Innate Recognition of Bacteria in Human Milk Is Mediated by a Milk-Derived Highly Expressed Pattern Recognition Receptor, Soluble Cd14

OpenAIRE

Labéta, Mario O.; Vidal, Karine; Nores, Julia E. Rey; Arias, Mauricio; Vita, Natalio; Morgan, B. Paul; Guillemot, Jean Claude; Loyaux, Denis; Ferrara, Pascual; Schmid, Daniel; Affolter, Michael; Borysiewicz, Leszek K.; Donnet-Hughes, Anne; Schiffrin, Eduardo J.

2000-01-01

Little is known about innate immunity to bacteria after birth in the hitherto sterile fetal intestine. Breast-feeding has long been associated with a lower incidence of gastrointestinal infections and inflammatory and allergic diseases. We found in human breast milk a 48-kD polypeptide, which we confirmed by mass spectrometry and sequencing to be a soluble form of the bacterial pattern recognition receptor CD14 (sCD14). Milk sCD14 (m-sCD14) concentrations were up to 20-fold higher than serum ...

Statistical Pattern Recognition

CERN Document Server

Webb, Andrew R

2011-01-01

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

[Effect of opioid receptors on acute stress-induced changes in recognition memory].

Science.gov (United States)

Liu, Ying; Wu, Yu-Wei; Qian, Zhao-Qiang; Yan, Cai-Fang; Fan, Ka-Min; Xu, Jin-Hui; Li, Xiao; Liu, Zhi-Qiang

2016-12-25

Although ample evidence has shown that acute stress impairs memory, the influences of acute stress on different phases of memory, such as acquisition, consolidation and retrieval, are different. Experimental data from both human and animals support that endogenous opioid system plays a role in stress, as endogenous opioid release is increased and opioid receptors are activated during stress experience. On the other hand, endogenous opioid system mediates learning and memory. The aim of the present study was to investigate the effect of acute forced swimming stress on recognition memory of C57 mice and the role of opioid receptors in this process by using a three-day pattern of new object recognition task. The results showed that 15-min acute forced swimming damaged the retrieval of recognition memory, but had no effect on acquisition and consolidation of recognition memory. No significant change of object recognition memory was found in mice that were given naloxone, an opioid receptor antagonist, by intraperitoneal injection. But intraperitoneal injection of naloxone before forced swimming stress could inhibit the impairment of recognition memory retrieval caused by forced swimming stress. The results of real-time PCR showed that acute forced swimming decreased the μ opioid receptor mRNA levels in whole brain and hippocampus, while the injection of naloxone before stress could reverse this change. These results suggest that acute stress may impair recognition memory retrieval via opioid receptors.

Pattern recognition & machine learning

CERN Document Server

Anzai, Y

1992-01-01

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Pattern Recognition Scavenger Receptor A/CD204 Regulates Airway Inflammatory Homeostasis Following Organic Dust Extract Exposures

Science.gov (United States)

Poole, Jill A.; Anderson, Leigh; Gleason, Angela M.; West, William W.; Romberger, Debra J.; Wyatt, Todd A.

2014-01-01

Exposure to agriculture organic dusts, comprised of a diversity of pathogen-associated molecular patterns, results in chronic airway diseases. The multi-functional class A macrophage scavenger receptor (SRA)/CD204 has emerged as an important class of pattern recognition receptors with broad ligand binding ability. Our objective was to determine the role of SRA in mediating repetitive and post-inflammatory organic dust extract (ODE)-induced airway inflammation. Wild-type (WT) and SRA knockout (KO) mice were intra-nasally treated with ODE or saline daily for 3 wk and immediately euthanized or allowed to recover for 1 wk. Results show that lung histopathologic changes were increased in SRA KO mice as compared to WT following repetitive ODE exposures marked predominately by increased size and distribution of lymphoid aggregates. After a 1-wk recovery from daily ODE treatments, there was significant resolution of lung injury in WT mice, but not SRA KO animals. The increased lung histopathology induced by ODE treatment was associated with decreased accumulation of neutrophils, but greater accumulation of CD4+ T-cells. The lung cytokine milieu induced by ODE was consistent with a TH1/TH17 polarization in both WT and SRA KO mice. Overall, our data demonstrate that SRA/CD204 plays an important role in the normative inflammatory lung response to ODE as evidenced by the enhanced dust-mediated injury viewed in the absence of this receptor. PMID:24491035

De Novo Transcriptome Analysis Shows That SAV-3 Infection Upregulates Pattern Recognition Receptors of the Endosomal Toll-Like and RIG-I-Like Receptor Signaling Pathways in Macrophage/Dendritic Like TO-Cells

Directory of Open Access Journals (Sweden)

Cheng Xu

2016-04-01

Full Text Available A fundamental step in cellular defense mechanisms is the recognition of “danger signals” made of conserved pathogen associated molecular patterns (PAMPs expressed by invading pathogens, by host cell germ line coded pattern recognition receptors (PRRs. In this study, we used RNA-seq and the Kyoto encyclopedia of genes and genomes (KEGG to identify PRRs together with the network pathway of differentially expressed genes (DEGs that recognize salmonid alphavirus subtype 3 (SAV-3 infection in macrophage/dendritic like TO-cells derived from Atlantic salmon (Salmo salar L headkidney leukocytes. Our findings show that recognition of SAV-3 in TO-cells was restricted to endosomal Toll-like receptors (TLRs 3 and 8 together with RIG-I-like receptors (RLRs and not the nucleotide-binding oligomerization domain-like receptors NOD-like receptor (NLRs genes. Among the RLRs, upregulated genes included the retinoic acid inducible gene I (RIG-I, melanoma differentiation association 5 (MDA5 and laboratory of genetics and physiology 2 (LGP2. The study points to possible involvement of the tripartite motif containing 25 (TRIM25 and mitochondrial antiviral signaling protein (MAVS in modulating RIG-I signaling being the first report that links these genes to the RLR pathway in SAV-3 infection in TO-cells. Downstream signaling suggests that both the TLR and RLR pathways use interferon (IFN regulatory factors (IRFs 3 and 7 to produce IFN-a2. The validity of RNA-seq data generated in this study was confirmed by quantitative real time qRT-PCR showing that genes up- or downregulated by RNA-seq were also up- or downregulated by RT-PCR. Overall, this study shows that de novo transcriptome assembly identify key receptors of the TLR and RLR sensors engaged in host pathogen interaction at cellular level. We envisage that data presented here can open a road map for future intervention strategies in SAV infection of salmon.

Oxytocin, vasopressin and estrogen receptor gene expression in relation to social recognition in female mice.

Science.gov (United States)

Clipperton-Allen, Amy E; Lee, Anna W; Reyes, Anny; Devidze, Nino; Phan, Anna; Pfaff, Donald W; Choleris, Elena

2012-02-28

Inter- and intra-species differences in social behavior and recognition-related hormones and receptors suggest that different distribution and/or expression patterns may relate to social recognition. We used qRT-PCR to investigate naturally occurring differences in expression of estrogen receptor-alpha (ERα), ER-beta (ERβ), progesterone receptor (PR), oxytocin (OT) and receptor, and vasopressin (AVP) and receptors in proestrous female mice. Following four 5 min exposures to the same two conspecifics, one was replaced with a novel mouse in the final trial (T5). Gene expression was examined in mice showing high (85-100%) and low (40-60%) social recognition scores (i.e., preferential novel mouse investigation in T5) in eight socially-relevant brain regions. Results supported OT and AVP involvement in social recognition, and suggest that in the medial preoptic area, increased OT and AVP mRNA, together with ERα and ERβ gene activation, relate to improved social recognition. Initial social investigation correlated with ERs, PR and OTR in the dorsolateral septum, suggesting that these receptors may modulate social interest without affecting social recognition. Finally, increased lateral amygdala gene activation in the LR mice may be associated with general learning impairments, while decreased lateral amygdala activity may indicate more efficient cognitive mechanisms in the HR mice. Copyright © 2011 Elsevier Inc. All rights reserved.

Rotation-invariant neural pattern recognition system with application to coin recognition.

Science.gov (United States)

Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

1992-01-01

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

Increased neutrophil expression of pattern recognition receptors during COPD exacerbations

NARCIS (Netherlands)

Pouwels, Simon D.; Van Geffen, Wouter H.; Jonker, Marnix R.; Kerstjens, Huib A. M.; Nawijn, Martijn C.; Heijink, Irene H.

Previously, we observed increased serum levels of damage-associated molecular patterns (DAMPs) during COPD exacerbations. Here, gene expression of DAMP receptors was measured in peripheral blood neutrophils of COPD patients during stable disease and severe acute exacerbation. The expression of

Human gestation-associated tissues express functional cytosolic nucleic acid sensing pattern recognition receptors.

Science.gov (United States)

Bryant, A H; Menzies, G E; Scott, L M; Spencer-Harty, S; Davies, L B; Smith, R A; Jones, R H; Thornton, C A

2017-07-01

The role of viral infections in adverse pregnancy outcomes has gained interest in recent years. Innate immune pattern recognition receptors (PRRs) and their signalling pathways, that yield a cytokine output in response to pathogenic stimuli, have been postulated to link infection at the maternal-fetal interface and adverse pregnancy outcomes. The objective of this study was to investigate the expression and functional response of nucleic acid ligand responsive Toll-like receptors (TLR-3, -7, -8 and -9), and retinoic acid-inducible gene 1 (RIG-I)-like receptors [RIG-I, melanoma differentiation-associated protein 5 (MDA5) and Laboratory of Genetics and Physiology 2(LGP2)] in human term gestation-associated tissues (placenta, choriodecidua and amnion) using an explant model. Immunohistochemistry revealed that these PRRs were expressed by the term placenta, choriodecidua and amnion. A statistically significant increase in interleukin (IL)-6 and/or IL-8 production in response to specific agonists for TLR-3 (Poly(I:C); low and high molecular weight), TLR-7 (imiquimod), TLR-8 (ssRNA40) and RIG-I/MDA5 (Poly(I:C)LyoVec) was observed; there was no response to a TLR-9 (ODN21798) agonist. A hierarchical clustering approach was used to compare the response of each tissue type to the ligands studied and revealed that the placenta and choriodecidua generate a more similar IL-8 response, while the choriodecidua and amnion generate a more similar IL-6 response to nucleic acid ligands. These findings demonstrate that responsiveness via TLR-3, TLR-7, TLR-8 and RIG-1/MDA5 is a broad feature of human term gestation-associated tissues with differential responses by tissue that might underpin adverse obstetric outcomes. © 2017 British Society for Immunology.

Pattern activation/recognition theory of mind.

Science.gov (United States)

du Castel, Bertrand

2015-01-01

In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.

Pattern recognition and string matching

CERN Document Server

Cheng, Xiuzhen

2002-01-01

The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica­ tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro­ vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...

Nucleic Acid Sensors Involved in the Recognition of HBV in the Liver–Specific in vivo Transfection Mouse Models—Pattern Recognition Receptors and Sensors for HBV

Directory of Open Access Journals (Sweden)

Chean Ring Leong

2015-04-01

Full Text Available Cellular innate immune system recognizing pathogen infection is critical for the host defense against viruses. Hepatitis B virus (HBV is a DNA virus with a unique life cycle whereby the DNA and RNA intermediates present at different phases. However, it is still unclear whether the viral DNA or RNA templates are recognized by the pattern-recognition receptors (PRRs to trigger host antiviral immune response. Here in this article, we review the recent advances in the progress of the HBV studies, focusing on the nucleic acid sensors and the pathways involved in the recognition of HBV in the liver–specific in vivo transfection mouse models. Hydrodynamic injection transfecting the hepatocytes in the gene-disrupted mouse model with the HBV replicative genome DNA has revealed that IFNAR and IRF3/7 are indispensable in HBV eradication in the mice liver but not the RNA sensing pathways. Interestingly, accumulating evidence of the recent studies has demonstrated that HBV markedly interfered with IFN-β induction and antiviral immunity mediated by the Stimulator of interferon genes (STING, which has been identified as a central factor in foreign DNA recognition and antiviral innate immunity. This review will present the current understanding of innate immunity in HBV infection and of the challenges for clearing of the HBV infection.

Data structures, computer graphics, and pattern recognition

CERN Document Server

Klinger, A; Kunii, T L

1977-01-01

Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

Nonlinear dynamics of pattern formation and pattern recognition in the rabbit olfactory bulb

Science.gov (United States)

Baird, Bill

1986-10-01

A mathematical model of the process of pattern recognition in the first olfactory sensory cortex of the rabbit is presented. It explains the formation and alteration of spatial patterns in neural activity observed experimentally during classical Pavlovian conditioning. On each inspiration of the animal, a surge of receptor input enters the olfactory bulb. EEG activity recorded at the surface of the bulb undergoes a transition from a low amplitude background state of temporal disorder to coherent oscillation. There is a distinctive spatial pattern of rms amplitude in this oscillation which changes reliably to a second pattern during each successful recognition by the animal of a conditioned stimulus odor. When a new odor is paired as conditioned stimulus, these patterns are replaced by new patterns that stabilize as the animal adapts to the new environment. I will argue that a unification of the theories of pattern formation and associative memory is required to account for these observations. This is achieved in a model of the bulb as a discrete excitable medium with spatially inhomogeneous coupling expressed by a connection matrix. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of competing unstable oscillatory modes. These may be created in the system by proper coupling and selectively evoked by specific classes of inputs. This allows a view of limit cycle attractors as “stored” fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

Pattern recognition receptor-mediated cytokine response in infants across 4 continents.

Science.gov (United States)

Smolen, Kinga K; Ruck, Candice E; Fortuno, Edgardo S; Ho, Kevin; Dimitriu, Pedro; Mohn, William W; Speert, David P; Cooper, Philip J; Esser, Monika; Goetghebuer, Tessa; Marchant, Arnaud; Kollmann, Tobias R

2014-03-01

Susceptibility to infection as well as response to vaccination varies among populations. To date, the underlying mechanisms responsible for these clinical observations have not been fully delineated. Because innate immunity instructs adaptive immunity, we hypothesized that differences between populations in innate immune responses may represent a mechanistic link to variation in susceptibility to infection or response to vaccination. Determine whether differences in innate immune responses exist among infants from different continents of the world. We determined the innate cytokine response following pattern recognition receptor (PRR) stimulation of whole blood from 2-year-old infants across 4 continents (Africa, North America, South America, and Europe). We found that despite the many possible genetic and environmental exposure differences in infants across 4 continents, innate cytokine responses were similar for infants from North America, South America, and Europe. However, cells from South African infants secreted significantly lower levels of cytokines than did cells from infants from the 3 other sites, and did so following stimulation of extracellular and endosomal but not cytosolic PRRs. Substantial differences in innate cytokine responses to PRR stimulation exist among different populations of infants that could not have been predicted. Delineating the underlying mechanism(s) for these differences will not only aid in improving vaccine-mediated protection but possibly also provide clues for the susceptibility to infection in different regions of the world. Copyright © 2013 The Authors. Published by Mosby, Inc. All rights reserved.

Acoustic Pattern Recognition on Android Devices

DEFF Research Database (Denmark)

Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

2013-01-01

an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

Expression of Pattern Recognition Receptors in Epithelial Cells Around Clinically Healthy Implants and Healthy Teeth.

Science.gov (United States)

Calcaterra, Roberta; Di Girolamo, Michele; Mirisola, Concetta; Baggi, Luigi

2016-06-01

Gingival epithelial cells have a pivotal role in the recognition of microorganisms and damage-associated molecular pattern molecules and in the regulation of the immune response. The investigation of the behavior of Toll-like receptors (TLRs) and nucleotide oligomerization domain (NOD) like receptors (NLRs) around a healthy implant may help to address the first step of periimplantitis pathogenesis. To investigate by quantitative real-time polymerase chain reaction, the mRNA expressions of TLR2, TLR3, TLR4, TLR5, TLR6, TLR9, NOD1, NOD2, and NLRP3 from gingival epithelial cells of the sulcus around healthy implants and around healthy teeth. Two types of implant-abutment systems with tube-in-tube interface were tested. After 6 months of implant restoration, gingival epithelial cells were obtained from the gingival sulcus around the implants and around the adjacent teeth of 10 patients. Our results did not reach statistical significance among the mRNA expressions of TLR2, TLR3, TLR4, TLR5, TLR6, TLR9, NOD1, NOD2, and NLRP3 in epithelial cells around the implant versus around natural teeth. This study shows that the implant-abutment systems tested did not induce an immune response by the surrounding epithelial cells at 6 months since their positioning, as well as in the adjacent clincally healthy teeth.

Pattern recognition and classification an introduction

CERN Document Server

Dougherty, Geoff

2012-01-01

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

Type I interferon production during herpes simplex virus infection is controlled by cell-type-specific viral recognition through Toll-like receptor 9, the mitochondrial antiviral signaling protein pathway, and novel recognition systems

DEFF Research Database (Denmark)

Rasmussen, Simon Brandtoft; Sørensen, Louise Nørgaard; Malmgaard, Lene

2007-01-01

Recognition of viruses by germ line-encoded pattern recognition receptors of the innate immune system is essential for rapid production of type I interferon (IFN) and early antiviral defense. We investigated the mechanisms of viral recognition governing production of type I IFN during herpes...... simplex virus (HSV) infection. We show that early production of IFN in vivo is mediated through Toll-like receptor 9 (TLR9) and plasmacytoid dendritic cells, whereas the subsequent alpha/beta IFN (IFN-alpha/beta) response is derived from several cell types and induced independently of TLR9...

Expression of pattern recognition receptors in liver biopsy specimens of children chronically infected with HBV and HCV

Directory of Open Access Journals (Sweden)

Wojciech Służewski

2011-10-01

Full Text Available Pattern recognition receptors (PRRs constitute a pivotal arm of innate immunity. Their distribution is widespread and not limited to cells of the immune system. Following our previous findings concerning the expression of Toll-like receptors (TLRs 2, 3 and 4 in chronic viral hepatitis C of children, we wished to search for other PRRs, including other TLRs, NOD-like receptors (NLRs and RIG-1-like helicase receptors (RLR in infected hepatocytes. Liver biopsy fragments from ten children with chronic hepatitis B and C were used and two others in which hepatotropic virus infection was excluded. Frozen sections of liver samples were subjected to ABC immunohistochemistry (IHC following incubation with a set of antibodies. Results of IHC findings were screened for correlation with clinical/laboratory data of patients. It was found that several PRRs could be shown in affected hepatocytes, but the incidence was higher in hepatitis C than in B. In hepatitis C, TLR1, 2, 4, NALP and RIG-1 helicase showed the most marked expression. In hepatitis B, TLR1, 3, 9, NOD1 and NALP expression were the most conspicuous. Expression PRRs in liver from hepatitis of unknown origin was much lower. It was also the case in cytospins from human hepatoma cell line. Several correlations between PRRs expression and clinical findings in patients could be shown by statistical exploration. In conclusion, this data suggests some role for PRRs in the pathogenesis of chronic viral hepatitis. (Folia Histochemica et Cytobiologica 2011; Vol. 49, No. 3, pp. 410–416

DMPD: Viral recognition by Toll-like receptors. [Dynamic Macrophage Pathway CSML Database

Lifescience Database Archive (English)

Full Text Available 17336545 Viral recognition by Toll-like receptors. Barton GM. Semin Immunol. 2007 F...eb;19(1):33-40. Epub 2007 Mar 2. (.png) (.svg) (.html) (.csml) Show Viral recognition by Toll-like receptors.... PubmedID 17336545 Title Viral recognition by Toll-like receptors. Authors Barton GM. Publication Semin Imm

Star pattern recognition algorithm aided by inertial information

Science.gov (United States)

Liu, Bao; Wang, Ke-dong; Zhang, Chao

2011-08-01

Star pattern recognition is one of the key problems of the celestial navigation. The traditional star pattern recognition approaches, such as the triangle algorithm and the star angular distance algorithm, are a kind of all-sky matching method whose recognition speed is slow and recognition success rate is not high. Therefore, the real time and reliability of CNS (Celestial Navigation System) is reduced to some extent, especially for the maneuvering spacecraft. However, if the direction of the camera optical axis can be estimated by other navigation systems such as INS (Inertial Navigation System), the star pattern recognition can be fulfilled in the vicinity of the estimated direction of the optical axis. The benefits of the INS-aided star pattern recognition algorithm include at least the improved matching speed and the improved success rate. In this paper, the direction of the camera optical axis, the local matching sky, and the projection of stars on the image plane are estimated by the aiding of INS firstly. Then, the local star catalog for the star pattern recognition is established in real time dynamically. The star images extracted in the camera plane are matched in the local sky. Compared to the traditional all-sky star pattern recognition algorithms, the memory of storing the star catalog is reduced significantly. Finally, the INS-aided star pattern recognition algorithm is validated by simulations. The results of simulations show that the algorithm's computation time is reduced sharply and its matching success rate is improved greatly.

MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

Directory of Open Access Journals (Sweden)

Artur Popko

2013-06-01

Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.

Fringe patterns generated by micro-optical sensors for pattern recognition.

Science.gov (United States)

Tamee, Kreangsak; Chaiwong, Khomyuth; Yothapakdee, Kriengsak; Yupapin, Preecha P

2015-01-01

We present a new result of pattern recognition generation scheme using a small-scale optical muscle sensing system, which consisted of an optical add-drop filter incorporating two nonlinear optical side ring resonators. When light from laser source enters into the system, the device is stimulated by an external physical parameter that introduces a change in the phase of light propagation within the sensing device, which can be formed by the interference fringe patterns. Results obtained have shown that the fringe patterns can be used to form the relationship between signal patterns and fringe pattern recognitions.

WC1 is a hybrid γδ TCR coreceptor and pattern recognition receptor for pathogenic bacteria.

Science.gov (United States)

Hsu, Haoting; Chen, Chuang; Nenninger, Ariel; Holz, Lauren; Baldwin, Cynthia L; Telfer, Janice C

2015-03-01

WC1 proteins are uniquely expressed on γδ T cells and belong to the scavenger receptor cysteine-rich (SRCR) superfamily. While present in variable, and sometimes high, numbers in the genomes of mammals and birds, in cattle there are 13 distinct genes (WC1-1 to WC1-13). All bovine WC1 proteins can serve as coreceptors for the TCR in a tyrosine phosphorylation dependent manner, and some are required for the γδ T cell response to Leptospira. We hypothesized that individual WC1 receptors encode Ag specificity via coligation of bacteria with the γδ TCR. SRCR domain binding was directly correlated with γδ T cell response, as WC1-3 SRCR domains from Leptospira-responsive cells, but not WC1-4 SRCR domains from Leptospira-nonresponsive cells, bound to multiple serovars of two Leptospira species, L. borgpetersenii, and L. interrogans. Three to five of eleven WC1-3 SRCR domains, but none of the eleven WC1-4 SRCR domains, interacted with Leptospira spp. and Borrelia burgdorferi, but not with Escherichia coli or Staphylococcus aureus. Mutational analysis indicated that the active site for bacterial binding in one of the SRCR domains is composed of amino acids in three discontinuous regions. Recombinant WC1 SRCR domains with the ability to bind leptospires inhibited Leptospira growth. Our data suggest that WC1 gene arrays play a multifaceted role in the γδ T cell response to bacteria, including acting as hybrid pattern recognition receptors and TCR coreceptors, and they may function as antimicrobials. Copyright © 2015 by The American Association of Immunologists, Inc.

Pattern Recognition Control Design

Science.gov (United States)

Gambone, Elisabeth A.

2018-01-01

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

Face recognition system and method using face pattern words and face pattern bytes

Science.gov (United States)

Zheng, Yufeng

2014-12-23

The present invention provides a novel system and method for identifying individuals and for face recognition utilizing facial features for face identification. The system and method of the invention comprise creating facial features or face patterns called face pattern words and face pattern bytes for face identification. The invention also provides for pattern recognitions for identification other than face recognition. The invention further provides a means for identifying individuals based on visible and/or thermal images of those individuals by utilizing computer software implemented by instructions on a computer or computer system and a computer readable medium containing instructions on a computer system for face recognition and identification.

Description and recognition of patterns in stochastic signals. [Electroencephalograms

Energy Technology Data Exchange (ETDEWEB)

Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

1975-10-01

A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

Improved pattern recognition systems by hybrid methods

International Nuclear Information System (INIS)

Duerr, B.; Haettich, W.; Tropf, H.; Winkler, G.; Fraunhofer-Gesellschaft zur Foerderung der Angewandten Forschung e.V., Karlsruhe

1978-12-01

This report describes a combination of statistical and syntactical pattern recongition methods. The hierarchically structured recognition system consists of a conventional statistical classifier, a structural classifier analysing the topological composition of the patterns, a stage reducing the number of hypotheses made by the first two stages, and a mixed stage based on a search for maximum similarity between syntactically generated prototypes and patterns. The stages work on different principles to avoid mistakes made in one stage in the other stages. This concept is applied to the recognition of numerals written without constraints. If no samples are rejected, a recognition rate of 99,5% is obtained. (orig.) [de

Degraded character recognition based on gradient pattern

Science.gov (United States)

Babu, D. R. Ramesh; Ravishankar, M.; Kumar, Manish; Wadera, Kevin; Raj, Aakash

2010-02-01

Degraded character recognition is a challenging problem in the field of Optical Character Recognition (OCR). The performance of an optical character recognition depends upon printed quality of the input documents. Many OCRs have been designed which correctly identifies the fine printed documents. But, very few reported work has been found on the recognition of the degraded documents. The efficiency of the OCRs system decreases if the input image is degraded. In this paper, a novel approach based on gradient pattern for recognizing degraded printed character is proposed. The approach makes use of gradient pattern of an individual character for recognition. Experiments were conducted on character image that is either digitally written or a degraded character extracted from historical documents and the results are found to be satisfactory.

Biochemical study of multiple drug recognition sites on central benzodiazepine receptors

Energy Technology Data Exchange (ETDEWEB)

Trifiletti, R.R.

1986-01-01

The benzodiazepine receptor complex of mammalian brain possesses recognition sites which mediate (at least in part) the pharmacologic actions of the 1,4-benzodiazepines and barbiturates. Evidence is provided suggesting the existence of least seven distinct drug recognition sites on this complex. Interactions between the various recognition sites have been explored using radioligand binding techniques. This information is utilized to provide a comprehensive scheme for characterizing receptor-active drugs on an anxiolytic-anticonvulsant/proconvulsant continuum using radioligand binding techniques, as well as a comprehensive program for identifying potential endogenous receptor-active substances. Further evidence is provided here supporting the notion of benzodiazepine recognition site heterogeneity. Classical 1,4-benzodiazepines do not appear to differentiate two populations of benzodiazepine receptors in an equilibrium sense, but appear to do so in a kinetic sense. An apparent physical separation of the two receptor subtypes can be achieved by differential solubilization. The benzodiazepine binding subunit can be identified by photoaffinity labeling with the benzodiazepine agonist (/sup 3/H)flunitrazepan. Conditions for reproducible partial proteolytic mapping of (/sup 3/H)flunitrazepam photoaffinity labeled receptors are established. From these maps, it is concluded that there are probably no major differences in the primary sequence of the benzodiazepine binding subunit in various regions of the rat central nervous system.

Pattern recognition in high energy physics

International Nuclear Information System (INIS)

Tenner, A.G.

1980-01-01

In high energy physics experiments tracks of elementary particles are recorded by different types of equipment. Coordinates of points of these tracks have to be measured for the geometrical reconstruction and the further analysis of the observed events. Pattern recognition methods may facilitate the detection of tracks or whole events and the separation of relevant from non-relevant information. They may also serve for the automation of measurement. Generally, all work is done by digital computation. In a bubble chamber tracks appear as strings of vapour bubbles that can be recorded photographically. Two methods of pattern recognition are discussed. The flying spot digitizer encodes the pattern on the photograph into point coordinates in the memory of a computer. The computer carries out the pattern recognition procedure entirely on the basis of the stored information. Cathode ray instruments scan the photograph by means of a computer steered optical device. Data acquisition from the film is performed in a feedback loop of the computation. In electronic experimental equipment tracks are defined by the spacial distribution of hits of counters (wire counters, scintillation counters, spark chambers). Pattern recognition is generally performed in various stages both by on-line and off-line equipment. Problems in the data handling arise both from the great abundance of data and from the time limits imposed on the on-line computation by high measuring rates. The on-line computation is carried out by hardwired logic, small computers, and to an increasing extent by microprocessors. (Auth.)

Pattern recognition methods in air pollution control

Energy Technology Data Exchange (ETDEWEB)

Tauber, S

1978-01-01

The use of pattern recognition methods for predicting air pollution developments is discussed. Computer analysis of historical pollution data allows comparison in graphical form. An example of crisis prediction for carbon monoxide concentrations, using the pattern recognition method of analysis, is presented. Results of the analysis agreed well with actual CO conditions. (6 graphs, 4 references, 1 table)

Optical Pattern Recognition for Missile Guidance.

Science.gov (United States)

1982-11-15

directed to novel pattern recognition algo- rithms (that allow pattern recognition and object classification in the face of various geometrical and...I wats EF5 = 50) p.j/t’ni 2 (for btith image pat tern recognitio itas a preproicessing oiperatiton. Ini devices). TIhe rt’ad light intensity (0.33t mW...electrodes on its large faces . This Priz light modulator and the motivation for its devel- SLM is known as the Prom (Pockels real-time optical opment. In Sec

Heteroditopic receptors for ion-pair recognition.

Science.gov (United States)

McConnell, Anna J; Beer, Paul D

2012-05-21

Ion-pair recognition is a new field of research emerging from cation and anion coordination chemistry. Specific types of heteroditopic receptor designs for ion pairs and the complexity of ion-pair binding are discussed to illustrate key concepts such as cooperativity. The importance of this area of research is reflected by the wide variety of potential applications of ion-pair receptors, including applications as membrane transport and salt solubilization agents and sensors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Recognition properties of receptors consisting of imidazole and indole recognition units towards carbohydrates

Directory of Open Access Journals (Sweden)

Monika Mazik

2010-02-01

Full Text Available Compounds 4 and 5, including both 4(5-substituted imidazole or 3-substituted indole units as the entities used in nature, and 2-aminopyridine group as a heterocyclic analogue of the asparagine/glutamine primary amide side chain, were prepared and their binding properties towards carbohydrates were studied. The design of these receptors was inspired by the binding motifs observed in the crystal structures of protein–carbohydrate complexes. 1H NMR spectroscopic titrations in competitive and non-competitive media as well as binding studies in two-phase systems, such as dissolution of solid carbohydrates in apolar media, revealed both highly effective recognition of neutral carbohydrates and interesting binding preferences of these acyclic compounds. Compared to the previously described acyclic receptors, compounds 4 and 5 showed significantly increased binding affinity towards β-galactoside. Both receptors display high β- vs. α-anomer binding preferences in the recognition of glycosides. It has been shown that both hydrogen bonding and interactions of the carbohydrate CH units with the aromatic rings of the receptors contribute to the stabilization of the receptor–carbohydrate complexes. The molecular modeling calculations, synthesis and binding properties of 4 and 5 towards selected carbohydrates are described and compared with those of the previously described receptors.

The Pandora software development kit for pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Marshall, J.S.; Thomson, M.A. [University of Cambridge, Cavendish Laboratory, Cambridge (United Kingdom)

2015-09-15

The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora software development kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e{sup +}e{sup -} linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber. (orig.)

Technical Reviews on Pattern Recognition in Process Analytical Technology

International Nuclear Information System (INIS)

Kim, Jong Yun; Choi, Yong Suk; Ji, Sun Kyung; Park, Yong Joon; Song, Kyu Seok; Jung, Sung Hee

2008-12-01

Pattern recognition is one of the first and the most widely adopted chemometric tools among many active research area in chemometrics such as design of experiment(DoE), pattern recognition, multivariate calibration, signal processing. Pattern recognition has been used to identify the origin of a wine and the time of year that the vine was grown by using chromatography, cause of fire by using GC/MS chromatography, detection of explosives and land mines, cargo and luggage inspection in seaports and airports by using a prompt gamma-ray activation analysis, and source apportionment of environmental pollutant by using a stable isotope ratio mass spectrometry. Recently, pattern recognition has been taken into account as a major chemometric tool in the so-called 'process analytical technology (PAT)', which is a newly-developed concept in the area of process analytics proposed by US Food and Drug Administration (US FDA). For instance, identification of raw material by using a pattern recognition analysis plays an important role for the effective quality control of the production process. Recently, pattern recognition technique has been used to identify the spatial distribution and uniformity of the active ingredients present in the product such as tablet by transforming the chemical data into the visual information

Pattern-Recognition Receptor Signaling Regulator mRNA Expression in Humans and Mice, and in Transient Inflammation or Progressive Fibrosis

Science.gov (United States)

Günthner, Roman; Kumar, Vankayala Ramaiah Santhosh; Lorenz, Georg; Anders, Hans-Joachim; Lech, Maciej

2013-01-01

The cell type-, organ-, and species-specific expression of the pattern-recognition receptors (PRRs) are well described but little is known about the respective expression profiles of their negative regulators. We therefore determined the mRNA expression levels of A20, CYLD, DUBA, ST2, CD180, SIGIRR, TANK, SOCS1, SOCS3, SHIP, IRAK-M, DOK1, DOK2, SHP1, SHP2, TOLLIP, IRF4, SIKE, NLRX1, ERBIN, CENTB1, and Clec4a2 in human and mouse solid organs. Humans and mice displayed significant differences between their respective mRNA expression patterns of these factors. Additionally, we characterized their expression profiles in mononuclear blood cells upon bacterial endotoxin, which showed a consistent induction of A20, SOCS3, IRAK-M, and Clec4a2 in human and murine cells. Furthermore, we studied the expression pattern in transient kidney ischemia-reperfusion injury versus post-ischemic atrophy and fibrosis in mice. A20, CD180, ST2, SOCS1, SOCS3, SHIP, IRAK-M, DOK1, DOK2, IRF4, CENTB1, and Clec4a2 were all induced, albeit at different times of injury and repair. Progressive fibrosis was associated with a persistent induction of these factors. Thus, the organ- and species-specific expression patterns need to be considered in the design and interpretation of studies related to PRR-mediated innate immunity, which seems to be involved in tissue injury, tissue regeneration and in progressive tissue scarring. PMID:24009023

Pattern recognition applied to uranium prospecting

Energy Technology Data Exchange (ETDEWEB)

Briggs, P L; Press, F [Massachusetts Inst. of Tech., Cambridge (USA). Dept. of Earth and Planetary Sciences

1977-07-14

It is stated that pattern recognition techniques provide one way of combining quantitative and descriptive geological data for mineral prospecting. A quantified decision process using computer-selected patterns of geological data has the potential for selecting areas with undiscovered deposits of uranium or other minerals. When a natural resource is mined more rapidly than it is discovered, its continued production becomes increasingly difficult, and it has been noted that, although a considerable uranium reserve may remain in the U.S.A., the discovery rate for uranium is decreasing exponentially with cumulative exploration footage drilled. Pattern recognition methods of organising geological information for prospecting may provide new predictive power, as well as insight into the occurrence of uranium ore deposits. Often the task of prospecting consists of three stages of information processing: (1) collection of data on known ore deposits; (2) noting any regularities common to the known examples of an ore; (3) selection of new exploration targets based on the results of the second stage. A logical pattern recognition algorithm is here described that implements this geological procedure to demonstrate the possibility of building a quantified uranium prospecting guide from diverse geologic data.

Application Of t-Cherry Junction Trees in Pattern Recognition

Directory of Open Access Journals (Sweden)

Edith Kovacs

2010-06-01

Full Text Available Pattern recognition aims to classify data (patterns based ei-
ther on a priori knowledge or on statistical information extracted from the data. In this paper we will concentrate on statistical pattern recognition using a new probabilistic approach which makes possible to select the so called 'informative' features. We develop a pattern recognition algorithm which is based on the conditional independence structure underlying the statistical data. Our method was succesfully applied on a real problem of recognizing Parkinson's disease on the basis of voice disorders.

Human serum albumin supported lipid patterns for the targeted recognition of microspheres coated by membrane based on ss-DNA hybridization

International Nuclear Information System (INIS)

Zhang Xiaoming; He Qiang; Cui Yue; Duan Li; Li Junbai

2006-01-01

Human serum albumin (HSA) patterns have been successfully fabricated for the deposition of lipid bilayer, 1,2-dimyristoyl-sglycerophosphate (DMPA), by making use of the micro-contact printing (μCP) technique and liposome fusion. Confocal laser scanning microscopy (CLSM) results indicate that lipid bilayer has been assembled in HSA patterns with a good stability. Such well-defined lipid patterns formed on HSA surface create possibility to incorporate specific components like channels or receptors for specific recognition. In view of this, microspheres coated with lipid membranes were immobilized in HSA-supported lipid patterns via the hybridization of complementary ss-DNAs. This procedure enables to transfer solid materials to a soft surface through a specific recognition

Type I interferon and pattern recognition receptor signaling following particulate matter inhalation

Directory of Open Access Journals (Sweden)

Erdely Aaron

2012-07-01

Full Text Available Abstract Background Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc – stainless steel (GMA-SS welding fume at 40 mg/m3 for 3 hr/d for 10 d and sacrificed 4 hr, 14 d and 28 d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling. Results The novel finding was a dominant type I interferon signaling network with the transcription factor Irf7 as a central component maintained through 28 d. Remarkably, these effects showed consistency across all tissues indicating a systemic type I interferon response that was complemented by changes in serum proteins (decreased MMP-9, CRP and increased VCAM1, oncostatin M, IP-10. In addition, pulmonary expression of interferon α and β and Irf7 specific pattern recognition receptors (PRR and signaling molecules (Ddx58, Ifih1, Dhx58, ISGF3 were induced, an effect that showed specificity when compared to other inflammatory exposures. Also, a canonical pathway indicated a coordinated response of multiple PRR and associated signaling molecules (Tlr7, Tlr2, Clec7a, Nlrp3, Myd88 to inhalation of GMA-SS. Conclusion This methodological approach has the potential to identify consistent, prominent and/or novel pathways and provides insight into mechanisms that contribute to pulmonary and systemic effects following toxicant exposure.

Data analysis and pattern recognition in multiple databases

CERN Document Server

Adhikari, Animesh; Pedrycz, Witold

2014-01-01

Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyse them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery, and mining patterns of select items provide different...

An Overview of Pathogen Recognition Receptors for Innate Immunity in Dental Pulp

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Ji-Hyun Jang

2015-01-01

Full Text Available Pathogen recognition receptors (PRRs are a class of germ line-encoded receptors that recognize pathogen-associated molecular patterns (PAMPs. The activation of PRRs is crucial for the initiation of innate immunity, which plays a key role in first-line defense until more specific adaptive immunity is developed. PRRs differ in the signaling cascades and host responses activated by their engagement and in their tissue distribution. Currently identified PRR families are the Toll-like receptors (TLRs, the C-type lectin receptors (CLRs, the nucleotide-binding oligomerization domain-like receptors (NLRs, the retinoic acid-inducible gene-I-like receptors (RLRs, and the AIM2-like receptor (ALR. The environment of the dental pulp is substantially different from that of other tissues of the body. Dental pulp resides in a low compliance root canal system that limits the expansion of pulpal tissues during inflammatory processes. An understanding of the PRRs in dental pulp is important for immunomodulation and hence for developing therapeutic targets in the field of endodontics. Here we comprehensively review recent finding on the PRRs and the mechanisms by which innate immunity is activated. We focus on the PRRs expressed on dental pulp and periapical tissues and their role in dental pulp inflammation.

Environmental Sound Recognition Using Time-Frequency Intersection Patterns

Directory of Open Access Journals (Sweden)

Xuan Guo

2012-01-01

Full Text Available Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations.

Type-2 fuzzy graphical models for pattern recognition

CERN Document Server

Zeng, Jia

2015-01-01

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

Algorithms for adaptive nonlinear pattern recognition

Science.gov (United States)

Schmalz, Mark S.; Ritter, Gerhard X.; Hayden, Eric; Key, Gary

2011-09-01

In Bayesian pattern recognition research, static classifiers have featured prominently in the literature. A static classifier is essentially based on a static model of input statistics, thereby assuming input ergodicity that is not realistic in practice. Classical Bayesian approaches attempt to circumvent the limitations of static classifiers, which can include brittleness and narrow coverage, by training extensively on a data set that is assumed to cover more than the subtense of expected input. Such assumptions are not realistic for more complex pattern classification tasks, for example, object detection using pattern classification applied to the output of computer vision filters. In contrast, we have developed a two step process, that can render the majority of static classifiers adaptive, such that the tracking of input nonergodicities is supported. Firstly, we developed operations that dynamically insert (or resp. delete) training patterns into (resp. from) the classifier's pattern database, without requiring that the classifier's internal representation of its training database be completely recomputed. Secondly, we developed and applied a pattern replacement algorithm that uses the aforementioned pattern insertion/deletion operations. This algorithm is designed to optimize the pattern database for a given set of performance measures, thereby supporting closed-loop, performance-directed optimization. This paper presents theory and algorithmic approaches for the efficient computation of adaptive linear and nonlinear pattern recognition operators that use our pattern insertion/deletion technology - in particular, tabular nearest-neighbor encoding (TNE) and lattice associative memories (LAMs). Of particular interest is the classification of nonergodic datastreams that have noise corruption with time-varying statistics. The TNE and LAM based classifiers discussed herein have been successfully applied to the computation of object classification in hyperspectral

DMPD: TLR9 as a key receptor for the recognition of DNA. [Dynamic Macrophage Pathway CSML Database

Lifescience Database Archive (English)

Full Text Available 18262306 TLR9 as a key receptor for the recognition of DNA. Kumagai Y, Takeuchi O, ...TLR9 as a key receptor for the recognition of DNA. PubmedID 18262306 Title TLR9 as a key receptor for the recognition

The principles of the pattern recognition of skeletal structures

International Nuclear Information System (INIS)

Motto, J.A.

