without an input (i.e., spontaneous activity) is modified by an input to generate a target response that is memorized for recall upon the same input. Suitable design of synaptic connections enables the model to memorize input/output (I/O) mappings equaling 70% of the total number of neurons, where the evoked activity distinguishes a target pattern from others. Spontaneous neural activity without an input shows chaotic dynamics but keeps some similarity with evoked activities, as reported in recent experimental studies.

  5. Quantitative phase microscopy using deep neural networks

    Science.gov (United States)

    Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

    2018-02-01

    Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

  6. Neural dynamics in reconfigurable silicon.

    Science.gov (United States)

    Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

    2010-10-01

    A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

  7. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  8. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  9. Neural network models of categorical perception.

    Science.gov (United States)

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  10. Deep Neural Network Detects Quantum Phase Transition

    Science.gov (United States)

    Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

    2018-03-01

    We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

  11. Recurrent Neural Network for Computing Outer Inverse.

    Science.gov (United States)

    Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin

    2016-05-01

    Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

  12. Electrospun Nanofibrous Materials for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Yee-Shuan Lee

    2011-02-01

    Full Text Available The use of biomaterials processed by the electrospinning technique has gained considerable interest for neural tissue engineering applications. The tissue engineering strategy is to facilitate the regrowth of nerves by combining an appropriate cell type with the electrospun scaffold. Electrospinning can generate fibrous meshes having fiber diameter dimensions at the nanoscale and these fibers can be nonwoven or oriented to facilitate neurite extension via contact guidance. This article reviews studies evaluating the effect of the scaffold’s architectural features such as fiber diameter and orientation on neural cell function and neurite extension. Electrospun meshes made of natural polymers, proteins and compositions having electrical activity in order to enhance neural cell function are also discussed.

  13. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  14. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

  15. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  16. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  17. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  18. Inversion of a lateral log using neural networks

    International Nuclear Information System (INIS)

    Garcia, G.; Whitman, W.W.

    1992-01-01

    In this paper a technique using neural networks is demonstrated for the inversion of a lateral log. The lateral log is simulated by a finite difference method which in turn is used as an input to a backpropagation neural network. An initial guess earth model is generated from the neural network, which is then input to a Marquardt inversion. The neural network reacts to gross and subtle data features in actual logs and produces a response inferred from the knowledge stored in the network during a training process. The neural network inversion of lateral logs is tested on synthetic and field data. Tests using field data resulted in a final earth model whose simulated lateral is in good agreement with the actual log data

  19. A comparative study of two neural networks for document retrieval

    International Nuclear Information System (INIS)

    Hui, S.C.; Goh, A.

    1997-01-01

    In recent years there has been specific interest in adopting advanced computer techniques in the field of document retrieval. This interest is generated by the fact that classical methods such as the Boolean search, the vector space model or even probabilistic retrieval cannot handle the increasing demands of end-users in satisfying their needs. The most recent attempt is the application of the neural network paradigm as a means of providing end-users with a more powerful retrieval mechanism. Neural networks are not only good pattern matchers but also highly versatile and adaptable. In this paper, we demonstrate how to apply two neural networks, namely Adaptive Resonance Theory and Fuzzy Kohonen Neural Network, for document retrieval. In addition, a comparison of these two neural networks based on performance is also given

  20. Designing neural networks that process mean values of random variables

    International Nuclear Information System (INIS)

    Barber, Michael J.; Clark, John W.

    2014-01-01

    We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

  1. Designing neural networks that process mean values of random variables

    Energy Technology Data Exchange (ETDEWEB)

    Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

    2014-06-13

    We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

  2. Introduction to neural networks with electric power applications

    International Nuclear Information System (INIS)

    Wildberger, A.M.; Hickok, K.A.