2006-01-01

Request of the skeletal system form a lage proportion of plain film radiographic examinations. A sound knowledge of normal radiographic appearances is vital if abnormal patterns are to be recognized.The ABCS, SPACED and SASNOES methods of applying pattern recognition to plain radiographers of bones and joints will be presented in an attempt to make pattern recognition and offer an opinion constitutes role extension of radiographers

Applications of chaotic neurodynamics in pattern recognition

Science.gov (United States)

Baird, Bill; Freeman, Walter J.; Eeckman, Frank H.; Yao, Yong

1991-08-01

Network algorithms and architectures for pattern recognition derived from neural models of the olfactory system are reviewed. These span a range from highly abstract to physiologically detailed, and employ the kind of dynamical complexity observed in olfactory cortex, ranging from oscillation to chaos. A simple architecture and algorithm for analytically guaranteed associative memory storage of analog patterns, continuous sequences, and chaotic attractors in the same network is described. A matrix inversion determines network weights, given prototype patterns to be stored. There are N units of capacity in an N node network with 3N2 weights. It costs one unit per static attractor, two per Fourier component of each sequence, and three to four per chaotic attractor. There are no spurious attractors, and for sequences there is a Liapunov function in a special coordinate system which governs the approach of transient states to stored trajectories. Unsupervised or supervised incremental learning algorithms for pattern classification, such as competitive learning or bootstrap Widrow-Hoff can easily be implemented. The architecture can be ''folded'' into a recurrent network with higher order weights that can be used as a model of cortex that stores oscillatory and chaotic attractors by a Hebb rule. Network performance is demonstrated by application to the problem of real-time handwritten digit recognition. An effective system with on-line learning has been written by Eeckman and Baird for the Macintosh. It utilizes static, oscillatory, and/or chaotic attractors of two kinds--Lorenze attractors, or attractors resulting from chaotically interacting oscillatory modes. The successful application to an industrial pattern recognition problem of a network architecture of considerable physiological and dynamical complexity, developed by Freeman and Yao, is described. The data sets of the problem come in three classes of difficulty, and performance of the biological network is

Pattern recognition of neurotransmitters using multimode sensing.

Science.gov (United States)

Stefan-van Staden, Raluca-Ioana; Moldoveanu, Iuliana; van Staden, Jacobus Frederick

2014-05-30

Pattern recognition is essential in chemical analysis of biological fluids. Reliable and sensitive methods for neurotransmitters analysis are needed. Therefore, we developed for pattern recognition of neurotransmitters: dopamine, epinephrine, norepinephrine a method based on multimode sensing. Multimode sensing was performed using microsensors based on diamond paste modified with 5,10,15,20-tetraphenyl-21H,23H-porphyrine, hemin and protoporphyrin IX in stochastic and differential pulse voltammetry modes. Optimized working conditions: phosphate buffer solution of pH 3.01 and KCl 0.1mol/L (as electrolyte support), were determined using cyclic voltammetry and used in all measurements. The lowest limits of quantification were: 10(-10)mol/L for dopamine and epinephrine, and 10(-11)mol/L for norepinephrine. The multimode microsensors were selective over ascorbic and uric acids and the method facilitated reliable assay of neurotransmitters in urine samples, and therefore, the pattern recognition showed high reliability (RSDneurotransmitters on biological fluids at a lower determination level than chromatographic methods. The sampling of the biological fluids referees only to the buffering (1:1, v/v) with a phosphate buffer pH 3.01, while for chromatographic methods the sampling is laborious. Accordingly with the statistic evaluation of the results at 99.00% confidence level, both modes can be used for pattern recognition and quantification of neurotransmitters with high reliability. The best multimode microsensor was the one based on diamond paste modified with protoporphyrin IX. Copyright © 2014 Elsevier B.V. All rights reserved.

Running Improves Pattern Separation during Novel Object Recognition.

Science.gov (United States)

Bolz, Leoni; Heigele, Stefanie; Bischofberger, Josef

2015-10-09

Running increases adult neurogenesis and improves pattern separation in various memory tasks including context fear conditioning or touch-screen based spatial learning. However, it is unknown whether pattern separation is improved in spontaneous behavior, not emotionally biased by positive or negative reinforcement. Here we investigated the effect of voluntary running on pattern separation during novel object recognition in mice using relatively similar or substantially different objects.We show that running increases hippocampal neurogenesis but does not affect object recognition memory with 1.5 h delay after sample phase. By contrast, at 24 h delay, running significantly improves recognition memory for similar objects, whereas highly different objects can be distinguished by both, running and sedentary mice. These data show that physical exercise improves pattern separation, independent of negative or positive reinforcement. In sedentary mice there is a pronounced temporal gradient for remembering object details. In running mice, however, increased neurogenesis improves hippocampal coding and temporally preserves distinction of novel objects from familiar ones.

Automated pattern recognition system for noise analysis

International Nuclear Information System (INIS)

Sides, W.H. Jr.; Piety, K.R.

1980-01-01

A pattern recognition system was developed at ORNL for on-line monitoring of noise signals from sensors in a nuclear power plant. The system continuousy measures the power spectral density (PSD) values of the signals and the statistical characteristics of the PSDs in unattended operation. Through statistical comparison of current with past PSDs (pattern recognition), the system detects changes in the noise signals. Because the noise signals contain information about the current operational condition of the plant, a change in these signals could indicate a change, either normal or abnormal, in the operational condition

A pattern recognition account of decision making.

Science.gov (United States)

Massaro, D W

1994-09-01

In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects' judgments should be described accurately by the fuzzy logical model of perception (FLMP)--a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.

Subspace methods for pattern recognition in intelligent environment

CERN Document Server

Jain, Lakhmi

2014-01-01

This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

The CC chemokine receptor 5 regulates olfactory and social recognition in mice.

Science.gov (United States)

Kalkonde, Y V; Shelton, R; Villarreal, M; Sigala, J; Mishra, P K; Ahuja, S S; Barea-Rodriguez, E; Moretti, P; Ahuja, S K

2011-12-01

Chemokines are chemotactic cytokines that regulate cell migration and are thought to play an important role in a broad range of inflammatory diseases. The availability of chemokine receptor blockers makes them an important therapeutic target. In vitro, chemokines are shown to modulate neurotransmission. However, it is not very clear if chemokines play a role in behavior and cognition. Here we evaluated the role of CC chemokine receptor 5 (CCR5) in various behavioral tasks in mice using Wt (Ccr5⁺/⁺) and Ccr5-null (Ccr5⁻/⁻)mice. Ccr5⁻/⁻ mice showed enhanced social recognition. Administration of CC chemokine ligand 3 (CCL3), one of the CCR5-ligands, impaired social recognition. Since the social recognition task is dependent on the sense of olfaction, we tested olfactory recognition for social and non-social scents in these mice. Ccr5⁻/⁻ mice had enhanced olfactory recognition for both these scents indicating that enhanced performance in social recognition task could be due to enhanced olfactory recognition in these mice. Spatial memory and aversive memory were comparable in Wt and Ccr5⁻/⁻ mice. Collectively, these results suggest that chemokines/chemokine receptors might play an important role in olfactory recognition tasks in mice and to our knowledge represents the first direct demonstration of an in vivo role of CCR5 in modulating social behavior in mice. These studies are important as CCR5 blockers are undergoing clinical trials and can potentially modulate behavior. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

Hepatitis B virus polymerase blocks pattern recognition receptor signaling via interaction with DDX3: implications for immune evasion.

Directory of Open Access Journals (Sweden)

Haifeng Wang

Full Text Available Viral infection leads to induction of pattern-recognition receptor signaling, which leads to interferon regulatory factor (IRF activation and ultimately interferon (IFN production. To establish infection, many viruses have strategies to evade the innate immunity. For the hepatitis B virus (HBV, which causes chronic infection in the liver, the evasion strategy remains uncertain. We now show that HBV polymerase (Pol blocks IRF signaling, indicating that HBV Pol is the viral molecule that effectively counteracts host innate immune response. In particular, HBV Pol inhibits TANK-binding kinase 1 (TBK1/IkappaB kinase-epsilon (IKKepsilon, the effector kinases of IRF signaling. Intriguingly, HBV Pol inhibits TBK1/IKKepsilon activity by disrupting the interaction between IKKepsilon and DDX3 DEAD box RNA helicase, which was recently shown to augment TBK1/IKKepsilon activity. This unexpected role of HBV Pol may explain how HBV evades innate immune response in the early phase of the infection. A therapeutic implication of this work is that a strategy to interfere with the HBV Pol-DDX3 interaction might lead to the resolution of life-long persistent infection.

Pseudomonas evades immune recognition of flagellin in both mammals and plants

NARCIS (Netherlands)

Bardoel, B.W.; Ent, S. van der; Pel, M.J.C.; Tommassen, J.; Pieterse, C.M.J.; Kessel, K.P.M. van; Strijp, J.A.G. van

2011-01-01

The building blocks of bacterial flagella, flagellin monomers, are potent stimulators of host innate immune systems. Recognition of flagellin monomers occurs by flagellin-specific pattern-recognition receptors, such as Toll-like receptor 5 (TLR5) in mammals and flagellin-sensitive 2 (FLS2) in

Porphyromonas gulae Activates Unprimed and Gamma Interferon-Primed Macrophages via the Pattern Recognition Receptors Toll-Like Receptor 2 (TLR2), TLR4, and NOD2.

Science.gov (United States)

Holden, James A; O'Brien-Simpson, Neil M; Lenzo, Jason C; Orth, Rebecca K H; Mansell, Ashley; Reynolds, Eric C

2017-09-01

Porphyromonas gulae is an anaerobic, Gram-negative coccobacillus that has been associated with periodontal disease in companion animals. The aims of this study were to analyze the ligation of pattern recognition receptors by P. gulae and the subsequent activation of macrophages. Exposure of HEK cells transfected with Toll-like receptors (TLRs) or NOD-like receptors to P. gulae resulted in the ligation of TLR2, TLR4, and NOD2. The effects of this engagement of receptors were investigated by measuring the synthesis of nitric oxide (NO), CD86 expression, and inflammatory cytokine production by wild-type, TLR2 -/- , and TLR4 -/- macrophages. The addition of P. gulae to unprimed and gamma interferon (IFN-γ)-primed (M1 phenotype) macrophages significantly increased the surface expression of CD86, but only M1 macrophages produced nitric oxide. P. gulae- induced expression of CD86 on unprimed macrophages was dependent on both TLR2 and TLR4, but CD86 expression and NO production in M1 macrophages were only TLR2 dependent. P. gulae induced an increase in secretion of interleukin-1α (IL-1α), IL-1β, IL-6, IL-12p70, IL-13, tumor necrosis factor alpha (TNF-α), granulocyte colony-stimulating factor (G-CSF), monocyte chemoattractant protein 1 (MCP-1), and macrophage inflammatory protein 1α (MIP-1α) by M1 macrophages compared to that by unprimed controls. Among these cytokines, secretion of IL-6 and TNF-α by M1 macrophages was dependent on either TLR2 or TLR4. Our data indicate that TLR2 and TLR4 are important for P. gulae activation of unprimed macrophages and that activation and effector functions induced in M1 macrophages by P. gulae are mainly dependent on TLR2. In conclusion, P. gulae induces a strong TLR2-dependent inflammatory M1 macrophage response which may be important in establishing the chronic inflammation associated with periodontal disease in companion animals. Copyright © 2017 American Society for Microbiology.

Porphyromonas gulae Activates Unprimed and Gamma Interferon-Primed Macrophages via the Pattern Recognition Receptors Toll-Like Receptor 2 (TLR2), TLR4, and NOD2

Science.gov (United States)

Holden, James A.; O'Brien-Simpson, Neil M.; Lenzo, Jason C.; Orth, Rebecca K. H.; Mansell, Ashley

2017-01-01

ABSTRACT Porphyromonas gulae is an anaerobic, Gram-negative coccobacillus that has been associated with periodontal disease in companion animals. The aims of this study were to analyze the ligation of pattern recognition receptors by P. gulae and the subsequent activation of macrophages. Exposure of HEK cells transfected with Toll-like receptors (TLRs) or NOD-like receptors to P. gulae resulted in the ligation of TLR2, TLR4, and NOD2. The effects of this engagement of receptors were investigated by measuring the synthesis of nitric oxide (NO), CD86 expression, and inflammatory cytokine production by wild-type, TLR2−/−, and TLR4−/− macrophages. The addition of P. gulae to unprimed and gamma interferon (IFN-γ)-primed (M1 phenotype) macrophages significantly increased the surface expression of CD86, but only M1 macrophages produced nitric oxide. P. gulae-induced expression of CD86 on unprimed macrophages was dependent on both TLR2 and TLR4, but CD86 expression and NO production in M1 macrophages were only TLR2 dependent. P. gulae induced an increase in secretion of interleukin-1α (IL-1α), IL-1β, IL-6, IL-12p70, IL-13, tumor necrosis factor alpha (TNF-α), granulocyte colony-stimulating factor (G-CSF), monocyte chemoattractant protein 1 (MCP-1), and macrophage inflammatory protein 1α (MIP-1α) by M1 macrophages compared to that by unprimed controls. Among these cytokines, secretion of IL-6 and TNF-α by M1 macrophages was dependent on either TLR2 or TLR4. Our data indicate that TLR2 and TLR4 are important for P. gulae activation of unprimed macrophages and that activation and effector functions induced in M1 macrophages by P. gulae are mainly dependent on TLR2. In conclusion, P. gulae induces a strong TLR2-dependent inflammatory M1 macrophage response which may be important in establishing the chronic inflammation associated with periodontal disease in companion animals. PMID:28630066

Single cell analysis of innate cytokine responses to pattern recognition receptor stimulation in children across four continents

Science.gov (United States)

Smolen, Kinga K; Cai, Bing; Fortuno, Edgardo S; Gelinas, Laura; Larsen, Martin; Speert, David P; Chamekh, Mustapha; Kollmann, Tobias R

2014-01-01

Innate immunity instructs adaptive immunity, and suppression of innate immunity is associated with increased risk for infection. We had previously shown that whole blood cellular components from a cohort of South African children secreted significantly lower levels of most cytokines following stimulation of pattern recognition receptors (PRR) as compared to whole blood from cohorts of Ecuadorian, Belgian, or Canadian children. To begin dissecting the responsible molecular mechanisms, we now set out to identify the relevant cellular source of these differences. Across the four cohorts represented in our study, we identified significant variation in the cellular composition of whole blood; however, significant reduction of the intracellular cytokine production on the single cell level was only detected in South African childrens’ monocytes, cDC, and pDC. We also uncovered a marked reduction in polyfunctionality for each of these cellular compartments in South African children as compared to children from other continents. Together our data identify differences in cell composition as well as profoundly lower functional responses of innate cells in our cohort of South African children. A possible link between altered innate immunity and increased risk for infection or lower response to vaccines in South African infants needs to be explored. PMID:25135829

Pattern recognition and modelling of earthquake registrations with interactive computer support

International Nuclear Information System (INIS)

Manova, Katarina S.

2004-01-01

The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

Structural pattern recognition methods based on string comparison for fusion databases

International Nuclear Information System (INIS)

Dormido-Canto, S.; Farias, G.; Dormido, R.; Vega, J.; Sanchez, J.; Duro, N.; Vargas, H.; Ratta, G.; Pereira, A.; Portas, A.

2008-01-01

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed

Structural pattern recognition methods based on string comparison for fusion databases

Energy Technology Data Exchange (ETDEWEB)

Dormido-Canto, S. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain)], E-mail: sebas@dia.uned.es; Farias, G.; Dormido, R. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain); Sanchez, J.; Duro, N.; Vargas, H. [Dpto. Informatica y Automatica - UNED 28040, Madrid (Spain); Ratta, G.; Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, 28040, Madrid (Spain)

2008-04-15

Databases for fusion experiments are designed to store several million waveforms. Temporal evolution signals show the same patterns under the same plasma conditions and, therefore, pattern recognition techniques allow the identification of similar plasma behaviours. This article is focused on the comparison of structural pattern recognition methods. A pattern can be composed of simpler sub-patterns, where the most elementary sub-patterns are known as primitives. Selection of primitives is an essential issue in structural pattern recognition methods, because they determine what types of structural components can be constructed. However, it should be noted that there is not a general solution to extract structural features (primitives) from data. So, four different ways to compute the primitives of plasma waveforms are compared: (1) constant length primitives, (2) adaptive length primitives, (3) concavity method and (4) concavity method for noisy signals. Each method defines a code alphabet and, in this way, the pattern recognition problem is carried out via string comparisons. Results of the four methods with the TJ-II stellarator databases will be discussed.

Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

CERN Document Server

Pisharady, Pramod Kumar; Poh, Loh Ai

2014-01-01

This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

Reactor noise analysis by statistical pattern recognition methods

International Nuclear Information System (INIS)

Howington, L.C.; Gonzalez, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and data compacting capabilities. System design emphasizes control of the false-alarm rate. Its abilities to learn normal patterns, to recognize deviations from these patterns, and to reduce the dimensionality of data with minimum error were evaluated by experiments at the Oak Ridge National Laboratory (ORNL) High-Flux Isotope Reactor. Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the pattern recognition system

Two Challenges of Correct Validation in Pattern Recognition

Directory of Open Access Journals (Sweden)

Thomas eNowotny

2014-09-01

Full Text Available Supervised pattern recognition is the process of mapping patterns to class labelsthat define their meaning. The core methods for pattern recognitionhave been developed by machine learning experts but due to their broadsuccess an increasing number of non-experts are now employing andrefining them. In this perspective I will discuss the challenge ofcorrect validation of supervised pattern recognition systems, in particular whenemployed by non-experts. To illustrate the problem I will give threeexamples of common errors that I have encountered in the lastyear. Much of this challenge can be addressed by strict procedure invalidation but there are remaining problems of correctlyinterpreting comparative work on exemplary data sets, which I willelucidate on the example of the well-used MNIST data set of handwrittendigits.

Pattern recognition methods for acoustic emission analysis

International Nuclear Information System (INIS)

Doctor, P.G.; Harrington, T.P.; Hutton, P.H.

1979-07-01

Models have been developed that relate the rate of acoustic emissions to structural integrity. The implementation of these techniques in the field has been hindered by the noisy environment in which the data must be taken. Acoustic emissions from noncritical sources are recorded in addition to those produced by critical sources, such as flaws. A technique is discussed for prescreening acoustic events and filtering out those that are produced by noncritical sources. The methodology that was investigated is pattern recognition. Three different pattern recognition techniques were applied to a data set that consisted of acoustic emissions caused by crack growth and acoustic signals caused by extraneous noise sources. Examination of the acoustic emission data presented has uncovered several features of the data that can provide a reasonable filter. Two of the most valuable features are the frequency of maximum response and the autocorrelation coefficient at Lag 13. When these two features and several others were combined with a least squares decision algorithm, 90% of the acoustic emissions in the data set were correctly classified. It appears possible to design filters that eliminate extraneous noise sources from flaw-growth acoustic emissions using pattern recognition techniques

Data complexity in pattern recognition

CERN Document Server

Kam Ho Tin

2006-01-01

Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. This book looks at data complexity and its role in shaping the theories and techniques in different disciplines

CD56 Is a Pathogen Recognition Receptor on Human Natural Killer Cells.

Science.gov (United States)

Ziegler, Sabrina; Weiss, Esther; Schmitt, Anna-Lena; Schlegel, Jan; Burgert, Anne; Terpitz, Ulrich; Sauer, Markus; Moretta, Lorenzo; Sivori, Simona; Leonhardt, Ines; Kurzai, Oliver; Einsele, Hermann; Loeffler, Juergen

2017-07-21

Aspergillus (A.) fumigatus is an opportunistic fungal mold inducing invasive aspergillosis (IA) in immunocompromised patients. Although antifungal activity of human natural killer (NK) cells was shown in previous studies, the underlying cellular mechanisms and pathogen recognition receptors (PRRs) are still unknown. Using flow cytometry we were able to show that the fluorescence positivity of the surface receptor CD56 significantly decreased upon fungal contact. To visualize the interaction site of NK cells and A. fumigatus we used SEM, CLSM and dSTORM techniques, which clearly demonstrated that NK cells directly interact with A. fumigatus via CD56 and that CD56 is re-organized and accumulated at this interaction site time-dependently. The inhibition of the cytoskeleton showed that the receptor re-organization was an active process dependent on actin re-arrangements. Furthermore, we could show that CD56 plays a role in the fungus mediated NK cell activation, since blocking of CD56 surface receptor reduced fungal mediated NK cell activation and reduced cytokine secretion. These results confirmed the direct interaction of NK cells and A. fumigatus, leading to the conclusion that CD56 is a pathogen recognition receptor. These findings give new insights into the functional role of CD56 in the pathogen recognition during the innate immune response.

Applications of evolutionary computation in image processing and pattern recognition

CERN Document Server

Cuevas, Erik; Perez-Cisneros, Marco

2016-01-01

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

MCL and mincle: C-type lectin receptors that sense damaged self and pathogen associated molecular patterns

Directory of Open Access Journals (Sweden)

Mark B Richardson

2014-06-01

Full Text Available MCL (macrophage C-type lectin and mincle (macrophage inducible C-type lectin comprise part of an extensive repertoire of pattern recognition receptors with the ability to sense damage associated and pathogen associated molecular patterns. In this review we cover the discovery and molecular characterization of these C-type lectin receptors, and highlight recent advances in the understanding of their roles in orchestrating the response of the immune system to bacterial and fungal infection, and damaged self. We also discuss the identification and structure-activity relationships of activating ligands, particularly trehalose dimycolate (TDM and related mycobacterial glycolipids, which have significant potential in the development of TH1/TH17 vaccination strategies.

Instruction of pattern recognition by MATLAB practice 1

International Nuclear Information System (INIS)

1999-06-01

This book describes the pattern recognition by MATLAB practice. It includes possibility and limit of AI, introduction of pattern recognition a vector and matrix, basic status and a probability theory, a random variable and probability distribution, statistical decision theory, data-mining, gaussian mixture model, a nerve cell modeling such as Hebb's learning rule, LMS learning rule, genetic algorithm, dynamic programming and DTW, HMN on Markov model and HMM's three problems and solution, introduction of SVM with KKT condition and margin optimum, kernel trick and MATLAB practice.

Differences in human skin between the epidermal growth factor receptor distribution detected by EGF binding and monoclonal antibody recognition

DEFF Research Database (Denmark)

Green, M R; Couchman, J R

1985-01-01

, the eccrine sweat glands, capillary system, and the hair follicle outer root sheath, generally similar in pattern to that previously reported for full-thickness rat skin and human epidermis. The same areas also bound EGF-R1 but in addition the monoclonal antibody recognized a cone of melanin containing......Two methods have been used to examine epidermal growth factor (EGF) receptor distribution in human scalp and foreskin. The first employed [125I]EGF viable explants and autoradiography to determine the EGF binding pattern while the second used a monoclonal antibody to the human EGF receptor to map...... whether EGF-R1 could recognize molecules unrelated to the EGF receptor, the EGF binding and EGF-R1 recognition profiles were compared on cultures of SVK14 cells, a SV40 transformed human keratinocyte cell line. EGF binding and EGF-R1 monoclonal antibody distribution on these cells was found to be similar...

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

Veldhuis, Raymond N.J.; Bazen, A.M.; Kauffman, J.A.; Hartel, Pieter H.; Delp, Edward J.; Wong, Ping W.

This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 x 44 piezoresistive elements is used to measure the grip pattern. An interface has been

Biometric verification based on grip-pattern recognition

NARCIS (Netherlands)

Veldhuis, Raymond N.J.; Bazen, A.M.; Kauffman, J.A.; Hartel, Pieter H.

This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 £ 44 piezoresistive elements is used to measure the grip pattern. An interface has been

Peptide Pattern Recognition for high-throughput protein sequence analysis and clustering

DEFF Research Database (Denmark)

Busk, Peter Kamp

2017-01-01

Large collections of protein sequences with divergent sequences are tedious to analyze for understanding their phylogenetic or structure-function relation. Peptide Pattern Recognition is an algorithm that was developed to facilitate this task but the previous version does only allow a limited...... number of sequences as input. I implemented Peptide Pattern Recognition as a multithread software designed to handle large numbers of sequences and perform analysis in a reasonable time frame. Benchmarking showed that the new implementation of Peptide Pattern Recognition is twenty times faster than...... the previous implementation on a small protein collection with 673 MAP kinase sequences. In addition, the new implementation could analyze a large protein collection with 48,570 Glycosyl Transferase family 20 sequences without reaching its upper limit on a desktop computer. Peptide Pattern Recognition...

Enhanced Arabidopsis pattern-triggered immunity by overexpression of cysteine-rich receptor-like kinases.

Science.gov (United States)

Yeh, Yu-Hung; Chang, Yu-Hsien; Huang, Pin-Yao; Huang, Jing-Bo; Zimmerli, Laurent

2015-01-01

Upon recognition of microbe-associated molecular patterns (MAMPs) such as the bacterial flagellin (or the derived peptide flg22) by pattern-recognition receptors (PRRs) such as the FLAGELLIN SENSING2 (FLS2), plants activate the pattern-triggered immunity (PTI) response. The L-type lectin receptor kinase-VI.2 (LecRK-VI.2) is a positive regulator of Arabidopsis thaliana PTI. Cysteine-rich receptor-like kinases (CRKs) possess two copies of the C-X8-C-X2-C (DUF26) motif in their extracellular domains and are thought to be involved in plant stress resistance, but data about CRK functions are scarce. Here, we show that Arabidopsis overexpressing the LecRK-VI.2-responsive CRK4, CRK6, and CRK36 demonstrated an enhanced PTI response and were resistant to virulent bacteria Pseudomonas syringae pv. tomato DC3000. Notably, the flg22-triggered oxidative burst was primed in CRK4, CRK6, and CRK36 transgenics and up-regulation of the PTI-responsive gene FLG22-INDUCED RECEPTOR-LIKE 1 (FRK1) was potentiated upon flg22 treatment in CRK4 and CRK6 overexpression lines or constitutively increased by CRK36 overexpression. PTI-mediated callose deposition was not affected by overexpression of CRK4 and CRK6, while CRK36 overexpression lines demonstrated constitutive accumulation of callose. In addition, Pst DC3000-mediated stomatal reopening was blocked in CRK4 and CRK36 overexpression lines, while overexpression of CRK6 induced constitutive stomatal closure suggesting a strengthening of stomatal immunity. Finally, bimolecular fluorescence complementation and co-immunoprecipitation analyses in Arabidopsis protoplasts suggested that the plasma membrane localized CRK4, CRK6, and CRK36 associate with the PRR FLS2. Association with FLS2 and the observation that overexpression of CRK4, CRK6, and CRK36 boosts specific PTI outputs and resistance to bacteria suggest a role for these CRKs in Arabidopsis innate immunity.

The Oxytocin Receptor Gene ( OXTR) and Face Recognition.

Science.gov (United States)

Verhallen, Roeland J; Bosten, Jenny M; Goodbourn, Patrick T; Lawrance-Owen, Adam J; Bargary, Gary; Mollon, J D

2017-01-01

A recent study has linked individual differences in face recognition to rs237887, a single-nucleotide polymorphism (SNP) of the oxytocin receptor gene ( OXTR; Skuse et al., 2014). In that study, participants were assessed using the Warrington Recognition Memory Test for Faces, but performance on Warrington's test has been shown not to rely purely on face recognition processes. We administered the widely used Cambridge Face Memory Test-a purer test of face recognition-to 370 participants. Performance was not significantly associated with rs237887, with 16 other SNPs of OXTR that we genotyped, or with a further 75 imputed SNPs. We also administered three other tests of face processing (the Mooney Face Test, the Glasgow Face Matching Test, and the Composite Face Test), but performance was never significantly associated with rs237887 or with any of the other genotyped or imputed SNPs, after corrections for multiple testing. In addition, we found no associations between OXTR and Autism-Spectrum Quotient scores.

Auditory orientation in crickets: Pattern recognition controls reactive steering

Science.gov (United States)

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

DMPD: Innate recognition of lipopolysaccharide by Toll-like receptor 4-MD-2. [Dynamic Macrophage Pathway CSML Database

Lifescience Database Archive (English)

Full Text Available 15051069 Innate recognition of lipopolysaccharide by Toll-like receptor 4-MD-2. Miy...ake K. Trends Microbiol. 2004 Apr;12(4):186-92. (.png) (.svg) (.html) (.csml) Show Innate recognition of lip...opolysaccharide by Toll-like receptor 4-MD-2. PubmedID 15051069 Title Innate recognition of lipopolysacchari

Deleted in malignant brain tumors-1 protein (DMBT1): a pattern recognition receptor with multiple binding sites.

Science.gov (United States)

Ligtenberg, Antoon J M; Karlsson, Niclas G; Veerman, Enno C I

2010-01-01

Deleted in Malignant Brain Tumors-1 protein (DMBT1), salivary agglutinin (DMBT1(SAG)), and lung glycoprotein-340 (DMBT1(GP340)) are three names for glycoproteins encoded by the same DMBT1 gene. All these proteins belong to the scavenger receptor cysteine-rich (SRCR) superfamily of proteins: a superfamily of secreted or membrane-bound proteins with SRCR domains that are highly conserved down to sponges, the most ancient metazoa. In addition to SRCR domains, all DMBT1s contain two CUB domains and one zona pellucida domain. The SRCR domains play a role in the function of DMBT1s, which is the binding of a broad range of pathogens including cariogenic streptococci, Helicobacter pylori and HIV. Mucosal defense proteins like IgA, surfactant proteins and lactoferrin also bind to DMBT1s through their SRCR domains. The binding motif on the SRCR domains comprises an 11-mer peptide in which a few amino acids are essential for binding (GRVEVLYRGSW). Adjacent to each individual SRCR domain are glycosylation domains, where the attached carbohydrate chains play a role in the binding of influenza A virus and Helicobacter pylori. The composition of the carbohydrate chains is not only donor specific, but also varies between different organs. These data demonstrate a role for DMBT1s as pattern recognition molecules containing various peptide and carbohydrate binding motifs.

Deleted in Malignant Brain Tumors-1 Protein (DMBT1: A Pattern Recognition Receptor with Multiple Binding Sites

Directory of Open Access Journals (Sweden)

Enno C. I. Veerman

2010-12-01

Full Text Available Deleted in Malignant Brain Tumors-1 protein (DMBT1, salivary agglutinin (DMBT1SAG, and lung glycoprotein-340 (DMBT1GP340 are three names for glycoproteins encoded by the same DMBT1 gene. All these proteins belong to the scavenger receptor cysteine-rich (SRCR superfamily of proteins: a superfamily of secreted or membrane-bound proteins with SRCR domains that are highly conserved down to sponges, the most ancient metazoa. In addition to SRCR domains, all DMBT1s contain two CUB domains and one zona pellucida domain. The SRCR domains play a role in the function of DMBT1s, which is the binding of a broad range of pathogens including cariogenic streptococci, Helicobacter pylori and HIV. Mucosal defense proteins like IgA, surfactant proteins and lactoferrin also bind to DMBT1s through their SRCR domains. The binding motif on the SRCR domains comprises an 11-mer peptide in which a few amino acids are essential for binding (GRVEVLYRGSW. Adjacent to each individual SRCR domain are glycosylation domains, where the attached carbohydrate chains play a role in the binding of influenza A virus and Helicobacter pylori. The composition of the carbohydrate chains is not only donor specific, but also varies between different organs. These data demonstrate a role for DMBT1s as pattern recognition molecules containing various peptide and carbohydrate binding motifs.

Perirhinal Cortex Muscarinic Receptor Blockade Impairs Taste Recognition Memory Formation

OpenAIRE

Gutiérrez, Ranier; De la Cruz, Vanesa; Rodriguez-Ortiz, Carlos J.; Bermudez-Rattoni, Federico

2004-01-01

The relevance of perirhinal cortical cholinergic and glutamatergic neurotransmission for taste recognition memory and learned taste aversion was assessed by microinfusions of muscarinic (scopolamine), NMDA (AP-5), and AMPA (NBQX) receptor antagonists. Infusions of scopolamine, but not AP5 or NBQX, prevented the consolidation of taste recognition memory using attenuation of neophobia as an index. In addition, learned taste aversion in both short- and long-term memory tests was exclusively impa...

Fuzzy tree automata and syntactic pattern recognition.

Science.gov (United States)

Lee, E T

1982-04-01

An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems.

The Role of Binocular Disparity in Rapid Scene and Pattern Recognition

Directory of Open Access Journals (Sweden)

Matteo Valsecchi

2013-04-01

Full Text Available We investigated the contribution of binocular disparity to the rapid recognition of scenes and simpler spatial patterns using a paradigm combining backward masked stimulus presentation and short-term match-to-sample recognition. First, we showed that binocular disparity did not contribute significantly to the recognition of briefly presented natural and artificial scenes, even when the availability of monocular cues was reduced. Subsequently, using dense random dot stereograms as stimuli, we showed that observers were in principle able to extract spatial patterns defined only by disparity under brief, masked presentations. Comparing our results with the predictions from a cue-summation model, we showed that combining disparity with luminance did not per se disrupt the processing of disparity. Our results suggest that the rapid recognition of scenes is mediated mostly by a monocular comparison of the images, although we can rely on stereo in fast pattern recognition.

Pattern recognition approach to nondestructive evaluation of materials

International Nuclear Information System (INIS)

Chen, C.H.

1987-01-01

In this paper, a pattern recognition approach to the ultrasonic nondestructive evaluation of materials is examined. Emphasis is placed on identifying effective features from time and frequency domains, correlation functions and impulse responses to classify aluminum plate specimens into three major defect geometry categories: flat, angular cut and circular hole defects. A multi-stage classification procedure is developed which can further determine the angles and sizes for defect characterization and classification. The research clearly demonstrates that the pattern recognition approach can significantly improve the nondestructive material evaluation capability of the ultrasonic methods without resorting to the solution of highly complex mathematical inverse problems

Application of pattern recognition techniques to crime analysis

Energy Technology Data Exchange (ETDEWEB)

Bender, C.F.; Cox, L.A. Jr.; Chappell, G.A.

1976-08-15

The initial goal was to evaluate the capabilities of current pattern recognition techniques when applied to existing computerized crime data. Performance was to be evaluated both in terms of the system's capability to predict crimes and to optimize police manpower allocation. A relation was sought to predict the crime's susceptibility to solution, based on knowledge of the crime type, location, time, etc. The preliminary results of this work are discussed. They indicate that automatic crime analysis involving pattern recognition techniques is feasible, and that efforts to determine optimum variables and techniques are warranted. 47 figures (RWR)

Pattern recognition receptors, sensing re(n)al danger

NARCIS (Netherlands)

Pulskens, W.P.C.

2012-01-01

Wilco Pulskens bestudeerde de rol van verschillende receptoren van het aangeboren immuunsysteem (waaronder Toll-like receptor-4 (TLR4) en het NLRP3-inflammasome) en liganden (bindingsmoleculen) die deze receptoren kunnen activeren tijdens acute en chronische nierschade. Met behulp van diermodellen

Quantum pattern recognition with multi-neuron interactions

Science.gov (United States)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Dentate gyrus supports slope recognition memory, shades of grey-context pattern separation and recognition memory, and CA3 supports pattern completion for object memory.

Science.gov (United States)

Kesner, Raymond P; Kirk, Ryan A; Yu, Zhenghui; Polansky, Caitlin; Musso, Nick D

2016-03-01

In order to examine the role of the dorsal dentate gyrus (dDG) in slope (vertical space) recognition and possible pattern separation, various slope (vertical space) degrees were used in a novel exploratory paradigm to measure novelty detection for changes in slope (vertical space) recognition memory and slope memory pattern separation in Experiment 1. The results of the experiment indicate that control rats displayed a slope recognition memory function with a pattern separation process for slope memory that is dependent upon the magnitude of change in slope between study and test phases. In contrast, the dDG lesioned rats displayed an impairment in slope recognition memory, though because there was no significant interaction between the two groups and slope memory, a reliable pattern separation impairment for slope could not be firmly established in the DG lesioned rats. In Experiment 2, in order to determine whether, the dDG plays a role in shades of grey spatial context recognition and possible pattern separation, shades of grey were used in a novel exploratory paradigm to measure novelty detection for changes in the shades of grey context environment. The results of the experiment indicate that control rats displayed a shades of grey-context pattern separation effect across levels of separation of context (shades of grey). In contrast, the DG lesioned rats displayed a significant interaction between the two groups and levels of shades of grey suggesting impairment in a pattern separation function for levels of shades of grey. In Experiment 3 in order to determine whether the dorsal CA3 (dCA3) plays a role in object pattern completion, a new task requiring less training and using a choice that was based on choosing the correct set of objects on a two-choice discrimination task was used. The results indicated that control rats displayed a pattern completion function based on the availability of one, two, three or four cues. In contrast, the dCA3 lesioned rats

An inverse problem approach to pattern recognition in industry

Directory of Open Access Journals (Sweden)

Ali Sever

2015-01-01

Full Text Available Many works have shown strong connections between learning and regularization techniques for ill-posed inverse problems. A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward. In this study, pattern recognition will be viewed as an ill-posed inverse problem and applications of methods from the theory of inverse problems to pattern recognition are studied. A new learning algorithm derived from a well-known regularization model is generated and applied to the task of reconstruction of an inhomogeneous object as pattern recognition. Particularly, it is demonstrated that pattern recognition can be reformulated in terms of inverse problems defined by a Riesz-type kernel. This reformulation can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. Finally, numerical experiments have been carried out with synthetic experimental data considering a reasonable level of noise. Good recoveries have been achieved with this methodology, and the results of these simulations are compatible with the existing methods. The comparison results show that the Regularization-based learning algorithm (RBA obtains a promising performance on the majority of the test problems. In prospects, this method can be used for the creation of automated systems for diagnostics, testing, and control in various fields of scientific and applied research, as well as in industry.

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

Chloroplastic protein NRIP1 mediates innate immune receptor recognition of a viral effector

Science.gov (United States)

Caplan, Jeffrey L.; Mamillapalli, Padmavathi; Burch-Smith, Tessa M.; Czymmek, Kirk; Dinesh-Kumar, S.P.

2008-01-01

Summary Plant innate immunity relies on the recognition of pathogen effector molecules by nucleotide-binding-leucine-rich repeat (NB-LRR) immune receptor families. Previously we have shown the N immune receptor, a member of TIR-NB-LRR family, indirectly recognizes the 50-kDa helicase (p50) domain of Tobacco mosaic virus (TMV) through its TIR domain. We have identified an N receptor-interacting protein, NRIP1, that directly interacts with both N's TIR domain and p50. NRIP1 is a functional rhodanese sulfurtransferase and is required for N to provide complete resistance to TMV. Interestingly, NRIP1 that normally localizes to the chloroplasts is recruited to the cytoplasm and nucleus by the p50 effector. As a consequence, NRIP1 interacts with N only in the presence of the p50 effector. Our findings show that a chloroplastic protein is intimately involved in pathogen recognition. We propose that N's activation requires a pre-recognition complex containing the p50 effector and NRIP1. PMID:18267075

Applications of pattern recognition theory in diagnostics of nuclear power plants

International Nuclear Information System (INIS)

Cech, J.

1982-01-01

The questions are discussed of the application of the theory of pattern recognition in the diagnostics of nuclear power plants. For the future use of recognition systems in the diagnostics of nuclear power plants it is obvious that like with other complex systems, optimal models will have to be used which will organize the optimal recognition algorithm. The conclusion is presented that for the needs of nuclear power plants special systems will be more suitable for pattern recognition than digital computers which are flexible and adaptible but have a lower decision rate, an insufficient working memory, complicated programs, etc. (Z.M.)

Sub-pattern based multi-manifold discriminant analysis for face recognition

Science.gov (United States)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

Distorted Pattern Recognition and Analysis with the Help of IEf Graph Representation

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Adam Sedziwy

2002-01-01

Full Text Available An algorithm for distorted pattern recognition is presented. lt's generalization of M Flasinski results (Pattern Recognition, 27, 1-16, 1992. A new formalism allows to make both qualitative and quantitive distortion analysis. It also enlarges parser flexibility by extending the set of patterns which may be recognized.