    1990-01-01

    This is an introduction to the general field of neural networks with emphasis on prospects for their application in the power industry. It is intended to provide enough background information for its audience to begin to follow technical developments in neural networks and to recognize those which might impact on electric power engineering. Beginning with a brief discussion of natural and artificial neurons, the characteristics of neural networks in general and how they learn, neural networks are compared with other modeling tools such as simulation and expert systems in order to provide guidance in selecting appropriate applications. In the power industry, possible applications include plant control, dispatching, and maintenance scheduling. In particular, neural networks are currently being investigated for enhancements to the Thermal Performance Advisor (TPA) which General Physics Corporation (GP) has developed to improve the efficiency of electric power generation

  3. Enteric neurospheres are not specific to neural crest cultures : Implications for neural stem cell therapies

    NARCIS (Netherlands)

    Binder, E. (Ellen); D. Natarajan (Dipa); J.E. Cooper (Julie E.); Kronfli, R. (Rania); Cananzi, M. (Mara); J.-M. Delalande (Jean-Marie); C. Mccann; A.J. Burns (Alan); N. Thapar (Nikhil)

    2015-01-01

    textabstractObjectives Enteric neural stem cells provide hope of curative treatment for enteric neuropathies. Current protocols for their harvesting from humans focus on the generation of 'neurospheres' from cultures of dissociated gut tissue. The study aims to better understand the derivation,

  4. Biologically Inspired Modular Neural Control for a Leg-Wheel Hybrid Robot

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Wörgötter, Florentin; Laksanacharoen, Pudit

    2014-01-01

    In this article we present modular neural control for a leg-wheel hybrid robot consisting of three legs with omnidirectional wheels. This neural control has four main modules having their functional origin in biological neural systems. A minimal recurrent control (MRC) module is for sensory signal...... processing and state memorization. Its outputs drive two front wheels while the rear wheel is controlled through a velocity regulating network (VRN) module. In parallel, a neural oscillator network module serves as a central pattern generator (CPG) controls leg movements for sidestepping. Stepping directions...... or they can serve as useful modules for other module-based neural control applications....

  5. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  6. Neural plasticity and its initiating conditions in tinnitus.

    Science.gov (United States)

    Roberts, L E

    2018-03-01

    Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

  7. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  8. Neural network decoder for quantum error correcting codes

    Science.gov (United States)

    Krastanov, Stefan; Jiang, Liang

    Artificial neural networks form a family of extremely powerful - albeit still poorly understood - tools used in anything from image and sound recognition through text generation to, in our case, decoding. We present a straightforward Recurrent Neural Network architecture capable of deducing the correcting procedure for a quantum error-correcting code from a set of repeated stabilizer measurements. We discuss the fault-tolerance of our scheme and the cost of training the neural network for a system of a realistic size. Such decoders are especially interesting when applied to codes, like the quantum LDPC codes, that lack known efficient decoding schemes.

  9. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. A canonical neural mechanism for behavioral variability

    Science.gov (United States)

    Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

    2017-05-01

    The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these `universal' statistics.

  11. Optimized Neural Network for Fault Diagnosis and Classification

    International Nuclear Information System (INIS)

    Elaraby, S.M.

    2005-01-01

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  12. The Neural Correlates of Humor Creativity

    OpenAIRE

    Amir, Ori; Biederman, Irving

    2016-01-01

    Unlike passive humor appreciation, the neural correlates of real-time humor creation have been unexplored. As a case study for creativity, humor generation uniquely affords a reliable assessment of a creative product’s quality with a clear and relatively rapid beginning and end, rendering it amenable to neuroimaging that has the potential for reflecting individual differences in expertise. Professional and amateur “improv” comedians and controls viewed New Yorker cartoon drawings while being ...

  13. Modulated error diffusion CGHs for neural nets

    Science.gov (United States)

    Vermeulen, Pieter J. E.; Casasent, David P.