Patterns recognition of electric brain activity using artificial neural networks

Science.gov (United States)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

Dopamine D4 receptor stimulation contributes to novel object recognition: Relevance to cognitive impairment in schizophrenia.

Science.gov (United States)

Miyauchi, Masanori; Neugebauer, Nichole M; Meltzer, Herbert Y

2017-04-01

Several atypical antipsychotic drugs (APDs) have high affinity for the dopamine (DA) D 4 receptor, but the relevance to the efficacy for the treatment of cognitive impairment associated with schizophrenia (CIAS) is poorly understood. The aim of this study was to investigate the effects of D 4 receptor stimulation or blockade on novel object recognition (NOR) in normal rats and on the sub-chronic phencyclidine (PCP)-induced novel object recognition deficit. The effect of the D 4 agonist, PD168077, and the D 4 antagonist, L-745,870, were studied alone, and in combination with clozapine and lurasidone. In normal rats, L-745,870 impaired novel object recognition, whereas PD168077 had no effect. PD168077 acutely reversed the sub-chronic phencyclidine-induced novel object recognition deficit. Co-administration of a sub-effective dose (SED) of PD168077 with a sub-effective dose of lurasidone also reversed this deficit, but a sub-effective dose of PD168077 with a sub-effective dose of clozapine, a more potent D 4 antagonist than lurasidone, did not reverse the sub-chronic phencyclidine-induced novel object recognition deficit. At a dose that did not induce a novel object recognition deficit, L-745,870 blocked the ability of clozapine, but not lurasidone, to reverse the novel object recognition deficit. D 4 receptor agonism has a beneficial effect on novel object recognition in sub-chronic PCP-treated rats and augments the cognitive enhancing efficacy of an atypical antipsychotic drug that lacks affinity for the D 4 receptor, lurasidone.

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Science.gov (United States)

Swartz, R. Andrew

2013-01-01

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

Directory of Open Access Journals (Sweden)

Wenjia Liu

2013-01-01

Full Text Available This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate.

Hopfield's Model of Patterns Recognition and Laws of Artistic Perception

Science.gov (United States)

Yevin, Igor; Koblyakov, Alexander

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’s model of patterns recognition.

Contrasting patterns of polymorphism and selection in bacterial-sensing toll-like receptor 4 in two house mouse subspecies

Czech Academy of Sciences Publication Activity Database

Fornůsková, Alena; Bryja, Josef; Vinkler, M.; Macholán, Miloš; Piálek, Jaroslav

2014-01-01

Roč. 4, č. 14 (2014), s. 2931-2944 ISSN 2045-7758 R&D Projects: GA ČR GA206/08/0640 Institutional support: RVO:68081766 ; RVO:67985904 Keywords : adaptive evolution * arms race * directional selection * host–pathogen interaction * MAMPs * Mus musculus * parasite-mediated selection * pattern-recognition receptors Subject RIV: EG - Zoology Impact factor: 2.320, year: 2014

Probiotic Bacteria Alter Pattern-Recognition Receptor Expression and Cytokine Profile in a Human Macrophage Model Challenged with Candida albicans and Lipopolysaccharide

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Victor H. Matsubara

2017-11-01

Full Text Available Probiotics are live microorganisms that confer benefits to the host health. The infection rate of potentially pathogenic organisms such as Candida albicans, the most common agent associated with mucosal candidiasis, can be reduced by probiotics. However, the mechanisms by which the probiotics interfere with the immune system are largely unknown. We evaluated the effect of probiotic bacteria on C. albicans challenged human macrophages. Macrophages were pretreated with lactobacilli alone (Lactobacillus rhamnosus LR32, Lactobacillus casei L324m, or Lactobacillus acidophilus NCFM or associated with Escherichia coli lipopolysaccharide (LPS, followed by the challenge with C. albicans or LPS in a co-culture assay. The expression of pattern-recognition receptors genes (CLE7A, TLR2, and TLR4 was determined by RT-qPCR, and dectin-1 reduced levels were confirmed by flow cytometry. The cytokine profile was determined by ELISA using the macrophage cell supernatant. Overall probiotic lactobacilli down-regulated the transcription of CLEC7A (p < 0.05, resulting in the decreased expression of dectin-1 on probiotic pretreated macrophages. The tested Lactobacillus species down-regulated TLR4, and increased TLR2 mRNA levels in macrophages challenged with C. albicans. The cytokines profile of macrophages challenged with C. albicans or LPS were altered by the probiotics, which generally led to increased levels of IL-10 and IL-1β, and reduction of IL-12 production by macrophages (p < 0.05. Our data suggest that probiotic lactobacilli impair the recognition of PAMPs by macrophages, and alter the production of pro/anti-inflammatory cytokines, thus modulating inflammation.

Threats of Password Pattern Leakage Using Smartwatch Motion Recognition Sensors

Directory of Open Access Journals (Sweden)

Jihun Kim

2017-06-01

Full Text Available Thanks to the development of Internet of Things (IoT technologies, wearable markets have been growing rapidly. Smartwatches can be said to be the most representative product in wearable markets, and involve various hardware technologies in order to overcome the limitations of small hardware. Motion recognition sensors are a representative example of those hardware technologies. However, smartwatches and motion recognition sensors that can be worn by users may pose security threats of password pattern leakage. In the present paper, passwords are inferred through experiments to obtain password patterns inputted by users using motion recognition sensors, and verification of the results and the accuracy of the results is shown.

Grip-pattern recognition: Applied to a smart gun

NARCIS (Netherlands)

Shang, X.

2008-01-01

In our work the verification performance of a biometric recognition system based on grip patterns, as part of a smart gun for use by the police ocers, has been investigated. The biometric features are extracted from a two-dimensional pattern of the pressure, exerted on the grip of a gun by the hand

Weighted Local Active Pixel Pattern (WLAPP for Face Recognition in Parallel Computation Environment

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Gundavarapu Mallikarjuna Rao

2013-10-01

Full Text Available Abstract  - The availability of multi-core technology resulted totally new computational era. Researchers are keen to explore available potential in state of art-machines for breaking the bearer imposed by serial computation. Face Recognition is one of the challenging applications on so ever computational environment. The main difficulty of traditional Face Recognition algorithms is lack of the scalability. In this paper Weighted Local Active Pixel Pattern (WLAPP, a new scalable Face Recognition Algorithm suitable for parallel environment is proposed.  Local Active Pixel Pattern (LAPP is found to be simple and computational inexpensive compare to Local Binary Patterns (LBP. WLAPP is developed based on concept of LAPP. The experimentation is performed on FG-Net Aging Database with deliberately introduced 20% distortion and the results are encouraging. Keywords — Active pixels, Face Recognition, Local Binary Pattern (LBP, Local Active Pixel Pattern (LAPP, Pattern computing, parallel workers, template, weight computation.  

The Role of Verbal Instruction and Visual Guidance in Training Pattern Recognition

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Jamie S. North

2017-09-01

Full Text Available We used a novel approach to examine whether it is possible to improve the perceptual–cognitive skill of pattern recognition using a video-based training intervention. Moreover, we investigated whether any improvements in pattern recognition transfer to an improved ability to make anticipation judgments. Finally, we compared the relative effectiveness of verbal and visual guidance interventions compared to a group that merely viewed the same sequences without any intervention and a control group that only completed pre- and post-tests. We found a significant effect for time of testing. Participants were more sensitive in their ability to perceive patterns and distinguish between novel and familiar sequences at post- compared to pre-test. However, this improvement was not influenced by the nature of the intervention, despite some trends in the data. An analysis of anticipation accuracy showed no change from pre- to post-test following the pattern recognition training intervention, suggesting that the link between pattern perception and anticipation may not be strong. We present a series of recommendations for scientists and practitioners when employing training methods to improve pattern recognition and anticipation.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

Science.gov (United States)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Artificial intelligence tools for pattern recognition

Science.gov (United States)

Acevedo, Elena; Acevedo, Antonio; Felipe, Federico; Avilés, Pedro

2017-06-01

In this work, we present a system for pattern recognition that combines the power of genetic algorithms for solving problems and the efficiency of the morphological associative memories. We use a set of 48 tire prints divided into 8 brands of tires. The images have dimensions of 200 x 200 pixels. We applied Hough transform to obtain lines as main features. The number of lines obtained is 449. The genetic algorithm reduces the number of features to ten suitable lines that give thus the 100% of recognition. Morphological associative memories were used as evaluation function. The selection algorithms were Tournament and Roulette wheel. For reproduction, we applied one-point, two-point and uniform crossover.

Pattern recognition of state variables by neural networks

International Nuclear Information System (INIS)

Faria, Eduardo Fernandes; Pereira, Claubia

1996-01-01

An artificial intelligence system based on artificial neural networks can be used to classify predefined events and emergency procedures. These systems are being used in different areas. In the nuclear reactors safety, the goal is the classification of events whose data can be processed and recognized by neural networks. In this works we present a preliminary simple system, using neural networks in the recognition of patterns the recognition of variables which define a situation. (author)

Discovery of a novel site of opioid action at the innate immune pattern-recognition receptor TLR4 and its role in addiction.

Science.gov (United States)

Jacobsen, Jonathan Henry W; Watkins, Linda R; Hutchinson, Mark R

2014-01-01

Opioids have historically, and continue to be, an integral component of pain management. However, despite pharmacokinetic and dynamic optimization over the past 100 years, opioids continue to produce many undesirable side effects such as tolerance, reward, and dependence. As such, opioids are liable for addiction. Traditionally, opioid addiction was viewed as a solely neuronal process, and while substantial headway has been made into understanding the molecular and cellular mechanisms mediating this process, research has however, been relatively ambivalent to how the rest of the central nervous system (CNS) responds to opioids. Evidence over the past 20 years has clearly demonstrated the importance of the immunocompetent cells of the CNS (glia) in many aspects of opioid pharmacology. Particular focus has been placed on microglia and astrocytes, who in response to opioids, become activated and release inflammatory mediators. Importantly, the mechanism underlying immune activation is beginning to be elucidated. Evidence suggests an innate immune pattern-recognition receptor (toll-like receptor 4) as an integral component underlying opioid-induced glial activation. The subsequent proinflammatory response may be viewed akin to neurotransmission creating a process termed central immune signaling. Translationally, we are beginning to appreciate the importance of central immune signaling as it contributes to many behavioral actions of addiction including reward, withdrawal, and craving. As such, the aim of this chapter is to review and integrate the neuronal and central immune signaling perspective of addiction. © 2014 Elsevier Inc. All rights reserved.

Granular neural networks, pattern recognition and bioinformatics

CERN Document Server

Pal, Sankar K; Ganivada, Avatharam

2017-01-01

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

Herpes simplex virus infection is sensed by both Toll-like receptors and retinoic acid-inducible gene- like receptors, which synergize to induce type I interferon production

DEFF Research Database (Denmark)

Rasmussen, Simon B; Jensen, Søren B; Nielsen, Christoffer

2009-01-01

The innate antiviral response is initiated by pattern recognition receptors, which recognize viral pathogen-associated molecular patterns. Here we show that retinoic acid-inducible gene (RIG)-I-like receptors (RLRs) in cooperation with Toll-like receptor (TLR) 9 is required for expression of type I...... interferons (IFNs) after infection with herpes simplex virus (HSV). Our work also identified RNase L as a critical component in IFN induction. Moreover, we found that TLR9 and RLRs activate distinct, as well as overlapping, intracellular signalling pathways. Thus, RLRs are important for recognition of HSV...

Perirhinal Cortex Muscarinic Receptor Blockade Impairs Taste Recognition Memory Formation

Science.gov (United States)

Gutierrez, Ranier; De la Cruz, Vanesa; Rodriguez-Ortiz, Carlos J.; Bermudez-Rattoni, Federico

2004-01-01

The relevance of perirhinal cortical cholinergic and glutamatergic neurotransmission for taste recognition memory and learned taste aversion was assessed by microinfusions of muscarinic (scopolamine), NMDA (AP-5), and AMPA (NBQX) receptor antagonists. Infusions of scopolamine, but not AP5 or NBQX, prevented the consolidation of taste recognition…

Using Pattern Classification and Recognition Techniques for Diagnostic and Prediction

Directory of Open Access Journals (Sweden)

MORARIU, N.

2007-04-01

Full Text Available The paper presents some aspects regarding the joint use of classification and recognition techniques for the activity evolution diagnostication and prediction by means of a set of indexes. Starting from the indexes set there is defined a measure on the patterns set, measure representing a scalar value that characterizes the activity analyzed at each time moment. A pattern is defined by the values of the indexes set at a given time. Over the classes set obtained by means of the classification and recognition techniques is defined a relation that allows the representation of the evolution from negative evolution towards positive evolution. For the diagnostication and prediction the following tools are used: pattern recognition and multilayer perceptron. The data set used in experiments describes the pollution due to CO2 emission from the consumption of fuels in Europe. The paper also presents the REFORME software written by the authors and the results of the experiment obtained with this software.

Pattern Recognition of the Multiple Sclerosis Syndrome

Science.gov (United States)

Stewart, Renee; Healey, Kathleen M.

2017-01-01

During recent decades, the autoimmune disease neuromyelitis optica spectrum disorder (NMOSD), once broadly classified under the umbrella of multiple sclerosis (MS), has been extended to include autoimmune inflammatory conditions of the central nervous system (CNS), which are now diagnosable with serum serological tests. These antibody-mediated inflammatory diseases of the CNS share a clinical presentation to MS. A number of practical learning points emerge in this review, which is geared toward the pattern recognition of optic neuritis, transverse myelitis, brainstem/cerebellar and hemispheric tumefactive demyelinating lesion (TDL)-associated MS, aquaporin-4-antibody and myelin oligodendrocyte glycoprotein (MOG)-antibody NMOSD, overlap syndrome, and some yet-to-be-defined/classified demyelinating disease, all unspecifically labeled under MS syndrome. The goal of this review is to increase clinicians’ awareness of the clinical nuances of the autoimmune conditions for MS and NMSOD, and to highlight highly suggestive patterns of clinical, paraclinical or imaging presentations in order to improve differentiation. With overlay in clinical manifestations between MS and NMOSD, magnetic resonance imaging (MRI) of the brain, orbits and spinal cord, serology, and most importantly, high index of suspicion based on pattern recognition, will help lead to the final diagnosis. PMID:29064441

Surveillance of a nuclear reactor core by use of a pattern recognition method

International Nuclear Information System (INIS)

Invernizzi, Michel.

1982-07-01

A pattern recognition system is described for the surveillance of a PWR reactor. This report contains four chapters. The first one succinctly deals with statistical pattern recognition principles. In the second chapter we show how a surveillance problem may be treated by pattern recognition and we present methods for surveillances (detection of abnormalities), controls (kind of running recognition) and diagnotics (kind of abnormality recognition). The third chapter shows a surveillance method of a nuclear plant. The signals used are the neutron noise observations made by the ionization chambers inserted in the reactor. Abnormality is defined in opposition with the training set witch is supposed to be an exhaustive summary of normality. In the fourth chapter we propose a scheme for an adaptative recognition and a method based on classes modelisations by hyper-spheres. This method has been tested on simulated training sets in two-dimensional feature spaces. It gives solutions to problems of non-linear separability [fr

Dopamine D1 receptor stimulation modulates the formation and retrieval of novel object recognition memory: Role of the prelimbic cortex.

Science.gov (United States)

Pezze, Marie A; Marshall, Hayley J; Fone, Kevin C F; Cassaday, Helen J

2015-11-01

Previous studies have shown that dopamine D1 receptor antagonists impair novel object recognition memory but the effects of dopamine D1 receptor stimulation remain to be determined. This study investigated the effects of the selective dopamine D1 receptor agonist SKF81297 on acquisition and retrieval in the novel object recognition task in male Wistar rats. SKF81297 (0.4 and 0.8 mg/kg s.c.) given 15 min before the sampling phase impaired novel object recognition evaluated 10 min or 24 h later. The same treatments also reduced novel object recognition memory tested 24 h after the sampling phase and when given 15 min before the choice session. These data indicate that D1 receptor stimulation modulates both the encoding and retrieval of object recognition memory. Microinfusion of SKF81297 (0.025 or 0.05 μg/side) into the prelimbic sub-region of the medial prefrontal cortex (mPFC) in this case 10 min before the sampling phase also impaired novel object recognition memory, suggesting that the mPFC is one important site mediating the effects of D1 receptor stimulation on visual recognition memory. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

Science.gov (United States)

Kaplan, Bernhard A.; Lansner, Anders

2014-01-01

Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures. PMID

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Science.gov (United States)

Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

2016-11-01

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

Pattern Recognition as a Human Centered non-Euclidean Problem

NARCIS (Netherlands)

Duin, R.P.W.

2010-01-01

Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition tries to bridge the gap between human judgment and measurements made by artificial sensors. This is done in two steps: representation

Immune receptors involved in Streptococcus suis recognition by dendritic cells.

Directory of Open Access Journals (Sweden)

Marie-Pier Lecours

Full Text Available Streptococcus suis is an important swine pathogen and an emerging zoonotic agent of septicemia and meningitis. Knowledge on host immune responses towards S. suis, and strategies used by this pathogen for subversion of these responses is scarce. The objective of this study was to identify the immune receptors involved in S. suis recognition by dendritic cells (DCs. Production of cytokines and expression of co-stimulatory molecules by DCs were shown to strongly rely on MyD88-dependent signaling pathways, suggesting that DCs recognize S. suis and become activated mostly through Toll-like receptor (TLR signaling. Supporting this fact, TLR2(-/- DCs were severely impaired in the release of several cytokines and the surface expression of CD86 and MHC-II. The release of IL-12p70 and CXC10, and the expression of CD40 were found to depend on signaling by both TLR2 and TLR9. The release of IL-23 and CXCL1 were partially dependent on NOD2. Finally, despite the fact that MyD88 signaling was crucial for DC activation and maturation, MyD88-dependent pathways were not implicated in S. suis internalization by DCs. This first study on receptors involved in DC activation by S. suis suggests a major involvement of MyD88 signaling pathways, mainly (but not exclusively through TLR2. A multimodal recognition involving a combination of different receptors seems essential for DC effective response to S. suis.

Pattern recognition neural-net by spatial mapping of biology visual field

Science.gov (United States)

Lin, Xin; Mori, Masahiko

2000-05-01

The method of spatial mapping in biology vision field is applied to artificial neural networks for pattern recognition. By the coordinate transform that is called the complex-logarithm mapping and Fourier transform, the input images are transformed into scale- rotation- and shift- invariant patterns, and then fed into a multilayer neural network for learning and recognition. The results of computer simulation and an optical experimental system are described.

Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

Directory of Open Access Journals (Sweden)

Bineng Zhong

2008-06-01

Full Text Available This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP, which is based on Daugman’s method for iris recognition and the local XOR pattern (LXP operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

Rough-fuzzy pattern recognition applications in bioinformatics and medical imaging

CERN Document Server

Maji, Pradipta

2012-01-01

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems dev

Proposal for the development of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Energy Technology Data Exchange (ETDEWEB)

Deptuch, Gregory; Hoff, Jim; Kwan, Simon; Lipton, Ron; Liu, Ted; Ramberg, Erik; Todri, Aida; Yarema, Ray; /Fermilab; Demarteua, Marcel,; Drake, Gary; Weerts, Harry; /Argonne /Chicago U. /Padua U. /INFN, Padua

2010-10-01

Future particle physics experiments looking for rare processes will have no choice but to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare process. The authors propose to develop a 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) chip for HEP applications, to advance the state-of-the-art for pattern recognition and track reconstruction for fast triggering.

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

Science.gov (United States)

Tongsong, Theera; Wanapirak, Chanane; Tantipalakorn, Charuwan; Tinnangwattana, Dangcheewan

2017-11-26

Objective: To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer. Methods: Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings. Results: A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively. Conclusion: IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed. Creative Commons Attribution License

RECOG-ORNL, Pattern Recognition Data Analysis

International Nuclear Information System (INIS)

Begovich, C.L.; Larson, N.M.

2000-01-01

Description of program or function: RECOG-ORNL, a general-purpose pattern recognition code, is a modification of the RECOG program, written at Lawrence Livermore National Laboratory. RECOG-ORNL contains techniques for preprocessing, analyzing, and displaying data, and for unsupervised and supervised learning. Data preprocessing routines transform the data into useful representations by auto-calling, selecting important variables, and/or adding products or transformations of the variables of the data set. Data analysis routines use correlations to evaluate the data and interrelationships among the data. Display routines plot the multidimensional patterns in two dimensions or plot histograms, patterns, or one variable versus another. Unsupervised learning techniques search for classes contained inherently in the data. Supervised learning techniques use known information about some of the data to generate predicted properties for an unknown set

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

Directory of Open Access Journals (Sweden)

Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

Spatial pattern recognition of seismic events in South West Colombia

Science.gov (United States)

Benítez, Hernán D.; Flórez, Juan F.; Duque, Diana P.; Benavides, Alberto; Lucía Baquero, Olga; Quintero, Jiber

2013-09-01

Recognition of seismogenic zones in geographical regions supports seismic hazard studies. This recognition is usually based on visual, qualitative and subjective analysis of data. Spatial pattern recognition provides a well founded means to obtain relevant information from large amounts of data. The purpose of this work is to identify and classify spatial patterns in instrumental data of the South West Colombian seismic database. In this research, clustering tendency analysis validates whether seismic database possesses a clustering structure. A non-supervised fuzzy clustering algorithm creates groups of seismic events. Given the sensitivity of fuzzy clustering algorithms to centroid initial positions, we proposed a methodology to initialize centroids that generates stable partitions with respect to centroid initialization. As a result of this work, a public software tool provides the user with the routines developed for clustering methodology. The analysis of the seismogenic zones obtained reveals meaningful spatial patterns in South-West Colombia. The clustering analysis provides a quantitative location and dispersion of seismogenic zones that facilitates seismological interpretations of seismic activities in South West Colombia.

Differential effects of m1 and m2 receptor antagonists in perirhinal cortex on visual recognition memory in monkeys.

Science.gov (United States)

Wu, Wei; Saunders, Richard C; Mishkin, Mortimer; Turchi, Janita

2012-07-01

Microinfusions of the nonselective muscarinic antagonist scopolamine into perirhinal cortex impairs performance on visual recognition tasks, indicating that muscarinic receptors in this region play a pivotal role in recognition memory. To assess the mnemonic effects of selective blockade in perirhinal cortex of muscarinic receptor subtypes, we locally infused either the m1-selective antagonist pirenzepine or the m2-selective antagonist methoctramine in animals performing one-trial visual recognition, and compared these scores with those following infusions of equivalent volumes of saline. Compared to these control infusions, injections of pirenzepine, but not of methoctramine, significantly impaired recognition accuracy. Further, similar doses of scopolamine and pirenzepine yielded similar deficits, suggesting that the deficits obtained earlier with scopolamine were due mainly, if not exclusively, to blockade of m1 receptors. The present findings indicate that m1 and m2 receptors have functionally dissociable roles, and that the formation of new visual memories is critically dependent on the cholinergic activation of m1 receptors located on perirhinal cells. Published by Elsevier Inc.

Pattern Recognition-Based Analysis of COPD in CT

DEFF Research Database (Denmark)

Sørensen, Lauge Emil Borch Laurs

recognition part is used to turn the texture measures, measured in a CT image of the lungs, into a quantitative measure of disease. This is done by applying a classifier that is trained on a training set of data examples with known lung tissue patterns. Different classification systems are considered, and we...... will in particular use the pattern recognition concepts of supervised learning, multiple instance learning, and dissimilarity representation-based classification. The proposed texture-based measures are applied to CT data from two different sources, one comprising low dose CT slices from subjects with manually...... annotated regions of emphysema and healthy tissue, and one comprising volumetric low dose CT images from subjects that are either healthy or suffer from COPD. Several experiments demonstrate that it is clearly beneficial to take the lung tissue texture into account when classifying or quantifying emphysema...

A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition

Directory of Open Access Journals (Sweden)

Zhenhua Zhang

2013-01-01

Full Text Available We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS, which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS and interval-valued IFS (IVIFS. And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.

Structural determinants for selective recognition of peptide ligands for endothelin receptor subtypes ETA and ETB.

Science.gov (United States)

Lättig, Jens; Oksche, Alexander; Beyermann, Michael; Rosenthal, Walter; Krause, Gerd

2009-07-01

The molecular basis for recognition of peptide ligands endothelin-1, -2 and -3 in endothelin receptors is poorly understood. Especially the origin of ligand selectivity for ET(A) or ET(B) is not clearly resolved. We derived sequence-structure-function relationships of peptides and receptors from mutational data and homology modeling. Our major findings are the dissection of peptide ligands into four epitopes and the delineation of four complementary structural portions on receptor side explaining ligand recognition in both endothelin receptor subtypes. In addition, structural determinants for ligand selectivity could be described. As a result, we could improve the selectivity of BQ3020 about 10-fold by a single amino acid substitution, validating our hypothesis for ligand selectivity caused by different entrances to the receptors' transmembrane binding sites. A narrow tunnel shape in ET(A) is restrictive for a selected group of peptide ligands' N-termini, whereas a broad funnel-shaped entrance in ET(B) accepts a variety of different shapes and properties of ligands.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

Science.gov (United States)

Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

2018-01-01

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

Do pattern recognition skills transfer across sports? A preliminary analysis.

Science.gov (United States)

Smeeton, Nicholas J; Ward, Paul; Williams, A Mark

2004-02-01

The ability to recognize patterns of play is fundamental to performance in team sports. While typically assumed to be domain-specific, pattern recognition skills may transfer from one sport to another if similarities exist in the perceptual features and their relations and/or the strategies used to encode and retrieve relevant information. A transfer paradigm was employed to compare skilled and less skilled soccer, field hockey and volleyball players' pattern recognition skills. Participants viewed structured and unstructured action sequences from each sport, half of which were randomly represented with clips not previously seen. The task was to identify previously viewed action sequences quickly and accurately. Transfer of pattern recognition skill was dependent on the participant's skill, sport practised, nature of the task and degree of structure. The skilled soccer and hockey players were quicker than the skilled volleyball players at recognizing structured soccer and hockey action sequences. Performance differences were not observed on the structured volleyball trials between the skilled soccer, field hockey and volleyball players. The skilled field hockey and soccer players were able to transfer perceptual information or strategies between their respective sports. The less skilled participants' results were less clear. Implications for domain-specific expertise, transfer and diversity across domains are discussed.

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

Science.gov (United States)

Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

2018-03-12

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

H-2 restriction: Independent recognition of H-2 and foreign antigen by a single receptor

Science.gov (United States)

Siliciano, Robert F.; Zacharchuk, Charles M.; Shin, Hyun S.

1980-01-01

We describe two situations in which the recognition of hapten can compensate for the lack of recognition of appropriate H-2 gene products in hapten-specific, H-2 restricted, T lymphocyte-mediated cytolysis. First, we show that although recognition of appropriate H-2 gene products is essential for the lysis of target cells bearing a low hapten density, significant hapten-specific lysis of H-2 inappropriate target cells is observed at high levels of target cell derivatization. Secondly, we show that hapten-conjugated anti-H-2 antibody inhibits cytolysis poorly even though its binding to target cell H-2 antigens is equivalent to that of underivatized antibody. These results suggest that hapten and H-2 are recognized independently and are therefore inconsistent with the altered-self model. Although our data do not exclude the dual-recognition model, we prefer to interpret them within the framework of a single-receptor model in which hapten and H-2 are recognized independently by receptors of identical idiotype on the T cell. We postulate that the affinity of these receptors for the relevant H-2 gene product is low enough so that the T cell is not activated by encounters with normal-self cells expressing that H-2 gene product. However, when self cells express in addition a foreign antigen that can also be recognized by the same receptor, then the force of T cell-target cell interaction may be increased sufficiently to activate T cell effector function. PMID:6966404

On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information

Science.gov (United States)

Stathopoulou, I.-O.; Alepis, E.; Tsihrintzis, G. A.; Virvou, M.

Towards realizing a multimodal affect recognition system, we are considering the advantages of assisting a visual-facial expression recognition system with keyboard-stroke pattern information. Our work is based on the assumption that the visual-facial and keyboard modalities are complementary to each other and that their combination can significantly improve the accuracy in affective user models. Specifically, we present and discuss the development and evaluation process of two corresponding affect recognition subsystems, with emphasis on the recognition of 6 basic emotional states, namely happiness, sadness, surprise, anger and disgust as well as the emotion-less state which we refer to as neutral. We find that emotion recognition by the visual-facial modality can be aided greatly by keyboard-stroke pattern information and the combination of the two modalities can lead to better results towards building a multimodal affect recognition system.

Quantitative EEG Applying the Statistical Recognition Pattern Method

DEFF Research Database (Denmark)

Engedal, Knut; Snaedal, Jon; Hoegh, Peter

2015-01-01

BACKGROUND/AIM: The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. METHODS...

Function and Dynamics of Tetraspanins during Antigen Recognition and Immunological Synapse Formation

Directory of Open Access Journals (Sweden)

Vera eRocha-Perugini

2016-01-01

Full Text Available Tetraspanin-enriched microdomains (TEMs are specialized membrane platforms driven by protein-protein interactions that integrate membrane receptors and adhesion molecules. Tetraspanins participate in antigen recognition and presentation by antigen presenting cells (APCs through the organization of pattern recognition receptors (PRRs and their downstream induced-signaling, as well as the regulation of MHC-II-peptide trafficking. T lymphocyte activation is triggered upon specific recognition of antigens present on the APC surface during immunological synapse (IS formation. This dynamic process is characterized by a defined spatial organization involving the compartmentalization of receptors and adhesion molecules in specialized membrane domains that are connected to the underlying cytoskeleton and signaling molecules. Tetraspanins contribute to the spatial organization and maturation of the IS by controlling receptor clustering and local accumulation of adhesion receptors and integrins, their downstream signaling and linkage to the actin cytoskeleton. This review offers a perspective on the important role of TEMs in the regulation of antigen recognition and presentation, and in the dynamics of IS architectural organization.

Function and Dynamics of Tetraspanins during Antigen Recognition and Immunological Synapse Formation

Science.gov (United States)

Rocha-Perugini, Vera; Sánchez-Madrid, Francisco; Martínez del Hoyo, Gloria

2016-01-01

Tetraspanin-enriched microdomains (TEMs) are specialized membrane platforms driven by protein–protein interactions that integrate membrane receptors and adhesion molecules. Tetraspanins participate in antigen recognition and presentation by antigen-­presenting cells (APCs) through the organization of pattern-recognition receptors (PRRs) and their downstream-induced signaling, as well as the regulation of MHC-II–peptide trafficking. T lymphocyte activation is triggered upon specific recognition of antigens present on the APC surface during immunological synapse (IS) formation. This dynamic process is characterized by a defined spatial organization involving the compartmentalization of receptors and adhesion molecules in specialized membrane domains that are connected to the underlying cytoskeleton and signaling molecules. Tetraspanins contribute to the spatial organization and maturation of the IS by controlling receptor clustering and local accumulation of adhesion receptors and integrins, their downstream signaling, and linkage to the actin cytoskeleton. This review offers a perspective on the important role of TEMs in the regulation of antigen recognition and presentation and in the dynamics of IS architectural organization. PMID:26793193

Face Recognition Using Local Quantized Patterns and Gabor Filters

Science.gov (United States)

Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

2015-05-01

The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

DNA pattern recognition using canonical correlation algorithm.

Science.gov (United States)

Sarkar, B K; Chakraborty, Chiranjib

2015-10-01

We performed canonical correlation analysis as an unsupervised statistical tool to describe related views of the same semantic object for identifying patterns. A pattern recognition technique based on canonical correlation analysis (CCA) was proposed for finding required genetic code in the DNA sequence. Two related but different objects were considered: one was a particular pattern, and other was test DNA sequence. CCA found correlations between two observations of the same semantic pattern and test sequence. It is concluded that the relationship possesses maximum value in the position where the pattern exists. As a case study, the potential of CCA was demonstrated on the sequence found from HIV-1 preferred integration sites. The subsequences on the left and right flanking from the integration site were considered as the two views, and statistically significant relationships were established between these two views to elucidate the viral preference as an important factor for the correlation.

Conditional Random Fields for Pattern Recognition Applied to Structured Data

Directory of Open Access Journals (Sweden)

Tom Burr

2015-07-01

Full Text Available Pattern recognition uses measurements from an input domain, X, to predict their labels from an output domain, Y. Image analysis is one setting where one might want to infer whether a pixel patch contains an object that is “manmade” (such as a building or “natural” (such as a tree. Suppose the label for a pixel patch is “manmade”; if the label for a nearby pixel patch is then more likely to be “manmade” there is structure in the output domain that can be exploited to improve pattern recognition performance. Modeling P(X is difficult because features between parts of the model are often correlated. Therefore, conditional random fields (CRFs model structured data using the conditional distribution P(Y|X = x, without specifying a model for P(X, and are well suited for applications with dependent features. This paper has two parts. First, we overview CRFs and their application to pattern recognition in structured problems. Our primary examples are image analysis applications in which there is dependence among samples (pixel patches in the output domain. Second, we identify research topics and present numerical examples.

Developement of 3D Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

International Nuclear Information System (INIS)

Deputch, G.; Hoff, J.; Lipton, R.; Liu, T.; Olsen, J.; Ramberg, E.; Wu, Jin-Yuan; Yarema, R.; Shochet, M.; Tang, F.; Demarteau, M.

2011-01-01

Many next-generation physics experiments will be characterized by the collection of large quantities of data, taken in rapid succession, from which scientists will have to unravel the underlying physical processes. In most cases, large backgrounds will overwhelm the physics signal. Since the quantity of data that can be stored for later analysis is limited, real-time event selection is imperative to retain the interesting events while rejecting the background. Scaling of current technologies is unlikely to satisfy the scientific needs of future projects, so investments in transformational new technologies need to be made. For example, future particle physics experiments looking for rare processes will have to address the demanding challenges of fast pattern recognition in triggering as detector hit density becomes significantly higher due to the high luminosity required to produce the rare processes. In this proposal, we intend to develop hardware-based technology that significantly advances the state-of-the-art for fast pattern recognition within and outside HEP using the 3D vertical integration technology that has emerged recently in industry. The ultimate physics reach of the LHC experiments will crucially depend on the tracking trigger's ability to help discriminate between interesting rare events and the background. Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing pattern recognition

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

International Nuclear Information System (INIS)

Gu Yu; Li Qiang

2014-01-01

We present a new pattern recognition system based on moving average and linear discriminant analysis (LDA), which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Hardware processors for pattern recognition tasks in experiments with wire chambers

International Nuclear Information System (INIS)

Verkerk, C.

1975-01-01

Hardware processors for pattern recognition tasks in experiments with multiwire proportional chambers or drift chambers are described. They vary from simple ones used for deciding in real time if particle trajectories are straight to complex ones for recognition of curved tracks. Schematics and block-diagrams of different processors are shown

Interaction of Hepatitis C virus proteins with pattern recognition receptors

Directory of Open Access Journals (Sweden)

Imran Muhammad

2012-06-01

Full Text Available Abstract Hepatitis C virus (HCV is an important human pathogen that causes acute and chronic hepatitis, cirrhosis and hepatocellular carcinoma worldwide. This positive stranded RNA virus is extremely efficient in establishing persistent infection by escaping immune detection or hindering the host immune responses. Recent studies have discovered two important signaling pathways that activate the host innate immunity against viral infection. One of these pathways utilizes members of Toll-like receptor (TLR family and the other uses the RNA helicase retinoic acid inducible gene I (RIG-I as the receptors for intracellular viral double stranded RNA (dsRNA, and activation of transcription factors. In this review article, we summarize the interaction of HCV proteins with various host receptors/sensors through one of these two pathways or both, and how they exploit these interactions to escape from host defense mechanisms. For this purpose, we searched data from Pubmed and Google Scholar. We found that three HCV proteins; Core (C, non structural 3/4 A (NS3/4A and non structural 5A (NS5A have direct interactions with these two pathways. Core protein only in the monomeric form stimulates TLR2 pathway assisting the virus to evade from the innate immune system. NS3/4A disrupts TLR3 and RIG-1 signaling pathways by cleaving Toll/IL-1 receptor domain-containing adapter inducing IFN-beta (TRIF and Cardif, the two important adapter proteins of these signaling cascades respectively, thus halting the defense against HCV. NS5A downmodulates the expressions of NKG2D on natural killer cells (NK cells via TLR4 pathway and impairs the functional ability of these cells. TLRs and RIG-1 pathways have a central role in innate immunity and despite their opposing natures to HCV proteins, when exploited together, HCV as an ever developing virus against host immunity is able to accumulate these mechanisms for near unbeatable survival.

A new concept of vertically integrated pattern recognition associative memory

International Nuclear Information System (INIS)

Liu, Ted; Hoff, Jim; Deptuch, Grzegorz; Yarema, Ray

2011-01-01

Hardware-based pattern recognition for fast triggering on particle tracks has been successfully used in high-energy physics experiments for some time. The CDF Silicon Vertex Trigger (SVT) at the Fermilab Tevatron is an excellent example. The method used there, developed in the 1990's, is based on algorithms that use a massively parallel associative memory architecture to identify patterns efficiently at high speed. However, due to much higher occupancy and event rates at the LHC, and the fact that the LHC detectors have a much larger number of channels in their tracking detectors, there is an enormous challenge in implementing fast pattern recognition for a track trigger, requiring about three orders of magnitude more associative memory patterns than what was used in the original CDF SVT. Scaling of current technologies is unlikely to satisfy the scientific needs of the future, and investments in transformational new technologies need to be made. In this paper, we will discuss a new concept of using the emerging 3D vertical integration technology to significantly advance the state-of-the-art for fast pattern recognition within and outside HEP. A generic R and D proposal based on this new concept, with a few institutions involved, has recently been submitted to DOE with the goal to design and perform the ASIC engineering necessary to realize a prototype device. The progress of this R and D project will be reported in the future. Here we will only focus on the concept of this new approach.

Hypothesis Support Mechanism for Mid-Level Visual Pattern Recognition

Science.gov (United States)

Amador, Jose J (Inventor)

2007-01-01

A method of mid-level pattern recognition provides for a pose invariant Hough Transform by parametrizing pairs of points in a pattern with respect to at least two reference points, thereby providing a parameter table that is scale- or rotation-invariant. A corresponding inverse transform may be applied to test hypothesized matches in an image and a distance transform utilized to quantify the level of match.

Wavelet-based moment invariants for pattern recognition

Science.gov (United States)

Chen, Guangyi; Xie, Wenfang

2011-07-01

Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.

Recent progress in invariant pattern recognition

Science.gov (United States)

Arsenault, Henri H.; Chang, S.; Gagne, Philippe; Gualdron Gonzalez, Oscar

1996-12-01

We present some recent results in invariant pattern recognition, including methods that are invariant under two or more distortions of position, orientation and scale. There are now a few methods that yield good results under changes of both rotation and scale. Some new methods are introduced. These include locally adaptive nonlinear matched filters, scale-adapted wavelet transforms and invariant filters for disjoint noise. Methods using neural networks will also be discussed, including an optical method that allows simultaneous classification of multiple targets.

Similarity-based pattern analysis and recognition

CERN Document Server

Pelillo, Marcello

2013-01-01

This accessible text/reference presents a coherent overview of the emerging field of non-Euclidean similarity learning. The book presents a broad range of perspectives on similarity-based pattern analysis and recognition methods, from purely theoretical challenges to practical, real-world applications. The coverage includes both supervised and unsupervised learning paradigms, as well as generative and discriminative models. Topics and features: explores the origination and causes of non-Euclidean (dis)similarity measures, and how they influence the performance of traditional classification alg

A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Directory of Open Access Journals (Sweden)

He-Yuan Lin

2008-03-01

Full Text Available A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

A Motion-Adaptive Deinterlacer via Hybrid Motion Detection and Edge-Pattern Recognition

Directory of Open Access Journals (Sweden)

Li Hsin-Te

2008-01-01

Full Text Available Abstract A novel motion-adaptive deinterlacing algorithm with edge-pattern recognition and hybrid motion detection is introduced. The great variety of video contents makes the processing of assorted motion, edges, textures, and the combination of them very difficult with a single algorithm. The edge-pattern recognition algorithm introduced in this paper exhibits the flexibility in processing both textures and edges which need to be separately accomplished by line average and edge-based line average before. Moreover, predicting the neighboring pixels for pattern analysis and interpolation further enhances the adaptability of the edge-pattern recognition unit when motion detection is incorporated. Our hybrid motion detection features accurate detection of fast and slow motion in interlaced video and also the motion with edges. Using only three fields for detection also renders higher temporal correlation for interpolation. The better performance of our deinterlacing algorithm with higher content-adaptability and less memory cost than the state-of-the-art 4-field motion detection algorithms can be seen from the subjective and objective experimental results of the CIF and PAL video sequences.