    1990-05-01

    New modulated error diffusion CGHs (computer generated holograms) for optical computing are considered. Specific attention is given to their use in optical matrix-vector, associative processor, neural net and optical interconnection architectures. We consider lensless CGH systems (many CGHs use an external Fourier transform (FT) lens), the Fresnel sampling requirements, the effects of finite CGH apertures (sample and hold inputs), dot size correction (for laser recorders), and new applications for this novel encoding method (that devotes attention to quantization noise effects).

  14. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  15. Rule extraction from minimal neural networks for credit card screening.

    Science.gov (United States)

    Setiono, Rudy; Baesens, Bart; Mues, Christophe

    2011-08-01

    While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.

  16. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  17. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  18. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  19. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  20. DNA methyltransferase 3b is dispensable for mouse neural crest development.

    Directory of Open Access Journals (Sweden)

    Bridget T Jacques-Fricke

    Full Text Available The neural crest is a population of multipotent cells that migrates extensively throughout vertebrate embryos to form diverse structures. Mice mutant for the de novo DNA methyltransferase DNMT3b exhibit defects in two neural crest derivatives, the craniofacial skeleton and cardiac ventricular septum, suggesting that DNMT3b activity is necessary for neural crest development. Nevertheless, the requirement for DNMT3b specifically in neural crest cells, as opposed to interacting cell types, has not been determined. Using a conditional DNMT3b allele crossed to the neural crest cre drivers Wnt1-cre and Sox10-cre, neural crest DNMT3b mutants were generated. In both neural crest-specific and fully DNMT3b-mutant embryos, cranial neural crest cells exhibited only subtle migration defects, with increased numbers of dispersed cells trailing organized streams in the head. In spite of this, the resulting cranial ganglia, craniofacial skeleton, and heart developed normally when neural crest cells lacked DNMT3b. This indicates that DNTM3b is not necessary in cranial neural crest cells for their development. We conclude that defects in neural crest derivatives in DNMT3b mutant mice reflect a requirement for DNMT3b in lineages such as the branchial arch mesendoderm or the cardiac mesoderm that interact with neural crest cells during formation of these structures.

  1. Interpretations of Frequency Domain Analyses of Neural Entrainment: Periodicity, Fundamental Frequency, and Harmonics.

    Science.gov (United States)

    Zhou, Hong; Melloni, Lucia; Poeppel, David; Ding, Nai

    2016-01-01

    Brain activity can follow the rhythms of dynamic sensory stimuli, such as speech and music, a phenomenon called neural entrainment. It has been hypothesized that low-frequency neural entrainment in the neural delta and theta bands provides a potential mechanism to represent and integrate temporal information. Low-frequency neural entrainment is often studied using periodically changing stimuli and is analyzed in the frequency domain using the Fourier analysis. The Fourier analysis decomposes a periodic signal into harmonically related sinusoids. However, it is not intuitive how these harmonically related components are related to the response waveform. Here, we explain the interpretation of response harmonics, with a special focus on very low-frequency neural entrainment near 1 Hz. It is illustrated why neural responses repeating at f Hz do not necessarily generate any neural response at f Hz in the Fourier spectrum. A strong neural response at f Hz indicates that the time scales of the neural response waveform within each cycle match the time scales of the stimulus rhythm. Therefore, neural entrainment at very low frequency implies not only that the neural response repeats at f Hz but also that each period of the neural response is a slow wave matching the time scale of a f Hz sinusoid.

  2. Function of FEZF1 during early neural differentiation of human embryonic stem cells.

    Science.gov (United States)