Pattern Recognition

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Aleš Procházka

2018-05-01

Full Text Available Multimodal signal analysis based on sophisticated sensors, efficient communicationsystems and fast parallel processing methods has a rapidly increasing range of multidisciplinaryapplications. The present paper is devoted to pattern recognition, machine learning, and the analysisof sleep stages in the detection of sleep disorders using polysomnography (PSG data, includingelectroencephalography (EEG, breathing (Flow, and electro-oculogram (EOG signals. The proposedmethod is based on the classification of selected features by a neural network system with sigmoidaland softmax transfer functions using Bayesian methods for the evaluation of the probabilities of theseparate classes. The application is devoted to the analysis of the sleep stages of 184 individualswith different diagnoses, using EEG and further PSG signals. Data analysis points to an averageincrease of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of theRapid Eye Movement (REM stages by 0.8% per 10 years. The mean classification accuracy for givensets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1 and89.6% (STD:1.9, respectively. The proposed methods enable the use of adaptive learning processesfor the detection and classification of health disorders based on prior specialist experience andman–machine interaction.

Listening for recollection: a multi-voxel pattern analysis of recognition memory retrieval strategies

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Joel R Quamme

2010-08-01

Full Text Available Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally-directed attentional state (listening for recollection that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects' recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. Specifically, we looked for brain regions that met the following criteria: 1 Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and 2 fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are listening for recollection at that moment should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally-directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before, suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection.

Recognition of neural brain activity patterns correlated with complex motor activity

Science.gov (United States)

Kurkin, Semen; Musatov, Vyacheslav Yu.; Runnova, Anastasia E.; Grubov, Vadim V.; Efremova, Tatyana Yu.; Zhuravlev, Maxim O.

2018-04-01

In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.

Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

Science.gov (United States)

Noh, Soo Rim; Isaacowitz, Derek M.

2014-01-01

While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

Independent Evolution of Strychnine Recognition by Bitter Taste Receptor Subtypes

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Ava Yuan Xue

2018-03-01

Full Text Available The 25 human bitter taste receptors (hT2Rs recognize thousands of structurally and chemically diverse bitter substances. The binding modes of human bitter taste receptors hT2R10 and hT2R46, which are responsible for strychnine recognition, were previously established using site-directed mutagenesis, functional assays, and molecular modeling. Here we construct a phylogenetic tree and reconstruct ancestral sequences of the T2R10 and T2R46 clades. We next analyze the binding sites in view of experimental data to predict their ability to recognize strychnine. This analysis suggests that the common ancestor of hT2R10 and hT2R46 is unlikely to bind strychnine in the same mode as either of its two descendants. Estimation of relative divergence times shows that hT2R10 evolved earlier than hT2R46. Strychnine recognition was likely acquired first by the earliest common ancestor of the T2R10 clade before the separation of primates from other mammals, and was highly conserved within the clade. It was probably independently acquired by the common ancestor of T2R43-47 before the homo-ape speciation, lost in most T2Rs within this clade, but enhanced in the hT2R46 after humans diverged from the rest of primates. Our findings suggest hypothetical strychnine T2R receptors in several species, and serve as an experimental guide for further study. Improved understanding of how bitter taste receptors acquire the ability to be activated by particular ligands is valuable for the development of sensors for bitterness and for potential toxicity.

Independent Evolution of Strychnine Recognition by Bitter Taste Receptor Subtypes

Science.gov (United States)

Xue, Ava Yuan; Di Pizio, Antonella; Levit, Anat; Yarnitzky, Tali; Penn, Osnat; Pupko, Tal; Niv, Masha Y.

2018-01-01

The 25 human bitter taste receptors (hT2Rs) recognize thousands of structurally and chemically diverse bitter substances. The binding modes of human bitter taste receptors hT2R10 and hT2R46, which are responsible for strychnine recognition, were previously established using site-directed mutagenesis, functional assays, and molecular modeling. Here we construct a phylogenetic tree and reconstruct ancestral sequences of the T2R10 and T2R46 clades. We next analyze the binding sites in view of experimental data to predict their ability to recognize strychnine. This analysis suggests that the common ancestor of hT2R10 and hT2R46 is unlikely to bind strychnine in the same mode as either of its two descendants. Estimation of relative divergence times shows that hT2R10 evolved earlier than hT2R46. Strychnine recognition was likely acquired first by the earliest common ancestor of the T2R10 clade before the separation of primates from other mammals, and was highly conserved within the clade. It was probably independently acquired by the common ancestor of T2R43-47 before the homo-ape speciation, lost in most T2Rs within this clade, but enhanced in the hT2R46 after humans diverged from the rest of primates. Our findings suggest hypothetical strychnine T2R receptors in several species, and serve as an experimental guide for further study. Improved understanding of how bitter taste receptors acquire the ability to be activated by particular ligands is valuable for the development of sensors for bitterness and for potential toxicity. PMID:29552563

Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?

Science.gov (United States)

Ashkenazi, Sarit; Mark-Zigdon, Nitza; Henik, Avishai

2013-01-01

The abilities of children diagnosed with developmental dyscalculia (DD) were examined in two types of object enumeration: subitizing, and small estimation (5-9 dots). Subitizing is usually defined as a fast and accurate assessment of a number of small dots (range 1 to 4 dots), and estimation is an imprecise process to assess a large number of items (range 5 dots or more). Based on reaction time (RT) and accuracy analysis, our results indicated a deficit in the subitizing and small estimation range among DD participants in relation to controls. There are indications that subitizing is based on pattern recognition, thus presenting dots in a canonical shape in the estimation range should result in a subitizing-like pattern. In line with this theory, our control group presented a subitizing-like pattern in the small estimation range for canonically arranged dots, whereas the DD participants presented a deficit in the estimation of canonically arranged dots. The present finding indicates that pattern recognition difficulties may play a significant role in both subitizing and subitizing deficits among those with DD. © 2012 Blackwell Publishing Ltd.

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

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Chia-Hung Lin

2010-01-01

Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Statistical pattern recognition for automatic writer identification and verification

NARCIS (Netherlands)

Bulacu, Marius Lucian

2007-01-01

The thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document

Sequential pattern recognition by maximum conditional informativity

Czech Academy of Sciences Publication Activity Database

Grim, Jiří

2014-01-01

Roč. 45, č. 1 (2014), s. 39-45 ISSN 0167-8655 R&D Projects: GA ČR(CZ) GA14-02652S; GA ČR(CZ) GA14-10911S Keywords : Multivariate statistics * Statistical pattern recognition * Sequential decision making * Product mixtures * EM algorithm * Shannon information Subject RIV: IN - Informatics, Computer Sci ence Impact factor: 1.551, year: 2014 http://library.utia.cas.cz/separaty/2014/RO/grim-0428565.pdf

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

OpenAIRE

Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

Involvement of hippocampal NMDA receptors in retrieval of spontaneous object recognition memory in rats.

Science.gov (United States)

Iwamura, Etsushi; Yamada, Kazuo; Ichitani, Yukio

2016-07-01

The involvement of hippocampal N-methyl-d-aspartate (NMDA) receptors in the retrieval process of spontaneous object recognition memory was investigated. The spontaneous object recognition test consisted of three phases. In the sample phase, rats were exposed to two identical objects several (2-5) times in the arena. After the sample phase, various lengths of delay intervals (24h-6 weeks) were inserted (delay phase). In the test phase in which both the familiar and the novel objects were placed in the arena, rats' novel object exploration behavior under the hippocampal treatment of NMDA receptor antagonist, AP5, or vehicle was observed. With 5 exposure sessions in the sample phase (experiment 1), AP5 treatment in the test phase significantly decreased discrimination ratio when the delay was 3 weeks but not when it was one week. On the other hand, with 2 exposure sessions in the sample phase (experiment 2) in which even vehicle-injected control animals could not discriminate the novel object from the familiar one with a 3 week delay, AP5 treatment significantly decreased discrimination ratio when the delay was one week, but not when it was 24h. Additional experiment (experiment 3) showed that the hippocampal treatment of an α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist, NBQX, decreased discrimination ratio with all delay intervals tested (24h-3 weeks). Results suggest that hippocampal NMDA receptors play an important role in the retrieval of spontaneous object recognition memory especially when the memory trace weakens. Copyright © 2016. Published by Elsevier B.V.

The murine cytomegalovirus M35 protein antagonizes type I IFN induction downstream of pattern recognition receptors by targeting NF-κB mediated transcription.

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Baca Chan

2017-05-01

Full Text Available The type I interferon (IFN response is imperative for the establishment of the early antiviral immune response. Here we report the identification of the first type I IFN antagonist encoded by murine cytomegalovirus (MCMV that shuts down signaling following pattern recognition receptor (PRR sensing. Screening of an MCMV open reading frame (ORF library identified M35 as a novel and strong negative modulator of IFNβ promoter induction following activation of both RNA and DNA cytoplasmic PRR. Additionally, M35 inhibits the proinflammatory cytokine response downstream of Toll-like receptors (TLR. Using a series of luciferase-based reporters with specific transcription factor binding sites, we determined that M35 targets NF-κB-, but not IRF-mediated, transcription. Expression of M35 upon retroviral transduction of immortalized bone marrow-derived macrophages (iBMDM led to reduced IFNβ transcription and secretion upon activation of stimulator of IFN genes (STING-dependent signaling. On the other hand, M35 does not antagonize interferon-stimulated gene (ISG 56 promoter induction or ISG transcription upon exogenous stimulation of the type I IFN receptor (IFNAR. M35 is present in the viral particle and, upon MCMV infection of fibroblasts, is immediately shuttled to the nucleus where it exerts its immunomodulatory effects. Deletion of M35 from the MCMV genome and hence from the viral particle resulted in elevated type I IFN transcription and secretion in vitro and in vivo. In the absence of M35, lower viral titers are observed during acute infection of the host, and productive infection in the salivary glands was not detected. In conclusion, the M35 protein is released by MCMV immediately upon infection in order to deftly inhibit the antiviral type I IFN response by targeting NF-κB-mediated transcription. The identification of this novel viral protein reinforces the importance of timely countermeasures in the complex relationship between virus and host.

Exploring How User Routine Affects the Recognition Performance of a Lock Pattern

NARCIS (Netherlands)

de Wide, Lisa; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

2015-01-01

To protect an Android smartphone against attackers, a lock pattern can be used. Nevertheless, shoulder-surfing and smudge attacks can be used to get access despite of this protection. To combat these attacks, biometric recognition can be added to the lock pattern, such that the lock-pattern

Automated target recognition and tracking using an optical pattern recognition neural network

Science.gov (United States)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Intrahippocampal injection of Cortistatin-14 impairs recognition memory consolidation in mice through activation of sst2, ghrelin and GABAA/B receptors.

Science.gov (United States)

Jiang, Jinhong; Peng, Yali; He, Zhen; Wei, Lijuan; Jin, Weidong; Wang, Xiaoli; Chang, Min

2017-07-01

Cortistatin-14 (CST-14), a neuropeptide related to somatostatin, is primarily localized within the cortex and hippocampus. In the hippocampus, CST-14 inhibits CA1 neuronal pyramidal cell firing and co-exists with GABA. However, its role in cognitive is still not clarified. The first aim of our study was to elucidate the role of CST-14 signaling in consolidation and reconsolidation of recognition memory in mice, using novel object recognition task. The results showed that central CST-14 induced in impairment of long-term and short-term recognition memory, indicating memory consolidation impairment effect. Similarly, we found that CST-14 did not impaired long-term and short-term reconsolidation recognition memory. To further investigate the underlying mechanisms of CST-14 in memory process, we used cyclosomatostatin (c-SOM, a selective sst 1-5 receptor antagonist), cyanamid154806 (a selective sst 2 receptor antagonist), ODN-8 (a high affinity and selectivity compound for sst 3 receptor), [d-Lys 3 ]GHRP-6 (a selective ghrelin receptor antagonist), picrotoxin (PTX, a GABA A receptor antagonist), and sacolfen (a GABA B receptor antagonist) to research its effects in recognition. Our results firstly indicated that the memory-impairing effects of CST-14 were significantly reversed by c-SOM, cyanamid154806, [d-Lys 3 ]GHRP-6, PTX and sacolfen, but not ODN-8, suggesting that the blockage of recognition memory consolidation induced by CST-14 involves sst 2 , ghrelin and GABA system. The present study provides a potential strategy to regulate memory processes, providing new evidence that reconsolidation is not a simple reiteration of consolidation. Copyright © 2017 Elsevier B.V. All rights reserved.

Hepatitis C Virus Frameshift/Alternate Reading Frame Protein Suppresses Interferon Responses Mediated by Pattern Recognition Receptor Retinoic-Acid-Inducible Gene-I.

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Seung Bum Park

Full Text Available Hepatitis C virus (HCV actively evades host interferon (IFN responses but the mechanisms of how it does so are not completely understood. In this study, we present evidence for an HCV factor that contributes to the suppression of retinoic-acid-inducible gene-I (RIG-I-mediated IFN induction. Expression of frameshift/alternate reading frame protein (F/ARFP from HCV -2/+1 frame in Huh7 hepatoma cells suppressed type I IFN responses stimulated by HCV RNA pathogen-associated molecular pattern (PAMP and poly(IC. The suppression occurred independently of other HCV factors; and activation of interferon stimulated genes, TNFα, IFN-λ1, and IFN-λ2/3 was likewise suppressed by HCV F/ARFP. Point mutations in the full-length HCV sequence (JFH1 genotype 2a strain were made to introduce premature termination codons in the -2/+1 reading frame coding for F/ARFP while preserving the original reading frame, which enhanced IFNα and IFNβ induction by HCV. The potentiation of IFN response by the F/ARFP mutations was diminished in Huh7.5 cells, which already have a defective RIG-I, and by decreasing RIG-I expression in Huh7 cells. Furthermore, adding F/ARFP back via trans-complementation suppressed IFN induction in the F/ARFP mutant. The F/ARFP mutants, on the other hand, were not resistant to exogenous IFNα. Finally, HCV-infected human liver samples showed significant F/ARFP antibody reactivity, compared to HCV-uninfected control livers. Therefore, HCV F/ARFP likely cooperates with other viral factors to suppress type I and III IFN induction occurring through the RIG-I signaling pathway. This study identifies a novel mechanism of pattern recognition receptor modulation by HCV and suggests a biological function of the HCV alternate reading frame in the modulation of host innate immunity.

Collectin CL-LK Is a Novel Soluble Pattern Recognition Receptor for Mycobacterium tuberculosis

DEFF Research Database (Denmark)

Troegeler, Anthony; Lugo-Villarino, Geanncarlo; Hansen, Søren

2015-01-01

Understanding the molecular components of immune recognition of the tuberculosis (TB) bacillus, Mycobacterium tuberculosis, can help designing novel strategies to combat TB. Here, we identify collectin CL-LK as a novel soluble C-type lectin able to bind M. tuberculosis, and characterize mycobacte......Understanding the molecular components of immune recognition of the tuberculosis (TB) bacillus, Mycobacterium tuberculosis, can help designing novel strategies to combat TB. Here, we identify collectin CL-LK as a novel soluble C-type lectin able to bind M. tuberculosis, and characterize...

Endogenous ligands for C-type lectin receptors: the true regulators of immune homeostasis.

Science.gov (United States)

García-Vallejo, Juan J; van Kooyk, Yvette

2009-07-01

C-type lectin receptors (CLRs) have long been known as pattern-recognition receptors implicated in the recognition of pathogens by the innate immune system. However, evidence is accumulating that many CLRs are also able to recognize endogenous 'self' ligands and that this recognition event often plays an important role in immune homeostasis. In the present review, we focus on the human and mouse CLRs for which endogenous ligands have been described. Special attention is given to the signaling events initiated upon recognition of the self ligand and the regulation of glycosylation as a switch modulating CLR recognition, and therefore, immune homeostasis.

LOCAL LINE BINARY PATTERN FOR FEATURE EXTRACTION ON PALM VEIN RECOGNITION

Directory of Open Access Journals (Sweden)

Jayanti Yusmah Sari

2015-08-01

Full Text Available In recent years, palm vein recognition has been studied to overcome problems in conventional systems in biometrics technology (finger print, face, and iris. Those problems in biometrics includes convenience and performance. However, due to the clarity of the palm vein image, the veins could not be segmented properly. To overcome this problem, we propose a palm vein recognition system using Local Line Binary Pattern (LLBP method that can extract robust features from the palm vein images that has unclear veins. LLBP is an advanced method of Local Binary Pattern (LBP, a texture descriptor based on the gray level comparison of a neighborhood of pixels. There are four major steps in this paper, Region of Interest (ROI detection, image preprocessing, features extraction using LLBP method, and matching using Fuzzy k-NN classifier. The proposed method was applied on the CASIA Multi-Spectral Image Database. Experimental results showed that the proposed method using LLBP has a good performance with recognition accuracy of 97.3%. In the future, experiments will be conducted to observe which parameter that could affect processing time and recognition accuracy of LLBP is needed

Ligand recognition by RAR and RXR receptors: binding and selectivity.

Science.gov (United States)

Sussman, Fredy; de Lera, Angel R

2005-10-06

Fundamental biological functions, most notably embriogenesis, cell growth, cell differentiation, and cell apoptosis, are in part regulated by a complex genomic network that starts with the binding (and activation) of retinoids to their cognate receptors, members of the superfamily of nuclear receptors. We have studied ligand recognition of retinoic receptors (RXRalpha and RARgamma) using a molecular-mechanics-based docking method. The protocol used in this work is able to rank the affinity of pairs of ligands for a single retinoid receptor, the highest values corresponding to those that adapt better to the shape of the binding site and generate the optimal set of electrostatic and apolar interactions with the receptor. Moreover, our studies shed light onto some of the energetic contributions to retinoid receptor ligand selectivity. In this regard we show that there is a difference in polarity between the binding site regions that anchor the carboxylate in RAR and RXR, which translates itself into large differences in the energy of interaction of both receptors with the same ligand. We observe that the latter energy change is canceled off by the solvation energy penalty upon binding. This energy compensation is borne out as well by experiments that address the effect of site-directed mutagenesis on ligand binding to RARgamma. The hypothesis that the difference in binding site polarity might be exploited to build RXR-selective ligands is tested with some compounds having a thiazolidinedione anchoring group.

Dopamine D1 receptor activation leads to object recognition memory in a coral reef fish.

Science.gov (United States)

Hamilton, Trevor J; Tresguerres, Martin; Kline, David I

2017-07-01

Object recognition memory is the ability to identify previously seen objects and is an adaptive mechanism that increases survival for many species throughout the animal kingdom. Previously believed to be possessed by only the highest order mammals, it is now becoming clear that fish are also capable of this type of memory formation. Similar to the mammalian hippocampus, the dorsolateral pallium regulates distinct memory processes and is modulated by neurotransmitters such as dopamine. Caribbean bicolour damselfish ( Stegastes partitus ) live in complex environments dominated by coral reef structures and thus likely possess many types of complex memory abilities including object recognition. This study used a novel object recognition test in which fish were first presented two identical objects, then after a retention interval of 10 min with no objects, the fish were presented with a novel object and one of the objects they had previously encountered in the first trial. We demonstrate that the dopamine D 1 -receptor agonist (SKF 38393) induces the formation of object recognition memories in these fish. Thus, our results suggest that dopamine-receptor mediated enhancement of spatial memory formation in fish represents an evolutionarily conserved mechanism in vertebrates. © 2017 The Author(s).

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

Science.gov (United States)

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

Science.gov (United States)

Chen, Bo; Zang, Chuanzhi

2009-03-01

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) Structural Health Monitoring Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

Science.gov (United States)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

Introduction of pattern recognition by MATLAB practice 2

International Nuclear Information System (INIS)

1999-06-01

The contents of this book starts introduction and examples of pattern recognition. This book describes a vector and matrix, basic statistics and a probability distribution, statistical decision theory and probability density function, liner shunt, vector quantizing and clustering GMM, PCA and KL conversion, LDA, ID 3, a nerve cell modeling, HMM, SVM and Ada boost. It has direction of MATLAB in the appendix.

Simultaneous pattern recognition and track fitting by the Kalman filtering method

International Nuclear Information System (INIS)

Billoir, P.

1990-01-01

A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. The fitted parameters and weight matrix of the candidate track are updated when adding a point, and give an increasing precision on prediction of the next point. Thus, pattern recognition and track fitting can be accomplished simultaneously. The method has been implemented and tested for track reconstruction for the vertex detector of the ZEUS experiment at DESY. Detailed procedures of the method and its performance are presented. Its flexibility is described as well. (orig.)

Hyaluronan functionalizing QDs as turn-on fluorescent probe for targeted recognition CD44 receptor

Science.gov (United States)

Zhou, Shang; Huo, Danqun; Hou, Changjun; Yang, Mei; Fa, Huanbao

2017-09-01

The recognition of tumor markers in living cancer cells has attracted increasing interest. In the present study, the turn-on fluorescence probe was designed based on the fluorescence of thiolated chitosan-coated CdTe QDs (CdTe/TCS QDs) quenched by hyaluronan, which could provide the low background signal for sensitive cellular imaging. This system is expected to offer specific recognition of CD44 receptor over other substances owing to the specific affinity of hyaluronan and CD44 receptor ( 8-9 kcal/mol). The probe is stable in aqueous and has little toxicity to living cells; thus, it can be utilized for targeted cancer cell imaging. The living lung cancer cell imaging experiments further demonstrate its value in recognizing cell-surface CD44 receptor with turn-on mode. In addition, the probe can be used to recognize and differentiate the subtypes of lung cancer cells based on the difference of CD44 expression on the surface of lung cancer cells. And, the western blot test further confirmed that the expression level of the CD44 receptor in lung cancer cells is different. Therefore, this probe may be potentially applied in recognizing lung cancer cells with higher contrast and sensitivity and provide new tools for cancer prognosis and therapy. [Figure not available: see fulltext.

Introduction of the human AVPR1A gene substantially alters brain receptor expression patterns and enhances aspects of social behavior in transgenic mice

Directory of Open Access Journals (Sweden)

Rhonda Charles

2014-08-01

Full Text Available Central arginine vasopressin receptor 1A (AVPR1A modulates a wide range of behaviors, including stress management and territorial aggression, as well as social bonding and recognition. Inter- and intra-species variations in the expression pattern of AVPR1A in the brain and downstream differential behavioral phenotypes have been attributed to differences in the non-coding regions of the AVPR1A gene, including polymorphic elements within upstream regulatory areas. Gene association studies have suggested a link between AVPR1A polymorphisms and autism, and AVPR1A has emerged as a potential pharmacological target for treatment of social cognitive impairments and mood and anxiety disorders. To further investigate the genetic mechanism giving rise to species differences in AVPR1A expression patterns and associated social behaviors, and to create a preclinical mouse model useful for screening drugs targeting AVPR1A, we engineered and extensively characterized bacterial artificial chromosome (BAC transgenic mice harboring the entire human AVPR1A locus with the surrounding regulatory elements. Compared with wild-type animals, the humanized mice displayed a more widely distributed ligand-AVPR1A binding pattern, which overlapped with that of primates. Furthermore, humanized AVPR1A mice displayed increased reciprocal social interactions compared with wild-type animals, but no differences in social approach and preference for social novelty were observed. Aspects of learning and memory, specifically novel object recognition and spatial relocation recognition, were unaffected. The biological alterations in humanized AVPR1A mice resulted in the rescue of the prepulse inhibition impairments that were observed in knockout mice, indicating conserved functionality. Although further behavioral paradigms and additional cohorts need to be examined in humanized AVPR1A mice, the results demonstrate that species-specific variations in the genomic content of regulatory

Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

Directory of Open Access Journals (Sweden)

Tomasz Ciszewski

2006-01-01

Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

Ficolins and FIBCD1: Soluble and membrane bound pattern recognition molecules with acetyl group selectivity

DEFF Research Database (Denmark)

Thomsen, Theresa; Schlosser, Anders; Holmskov, Uffe

2011-01-01

as pattern recognition molecules. Ficolins are soluble oligomeric proteins composed of trimeric collagen-like regions linked to fibrinogen-related domains (FReDs) that have the ability to sense molecular patterns on both pathogens and apoptotic cell surfaces and activate the complement system. The ficolins......D-containing molecules, and discusses structural resemblance but also diversity in recognition of acetylated ligands....

Adaptive pattern recognition in real-time video-based soccer analysis

DEFF Research Database (Denmark)

Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc

2017-01-01

are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online......Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results......, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data...

The time course of individual face recognition: A pattern analysis of ERP signals.

Science.gov (United States)

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Neurocomputing methods for pattern recognition in nuclear physics

Energy Technology Data Exchange (ETDEWEB)

Gyulassy, M.; Dong, D.; Harlander, M. [Lawrence Berkeley Lab., CA (United States)

1991-12-31

We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

Pattern recognition as a method of data analysis

Energy Technology Data Exchange (ETDEWEB)

Caputo, M.

1978-11-15

The method of pattern recognition has been used in biological and social sciences and has been recently introduced for the solution of geological and geophysical problems such as oil and ore prospecting and seismological prediction. The method is briefly illustrated by an application to earthquake prediction in Italy in which topographic and geologic maps are used in conjunction with earthquake catalogs. 3 figures, 1 table.

Common polymorphism in the oxytocin receptor gene (OXTR) is associated with human social recognition skills.

Science.gov (United States)

Skuse, David H; Lori, Adriana; Cubells, Joseph F; Lee, Irene; Conneely, Karen N; Puura, Kaija; Lehtimäki, Terho; Binder, Elisabeth B; Young, Larry J

2014-02-04

The neuropeptides oxytocin and vasopressin are evolutionarily conserved regulators of social perception and behavior. Evidence is building that they are critically involved in the development of social recognition skills within rodent species, primates, and humans. We investigated whether common polymorphisms in the genes encoding the oxytocin and vasopressin 1a receptors influence social memory for faces. Our sample comprised 198 families, from the United Kingdom and Finland, in whom a single child had been diagnosed with high-functioning autism. Previous research has shown that impaired social perception, characteristic of autism, extends to the first-degree relatives of autistic individuals, implying heritable risk. Assessments of face recognition memory, discrimination of facial emotions, and direction of gaze detection were standardized for age (7-60 y) and sex. A common SNP in the oxytocin receptor (rs237887) was strongly associated with recognition memory in combined probands, parents, and siblings after correction for multiple comparisons. Homozygotes for the ancestral A allele had impairments in the range -0.6 to -1.15 SD scores, irrespective of their diagnostic status. Our findings imply that a critical role for the oxytocin system in social recognition has been conserved across perceptual boundaries through evolution, from olfaction in rodents to visual memory in humans.

Definition of new 3D invariants. Applications to pattern recognition problems with neural networks

International Nuclear Information System (INIS)

Proriol, J.

1996-01-01

We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the defined 3D invariants which can be used with neural networks to solve pattern recognition problems. We describe some methods to use the 2 D approximants. This work is an extension of previous 3D invariants used to solve some high energy physics problems. (author)

Digital and optical shape representation and pattern recognition; Proceedings of the Meeting, Orlando, FL, Apr. 4-6, 1988

Science.gov (United States)

Juday, Richard D. (Editor)

1988-01-01

The present conference discusses topics in pattern-recognition correlator architectures, digital stereo systems, geometric image transformations and their applications, topics in pattern recognition, filter algorithms, object detection and classification, shape representation techniques, and model-based object recognition methods. Attention is given to edge-enhancement preprocessing using liquid crystal TVs, massively-parallel optical data base management, three-dimensional sensing with polar exponential sensor arrays, the optical processing of imaging spectrometer data, hybrid associative memories and metric data models, the representation of shape primitives in neural networks, and the Monte Carlo estimation of moment invariants for pattern recognition.

A study on the extraction of feature variables for the pattern recognition for welding flaws

International Nuclear Information System (INIS)

Kim, J. Y.; Kim, C. H.; Kim, B. H.

1996-01-01

In this study, the researches classifying the artificial and natural flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing, feature extraction, feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear discriminant function classifier, the empirical Bayesian classifier. Also, the pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack, lack of penetration, lack of fusion, porosity, and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately teamed the neural network classifier is better than stastical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

Negative regulation of Toll-like receptor signalling 

Directory of Open Access Journals (Sweden)

Halina Antosz

2013-04-01

Full Text Available The mechanism of innate immunity is based on the pattern recognition receptors (PRR that recognize molecular patterns associated with pathogens (PAMPs. Among PRR receptors Toll-like receptors (TLR are distinguished. As a result of contact with pathogens, TLRs activate specific intracellular signaling pathways. It happens through proteins such as adaptor molecules, e.g. MyD88, TIRAP, TRIF, TRAM, and IPS-1, which participate in the cascade activation of kinases (IKK, MAP, RIP-1, TBK-1 as well as transcription factors (NF-κB, AP-1 and regulatory factor (IRF3. The result of this activation is the production of active proinflammatory cytokines, chemokines, interferons and enzymes. The PRR pathways are controlled by extra – and intracellular molecules to prevent overexpression of PRR. They include soluble receptors (sTLR, transmembrane proteins (ST2, SIGIRR, RP105, TRAIL-R and intracellular inhibitors (SOCS-1, SOCS-3, sMyD88, TOLLIP, IRAK-M, SARM, A20, β-arrestin, CYLD, SHP. These molecules maintain the balance between activation and inhibition and ensure balancing of the beneficial and adverse effects of antigen recognition.

Double-Barrier Memristive Devices for Unsupervised Learning and Pattern Recognition.

Science.gov (United States)

Hansen, Mirko; Zahari, Finn; Ziegler, Martin; Kohlstedt, Hermann

2017-01-01

The use of interface-based resistive switching devices for neuromorphic computing is investigated. In a combined experimental and numerical study, the important device parameters and their impact on a neuromorphic pattern recognition system are studied. The memristive cells consist of a layer sequence Al/Al 2 O 3 /Nb x O y /Au and are fabricated on a 4-inch wafer. The key functional ingredients of the devices are a 1.3 nm thick Al 2 O 3 tunnel barrier and a 2.5 mm thick Nb x O y memristive layer. Voltage pulse measurements are used to study the electrical conditions for the emulation of synaptic functionality of single cells for later use in a recognition system. The results are evaluated and modeled in the framework of the plasticity model of Ziegler et al. Based on this model, which is matched to experimental data from 84 individual devices, the network performance with regard to yield, reliability, and variability is investigated numerically. As the network model, a computing scheme for pattern recognition and unsupervised learning based on the work of Querlioz et al. (2011), Sheridan et al. (2014), Zahari et al. (2015) is employed. This is a two-layer feedforward network with a crossbar array of memristive devices, leaky integrate-and-fire output neurons including a winner-takes-all strategy, and a stochastic coding scheme for the input pattern. As input pattern, the full data set of digits from the MNIST database is used. The numerical investigation indicates that the experimentally obtained yield, reliability, and variability of the memristive cells are suitable for such a network. Furthermore, evidence is presented that their strong I - V non-linearity might avoid the need for selector devices in crossbar array structures.

Visual Scanning Patterns and Executive Function in Relation to Facial Emotion Recognition in Aging

Science.gov (United States)

Circelli, Karishma S.; Clark, Uraina S.; Cronin-Golomb, Alice

2012-01-01

Objective The ability to perceive facial emotion varies with age. Relative to younger adults (YA), older adults (OA) are less accurate at identifying fear, anger, and sadness, and more accurate at identifying disgust. Because different emotions are conveyed by different parts of the face, changes in visual scanning patterns may account for age-related variability. We investigated the relation between scanning patterns and recognition of facial emotions. Additionally, as frontal-lobe changes with age may affect scanning patterns and emotion recognition, we examined correlations between scanning parameters and performance on executive function tests. Methods We recorded eye movements from 16 OA (mean age 68.9) and 16 YA (mean age 19.2) while they categorized facial expressions and non-face control images (landscapes), and administered standard tests of executive function. Results OA were less accurate than YA at identifying fear (precognition of sad expressions and with scanning patterns for fearful, sad, and surprised expressions. Conclusion We report significant age-related differences in visual scanning that are specific to faces. The observed relation between scanning patterns and executive function supports the hypothesis that frontal-lobe changes with age may underlie some changes in emotion recognition. PMID:22616800

A Temporally Distinct Role for Group I and Group II Metabotropic Glutamate Receptors in Object Recognition Memory

Science.gov (United States)

Brown, Malcolm Watson; Warburton, Elizabeth Clea; Barker, Gareth Robert Isaac; Bashir, Zafar Iqbal

2006-01-01

Recognition memory, involving the ability to discriminate between a novel and familiar object, depends on the integrity of the perirhinal cortex (PRH). Glutamate, the main excitatory neurotransmitter in the cortex, is essential for many types of memory processes. Of the subtypes of glutamate receptor, metabotropic receptors (mGluRs) have received…

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1976-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system

Multivariate statistical pattern recognition system for reactor noise analysis

International Nuclear Information System (INIS)

Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

1975-01-01

A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

Human Walking Pattern Recognition Based on KPCA and SVM with Ground Reflex Pressure Signal

Directory of Open Access Journals (Sweden)

Zhaoqin Peng

2013-01-01

Full Text Available Algorithms based on the ground reflex pressure (GRF signal obtained from a pair of sensing shoes for human walking pattern recognition were investigated. The dimensionality reduction algorithms based on principal component analysis (PCA and kernel principal component analysis (KPCA for walking pattern data compression were studied in order to obtain higher recognition speed. Classifiers based on support vector machine (SVM, SVM-PCA, and SVM-KPCA were designed, and the classification performances of these three kinds of algorithms were compared using data collected from a person who was wearing the sensing shoes. Experimental results showed that the algorithm fusing SVM and KPCA had better recognition performance than the other two methods. Experimental outcomes also confirmed that the sensing shoes developed in this paper can be employed for automatically recognizing human walking pattern in unlimited environments which demonstrated the potential application in the control of exoskeleton robots.

Pattern recognition approach to quantify the atomic structure of graphene

DEFF Research Database (Denmark)

Kling, Jens; Vestergaard, Jacob Schack; Dahl, Anders Bjorholm

2014-01-01

We report a pattern recognition approach to detect the atomic structure in high-resolution transmission electron microscopy images of graphene. The approach provides quantitative information such as carbon-carbon bond lengths and bond length variations on a global and local scale alike. © 2014...

New pattern recognition system in the e-nose for Chinese spirit identification

International Nuclear Information System (INIS)

Zeng Hui; Li Qiang; Gu Yu

2016-01-01

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance (QCM) principle, and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an 8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value (A), root-mean-square value (RMS), shape factor value (S f ), crest factor value (C f ), impulse factor value (I f ), clearance factor value (CL f ), kurtosis factor value (K v ) are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis (PCA) method. Finally the back propagation (BP) neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively. (paper)

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

Directory of Open Access Journals (Sweden)

Shouyi Yin

2015-01-01

Full Text Available Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Transgenic expression of the rice Xa21 pattern-recognition receptor in banana (Musa sp.) confers resistance to Xanthomonas campestris pv. musacearum.

Science.gov (United States)

Tripathi, Jaindra N; Lorenzen, Jim; Bahar, Ofir; Ronald, Pamela; Tripathi, Leena

2014-08-01

Banana Xanthomonas wilt (BXW), caused by the bacterium Xanthomonas campestris pv. musacearum (Xcm), is the most devastating disease of banana in east and central Africa. The spread of BXW threatens the livelihood of millions of African farmers who depend on banana for food security and income. There are no commercial chemicals, biocontrol agents or resistant cultivars available to control BXW. Here, we take advantage of the robust resistance conferred by the rice pattern-recognition receptor (PRR), XA21, to the rice pathogen Xanthomonas oryzae pv. oryzae (Xoo). We identified a set of genes required for activation of Xa21-mediated immunity (rax) that were conserved in both Xoo and Xcm. Based on the conservation, we hypothesized that intergeneric transfer of Xa21 would confer resistance to Xcm. We evaluated 25 transgenic lines of the banana cultivar 'Gonja manjaya' (AAB) using a rapid bioassay and 12 transgenic lines in the glasshouse for resistance against Xcm. About 50% of the transgenic lines showed complete resistance to Xcm in both assays. In contrast, all of the nontransgenic control plants showed severe symptoms that progressed to complete wilting. These results indicate that the constitutive expression of the rice Xa21 gene in banana results in enhanced resistance against Xcm. Furthermore, this work demonstrates the feasibility of PRR gene transfer between monocotyledonous species and provides a valuable new tool for controlling the BXW pandemic of banana, a staple food for 100 million people in east Africa. © 2014 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.

Scavenger receptor-mediated recognition of maleyl bovine plasma albumin and the demaleylated protein in human monocyte macrophages

International Nuclear Information System (INIS)

Haberland, M.E.; Fogelman, A.M.

1985-01-01

Maleyl bovine plasma albumin competed on an equimolar basis with malondialdehyde low density lipoprotein (LDL) in suppressing the lysosomal hydrolysis of 125 I-labeled malondialdehyde LDL mediated by the scavenger receptor of human monocyte macrophages. Maleyl bovine plasma albumin, in which 94% of the amino groups were modified, exhibited an anodic mobility in agarose electrophoresis 1.7 times that of the native protein. Incubation of maleyl bovine plasma albumin at pH 3.5 regenerated the free amino groups and restored the protein to the same electrophoretic mobility as native albumin. Although ligands recognized by the scavenger receptor typically are anionic, the authors propose that addition of new negative charge achieved by maleylation, rather than directly forming the receptor binding site(s), induces conformational changes in albumin as a prerequisite to expression of the recognition domain(s). They conclude that the primary sequence of albumin, rather than addition of new negative charge, provides the recognition determinant(s) essential for interaction of maleyl bovine plasma albumin with the scavenger receptor

Implementation theory of distortion-invariant pattern recognition for optical and digital signal processing systems

Science.gov (United States)

Lhamon, Michael Earl

A pattern recognition system which uses complex correlation filter banks requires proportionally more computational effort than single-real valued filters. This introduces increased computation burden but also introduces a higher level of parallelism, that common computing platforms fail to identify. As a result, we consider algorithm mapping to both optical and digital processors. For digital implementation, we develop computationally efficient pattern recognition algorithms, referred to as, vector inner product operators that require less computational effort than traditional fast Fourier methods. These algorithms do not need correlation and they map readily onto parallel digital architectures, which imply new architectures for optical processors. These filters exploit circulant-symmetric matrix structures of the training set data representing a variety of distortions. By using the same mathematical basis as with the vector inner product operations, we are able to extend the capabilities of more traditional correlation filtering to what we refer to as "Super Images". These "Super Images" are used to morphologically transform a complicated input scene into a predetermined dot pattern. The orientation of the dot pattern is related to the rotational distortion of the object of interest. The optical implementation of "Super Images" yields feature reduction necessary for using other techniques, such as artificial neural networks. We propose a parallel digital signal processor architecture based on specific pattern recognition algorithms but general enough to be applicable to other similar problems. Such an architecture is classified as a data flow architecture. Instead of mapping an algorithm to an architecture, we propose mapping the DSP architecture to a class of pattern recognition algorithms. Today's optical processing systems have difficulties implementing full complex filter structures. Typically, optical systems (like the 4f correlators) are limited to phase

Potent neutralization of hepatitis A virus reveals a receptor mimic mechanism and the receptor recognition site.

Science.gov (United States)

Wang, Xiangxi; Zhu, Ling; Dang, Minghao; Hu, Zhongyu; Gao, Qiang; Yuan, Shuai; Sun, Yao; Zhang, Bo; Ren, Jingshan; Kotecha, Abhay; Walter, Thomas S; Wang, Junzhi; Fry, Elizabeth E; Stuart, David I; Rao, Zihe

2017-01-24

Hepatitis A virus (HAV) infects ∼1.4 million people annually and, although there is a vaccine, there are no licensed therapeutic drugs. HAV is unusually stable (making disinfection problematic) and little is known of how it enters cells and releases its RNA. Here we report a potent HAV-specific monoclonal antibody, R10, which neutralizes HAV infection by blocking attachment to the host cell. High-resolution cryo-EM structures of HAV full and empty particles and of the complex of HAV with R10 Fab reveal the atomic details of antibody binding and point to a receptor recognition site at the pentamer interface. These results, together with our observation that the R10 Fab destabilizes the capsid, suggest the use of a receptor mimic mechanism to neutralize virus infection, providing new opportunities for therapeutic intervention.

Pattern recognition in spectra

International Nuclear Information System (INIS)

Gebran, M; Paletou, F

2017-01-01

We present a new automated procedure that simultaneously derives the effective temperature T eff , surface gravity log g , metallicity [ Fe/H ], and equatorial projected rotational velocity v e sin i for stars. The procedure is inspired by the well-known PCA-based inversion of spectropolarimetric full-Stokes solar data, which was used both for Zeeman and Hanle effects. The efficiency and accuracy of this procedure have been proven for FGK, A, and late type dwarf stars of K and M spectral types. Learning databases are generated from the Elodie stellar spectra library using observed spectra for which fundamental parameters were already evaluated or with synthetic data. The synthetic spectra are calculated using ATLAS9 model atmospheres. This technique helped us to detect many peculiar stars such as Am, Ap, HgMn, SiEuCr and binaries. This fast and efficient technique could be used every time a pattern recognition is needed. One important application is the understanding of the physical properties of planetary surfaces by comparing aboard instrument data to synthetic ones. (paper)

Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks

DEFF Research Database (Denmark)

Garrido, Jesús A.; Luque, Niceto R.; Tolu, Silvia

2016-01-01

The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly...... and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity...... effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input...

Pattern-recognition software detecting the onset of failures in complex systems

International Nuclear Information System (INIS)

Mott, J.; King, R.

1987-01-01

A very general mathematical framework for embodying learned data from a complex system and combining it with a current observation to estimate the true current state of the system has been implemented using nearly universal pattern-recognition algorithms and applied to surveillance of the EBR-II power plant. In this application the methodology can provide signal validation and replacement of faulty signals on a near-real-time basis for hundreds of plant parameters. The mathematical framework, the pattern-recognition algorithms, examples of the learning and estimating process, and plant operating decisions made using this methodology are discussed. The entire methodology has been reduced to a set of FORTRAN subroutines which are small, fast, robust and executable on a personal computer with a serial link to the system's data acquisition computer, or on the data acquisition computer itself

Extended pattern recognition scheme for self-learning kinetic Monte Carlo simulations

International Nuclear Information System (INIS)

Shah, Syed Islamuddin; Nandipati, Giridhar; Kara, Abdelkader; Rahman, Talat S

2012-01-01

We report the development of a pattern recognition scheme that takes into account both fcc and hcp adsorption sites in performing self-learning kinetic Monte Carlo (SLKMC-II) simulations on the fcc(111) surface. In this scheme, the local environment of every under-coordinated atom in an island is uniquely identified by grouping fcc sites, hcp sites and top-layer substrate atoms around it into hexagonal rings. As the simulation progresses, all possible processes, including those such as shearing, reptation and concerted gliding, which may involve fcc-fcc, hcp-hcp and fcc-hcp moves are automatically found, and their energetics calculated on the fly. In this article we present the results of applying this new pattern recognition scheme to the self-diffusion of 9-atom islands (M 9 ) on M(111), where M = Cu, Ag or Ni.

Foundations for a syntatic pattern recognition system for genomic DNA sequences

Energy Technology Data Exchange (ETDEWEB)

Searles, D.B.

1993-03-01

The goal of the proposed work is the creation of a software system that will perform sophisticated pattern recognition and related functions at a level of abstraction and with expressive power beyond current general-purpose pattern-matching systems for biological sequences; and with a more uniform language, environment, and graphical user interface, and with greater flexibility, extensibility, embeddability, and ability to incorporate other algorithms, than current special-purpose analytic software.

Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition

Science.gov (United States)

Schwanke, Joerg; Megnet, Roland; Jensch, Peter F.

1993-03-01

The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

Modulation of pathogen recognition by autophagy

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Ji Eun eOh

2012-03-01

Full Text Available Autophagy is an ancient biological process for maintaining cellular homeostasis by degradation of long-lived cytosolic proteins and organelles. Recent studies demonstrated that autophagy is availed by immune cells to regulate innate immunity. On the one hand, cells exert direct effector function by degrading intracellular pathogens; on the other hand, autophagy modulates pathogen recognition and downstream signaling for innate immune responses. Pathogen recognition via pattern recognition receptors induces autophagy. The function of phagocytic cells is enhanced by recruitment of autophagy-related proteins. Moreover, autophagy acts as a delivery system for viral replication complexes to migrate to the endosomal compartments where virus sensing occurs. In another case, key molecules of the autophagic pathway have been found to negatively regulate immune signaling, thus preventing aberrant activation of cytokine production and consequent immune responses. In this review, we focus on the recent advances in the role of autophagy in pathogen recognition and modulation of innate immune responses.

Interactions of the humoral pattern recognition molecule PTX3 with the complement system

DEFF Research Database (Denmark)

Doni, Andrea; Garlanda, Cecilia; Bottazzi, Barbara

2012-01-01

The innate immune system comprises a cellular and a humoral arm. The long pentraxin PTX3 is a fluid phase pattern recognition molecule, which acts as an essential component of the humoral arm of innate immunity. PTX3 has antibody-like properties including interactions with complement components....... PTX3 interacts with C1q, ficolin-1 and ficolin-2 as well as mannose-binding lectin, recognition molecules in the classical and lectin complement pathways. The formation of these heterocomplexes results in cooperative pathogen recognition and complement activation. Interactions with C4b binding protein...

A novel Arabidopsis CHITIN ELICITOR RECEPTOR KINASE 1 (CERK1) mutant with enhanced pathogen-induced cell death and altered receptor processing.

Science.gov (United States)

Petutschnig, Elena K; Stolze, Marnie; Lipka, Ulrike; Kopischke, Michaela; Horlacher, Juliane; Valerius, Oliver; Rozhon, Wilfried; Gust, Andrea A; Kemmerling, Birgit; Poppenberger, Brigitte; Braus, Gerhard H; Nürnberger, Thorsten; Lipka, Volker

2014-12-01

Plants detect pathogens by sensing microbe-associated molecular patterns (MAMPs) through pattern recognition receptors. Pattern recognition receptor complexes also have roles in cell death control, but the underlying mechanisms are poorly understood. Here, we report isolation of cerk1-4, a novel mutant allele of the Arabidopsis chitin receptor CERK1 with enhanced defense responses. We identified cerk1-4 in a forward genetic screen with barley powdery mildew and consequently characterized it by pathogen assays, mutant crosses and analysis of defense pathways. CERK1 and CERK1-4 proteins were analyzed biochemically. The cerk1-4 mutation causes an amino acid exchange in the CERK1 ectodomain. Mutant plants maintain chitin signaling capacity but exhibit hyper-inducible salicylic acid concentrations and deregulated cell death upon pathogen challenge. In contrast to chitin signaling, the cerk1-4 phenotype does not require kinase activity and is conferred by the N-terminal part of the receptor. CERK1 undergoes ectodomain shedding, a well-known process in animal cell surface proteins. Wild-type plants contain the full-length CERK1 receptor protein as well as a soluble form of the CERK1 ectodomain, whereas cerk1-4 plants lack the N-terminal shedding product. Our work suggests that CERK1 may have a chitin-independent role in cell death control and is the first report of ectodomain shedding in plants. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

NMDA receptors are not required for pattern completion during associative memory recall.

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Bing Mei

2011-04-01

Full Text Available Pattern completion, the ability to retrieve complete memories initiated by subsets of external cues, has been a major focus of many computation models. A previously study reports that such pattern completion requires NMDA receptors in the hippocampus. However, such a claim was derived from a non-inducible gene knockout experiment in which the NMDA receptors were absent throughout all stages of memory processes as well as animal's adult life. This raises the critical question regarding whether the previously described results were truly resulting from the requirement of the NMDA receptors in retrieval. Here, we have examined the role of the NMDA receptors in pattern completion via inducible knockout of NMDA receptors limited to the memory retrieval stage. By using two independent mouse lines, we found that inducible knockout mice, lacking NMDA receptor in either forebrain or hippocampus CA1 region at the time of memory retrieval, exhibited normal recall of associative spatial reference memory regardless of whether retrievals took place under full-cue or partial-cue conditions. Moreover, systemic antagonism of NMDA receptor during retention tests also had no effect on full-cue or partial-cue recall of spatial water maze memories. Thus, both genetic and pharmacological experiments collectively demonstrate that pattern completion during spatial associative memory recall does not require the NMDA receptor in the hippocampus or forebrain.

Impact of Sliding Window Length in Indoor Human Motion Modes and Pose Pattern Recognition Based on Smartphone Sensors

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Gaojing Wang

2018-06-01

Full Text Available Human activity recognition (HAR is essential for understanding people’s habits and behaviors, providing an important data source for precise marketing and research in psychology and sociology. Different approaches have been proposed and applied to HAR. Data segmentation using a sliding window is a basic step during the HAR procedure, wherein the window length directly affects recognition performance. However, the window length is generally randomly selected without systematic study. In this study, we examined the impact of window length on smartphone sensor-based human motion and pose pattern recognition. With data collected from smartphone sensors, we tested a range of window lengths on five popular machine-learning methods: decision tree, support vector machine, K-nearest neighbor, Gaussian naïve Bayesian, and adaptive boosting. From the results, we provide recommendations for choosing the appropriate window length. Results corroborate that the influence of window length on the recognition of motion modes is significant but largely limited to pose pattern recognition. For motion mode recognition, a window length between 2.5–3.5 s can provide an optimal tradeoff between recognition performance and speed. Adaptive boosting outperformed the other methods. For pose pattern recognition, 0.5 s was enough to obtain a satisfactory result. In addition, all of the tested methods performed well.

PCI bus content-addressable-memory (CAM) implementation on FPGA for pattern recognition/image retrieval in a distributed environment

Science.gov (United States)

Megherbi, Dalila B.; Yan, Yin; Tanmay, Parikh; Khoury, Jed; Woods, C. L.

2004-11-01

Recently surveillance and Automatic Target Recognition (ATR) applications are increasing as the cost of computing power needed to process the massive amount of information continues to fall. This computing power has been made possible partly by the latest advances in FPGAs and SOPCs. In particular, to design and implement state-of-the-Art electro-optical imaging systems to provide advanced surveillance capabilities, there is a need to integrate several technologies (e.g. telescope, precise optics, cameras, image/compute vision algorithms, which can be geographically distributed or sharing distributed resources) into a programmable system and DSP systems. Additionally, pattern recognition techniques and fast information retrieval, are often important components of intelligent systems. The aim of this work is using embedded FPGA as a fast, configurable and synthesizable search engine in fast image pattern recognition/retrieval in a distributed hardware/software co-design environment. In particular, we propose and show a low cost Content Addressable Memory (CAM)-based distributed embedded FPGA hardware architecture solution with real time recognition capabilities and computing for pattern look-up, pattern recognition, and image retrieval. We show how the distributed CAM-based architecture offers a performance advantage of an order-of-magnitude over RAM-based architecture (Random Access Memory) search for implementing high speed pattern recognition for image retrieval. The methods of designing, implementing, and analyzing the proposed CAM based embedded architecture are described here. Other SOPC solutions/design issues are covered. Finally, experimental results, hardware verification, and performance evaluations using both the Xilinx Virtex-II and the Altera Apex20k are provided to show the potential and power of the proposed method for low cost reconfigurable fast image pattern recognition/retrieval at the hardware/software co-design level.

Scanning patterns of faces do not explain impaired emotion recognition in Huntington Disease: Evidence for a high level mechanism

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Marieke evan Asselen

2012-02-01

Full Text Available Previous studies in patients with amygdala lesions suggested that deficits in emotion recognition might be mediated by impaired scanning patterns of faces. Here we investigated whether scanning patterns also contribute to the selective impairment in recognition of disgust in Huntington disease (HD. To achieve this goal, we recorded eye movements during a two-alternative forced choice emotion recognition task. HD patients in presymptomatic (n=16 and symptomatic (n=9 disease stages were tested and their performance was compared to a control group (n=22. In our emotion recognition task, participants had to indicate whether a face reflected one of six basic emotions. In addition, and in order to define whether emotion recognition was altered when the participants were forced to look at a specific component of the face, we used a second task where only limited facial information was provided (eyes/mouth in partially masked faces. Behavioural results showed no differences in the ability to recognize emotions between presymptomatic gene carriers and controls. However, an emotion recognition deficit was found for all 6 basic emotion categories in early stage HD. Analysis of eye movement patterns showed that patient and controls used similar scanning strategies. Patterns of deficits were similar regardless of whether parts of the faces were masked or not, thereby confirming that selective attention to particular face parts is not underlying the deficits. These results suggest that the emotion recognition deficits in symptomatic HD patients cannot be explained by impaired scanning patterns of faces. Furthermore, no selective deficit for recognition of disgust was found in presymptomatic HD patients.

Application of ann-based decision making pattern recognition to fishing operations

Energy Technology Data Exchange (ETDEWEB)

Akhlaghinia, M.; Torabi, F.; Wilton, R.R. [University of Regina, Saskatchewan (Canada). Faculty of Engineering. Dept. of Petroleum Engineering], e-mail: Farshid.Torabi@uregina.ca

2010-10-15

Decision making is a crucial part of fishing operations. Proper decisions should be made to prevent wasted time and associated costs on unsuccessful operations. This paper presents a novel model to help drilling managers decide when to commence and when to quit a fishing operation. A decision making model based on Artificial Neural Network (ANN) has been developed that utilizes Pattern Recognition based on 181 fishing incidents from one of the most fish-prone fields of the southwest of Iran. All parameters chosen to train the ANN-Based Pattern Recognition Tool are assumed to play a role in the success of the fishing operation and are therefore used to decide whether a fishing operation should be performed or not. If the tool deems the operation suitable for consideration, a cost analysis of the fishing operation can then be performed to justify its overall cost. (author)

Serotonin 2a Receptor and serotonin 1a receptor interact within the medial prefrontal cortex during recognition memory in mice

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Juan Facundo Morici

2015-12-01

Full Text Available Episodic memory, can be defined as the memory for unique events. The serotonergic system one of the main neuromodulatory systems in the brain appears to play a role in it. The serotonin 2a receptor (5-HT2aR one of the principal post-synaptic receptors for 5-HT in the brain, is involved in neuropsychiatric and neurological disorders associated with memory deficits. Recognition memory can be defined as the ability to recognize if a particular event or item was previously encountered and is thus considered, under certain conditions, a form of episodic memory. As human data suggest that a constitutively decrease of 5-HT2A signaling might affect episodic memory performance we decided to compare the performance of mice with disrupted 5-HT2aR signaling (htr2a -/- with wild type (htr2a+/+ littermates in different recognition memory and working memory tasks that differed in the level of proactive interference. We found that ablation of 5-HT2aR signaling throughout development produces a deficit in tasks that cannot be solved by single item strategy suggesting that 5-HT2aR signaling is involved in interference resolution. We also found that in the absence of 5-HT2aR signaling serotonin has a deleterious effect on recognition memory retrieval through the activation of 5-HT1aR in the medial prefrontal cortex.

Pattern recognition techniques for horizontal and vertically upward multiphase flow measurement

Science.gov (United States)

Arubi, Tesi I. M.; Yeung, Hoi

2012-03-01

The oil and gas industry need for high performing and low cost multiphase meters is ever more justified given the rapid depletion of conventional oil reserves that has led oil companies to develop smaller and marginal fields and reservoirs in remote locations and deep offshore, thereby placing great demands for compact and more cost effective solutions of on-line continuous multiphase flow measurement for well testing, production monitoring, production optimisation, process control and automation. The pattern recognition approach for clamp-on multiphase measurement employed in this study provides one means for meeting this need. High speed caesium-137 radioisotope-based densitometers were installed vertically at the top of a 50.8mm and 101.6mm riser as well as horizontally at the riser base in the Cranfield University multiphase flow test facility. A comprehensive experimental campaign comprising flow conditions typical of operating conditions found in the Petroleum Industry was conducted. The application of a single gamma densitometer unit, in conjunction with pattern recognition techniques to determine both the phase volume fractions and velocities to yield the individual phase flow rates of horizontal and vertically upward multiphase flows was investigated. The pattern recognition systems were trained to map the temporal fluctuations in the multiphase mixture density with the individual phase flow rates using statistical features extracted from the gamma counts signals as their inputs. Initial results yielded individual phase flow rate predictions to within ±5% relative error for the two phase airwater flows and ±10% for three phase air-oil-water flows data.

Characteristics in Molecular Vibrational Frequency Patterns between Agonists and Antagonists of Histamine Receptors

Directory of Open Access Journals (Sweden)

S. June Oh

2012-06-01

Full Text Available To learn the differences between the structure-activity relationship and molecular vibration-activity relationship in the ligand-receptor interaction of the histamine receptor, 47 ligands of the histamine receptor were analyzed by structural similarity and molecular vibrational frequency patterns. The radial tree that was produced by clustering analysis of molecular vibrational frequency patterns shows its potential for the functional classification of histamine receptor ligands.

Linear Programming and Its Application to Pattern Recognition Problems

Science.gov (United States)

Omalley, M. J.

1973-01-01

Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.

Binary pattern flavored feature extractors for Facial Expression Recognition: An overview

DEFF Research Database (Denmark)

Kristensen, Rasmus Lyngby; Tan, Zheng-Hua; Ma, Zhanyu

2015-01-01

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression Recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER...

A pattern recognition approach to transistor array parameter variance

Science.gov (United States)

da F. Costa, Luciano; Silva, Filipi N.; Comin, Cesar H.

2018-06-01

The properties of semiconductor devices, including bipolar junction transistors (BJTs), are known to vary substantially in terms of their parameters. In this work, an experimental approach, including pattern recognition concepts and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), was used to experimentally investigate the variation among BJTs belonging to integrated circuits known as transistor arrays. It was shown that a good deal of the devices variance can be captured using only two PCA axes. It was also verified that, though substantially small variation of parameters is observed for BJT from the same array, larger variation arises between BJTs from distinct arrays, suggesting the consideration of device characteristics in more critical analog designs. As a consequence of its supervised nature, LDA was able to provide a substantial separation of the BJT into clusters, corresponding to each transistor array. In addition, the LDA mapping into two dimensions revealed a clear relationship between the considered measurements. Interestingly, a specific mapping suggested by the PCA, involving the total harmonic distortion variation expressed in terms of the average voltage gain, yielded an even better separation between the transistor array clusters. All in all, this work yielded interesting results from both semiconductor engineering and pattern recognition perspectives.

Knowledge fusion: An approach to time series model selection followed by pattern recognition

International Nuclear Information System (INIS)

Bleasdale, S.A.; Burr, T.L.; Scovel, J.C.; Strittmatter, R.B.

1996-03-01

This report describes work done during FY 95 that was sponsored by the Department of Energy, Office of Nonproliferation and National Security, Knowledge Fusion Project. The project team selected satellite sensor data to use as the one main example for the application of its analysis algorithms. The specific sensor-fusion problem has many generic features, which make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series that define a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. An accompanying report (Ref 1) describes the implementation and application of this 2-step process for separating events from unusual background and applies a suite of forecasting methods followed by a suite of pattern recognition methods. This report goes into more detail about one of the forecasting methods and one of the pattern recognition methods and is applied to the same kind of satellite-sensor data that is described in Ref. 1

Comparison of eye imaging pattern recognition using neural network

Science.gov (United States)

Bukhari, W. M.; Syed A., M.; Nasir, M. N. M.; Sulaima, M. F.; Yahaya, M. S.

2015-05-01

The beauty of eye recognition system that it is used in automatic identifying and verifies a human weather from digital images or video source. There are various behaviors of the eye such as the color of the iris, size of pupil and shape of the eye. This study represents the analysis, design and implementation of a system for recognition of eye imaging. All the eye images that had been captured from the webcam in RGB format must through several techniques before it can be input for the pattern and recognition processes. The result shows that the final value of weight and bias after complete training 6 eye images for one subject is memorized by the neural network system and be the reference value of the weight and bias for the testing part. The target classifies to 5 different types for 5 subjects. The eye images can recognize the subject based on the target that had been set earlier during the training process. When the values between new eye image and the eye image in the database are almost equal, it is considered the eye image is matched.

CNNs flag recognition preprocessing scheme based on gray scale stretching and local binary pattern

Science.gov (United States)

Gong, Qian; Qu, Zhiyi; Hao, Kun

2017-07-01

Flag is a rather special recognition target in image recognition because of its non-rigid features with the location, scale and rotation characteristics. The location change can be handled well by the depth learning algorithm Convolutional Neural Networks (CNNs), but the scale and rotation changes are quite a challenge for CNNs. Since it has good rotation and gray scale invariance, the local binary pattern (LBP) is combined with grayscale stretching and CNNs to make LBP and grayscale stretching as CNNs pretreatment, which can not only significantly improve the efficiency of flag recognition, but can also evaluate the recognition effect through ROC, accuracy, MSE and quality factor.

Compact holographic optical neural network system for real-time pattern recognition

Science.gov (United States)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

Complement activating soluble pattern recognition molecules with collagen-like regions, mannan-binding lectin, ficolins and associated proteins

DEFF Research Database (Denmark)

Thiel, Steffen

2007-01-01

Mannan-binding lectin (MBL), L-ficolin, M-ficolin and H-ficolin are all complement activating soluble pattern recognition molecules with recognition domains linked to collagen-like regions. All four may form complexes with four structurally related proteins, the three MBL-associated serine...... proteases (MASPs), MASP-1, MASP-2 and MASP-3, and a smaller MBL-associated protein (MAp19). The four recognition molecules recognize patterns of carbohydrate or acetyl-group containing ligands. After binding to the relevant targets all four are able to activate the complement system. We thus have a system...... where four different and/or overlapping patterns of microbial origin or patterns of altered-self may be recognized, but in all cases the signalling molecules, the MASPs, are shared. MASP-1 and MASP-3 are formed from one gene, MASP1/3, by alternative splicing generating two different mRNAs from a single...

Deletion of the GluA1 AMPA receptor subunit impairs recency-dependent object recognition memory

Science.gov (United States)

Sanderson, David J.; Hindley, Emma; Smeaton, Emily; Denny, Nick; Taylor, Amy; Barkus, Chris; Sprengel, Rolf; Seeburg, Peter H.; Bannerman, David M.

2011-01-01

Deletion of the GluA1 AMPA receptor subunit impairs short-term spatial recognition memory. It has been suggested that short-term recognition depends upon memory caused by the recent presentation of a stimulus that is independent of contextual–retrieval processes. The aim of the present set of experiments was to test whether the role of GluA1 extends to nonspatial recognition memory. Wild-type and GluA1 knockout mice were tested on the standard object recognition task and a context-independent recognition task that required recency-dependent memory. In a first set of experiments it was found that GluA1 deletion failed to impair performance on either of the object recognition or recency-dependent tasks. However, GluA1 knockout mice displayed increased levels of exploration of the objects in both the sample and test phases compared to controls. In contrast, when the time that GluA1 knockout mice spent exploring the objects was yoked to control mice during the sample phase, it was found that GluA1 deletion now impaired performance on both the object recognition and the recency-dependent tasks. GluA1 deletion failed to impair performance on a context-dependent recognition task regardless of whether object exposure in knockout mice was yoked to controls or not. These results demonstrate that GluA1 is necessary for nonspatial as well as spatial recognition memory and plays an important role in recency-dependent memory processes. PMID:21378100

Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition

NARCIS (Netherlands)

Leon Rincon, Carlos; Moreno, José Fernando; Cely, Jorge

2017-01-01

The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks’

Ultrasonic pattern recognition study of feedwater nozzle inner radius indication

International Nuclear Information System (INIS)

Yoneyama, H.; Takama, S.; Kishigami, M.; Sasahara, T.; Ando, H.

1983-01-01

A study was made to distinguish defects on feed-water nozzle inner radius from noise echo caused by stainless steel cladding by using ultrasonic pattern recognition method with frequency analysis technique. Experiment has been successfully performed on flat clad plates and nozzle mock-up containing fatigue cracks and the following results which shows the high capability of frequency analysis technique are obtained

An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

Directory of Open Access Journals (Sweden)

Xiao-Dong Zhang

2011-12-01

Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

EBR-II [Experimental Breeder Reactor-II] system surveillance using pattern recognition software

International Nuclear Information System (INIS)

Mott, J.E.; Radtke, W.H.; King, R.W.

1986-02-01

The problem of most accurately determining the Experimental Breeder Reactor-II (EBR-II) reactor outlet temperature from currently available plant signals is investigated. Historically, the reactor outlet pipe was originally instrumented with 8 temperature sensors but, during 22 years of operation, all these instruments have failed except for one remaining thermocouple, and its output had recently become suspect. Using pattern recognition methods to compare values of 129 plant signals for similarities over a 7 month period spanning reconfiguration of the core and recalibration of many plant signals, it was determined that the remaining reactor outlet pipe thermocouple is still useful as an indicator of true mixed mean reactor outlet temperature. Application of this methodology to investigate one specific signal has automatically validated the vast majority of the 129 signals used for pattern recognition and also highlighted a few inconsistent signals for further investigation

ALBEDO PATTERN RECOGNITION AND TIME-SERIES ANALYSES IN MALAYSIA

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S. A. Salleh

2012-07-01

Full Text Available Pattern recognition and time-series analyses will enable one to evaluate and generate predictions of specific phenomena. The albedo pattern and time-series analyses are very much useful especially in relation to climate condition monitoring. This study is conducted to seek for Malaysia albedo pattern changes. The pattern recognition and changes will be useful for variety of environmental and climate monitoring researches such as carbon budgeting and aerosol mapping. The 10 years (2000–2009 MODIS satellite images were used for the analyses and interpretation. These images were being processed using ERDAS Imagine remote sensing software, ArcGIS 9.3, the 6S code for atmospherical calibration and several MODIS tools (MRT, HDF2GIS, Albedo tools. There are several methods for time-series analyses were explored, this paper demonstrates trends and seasonal time-series analyses using converted HDF format MODIS MCD43A3 albedo land product. The results revealed significance changes of albedo percentages over the past 10 years and the pattern with regards to Malaysia's nebulosity index (NI and aerosol optical depth (AOD. There is noticeable trend can be identified with regards to its maximum and minimum value of the albedo. The rise and fall of the line graph show a similar trend with regards to its daily observation. The different can be identified in term of the value or percentage of rises and falls of albedo. Thus, it can be concludes that the temporal behavior of land surface albedo in Malaysia have a uniform behaviours and effects with regards to the local monsoons. However, although the average albedo shows linear trend with nebulosity index, the pattern changes of albedo with respects to the nebulosity index indicates that there are external factors that implicates the albedo values, as the sky conditions and its diffusion plotted does not have uniform trend over the years, especially when the trend of 5 years interval is examined, 2000 shows high

Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach.

Directory of Open Access Journals (Sweden)

Andre F Marquand

Full Text Available Progressive supranuclear palsy (PSP, multiple system atrophy (MSA and idiopathic Parkinson's disease (IPD can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs. An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i a subcortical motor network; (ii each of its component regions and (iii the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.

Oxytocin and the oxytocin receptor underlie intrastrain, but not interstrain, social recognition.

Science.gov (United States)

Macbeth, A H; Lee, H-J; Edds, J; Young, W S

2009-07-01

We studied three lines of oxytocin (Oxt) and oxytocin receptor (Oxtr) knockout (KO) male mice [Oxt(-/-), total Oxtr(-/-) and partial forebrain Oxtr (Oxtr(FB/FB))] with established deficits in social recognition to further refine our understanding of their deficits with regard to stimulus female's strain. We used a modified social discrimination paradigm in which subjects are singly housed only for the duration of the test. Additionally, stimulus females are singly housed throughout testing and are presented within corrals for rapid comparison of investigation by subject males. Wild-type (WT) males from all three lines discriminated between familiar and novel females of three different strains (C57BL/6, BALB/c and Swiss-Webster). No KO males discriminated between familiar and novel BALB/c or C57BL/6 females. Male Oxt(-/-) and Oxtr(-/-) mice, but not Oxtr(FB/FB) mice, discriminated between familiar and novel Swiss-Webster females. As this might indicate a global deficit in individual recognition for Oxtr(FB/FB) males, we examined their ability to discriminate between females from different strains and compared performance with Oxtr(-/-) males. WT and KO males from both lines were able to distinguish between familiar and novel females from different strains, indicating the social recognition deficit is not universal. Instead, we hypothesize that the Oxtr is involved in 'fine' intrastrain recognition, but is less important in 'broad' interstrain recognition. We also present the novel finding of decreased investigation across tests, which is likely an artifact of repeated testing and not because of stimulus female's strain or age of subject males.

WAY 267,464, a non-peptide oxytocin receptor agonist, impairs social recognition memory in rats through a vasopressin 1A receptor antagonist action.

Science.gov (United States)

Hicks, Callum; Ramos, Linnet; Reekie, Tristan A; Narlawar, Rajeshwar; Kassiou, Michael; McGregor, Iain S

2015-08-01

Recent in vitro studies suggest that the oxytocin receptor (OTR) agonist WAY 267,464 has vasopressin 1A receptor (V1AR) antagonist effects. This might limit its therapeutic potential due to the positive involvement of the V1AR in social behavior. The objective of this study was to assess functional V1AR antagonist-like effects of WAY 267,464 in vivo using a test of social recognition memory. Adult experimental rats were tested for their recognition of a juvenile conspecific rat that they had briefly met 30 or 120 min previously. The modulatory effects of vasopressin (AVP), the selective V1AR antagonist SR49059, and WAY 267,464 were examined together with those of the selective OTR antagonist Compound 25 (C25). Drugs were administered immediately after the first meeting. Control rats showed recognition of juveniles at a 30 min, but not a 120 min retention interval. AVP (0.005, but not 0.001 mg/kg intraperitoneal (i.p.)) improved memory such that recognition was evident after 120 min. This was prevented by pretreatment with SR49059 (1 mg/kg) and WAY 267,464 (10, 30, and 100 mg/kg). Given alone, SR49059 (1 mg/kg) and WAY 267,464 (30 and 100 mg/kg) impaired memory at a 30 min retention interval. The impairment with WAY 267,464 was not prevented by C25 (5 mg/kg), suggesting V1AR rather than OTR mediation of the effect. Given alone, C25 also impaired memory. These results highlight a tonic role for endogenous AVP (and oxytocin) in social recognition memory and indicate that WAY 267,464 functions in vivo as a V1AR antagonist to prevent the memory-enhancing effects of AVP.

Activation of toll-like receptors and dendritic cells by a broad range of bacterial molecules

NARCIS (Netherlands)

Boele, L.C.L.; Bajramovic, J.J.; Vries, A.M.M.B.C. de; Voskamp-Visser, I.A.I.; Kaman, W.E.; Kleij, D. van der

2009-01-01

Activation of pattern recognition receptors such as Toll-like receptors (TLRs) by pathogens leads to activation and maturation of dendritic cells (DC), which orchestrate the development of the adaptive immune response. To create an overview of the effects of a broad range of pathogenic bacteria,

Improved localization of cellular membrane receptors using combined fluorescence microscopy and simultaneous topography and recognition imaging

International Nuclear Information System (INIS)

Duman, M; Pfleger, M; Chtcheglova, L A; Neundlinger, I; Bozna, B L; Ebner, A; Schuetz, G J; Hinterdorfer, P; Zhu, R; Mayer, B; Rankl, C; Moertelmaier, M; Kada, G; Kienberger, F; Salio, M; Shepherd, D; Polzella, P; Cerundolo, V; Dieudonne, M

2010-01-01

The combination of fluorescence microscopy and atomic force microscopy has a great potential in single-molecule-detection applications, overcoming many of the limitations coming from each individual technique. Here we present a new platform of combined fluorescence and simultaneous topography and recognition imaging (TREC) for improved localization of cellular receptors. Green fluorescent protein (GFP) labeled human sodium-glucose cotransporter (hSGLT1) expressed Chinese Hamster Ovary (CHO) cells and endothelial cells (MyEnd) from mouse myocardium stained with phalloidin-rhodamine were used as cell systems to study AFM topography and fluorescence microscopy on the same surface area. Topographical AFM images revealed membrane features such as lamellipodia, cytoskeleton fibers, F-actin filaments and small globular structures with heights ranging from 20 to 30 nm. Combined fluorescence and TREC imaging was applied to detect density, distribution and localization of YFP-labeled CD1d molecules on α-galactosylceramide (αGalCer)-loaded THP1 cells. While the expression level, distribution and localization of CD1d molecules on THP1 cells were detected with fluorescence microscopy, the nanoscale distribution of binding sites was investigated with molecular recognition imaging by using a chemically modified AFM tip. Using TREC on the inverted light microscope, the recognition sites of cell receptors were detected in recognition images with domain sizes ranging from ∼ 25 to ∼ 160 nm, with the smaller domains corresponding to a single CD1d molecule.

Improved localization of cellular membrane receptors using combined fluorescence microscopy and simultaneous topography and recognition imaging

Energy Technology Data Exchange (ETDEWEB)

Duman, M; Pfleger, M; Chtcheglova, L A; Neundlinger, I; Bozna, B L; Ebner, A; Schuetz, G J; Hinterdorfer, P [Institute for Biophysics, University of Linz, Altenbergerstrasse 69, A-4040 Linz (Austria); Zhu, R; Mayer, B [Christian Doppler Laboratory for Nanoscopic Methods in Biophysics, Institute for Biophysics, University of Linz, Altenbergerstrasse 69, A-4040 Linz (Austria); Rankl, C; Moertelmaier, M; Kada, G; Kienberger, F [Agilent Technologies Austria GmbH, Aubrunnerweg 11, A-4040 Linz (Austria); Salio, M; Shepherd, D; Polzella, P; Cerundolo, V [Cancer Research UK Tumor Immunology Group, Weatherall Institute of Molecular Medicine, Nuffield Department of Medicine, University of Oxford, Oxford OX3 9DS (United Kingdom); Dieudonne, M, E-mail: ferry_kienberger@agilent.com [Agilent Technologies Belgium, Wingepark 51, Rotselaar, AN B-3110 (Belgium)

2010-03-19

The combination of fluorescence microscopy and atomic force microscopy has a great potential in single-molecule-detection applications, overcoming many of the limitations coming from each individual technique. Here we present a new platform of combined fluorescence and simultaneous topography and recognition imaging (TREC) for improved localization of cellular receptors. Green fluorescent protein (GFP) labeled human sodium-glucose cotransporter (hSGLT1) expressed Chinese Hamster Ovary (CHO) cells and endothelial cells (MyEnd) from mouse myocardium stained with phalloidin-rhodamine were used as cell systems to study AFM topography and fluorescence microscopy on the same surface area. Topographical AFM images revealed membrane features such as lamellipodia, cytoskeleton fibers, F-actin filaments and small globular structures with heights ranging from 20 to 30 nm. Combined fluorescence and TREC imaging was applied to detect density, distribution and localization of YFP-labeled CD1d molecules on {alpha}-galactosylceramide ({alpha}GalCer)-loaded THP1 cells. While the expression level, distribution and localization of CD1d molecules on THP1 cells were detected with fluorescence microscopy, the nanoscale distribution of binding sites was investigated with molecular recognition imaging by using a chemically modified AFM tip. Using TREC on the inverted light microscope, the recognition sites of cell receptors were detected in recognition images with domain sizes ranging from {approx} 25 to {approx} 160 nm, with the smaller domains corresponding to a single CD1d molecule.

Pattern recognition receptor responses in children with chronic hepatitis B virus infection

DEFF Research Database (Denmark)

Heiberg, Ida Louise; Winther, Thilde Nordmann; Paludan, Søren Riis

2012-01-01

Several studies have demonstrated that hepatitis B virus (HBV) affects the expression and function of Toll like receptors (TLRs), but data on TLR function in HBV infection are mainly from adult patients. The natural history of chronic hepatitis B (CHB) infection is distinctly different in childre...

Laminar pattern of cholinergic and adrenergic receptors in rat visual cortex using quantitative receptor autoradiography

International Nuclear Information System (INIS)

Schliebs, R.; Walch, C.

1989-01-01

The laminar distribution of muscarinic acetylcholine receptors, including the M1-receptor subtype, of beta-adrenergic receptors, and noradrenaline uptake sites, was studied in the adult rat visual, frontal, somatosensory and motor cortex, using quantitative receptor autoradiography. In the visual cortex, the highest density of muscarinic acetylcholine receptors was found in layer I. From layer II/III to layer V binding decreases continueously reaching a constant binding level in layers V and VI. This laminar pattern of muscarinic receptor density differs somewhat from that observed in the non-visual cortical regions examined: layer II/III contained the highest receptor density followed by layer I and IV: lowest density was found in layer V and VI. The binding profile of the muscarinic cholinergic M1-subtype through the visual cortex shows a peak in cortical layer II and in the upper part of layer VI, whereas in the non-visual cortical regions cited the binding level was high in layer II/III, moderate in layer I and IV, and low in layer VI. Layers I to IV of the visual cortex contained the highest beta-adrenergic receptor densities, whereas only low binding levels were observed in the deeper layers. A similar laminar distribution was found also in the frontal, somatosensory and motor cortex. The density of noradrenaline uptake sites was high in all layers of the cortical regions studied, but with noradrenaline uptake sites somewhat more concentrated in the superficial layers than in deeper ones. The distinct laminar pattern of cholinergic and noradrenergic receptor sites indicates a different role for acetylcholine and noradrenaline in the functional anatomy of the cerebral cortex, and in particular, the visual cortex. (author)

Laminar pattern of cholinergic and adrenergic receptors in rat visual cortex using quantitative receptor autoradiography

Energy Technology Data Exchange (ETDEWEB)

Schliebs, R; Walch, C [Leipzig Univ. (German Democratic Republic). Bereich Medizin; Stewart, M G [Open Univ., Milton Keynes (UK)

1989-01-01

The laminar distribution of muscarinic acetylcholine receptors, including the M1-receptor subtype, of beta-adrenergic receptors, and noradrenaline uptake sites, was studied in the adult rat visual, frontal, somatosensory and motor cortex, using quantitative receptor autoradiography. In the visual cortex, the highest density of muscarinic acetylcholine receptors was found in layer I. From layer II/III to layer V binding decreases continueously reaching a constant binding level in layers V and VI. This laminar pattern of muscarinic receptor density differs somewhat from that observed in the non-visual cortical regions examined: layer II/III contained the highest receptor density followed by layer I and IV: lowest density was found in layer V and VI. The binding profile of the muscarinic cholinergic M1-subtype through the visual cortex shows a peak in cortical layer II and in the upper part of layer VI, whereas in the non-visual cortical regions cited the binding level was high in layer II/III, moderate in layer I and IV, and low in layer VI. Layers I to IV of the visual cortex contained the highest beta-adrenergic receptor densities, whereas only low binding levels were observed in the deeper layers. A similar laminar distribution was found also in the frontal, somatosensory and motor cortex. The density of noradrenaline uptake sites was high in all layers of the cortical regions studied, but with noradrenaline uptake sites somewhat more concentrated in the superficial layers than in deeper ones. The distinct laminar pattern of cholinergic and noradrenergic receptor sites indicates a different role for acetylcholine and noradrenaline in the functional anatomy of the cerebral cortex, and in particular, the visual cortex. (author).

Three dimensional pattern recognition using feature-based indexing and rule-based search

Science.gov (United States)

Lee, Jae-Kyu

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells. This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene. Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage. Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

Science.gov (United States)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

A New Functional Classification of Glucuronoyl Esterases by Peptide Pattern Recognition

DEFF Research Database (Denmark)

Wittrup Agger, Jane; Busk, Peter Kamp; Pilgaard, Bo

2017-01-01

of characterized enzymes exist and the exact activity is still uncertain. Here peptide pattern recognition is used as a bioinformatic tool to identify and group new CE15 proteins that are likely to have glucuronoyl esterase activity. 1024 CE15-like sequences were drawn from GenBank and grouped into 24 groups...

Pattern recognition for cache management in distributed medical imaging environments.

Science.gov (United States)

Viana-Ferreira, Carlos; Ribeiro, Luís; Matos, Sérgio; Costa, Carlos

2016-02-01

Traditionally, medical imaging repositories have been supported by indoor infrastructures with huge operational costs. This paradigm is changing thanks to cloud outsourcing which not only brings technological advantages but also facilitates inter-institutional workflows. However, communication latency is one main problem in this kind of approaches, since we are dealing with tremendous volumes of data. To minimize the impact of this issue, cache and prefetching are commonly used. The effectiveness of these mechanisms is highly dependent on their capability of accurately selecting the objects that will be needed soon. This paper describes a pattern recognition system based on artificial neural networks with incremental learning to evaluate, from a set of usage pattern, which one fits the user behavior at a given time. The accuracy of the pattern recognition model in distinct training conditions was also evaluated. The solution was tested with a real-world dataset and a synthesized dataset, showing that incremental learning is advantageous. Even with very immature initial models, trained with just 1 week of data samples, the overall accuracy was very similar to the value obtained when using 75% of the long-term data for training the models. Preliminary results demonstrate an effective reduction in communication latency when using the proposed solution to feed a prefetching mechanism. The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.

Hippocampal NMDA receptors are involved in rats' spontaneous object recognition only under high memory load condition.

Science.gov (United States)

Sugita, Manami; Yamada, Kazuo; Iguchi, Natsumi; Ichitani, Yukio

2015-10-22

The possible involvement of hippocampal N-methyl-D-aspartate (NMDA) receptors in spontaneous object recognition was investigated in rats under different memory load conditions. We first estimated rats' object memory span using 3-5 objects in "Different Objects Task (DOT)" in order to confirm the highest memory load condition in object recognition memory. Rats were allowed to explore a field in which 3 (3-DOT), 4 (4-DOT), or 5 (5-DOT) different objects were presented. After a delay period, they were placed again in the same field in which one of the sample objects was replaced by another object, and their object exploration behavior was analyzed. Rats could differentiate the novel object from the familiar ones in 3-DOT and 4-DOT but not in 5-DOT, suggesting that rats' object memory span was about 4. Then, we examined the effects of hippocampal AP5 infusion on performance in both 2-DOT (2 different objects were used) and 4-DOT. The drug treatment before the sample phase impaired performance only in 4-DOT. These results suggest that hippocampal NMDA receptors play a critical role in spontaneous object recognition only when the memory load is high. Copyright © 2015 Elsevier B.V. All rights reserved.