    Liu, Xin; Su, Pei; Lu, Lisha; Feng, Zicen; Wang, Hongtao; Zhou, Jiaxi

    2018-01-01

    The understanding of the mechanism underlying human neural development has been hampered due to lack of a cellular system and complicated ethical issues. Human embryonic stem cells (hESCs) provide an invaluable model for dissecting human development because of unlimited self-renewal and the capacity to differentiate into nearly all cell types in the human body. In this study, using a chemical defined neural induction protocol and molecular profiling, we identified Fez family zinc finger 1 (FEZF1) as a potential regulator of early human neural development. FEZF1 is rapidly up-regulated during neural differentiation in hESCs and expressed before PAX6, a well-established marker of early human neural induction. We generated FEZF1-knockout H1 hESC lines using CRISPR-CAS9 technology and found that depletion of FEZF1 abrogates neural differentiation of hESCs. Moreover, loss of FEZF1 impairs the pluripotency exit of hESCs during neural specification, which partially explains the neural induction defect caused by FEZF1 deletion. However, enforced expression of FEZF1 itself fails to drive neural differentiation in hESCs, suggesting that FEZF1 is necessary but not sufficient for neural differentiation from hESCs. Taken together, our findings identify one of the earliest regulators expressed upon neural induction and provide insight into early neural development in human.

  3. Prostate Cancer Stem-Like Cells | Center for Cancer Research

    Science.gov (United States)

    Prostate cancer is the third leading cause of cancer-related death among men, killing an estimated 27,000 men each year in the United States. Men with advanced prostate cancer often become resistant to conventional therapies. Many researchers speculate that the emergence of resistance is due to the presence of cancer stem cells, which are believed to be a small subpopulation

  4. Glioblastoma stem-like cells give rise to tumour endothelium

    NARCIS (Netherlands)

    Wang, Rong; Chadalavada, Kalyani; Wilshire, Jennifer; Kowalik, Urszula; Hovinga, Koos E.; Geber, Adam; Fligelman, Boris; Leversha, Margaret; Brennan, Cameron; Tabar, Viviane

    2010-01-01

    Glioblastoma (GBM) is among the most aggressive of human cancers. A key feature of GBMs is the extensive network of abnormal vasculature characterized by glomeruloid structures and endothelial hyperplasia. Yet the mechanisms of angiogenesis and the origin of tumour endothelial cells remain poorly

  5. Burst firing enhances neural output correlation

    Directory of Open Access Journals (Sweden)

    Ho Ka eChan

    2016-05-01

    Full Text Available Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.

  6. A fully implantable rodent neural stimulator

    Science.gov (United States)

    Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

    2012-02-01

    The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

  7. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  8. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  9. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  10. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  11. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  12. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  13. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  14. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  15. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.

    Science.gov (United States)

    Leynes, Andrew P; Yang, Jaewon; Wiesinger, Florian; Kaushik, Sandeep S; Shanbhag, Dattesh D; Seo, Youngho; Hope, Thomas A; Larson, Peder E Z

    2018-05-01

    Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density-weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUV max was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

  16. Statistical physics of interacting neural networks

    Science.gov (United States)

    Kinzel, Wolfgang; Metzler, Richard; Kanter, Ido

    2001-12-01

    Recent results on the statistical physics of time series generation and prediction are presented. A neural network is trained on quasi-periodic and chaotic sequences and overlaps to the sequence generator as well as the prediction errors are calculated numerically. For each network there exists a sequence for which it completely fails to make predictions. Two interacting networks show a transition to perfect synchronization. A pool of interacting networks shows good coordination in the minority game-a model of competition in a closed market. Finally, as a demonstration, a perceptron predicts bit sequences produced by human beings.

  17. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...

  18. Replicable Expansion and Differentiation of Neural Precursors from Adult Canine Skin

    Directory of Open Access Journals (Sweden)

    Thomas Duncan

    2017-08-01

    Full Text Available Repopulation of brain circuits by neural precursors is a potential therapeutic strategy for neurodegenerative disorders; however, choice of cell is critical. Previously, we introduced a two-step culture system that generates a high yield of neural precursors from small samples of adult canine skin. Here, we probe their gene and protein expression profiles in comparison with dermal fibroblasts and brain-derived neural stem cells and characterize their neuronal potential. To date, we have produced >50 skin-derived neural precursor (SKN lines. SKNs can be cultured in a highly replicable fashion and uniformly express a panel of identifying markers. Upon differentiation, they self-upregulate neural specification genes, generating neurons with basic electrophysiological functionality. This unique population of neural precursors, derived from mature skin, overcomes many of the practical issues that have limited clinical translation of alternative cell types. Easily accessible, neuronally committed, and patient specific, SKNs may have potential for the treatment of brain disorders.