Migration and chemokine receptor pattern of colitis-preventing DX5+NKT cells.

Science.gov (United States)

Hornung, Matthias; Werner, Jens M; Farkas, Stefan; Schlitt, Hans J; Geissler, Edward K

2011-11-01

DX5(+)NKT cells are a subpopulation of NKT cells expressing both T cell receptor and NK cell markers that show an immune-regulating function. Transferred DX5(+)NKT cells from immune competent Balb/c mice can prevent or reduce induced colitis in severe combined immunodeficient (SCID) mice. Here, we investigated the in vivo migration of DX5(+)NKT cells and their corresponding chemokine receptor patterns. DX5(+)NKT cells were isolated from spleens of Balb/c mice and transferred into Balb/c SCID mice. After 2 and 8 days, in vivo migration was examined using in vivo microscopy. In addition, the chemokine receptor pattern was analyzed with fluorescence-activated cell sorting (FACS) and the migration assay was performed. Our results show that labeled DX5(+)NKT cells were primarily detectable in mesenteric lymph nodes and spleen after transfer. After 8 days, DX5(+)NKT cells were observed in the colonic tissues, especially the appendix. FACS analysis of chemokine receptors in DX5(+)NKT cells revealed expression of CCR3, CCR6, CCR9, CXCR3, CXCR4, and CXCR6, but no CCR5, CXCR5, or the lymphoid homing receptor CCR7. Stimulation upregulated especially CCR7 expression, and chemokine receptor patterns were different between splenic and liver DX5(+)NKT cells. These data indicate that colitis-preventing DX5(+)NKT cells need to traffic through lymphoid organs to execute their immunological function at the site of inflammation. Furthermore, DX5(+)NKT cells express a specific chemokine receptor pattern with an upregulation of the lymphoid homing receptor CCR7 after activation.

Reptile Toll-like receptor 5 unveils adaptive evolution of bacterial flagellin recognition.

Science.gov (United States)

Voogdt, Carlos G P; Bouwman, Lieneke I; Kik, Marja J L; Wagenaar, Jaap A; van Putten, Jos P M

2016-01-07

Toll-like receptors (TLR) are ancient innate immune receptors crucial for immune homeostasis and protection against infection. TLRs are present in mammals, birds, amphibians and fish but have not been functionally characterized in reptiles despite the central position of this animal class in vertebrate evolution. Here we report the cloning, characterization, and function of TLR5 of the reptile Anolis carolinensis (Green Anole lizard). The receptor (acTLR5) displays the typical TLR protein architecture with 22 extracellular leucine rich repeats flanked by a N- and C-terminal leucine rich repeat domain, a membrane-spanning region, and an intracellular TIR domain. The receptor is phylogenetically most similar to TLR5 of birds and most distant to fish TLR5. Transcript analysis revealed acTLR5 expression in multiple lizard tissues. Stimulation of acTLR5 with TLR ligands demonstrated unique responsiveness towards bacterial flagellin in both reptile and human cells. Comparison of acTLR5 and human TLR5 using purified flagellins revealed differential sensitivity to Pseudomonas but not Salmonella flagellin, indicating development of species-specific flagellin recognition during the divergent evolution of mammals and reptiles. Our discovery of reptile TLR5 fills the evolutionary gap regarding TLR conservation across vertebrates and provides novel insights in functional evolution of host-microbe interactions.

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

Energy Technology Data Exchange (ETDEWEB)

Deptuch, Gregory [Fermilab; Hoff, James [Fermilab; Jindariani, Sergo [Fermilab; Liu, Tiehui [Fermilab; Olsen, Jamieson [Fermilab; Tran, Nhan [Fermilab; Joshi, Siddhartha [Northwestern U.; Li, Dawei [Northwestern U.; Ogrenci-Memik, Seda [Northwestern U.

2017-09-24

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The first step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.

Pseudomonas Evades Immune Recognition of Flagellin in Both Mammals and Plants

Science.gov (United States)

Bardoel, Bart W.; van der Ent, Sjoerd; Pel, Michiel J. C.; Tommassen, Jan; Pieterse, Corné M. J.; van Kessel, Kok P. M.; van Strijp, Jos A. G.

2011-01-01

The building blocks of bacterial flagella, flagellin monomers, are potent stimulators of host innate immune systems. Recognition of flagellin monomers occurs by flagellin-specific pattern-recognition receptors, such as Toll-like receptor 5 (TLR5) in mammals and flagellin-sensitive 2 (FLS2) in plants. Activation of these immune systems via flagellin leads eventually to elimination of the bacterium from the host. In order to prevent immune activation and thus favor survival in the host, bacteria secrete many proteins that hamper such recognition. In our search for Toll like receptor (TLR) antagonists, we screened bacterial supernatants and identified alkaline protease (AprA) of Pseudomonas aeruginosa as a TLR5 signaling inhibitor as evidenced by a marked reduction in IL-8 production and NF-κB activation. AprA effectively degrades the TLR5 ligand monomeric flagellin, while polymeric flagellin (involved in bacterial motility) and TLR5 itself resist degradation. The natural occurring alkaline protease inhibitor AprI of P. aeruginosa blocked flagellin degradation by AprA. P. aeruginosa aprA mutants induced an over 100-fold enhanced activation of TLR5 signaling, because they fail to degrade excess monomeric flagellin in their environment. Interestingly, AprA also prevents flagellin-mediated immune responses (such as growth inhibition and callose deposition) in Arabidopsis thaliana plants. This was due to decreased activation of the receptor FLS2 and clearly demonstrated by delayed stomatal closure with live bacteria in plants. Thus, by degrading the ligand for TLR5 and FLS2, P. aeruginosa escapes recognition by the innate immune systems of both mammals and plants. PMID:21901099

Pattern recognition algorithms for data mining scalability, knowledge discovery and soft granular computing

CERN Document Server

Pal, Sankar K

2004-01-01

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

Science.gov (United States)

Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

2016-01-01

Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

Finger crease pattern recognition using Legendre moments and principal component analysis

Science.gov (United States)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Pattern recognition applied to infrared images for early alerts in fog

Science.gov (United States)

Boucher, Vincent; Marchetti, Mario; Dumoulin, Jean; Cord, Aurélien

2014-09-01

Fog conditions are the cause of severe car accidents in western countries because of the poor induced visibility. Its forecast and intensity are still very difficult to predict by weather services. Infrared cameras allow to detect and to identify objects in fog while visibility is too low for eye detection. Over the past years, the implementation of cost effective infrared cameras on some vehicles has enabled such detection. On the other hand pattern recognition algorithms based on Canny filters and Hough transformation are a common tool applied to images. Based on these facts, a joint research program between IFSTTAR and Cerema has been developed to study the benefit of infrared images obtained in a fog tunnel during its natural dissipation. Pattern recognition algorithms have been applied, specifically on road signs which shape is usually associated to a specific meaning (circular for a speed limit, triangle for an alert, …). It has been shown that road signs were detected early enough in images, with respect to images in the visible spectrum, to trigger useful alerts for Advanced Driver Assistance Systems.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

Science.gov (United States)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Calix[4]arenes Containing a Ureido Functionality on the Lower Rim as Highly Efficient Receptors for Anion Recognition.

Czech Academy of Sciences Publication Activity Database

Klejch, T.; Slavíček, J.; Hudeček, O.; Eigner, V.; Gutierrez, Natalia Andrea; Cuřínová, Petra; Lhoták, P.

2016-01-01

Roč. 40, č. 9 (2016), s. 7935-7942 ISSN 1144-0546 Institutional support: RVO:67985858 Keywords : calix[4]arene * anion recognition * receptors Subject RIV: CC - Organic Chemistry Impact factor: 3.269, year: 2016

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

Science.gov (United States)

Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Loss of GluN2D subunit results in social recognition deficit, social stress, 5-HT2C receptor dysfunction, and anhedonia in mice.

Science.gov (United States)

Yamamoto, Hideko; Kamegaya, Etsuko; Hagino, Yoko; Takamatsu, Yukio; Sawada, Wakako; Matsuzawa, Maaya; Ide, Soichiro; Yamamoto, Toshifumi; Mishina, Masayoshi; Ikeda, Kazutaka

2017-01-01

The N-methyl-d-aspartate (NMDA) receptor channel is involved in various physiological functions, including learning and memory. The GluN2D subunit of the NMDA receptor has low expression in the mature brain, and its role is not fully understood. In the present study, the effects of GluN2D subunit deficiency on emotional and cognitive function were investigated in GluN2D knockout (KO) mice. We found a reduction of motility (i.e., a depressive-like state) in the tail suspension test and a reduction of sucrose preference (i.e., an anhedonic state) in GluN2D KO mice that were group-housed with littermates. Despite apparently normal olfactory function and social interaction, GluN2D KO mice exhibited a decrease in preference for social novelty, suggesting a deficit in social recognition or memory. Golgi-Cox staining revealed a reduction of the complexity of dendritic trees in the accessory olfactory bulb in GluN2D KO mice, suggesting a deficit in pheromone processing pathway activation, which modulates social recognition. The deficit in social recognition may result in social stress in GluN2D KO mice. Isolation housing is a procedure that has been shown to reduce stress in mice. Interestingly, 3-week isolation and treatment with agomelatine or the 5-hydroxytryptamine-2C (5-HT 2C ) receptor antagonist SB242084 reversed the anhedonic-like state in GluN2D KO mice. In contrast, treatment with the 5-HT 2C receptor agonist CP809101 induced depressive- and anhedonic-like states in isolated GluN2D KO mice. These results suggest that social stress that is caused by a deficit in social recognition desensitizes 5-HT 2c receptors, followed by an anhedonic- and depressive-like state, in GluN2D KO mice. The GluN2D subunit of the NMDA receptor appears to be important for the recognition of individuals and development of normal emotionality in mice. 5-HT 2C receptor antagonism may be a therapeutic target for treating social stress-induced anhedonia. This article is part of the Special

Assay for the pattern recognition molecule collectin liver 1 (CL-L1)

DEFF Research Database (Denmark)

Axelgaard, Esben; Jensenius, Jens Christian; Thiel, Steffen

Collectin liver 1 (also termed collectin 10 and CL-L1) is a C-type lectin that functions as a pattern recognition molecule (PRM) in the innate immune system1. We have produced antibodies against CL-L1 and have developed a sandwich-type time-resolved immuno-fluorometric assay (TRIFMA...... to co-purify with MASPs, possibly rendering it a role in complement. CL-L1 showed binding activity towards mannose-TSK beads in a Ca2+-dependent manner. This binding could be inhibited by mannose and glucose, but not by galactose, indicating that CL-L1 binds via its carbohydrate-recognition domain (CRD)....

Pattern recognition in menstrual bleeding diaries by statistical cluster analysis

Directory of Open Access Journals (Sweden)

Wessel Jens

2009-07-01

Full Text Available Abstract Background The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs. Methods We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries. The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed. Results The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated. Conclusion Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

Science.gov (United States)

Huang, Qi; Yang, Dapeng; Jiang, Li; Zhang, Huajie; Liu, Hong; Kotani, Kiyoshi

2017-01-01

Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC), by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC). We compared PAC performance with incremental support vector classifier (ISVC) and non-adapting SVC (NSVC) in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05) and ISVC (13.38% ± 2.62%, p = 0.001), and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle). PMID:28608824

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG Pattern Recognition

Directory of Open Access Journals (Sweden)

Qi Huang

2017-06-01

Full Text Available Performance degradation will be caused by a variety of interfering factors for pattern recognition-based myoelectric control methods in the long term. This paper proposes an adaptive learning method with low computational cost to mitigate the effect in unsupervised adaptive learning scenarios. We presents a particle adaptive classifier (PAC, by constructing a particle adaptive learning strategy and universal incremental least square support vector classifier (LS-SVC. We compared PAC performance with incremental support vector classifier (ISVC and non-adapting SVC (NSVC in a long-term pattern recognition task in both unsupervised and supervised adaptive learning scenarios. Retraining time cost and recognition accuracy were compared by validating the classification performance on both simulated and realistic long-term EMG data. The classification results of realistic long-term EMG data showed that the PAC significantly decreased the performance degradation in unsupervised adaptive learning scenarios compared with NSVC (9.03% ± 2.23%, p < 0.05 and ISVC (13.38% ± 2.62%, p = 0.001, and reduced the retraining time cost compared with ISVC (2 ms per updating cycle vs. 50 ms per updating cycle.

Advantage of the Highly Restricted Odorant Receptor Expression Pattern in Chemosensory Neurons of Drosophila.

Science.gov (United States)

Tharadra, Sana Khalid; Medina, Adriana; Ray, Anandasankar

2013-01-01

A fundamental molecular feature of olfactory systems is that individual neurons express only one receptor from a large odorant receptor gene family. While numerous theories have been proposed, the functional significance and evolutionary advantage of generating a sophisticated one-receptor-per neuron expression pattern is not well understood. Using the genetically tractable Drosophila melanogaster as a model, we demonstrate that the breakdown of this highly restricted expression pattern of an odorant receptor in neurons leads to a deficit in the ability to exploit new food sources. We show that animals with ectopic co-expression of odorant receptors also have a competitive disadvantage in a complex environment with limiting food sources. At the level of the olfactory system, we find changes in both the behavioral and electrophysiological responses to odorants that are detected by endogenous receptors when an olfactory receptor is broadly misexpressed in chemosensory neurons. Taken together these results indicate that restrictive expression patterns and segregation of odorant receptors to individual neuron classes are important for sensitive odor-detection and appropriate olfactory behaviors.

Pattern recognition trigger electronics for an imaging atmospheric Cherenkov telescope

International Nuclear Information System (INIS)

Bradbury, S.M.; Rose, H.J.

2002-01-01

For imaging atmospheric Cherenkov telescopes, which aim to detect electromagnetic air showers with cameras consisting of several hundred photomultiplier pixels, the single pixel trigger rate is dominated by fluctuations in night sky brightness and by ion feedback in the photomultipliers. Pattern recognition trigger electronics may be used to reject night sky background images, thus reducing the data rate to a manageable level. The trigger system described here detects patterns of 2, 3 or 4 adjacent pixel signals within a 331 pixel camera and gives a positive trigger decision in 65 ns. The candidate pixel pattern is compared with the contents of a pre-programmed memory. With the trigger decision timing controlled by a fixed delay the time-jitter inherent in the use of programmable gate arrays is avoided. This system is now in routine operation at the Whipple 10 m Telescope

Playing tag with ANN: boosted top identification with pattern recognition

International Nuclear Information System (INIS)

Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu; Lee, Seung J.; Perelstein, Maxim

2015-01-01

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p T in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Playing tag with ANN: boosted top identification with pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

2015-07-17

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

Fast pattern recognition with the ATLAS L1Track trigger for HL-LHC

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00530554; The ATLAS collaboration

2017-01-01

A fast hardware based track trigger is being developed in ATLAS for the High Luminosity upgrade of the Large Hadron Collider. The goal is to achieve trigger levels in the high pile-up conditions of the High Luminosity Large Hadron Collider that are similar or better than those achieved at low pile-up conditions by adding tracking information to the ATLAS hardware trigger. A method for fast pattern recognition using the Hough transform is investigated. In this method, detector hits are mapped onto a 2D parameter space with one parameter related to the transverse momentum and one to the initial track direction. The performance of the Hough transform is studied at different pile-up values. It is also compared, using full event simulation of events with average pile-up of 200, with a method based on matching detector hits to pattern banks of simulated tracks stored in a custom made Associative Memory ASICs. The pattern recognition is followed by a track fitting step which calculates the track parameters. The spee...

Hidden Markov model analysis reveals the advantage of analytic eye movement patterns in face recognition across cultures.

Science.gov (United States)

Chuk, Tim; Crookes, Kate; Hayward, William G; Chan, Antoni B; Hsiao, Janet H

2017-12-01

It remains controversial whether culture modulates eye movement behavior in face recognition. Inconsistent results have been reported regarding whether cultural differences in eye movement patterns exist, whether these differences affect recognition performance, and whether participants use similar eye movement patterns when viewing faces from different ethnicities. These inconsistencies may be due to substantial individual differences in eye movement patterns within a cultural group. Here we addressed this issue by conducting individual-level eye movement data analysis using hidden Markov models (HMMs). Each individual's eye movements were modeled with an HMM. We clustered the individual HMMs according to their similarities and discovered three common patterns in both Asian and Caucasian participants: holistic (looking mostly at the face center), left-eye-biased analytic (looking mostly at the two individual eyes in addition to the face center with a slight bias to the left eye), and right-eye-based analytic (looking mostly at the right eye in addition to the face center). The frequency of participants adopting the three patterns did not differ significantly between Asians and Caucasians, suggesting little modulation from culture. Significantly more participants (75%) showed similar eye movement patterns when viewing own- and other-race faces than different patterns. Most importantly, participants with left-eye-biased analytic patterns performed significantly better than those using either holistic or right-eye-biased analytic patterns. These results suggest that active retrieval of facial feature information through an analytic eye movement pattern may be optimal for face recognition regardless of culture. Copyright © 2017 Elsevier B.V. All rights reserved.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Energy Technology Data Exchange (ETDEWEB)

Acciarri, R.; Bagby, L.; Baller, B.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Greenlee, H.; James, C.; Jostlein, H.; Ketchum, W.; Kirby, M.; Kobilarcik, T.; Lockwitz, S.; Lundberg, B.; Marchionni, A.; Moore, C.D.; Palamara, O.; Pavlovic, Z.; Raaf, J.L.; Schukraft, A.; Snider, E.L.; Spentzouris, P.; Strauss, T.; Toups, M.; Wolbers, S.; Yang, T.; Zeller, G.P. [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Adams, C. [Harvard University, Cambridge, MA (United States); Yale University, New Haven, CT (United States); An, R.; Littlejohn, B.R.; Martinez Caicedo, D.A. [Illinois Institute of Technology (IIT), Chicago, IL (United States); Anthony, J.; Escudero Sanchez, L.; De Vries, J.J.; Marshall, J.; Smith, A.; Thomson, M. [University of Cambridge, Cambridge (United Kingdom); Asaadi, J. [University of Texas, Arlington, TX (United States); Auger, M.; Ereditato, A.; Goeldi, D.; Kreslo, I.; Lorca, D.; Luethi, M.; Rudolf von Rohr, C.; Sinclair, J.; Weber, M. [Universitaet Bern, Bern (Switzerland); Balasubramanian, S.; Fleming, B.T.; Gramellini, E.; Hackenburg, A.; Luo, X.; Russell, B.; Tufanli, S. [Yale University, New Haven, CT (United States); Barnes, C.; Mousseau, J.; Spitz, J. [University of Michigan, Ann Arbor, MI (United States); Barr, G.; Bass, M.; Del Tutto, M.; Laube, A.; Soleti, S.R.; De Pontseele, W.V. [University of Oxford, Oxford (United Kingdom); Bay, F. [TUBITAK Space Technologies Research Institute, Ankara (Turkey); Bishai, M.; Chen, H.; Joshi, J.; Kirby, B.; Li, Y.; Mooney, M.; Qian, X.; Viren, B.; Zhang, C. [Brookhaven National Laboratory (BNL), Upton, NY (United States); Blake, A.; Devitt, D.; Lister, A.; Nowak, J. [Lancaster University, Lancaster (United Kingdom); Bolton, T.; Horton-Smith, G.; Meddage, V.; Rafique, A. [Kansas State University (KSU), Manhattan, KS (United States); Camilleri, L.; Caratelli, D.; Crespo-Anadon, J.I.; Fadeeva, A.A.; Genty, V.; Kaleko, D.; Seligman, W.; Shaevitz, M.H. [Columbia University, New York, NY (United States); Church, E. [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Cianci, D.; Karagiorgi, G. [Columbia University, New York, NY (United States); The University of Manchester (United Kingdom); Cohen, E.; Piasetzky, E. [Tel Aviv University, Tel Aviv (Israel); Collin, G.H.; Conrad, J.M.; Hen, O.; Hourlier, A.; Moon, J.; Wongjirad, T.; Yates, L. [Massachusetts Institute of Technology (MIT), Cambridge, MA (United States); Convery, M.; Eberly, B.; Rochester, L.; Tsai, Y.T.; Usher, T. [SLAC National Accelerator Laboratory, Menlo Park, CA (United States); Dytman, S.; Graf, N.; Jiang, L.; Naples, D.; Paolone, V.; Wickremasinghe, D.A. [University of Pittsburgh, Pittsburgh, PA (United States); Esquivel, J.; Hamilton, P.; Pulliam, G.; Soderberg, M. [Syracuse University, Syracuse, NY (United States); Foreman, W.; Ho, J.; Schmitz, D.W.; Zennamo, J. [University of Chicago, IL (United States); Furmanski, A.P.; Garcia-Gamez, D.; Hewes, J.; Hill, C.; Murrells, R.; Porzio, D.; Soeldner-Rembold, S.; Szelc, A.M. [The University of Manchester (United Kingdom); Garvey, G.T.; Huang, E.C.; Louis, W.C.; Mills, G.B.; De Water, R.G.V. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Gollapinni, S. [Kansas State University (KSU), Manhattan, KS (United States); University of Tennessee, Knoxville, TN (United States); and others

2018-01-15

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies. (orig.)

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

Science.gov (United States)

Acciarri, R.; Adams, C.; An, R.; Anthony, J.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fadeeva, A. A.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Hourlier, A.; Huang, E.-C.; James, C.; Jan de Vries, J.; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; Rudolf von Rohr, C.; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van de Water, R. G.; Viren, B.; Weber, M.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2018-01-01

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.

Pattern recognition in cyclic and discrete skills performance from inertial measurement units

NARCIS (Netherlands)

Seifert, Ludovic; L'Hermette, Maxime; Komar, John; Orth, Dominic; Mell, Florian; Merriaux, Pierre; Grenet, Pierre; Caritu, Yanis; Hérault, Romain; Dovgalecs, Vladislavs; Davids, Keith

2014-01-01

The aim of this study is to compare and validate an Inertial Measurement Unit (IMU) relative to an optic system, and to propose methods for pattern recognition to capture behavioural dynamics during sport performance. IMU validation was conducted by comparing the motions of the two arms of a

Evolution of the C-Type Lectin-Like Receptor Genes of the DECTIN-1 Cluster in the NK Gene Complex

Directory of Open Access Journals (Sweden)

Susanne Sattler

2012-01-01

Full Text Available Pattern recognition receptors are crucial in initiating and shaping innate and adaptive immune responses and often belong to families of structurally and evolutionarily related proteins. The human C-type lectin-like receptors encoded in the DECTIN-1 cluster within the NK gene complex contain prominent receptors with pattern recognition function, such as DECTIN-1 and LOX-1. All members of this cluster share significant homology and are considered to have arisen from subsequent gene duplications. Recent developments in sequencing and the availability of comprehensive sequence data comprising many species showed that the receptors of the DECTIN-1 cluster are not only homologous to each other but also highly conserved between species. Even in Caenorhabditis elegans, genes displaying homology to the mammalian C-type lectin-like receptors have been detected. In this paper, we conduct a comprehensive phylogenetic survey and give an up-to-date overview of the currently available data on the evolutionary emergence of the DECTIN-1 cluster genes.

Aryl hydrocarbon receptor overexpression in miniaturized follicles in female pattern hair loss.

Science.gov (United States)

Ramos, Paulo Müller; Brianezi, Gabrielli; Martins, Ana Carolina Pereira; Silva, Márcia Guimarães da; Marques, Mariângela Esther Alencar; Miot, Hélio Amante

2017-01-01

The etiopathogenesis of female pattern hair loss is still poorly understood. In addition to genetic and hormonal elements, environmental factors could be involved. The aryl hydrocarbon receptor is expressed in keratinocytes and can be activated by environmental pollutants leading to alterations in the cell cycle, inflammation, and apoptosis. Here we demonstrate the overexpression of nuclear aryl hydrocarbon receptors in miniaturized hair follicles in female pattern hair loss.

Pattern recognition with magnonic holographic memory device

International Nuclear Information System (INIS)

Kozhevnikov, A.; Dudko, G.; Filimonov, Y.; Gertz, F.; Khitun, A.

2015-01-01

In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed

The DELPHI Silicon Tracker in the global pattern recognition

CERN Document Server

Elsing, M

2000-01-01

ALEPH and DELPHI were the first experiments operating a silicon vertex detector at LEP. During the past 10 years of data taking the DELPHI Silicon Tracker was upgraded three times to follow the different tracking requirements for LEP 1 and LEP 2 as well as to improve the tracking performance. Several steps in the development of the pattern recognition software were done in order to understand and fully exploit the silicon tracker information. This article gives an overview of the final algorithms and concepts of the track reconstruction using the Silicon Tracker in DELPHI.

The DELPHI Silicon Tracker in the global pattern recognition

International Nuclear Information System (INIS)

Elsing, M.

2000-01-01

ALEPH and DELPHI were the first experiments operating a silicon vertex detector at LEP. During the past 10 years of data taking the DELPHI Silicon Tracker was upgraded three times to follow the different tracking requirements for LEP 1 and LEP 2 as well as to improve the tracking performance. Several steps in the development of the pattern recognition software were done in order to understand and fully exploit the silicon tracker information. This article gives an overview of the final algorithms and concepts of the track reconstruction using the Silicon Tracker in DELPHI

Effects of selective activation of M1 and M4 muscarinic receptors on object recognition memory performance in rats.

Science.gov (United States)

Galloway, Claire R; Lebois, Evan P; Shagarabi, Shezza L; Hernandez, Norma A; Manns, Joseph R

2014-01-01

Acetylcholine signaling through muscarinic receptors has been shown to benefit memory performance in some conditions, but pan-muscarinic activation also frequently leads to peripheral side effects. Drug therapies that selectively target M1 or M4 muscarinic receptors could potentially improve memory while minimizing side effects mediated by the other muscarinic receptor subtypes. The ability of three recently developed drugs that selectively activate M1 or M4 receptors to improve recognition memory was tested by giving Long-Evans rats subcutaneous injections of three different doses of the M1 agonist VU0364572, the M1 positive allosteric modulator BQCA or the M4 positive allosteric modulator VU0152100 before performing an object recognition memory task. VU0364572 at 0.1 mg/kg, BQCA at 1.0 mg/kg and VU0152100 at 3.0 and 30.0 mg/kg improved the memory performance of rats that performed poorly at baseline, yet the improvements in memory performance were the most statistically robust for VU0152100 at 3.0 mg/kg. The results suggested that selective M1 and M4 receptor activation each improved memory but that the likelihood of obtaining behavioral efficacy at a given dose might vary between subjects even in healthy groups depending on baseline performance. These results also highlighted the potential of drug therapies that selectively target M1 or M4 receptors to improve memory performance in individuals with impaired memory.

Pattern recognition methodologies and deterministic evaluation of seismic hazard: A strategy to increase earthquake preparedness

International Nuclear Information System (INIS)

Peresan, Antonella; Panza, Giuliano F.; Gorshkov, Alexander I.; Aoudia, Abdelkrim

2001-05-01

Several algorithms, structured according to a general pattern-recognition scheme, have been developed for the space-time identification of strong events. Currently, two of such algorithms are applied to the Italian territory, one for the recognition of earthquake-prone areas and the other, namely CN algorithm, for earthquake prediction purposes. These procedures can be viewed as independent experts, hence they can be combined to better constrain the alerted seismogenic area. We examine here the possibility to integrate CN intermediate-term medium-range earthquake predictions, pattern recognition of earthquake-prone areas and deterministic hazard maps, in order to associate CN Times of Increased Probability (TIPs) to a set of appropriate scenarios of ground motion. The advantage of this procedure mainly consists in the time information provided by predictions, useful to increase preparedness of safety measures and to indicate a priority for detailed seismic risk studies to be performed at a local scale. (author)

Recognition of building group patterns in topographic maps based on graph partitioning and random forest

Science.gov (United States)

He, Xianjin; Zhang, Xinchang; Xin, Qinchuan

2018-02-01

Recognition of building group patterns (i.e., the arrangement and form exhibited by a collection of buildings at a given mapping scale) is important to the understanding and modeling of geographic space and is hence essential to a wide range of downstream applications such as map generalization. Most of the existing methods develop rigid rules based on the topographic relationships between building pairs to identify building group patterns and thus their applications are often limited. This study proposes a method to identify a variety of building group patterns that allow for map generalization. The method first identifies building group patterns from potential building clusters based on a machine-learning algorithm and further partitions the building clusters with no recognized patterns based on the graph partitioning method. The proposed method is applied to the datasets of three cities that are representative of the complex urban environment in Southern China. Assessment of the results based on the reference data suggests that the proposed method is able to recognize both regular (e.g., the collinear, curvilinear, and rectangular patterns) and irregular (e.g., the L-shaped, H-shaped, and high-density patterns) building group patterns well, given that the correctness values are consistently nearly 90% and the completeness values are all above 91% for three study areas. The proposed method shows promises in automated recognition of building group patterns that allows for map generalization.

Inductive class representation and its central role in pattern recognition

Energy Technology Data Exchange (ETDEWEB)

Goldfarb, L. [Univ. of New Brunswick, Fredericton, New Brunswick (Canada)

1996-12-31

The definition of inductive learning (IL) based on the new concept of inductive class representation (ICR) is given. The ICR, in addition to the ability to recognize a noise-corrupted object from the class, must also provide the means to generate every element in the resulting approximation of the class, i.e., the emphasis is on the generative capability of the ICR. Thus, the IL problem absorbs the main difficulties associated with a satisfactory formulation of the pattern recognition problem. This formulation of the IL problem appeared gradually as a result of the development of a fundamentally new formal model of IL--evolving transformation system (ETS) model. The model with striking clarity suggests that IL is the basic process which produces all the necessary {open_quotes}structures{close_quotes} for the recognition process, which is built directly on top of it. Based on the training set, the IL process, constructs optimal discriminatory (symbolic) weighted {open_quotes}features{close_quotes} which induce the corresponding optimal (symbolic) distance measure. The distance measure is a generalization of the weighted Levenshtein, or edit, distance defined on strings over a finite alphabet. The ETS model has emerged as a result of an attempt to unify two basic, but inadequate, approaches to pattern recognition: the classical vector space based and the syntactic approaches. ETS also elucidates with remarkable clarity the nature of the interrelationships between the corresponding symbolic and numeric mechanisms, in which the symbolic mechanisms play a more fundamental part. The model, in fact, suggests the first formal definition of the symbolic mathematical structure and also suggests a fundamentally different, more satisfactory, way of introducing the concept of fuzziness. The importance of the ICR concept to semiotics and semantics should become apparent as soon as one fully realizes that it represents the class and specifies the semantics of the class.

Pseudomonas evades immune recognition of flagellin in both mammals and plants.

Directory of Open Access Journals (Sweden)

Bart W Bardoel

2011-08-01

Full Text Available The building blocks of bacterial flagella, flagellin monomers, are potent stimulators of host innate immune systems. Recognition of flagellin monomers occurs by flagellin-specific pattern-recognition receptors, such as Toll-like receptor 5 (TLR5 in mammals and flagellin-sensitive 2 (FLS2 in plants. Activation of these immune systems via flagellin leads eventually to elimination of the bacterium from the host. In order to prevent immune activation and thus favor survival in the host, bacteria secrete many proteins that hamper such recognition. In our search for Toll like receptor (TLR antagonists, we screened bacterial supernatants and identified alkaline protease (AprA of Pseudomonas aeruginosa as a TLR5 signaling inhibitor as evidenced by a marked reduction in IL-8 production and NF-κB activation. AprA effectively degrades the TLR5 ligand monomeric flagellin, while polymeric flagellin (involved in bacterial motility and TLR5 itself resist degradation. The natural occurring alkaline protease inhibitor AprI of P. aeruginosa blocked flagellin degradation by AprA. P. aeruginosa aprA mutants induced an over 100-fold enhanced activation of TLR5 signaling, because they fail to degrade excess monomeric flagellin in their environment. Interestingly, AprA also prevents flagellin-mediated immune responses (such as growth inhibition and callose deposition in Arabidopsis thaliana plants. This was due to decreased activation of the receptor FLS2 and clearly demonstrated by delayed stomatal closure with live bacteria in plants. Thus, by degrading the ligand for TLR5 and FLS2, P. aeruginosa escapes recognition by the innate immune systems of both mammals and plants.

Recent developments in pattern recognition with applications in high-energy physics

International Nuclear Information System (INIS)

Bischof, H.; Fruehwirth, R.

1998-01-01

In this contribution we consider the track finding and fitting problem from the pattern recognition and computer vision point of view. In particular, we consider it as a problem of recovering parametric models from noisy measurements. We review major approaches like neural networks, Hough transforms, Kalman filtering techniques, etc, which have been previously used in that area, and point out the problems of these approaches. Then we present an algorithm, originally developed in the area of computer vision, to fit different types of curves to noisy edge data, and demonstrate how it can be used for track finding and fitting. The algorithm is based on two principles: (a) a data driven exploration produces many hypotheses of possible tracks; (b) a selection procedure based on the Minimum Description Length Principle (MDL), selects those hypotheses which are needed to explain the data. The results of the algorithm are a number of tracks, and a set of outlier points (which cannot be explained by tracks according to the MDL-principle). We discuss how knowledge about the detectors and the underlying processes can be incorporated in the algorithm. Finally, we demonstrate the algorithm on 2D and 3D pattern recognition tasks. (author)

Optimizing pattern recognition-based control for partial-hand prosthesis application.

Science.gov (United States)

Earley, Eric J; Adewuyi, Adenike A; Hargrove, Levi J

2014-01-01

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (pgrasps available to the classifier significantly decrease classification error (pgrasp.

Rotation, scale, and translation invariant pattern recognition using feature extraction

Science.gov (United States)

Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

1997-03-01

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

Segmentation of turbo generator and reactor coolant pump vibratory patterns: a syntactic pattern recognition approach

International Nuclear Information System (INIS)

Tira, Z.

1993-02-01

This study was undertaken in the context of turbogenerator and reactor coolant pump vibration surveillance. Vibration meters are used to monitor equipment condition. An anomaly will modify the signal mean. At the present time, the expert system DIVA, developed to automate diagnosis, requests the operator to identify the nature of the pattern change thus indicated. In order to minimize operator intervention, we have to automate on the one hand classification and on the other hand, detection and segmentation of the patterns. The purpose of this study is to develop a new automatic system for the segmentation and classification of signals. The segmentation is based on syntactic pattern recognition. For the classification, a decision tree is used. The signals to process are the rms values of the vibrations measured on rotating machines. These signals are randomly sampled. All processing is automatic and no a priori statistical knowledge on the signals is required. The segmentation performances are assessed by tests on vibratory signals. (author). 31 figs

Hand biometric recognition based on fused hand geometry and vascular patterns.

Science.gov (United States)

Park, GiTae; Kim, Soowon

2013-02-28

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%.

Metal oxide nanosensors using polymeric membranes, enzymes and antibody receptors as ion and molecular recognition elements.

Science.gov (United States)

Willander, Magnus; Khun, Kimleang; Ibupoto, Zafar Hussain

2014-05-16

The concept of recognition and biofunctionality has attracted increasing interest in the fields of chemistry and material sciences. Advances in the field of nanotechnology for the synthesis of desired metal oxide nanostructures have provided a solid platform for the integration of nanoelectronic devices. These nanoelectronics-based devices have the ability to recognize molecular species of living organisms, and they have created the possibility for advanced chemical sensing functionalities with low limits of detection in the nanomolar range. In this review, various metal oxides, such as ZnO-, CuO-, and NiO-based nanosensors, are described using different methods (receptors) of functionalization for molecular and ion recognition. These functionalized metal oxide surfaces with a specific receptor involve either a complex formation between the receptor and the analyte or an electrostatic interaction during the chemical sensing of analytes. Metal oxide nanostructures are considered revolutionary nanomaterials that have a specific surface for the immobilization of biomolecules with much needed orientation, good conformation and enhanced biological activity which further improve the sensing properties of nanosensors. Metal oxide nanostructures are associated with certain unique optical, electrical and molecular characteristics in addition to unique functionalities and surface charge features which shows attractive platforms for interfacing biorecognition elements with effective transducing properties for signal amplification. There is a great opportunity in the near future for metal oxide nanostructure-based miniaturization and the development of engineering sensor devices.

Roles of dental pulp fibroblasts in the recognition of bacterium-related factors and subsequent development of pulpitis

Directory of Open Access Journals (Sweden)

Tadashi Nakanishi

2011-08-01

Full Text Available As caries-related bacteria invade deeply into dentin and come into close proximity to the pulp, inflammatory cells (such as lymphocytes, macrophages and neutrophils infiltrate into the bacterium-invaded area and consequently pulpitis develops. Many types of cytokines and adhesion molecules are responsible for the initiation and progression of pulpitis. Dental pulp fibroblasts, a major cell type in the dental pulp, also have capacity to produce pro-inflammatory cytokines and express adhesion molecules in response to pathogen-associated molecular patterns (PAMPs, including lipopolysaccharide. The innate immune system senses microbial infection using pattern recognition receptors, such as Toll-like receptors (TLRs and nucleotide-binding oligomerization domain (NOD, for PAMPs. In this review, we summarize the roles of dental pulp fibroblasts in the recognition of invaded bacterium-related factors via TLR and NOD pathways, and the subsequent pulpal immune responses, leading to progressive pulpitis.

BIOCAT: a pattern recognition platform for customizable biological image classification and annotation.

Science.gov (United States)

Zhou, Jie; Lamichhane, Santosh; Sterne, Gabriella; Ye, Bing; Peng, Hanchuan

2013-10-04

Pattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories. We develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience. BIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

Science.gov (United States)

Pereira Marinho, Eraldo

2014-03-01

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.

Pattern recognition issues on anisotropic smoothed particle hydrodynamics

International Nuclear Information System (INIS)

Marinho, Eraldo Pereira

2014-01-01

This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation

Real-time intelligent pattern recognition algorithm for surface EMG signals

Directory of Open Access Journals (Sweden)

Jahed Mehran

2007-12-01

Full Text Available Abstract Background Electromyography (EMG is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP and least mean square (LMS is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD and time-frequency representation (TFR. Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal

Structural Probing of Off-Target G Protein-Coupled Receptor Activities within a Series of Adenosine/Adenine Congeners

Science.gov (United States)

Paoletta, Silvia; Tosh, Dilip K.; Salvemini, Daniela; Jacobson, Kenneth A.

2014-01-01

We studied patterns of off-target receptor interactions, mostly at G protein-coupled receptors (GPCRs) in the µM range, of nucleoside derivatives that are highly engineered for nM interaction with adenosine receptors (ARs). Because of the considerable interest of using AR ligands for treating diseases of the CNS, we used the Psychoactive Drug Screening Program (PDSP) for probing promiscuity of these adenosine/adenine congeners at 41 diverse receptors, channels and a transporter. The step-wise truncation of rigidified, trisubstituted (at N6, C2, and 5′ positions) nucleosides revealed unanticipated interactions mainly with biogenic amine receptors, such as adrenergic receptors and serotonergic receptors, with affinities as high as 61 nM. The unmasking of consistent sets of structure activity relationship (SAR) at novel sites suggested similarities between receptor families in molecular recognition. Extensive molecular modeling of the GPCRs affected suggested binding modes of the ligands that supported the patterns of SAR at individual receptors. In some cases, the ligand docking mode closely resembled AR binding and in other cases the ligand assumed different orientations. The recognition patterns for different GPCRs were clustered according to which substituent groups were tolerated and explained in light of the complementarity with the receptor binding site. Thus, some likely off-target interactions, a concern for secondary drug effects, can be predicted for analogues of this set of substructures, aiding the design of additional structural analogues that either eliminate or accentuate certain off-target activities. Moreover, similar analyses could be performed for unrelated structural families for other GPCRs. PMID:24859150

Study of problems met in muon pattern recognition for a deep inelastic scattering experiment at the S.P.S

International Nuclear Information System (INIS)

Besson, C.

1976-01-01

The problems of the muon pattern recognition are studied for a muon-proton deep inelastic scattering experiment at the S.P.S. The pattern recognition program is described together with the problems caused by some characteristics of the apparatus of the European muon collaboration. Several reconstruction technics are compared, and a way of handling big drift chamber problems is found. Some results on Monte-Carlo tracks are given [fr

Pattern recognition with simple oscillating circuits

International Nuclear Information System (INIS)

Hoelzel, R W; Krischer, K

2011-01-01

Neural network devices that inherently possess parallel computing capabilities are generally difficult to construct because of the large number of neuron-neuron connections. However, there exists a theoretical approach (Hoppensteadt and Izhikevich 1999 Phys. Rev. Lett. 82 2983) that forgoes the individual connections and uses only a global coupling: systems of weakly coupled oscillators with a time-dependent global coupling are capable of performing pattern recognition in an associative manner similar to Hopfield networks. The information is stored in the phase shifts of the individual oscillators. However, to date, even the feasibility of controlling phase shifts with this kind of coupling has not yet been established experimentally. We present an experimental realization of this neural network device. It consists of eight sinusoidal electrical van der Pol oscillators that are globally coupled through a variable resistor with the electric potential as the coupling variable. We estimate an effective value of the phase coupling strength in our experiment. For that, we derive a general approach that allows one to compare different experimental realizations with each other as well as with phase equation models. We demonstrate that individual phase shifts of oscillators can be experimentally controlled by a weak global coupling. Furthermore, supplied with a distorted input image, the oscillating network can indeed recognize the correct image out of a set of predefined patterns. It can therefore be used as the processing unit of an associative memory device.

Bifurcation analysis of oscillating network model of pattern recognition in the rabbit olfactory bulb

Science.gov (United States)

Baird, Bill

1986-08-01

A neural network model describing pattern recognition in the rabbit olfactory bulb is analysed to explain the changes in neural activity observed experimentally during classical Pavlovian conditioning. EEG activity recorded from an 8×8 arry of 64 electrodes directly on the surface on the bulb shows distinct spatial patterns of oscillation that correspond to the animal's recognition of different conditioned odors and change with conditioning to new odors. The model may be considered a variant of Hopfield's model of continuous analog neural dynamics. Excitatory and inhibitory cell types in the bulb and the anatomical architecture of their connection requires a nonsymmetric coupling matrix. As the mean input level rises during each breath of the animal, the system bifurcates from homogenous equilibrium to a spatially patterned oscillation. The theory of multiple Hopf bifurcations is employed to find coupled equations for the amplitudes of these unstable oscillatory modes independent of frequency. This allows a view of stored periodic attractors as fixed points of a gradient vector field and thereby recovers the more familiar dynamical systems picture of associative memory.

Fast pattern recognition with the ATLAS L1 track trigger for the HL-LHC

CERN Document Server

Martensson, Mikael; The ATLAS collaboration

2016-01-01

A fast hardware based track trigger for high luminosity upgrade of the Large Hadron Collider (HL- LHC) is being developed in ATLAS. The goal is to achieve trigger levels in high pileup collisions that are similar or even better than those achieved at low pile-up running of LHC by adding tracking information to the ATLAS hardware trigger which is currently based on information from calorimeters and muon trigger chambers only. Two methods for fast pattern recognition are investigated. The first is based on matching tracker hits to pattern banks of simulated high momentum tracks which are stored in a custom made Associative Memory (AM) ASIC. The second is based on the Hough transform where detector hits are transformed into 2D Hough space with one variable related to track pt and one to track direction. Hits found by pattern recognition will be sent to a track fitting step which calculates the track parameters . The speed and precision of the track fitting depends on the quality of the hits selected by the patte...

Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns

Science.gov (United States)

Park, GiTae; Kim, Soowon

2013-01-01

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%. PMID:23449119

GABAB-receptor activation alters the firing pattern of dopamine neurons in the rat substantia nigra.

Science.gov (United States)

Engberg, G; Kling-Petersen, T; Nissbrandt, H

1993-11-01

Previous electrophysiological experiments have emphasized the importance of the firing pattern for the functioning of midbrain dopamine (DA) neurons. In this regard, excitatory amino acid receptors appear to constitute an important modulatory control mechanism. In the present study, extracellular recording techniques were used to investigate the significance of GABAB-receptor activation for the firing properties of DA neurons in the substantia nigra (SN) in the rat. Intravenous administration of the GABAB-receptor agonist baclofen (1-16 mg/kg) was associated with a dose-dependent regularization of the firing pattern, concomitant with a reduction in burst firing. At higher doses (16-32 mg/kg), the firing rate of the DA neurons was dose-dependently decreased. Also, microiontophoretic application of baclofen regularized the firing pattern of nigral DA neurons, including a reduction of burst firing. Both the regularization of the firing pattern and inhibition of firing rate produced by systemic baclofen administration was antagonized by the GABAB-receptor antagonist CGP 35348 (200 mg/kg, i.v.). The GABAA-receptor agonist muscimol produced effects on the firing properties of DA neurons that were opposite to those observed following baclofen, i.e., an increase in firing rate accompanied by a decreased regularity. The NMDA receptor antagonist MK 801 (0.4-3.2 mg/kg, i.v.) produced a moderate, dose-dependent increase in the firing rate of the nigral DA neurons as well as a slightly regularized firing pattern. Pretreatment with MK 801 (3.2 mg/kg, i.v., 3-10 min) did neither promote nor prevent the regularization of the firing pattern or inhibition of firing rate on the nigral DA neurons produced by baclofen. The present results clearly show that GABAB-receptors can alter the firing pattern of nigral DA neurons, hereby counterbalancing the previously described ability of glutamate to induce burst firing activity on these neurons.

Fluid pipeline system leak detection based on neural network and pattern recognition

International Nuclear Information System (INIS)

Tang Xiujia

1998-01-01

The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation

Directory of Open Access Journals (Sweden)

Carlos Fernández-Llatas

2013-10-01

Full Text Available Born in the early nineteen nineties, evidence-based medicine (EBM is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

HPLC fingerprint analysis combined with chemometrics for pattern recognition of ginger.

Science.gov (United States)

Feng, Xu; Kong, Weijun; Wei, Jianhe; Ou-Yang, Zhen; Yang, Meihua

2014-03-01

Ginger, the fresh rhizome of Zingiber officinale Rosc. (Zingiberaceae), has been used worldwide; however, for a long time, there has been no standard approbated internationally for its quality control. To establish an efficacious and combinational method and pattern recognition technique for quality control of ginger. A simple, accurate and reliable method based on high-performance liquid chromatography with photodiode array (HPLC-PDA) detection was developed for establishing the chemical fingerprints of 10 batches of ginger from different markets in China. The method was validated in terms of precision, reproducibility and stability; and the relative standard deviations were all less than 1.57%. On the basis of this method, the fingerprints of 10 batches of ginger samples were obtained, which showed 16 common peaks. Coupled with similarity evaluation software, the similarities between each fingerprint of the sample and the simulative mean chromatogram were in the range of 0.998-1.000. Then, the chemometric techniques, including similarity analysis, hierarchical clustering analysis and principal component analysis were applied to classify the ginger samples. Consistent results were obtained to show that ginger samples could be successfully classified into two groups. This study revealed that HPLC-PDA method was simple, sensitive and reliable for fingerprint analysis, and moreover, for pattern recognition and quality control of ginger.

Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

International Nuclear Information System (INIS)

Tahat Amani; Marti Jordi; Khwaldeh Ali; Tahat Kaher

2014-01-01

In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies. (condensed matter: structural, mechanical, and thermal properties)

Effects of repeated 9 and 30-day exposure to extremely low-frequency electromagnetic fields on social recognition behavior and estrogen receptors expression in olfactory bulb of Wistar female rats.

Science.gov (United States)

Bernal-Mondragón, C; Arriaga-Avila, V; Martínez-Abundis, E; Barrera-Mera, B; Mercado-Gómez, O; Guevara-Guzmán, R

2017-02-01

We investigated the short- and long-term effects of extremely low-frequency electromagnetic fields (EMF) on social recognition behavior and expression of α- and β-estrogen receptors (ER). Rats were exposed to 60-Hz electromagnetic fields for 9 or 30 days and tested for social recognition behavior. Immunohistochemistry and western blot assays were performed to evaluate α- and β-ER expression in the olfactory bulb of intact, ovariectomized (OVX), and ovariectomized+estradiol (E2) replacement (OVX+E2). Ovariectomization showed impairment of social recognition after 9 days of EMF exposure and a complete recovery after E2 replacement and so did those after 30 days. Short EMF exposure increased expression of β-ER in intact, but not in the others. Longer exposure produced a decrease in intact but an increase in OVX and OVX+E2. Our findings suggest a significant role for β-estrogen receptors and a lack of effect for α-estrogen receptors on a social recognition task. EMF: extremely low frequency electromagnetic fields; ERs: estrogen receptors; OB: olfactory bulb; OVX: ovariectomized; OVX + E 2 : ovariectomized + estradiol replacement; IEI: interexposure interval; β-ER: beta estrogen receptor; E 2 : replacement of estradiol; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; WB: Western blot; PBS: phosphate-buffer saline; PB: phosphate-buffer.

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

CERN Document Server

Acciarri, R.; An, R.; Anthony, J.; Asaadi, J.; Auger, M.; Bagby, L.; Balasubramanian, S.; Baller, B.; Barnes, C.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Cohen, E.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fadeeva, A. A.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garcia-Gamez, D.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; Hourlier, A.; Huang, E.-C.; James, C.; Jan de Vries, J.; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Piasetzky, E.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; Rudolf von Rohr, C.; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Smith, A.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van De Pontseele, W.; Van de Water, R. G.; Viren, B.; Weber, M.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Yates, L.; Zeller, G. P.; Zennamo, J.; Zhang, C.

2017-01-01

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the...

Activation of Supraoptic Oxytocin Neurons by Secretin Facilitates Social Recognition.

Science.gov (United States)

Takayanagi, Yuki; Yoshida, Masahide; Takashima, Akihide; Takanami, Keiko; Yoshida, Shoma; Nishimori, Katsuhiko; Nishijima, Ichiko; Sakamoto, Hirotaka; Yamagata, Takanori; Onaka, Tatsushi

2017-02-01

Social recognition underlies social behavior in animals, and patients with psychiatric disorders associated with social deficits show abnormalities in social recognition. Oxytocin is implicated in social behavior and has received attention as an effective treatment for sociobehavioral deficits. Secretin receptor-deficient mice show deficits in social behavior. The relationship between oxytocin and secretin concerning social behavior remains to be determined. Expression of c-Fos in oxytocin neurons and release of oxytocin from their dendrites after secretin application were investigated. Social recognition was examined after intracerebroventricular or local injection of secretin, oxytocin, or an oxytocin receptor antagonist in rats, oxytocin receptor-deficient mice, and secretin receptor-deficient mice. Electron and light microscopic immunohistochemical analysis was also performed to determine whether oxytocin neurons extend their dendrites into the medial amygdala. Supraoptic oxytocin neurons expressed the secretin receptor. Secretin activated supraoptic oxytocin neurons and facilitated oxytocin release from dendrites. Secretin increased acquisition of social recognition in an oxytocin receptor-dependent manner. Local application of secretin into the supraoptic nucleus facilitated social recognition, and this facilitation was blocked by an oxytocin receptor antagonist injected into, but not outside of, the medial amygdala. In the medial amygdala, dendrite-like thick oxytocin processes were found to extend from the supraoptic nucleus. Furthermore, oxytocin treatment restored deficits of social recognition in secretin receptor-deficient mice. The results of our study demonstrate that secretin-induced dendritic oxytocin release from supraoptic neurons enhances social recognition. The newly defined secretin-oxytocin system may lead to a possible treatment for social deficits. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights

Metal Oxide Nanosensors Using Polymeric Membranes, Enzymes and Antibody Receptors as Ion and Molecular Recognition Elements

Directory of Open Access Journals (Sweden)

Magnus Willander

2014-05-01

Full Text Available The concept of recognition and biofunctionality has attracted increasing interest in the fields of chemistry and material sciences. Advances in the field of nanotechnology for the synthesis of desired metal oxide nanostructures have provided a solid platform for the integration of nanoelectronic devices. These nanoelectronics-based devices have the ability to recognize molecular species of living organisms, and they have created the possibility for advanced chemical sensing functionalities with low limits of detection in the nanomolar range. In this review, various metal oxides, such as ZnO-, CuO-, and NiO-based nanosensors, are described using different methods (receptors of functionalization for molecular and ion recognition. These functionalized metal oxide surfaces with a specific receptor involve either a complex formation between the receptor and the analyte or an electrostatic interaction during the chemical sensing of analytes. Metal oxide nanostructures are considered revolutionary nanomaterials that have a specific surface for the immobilization of biomolecules with much needed orientation, good conformation and enhanced biological activity which further improve the sensing properties of nanosensors. Metal oxide nanostructures are associated with certain unique optical, electrical and molecular characteristics in addition to unique functionalities and surface charge features which shows attractive platforms for interfacing biorecognition elements with effective transducing properties for signal amplification. There is a great opportunity in the near future for metal oxide nanostructure-based miniaturization and the development of engineering sensor devices.

Pharmacologic suppression of target cell recognition by engineered T cells expressing chimeric T-cell receptors.

Science.gov (United States)

Alvarez-Vallina, L; Yañez, R; Blanco, B; Gil, M; Russell, S J

2000-04-01

Adoptive therapy with autologous T cells expressing chimeric T-cell receptors (chTCRs) is of potential interest for the treatment of malignancy. To limit possible T-cell-mediated damage to normal tissues that weakly express the targeted tumor antigen (Ag), we have tested a strategy for the suppression of target cell recognition by engineered T cells. Jurkat T cells were transduced with an anti-hapten chTCR tinder the control of a tetracycline-suppressible promoter and were shown to respond to Ag-positive (hapten-coated) but not to Ag-negative target cells. The engineered T cells were then reacted with hapten-coated target cells at different effector to target cell ratios before and after exposure to tetracycline. When the engineered T cells were treated with tetracycline, expression of the chTCR was greatly decreased and recognition of the hapten-coated target cells was completely suppressed. Tetracycline-mediated suppression of target cell recognition by engineered T cells may be a useful strategy to limit the toxicity of the approach to cancer gene therapy.

Identification of a β-glucosidase from the Mucor circinelloides genome by peptide pattern recognition

DEFF Research Database (Denmark)

Yuhong, Huang; Busk, Peter Kamp; Grell, Morten Nedergaard

2014-01-01

Mucor circinelloides produces plant cell wall degrading enzymes that allow it to grow on complex polysaccharides. Although the genome of M. circinelloides has been sequenced, only few plant cell wall degrading enzymes are annotated in this species. We applied peptide pattern recognition, which...

Application of pattern recognition techniques to the detection of the Phenix reactor control rods vibrations

International Nuclear Information System (INIS)

Zwingelstein, G.; Deat, M.; Le Guillou, G.

1979-01-01

The incipient detection of control rods vibrations is very important for the safety of the operating plants. This detection can be achieved by an analysis of the peaks of the power spectrum density of the neutron noise. Pattern Recognition techniques were applied to detect the rod vibrations which occured at the fast breeder Phenix (250MWe). In the first part we give a description of the basic pattern which is used to characterize the behavior of the plant. The pattern is considered as column vector in n dimensional Euclidian space where the components are the samples of the power spectral density of the neutron noise. In the second part, a recursive learning procedure of the normal patterns which provides the mean and the variance of the estimates is described. In the third part the classification problem has been framed in terms of a partitioning procedure in n dimensional space which encloses regions corresponding to normal operations. This pattern recognition scheme was applied to the detection of rod vibrations with neutron data collected at the Phenix site before and after occurence of the vibrations. The analysis was carried out with a 42-dimensional measurement space. The learned pattern was estimated with 150 measurement vectors which correspond to the period without vibrations. The efficiency of the surveillance scheme is then demonstrated by processing separately 119 measurement vectors recorded during the rod vibration period

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Science.gov (United States)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

The Arabidopsis flagellin receptor FLS2 mediates the perception of Xanthomonas Ax21 secreted peptides.

Science.gov (United States)

Danna, Cristian H; Millet, Yves A; Koller, Teresa; Han, Sang-Wook; Bent, Andrew F; Ronald, Pamela C; Ausubel, Frederick M

2011-05-31

Detection of microbes by plants relies in part on an array of pattern-recognition receptors that recognize conserved microbial signatures, so-called "microbe-associated molecular patterns." The Arabidopsis thaliana receptor-like kinase FLS2 is the pattern-recognition receptor for bacterial flagellin. Similarly to FLS2, the rice transmembrane protein XA21 is the receptor for the sulfated form of the Xanthomonas oryzae pv. oryzae secreted protein Ax21. Here we show that Ax21-derived peptides activate Arabidopsis immunity, triggering responses similar to those elicited by flagellin, including an oxidative burst, induction of defense-response genes, and enhanced resistance to bacterial pathogens. To identify Arabidopsis Xa21 functional homologs, we used a reverse genetics approach to screen T-DNA insertion mutants corresponding to all 47 of the Arabidopsis genes encoding non-RD kinases belonging to the interleukin-1 receptor-associated kinase (IRAK) family. Surprisingly, among all of these mutant lines, only fls2 mutants exhibited a significant loss of response to Ax21-derived peptides. Ax21 peptides also failed to activate defense-related responses in an fls2-24 mutant that does not bind Flg22. Moreover, a Flg22Δ2 variant of Flg22 that binds to FLS2 but does not activate FLS2-mediated signaling suppressed Ax21-derived peptide signaling, indicating mutually exclusive perception of Flg22 or Ax21 peptides by FLS2. The data indicate that FLS2 functions beyond flagellin perception to detect other microbe-associated molecular patterns.

Fault diagnosis and performance monitoring for pumps by means of vibration measurement and pattern recognition

International Nuclear Information System (INIS)

Grabner, A.; Weiss, F.P.

1984-12-01

In recent years the early detection of malfunctions with noise and vibration analysis techniques has become a more and more important method for increasing availability and safety of various components in technical plants. The possibility of pattern recognition assisted vibration monitoring and its practical realization are demonstrated by failure diagnosis and trend analysis of the condition of large centrifugal pumps in hydraulic circuits. Some problems as, e.g., the finding of dynamic failure models, signal analysis, feature extraction and statistical pattern recognition, which helps automatically to decide whether the pump works normally or not, are discussed in more detail. In the paper it is shown that for various types of machines the chance of success of condition based maintenance can be enhanced by such an automatic vibration monitoring. (author)

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

Science.gov (United States)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition.

Science.gov (United States)

Lu, Zhiyuan; Chen, Xiang; Zhang, Xu; Tong, Kay-Yu; Zhou, Ping

2017-08-01

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

VIPRAM_L1CMS: a 2-Tier 3D Architecture for Pattern Recognition for Track Finding

Energy Technology Data Exchange (ETDEWEB)

Hoff, J. R. [Fermilab; Joshi, Joshi,S. [Northwestern U.; Liu, Liu, [Fermilab; Olsen, J. [Fermilab; Shenai, A. [Fermilab

2017-06-15

In HEP tracking trigger applications, flagging an individual detector hit is not important. Rather, the path of a charged particle through many detector layers is what must be found. Moreover, given the increased luminosity projected for future LHC experiments, this type of track finding will be required within the Level 1 Trigger system. This means that future LHC experiments require not just a chip capable of high-speed track finding but also one with a high-speed readout architecture. VIPRAM_L1CMS is 2-Tier Vertically Integrated chip designed to fulfill these requirements. It is a complete pipelined Pattern Recognition Associative Memory (PRAM) architecture including pattern recognition, result sparsification, and readout for Level 1 trigger applications in CMS with 15-bit wide detector addresses and eight detector layers included in the track finding. Pattern recognition is based on classic Content Addressable Memories with a Current Race Scheme to reduce timing complexity and a 4-bit Selective Precharge to minimize power consumption. VIPRAM_L1CMS uses a pipelined set of priority-encoded binary readout structures to sparsify and readout active road flags at frequencies of at least 100MHz. VIPRAM_L1CMS is designed to work directly with the Pulsar2b Architecture.

Soluble Collectin-12 (CL-12) Is a Pattern Recognition Molecule Initiating Complement Activation via the Alternative Pathway

DEFF Research Database (Denmark)

Ma, Ying Jie; Hein, Estrid; Munthe-Fog, Lea

2015-01-01

and may recognize certain bacteria and fungi, leading to opsonophagocytosis. However, based on its structural and functional similarities with soluble collectins, we hypothesized the existence of a fluid-phase analog of CL-12 released from cells, which may function as a soluble pattern-recognition...... of the terminal complement complex. These results demonstrate the existence of CL-12 in a soluble form and indicate a novel mechanism by which the alternative pathway of complement may be triggered directly by a soluble pattern-recognition molecule....... nonreducing conditions it presented multimeric assembly forms. Immunoprecipitation and Western blot analysis of human umbilical cord plasma enabled identification of a natural soluble form of CL-12 having an electrophoretic mobility pattern close to that of shed soluble recombinant CL-12. Soluble CL-12 could...

Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition.

Science.gov (United States)

Lu, Wei; Teng, Jun; Zhou, Qiushi; Peng, Qiexin

2018-02-01

The stress in structural steel members is the most useful and directly measurable physical quantity to evaluate the structural safety in structural health monitoring, which is also an important index to evaluate the stress distribution and force condition of structures during structural construction and service phases. Thus, it is common to set stress as a measure in steel structural monitoring. Considering the economy and the importance of the structural members, there are only a limited number of sensors that can be placed, which means that it is impossible to obtain the stresses of all members directly using sensors. This study aims to develop a stress response prediction method for locations where there are insufficent sensors, using measurements from a limited number of sensors and pattern recognition. The detailed improved aspects are: (1) a distributed computing process is proposed, where the same pattern is recognized by several subsets of measurements; and (2) the pattern recognition using the subset of measurements is carried out by considering the optimal number of sensors and number of fusion patterns. The validity and feasibility of the proposed method are verified using two examples: the finite-element simulation of a single-layer shell-like steel structure, and the structural health monitoring of the space steel roof of Shenzhen Bay Stadium; for the latter, the anti-noise performance of this method is verified by the stress measurements from a real-world project.

Muscle Sensor Model Using Small Scale Optical Device for Pattern Recognitions

Directory of Open Access Journals (Sweden)

Kreangsak Tamee

2013-01-01

Full Text Available A new sensor system for measuring contraction and relaxation of muscles by using a PANDA ring resonator is proposed. The small scale optical device is designed and configured to perform the coupling effects between the changes in optical device phase shift and human facial muscle movement, which can be used to form the relationship between optical phase shift and muscle movement. By using the Optiwave and MATLAB programs, the results obtained have shown that the measurement of the contraction and relaxation of muscles can be obtained after the muscle movements, in which the unique pattern of individual muscle movement from facial expression can be established. The obtained simulation results, that is, interference signal patterns, can be used to form the various pattern recognitions, which are useful for the human machine interface and the human computer interface application and discussed in detail.

Shape-based hand recognition approach using the morphological pattern spectrum

Science.gov (United States)

Ramirez-Cortes, Juan Manuel; Gomez-Gil, Pilar; Sanchez-Perez, Gabriel; Prieto-Castro, Cesar

2009-01-01

We propose the use of the morphological pattern spectrum, or pecstrum, as the base of a biometric shape-based hand recognition system. The system receives an image of the right hand of a subject in an unconstrained pose, which is captured with a commercial flatbed scanner. According to pecstrum property of invariance to translation and rotation, the system does not require the use of pegs for a fixed hand position, which simplifies the image acquisition process. This novel feature-extraction method is tested using a Euclidean distance classifier for identification and verification cases, obtaining 97% correct identification, and an equal error rate (EER) of 0.0285 (2.85%) for the verification mode. The obtained results indicate that the pattern spectrum represents a good feature-extraction alternative for low- and medium-level hand-shape-based biometric applications.

Pathogen recognition in the innate immune response.

Science.gov (United States)

Kumar, Himanshu; Kawai, Taro; Akira, Shizuo

2009-04-28

Immunity against microbial pathogens primarily depends on the recognition of pathogen components by innate receptors expressed on immune and non-immune cells. Innate receptors are evolutionarily conserved germ-line-encoded proteins and include TLRs (Toll-like receptors), RLRs [RIG-I (retinoic acid-inducible gene-I)-like receptors] and NLRs (Nod-like receptors). These receptors recognize pathogens or pathogen-derived products in different cellular compartments, such as the plasma membrane, the endosomes or the cytoplasm, and induce the expression of cytokines, chemokines and co-stimulatory molecules to eliminate pathogens and instruct pathogen-specific adaptive immune responses. In the present review, we will discuss the recent progress in the study of pathogen recognition by TLRs, RLRs and NLRs and their signalling pathways.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm.

Science.gov (United States)

Bourobou, Serge Thomas Mickala; Yoo, Younghwan

2015-05-21

This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things) based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen's temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm

Directory of Open Access Journals (Sweden)

Serge Thomas Mickala Bourobou

2015-05-01

Full Text Available This paper discusses the possibility of recognizing and predicting user activities in the IoT (Internet of Things based smart environment. The activity recognition is usually done through two steps: activity pattern clustering and activity type decision. Although many related works have been suggested, they had some limited performance because they focused only on one part between the two steps. This paper tries to find the best combination of a pattern clustering method and an activity decision algorithm among various existing works. For the first step, in order to classify so varied and complex user activities, we use a relevant and efficient unsupervised learning method called the K-pattern clustering algorithm. In the second step, the training of smart environment for recognizing and predicting user activities inside his/her personal space is done by utilizing the artificial neural network based on the Allen’s temporal relations. The experimental results show that our combined method provides the higher recognition accuracy for various activities, as compared with other data mining classification algorithms. Furthermore, it is more appropriate for a dynamic environment like an IoT based smart home.

Application of neural network and pattern recognition software to the automated analysis of continuous nuclear monitoring of on-load reactors

Energy Technology Data Exchange (ETDEWEB)

Howell, J.A.; Eccleston, G.W.; Halbig, J.K.; Klosterbuer, S.F. [Los Alamos National Lab., NM (United States); Larson, T.W. [California Polytechnic State Univ., San Luis Obispo, CA (US)

1993-08-01

Automated analysis using pattern recognition and neural network software can help interpret data, call attention to potential anomalies, and improve safeguards effectiveness. Automated software analysis, based on pattern recognition and neural networks, was applied to data collected from a radiation core discharge monitor system located adjacent to an on-load reactor core. Unattended radiation sensors continuously collect data to monitor on-line refueling operations in the reactor. The huge volume of data collected from a number of radiation channels makes it difficult for a safeguards inspector to review it all, check for consistency among the measurement channels, and find anomalies. Pattern recognition and neural network software can analyze large volumes of data from continuous, unattended measurements, thereby improving and automating the detection of anomalies. The authors developed a prototype pattern recognition program that determines the reactor power level and identifies the times when fuel bundles are pushed through the core during on-line refueling. Neural network models were also developed to predict fuel bundle burnup to calculate the region on the on-load reactor face from which fuel bundles were discharged based on the radiation signals. In the preliminary data set, which was limited and consisted of four distinct burnup regions, the neural network model correctly predicted the burnup region with an accuracy of 92%.

Effect of physical workload and modality of information presentation on pattern recognition and navigation task performance by high-fit young males.

Science.gov (United States)

Zahabi, Maryam; Zhang, Wenjuan; Pankok, Carl; Lau, Mei Ying; Shirley, James; Kaber, David

2017-11-01

Many occupations require both physical exertion and cognitive task performance. Knowledge of any interaction between physical demands and modalities of cognitive task information presentation can provide a basis for optimising performance. This study examined the effect of physical exertion and modality of information presentation on pattern recognition and navigation-related information processing. Results indicated males of equivalent high fitness, between the ages of 18 and 34, rely more on visual cues vs auditory or haptic for pattern recognition when exertion level is high. We found that navigation response time was shorter under low and medium exertion levels as compared to high intensity. Navigation accuracy was lower under high level exertion compared to medium and low levels. In general, findings indicated that use of the haptic modality for cognitive task cueing decreased accuracy in pattern recognition responses. Practitioner Summary: An examination was conducted on the effect of physical exertion and information presentation modality in pattern recognition and navigation. In occupations requiring information presentation to workers, who are simultaneously performing a physical task, the visual modality appears most effective under high level exertion while haptic cueing degrades performance.

Spatial profiling of nuclear receptor transcription patterns over the course of Drosophila development.

Science.gov (United States)

Wilk, Ronit; Hu, Jack; Krause, Henry M

2013-07-08

Previous work has shown that many of the 18 family members of Drosophila nuclear receptor transcription factors function in a temporal hierarchy to coordinate developmental progression and growth with the rate limiting process of metabolism. To gain further insight into these interactions and processes, we have undertaken a whole-family analysis of nuclear receptor mRNA spatial expression patterns over the entire process of embryogenesis, as well as the 3rd instar wandering larva stage, by using high-resolution fluorescence in situ hybridization. Overall, the patterns of expression are remarkably consistent with previously mapped spatial activity profiles documented during the same time points, with similar hot spots and temporal profiles in endocrine and metabolically important tissues. Among the more remarkable of the findings is that the majority of mRNA expression patterns observed show striking subcellular distributions, indicating potentially critical roles in the control of protein synthesis and subsequent subcellular distributions. These patterns will serve as a useful reference for future studies on the tissue-specific roles and interactions of nuclear receptor proteins, partners, cofactors and ligands.

New method for recognition of sterol signalling molecules: Methinium salts as receptors for sulphated steroids

Czech Academy of Sciences Publication Activity Database

Kejík, Z.; Bříza, T.; Králová, Jarmila; Mikula, I.; Poučková, P.; Martásek, P.; Král, V.

2015-01-01

Roč. 94, February 2015 (2015), s. 15-20 ISSN 1878-5867 R&D Projects: GA TA ČR(CZ) TE01020028; GA ČR(CZ) GAP303/11/1291; GA MŠk(CZ) LH14008; GA MŠk(CZ) CZ.1.07/2.300/30.0060; GA MŠk(CZ) ED1.1.00/02.0109 Institutional support: RVO:68378050 Keywords : Polymethinium salts * Sulphated sterols * Molecular recognition * Synthetic receptors Subject RIV: EB - Genetics ; Molecular Biology

Utility and recognition of lines and linear patterns on electronic displays depicting aeronautical charting information

Science.gov (United States)

2009-01-01

This report describes a study conducted to explore the utility and recognition of lines and linear patterns on electronic displays depicting aeronautical charting information. The study gathered data from a large number of pilots who conduct all type...

Track recognition with an associative pattern memory

International Nuclear Information System (INIS)

Bok, H.W. den; Visschers, J.L.; Borgers, A.J.; Lourens, W.

1991-01-01

Using Programmable Gate Arrays (PGAs), a prototype for a fast Associative Pattern Memory module has been realized. The associative memory performs the recognition of tracks within the hadron detector data acquisition system at NIKHEF-K. The memory matches the detector state with a set of 24 predefined tracks to identify the particle tracks that occur during an event. This information enables the trigger hardware to classify and select or discriminate the event. Mounted on a standard size (6U) VME board, several PGAs together form an associative memory. The internal logic architecture of the Gate Array is used in such a way as to minimize signal propagation delay. The memory cells, containing a binary representation of the particle tracks, are dynamically loadable through a VME bus interface, providing a high level of flexibility. The hadron detector and its readout system are briefly described and our track representation method is presented. Results from measurements under experimental conditions are discussed. (orig.)

Authentication and distinction of Shenmai injection with HPLC fingerprint analysis assisted by pattern recognition techniques

Directory of Open Access Journals (Sweden)

Xue-Feng Lu

2012-10-01

Full Text Available In this paper, the feasibility and advantages of employing high performance liquid chromatographic (HPLC fingerprints combined with pattern recognition techniques for quality control of Shenmai injection were investigated and demonstrated. The Similarity Evaluation System was employed to evaluate the similarities of samples of Shenmai injection, and the HPLC generated chromatographic data were analyzed using hierarchical clustering analysis (HCA and soft independent modeling of class analogy (SIMCA. Consistent results were obtained to show that the authentic samples and the blended samples were successfully classified by SIMCA, which could be applied to accurate discrimination and quality control of Shenmai injection. Furthermore, samples could also be grouped in accordance with manufacturers. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of Shenmai injection. Keywords: Shenmai injection, High performance liquid chromatography, Fingerprint, Pattern recognition

PATTER, Pattern Recognition Data Analysis

International Nuclear Information System (INIS)

Cox, L.C. Jr.; Bender, C.F.

1986-01-01

1 - Description of program or function: PATTER is an interactive program with extensive facilities for modeling analytical processes and solving complex data analysis problems using statistical methods, spectral analysis, and pattern recognition techniques. PATTER addresses the type of problem generally stated as follows: given a set of objects and a list of measurements made on these objects, is it possible to find or predict a property of the objects which is not directly measurable but is known to define some unknown relationship? When employed intelligently, PATTER will act upon a data set in such a way it becomes apparent if useful information, beyond that already discerned, is contained in the data. 2 - Method of solution: In order to solve the general problem, PATTER contains preprocessing techniques to produce new variables that are related to the values of the measurements which may reduce the number of variables and/or reveal useful information about the 'obscure' property; display techniques to represent the variable space in some way that can be easily projected onto a two- or three-dimensional plot for human observation to see if any significant clustering of points occurs; and learning techniques based on both unsupervised and supervised methods, to extract as much information from the data as possible so that the optimum solution can be found

Probabilistic Neural Networks for Chemical Sensor Array Pattern Recognition: Comparison Studies, Improvements and Automated Outlier Rejection

National Research Council Canada - National Science Library

Shaffer, Ronald E

1998-01-01

For application to chemical sensor arrays, the ideal pattern recognition is accurate, fast, simple to train, robust to outliers, has low memory requirements, and has the ability to produce a measure...

Application of PSO for solving problems of pattern recognition

Directory of Open Access Journals (Sweden)

S. N. Chukanov

2016-01-01

Full Text Available The problem of estimating the norm of the distance between the two closed smooth curves for pattern recognition is considered. Diffeomorphic transformation curves based on the model of large deformation with the transformation of the starting points of domain in required is formed on the basis of which depends on time-dependent vector field of velocity is considered. The action of the translation, rotation and scaling closed curve, the invariants of the action of these groups are considered. The position of curves is normalized by centering, bringing the principal axes of the image to the axes of the coordinate system and bringing the area of a closed curve corresponding to one. For estimating of the norm of the distance between two closed curves is formed the functional corresponding normalized distance between the two curves, and the equation of evolution diffeomorphic transformations. The equation of evolution allows to move objects along trajectories which correspond to diffeomorphic transformations. The diffeomorphisms do not change the topology along the geodesic trajectories. The problem of inexact comparing the minimized functional contains a term that estimates the exactness of shooting points in the required positions. In the equation of evolution is introduced the variance of conversion error. An algorithm for solving the equation of diffeomorphic transformation is proposed, built on the basis of PSO, which can significantly reduce the number of computing operations, compared with gradient methods for solving. The developed algorithms can be used in bioinformatics and biometrics systems, classification of images and objects, machine vision systems, neuroimaging, for pattern recognition and object tracking systems. Algorithm for estimating the norm of distance between the closed curves by diffeomorphic transformation can spread to spatial objects (curves, surfaces, manifolds.

New Digital Approach to CNN On-chip Implementation for Pattern Recognition

OpenAIRE

Durackova, Daniela

2008-01-01

We developed a novel simulator for the CNN using the program tool Visual Basic for Application. Its algorithm is based on the same principle as the planned designed circuit. The network can process the patterns with 400 point recognition. The created universal simulator can change various simulation parameters. We found that the rounding at multiplication is not as important as we previously expected. On the basis of the simulations we designed a novel digital CNN cell implemented on a chip. ...

Application of an automatic pattern recognition for aleatory signals for the surveillance of nuclear reactor and rotating machinery

International Nuclear Information System (INIS)

Nascimento, J.A. do.

1982-02-01

An automatic pattern recognition program PSDREC, developed for the surveillance of nuclear reactor and rotating machinery is described and the relevant theory is outlined. Pattern recognition analysis of noise signals is a powerful technique for assessing 'system normality' in dynamic systems. This program, with applies 8 statistical tests to calculated power spectral density (PSD) distribution, was earlier installed in a PDP-11/45 computer at IPEN. To analyse recorded signals from three systems, namely an operational BWR power reactor (neutron signals), a water pump and a diesel engine (vibration signals) this technique was used. Results of the tests are considered satisfactory. (Author) [pt

Vaginal epithelial cell-derived S100 alarmins induced by Candida albicans via pattern recognition receptor interactions are sufficient but not necessary for the acute neutrophil response during experimental vaginal candidiasis.

Science.gov (United States)

Yano, Junko; Palmer, Glen E; Eberle, Karen E; Peters, Brian M; Vogl, Thomas; McKenzie, Andrew N; Fidel, Paul L

2014-02-01

Vulvovaginal candidiasis (VVC), caused by Candida albicans, affects women worldwide. Animal and clinical studies suggest that the immunopathogenic inflammatory condition of VVC is initiated by S100 alarmins in response to C. albicans, which stimulate polymorphonuclear neutrophil (PMN) migration to the vagina. The purpose of this study was to extend previous in vitro data and determine the requirement for the alarmin S100A8 in the PMN response and to evaluate pattern recognition receptors (PRRs) that initiate the response. For the former, PMN migration was evaluated in vitro or in vivo in the presence or absence of S100 alarmins initiated by several approaches. For the latter, vaginal epithelial cells were evaluated for PRR expression and C. albicans-induced S100A8 and S100A9 mRNAs, followed by evaluation of the PMN response in inoculated PRR-deficient mice. Results revealed that, consistent with previously reported in vitro data, eukaryote-derived S100A8, but not prokaryote-derived recombinant S100A8, induced significant PMN chemotaxis in vivo. Conversely, a lack of biologically active S100A8 alarmin, achieved by antibody neutralization or by using S100A9(-/-) mice, had no effect on the PMN response in vivo. In PRR analyses, whereas Toll-like receptor 4 (TLR4)- and SIGNR1-deficient vaginal epithelial cells showed a dramatic reduction in C. albicans-induced S100A8/S100A9 mRNAs in vitro, inoculated mice deficient in these PRRs showed PMN migration similar to that in wild-type controls. These results suggest that S100A8 alarmin is sufficient, but not necessary, to induce PMN migration during VVC and that the vaginal PMN response to C. albicans involves PRRs in addition to SIGNR1 and TLR4, or other induction pathways.

Mechanisms and Neural Basis of Object and Pattern Recognition: A Study with Chess Experts

Science.gov (United States)

Bilalic, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-01-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and…

Pattern-recognition system application to EBR-II plant-life extension

International Nuclear Information System (INIS)

King, R.W.; Radtke, W.H.; Mott, J.E.

1988-01-01

A computer-based pattern-recognition system, the System State Analyzer (SSA), is being used as part of the EBR-II plant-life extension program for detection of degradation and other abnormalities in plant systems. The SSA is used for surveillance of the EBR-II primary system instrumentation, primary sodium pumps, and plant heat balances. Early results of this surveillance indicate that the SSA can detect instrumentation degradation and system performance degradation over varying time intervals, and can provide derived signal values to replace signals from failed critical sensors. These results are being used in planning for extended-life operation of EBR-II

Infrared target recognition based on improved joint local ternary pattern

Science.gov (United States)

Sun, Junding; Wu, Xiaosheng

2016-05-01

This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods.

REMOVAL OF SPECTRO-POLARIMETRIC FRINGES BY TWO-DIMENSIONAL PATTERN RECOGNITION

International Nuclear Information System (INIS)

Casini, R.; Judge, P. G.; Schad, T. A.

2012-01-01

We present a pattern-recognition-based approach to the problem of the removal of polarized fringes from spectro-polarimetric data. We demonstrate that two-dimensional principal component analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us, in principle, to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.

Toll-like receptors as therapeutic targets in cystic fibrosis.

LENUS (Irish Health Repository)

Greene, Catherine M

2008-12-01

Background: Toll-like receptors (TLRs) are pattern recognition receptors that act as a first-line of defence in the innate immune response by recognising and responding to conserved molecular patterns in microbial factors and endogenous danger signals. Cystic fibrosis (CF)-affected airways represent a milieu potentially rich in TLR agonists and the chronic inflammatory phenotype evident in CF airway epithelial cells is probably due in large part to activation of TLRs. Objective\\/methods: To examine the prospects of developing novel therapies for CF by targeting TLRs. We outline the expression and function of TLRs and explore the therapeutic potential of naturally-occurring and synthetic TLR inhibitors for CF. Results\\/conclusion: Modulation of TLRs has therapeutic potential for the inflammatory lung manifestations of CF.

Social instability stress in adolescent male rats reduces social interaction and social recognition performance and increases oxytocin receptor binding.

Science.gov (United States)

Hodges, Travis E; Baumbach, Jennet L; Marcolin, Marina L; Bredewold, Remco; Veenema, Alexa H; McCormick, Cheryl M

2017-09-17

Social experiences in adolescence are essential for displaying context-appropriate social behaviors in adulthood. We previously found that adult male rats that underwent social instability stress (SS) in adolescence had reduced social interactions with unfamiliar peers compared with non-stressed controls (CTL). Here we determined whether SS altered social recognition and social reward and brain oxytocin and vasopressin receptor density in adolescence. We confirmed that SS rats spent less time interacting with unfamiliar peers than did CTL rats (p=0.006). Furthermore, CTL rats showed a preference for novel over familiar conspecifics in a social recognition test whereas SS rats did not, which may reflect reduced recognition, impaired memory, or reduced preference for novelty in SS rats. The reward value of social interactions was not affected by SS based on conditioned place preference tests and based on the greater time SS rats spent investigating stimulus rats than did CTL rats when the stimulus rat was behind wire mesh (p=0.03). Finally, oxytocin receptor binding density was higher in the dorsal lateral septum and nucleus accumbens shell in SS rats compared with CTL rats (p=0.02, p=0.01, respectively). No effect of SS was found for vasopressin 1a receptor binding density in any of the brain regions analyzed. We discuss the extent to which the differences in social behavior exhibited after social instability in adolescence involve changes in social salience and social competency, and the possibility that changes in oxytocin signaling in the brain underlie the differences in social behavior. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Use of nonstatistical techniques for pattern recognition to detect risk groups among liquidators of the Chernobyl NPP accident aftereffects

International Nuclear Information System (INIS)

Blinov, N.N.; Guslistyj, V.P.; Misyurev, A.V.; Novitskaya, N.N.; Snigireva, G.P.