  19. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

    2018-04-01

    By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

  20. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

  1. Neural and Fuzzy Adaptive Control of Induction Motor Drives

    International Nuclear Information System (INIS)

    Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

    2008-01-01

    This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

  2. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Direct reprogramming of somatic cells into neural stem cells or neurons for neurological disorders.

    Science.gov (United States)

    Hou, Shaoping; Lu, Paul

    2016-01-01

    Direct reprogramming of somatic cells into neurons or neural stem cells is one of the most important frontier fields in current neuroscience research. Without undergoing the pluripotency stage, induced neurons or induced neural stem cells are a safer and timelier manner resource in comparison to those derived from induced pluripotent stem cells. In this prospective, we review the recent advances in generation of induced neurons and induced neural stem cells in vitro and in vivo and their potential treatments of neurological disorders.

  4. Wind power prediction based on genetic neural network

    Science.gov (United States)

    Zhang, Suhan

    2017-04-01

    The scale of grid connected wind farms keeps increasing. To ensure the stability of power system operation, make a reasonable scheduling scheme and improve the competitiveness of wind farm in the electricity generation market, it's important to accurately forecast the short-term wind power. To reduce the influence of the nonlinear relationship between the disturbance factor and the wind power, the improved prediction model based on genetic algorithm and neural network method is established. To overcome the shortcomings of long training time of BP neural network and easy to fall into local minimum and improve the accuracy of the neural network, genetic algorithm is adopted to optimize the parameters and topology of neural network. The historical data is used as input to predict short-term wind power. The effectiveness and feasibility of the method is verified by the actual data of a certain wind farm as an example.

  5. Recurrent Neural Network Method for Automatic Generation of Music%面向自动音乐生成的深度递归神经网络方法

    Institute of Scientific and Technical Information of China (English)

    王程; 周婉; 何军

    2017-01-01

    This paper introduces the method of computer music generation based on the character-level long short term memory net-work ( LSTM) ,and analyzes the effects of different network structures on the computer music generation. Compared with the existing methods,the advantages of this method are that it can be trained and optimized in an end-to-end fashion,and the network structure is simple and intuitive. A more interesting feature of the proposed approach is that it does not depend on feature engineering. Music fea-tures are automatically learned by the LSTM network. Through experimental validation,significant melodies can be generated by dif-ferent LSTM network structures with proper training.%本文介绍了基于字符级长短期记忆网络(LSTM)计算机音乐生成方法,并分析了其不同网络结构在计算机音乐生成的效果.与现有的方法相比,基于字符级递归神经网络的音乐生成算法的优点是可以实现端到端训练,网络结构简单直观.本文方法的一种更为有意义的特征在于,音乐旋律无需通过繁琐的特征工程来获得,而是直接通过LSTM网络的学习自动获得.通过实验验证发现,不同LSTM网络结构在经过合适的训练之后均能够生成具有明显旋律的音乐序列.

  6. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  7. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  8. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  9. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  10. Rotation Invariance Neural Network

    OpenAIRE

    Li, Shiyuan

    2017-01-01

    Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

  11. Neural Mechanisms of Foraging

    OpenAIRE

    Kolling, Nils; Behrens, Timothy EJ; Mars, Rogier B; Rushworth, Matthew FS

    2012-01-01

    Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-ma...

  12. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  13. In vitro effects of Epidiferphane™ on adult human neural progenitor cells

    Science.gov (United States)

    Neural stem cells have the capacity to respond to their environment, migrate to the injury site and generate functional cell types, and thus they hold great promise for cell therapies. In addition to representing a source for central nervous system (CNS) repair, neural stem and progenitor cells als...