1993-01-01

Attempt of using of the nonstatistical techniques for pattern recognition to detect the risk groups among liquidators of the Chernobyl NPP accident aftereffects was described. 14 hematologic, biochemical and biophysical blood serum parameters of the group of liquidators of the Chernobyl NPP accident impact as well as the group of donors free of any radiation dose (controlled group) were taken as the diagnostic parameters. Modification of the nonstatistical techniques for pattern recognition based on the assessment calculations were used. The patients were divided into risk group at the truth ∼ 80%

Receptor Oligomerization as a Process Modulating Cellular Semiotics

DEFF Research Database (Denmark)

Giorgi, Franco; Bruni, Luis Emilio; Maggio, Roberto

2010-01-01

be another level of quality control that may help maintaining GPCRs rather stable throughout evolution. We propose here receptor oligomerization to be a basic molecular mechanism controlling GPCRs redundancy in many different cell types, and the plasma membrane as the first hierarchical cell structure...... at which selective categorical sensing may occur. Categorical sensing can be seen as the cellular capacity for identifying and ordering complex patterns of mixed signals out of a contextual matrix, i.e., the recognition of meaningful patterns out of ubiquitous signals. In this context, redundancy...

The C-type lectin receptor SIGNR3 binds to fungi present in commensal microbiota and influences immune regulation in experimental colitis

Directory of Open Access Journals (Sweden)

Magdalena eEriksson

2013-07-01

Full Text Available Inflammatory bowel disease is a condition of acute and chronic inflammation of the gut. An important factor contributing to pathogenesis is a dysregulated mucosal immunity against commensal bacteria and fungi. Host pattern recognition receptors sense commensals in the gut and are involved in maintaining the balance between controlled responses to pathogens and overwhelming innate immune activation. C-type lectin receptors (CLRs are pattern recognition receptors recognizing glycan structures on pathogens and self-antigens. Here we examined the role of the murine CLR SIGNR3 in the recognition of commensals and its involvement in intestinal immunity. SIGNR3 is the closest murine homologue of the human DC-SIGN receptor recognizing similar carbohydrate ligands such as terminal fucose or high-mannose glycans. We discovered that SIGNR3 recognizes fungi present in the commensal microbiota. To analyze if this interaction impacts the intestinal immunity against microbiota, the dextran sulfate sodium (DSS-induced colitis model was employed. SIGNR3-/- mice exhibited an increased weight loss associated with more severe colitis symptoms compared to wild-type control mice. The increased inflammation in SIGNR3-/- mice was accompanied by a higher level of TNF-α in colon. Our findings demonstrate for the first time that SIGNR3 recognizes intestinal fungi and has an immune regulatory role in colitis.

Detection of sunn pest-damaged wheat samples using visible/near-infrared spectroscopy based on pattern recognition.

Science.gov (United States)

Basati, Zahra; Jamshidi, Bahareh; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-05-30

The presence of sunn pest-damaged grains in wheat mass reduces the quality of flour and bread produced from it. Therefore, it is essential to assess the quality of the samples in collecting and storage centers of wheat and flour mills. In this research, the capability of visible/near-infrared (Vis/NIR) spectroscopy combined with pattern recognition methods was investigated for discrimination of wheat samples with different percentages of sunn pest-damaged. To this end, various samples belonging to five classes (healthy and 5%, 10%, 15% and 20% unhealthy) were analyzed using Vis/NIR spectroscopy (wavelength range of 350-1000 nm) based on both supervised and unsupervised pattern recognition methods. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) as the unsupervised techniques and soft independent modeling of class analogies (SIMCA) and partial least squares-discriminant analysis (PLS-DA) as supervised methods were used. The results showed that Vis/NIR spectra of healthy samples were correctly clustered using both PCA and HCA. Due to the high overlapping between the four unhealthy classes (5%, 10%, 15% and 20%), it was not possible to discriminate all the unhealthy samples in individual classes. However, when considering only the two main categories of healthy and unhealthy, an acceptable degree of separation between the classes can be obtained after classification with supervised pattern recognition methods of SIMCA and PLS-DA. SIMCA based on PCA modeling correctly classified samples in two classes of healthy and unhealthy with classification accuracy of 100%. Moreover, the power of the wavelengths of 839 nm, 918 nm and 995 nm were more than other wavelengths to discriminate two classes of healthy and unhealthy. It was also concluded that PLS-DA provides excellent classification results of healthy and unhealthy samples (R 2  = 0.973 and RMSECV = 0.057). Therefore, Vis/NIR spectroscopy based on pattern recognition techniques

Targeting Multiple Tumors Using T-Cells Engineered to Express a Natural Cytotoxicity Receptor 2-Based Chimeric Receptor

Directory of Open Access Journals (Sweden)

Vasyl Eisenberg

2017-09-01

Full Text Available Recent developments in cancer treatment are demonstrating the increasing and powerful potential of immunotherapeutic strategies. In this regard, the adoptive transfer of tumor-specific T-lymphocytes approaches can lead to tumor regression in cancer patients. More recently, the use of T-cells genetically engineered to express cancer-specific receptors such as the anti-CD19 chimeric antigen receptor (CAR continues to show promise for the treatment of hematological malignancies. Still, there is a crucial need to develop efficient CAR-T cell approaches for the treatment of solid tumors. It has been shown that other lymphocytes such as natural killer (NK cells can demonstrate potent antitumor function—nonetheless, their use in immunotherapy is rather limited due to difficulties in expanding these cells to therapeutically relevant numbers and to suppression by endogenous inhibitory mechanisms. Cancer recognition by NK cells is partly mediated by molecules termed natural cytotoxicity receptors (NCRs. In the present study, we hypothesize that it is possible to endow T-cells with an NK recognition pattern, providing them with a mean to recognize tumor cells, in a non-MHC restricted way. To test this, we genetically modified human T-cells with different chimeric receptors based on the human NCR2 molecule and then assessed their antitumor activity in vitro and in vivo. Our results show that expression in primary lymphocytes of an NCR2-derived CAR, termed s4428z, confers T-cells with the ability to specifically recognize heterogeneous tumors and to mediate tumor cytotoxicity in a mouse model. This study demonstrates the benefit of combining tumor recognition capability of NK cells with T cell effectiveness to improve cancer immunotherapy.

Behavioral and Physiological Neural Network Analyses: A Common Pathway toward Pattern Recognition and Prediction

Science.gov (United States)

Ninness, Chris; Lauter, Judy L.; Coffee, Michael; Clary, Logan; Kelly, Elizabeth; Rumph, Marilyn; Rumph, Robin; Kyle, Betty; Ninness, Sharon K.

2012-01-01

Using 3 diversified datasets, we explored the pattern-recognition ability of the Self-Organizing Map (SOM) artificial neural network as applied to diversified nonlinear data distributions in the areas of behavioral and physiological research. Experiment 1 employed a dataset obtained from the UCI Machine Learning Repository. Data for this study…

Endometrial transcription of microbial molecular patterns receptors in Gyr and F1 Holstein x Gyr postpartum cows

Directory of Open Access Journals (Sweden)

T.M. Martins

Full Text Available ABSTRACT Zebu and Holstein x Zebu crossbred have low incidence of uterine infection when compared to Holstein cows. Resistance to uterine infections may be associated with the ability to recognize invading microorganisms. Endometrial transcription of microbial molecular patterns receptors has been investigated in the postpartum period of Holstein cows, but it is completely unknown in Zebu or Holstein x Zebu cows. In this study, 9 Gyr and 12 F1 Holstein x Gyr cows were submitted to endometrial biopsies at the first and seventh days postpartum, with the objective to measure transcription levels of toll-like receptors (TLRs 1/6, 2, 4, 5, and 9; nucleotide-binding oligomerization domain (NOD-like receptors 1 and 2; and coreceptors cluster of differentiation 14 (CD14 and myeloid differentiation protein-2 (MD-2. There was a significant (P<0.05 decrease in transcription of TLR5 in Gyr, and an increase in transcription of TLR9 in F1 cows, between the first and seventh day postpartum. Both groups had low incidences of uterine infections up to 42 days postpartum. Uterine involution completed at 27.7 ± 10.1 and 25.1 ± 4.7 days postpartum for Gyr and F1 cows, respectively. In Gyr cows, higher transcription levels of TLR1/6 and NOD1 correlated to a longer period required for uterine involution. In F1 cows, lower levels of TLR1/6, TLR2 and NOD2 correlated to a longer period required for uterine involution. In conclusion, some pathogen recognition receptors associated significantly with the time required for uterine involution in Gyr and F1 cows.

Differential ontogenetic patterns of levocabastine-sensitive neurotensin NT2 receptors and of NT1 receptors in the rat brain revealed by in situ hybridization.

Science.gov (United States)

Lépée-Lorgeoux, I; Betancur, C; Rostène, W; Pélaprat, D

1999-03-12

The postnatal ontogeny of the levocabastine-sensitive neurotensin receptor (NT2) mRNA was studied by in situ hybridization in the rat brain and compared with the distribution of the levocabastine-insensitive NT1 receptor. NT2 receptor mRNA was absent at birth from all brain structures except the ependymal cell layer lining the ventricles. The development of NT2 receptor mRNA followed three ontogenetic patterns. The first pattern, involving the majority of the cerebral gray matter, was characterized by a continuous increase from postnatal day 5 (P5) to P30. The second one, involving regions rich in myelinated fibers such as the corpus callosum and lacunosum moleculare layer of the hippocampus, exhibited a pronounced increase between P5 and P10, peaked at P15 and was followed by a plateau or a slight decrease. The third pattern was observed in the ependymal cell layer lining the olfactory and lateral ventricles, where the high labeling already present at birth continued to increase during development. These different developmental patterns could reflect the variety of cells expressing NT2 receptor mRNA, including neurons, protoplasmic astrocytes in gray matter, fibrous astrocytes present in myelinated fibers tracts, and ependymal cells. In contrast, NT1 receptor mRNA, which seems to be associated only with neurons, was highly and transiently expressed during the perinatal period in the cerebral cortex, hippocampus and striatal neuroepithelium. Other regions, notably the ventral tegmental area and substantia nigra compacta, exhibited a gradual increase in NT1 receptor signal, reaching adult levels by P21. Both the differential localization and ontogenetic profiles of NT1 and NT2 receptor mRNAs suggest different involvement of these two receptors in brain functions and development. Copyright 1999 Elsevier Science B.V.

Application of pattern recognition methods for evaluating the immune status in patients

International Nuclear Information System (INIS)

Stavitsky, R.B.; Guslistyj, I.V.; Miroshnichenko, I.V.; Karklinskaya, O.N.; Ryabinina, I.D.; Kosova, I.P.; Stolpnikova, V.N.; Malaeva, N.S.; Latypova, I.I.; Lebedev, L.A.

2001-01-01

The effectiveness of mathematical tools for pattern recognition as applied to numerical assessments of the immune status of patients exposed to ecological hazards is evaluated by experimentation. The immune status is estimated according to a two-class scheme (norm/abnormality) based on blood indicators of immunity for the patients examined. The task of categorizing patients by immunological parameters of blood is shown to be resolved with high effectiveness for determining the immune status [ru

Investigations into the involvement of NMDA mechanisms in recognition memory.

Science.gov (United States)

Warburton, E Clea; Barker, Gareth R I; Brown, Malcom W

2013-11-01

This review will focus on evidence showing that NMDA receptor neurotransmission is critical for synaptic plasticity processes within brain regions known to be necessary for the formation of object recognition memories. The aim will be to provide evidence concerning NMDA mechanisms related to recognition memory processes and show that recognition memory for objects, places or associations between objects and places depends on NMDA neurotransmission within the perirhinal cortex, temporal association cortex medial prefrontal cortex and hippocampus. Administration of the NMDA antagonist AP5, selectively into each of these brain regions has revealed that the extent of the involvement NMDA receptors appears dependent on the type of information required to solve the recognition memory task; thus NMDA receptors in the perirhinal cortex are crucial for the encoding of long-term recognition memory for objects, and object-in-place associations, but not for short-term recognition memory or for retrieval. In contrast the hippocampus and medial prefrontal cortex are required for both long-term and short-term recognition memory for places or associations between objects and places, or for recognition memory tasks that have a temporal component. Such studies have therefore confirmed that the multiple brain regions make distinct contributions to recognition memory but in addition that more than one synaptic plasticity process must be involved. This article is part of the Special Issue entitled 'Glutamate Receptor-Dependent Synaptic Plasticity'. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Defect Localization Capabilities of a Global Detection Scheme: Spatial Pattern Recognition Using Full-field Vibration Test Data in Plates

Science.gov (United States)

Saleeb, A. F.; Prabhu, M.; Arnold, S. M. (Technical Monitor)

2002-01-01

Recently, a conceptually simple approach, based on the notion of defect energy in material space has been developed and extensively studied (from the theoretical and computational standpoints). The present study focuses on its evaluation from the viewpoint of damage localization capabilities in case of two-dimensional plates; i.e., spatial pattern recognition on surfaces. To this end, two different experimental modal test results are utilized; i.e., (1) conventional modal testing using (white noise) excitation and accelerometer-type sensors and (2) pattern recognition using Electronic speckle pattern interferometry (ESPI), a full field method capable of analyzing the mechanical vibration of complex structures. Unlike the conventional modal testing technique (using contacting accelerometers), these emerging ESPI technologies operate in a non-contacting mode, can be used even under hazardous conditions with minimal or no presence of noise and can simultaneously provide measurements for both translations and rotations. Results obtained have clearly demonstrated the robustness and versatility of the global NDE scheme developed. The vectorial character of the indices used, which enabled the extraction of distinct patterns for localizing damages proved very useful. In the context of the targeted pattern recognition paradigm, two algorithms were developed for the interrogation of test measurements; i.e., intensity contour maps for the damaged index, and the associated defect energy vector field plots.

Contrasted evolutionary histories of two Toll-like receptors (Tlr4 and Tlr7) in wild rodents (MURINAE)

Czech Academy of Sciences Publication Activity Database

Fornůsková, Alena; Vinkler, M.; Pages, M.; Galan, M.; Jousselin, E.; Cerqueira, F.; Morand, S.; Charbonnel, N.; Bryja, Josef; Cosson, J.-F.

2013-01-01

Roč. 13, č. 194 (2013), s. 194 ISSN 1471-2148 R&D Projects : GA ČR GA206/08/0640 Institutional support: RVO:68081766 Keywords : Arms race * Host-pathogen interaction * Pattern recognition receptors * Adaptive evolution * Pathogen-Associated Molecular Pattern (PAMP) Subject RIV: EG - Zoology Impact factor: 3.407, year: 2013 http://www.biomedcentral.com/1471-2148/13/194

Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

Science.gov (United States)

Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

2015-01-01

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

Effects of the Maximum Luminance in a Medical-grade Liquid-crystal Display on the Recognition Time of a Test Pattern: Observer Performance Using Landolt Rings.

Science.gov (United States)

Doi, Yasuhiro; Matsuyama, Michinobu; Ikeda, Ryuji; Hashida, Masahiro

2016-07-01

This study was conducted to measure the recognition time of the test pattern and to investigate the effects of the maximum luminance in a medical-grade liquid-crystal display (LCD) on the recognition time. Landolt rings as signals of the test pattern were used with four random orientations, one on each of the eight gray-scale steps. Ten observers input the orientation of the gap on the Landolt rings using cursor keys on the keyboard. The recognition times were automatically measured from the display of the test pattern on the medical-grade LCD to the input of the orientation of the gap in the Landolt rings. The maximum luminance in this study was set to one of four values (100, 170, 250, and 400 cd/m(2)), for which the corresponding recognition times were measured. As a result, the average recognition times for each observer with maximum luminances of 100, 170, 250, and 400 cd/m(2) were found to be 3.96 to 7.12 s, 3.72 to 6.35 s, 3.53 to 5.97 s, and 3.37 to 5.98 s, respectively. The results indicate that the observer's recognition time is directly proportional to the luminance of the medical-grade LCD. Therefore, it is evident that the maximum luminance of the medical-grade LCD affects the test pattern recognition time.

An Ultrasonic Pattern Recognition Approach to Welding Defect Classification

International Nuclear Information System (INIS)

Song, Sung Jin

1995-01-01

Classification of flaws in weldments from their ultrasonic scattering signals is very important in quantitative nondestructive evaluation. This problem is ideally suited to a modern ultrasonic pattern recognition technique. Here brief discussion on systematic approach to this methodology is presented including ultrasonic feature extraction, feature selection and classification. A stronger emphasis is placed on probabilistic neural networks as efficient classifiers for many practical classification problems. In an example probabilistic neural networks are applied to classify flaws in weldments into 3 classes such as cracks, porosity and slag inclusions. Probabilistic nets are shown to be able to exhibit high performance of other classifiers without any training time overhead. In addition, forward selection scheme for sensitive features is addressed to enhance network performance

ICRPfinder: a fast pattern design algorithm for coding sequences and its application in finding potential restriction enzyme recognition sites

Directory of Open Access Journals (Sweden)

Stafford Phillip

2009-09-01

Full Text Available Abstract Background Restriction enzymes can produce easily definable segments from DNA sequences by using a variety of cut patterns. There are, however, no software tools that can aid in gene building -- that is, modifying wild-type DNA sequences to express the same wild-type amino acid sequences but with enhanced codons, specific cut sites, unique post-translational modifications, and other engineered-in components for recombinant applications. A fast DNA pattern design algorithm, ICRPfinder, is provided in this paper and applied to find or create potential recognition sites in target coding sequences. Results ICRPfinder is applied to find or create restriction enzyme recognition sites by introducing silent mutations. The algorithm is shown capable of mapping existing cut-sites but importantly it also can generate specified new unique cut-sites within a specified region that are guaranteed not to be present elsewhere in the DNA sequence. Conclusion ICRPfinder is a powerful tool for finding or creating specific DNA patterns in a given target coding sequence. ICRPfinder finds or creates patterns, which can include restriction enzyme recognition sites, without changing the translated protein sequence. ICRPfinder is a browser-based JavaScript application and it can run on any platform, in on-line or off-line mode.

Architecture of top down, parallel pattern recognition system TOPS and its application to the MR head images

International Nuclear Information System (INIS)

Matsunoshita, Jun-ichi; Akamatsu, Shigeo; Yamamoto, Shinji.

1993-01-01

This paper describes about the system architecture of a new image recognition system TOPS (top-down parallel pattern recognition system), and its application to the automatic extraction of brain organs (cerebrum, cerebellum, brain stem) from 3D-MRI images. Main concepts of TOPS are as follows: (1) TOPS is the top-down type recognition system, which allows parallel models in each level of hierarchy structure. (2) TOPS allows parallel image processing algorithms for one purpose (for example, for extraction of one special organ). This results in multiple candidates for one purpose, and judgment to get unique solution for it will be made at upper level of hierarchy structure. (author)

A pattern recognition mezzanine based on associative memory and FPGA technology for L1 track triggering at HL-LHC

International Nuclear Information System (INIS)

Alunni, L.; Biesuz, N.; Bilei, G.M.; Citraro, S.; Crescioli, F.; Fanò, L.; Fedi, G.; Magalotti, D.; Magazzù, G.; Servoli, L.; Storchi, L.; Palla, F.; Placidi, P.; Papi, A.; Piadyk, Y.; Rossi, E.; Spiezia, A.

2016-01-01

The increase of luminosity at HL-LHC will require the introduction of tracker information at Level-1 trigger system for the experiments to maintain an acceptable trigger rate to select interesting events despite the one order of magnitude increase in the minimum bias interactions. To extract in the required latency the track information a dedicated hardware has to be used. We present the tests of a prototype system (Pattern Recognition Mezzanine) as core of pattern recognition and track fitting for HL-LHC ATLAS and CMS experiments, combining the power of both Associative Memory custom ASIC and modern Field Programmable Gate Array (FPGA) devices.

A pattern recognition mezzanine based on associative memory and FPGA technology for L1 track triggering at HL-LHC

Science.gov (United States)

Alunni, L.; Biesuz, N.; Bilei, G. M.; Citraro, S.; Crescioli, F.; Fanò, L.; Fedi, G.; Magalotti, D.; Magazzù, G.; Servoli, L.; Storchi, L.; Palla, F.; Placidi, P.; Papi, A.; Piadyk, Y.; Rossi, E.; Spiezia, A.

2016-07-01

The increase of luminosity at HL-LHC will require the introduction of tracker information at Level-1 trigger system for the experiments to maintain an acceptable trigger rate to select interesting events despite the one order of magnitude increase in the minimum bias interactions. To extract in the required latency the track information a dedicated hardware has to be used. We present the tests of a prototype system (Pattern Recognition Mezzanine) as core of pattern recognition and track fitting for HL-LHC ATLAS and CMS experiments, combining the power of both Associative Memory custom ASIC and modern Field Programmable Gate Array (FPGA) devices.

A pattern recognition mezzanine based on associative memory and FPGA technology for L1 track triggering at HL-LHC

Energy Technology Data Exchange (ETDEWEB)

Alunni, L. [INFN Sezione di Perugia (Italy); Biesuz, N. [INFN Sezione di Pisa (Italy); Bilei, G.M. [INFN Sezione di Perugia (Italy); Citraro, S. [Università di Pisa, Pisa (Italy); Crescioli, F. [LPNHE, Paris (France); Fanò, L. [INFN Sezione di Perugia (Italy); Fedi, G., E-mail: giacomo.fedi@pi.infn.it [INFN Sezione di Pisa (Italy); Magalotti, D. [INFN Sezione di Perugia (Italy); UNIMORE, Modena (Italy); Magazzù, G. [INFN Sezione di Pisa (Italy); Servoli, L.; Storchi, L. [INFN Sezione di Perugia (Italy); Palla, F. [INFN Sezione di Pisa (Italy); Placidi, P. [INFN Sezione di Perugia (Italy); DIEI, Perugia (Italy); Papi, A. [INFN Sezione di Perugia (Italy); Piadyk, Y. [LPNHE, Paris (France); Rossi, E. [INFN Sezione di Pisa (Italy); Spiezia, A. [IHEP (China)

2016-07-11

The increase of luminosity at HL-LHC will require the introduction of tracker information at Level-1 trigger system for the experiments to maintain an acceptable trigger rate to select interesting events despite the one order of magnitude increase in the minimum bias interactions. To extract in the required latency the track information a dedicated hardware has to be used. We present the tests of a prototype system (Pattern Recognition Mezzanine) as core of pattern recognition and track fitting for HL-LHC ATLAS and CMS experiments, combining the power of both Associative Memory custom ASIC and modern Field Programmable Gate Array (FPGA) devices.

Compact holographic memory and its application to optical pattern recognition

Science.gov (United States)

Chao, Tien-Hsin; Reyes, George F.; Zhou, Hanying

2001-03-01

JPL is developing a high-density, nonvolatile Compact Holographic Data Storage (CHDS) system to enable large- capacity, high-speed, low power consumption, and read/write of data for commercial and space applications. This CHDS system consists of laser diodes, photorefractive crystal, spatial light modulator, photodetector array, and I/O electronic interface. In operation, pages of information would be recorded and retrieved with random access and high- speed. In this paper, recent technology progress in developing this CHDS at JPL will be presented. The recent applications of the CHDS to optical pattern recognition, as a high-density, high transfer rate memory bank will also be discussed.

6th International Conference on Pattern Recognition and Machine Intelligence

CERN Document Server

Gawrysiak, Piotr; Kryszkiewicz, Marzena; Rybiński, Henryk

2016-01-01

This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results.

Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

Directory of Open Access Journals (Sweden)

Pavol Partila

2015-01-01

Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

Toll-Like Receptors: Role in Dermatological Disease

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Aswin Hari

2010-01-01

Full Text Available Toll-like receptors (TLRs are a class of conserved receptors that recognize pathogen-associated molecular patterns (PAMPs present in microbes. In humans, at least ten TLRs have been identified, and their recognition targets range from bacterial endotoxins to lipopeptides, DNA, dsRNA, ssRNA, fungal products, and several host factors. Of dermatological interest, these receptors are expressed on several skin cells including keratinocytes, melanocytes, and Langerhans cells. TLRs are essential in identifying microbial products and are known to link the innate and adaptive immune systems. Over the years, there have been significant advances in our understanding of TLRs in skin inflammation, cutaneous malignancies, and defence mechanisms. In this paper, we will describe the association between TLRs and various skin pathologies and discuss proposed TLR therapeutics.

Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication.

Science.gov (United States)

Dhir, L; Habib, N E; Monro, D M; Rakshit, S

2010-06-01

The purpose of this study was to investigate the effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Prospective non-comparative cohort study. Images of 15 subjects were captured before (enrolment), and 5, 10, and 15 min after instillation of mydriatics before routine cataract surgery. After cataract surgery, images were captured 2 weeks thereafter. Enrolled and test images (after pupillary dilation and after cataract surgery) were segmented to extract the iris. This was then unwrapped onto a rectangular format for normalization and a novel method using the Discrete Cosine Transform was applied to encode the image into binary bits. The numerical difference between two iris codes (Hamming distance, HD) was calculated. The HD between identification and enrolment codes was used as a score and was compared with a confidence threshold for specific equipment, giving a match or non-match result. The Correct Recognition Rate (CRR) and Equal Error Rates (EERs) were calculated to analyse overall system performance. After cataract surgery, perfect identification and verification was achieved, with zero false acceptance rate, zero false rejection rate, and zero EER. After pupillary dilation, non-elastic deformation occurs and a CRR of 86.67% and EER of 9.33% were obtained. Conventional circle-based localization methods are inadequate. Matching reliability decreases considerably with increase in pupillary dilation. Cataract surgery has no effect on iris pattern recognition, whereas pupil dilation may be used to defeat an iris-based authentication system.

ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

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VIMALA C.

2015-05-01

Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

Molecular Recognition of Corticotropin releasing Factor by Its G protein-coupled Receptor CRFR1

Energy Technology Data Exchange (ETDEWEB)

Pioszak, Augen A.; Parker, Naomi R.; Suino-Powell, Kelly; Xu, H. Eric (Van Andel)

2009-01-15

The bimolecular interaction between corticotropin-releasing factor (CRF), a neuropeptide, and its type 1 receptor (CRFR1), a class B G-protein-coupled receptor (GPCR), is crucial for activation of the hypothalamic-pituitary-adrenal axis in response to stress, and has been a target of intense drug design for the treatment of anxiety, depression, and related disorders. As a class B GPCR, CRFR1 contains an N-terminal extracellular domain (ECD) that provides the primary ligand binding determinants. Here we present three crystal structures of the human CRFR1 ECD, one in a ligand-free form and two in distinct CRF-bound states. The CRFR1 ECD adopts the alpha-beta-betaalpha fold observed for other class B GPCR ECDs, but the N-terminal alpha-helix is significantly shorter and does not contact CRF. CRF adopts a continuous alpha-helix that docks in a hydrophobic surface of the ECD that is distinct from the peptide-binding site of other class B GPCRs, thereby providing a basis for the specificity of ligand recognition between CRFR1 and other class B GPCRs. The binding of CRF is accompanied by clamp-like conformational changes of two loops of the receptor that anchor the CRF C terminus, including the C-terminal amide group. These structural studies provide a molecular framework for understanding peptide binding and specificity by the CRF receptors as well as a template for designing potent and selective CRFR1 antagonists for therapeutic applications.

The secreted peptide PIP1 amplifies immunity through receptor-like kinase 7.

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Shuguo Hou

2014-09-01

Full Text Available In plants, innate immune responses are initiated by plasma membrane-located pattern recognition receptors (PRRs upon recognition of elicitors, including exogenous pathogen-associated molecular patterns (PAMPs and endogenous damage-associated molecular patterns (DAMPs. Arabidopsis thaliana produces more than 1000 secreted peptide candidates, but it has yet to be established whether any of these act as elicitors. Here we identified an A. thaliana gene family encoding precursors of PAMP-induced secreted peptides (prePIPs through an in-silico approach. The expression of some members of the family, including prePIP1 and prePIP2, is induced by a variety of pathogens and elicitors. Subcellular localization and proteolytic processing analyses demonstrated that the prePIP1 product is secreted into extracellular spaces where it is cleaved at the C-terminus. Overexpression of prePIP1 and prePIP2, or exogenous application of PIP1 and PIP2 synthetic peptides corresponding to the C-terminal conserved regions in prePIP1 and prePIP2, enhanced immune responses and pathogen resistance in A. thaliana. Genetic and biochemical analyses suggested that the receptor-like kinase 7 (RLK7 functions as a receptor of PIP1. Once perceived by RLK7, PIP1 initiates overlapping and distinct immune signaling responses together with the DAMP PEP1. PIP1 and PEP1 cooperate in amplifying the immune responses triggered by the PAMP flagellin. Collectively, these studies provide significant insights into immune modulation by Arabidopsis endogenous secreted peptides.

Pattern recognition application for surveillance of abnormal conditions in a nuclear reactor

International Nuclear Information System (INIS)

Pepelyshev, Yu.N.; Dzwinel, W.

1990-01-01

The system to monitor abnormal conditions in a nuclear reactor, based on the noise analysis of the reactor basic parameters such as power, temperature and coolant flow rate, has been developed. The pattern recognition techniques such as clustering, cluster analysis, feature selection and clusters visualization methods form the basis of the software. Apart from non-hierarchical clustering procedures applied earlier, the hierarchical one is recommended. The system application for IBR-2 Dubna reactor diagnostics is shown. 10 refs.; 6 figs

Graphical symbol recognition

OpenAIRE

K.C. , Santosh; Wendling , Laurent

2015-01-01

International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

An Application of Discriminant Analysis to Pattern Recognition of Selected Contaminated Soil Features in Thin Sections

DEFF Research Database (Denmark)

Ribeiro, Alexandra B.; Nielsen, Allan Aasbjerg

1997-01-01

qualitative microprobe results: present elements Al, Si, Cr, Fe, As (associated with others). Selected groups of calibrated images (same light conditions and magnification) submitted to discriminant analysis, in order to find a pattern of recognition in the soil features corresponding to contamination already...

Cytotoxic T lymphocyte responses by chimeric thymocytes. Self-recognition is determined early in T cell development

International Nuclear Information System (INIS)

Kruisbeek, A.M.; Hodes, R.J.; Singer, A.

1981-01-01

In this study the cytotoxic T lymphocyte (CTL) recognition pattern of thymocytes from recently reconstituted parent leads to F1 and F1 leads to parent radiation bone marrow chimeras was investigated. Chimeric thymocytes were entirely of donor origin approximately 4 weeks after irradiation and reconstitution but were not capable of autonomously generating either alloreactive or trinitrophenyl (TNP)-modified-self-reactive CTL responses. These experiments demonstrte that even at the earliest time CTL effectors of donor origin from the thymuses of chimeras can be studied, their self-receptor repertoire has already been restricted to recognition of host MHC determinants. These results support the cocept that the host environment influences the self-recognition capacity of T cells at the pre- or intrathymic stage of differentation

Pipeline Structural Damage Detection Using Self-Sensing Technology and PNN-Based Pattern Recognition

International Nuclear Information System (INIS)

Lee, Chang Gil; Park, Woong Ki; Park, Seung Hee

2011-01-01

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach

Recognition of damage-associated, nucleic acid-related molecular patterns during inflammation and vaccination

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Nao eJounai

2013-01-01

Full Text Available All mammalian cells are equipped with large numbers of sensors for protection from various sorts of invaders, who, in turn, are equipped with molecules containing pathogen-associated molecular patterns (PAMPs. Once these sensors recognize non-self antigens containing PAMPs, various physiological responses including inflammation are induced to eliminate the pathogens. However, the host sometimes suffers from chronic infection or continuous injuries, resulting in production of self-molecules containing damage-associated molecular patterns (DAMPs. DAMPs are also responsible for the elimination of pathogens, but promiscuous recognition of DAMPs through sensors against PAMPs has been reported. Accumulation of DAMPs leads to massive inflammation and continuous production of DAMPs; that is, a vicious circle leading to the development of autoimmune disease. From a vaccinological point of view, the accurate recognition of both PAMPs and DAMPs is important for vaccine immunogenicity, because vaccine adjuvants are composed of several PAMPs and/or DAMPs, which are also associated with severe adverse events after vaccination. Here, we review as the roles of PAMPs and DAMPs upon infection with pathogens or inflammation, and the sensors responsible for recognizing them, as well as their relationship with the development of autoimmune disease or the immunogenicity of vaccines.

Is it worth changing pattern recognition methods for structural health monitoring?

Science.gov (United States)

Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

2017-05-01

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

[Fuzzing pattern recognition study on Raman spectrum of tumor peripheral tissue].

Science.gov (United States)

Luo, Lei; Zhao, Yuan-li; Ge, Xiang-hong; Zhang, Xiao-dong; Hao, Zhi-fang; Lü, Jing

2006-06-01

On the basis of some theories about fuzzing pattern recognition, the present article studied the data preprocessing of the Raman spectrum of tumor peripheral tissue, and feature extraction and selection. According to these features the authors improved the leaning towards the bigger membership function of trapezoidal distribution. The authors built the membership function of Raman spectrum of tumor peripheral tissue which belongs to malignant tumor on the basis of 40 specimens, and designed the classifier. The test of other 40 specimens showed that the discrimination of malignant tumor is 82.4%, while that of beginning tumor is 73.9%.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

Science.gov (United States)

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition.

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Alicia Alva

Full Text Available Tuberculosis control efforts are hampered by a mismatch in diagnostic technology: modern optimal diagnostic tests are least available in poor areas where they are needed most. Lack of adequate early diagnostics and MDR detection is a critical problem in control efforts. The Microscopic Observation Drug Susceptibility (MODS assay uses visual recognition of cording patterns from Mycobacterium tuberculosis (MTB to diagnose tuberculosis infection and drug susceptibility directly from a sputum sample in 7-10 days with a low cost. An important limitation that laboratories in the developing world face in MODS implementation is the presence of permanent technical staff with expertise in reading MODS. We developed a pattern recognition algorithm to automatically interpret MODS results from digital images. The algorithm using image processing, feature extraction and pattern recognition determined geometrical and illumination features used in an object-model and a photo-model to classify TB-positive images. 765 MODS digital photos were processed. The single-object model identified MTB (96.9% sensitivity and 96.3% specificity and was able to discriminate non-tuberculous mycobacteria with a high specificity (97.1% M. avium, 99.1% M. chelonae, and 93.8% M. kansasii. The photo model identified TB-positive samples with 99.1% sensitivity and 99.7% specificity. This algorithm is a valuable tool that will enable automatic remote diagnosis using Internet or cellphone telephony. The use of this algorithm and its further implementation in a telediagnostics platform will contribute to both faster TB detection and MDR TB determination leading to an earlier initiation of appropriate treatment.

Differential theory of learning for efficient neural network pattern recognition

Science.gov (United States)

Hampshire, John B., II; Vijaya Kumar, Bhagavatula

1993-09-01

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Speech Recognition

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Adrian Morariu

2009-01-01

Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi.

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Kriti Tyagi

Full Text Available The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites.Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively.Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1 showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3 showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs for human erythrocyte receptors. However, the third protein (PkTRAg67.1 utilized the additional but different human erythrocyte receptor(s as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite.Recognition and sharing of human erythrocyte receptor(s by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host.

Recognition of Human Erythrocyte Receptors by the Tryptophan-Rich Antigens of Monkey Malaria Parasite Plasmodium knowlesi.

Science.gov (United States)

Tyagi, Kriti; Gupta, Deepali; Saini, Ekta; Choudhary, Shilpa; Jamwal, Abhishek; Alam, Mohd Shoeb; Zeeshan, Mohammad; Tyagi, Rupesh K; Sharma, Yagya D

2015-01-01

The monkey malaria parasite Plasmodium knowlesi also infect humans. There is a lack of information on the molecular mechanisms that take place between this simian parasite and its heterologous human host erythrocytes leading to this zoonotic disease. Therefore, we investigated here the binding ability of P. knowlesi tryptophan-rich antigens (PkTRAgs) to the human erythrocytes and sharing of the erythrocyte receptors between them as well as with other commonly occurring human malaria parasites. Six PkTRAgs were cloned and expressed in E.coli as well as in mammalian CHO-K1 cell to determine their human erythrocyte binding activity by cell-ELISA, and in-vitro rosetting assay, respectively. Three of six PkTRAgs (PkTRAg38.3, PkTRAg40.1, and PkTRAg67.1) showed binding to human erythrocytes. Two of them (PkTRAg40.1 and PkTRAg38.3) showed cross-competition with each other as well as with the previously described P.vivax tryptophan-rich antigens (PvTRAgs) for human erythrocyte receptors. However, the third protein (PkTRAg67.1) utilized the additional but different human erythrocyte receptor(s) as it did not cross-compete for erythrocyte binding with either of these two PkTRAgs as well as with any of the PvTRAgs. These three PkTRAgs also inhibited the P.falciparum parasite growth in in-vitro culture, further indicating the sharing of human erythrocyte receptors by these parasite species and the biological significance of this receptor-ligand interaction between heterologous host and simian parasite. Recognition and sharing of human erythrocyte receptor(s) by PkTRAgs with human parasite ligands could be part of the strategy adopted by the monkey malaria parasite to establish inside the heterologous human host.

Exploitation of the Toll-like receptor system in cancer: a doubled-edged sword?

LENUS (Irish Health Repository)

Killeen, S D

2012-02-03

The toll-like receptor (TLR) system constitutes a pylogenetically ancient, evolutionary conserved, archetypal pattern recognition system, which underpins pathogen recognition by and activation of the immune system. Toll-like receptor agonists have long been used as immunoadjuvants in anti cancer immunotherapy. However, TLRs are increasingly implicated in human disease pathogenesis and an expanding body of both clinical and experimental evidence suggests that the neoplastic process may subvert TLR signalling pathways to advance cancer progression. Recent discoveries in the TLR system open a multitude of potential therapeutic avenues. Extrapolation of such TLR system manipulations to a clinical oncological setting demands care to prevent potentially deleterious activation of TLR-mediated survival pathways. Thus, the TLR system is a double-edge sword, which needs to be carefully wielded in the setting of neoplastic disease.

Landsat TM band 431 combine on clustering analysis for pattern recognition land use using idrisi 4.2 software

International Nuclear Information System (INIS)

Wiweka, Arief H.; Izzawati, Tjahyaningsih A.

1997-01-01

The recognition of earth object's pattern which is recorded on remote sensing digital image can do by classification process based on the group of spectral pixel value. The spectral assessment on a spatial which represent the object characteristic can be helped through supervised or unsupervised. On certain case, there no media, such as maps, airborne, photo, the capability of field observation and the knowledge of object's location. Classification process can be done by clustering. The group of pixel based on the wide of the whole value interval of spectral image, then the class group base on the desired accuracy. The clustering method in Idris 4.2 software equipments are sequential method, statistic, iso data, and RGB. The clustering existence can help pre-process pattern recognition

Molecular recognition of AT-DNA sequences by the induced CD pattern of dibenzotetraaza[14]annulene (DBTAA)-adenine derivatives.

Science.gov (United States)

Stojković, Marijana Radić; Skugor, Marko; Dudek, Lukasz; Grolik, Jarosław; Eilmes, Julita; Piantanida, Ivo

2014-01-01

An investigation of the interactions of two novel and several known DBTAA-adenine conjugates with double-stranded DNA and RNA has revealed the DNA/RNA groove as the dominant binding site, which is in contrast to the majority of previously studied DBTAA analogues (DNA/RNA intercalators). Only DBTAA-propyladenine conjugates revealed the molecular recognition of AT-DNA by an ICD band pattern > 300 nm, whereas significant ICD bands did not appear for other ds-DNA/RNA. A structure-activity relation for the studied series of compounds showed that the essential structural features for the ICD recognition are a) the presence of DNA-binding appendages (adenine side chain and positively charged side chain) on both DBTAA side chains, and b) the presence of a short propyl linker, which does not support intramolecular aromatic stacking between DBTAA and adenine. The observed AT-DNA-ICD pattern differs from previously reported ss-DNA (poly dT) ICD recognition by a strong negative ICD band at 350 nm, which allows for the dynamic differentiation between ss-DNA (poly dT) and coupled ds-AT-DNA.

Pattern Recognition by Humans and Machines

International Nuclear Information System (INIS)