  14. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  15. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  16. Stimulus-dependent suppression of chaos in recurrent neural networks

    International Nuclear Information System (INIS)

    Rajan, Kanaka; Abbott, L. F.; Sompolinsky, Haim

    2010-01-01

    Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, but they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a nonmonotonic function of stimulus frequency, revealing a 'resonant' frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate.

  17. Neural crest stem cell multipotency requires Foxd3 to maintain neural potential and repress mesenchymal fates.

    Science.gov (United States)

    Mundell, Nathan A; Labosky, Patricia A

    2011-02-01

    Neural crest (NC) progenitors generate a wide array of cell types, yet molecules controlling NC multipotency and self-renewal and factors mediating cell-intrinsic distinctions between multipotent versus fate-restricted progenitors are poorly understood. Our earlier work demonstrated that Foxd3 is required for maintenance of NC progenitors in the embryo. Here, we show that Foxd3 mediates a fate restriction choice for multipotent NC progenitors with loss of Foxd3 biasing NC toward a mesenchymal fate. Neural derivatives of NC were lost in Foxd3 mutant mouse embryos, whereas abnormally fated NC-derived vascular smooth muscle cells were ectopically located in the aorta. Cranial NC defects were associated with precocious differentiation towards osteoblast and chondrocyte cell fates, and individual mutant NC from different anteroposterior regions underwent fate changes, losing neural and increasing myofibroblast potential. Our results demonstrate that neural potential can be separated from NC multipotency by the action of a single gene, and establish novel parallels between NC and other progenitor populations that depend on this functionally conserved stem cell protein to regulate self-renewal and multipotency.

  18. C-RNN-GAN: Continuous recurrent neural networks with adversarial training

    OpenAIRE

    Mogren, Olof

    2016-01-01

    Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.

  19. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  20. Neural network classifier of attacks in IP telephony

    Science.gov (United States)

    Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

    2014-05-01

    Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

  1. PWR system simulation and parameter estimation with neural networks

    International Nuclear Information System (INIS)

    Akkurt, Hatice; Colak, Uener

    2002-01-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected

  2. PWR system simulation and parameter estimation with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr

    2002-11-01

    A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.

  3. Application of neural networks to group technology

    Science.gov (United States)

    Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

    1991-08-01

    Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

  4. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

  5. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  6. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  7. Neural-net based real-time economic dispatch for thermal power plants

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)

    1996-12-01

    This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.

  8. predicting water levels at kainji dam using artificial neural networks

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... Apart from insufficient number of power generation plants, existing ones are ... ture and/or functional aspects of biological neural net-. Nigerian Journal of ... Their model used the radiosonde-based 700-hPa wind direction and ...

  9. Neural correlates of rhythmic expectancy

    Directory of Open Access Journals (Sweden)

    Theodore P. Zanto

    2006-01-01

    Full Text Available Temporal expectancy is thought to play a fundamental role in the perception of rhythm. This review summarizes recent studies that investigated rhythmic expectancy by recording neuroelectric activity with high temporal resolution during the presentation of rhythmic patterns. Prior event-related brain potential (ERP studies have uncovered auditory evoked responses that reflect detection of onsets, offsets, sustains,and abrupt changes in acoustic properties such as frequency, intensity, and spectrum, in addition to indexing higher-order processes such as auditory sensory memory and the violation of expectancy. In our studies of rhythmic expectancy, we measured emitted responses - a type of ERP that occurs when an expected event is omitted from a regular series of stimulus events - in simple rhythms with temporal structures typical of music. Our observations suggest that middle-latency gamma band (20-60 Hz activity (GBA plays an essential role in auditory rhythm processing. Evoked (phase-locked GBA occurs in the presence of physically presented auditory events and reflects the degree of accent. Induced (non-phase-locked GBA reflects temporally precise expectancies for strongly and weakly accented events in sound patterns. Thus far, these findings support theories of rhythm perception that posit temporal expectancies generated by active neural processes.

  10. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  11. An efficient automated parameter tuning framework for spiking neural networks.

    Science.gov (United States)

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  12. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

    2018-02-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Storage capacity and retrieval time of small-world neural networks

    International Nuclear Information System (INIS)

    Oshima, Hiraku; Odagaki, Takashi

    2007-01-01

    To understand the influence of structure on the function of neural networks, we study the storage capacity and the retrieval time of Hopfield-type neural networks for four network structures: regular, small world, random networks generated by the Watts-Strogatz (WS) model, and the same network as the neural network of the nematode Caenorhabditis elegans. Using computer simulations, we find that (1) as the randomness of network is increased, its storage capacity is enhanced; (2) the retrieval time of WS networks does not depend on the network structure, but the retrieval time of C. elegans's neural network is longer than that of WS networks; (3) the storage capacity of the C. elegans network is smaller than that of networks generated by the WS model, though the neural network of C. elegans is considered to be a small-world network

  14. Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise

    Directory of Open Access Journals (Sweden)

    Mehrshad Salmasi

    2012-07-01

    Full Text Available Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations, noise signals from a SPIB database are used. In order to compare the networks appropriately, equal number of layers and neurons are considered for the networks. Moreover, training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen, the ability of recurrent neural network in noise attenuation is better than feedforward network.

  15. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  16. Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

    Science.gov (United States)

    Goudar, Vishwa; Buonomano, Dean V

    2018-03-14

    Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

  17. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  18. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  19. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  20. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  1. Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise

    OpenAIRE

    Mehrshad Salmasi; Homayoun Mahdavi-Nasab

    2012-01-01

    Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in n...

  2. The use of global image characteristics for neural network pattern recognitions

    Science.gov (United States)

    Kulyas, Maksim O.; Kulyas, Oleg L.; Loshkarev, Aleksey S.

    2017-04-01

    The recognition system is observed, where the information is transferred by images of symbols generated by a television camera. For descriptors of objects the coefficients of two-dimensional Fourier transformation generated in a special way. For solution of the task of classification the one-layer neural network trained on reference images is used. Fast learning of a neural network with a single neuron calculation of coefficients is applied.

  3. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  4. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  5. A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

    Science.gov (United States)

    von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

    2016-10-26

    The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability

  6. Workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hashem, S.

    1995-03-01

    This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc.

  7. Workshop on environmental and energy applications of neural networks

    International Nuclear Information System (INIS)

    Hashem, S.

    1995-03-01

    This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc

  8. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  9. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  10. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  11. Neural circuitry and immunity

    Science.gov (United States)

    Pavlov, Valentin A.; Tracey, Kevin J.

    2015-01-01

    Research during the last decade has significantly advanced our understanding of the molecular mechanisms at the interface between the nervous system and the immune system. Insight into bidirectional neuroimmune communication has characterized the nervous system as an important partner of the immune system in the regulation of inflammation. Neuronal pathways, including the vagus nerve-based inflammatory reflex are physiological regulators of immune function and inflammation. In parallel, neuronal function is altered in conditions characterized by immune dysregulation and inflammation. Here, we review these regulatory mechanisms and describe the neural circuitry modulating immunity. Understanding these mechanisms reveals possibilities to use targeted neuromodulation as a therapeutic approach for inflammatory and autoimmune disorders. These findings and current clinical exploration of neuromodulation in the treatment of inflammatory diseases defines the emerging field of Bioelectronic Medicine. PMID:26512000

  12. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  13. Generating Units

    Data.gov (United States)

    Department of Homeland Security — Generating Units are any combination of physically connected generators, reactors, boilers, combustion turbines, and other prime movers operated together to produce...

  14. Iterative free-energy optimization for recurrent neural networks (INFERNO)

    Science.gov (United States)

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439

  15. Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

    OpenAIRE

    Lee, Hyeungill; Han, Sungyeob; Lee, Jungwoo

    2017-01-01

    We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier ...

  16. Optimisation of milling parameters using neural network

    Directory of Open Access Journals (Sweden)

    Lipski Jerzy

    2017-01-01

    Full Text Available The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3. The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3 and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone, connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

  17. The Neural Correlates of Humor Creativity.

    Science.gov (United States)

    Amir, Ori; Biederman, Irving

    2016-01-01

    Unlike passive humor appreciation, the neural correlates of real-time humor creation have been unexplored. As a case study for creativity, humor generation uniquely affords a reliable assessment of a creative product's quality with a clear and relatively rapid beginning and end, rendering it amenable to neuroimaging that has the potential for reflecting individual differences in expertise. Professional and amateur "improv" comedians and controls viewed New Yorker cartoon drawings while being scanned. For each drawing, they were instructed to generate either a humorous or a mundane caption. Greater comedic experience was associated with decreased activation in the striatum and medial prefrontal cortex (mPFC), but increased activation in temporal association regions (TMP). Less experienced comedians manifested greater activation of mPFC, reflecting their deliberate search through TMP association space. Professionals, by contrast, tend to reap the fruits of their spontaneous associations with reduced reliance on top-down guided search.

  18. The Neural Correlates of Humor Creativity

    Directory of Open Access Journals (Sweden)

    Ori Amir

    2016-11-01

    Full Text Available Unlike passive humor appreciation, the neural correlates of real-time humor creation have been unexplored. As a case study for creativity, humor generation uniquely affords a reliable assessment of a creative product’s quality with a clear and relatively rapid beginning and end, rendering it amenable to neuroimaging that has the potential for reflecting individual differences in expertise. Professional and amateur improv comedians and controls viewed New Yorker cartoon drawings while being scanned. For each drawing, they were instructed to generate either a humorous or a mundane caption. Greater comedic experience was associated with decreased activation in the striatum and medial prefrontal cortex (mPFC, but increased activation in temporal association regions (TMP. Less experienced comedians manifested greater activation of mPFC, reflecting their deliberate search through TMP association space. Professionals, by contrast, tend to reap the fruits of their spontaneous associations with reduced reliance on top-down guided search.

  19. The Neural Correlates of Humor Creativity

    Science.gov (United States)

    Amir, Ori; Biederman, Irving

    2016-01-01

    Unlike passive humor appreciation, the neural correlates of real-time humor creation have been unexplored. As a case study for creativity, humor generation uniquely affords a reliable assessment of a creative product’s quality with a clear and relatively rapid beginning and end, rendering it amenable to neuroimaging that has the potential for reflecting individual differences in expertise. Professional and amateur “improv” comedians and controls viewed New Yorker cartoon drawings while being scanned. For each drawing, they were instructed to generate either a humorous or a mundane caption. Greater comedic experience was associated with decreased activation in the striatum and medial prefrontal cortex (mPFC), but increased activation in temporal association regions (TMP). Less experienced comedians manifested greater activation of mPFC, reflecting their deliberate search through TMP association space. Professionals, by contrast, tend to reap the fruits of their spontaneous associations with reduced reliance on top-down guided search. PMID:27932965

  20. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  1. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  2. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  3. Novel Adaptive Forward Neural MIMO NARX Model for the Identification of Industrial 3-DOF Robot Arm Kinematics

    OpenAIRE

    Ho Pham Huy Anh; Nguyen Thanh Nam

    2012-01-01

    In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3‐DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model‐based identification process using experimental input‐output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural M...

  4. Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative...

  5. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    network should be able to predict the vehicle's future states at next time step. A complete 4-D trajectory are then generated step by step using the trained neural network. Experiments in this work show that the neural network can approximate the sUAV's model and predict the trajectory accurately.

  6. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    Zak, Michail A.

    1992-01-01

    Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

  7. Feed forward neural networks modeling for K-P interactions

    International Nuclear Information System (INIS)

    El-Bakry, M.Y.

    2003-01-01

    Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental