WorldWideScience

Sample records for learning identify essential

  1. Identifying essential genes in bacterial metabolic networks with machine learning methods

    Science.gov (United States)

    2010-01-01

    Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism. PMID:20438628

  2. Learning Zimbra Server essentials

    CERN Document Server

    Kouka, Abdelmonam

    2013-01-01

    A standard tutorial approach which will guide the readers on all of the intricacies of the Zimbra Server.If you are any kind of Zimbra user, this book will be useful for you, from newbies to experts who would like to learn how to setup a Zimbra server. If you are an IT administrator or consultant who is exploring the idea of adopting, or have already adopted Zimbra as your mail server, then this book is for you. No prior knowledge of Zimbra is required.

  3. Learning Neuroimaging. 100 essential cases

    International Nuclear Information System (INIS)

    Asis Bravo-Rodriguez, Francisco de; Diaz-Aguilera, Rocio; Hygino da Cruz, Luiz Celso

    2012-01-01

    Neuroradiology is the branch of radiology that comprises both imaging and invasive procedures related to the brain, spine and spinal cord, head, neck, organs of special sense (eyes, ears, nose), cranial and spinal nerves, and cranial, cervical, and spinal vessels. Special training and skills are required to enable the neuroradiologist to function as an expert diagnostic and therapeutic consultant and practitioner. In addition to knowledge of imaging findings, the neuroradiologist is required to learn the fundamentals of structural and functional neuroanatomy, neuropathology, and neuropathophysiology as well as the clinical manifestations of diseases of the brain, spine and spinal cord, head, neck, and organs of special sense. This book is intended as an introduction to neuroradiology and aims to provide the reader with a comprehensive overview of this highly specialized radiological subspecialty. One hundred illustrated cases from clinical practice are presented in a standard way. Each case is supported by representative images and is divided into three parts: a brief summary of the patient's medical history, a discussion of the disease, and a description of the most characteristic imaging features of the disorder. The focus is not only on common neuroradiological entities such as stroke and acute head trauma but also on less frequent disorders that the practitioner should recognize. Learning Neuroimaging: 100 Essential Cases is an ideal resource for neuroradiology and radiology residents, neurology residents, neurosurgery residents, nurses, radiology technicians, and medical students. (orig.)

  4. Learning Neuroimaging. 100 essential cases

    Energy Technology Data Exchange (ETDEWEB)

    Asis Bravo-Rodriguez, Francisco de [Reina Sofia University Hospital, Cordoba (Spain). Diagnostic and Therapeutics Neuroradiology; Diaz-Aguilera, Rocio [Alto Guadalquivir Hospital, Andujar, Jaen (Spain). Dept. of Radiology; Hygino da Cruz, Luiz Celso [Universidade Federal do Rio de Janeiro (Brazil). CDPI and IRM Ressonancia Magnetica

    2012-07-01

    Neuroradiology is the branch of radiology that comprises both imaging and invasive procedures related to the brain, spine and spinal cord, head, neck, organs of special sense (eyes, ears, nose), cranial and spinal nerves, and cranial, cervical, and spinal vessels. Special training and skills are required to enable the neuroradiologist to function as an expert diagnostic and therapeutic consultant and practitioner. In addition to knowledge of imaging findings, the neuroradiologist is required to learn the fundamentals of structural and functional neuroanatomy, neuropathology, and neuropathophysiology as well as the clinical manifestations of diseases of the brain, spine and spinal cord, head, neck, and organs of special sense. This book is intended as an introduction to neuroradiology and aims to provide the reader with a comprehensive overview of this highly specialized radiological subspecialty. One hundred illustrated cases from clinical practice are presented in a standard way. Each case is supported by representative images and is divided into three parts: a brief summary of the patient's medical history, a discussion of the disease, and a description of the most characteristic imaging features of the disorder. The focus is not only on common neuroradiological entities such as stroke and acute head trauma but also on less frequent disorders that the practitioner should recognize. Learning Neuroimaging: 100 Essential Cases is an ideal resource for neuroradiology and radiology residents, neurology residents, neurosurgery residents, nurses, radiology technicians, and medical students. (orig.)

  5. Four Essential Dimensions of Workplace Learning

    Science.gov (United States)

    Hopwood, Nick

    2014-01-01

    Purpose: This conceptual paper aims to argue that times, spaces, bodies and things constitute four essential dimensions of workplace learning. It examines how practices relate or hang together, taking Gherardi's texture of practices or connectedness in action as the foundation for making visible essential but often overlooked dimensions of…

  6. National Assessment of College Student Learning: Identifying College Graduates' Essential Skills in Writing, Speech and Listening, and Critical Thinking. Final Project Report.

    Science.gov (United States)

    Jones, Elizabeth A.; And Others

    This study used an iterative Delphi survey process of about 600 faculty, employers, and policymakers to identify writing, speech and listening, and critical thinking skills that college graduates should achieve to become effective employees and citizens (National Education Goal 6). Participants reached a consensus about the importance in critical…

  7. Focusing on the essentials: learning for performance.

    Science.gov (United States)

    Murphy, Catherine J

    2008-12-10

    As The World health report 2006 emphasized, there is increasing consensus that training programmes should focus on "know-how" instead of "know-all." Health workers need to know how to do the job they will be expected to do. IntraHealth International's Learning for performance: a guide and toolkit for health worker training and education programs offers a step-by-step, customizable approach designed to develop the right skills linked to job responsibilities. Using Learning for performance (LFP) yields more efficient training that focuses on what is essential for health workers to do their jobs and on effective learning methods, while addressing the factors that ensure application of new skills on the job. This brief communication describes the Learning for performance approach and initial findings from its application for pre-service education and in-service training in three countries: India, Mali and Bangladesh. Based on IntraHealth's experiences, the author provides thoughts on how LFP's performance-based learning approach can be a useful tool in training scale-up to strengthen human resources for health.

  8. Focusing on the essentials: learning for performance

    Directory of Open Access Journals (Sweden)

    Murphy Catherine J

    2008-12-01

    Full Text Available Abstract As The World health report 2006 emphasized, there is increasing consensus that training programmes should focus on "know-how" instead of "know-all." Health workers need to know how to do the job they will be expected to do. IntraHealth International's Learning for performance: a guide and toolkit for health worker training and education programs offers a step-by-step, customizable approach designed to develop the right skills linked to job responsibilities. Using Learning for performance (LFP yields more efficient training that focuses on what is essential for health workers to do their jobs and on effective learning methods, while addressing the factors that ensure application of new skills on the job. This brief communication describes the Learning for performance approach and initial findings from its application for pre-service education and in-service training in three countries: India, Mali and Bangladesh. Based on IntraHealth's experiences, the author provides thoughts on how LFP's performance-based learning approach can be a useful tool in training scale-up to strengthen human resources for health.

  9. Learning commons evolution and collaborative essentials

    CERN Document Server

    Schader, Barbara

    2008-01-01

    This book examines successfully planned and implemented learning commons at several different academic institutions around the world. These case studies provide a methodology for effective planning, implementation and assessment. Practical information is provided on how to collaborate with campus stakeholders, estimate budgeting and staffing and determine the equipment, hardware and software needs. Also provided are memoranda of understandings (MOUs), planning checklists and assessment tools. This book reflects a unifying focus on both the evolution of learning commons to learning spaces and t

  10. Mobile Learning Devices. Essentials for Principals

    Science.gov (United States)

    Rogers, Kipp D.

    2011-01-01

    In "Mobile Learning Devices," the author helps educators confront and overcome their fears and doubts about using mobile learning devices (MLDs) such as cell phones, personal digital assistants, MP3 players, handheld games, digital audio players, and laptops in classrooms. School policies that ban such tools are outdated, the author suggests;…

  11. Feedback: an essential element of student learning in clinical practice.

    Science.gov (United States)

    Clynes, Mary P; Raftery, Sara E C

    2008-11-01

    Clinical practice is an essential component of the nursing curriculum. In order for the student to benefit fully from the experience regular performance feedback is required. Feedback should provide the student with information on current practice and offer practical advice for improved performance. The importance of feedback is widely acknowledged however it appears that there is inconsistency in its provision to students. The benefits of feedback include increased student confidence, motivation and self-esteem as well as improved clinical practice. Benefits such as enhanced interpersonal skills and a sense of personal satisfaction also accrue to the supervisor. Barriers to the feedback process are identified as inadequate supervisor training and education, unfavourable ward learning environment and insufficient time spent with students. In addition to the appropriate preparation of the supervisor effective feedback includes an appreciation of the steps of the feedback process, an understanding of the student response to feedback and effective communication skills.

  12. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    Science.gov (United States)

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  13. Machine learning for identifying botnet network traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2013-01-01

    . Due to promise of non-invasive and resilient detection, botnet detection based on network traffic analysis has drawn a special attention of the research community. Furthermore, many authors have turned their attention to the use of machine learning algorithms as the mean of inferring botnet......-related knowledge from the monitored traffic. This paper presents a review of contemporary botnet detection methods that use machine learning as a tool of identifying botnet-related traffic. The main goal of the paper is to provide a comprehensive overview on the field by summarizing current scientific efforts....... The contribution of the paper is three-fold. First, the paper provides a detailed insight on the existing detection methods by investigating which bot-related heuristic were assumed by the detection systems and how different machine learning techniques were adapted in order to capture botnet-related knowledge...

  14. Identifying the Essential Elements of Effective Science Communication: What Do the Experts Say?

    Science.gov (United States)

    Bray, Belinda; France, Bev; Gilbert, John K.

    2012-01-01

    Experts in science communication were asked to identify the essential elements of a science communication course for post-graduate students. A Delphi methodology provided a framework for a research design that accessed their opinions and allowed them to contribute to, reflect on and identify 10 essential elements. There was a high level of…

  15. Identifying Adverse Drug Events by Relational Learning.

    Science.gov (United States)

    Page, David; Costa, Vítor Santos; Natarajan, Sriraam; Barnard, Aubrey; Peissig, Peggy; Caldwell, Michael

    2012-07-01

    The pharmaceutical industry, consumer protection groups, users of medications and government oversight agencies are all strongly interested in identifying adverse reactions to drugs. While a clinical trial of a drug may use only a thousand patients, once a drug is released on the market it may be taken by millions of patients. As a result, in many cases adverse drug events (ADEs) are observed in the broader population that were not identified during clinical trials. Therefore, there is a need for continued, post-marketing surveillance of drugs to identify previously-unanticipated ADEs. This paper casts this problem as a reverse machine learning task , related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events.

  16. Identifying QCD Transition Using Deep Learning

    Science.gov (United States)

    Zhou, Kai; Pang, Long-gang; Su, Nan; Petersen, Hannah; Stoecker, Horst; Wang, Xin-Nian

    2018-02-01

    In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, V). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective "EoS-meter" in detecting the nature of the QCD transition.

  17. Understanding the essential elements of work-based learning and its relevance to everyday clinical practice.

    Science.gov (United States)

    Williams, Caroline

    2010-09-01

    To critically review the work-based learning literature and explore the implications of the findings for the development of work-based learning programmes. With NHS budgets under increasing pressure, and challenges to the impact of classroom-based learning on patient outcomes, work-based learning is likely to come under increased scrutiny as a potential solution. Evidence from higher education institutions suggests that work-based learning can improve practice, but in many cases it is perceived as little more than on-the-job training to perform tasks. The CINAHL database was searched using the keywords work-based learning, work-place learning and practice-based learning. Those articles that had a focus on post-registration nursing were selected and critically reviewed. Using the review of the literature, three key issues were explored. Work-based learning has the potential to change practice. Learning how to learn and critical reflection are key features. For effective work-based learning nurses need to take control of their own learning, receive support to critically reflect on their practice and be empowered to make changes to that practice. A critical review of the literature has identified essential considerations for the implementation of work-based learning. A change in culture from classroom to work-based learning requires careful planning and consideration of learning cultures. To enable effective work-based learning, nurse managers need to develop a learning culture in their workplace. They should ensure that skilled facilitation is provided to support staff with critical reflection and effecting changes in practice. CONTRIBUTION TO NEW KNOWLEDGE: This paper has identified three key issues that need to be considered in the development of work-based learning programmes. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  18. Identifying essential proteins based on sub-network partition and prioritization by integrating subcellular localization information.

    Science.gov (United States)

    Li, Min; Li, Wenkai; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

    2018-06-14

    Essential proteins are important participants in various life activities and play a vital role in the survival and reproduction of living organisms. Identification of essential proteins from protein-protein interaction (PPI) networks has great significance to facilitate the study of human complex diseases, the design of drugs and the development of bioinformatics and computational science. Studies have shown that highly connected proteins in a PPI network tend to be essential. A series of computational methods have been proposed to identify essential proteins by analyzing topological structures of PPI networks. However, the high noise in the PPI data can degrade the accuracy of essential protein prediction. Moreover, proteins must be located in the appropriate subcellular localization to perform their functions, and only when the proteins are located in the same subcellular localization, it is possible that they can interact with each other. In this paper, we propose a new network-based essential protein discovery method based on sub-network partition and prioritization by integrating subcellular localization information, named SPP. The proposed method SPP was tested on two different yeast PPI networks obtained from DIP database and BioGRID database. The experimental results show that SPP can effectively reduce the effect of false positives in PPI networks and predict essential proteins more accurately compared with other existing computational methods DC, BC, CC, SC, EC, IC, NC. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. Learning environments matter: Identifying influences on the ...

    African Journals Online (AJOL)

    Hennie

    The students completed the Student Motivation for Science Learning questionnaire. ... (1999), which gave the South African education system the opportunity to benchmark mathematics and .... petition and rewards (Ramnarain, 2013; Vedder-.

  20. Identifying Gatekeepers in Online Learning Networks

    Science.gov (United States)

    Gursakal, Necmi; Bozkurt, Aras

    2017-01-01

    The rise of the networked society has not only changed our perceptions but also the definitions, roles, processes and dynamics of online learning networks. From offline to online worlds, networks are everywhere and gatekeepers are an important entity in these networks. In this context, the purpose of this paper is to explore gatekeeping and…

  1. Learning environments matter: Identifying influences on the ...

    African Journals Online (AJOL)

    In the light of the poor academic achievement in science by secondary school students in South Africa, students' motivation for science learning should be enhanced. It is argued that this can only be achieved with insight into which motivational factors to target, with due consideration of the diversity in schools. The study ...

  2. Learning to cooperate is essential for progress in physics

    Science.gov (United States)

    Dickau, Jonathan J.

    2012-06-01

    At the 10th Frontiers of Fundamental Physics symposium, Gerard't Hooft stated that, for some of the advances we hope to see in Physics during the future, there must be a great deal of cooperation between physicists from different disciplines, as well as mathematicians, programmers, technologists, and others. This requires us to evolve a new mindset; however, as so much of our past progress has come out of a fiercely competitive process - especially since a critical review of our ideas about reality remains essential to making clear progress and checking our progress. We must also address the fact that some frameworks appear incompatible, as with relativity and quantum mechanics, whose unification remains distant despite years of attempts to find a quantum gravity theory. I explore the idea that playful exploration, using both left-brained and right-brained approaches to learning, allows us to resolve conflicting ideas by taking advantage of innate human developmental and learning strategies and brain structure. It may thus foster the kind of interdisciplinary cooperation we are hoping to see.

  3. Candidate Essential Genes in Burkholderia cenocepacia J2315 Identified by Genome-Wide TraDIS

    KAUST Repository

    Wong, Yee-Chin

    2016-08-22

    Burkholderia cenocepacia infection often leads to fatal cepacia syndrome in cystic fibrosis patients. However, antibiotic therapy rarely results in complete eradication of the pathogen due to its intrinsic resistance to many clinically available antibiotics. Recent attention has turned to the identification of essential genes as the proteins encoded by these genes may serve as potential targets for development of novel antimicrobials. In this study, we utilized TraDIS (Transposon Directed Insertion-site Sequencing) as a genome-wide screening tool to facilitate the identification of B. cenocepacia genes essential for its growth and viability. A transposon mutant pool consisting of approximately 500,000 mutants was successfully constructed, with more than 400,000 unique transposon insertion sites identified by computational analysis of TraDIS datasets. The saturated library allowed for the identification of 383 genes that were predicted to be essential in B. cenocepacia. We extended the application of TraDIS to identify conditionally essential genes required for in vitro growth and revealed an additional repertoire of 439 genes to be crucial for B. cenocepacia growth under nutrient-depleted conditions. The library of B. cenocepacia mutants can subsequently be subjected to various biologically related conditions to facilitate the discovery of genes involved in niche adaptation as well as pathogenicity and virulence.

  4. Candidate Essential Genes in Burkholderia cenocepacia J2315 Identified by Genome-Wide TraDIS

    KAUST Repository

    Wong, Yee-Chin; Abd El Ghany, Moataz; Naeem, Raeece; Lee, Kok-Wei; Tan, Yung-Chie; Pain, Arnab; Nathan, Sheila

    2016-01-01

    Burkholderia cenocepacia infection often leads to fatal cepacia syndrome in cystic fibrosis patients. However, antibiotic therapy rarely results in complete eradication of the pathogen due to its intrinsic resistance to many clinically available antibiotics. Recent attention has turned to the identification of essential genes as the proteins encoded by these genes may serve as potential targets for development of novel antimicrobials. In this study, we utilized TraDIS (Transposon Directed Insertion-site Sequencing) as a genome-wide screening tool to facilitate the identification of B. cenocepacia genes essential for its growth and viability. A transposon mutant pool consisting of approximately 500,000 mutants was successfully constructed, with more than 400,000 unique transposon insertion sites identified by computational analysis of TraDIS datasets. The saturated library allowed for the identification of 383 genes that were predicted to be essential in B. cenocepacia. We extended the application of TraDIS to identify conditionally essential genes required for in vitro growth and revealed an additional repertoire of 439 genes to be crucial for B. cenocepacia growth under nutrient-depleted conditions. The library of B. cenocepacia mutants can subsequently be subjected to various biologically related conditions to facilitate the discovery of genes involved in niche adaptation as well as pathogenicity and virulence.

  5. Candidate essential genes in Burkholderia cenocepacia J2315 identified by genome-wide TraDIS

    Directory of Open Access Journals (Sweden)

    Yee-Chin Wong

    2016-08-01

    Full Text Available Burkholderia cenocepacia infection often leads to fatal cepacia syndrome in cystic fibrosis patients. However, antibiotic therapy rarely results in complete eradication of the pathogen due to its intrinsic resistance to many clinically available antibiotics. Recent attention has turned to the identification of essential genes as the proteins encoded by these genes may serve as potential targets for development of novel antimicrobials. In this study, we utilized TraDIS (Transposon Directed Insertion-site Sequencing as a genome-wide screening tool to facilitate the identification of B. cenocepacia genes essential for its growth and viability. A transposon mutant pool consisting of approximately 500,000 mutants was successfully constructed, with more than 400,000 unique transposon insertion sites identified by computational analysis of TraDIS datasets. The saturated library allowed for the identification of 383 genes that were predicted to be essential in B. cenocepacia. We extended the application of TraDIS to identify conditionally essential genes required for in vitro growth and revealed an additional repertoire of 439 genes to be crucial for B. cenocepacia growth under nutrient-depleted conditions. The library of B. cenocepacia mutants can subsequently be subjected to various biologically related conditions to facilitate the discovery of genes involved in niche adaptation as well as pathogenicity and virulence.

  6. Identifying the essential components of cultural competence in a Chinese nursing context: A qualitative study.

    Science.gov (United States)

    Cai, Duanying; Kunaviktikul, Wipada; Klunklin, Areewan; Sripusanapan, Acharaporn; Avant, Patricia Kay

    2017-06-01

    This qualitative study using semi-structured interviews was conducted to identify the essential components of cultural competence from the perspective of Chinese nurses. A purposive sample of 20 nurse experts, including senior clinical nurses, nurse administrators, and educators in transcultural nursing, was recruited. Using thematic analysis, four themes: awareness, attitudes, knowledge, and skills, with two subthemes for each, were identified. Notably, culture in China was understood in a broad way. The participants' responses focused upon demographic attributes, individuality, and efforts to facilitate quality care rather than on the cultural differences of ethnicity and race and developing the capacity to change discrimination or health disparities. A greater understanding of cultural competence in the Chinese nursing context, in which a dominant cultural group exists, is essential to facilitate the provision of culturally competent care to diverse populations. © 2016 John Wiley & Sons Australia, Ltd.

  7. The systematic functional analysis of plasmodium protein kinases identifies essential regulators of mosquito transmission

    KAUST Repository

    Tewari, Rita; Straschil, Ursula; Bateman, Alex; Bö hme, Ulrike; Cherevach, Inna; Gong, Peng; Pain, Arnab; Billker, Oliver

    2010-01-01

    Although eukaryotic protein kinases (ePKs) contribute to many cellular processes, only three Plasmodium falciparum ePKs have thus far been identified as essential for parasite asexual blood stage development. To identify pathways essential for parasite transmission between their mammalian host and mosquito vector, we undertook a systematic functional analysis of ePKs in the genetically tractable rodent parasite Plasmodium berghei. Modeling domain signatures of conventional ePKs identified 66 putative Plasmodium ePKs. Kinomes are highly conserved between Plasmodium species. Using reverse genetics, we show that 23 ePKs are redundant for asexual erythrocytic parasite development in mice. Phenotyping mutants at four life cycle stages in Anopheles stephensi mosquitoes revealed functional clusters of kinases required for sexual development and sporogony. Roles for a putative SR protein kinase (SRPK) in microgamete formation, a conserved regulator of clathrin uncoating (GAK) in ookinete formation, and a likely regulator of energy metabolism (SNF1/KIN) in sporozoite development were identified. 2010 Elsevier Inc.

  8. The systematic functional analysis of plasmodium protein kinases identifies essential regulators of mosquito transmission

    KAUST Repository

    Tewari, Rita

    2010-10-21

    Although eukaryotic protein kinases (ePKs) contribute to many cellular processes, only three Plasmodium falciparum ePKs have thus far been identified as essential for parasite asexual blood stage development. To identify pathways essential for parasite transmission between their mammalian host and mosquito vector, we undertook a systematic functional analysis of ePKs in the genetically tractable rodent parasite Plasmodium berghei. Modeling domain signatures of conventional ePKs identified 66 putative Plasmodium ePKs. Kinomes are highly conserved between Plasmodium species. Using reverse genetics, we show that 23 ePKs are redundant for asexual erythrocytic parasite development in mice. Phenotyping mutants at four life cycle stages in Anopheles stephensi mosquitoes revealed functional clusters of kinases required for sexual development and sporogony. Roles for a putative SR protein kinase (SRPK) in microgamete formation, a conserved regulator of clathrin uncoating (GAK) in ookinete formation, and a likely regulator of energy metabolism (SNF1/KIN) in sporozoite development were identified. 2010 Elsevier Inc.

  9. Proteomic profiling of Plasmodium sporozoite maturation identifies new proteins essential for parasite development and infectivity

    DEFF Research Database (Denmark)

    Lasonder, Edwin; Janse, Chris J; van Gemert, Geert-Jan

    2008-01-01

    Plasmodium falciparum sporozoites that develop and mature inside an Anopheles mosquito initiate a malaria infection in humans. Here we report the first proteomic comparison of different parasite stages from the mosquito -- early and late oocysts containing midgut sporozoites, and the mature...... whose annotation suggest an involvement in sporozoite maturation, motility, infection of the human host and associated metabolic adjustments. Analyses of proteins identified in the P. falciparum sporozoite proteomes by orthologous gene disruption in the rodent malaria parasite, P. berghei, revealed...... three previously uncharacterized Plasmodium proteins that appear to be essential for sporozoite development at distinct points of maturation in the mosquito. This study sheds light on the development and maturation of the malaria parasite in an Anopheles mosquito and also identifies proteins that may...

  10. 30 CFR 285.803 - How must I conduct my approved activities to protect essential fish habitats identified and...

    Science.gov (United States)

    2010-07-01

    ... protect essential fish habitats identified and described under the Magnuson-Stevens Fishery Conservation... Act? (a) If, during the conduct of your approved activities, MMS finds that essential fish habitat or... adverse affects on Essential Fish Habitat will be incorporated as terms and conditions in the lease and...

  11. Guided genetic screen to identify genes essential in the regeneration of hair cells and other tissues.

    Science.gov (United States)

    Pei, Wuhong; Xu, Lisha; Huang, Sunny C; Pettie, Kade; Idol, Jennifer; Rissone, Alberto; Jimenez, Erin; Sinclair, Jason W; Slevin, Claire; Varshney, Gaurav K; Jones, MaryPat; Carrington, Blake; Bishop, Kevin; Huang, Haigen; Sood, Raman; Lin, Shuo; Burgess, Shawn M

    2018-01-01

    Regenerative medicine holds great promise for both degenerative diseases and traumatic tissue injury which represent significant challenges to the health care system. Hearing loss, which affects hundreds of millions of people worldwide, is caused primarily by a permanent loss of the mechanosensory receptors of the inner ear known as hair cells. This failure to regenerate hair cells after loss is limited to mammals, while all other non-mammalian vertebrates tested were able to completely regenerate these mechanosensory receptors after injury. To understand the mechanism of hair cell regeneration and its association with regeneration of other tissues, we performed a guided mutagenesis screen using zebrafish lateral line hair cells as a screening platform to identify genes that are essential for hair cell regeneration, and further investigated how genes essential for hair cell regeneration were involved in the regeneration of other tissues. We created genetic mutations either by retroviral insertion or CRISPR/Cas9 approaches, and developed a high-throughput screening pipeline for analyzing hair cell development and regeneration. We screened 254 gene mutations and identified 7 genes specifically affecting hair cell regeneration. These hair cell regeneration genes fell into distinct and somewhat surprising functional categories. By examining the regeneration of caudal fin and liver, we found these hair cell regeneration genes often also affected other types of tissue regeneration. Therefore, our results demonstrate guided screening is an effective approach to discover regeneration candidates, and hair cell regeneration is associated with other tissue regeneration.

  12. Electronic monitoring of adherence to inhaled corticosteroids: an essential tool in identifying severe asthma in children.

    Science.gov (United States)

    Jochmann, Anja; Artusio, Luca; Jamalzadeh, Angela; Nagakumar, Prasad; Delgado-Eckert, Edgar; Saglani, Sejal; Bush, Andrew; Frey, Urs; Fleming, Louise J

    2017-12-01

    International guidelines recommend that severe asthma can only be diagnosed after contributory factors, including adherence, have been addressed. Accurate assessment of adherence is difficult in clinical practice. We hypothesised that electronic monitoring in children would identify nonadherence, thus delineating the small number with true severe asthma.Asthmatic children already prescribed inhaled corticosteroids were prospectively recruited and persistence of adherence assessed using electronic monitoring devices. Spirometry, airway inflammation and asthma control were measured at the start and end of the monitoring period.93 children (62 male; median age 12.4 years) were monitored for a median of 92 days. Median (range) monitored adherence was 74% (21-99%). We identified four groups: 1) good adherence during monitoring with improved control, 24% (likely previous poor adherence); 2) good adherence with poor control, 18% (severe therapy-resistant asthma); 3) poor adherence with good control, 26% (likely overtreated); and 4) poor adherence with poor control, 32%. No clinical parameter prior to monitoring distinguished these groups.Electronic monitoring is a useful tool for identifying children in whom a step up in treatment is indicated. Different approaches are needed in those who are controlled when adherent or who are nonadherent. Electronic monitoring is essential in a paediatric severe asthma clinic. Copyright ©ERS 2017.

  13. Genome-wide CRISPR/Cas9 Screen Identifies Host Factors Essential for Influenza Virus Replication

    Directory of Open Access Journals (Sweden)

    Julianna Han

    2018-04-01

    Full Text Available Summary: The emergence of influenza A viruses (IAVs from zoonotic reservoirs poses a great threat to human health. As seasonal vaccines are ineffective against zoonotic strains, and newly transmitted viruses can quickly acquire drug resistance, there remains a need for host-directed therapeutics against IAVs. Here, we performed a genome-scale CRISPR/Cas9 knockout screen in human lung epithelial cells with a human isolate of an avian H5N1 strain. Several genes involved in sialic acid biosynthesis and related glycosylation pathways were highly enriched post-H5N1 selection, including SLC35A1, a sialic acid transporter essential for IAV receptor expression and thus viral entry. Importantly, we have identified capicua (CIC as a negative regulator of cell-intrinsic immunity, as loss of CIC resulted in heightened antiviral responses and restricted replication of multiple viruses. Therefore, our study demonstrates that the CRISPR/Cas9 system can be utilized for the discovery of host factors critical for the replication of intracellular pathogens. : Using a genome-wide CRISPR/Cas9 screen, Han et al. demonstrate that the major hit, the sialic acid transporter SLC35A1, is an essential host factor for IAV entry. In addition, they identify the DNA-binding transcriptional repressor CIC as a negative regulator of cell-intrinsic immunity. Keywords: CRISPR/Cas9 screen, GeCKO, influenza virus, host factors, sialic acid pathway, SLC35A1, Capicua, CIC, cell-intrinsic immunity, H5N1

  14. Social-Emotional Learning Is Essential to Classroom Management

    Science.gov (United States)

    Jones, Stephanie M.; Bailey, Rebecca; Jacob, Robin

    2014-01-01

    Research tells us that children's social-emotional development can propel learning. A new program, SECURe, embeds that research into classroom management strategies that improve teaching and learning. Across all classrooms and grade levels, four principles of effective management are constant: Effective classroom management is based in…

  15. Learning Essential Terms and Concepts in Statistics and Accounting

    Science.gov (United States)

    Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

    2014-01-01

    This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

  16. Machine learning in Python essential techniques for predictive analysis

    CERN Document Server

    Bowles, Michael

    2015-01-01

    Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, d

  17. A CRISPR-Based Screen Identifies Genes Essential for West-Nile-Virus-Induced Cell Death.

    Science.gov (United States)

    Ma, Hongming; Dang, Ying; Wu, Yonggan; Jia, Gengxiang; Anaya, Edgar; Zhang, Junli; Abraham, Sojan; Choi, Jang-Gi; Shi, Guojun; Qi, Ling; Manjunath, N; Wu, Haoquan

    2015-07-28

    West Nile virus (WNV) causes an acute neurological infection attended by massive neuronal cell death. However, the mechanism(s) behind the virus-induced cell death is poorly understood. Using a library containing 77,406 sgRNAs targeting 20,121 genes, we performed a genome-wide screen followed by a second screen with a sub-library. Among the genes identified, seven genes, EMC2, EMC3, SEL1L, DERL2, UBE2G2, UBE2J1, and HRD1, stood out as having the strongest phenotype, whose knockout conferred strong protection against WNV-induced cell death with two different WNV strains and in three cell lines. Interestingly, knockout of these genes did not block WNV replication. Thus, these appear to be essential genes that link WNV replication to downstream cell death pathway(s). In addition, the fact that all of these genes belong to the ER-associated protein degradation (ERAD) pathway suggests that this might be the primary driver of WNV-induced cell death. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  18. Managing Resistance: An Essential Consulting Skill for Learning Disabilities Teachers.

    Science.gov (United States)

    Friend, Marilyn; Bauwens, Jeanne

    1988-01-01

    The article explores characteristics of resistance by general educators to special education consultation programs. It offers teachers of learning disabled students strategies for managing specific types of resistance as well as a general plan for minimizing resistance as well as suggestions for evaluating the impact of resistance management…

  19. Identifying Different Registers of Digital Literacy in Virtual Learning Environments

    Science.gov (United States)

    Knutsson, Ola; Blasjo, Mona.; Hallsten, Stina; Karlstrom, Petter

    2012-01-01

    In this paper social semiotics, and systemic functional linguistics in particular, are used in order to identify registers of digital literacy in the use of virtual learning environments. The framework of social semiotics provides means to systemize and discuss digital literacy as a linguistic and semiotic issue. The following research question…

  20. Identifying learning characteristics of the gifted Students in the ...

    African Journals Online (AJOL)

    The failure of schools, teachers and counsellors to identify gifted students as well as responding to their unique characteristics and learning needs give rise to this paper. Gifted learners possess high level of intelligence than their peers, but are disadvantaged in the sense that they are not given the opportunity to reach their ...

  1. Affective Learning and Personal Information Management: Essential Components of Information Literacy

    Science.gov (United States)

    Cahoy, Ellysa Stern

    2013-01-01

    "Affective competence," managing the feelings and emotions that students encounter throughout the content creation/research process, is essential to academic success. Just as it is crucial for students to acquire core literacies, it is essential that they learn how to manage the anxieties and emotions that will emerge throughout all…

  2. Essential Tremor: What We Can Learn from Current Pharmacotherapy

    Directory of Open Access Journals (Sweden)

    William Ondo

    2016-03-01

    Full Text Available Background: The pathophysiology of essential tremor, especially at the cellular level, is poorly understood. Although no drug has been specifically designed to treat essential tremor, several medications improve tremor, and others worsen it. Studying the mechanism of actions of these medications can help our understanding of tremor pathophysiology and contribute to future rational drug design. Methods: We reviewed literature, concentrating on mechanisms of action, of various medications that mitigate tremor. Results: Many medications have multiple mechanisms of actions, making simple correlations difficult. Medications that increase the duration of opening of gamma-aminobutyric acid (GABA-A receptors are most consistently associated with tremor improvement. Interestingly, drugs that increase GABA availability have not been associated with improved tremor. Other mechanisms possibly associated with tremor improvement include antagonism of alpha-2 delta subunits associated with calcium channels, inhibition of carbonic anhydrase, and inhibition of the synaptic vesicle protein 2A. Drugs that block voltage-gaited sodium channels do not affect tremor. The ideal beta-adrenergic blocker requires B2 affinity (non-cardiac selective, has no sympathomimetic properties, does not require membrane stabilization properties, and may benefit from good central nervous system penetration. Discussion: To date, serendipitous observations have provided most of our understanding of tremor cellular physiology. Based on similarities to currently effective drugs or rational approximations and inferences, several currently available agents should be considered for tremor trials.

  3. Essential Tremor: What We Can Learn from Current Pharmacotherapy.

    Science.gov (United States)

    Ondo, William

    2016-01-01

    The pathophysiology of essential tremor, especially at the cellular level, is poorly understood. Although no drug has been specifically designed to treat essential tremor, several medications improve tremor, and others worsen it. Studying the mechanism of actions of these medications can help our understanding of tremor pathophysiology and contribute to future rational drug design. We reviewed literature, concentrating on mechanisms of action, of various medications that mitigate tremor. Many medications have multiple mechanisms of actions, making simple correlations difficult. Medications that increase the duration of opening of gamma-aminobutyric acid (GABA)-A receptors are most consistently associated with tremor improvement. Interestingly, drugs that increase GABA availability have not been associated with improved tremor. Other mechanisms possibly associated with tremor improvement include antagonism of alpha-2 delta subunits associated with calcium channels, inhibition of carbonic anhydrase, and inhibition of the synaptic vesicle protein 2A. Drugs that block voltage-gaited sodium channels do not affect tremor. The ideal beta-adrenergic blocker requires B2 affinity (non-cardiac selective), has no sympathomimetic properties, does not require membrane stabilization properties, and may benefit from good central nervous system penetration. To date, serendipitous observations have provided most of our understanding of tremor cellular physiology. Based on similarities to currently effective drugs or rational approximations and inferences, several currently available agents should be considered for tremor trials.

  4. Blender Foundations The Essential Guide to Learning Blender 26

    CERN Document Server

    Hess, Roland

    2010-01-01

    Blender Foundations is the definitive resource for getting started with 3D art in Blender, one of the most popular 3D/Animation tools on the market . With the expert insight and experience of Roland Hess, noted Blender expert and author, animators and artists will learn the basics starting with the revised 2.6 interface, modeling tools, sculpting, lighting and materials through rendering, compositing and video editing. Some of the new features covered include the completely re-thought interface, the character animation and keying system, and the smoke simulator. More than just a tutorial gui

  5. Diversity Within Unity: Essential Principles for Teaching and Learning in a Multicultural Society.

    Science.gov (United States)

    Banks, James A.; Cookson, Peter; Gay, Geneva; Hawley, Willis D.; Irvine, Jacqueline Jordan; Nieto, Sonia; Schofield, Janet Ward; Stephen, Walter G.

    2001-01-01

    Discusses 12 essential principles to help schools teach democratic values in a multicultural society. Derived from findings of the Multicultural Education Consensus Panel to review and synthesize research on diversity, principles are organized into five categories: Teacher learning; student learning; intergroup relations; school governance,…

  6. Student Motivation from and Resistance to Active Learning Rooted in Essential Science Practices

    Science.gov (United States)

    Owens, David C.; Sadler, Troy D.; Barlow, Angela T.; Smith-Walters, Cindi

    2017-12-01

    Several studies have found active learning to enhance students' motivation and attitudes. Yet, faculty indicate that students resist active learning and censure them on evaluations after incorporating active learning into their instruction, resulting in an apparent paradox. We argue that the disparity in findings across previous studies is the result of variation in the active learning instruction that was implemented. The purpose of this study was to illuminate sources of motivation from and resistance to active learning that resulted from a novel, exemplary active-learning approach rooted in essential science practices and supported by science education literature. This approach was enacted over the course of 4 weeks in eight sections of an introductory undergraduate biology laboratory course. A plant concept inventory, administered to students as a pre-, post-, and delayed-posttest indicated significant proximal and distal learning gains. Qualitative analysis of open-response questionnaires and interviews elucidated sources of motivation and resistance that resulted from this active-learning approach. Several participants indicated this approach enhanced interest, creativity, and motivation to prepare, and resulted in a challenging learning environment that facilitated the sharing of diverse perspectives and the development of a community of learners. Sources of resistance to active learning included participants' unfamiliarity with essential science practices, having to struggle with uncertainty in the absence of authoritative information, and the extra effort required to actively construct knowledge as compared to learning via traditional, teacher-centered instruction. Implications for implementation, including tips for reducing student resistance to active learning, are discussed.

  7. Towards identifying Collaborative Learning groups using Social Media

    Directory of Open Access Journals (Sweden)

    Selver Softic

    2012-11-01

    Full Text Available This work reports about the preliminary results and ongoing research based upon profiling collaborative learning groups of persons within the social micro-blogging platforms like Twitter that share potentially common interests on special topic. Hereby the focus is held on spontaneously initiated collaborative learning in Social Media and detection of collaborative learning groups based upon their communication dynamics. Research questions targeted to be answered are: are there any useful data mining algorithms to fulfill the task of pre-selection and clustering of users in social networks, how good do they perform, and what are the metrics that could be used for detection and evaluation in the realm of this task. Basic approach presented here uses as preamble hypothesis that users and their interests in Social Networks can be identified through content generated by them and content they consume. Special focus is held on topic oriented approach as least common bounding point. Those should be also the basic criteria used to detect and outline the learning groups. The aim of this work is to deliver first scientific pre-work for successfully implementation of recommender systems using social network metrics and content features of social network users for the purposes of better learning group communication and information consumption.

  8. Analysis of pan-genome to identify the core genes and essential genes of Brucella spp.

    Science.gov (United States)

    Yang, Xiaowen; Li, Yajie; Zang, Juan; Li, Yexia; Bie, Pengfei; Lu, Yanli; Wu, Qingmin

    2016-04-01

    Brucella spp. are facultative intracellular pathogens, that cause a contagious zoonotic disease, that can result in such outcomes as abortion or sterility in susceptible animal hosts and grave, debilitating illness in humans. For deciphering the survival mechanism of Brucella spp. in vivo, 42 Brucella complete genomes from NCBI were analyzed for the pan-genome and core genome by identification of their composition and function of Brucella genomes. The results showed that the total 132,143 protein-coding genes in these genomes were divided into 5369 clusters. Among these, 1710 clusters were associated with the core genome, 1182 clusters with strain-specific genes and 2477 clusters with dispensable genomes. COG analysis indicated that 44 % of the core genes were devoted to metabolism, which were mainly responsible for energy production and conversion (COG category C), and amino acid transport and metabolism (COG category E). Meanwhile, approximately 35 % of the core genes were in positive selection. In addition, 1252 potential essential genes were predicted in the core genome by comparison with a prokaryote database of essential genes. The results suggested that the core genes in Brucella genomes are relatively conservation, and the energy and amino acid metabolism play a more important role in the process of growth and reproduction in Brucella spp. This study might help us to better understand the mechanisms of Brucella persistent infection and provide some clues for further exploring the gene modules of the intracellular survival in Brucella spp.

  9. Acquired RhD mosaicism identifies fibrotic transformation of thrombopoietin receptor-mutated essential thrombocythemia.

    Science.gov (United States)

    Montemayor-Garcia, Celina; Coward, Rebecca; Albitar, Maher; Udani, Rupa; Jain, Prachi; Koklanaris, Eleftheria; Battiwalla, Minoo; Keel, Siobán; Klein, Harvey G; Barrett, A John; Ito, Sawa

    2017-09-01

    Acquired copy-neutral loss of heterozygosity has been described in myeloid malignant progression with an otherwise normal karyotype. A 65-year-old woman with MPL-mutated essential thrombocythemia and progression to myelofibrosis was noted upon routine pretransplant testing to have mixed field reactivity with anti-D and an historic discrepancy in RhD type. The patient had never received transfusions or transplantation. Gel immunoagglutination revealed group A red blood cells and a mixed-field reaction for the D phenotype, with a predominant D-negative population and a small subset of circulating red blood cells carrying the D antigen. Subsequent genomic microarray single nucleotide polymorphism profiling revealed copy-neutral loss of heterozygosity of chromosome 1 p36.33-p34.2, a known molecular mechanism underlying fibrotic progression of MPL-mutated essential thrombocythemia. The chromosomal region affected by this copy-neutral loss of heterozygosity encompassed the RHD, RHCE, and MPL genes. We propose a model of chronological molecular events that is supported by RHD zygosity assays in peripheral lymphoid and myeloid-derived cells. Copy-neutral loss of heterozygosity events that lead to clonal selection and myeloid malignant progression may also affect the expression of adjacent unrelated genes, including those encoding for blood group antigens. Detection of mixed-field reactions and investigation of discrepant blood typing results are important for proper transfusion support of these patients and can provide useful surrogate markers of myeloproliferative disease progression. © 2017 AABB.

  10. A newly identified essential complex, Dre2-Tah18, controls mitochondria integrity and cell death after oxidative stress in yeast.

    Directory of Open Access Journals (Sweden)

    Laurence Vernis

    Full Text Available A mutated allele of the essential gene TAH18 was previously identified in our laboratory in a genetic screen for new proteins interacting with the DNA polymerase delta in yeast [1]. The present work shows that Tah18 plays a role in response to oxidative stress. After exposure to lethal doses of H(2O(2, GFP-Tah18 relocalizes to the mitochondria and controls mitochondria integrity and cell death. Dre2, an essential Fe/S cluster protein and homologue of human anti-apoptotic Ciapin1, was identified as a molecular partner of Tah18 in the absence of stress. Moreover, Ciapin1 is able to replace yeast Dre2 in vivo and physically interacts with Tah18. Our results are in favour of an oxidative stress-induced cell death in yeast that involves mitochondria and is controlled by the newly identified Dre2-Tah18 complex.

  11. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  12. Identifying Students learning Styles as a Way to Promote Learning Quality

    Directory of Open Access Journals (Sweden)

    Jafar Sadegh Tabrizi

    2013-05-01

    Full Text Available Introduction: The major part of peoples knowledge, skills and abilities are achieved during the complex process called learning. Learning is not simply the product of mere intelligence and capabilities of individual; it also depends on other factors such as personality traits, personal interests, and t ype of duty and di fferent methods and st yles. The understanding of each individual fits with his/her learning style. The aim of this study was to determine the learning st yles of Health Care Management students in Tabriz University of Medical Sciences. Methods: Learning styles of 55 Health Services Management students in Tabriz Health and Nutrition Faculty were evaluated in 2009 using a twelve-question Kolb questionnaire in a descriptive study. The data was anal yzed using SPSS. And the frequency of students learning styles was identified by their ages and averages. Results: In this study, 69% of the students were female and the dominant learning method was Assimilator (42%. Other styles with a regard to their frequency were Diverge (24%, Coverage (22%and Accommodator (12%. In the present study,no statistically significant relationship was found in learning styles between the gender (p= 0.644and average (p = 0.676of the students. Conclusion: Assimilator and Diverge methods were the most common ones among the management students. Hence, to improve the quality of learning in this group of students, it is proposed that the teachers use interactive and creative teaching methods such as small and la rge group discussion,brain storming, problem solving, debate-based learning, self-learning and lecturing.

  13. Semi-Supervised Learning to Identify UMLS Semantic Relations.

    Science.gov (United States)

    Luo, Yuan; Uzuner, Ozlem

    2014-01-01

    The UMLS Semantic Network is constructed by experts and requires periodic expert review to update. We propose and implement a semi-supervised approach for automatically identifying UMLS semantic relations from narrative text in PubMed. Our method analyzes biomedical narrative text to collect semantic entity pairs, and extracts multiple semantic, syntactic and orthographic features for the collected pairs. We experiment with seeded k-means clustering with various distance metrics. We create and annotate a ground truth corpus according to the top two levels of the UMLS semantic relation hierarchy. We evaluate our system on this corpus and characterize the learning curves of different clustering configuration. Using KL divergence consistently performs the best on the held-out test data. With full seeding, we obtain macro-averaged F-measures above 70% for clustering the top level UMLS relations (2-way), and above 50% for clustering the second level relations (7-way).

  14. Linux Essentials

    CERN Document Server

    Smith, Roderick W

    2012-01-01

    A unique, full-color introduction to Linux fundamentals Serving as a low-cost, secure alternative to expensive operating systems, Linux is a UNIX-based, open source operating system. Full-color and concise, this beginner's guide takes a learning-by-doing approach to understanding the essentials of Linux. Each chapter begins by clearly identifying what you will learn in the chapter, followed by a straightforward discussion of concepts that leads you right into hands-on tutorials. Chapters conclude with additional exercises and review questions, allowing you to reinforce and measure your underst

  15. Identifying Students' Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning

    Science.gov (United States)

    Bouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam

    2012-01-01

    Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…

  16. Learning Bayesian network structure: towards the essential graph by integer linear programming tools

    Czech Academy of Sciences Publication Activity Database

    Studený, Milan; Haws, D.

    2014-01-01

    Roč. 55, č. 4 (2014), s. 1043-1071 ISSN 0888-613X R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * integer linear programming * characteristic imset * essential graph Subject RIV: BA - General Mathematics Impact factor: 2.451, year: 2014 http://library.utia.cas.cz/separaty/2014/MTR/studeny-0427002.pdf

  17. Learning environments matter: Identifying influences on the motivation to learn science

    Directory of Open Access Journals (Sweden)

    Salomé Schulze

    2015-05-01

    Full Text Available In the light of the poor academic achievement in science by secondary school students in South Africa, students' motivation for science learning should be enhanced. It is argued that this can only be achieved with insight into which motivational factors to target, with due consideration of the diversity in schools. The study therefore explored the impact of six motivational factors for science learning in a sample of 380 Grade Nine boys and girls from three racial groups, in both public and independent schools. The students completed the Student Motivation for Science Learning questionnaire. Significant differences were identified between different groups and school types. The study is important for identifying the key role of achievement goals, science learning values and science self-efficacies. The main finding emphasises the significant role played by science teachers in motivating students for science in terms of the learning environments that they create. This has important implications for future research, aimed at a better understanding of these environments. Such insights are needed to promote scientific literacy among the school students, and so contribute to the improvement of science achievement in South Africa.

  18. An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

    Science.gov (United States)

    Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao

    Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus , which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

  19. Carbon Stars Identified from LAMOST DR4 Using Machine Learning

    Science.gov (United States)

    Li, Yin-Bi; Luo, A.-Li; Du, Chang-De; Zuo, Fang; Wang, Meng-Xin; Zhao, Gang; Jiang, Bi-Wei; Zhang, Hua-Wei; Liu, Chao; Qin, Li; Wang, Rui; Du, Bing; Guo, Yan-Xin; Wang, Bo; Han, Zhan-Wen; Xiang, Mao-Sheng; Huang, Yang; Chen, Bing-Qiu; Chen, Jian-Jun; Kong, Xiao; Hou, Wen; Song, Yi-Han; Wang, You-Fen; Wu, Ke-Fei; Zhang, Jian-Nan; Zhang, Yong; Wang, Yue-Fei; Cao, Zi-Huang; Hou, Yong-Hui; Zhao, Yong-Heng

    2018-02-01

    In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find these stars from more than 7 million spectra. As a by-product, 17 carbon-enhanced metal-poor turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes: 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes, C-J(H), C-J(R), and C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Besides spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and that they are likely to be in binary systems with compact white dwarf companions.

  20. Identifying Student Types in a Gamified Learning Experience

    Science.gov (United States)

    Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel

    2014-01-01

    Gamification of education is a recent trend, and early experiments showed promising results. Students seem not only to perform better, but also to participate more and to feel more engaged with gamified learning. However, little is known regarding how different students are affected by gamification and how their learning experience may vary. In…

  1. Identifying Learning Preferences in Vocational Education and Training Classroom Settings

    Science.gov (United States)

    Smith, Peter J.

    2006-01-01

    This research was designed to assess whether teachers and trainers of vocational learners noted and valued differences in individual learning preferences and, if so, how those differences were observed in natural classroom, workshop or other formal learning settings. Data were collected from six vocational education and training (VET) learning…

  2. Digital Games and Learning: Identifying Pathways of Influence

    Science.gov (United States)

    Subrahmanyam, Kaveri; Renukarya, Bhavya

    2015-01-01

    Digital games and gamelike contexts have become an integral part of young people's lives, and scholars have speculated about their potential to engage and enhance children's learning. Given that digital games are complex systems, we propose that different aspects of game features and game play might influence learning in different ways. Drawing on…

  3. Mutational analysis to identify the residues essential for the inhibition of N-acetyl glutamate kinase of Corynebacterium glutamicum.

    Science.gov (United States)

    Huang, Yuanyuan; Zhang, Hao; Tian, Hongming; Li, Cheng; Han, Shuangyan; Lin, Ying; Zheng, Suiping

    2015-09-01

    N-acetyl glutamate kinase (NAGK) is a key enzyme in the synthesis of L-arginine that is inhibited by its end product L-arginine in Corynebacterium glutamicum (C. glutamicum). In this study, the potential binding sites of arginine and the residues essential for its inhibition were identified by homology modeling, inhibitor docking, and site-directed mutagenesis. The allosteric inhibition of NAGK was successfully alleviated by a mutation, as determined through analysis of mutant enzymes, which were overexpressed in vivo in C. glutamicum ATCC14067. Analysis of the mutant enzymes and docking analysis demonstrated that residue W23 positions an arginine molecule, and the interaction between arginine and residues L282, L283, and T284 may play an important role in the remote inhibitory process. Based on the results of the docking analysis of the effective mutants, we propose a linkage mechanism for the remote allosteric regulation of NAGK activity, in which residue R209 may play an essential role. In this study, the structure of the arginine-binding site of C. glutamicum NAGK (CgNAGK) was successfully predicted and the roles of the relevant residues were identified, providing new insight into the allosteric regulation of CgNAGK activity and a solid platform for the future construction of an optimized L-arginine producing strain.

  4. Informal Science learning in PIBID: identifying and interpreting the strands

    Directory of Open Access Journals (Sweden)

    Thomas Barbosa Fejolo

    2013-10-01

    Full Text Available This paper presents a research on informal Science learning in the context of the Institutional Scholarship Program Initiation to Teaching (PIBID. We take as reference the strands of informal Science learning (FAC, representing six dimensions of learning, they are: 1 Development of interest in Science; 2 Understanding of scientific knowledge; 3 Engaging in scientific reasoning; 4 Reflection on Science; 5 Engagement in scientific practice; 6 Identification with Science. For the lifting data, it was used the filming record of the interactions and dialogues of undergraduate students while performing activities of Optical Spectroscopy in the laboratory. Based on the procedures of content analysis and interpretations through communication, we investigate which of the six strands were present during the action of the students in activities. As a result we have drawn a learning profile for each student by distributing communications in different strands of informal Science learning.

  5. On the Use of Machine Learning for Identifying Botnet Network Traffic

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    contemporary approaches use machine learning techniques for identifying malicious traffic. This paper presents a survey of contemporary botnet detection methods that rely on machine learning for identifying botnet network traffic. The paper provides a comprehensive overview on the existing scientific work thus...... contributing to the better understanding of capabilities, limitations and opportunities of using machine learning for identifying botnet traffic. Furthermore, the paper outlines possibilities for the future development of machine learning-based botnet detection systems....

  6. Embedding Literacy and Essential Skills in Workplace Learning: Breaking the Solitudes

    Science.gov (United States)

    Derrick, Jay

    2012-01-01

    This paper attempts to identify some tools to help practioners think about, debate and plan Workplace Literacy and Essential Skills (WLES) programs. Such tools are necessary so that discussions between practitioners aiming to clarify good practice and successful approaches can get beyond mere descriptions of what happened. In order to compare and…

  7. Identifying student stuck states in programmingassignments using machine learning

    OpenAIRE

    Lindell, Johan

    2014-01-01

    Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...

  8. Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

    Directory of Open Access Journals (Sweden)

    Jun Ren

    2014-01-01

    Full Text Available Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.

  9. Mutagenesis Screen Identifies agtpbp1 and eps15L1 as Essential for T lymphocyte Development in Zebrafish.

    Science.gov (United States)

    Seiler, Christoph; Gebhart, Nichole; Zhang, Yong; Shinton, Susan A; Li, Yue-sheng; Ross, Nicola L; Liu, Xingjun; Li, Qin; Bilbee, Alison N; Varshney, Gaurav K; LaFave, Matthew C; Burgess, Shawn M; Balciuniene, Jorune; Balciunas, Darius; Hardy, Richard R; Kappes, Dietmar J; Wiest, David L; Rhodes, Jennifer

    2015-01-01

    Genetic screens are a powerful tool to discover genes that are important in immune cell development and function. The evolutionarily conserved development of lymphoid cells paired with the genetic tractability of zebrafish make this a powerful model system for this purpose. We used a Tol2-based gene-breaking transposon to induce mutations in the zebrafish (Danio rerio, AB strain) genome, which served the dual purpose of fluorescently tagging cells and tissues that express the disrupted gene and provided a means of identifying the disrupted gene. We identified 12 lines in which hematopoietic tissues expressed green fluorescent protein (GFP) during embryonic development, as detected by microscopy. Subsequent analysis of young adult fish, using a novel approach in which single cell suspensions of whole fish were analyzed by flow cytometry, revealed that 8 of these lines also exhibited GFP expression in young adult cells. An additional 15 lines that did not have embryonic GFP+ hematopoietic tissue by microscopy, nevertheless exhibited GFP+ cells in young adults. RT-PCR analysis of purified GFP+ populations for expression of T and B cell-specific markers identified 18 lines in which T and/or B cells were fluorescently tagged at 6 weeks of age. As transposon insertion is expected to cause gene disruption, these lines can be used to assess the requirement for the disrupted genes in immune cell development. Focusing on the lines with embryonic GFP+ hematopoietic tissue, we identified three lines in which homozygous mutants exhibited impaired T cell development at 6 days of age. In two of the lines we identified the disrupted genes, agtpbp1 and eps15L1. Morpholino-mediated knockdown of these genes mimicked the T cell defects in the corresponding mutant embryos, demonstrating the previously unrecognized, essential roles of agtpbp1 and eps15L1 in T cell development.

  10. Mutagenesis Screen Identifies agtpbp1 and eps15L1 as Essential for T lymphocyte Development in Zebrafish.

    Directory of Open Access Journals (Sweden)

    Christoph Seiler

    Full Text Available Genetic screens are a powerful tool to discover genes that are important in immune cell development and function. The evolutionarily conserved development of lymphoid cells paired with the genetic tractability of zebrafish make this a powerful model system for this purpose. We used a Tol2-based gene-breaking transposon to induce mutations in the zebrafish (Danio rerio, AB strain genome, which served the dual purpose of fluorescently tagging cells and tissues that express the disrupted gene and provided a means of identifying the disrupted gene. We identified 12 lines in which hematopoietic tissues expressed green fluorescent protein (GFP during embryonic development, as detected by microscopy. Subsequent analysis of young adult fish, using a novel approach in which single cell suspensions of whole fish were analyzed by flow cytometry, revealed that 8 of these lines also exhibited GFP expression in young adult cells. An additional 15 lines that did not have embryonic GFP+ hematopoietic tissue by microscopy, nevertheless exhibited GFP+ cells in young adults. RT-PCR analysis of purified GFP+ populations for expression of T and B cell-specific markers identified 18 lines in which T and/or B cells were fluorescently tagged at 6 weeks of age. As transposon insertion is expected to cause gene disruption, these lines can be used to assess the requirement for the disrupted genes in immune cell development. Focusing on the lines with embryonic GFP+ hematopoietic tissue, we identified three lines in which homozygous mutants exhibited impaired T cell development at 6 days of age. In two of the lines we identified the disrupted genes, agtpbp1 and eps15L1. Morpholino-mediated knockdown of these genes mimicked the T cell defects in the corresponding mutant embryos, demonstrating the previously unrecognized, essential roles of agtpbp1 and eps15L1 in T cell development.

  11. Essential Features of Serious Games Design in Higher Education: Linking Learning Attributes to Game Mechanics

    Science.gov (United States)

    Lameras, Petros; Arnab, Sylvester; Dunwell, Ian; Stewart, Craig; Clarke, Samantha; Petridis, Panagiotis

    2017-01-01

    This paper consolidates evidence and material from a range of specialist and disciplinary fields to provide an evidence-based review and synthesis on the design and use of serious games in higher education. Search terms identified 165 papers reporting conceptual and empirical evidence on how learning attributes and game mechanics may be planned,…

  12. The essential skills required by librarians to support medical virtual learning programs.

    Science.gov (United States)

    Soleymani, Mohammad Reza; Akbari, Zahra; Mojiri, Shahin

    2016-01-01

    Background: With the recent spread of virtual learning programs in universities, especially in the field of medical sciences, libraries play a crucial role to support these programs. This study aimed at investigating the skills required by librarians to support virtual learning programs in Isfahan University and Isfahan University of Medical Sciences. Methods: This was an applied survey study. The population of the study includes all librarians working in Isfahan University and Isfahan University of Medical Sciences. A sample of 89 librarians was selected by stratified random sampling. Data were collected by a researcher-made questionnaire, the validity of which was confirmed by specialists in the fields of librarianship and information sciences and virtual learning, and its reliability was determined to be 0.92, using Cronbach's Alpha. The questionnaire consisted of 51 items designed to evaluate the librarians' virtual learning skills using Likert scale. Descriptive and inferential statistics were used to analyze the findings. Results: The findings of this study revealed that librarians had low level of skills with respect to the online reference services, and familiarity with virtual learning environment. They also showed low and average level of skills with respect to their general information technology, communication skills, ability to teach electronic information literacy and ability to create access to electronic resources. The results revealed no significant difference between the librarians of the two universities, or between male and female librarians. However, librarians with educational background in librarianship and information sciences were significantly more skillful and competent than their colleagues. Conclusion: Despite the crucial role of libraries in supporting virtual learning programs, the librarians in Isfahan University and Isfahan University of Medical Sciences had low-level skills to play such an important role. Therefore, it is essential

  13. Identifying child abuse through text mining and machine learning

    NARCIS (Netherlands)

    Amrit, Chintan; Paauw, Tim; Aly, Robin; Lavric, Miha

    2017-01-01

    In this paper, we describe how we used text mining and analysis to identify and predict cases of child abuse in a public health institution. Such institutions in the Netherlands try to identify and prevent different kinds of abuse. A significant part of the medical data that the institutions have on

  14. Teaching/learning strategies for the essentials of baccalaureate nursing education for entry-level community/public health nursing.

    Science.gov (United States)

    Callen, Bonnie; Smith, Claudia M; Joyce, Barbara; Lutz, Jayne; Brown-Schott, Nancy; Block, Derryl

    2013-01-01

    The purpose of this article is to describe teaching/learning strategies for each of the 15 Essentials of Baccalaureate Nursing Education for Entry-Level Community/Public Health Nursing (ACHNE, 2009). Carper's ways of knowing serve as foundations for creating classroom and clinical experiences that focus on clinical action with community as client. Each community/public health essential is defined with relevance to community/public health nursing practice. Five teaching/learning strategies have been delineated for each essential with suggestions of teaching resources and/or target population application. Teaching/learning strategies that focus on community as client, population health, and the essential knowledge and competencies of C/PH nursing will help ensure preparation of baccalaureate prepared nurses with knowledge and skills to improve the health of populations. © 2013 Wiley Periodicals, Inc.

  15. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Science.gov (United States)

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

  16. Systems Analysis of Lactose Metabolism in Trichoderma reesei Identifies a Lactose Permease That Is Essential for Cellulase Induction

    Science.gov (United States)

    Ivanova, Christa; Bååth, Jenny A.; Seiboth, Bernhard; Kubicek, Christian P.

    2013-01-01

    Trichoderma reesei colonizes predecayed wood in nature and metabolizes cellulose and hemicellulose from the plant biomass. The respective enzymes are industrially produced for application in the biofuel and biorefinery industry. However, these enzymes are also induced in the presence of lactose (1,4-0-ß-d-galactopyranosyl-d-glucose), a waste product from cheese manufacture or whey processing industries. In fact, lactose is the only soluble carbon source that induces these enzymes in T. reesei on an industrial level but the reason for this unique phenomenon is not understood. To answer this question, we used systems analysis of the T. reesei transcriptome during utilization of lactose. We found that the respective CAZome encoded all glycosyl hydrolases necessary for cellulose degradation and particularly for the attack of monocotyledon xyloglucan, from which ß-galactosides could be released that may act as the inducers of T. reesei’s cellulases and hemicellulases. In addition, lactose also induces a high number of putative transporters of the major facilitator superfamily. Deletion of fourteen of them identified one gene that is essential for lactose utilization and lactose uptake, and for cellulase induction by lactose (but not sophorose) in pregrown mycelia of T. reesei. These data shed new light on the mechanism by which T. reesei metabolizes lactose and offers strategies for its improvement. They also illuminate the key role of ß-D-galactosides in habitat specificity of this fungus. PMID:23690947

  17. ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

    Science.gov (United States)

    Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

    2015-07-01

    In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  18. ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes

    Science.gov (United States)

    Hua, Zhi-Gang; Lin, Yan; Yuan, Ya-Zhou; Yang, De-Chang; Wei, Wen; Guo, Feng-Biao

    2015-01-01

    In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions. PMID:25977299

  19. Supporting Teachers in Identifying Students' Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach

    Science.gov (United States)

    Graf, Sabine; Kinshuk; Liu, Tzu-Chien

    2009-01-01

    In learning management systems (LMSs), teachers have more difficulties to notice and know how individual students behave and learn in a course, compared to face-to-face education. Enabling teachers to know their students' learning styles and making students aware of their own learning styles increases teachers' and students' understanding about…

  20. Use of genomics to identify bacterial undecaprenyl pyrophosphate synthetase: cloning, expression, and characterization of the essential uppS gene.

    Science.gov (United States)

    Apfel, C M; Takács, B; Fountoulakis, M; Stieger, M; Keck, W

    1999-01-01

    The prenyltransferase undecaprenyl pyrophosphate synthetase (di-trans,poly-cis-decaprenylcistransferase; EC 2.5.1.31) was purified from the soluble fraction of Escherichia coli by TSK-DEAE, ceramic hydroxyapatite, TSK-ether, Superdex 200, and heparin-Actigel chromatography. The protein was labeled with the photolabile analogue of the farnesyl pyrophosphate analogue (E, E)-[1-3H]-(2-diazo-3-trifluoropropionyloxy)geranyl diphosphate and was detected on a sodium dodecyl sulfate-polyacrylamide gel as a protein with an apparent molecular mass of 29 kDa. This protein band was cut out from the gel, trypsin digested, and subjected to matrix-assisted laser desorption ionization mass spectrometric analysis. Comparison of the experimental data with computer-simulated trypsin digest data for all E. coli proteins yielded a single match with a protein of unassigned function (SWISS-PROT Q47675; YAES_ECOLI). Sequences with strong similarity indicative of homology to this protein were identified in 25 bacterial species, in Saccharomyces cerevisiae, and in Caenorhabditis elegans. The homologous genes (uppS) were cloned from E. coli, Haemophilus influenzae, and Streptococcus pneumoniae, expressed in E. coli as amino-terminal His-tagged fusion proteins, and purified over a Ni2+ affinity column. An untagged version of the E. coli uppS gene was also cloned and expressed, and the protein purified in two chromatographic steps. We were able to detect Upp synthetase activity for all purified enzymes. Further, biochemical characterization revealed no differences between the recombinant untagged E. coli Upp synthetase and the three His-tagged fusion proteins. All enzymes were absolutely Triton X-100 and MgCl2 dependent. With the use of a regulatable gene disruption system, we demonstrated that uppS is essential for growth in S. pneumoniae R6.

  1. A Noise Trimming and Positional Significance of Transposon Insertion System to Identify Essential Genes in Yersinia pestis

    Science.gov (United States)

    Yang, Zheng Rong; Bullifent, Helen L.; Moore, Karen; Paszkiewicz, Konrad; Saint, Richard J.; Southern, Stephanie J.; Champion, Olivia L.; Senior, Nicola J.; Sarkar-Tyson, Mitali; Oyston, Petra C. F.; Atkins, Timothy P.; Titball, Richard W.

    2017-02-01

    Massively parallel sequencing technology coupled with saturation mutagenesis has provided new and global insights into gene functions and roles. At a simplistic level, the frequency of mutations within genes can indicate the degree of essentiality. However, this approach neglects to take account of the positional significance of mutations - the function of a gene is less likely to be disrupted by a mutation close to the distal ends. Therefore, a systematic bioinformatics approach to improve the reliability of essential gene identification is desirable. We report here a parametric model which introduces a novel mutation feature together with a noise trimming approach to predict the biological significance of Tn5 mutations. We show improved performance of essential gene prediction in the bacterium Yersinia pestis, the causative agent of plague. This method would have broad applicability to other organisms and to the identification of genes which are essential for competitiveness or survival under a broad range of stresses.

  2. Identifying events that impact self-efficacy in physics learning

    Directory of Open Access Journals (Sweden)

    Vashti Sawtelle

    2012-09-01

    Full Text Available We present a method of analyzing the development of self-efficacy in real time using a framework of self-efficacy opportunities (SEOs. Considerable research has shown a connection between self-efficacy, or the confidence in one’s own ability to perform a task, and success in science fields. Traditional methods of investigating the development of self-efficacy have required participants to recollect past events. This reliance on participant memory makes it difficult to understand what impact particular events may have on developing self-efficacy in the moment. We use video recordings of three undergraduate Modeling Instruction students solving a physics problem to characterize SEOs in a moment-by-moment analysis. We then validate these characterizations of the development of self-efficacy by reviewing the problem-solving session with the participants and find evidence that the SEOs identified are taken up and impact self-efficacy. This characterization and validation of SEOs in the moment represents a first step towards establishing a methodology for analyzing the development of self-efficacy in real time.

  3. Reaching Consensus on Essential Biomedical Science Learning Objectives in a Dental Curriculum.

    Science.gov (United States)

    Best, Leandra; Walton, Joanne N; Walker, Judith; von Bergmann, HsingChi

    2016-04-01

    This article describes how the University of British Columbia Faculty of Dentistry reached consensus on essential basic biomedical science objectives for DMD students and applied the information to the renewal of its DMD curriculum. The Delphi Method was used to build consensus among dental faculty members and students regarding the relevance of over 1,500 existing biomedical science objectives. Volunteer panels of at least three faculty members (a basic scientist, a general dentist, and a dental specialist) and a fourth-year dental student were formed for each of 13 biomedical courses in the first two years of the program. Panel members worked independently and anonymously, rating each course objective as "need to know," "nice to know," "irrelevant," or "don't know." Panel members were advised after each round which objectives had not yet achieved a 75% consensus and were asked to reconsider their ratings. After a maximum of three rounds to reach consensus, a second group of faculty experts reviewed and refined the results to establish the biomedical science objectives for the renewed curriculum. There was consensus on 46% of the learning objectives after round one, 80% after round two, and 95% after round three. The second expert group addressed any remaining objectives as part of its review process. Only 47% of previous biomedical science course objectives were judged to be essential or "need to know" for the general dentist. The consensus reached by participants in the Delphi Method panels and a second group of faculty experts led to a streamlined, better integrated DMD curriculum to prepare graduates for future practice.

  4. Development of alternating current circuit simulation as essential learning support for senior high school student

    Directory of Open Access Journals (Sweden)

    Mayang Dwinta Trisniarti

    2017-02-01

    Full Text Available In this study an interactive simulation of Alternating Current circuit was developed by using Articulate Storyline 2 and Adobe Flash CS 6 programs. The aim of this study was providing a computer interactive simulation as essential learning support for Senior High School student. One of the most important features of AC circuit simulation is the easily and continuous material to attain learning objectivity and interest toward students. This AC circuit simulation is built to create real-time sine wave graphs so that student could compare the result if the variable were changed gradually. The validation is held through several experts and reviewers due to get obtained through questionnaires. The results of this research could be concluded that AC circuit simulation for Senior High School Physics have good criteria based on user interface, i.e. 50% of respondents rated enough, 16.67% of respondents rated good, and 33.33% of respondents rated very good. Based on maintenance, i.e. 50% of respondents rated enough, 20% of respondents rated good, and 30% of respondents rated very good. Then based on usability, i.e. 6.67% of respondents rated good and 93.33% rated very good. Furthermore, based on understanding, i.e. 6.67% of respondents rated enough, 30% of respondents rated good, and 73.33% of respondents rated very good. The use of AC circuit simulation could improve the senior high school students’ cognitive ability on the Physics’s course, i.e. with the average score increased from 68.67 to 80.5 based on 30 students.

  5. Organisational Issues for E-Learning: Critical Success Factors as Identified by HE Practitioners

    Science.gov (United States)

    McPherson, Maggie; Nunes, Miguel Baptista

    2006-01-01

    Purpose: The purpose of this paper is to report on a research project that identified organisational critical success factors (CSFs) for e-learning implementation in higher education (HE). These CSFs can be used as a theoretical foundation upon which to base decision-making and strategic thinking about e-learning. Design/methodology/approach: The…

  6. An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images.

    Science.gov (United States)

    Hashimoto, Shinichi; Ogihara, Hiroyuki; Suenaga, Masato; Fujita, Yusuke; Terai, Shuji; Hamamoto, Yoshihiko; Sakaida, Isao

    2017-08-01

    Visibility in capsule endoscopic images is presently evaluated through intermittent analysis of frames selected by a physician. It is thus subjective and not quantitative. A method to automatically quantify the visibility on capsule endoscopic images has not been reported. Generally, when designing automated image recognition programs, physicians must provide a training image; this process is called supervised learning. We aimed to develop a novel automated self-learning quantification system to identify visible areas on capsule endoscopic images. The technique was developed using 200 capsule endoscopic images retrospectively selected from each of three patients. The rate of detection of visible areas on capsule endoscopic images between a supervised learning program, using training images labeled by a physician, and our novel automated self-learning program, using unlabeled training images without intervention by a physician, was compared. The rate of detection of visible areas was equivalent for the supervised learning program and for our automatic self-learning program. The visible areas automatically identified by self-learning program correlated to the areas identified by an experienced physician. We developed a novel self-learning automated program to identify visible areas in capsule endoscopic images.

  7. Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

    NARCIS (Netherlands)

    Drachsler, Hendrik; Hummel, Hans; Koper, Rob

    2008-01-01

    Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.

  8. PRE-CLINICAL EVALUATION OF EXTRACTS AND ESSENTIAL OILS FROM BETEL-LIKE SCENT PIPER SPECIES IDENTIFIED POTENTIAL CANCER TREATMENT.

    Science.gov (United States)

    Sanubol, Arisa; Chaveerach, Arunrat; Tanee, Tawatchai; Sudmoon, Runglawan

    2017-01-01

    Nine Piper species with betel-like scents are sources of industrial and medicinal aromatic chemicals, but there is lack of information on cytotoxicity and genotoxicity for human safety, including how these plants impact human cervical cancer cell line. Plant leaves were extracted with hexane and hydro-distilled for essential oils. The extracts and oils were pre-clinically studied based on cyto - and genotoxicity using microculture tetrazolium (MTT) and comet assays. The crude extracts showed an IC 50 in leukocytes and HeLa cells of 58.59-97.31 mg/ml and 34.91-101.79 mg/ml, the LD 50 is higher than 5000 mg/kg. With lower values than the crude extracts, the essential oils showed an IC 50 in leukocytes and HeLa cells of 0.023-0.059 μg/ml and 0.025-0.043 μg/ml the LD 50 is less than 50 mg/kg. IC 50 values showed that the essential oils were highly toxic than the crude extracts. At the level of human genetic materials, the crude extracts of two species, including P. betloides and P. crocatum , showed a significant toxicity ( p Piper species showed insignificant toxicity in leukocytes. For HeLa cells, the eight-studied species showed significant toxicity in HeLa cells, whereas only P. submultinerve showed insignificant toxicity. The crude extracts and essential oils should be tested as putative cervical cancer treatments due to less toxicity in human normal cells.

  9. CRISPR/Cas9-Mediated Rapid Generation of Multiple Mouse Lines Identified Ccdc63 as Essential for Spermiogenesis

    Directory of Open Access Journals (Sweden)

    Samantha A. M. Young

    2015-10-01

    Full Text Available Spermatozoa are flagellated cells whose role in fertilization is dependent on their ability to move towards an oocyte. The structure of the sperm flagella is highly conserved across species, and much of what is known about this structure is derived from studies utilizing animal models. One group of proteins essential for the movement of the flagella are the dyneins. Using the advanced technology of CRISPR/Cas9 we have targeted three dynein group members; Dnaic1, Wdr63 and Ccdc63 in mice. All three of these genes are expressed strongly in the testis. We generated mice with amino acid substitutions in Dnaic1 to analyze two specific phosphorylation events at S124 and S127, and generated simple knockouts of Wdr63 and Ccdc63. We found that the targeted phosphorylation sites in Dnaic1 were not essential for male fertility. Similarly, Wdr63 was not essential for male fertility; however, Ccdc63 removal resulted in sterile male mice due to shortened flagella. This study demonstrates the versatility of the CRISPR/Cas9 system to generate animal models of a highly complex system by introducing point mutations and simple knockouts in a fast and efficient manner.

  10. Identifying and building consensus about the essential competencies for R and D managers - case study at IPEN

    International Nuclear Information System (INIS)

    Gimenes, Celso Huerta

    2009-01-01

    The capacity of innovation and the technological development, in an R and D institute depend, among other things, on a management body with special competencies. The present work developed and applied a methodology to elicit, filter and prioritize the set of managerial competencies considered essential. The method was applied and tested in the Institute of Energetic and Nuclear Research - IPEN, which besides R and D, it is also dedicated to education and production. The methodology includes the following elements: (a) search, compilation and consolidation of competencies description, which are considered essential in similar institutions; (b) statements fitting and grouping according to seven BNQA's criteria; (c) filtering and reduction of the population survey list, concentrating on the parts which are of interest to the IPEN; (d) prioritization in two Delphi rounds, with the same population; (e) analysis and format of the resulting products, using single and multi-varied descriptive statistical techniques. The work was well-succeeded and, taking into account the high rate of reply to the Delphi survey, the obtained findings may be considered reliable. (author)

  11. Ecologies of Learning: How Culture and Context Impact Outcomes of Workplace Literacy and Essential Skills

    Science.gov (United States)

    Merrifield, Juliet

    2012-01-01

    Learning always takes place in a particular context and culture, yet educators have tended to focus their attention mainly on the form of learning, its methodology, content and teaching approach. While these can and do affect learning and its results, this paper looks beyond the particulars of the program to explore how the context and culture of…

  12. Newly identified essential amino acid residues affecting ^8-sphingolipid desaturase activity revealed by site-directed mutagenesis

    Science.gov (United States)

    In order to identify amino acid residues crucial for the enzymatic activity of ^8-sphingolipid desaturases, a sequence comparison was performed among ^8-sphingolipid desaturases and ^6-fatty acid desaturase from various plants. In addition to the known conserved cytb5 (cytochrome b5) HPGG motif and...

  13. Using Active Learning to Identify Health Information Technology Related Patient Safety Events.

    Science.gov (United States)

    Fong, Allan; Howe, Jessica L; Adams, Katharine T; Ratwani, Raj M

    2017-01-18

    The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.

  14. Learning essentials: what graduates of mental health nursing programmes need to know from an industry perspective.

    Science.gov (United States)

    McAllister, Margaret; Happell, Brenda; Flynn, Trudi

    2014-12-01

    To explore the perspectives of nursing directors in mental health in Queensland, Australia, regarding the skills and attributes of graduates of comprehensive nursing programme to provide an industry perspective and thus augment knowledge from theoretical and professional dimensions. There is a worldwide shortage of appropriately qualified nurses with the knowledge, skills and attitudes to work effectively in mental health services. Within Australia, this has been well documented since the introduction of comprehensive nursing education. The underrepresentation of mental health content in undergraduate curricula has been identified as the primary reason for nursing graduates not being adequately prepared for practice in this field. To date, this issue has primarily been addressed from the perspective of university academics, with the voice of industry relatively silent in the published literature. Qualitative exploratory. In-depth telephone interviews with Director of Nursing (Mental Health) in Queensland, Australia. The concerns of participants were expressed in six main themes: (1) foundational knowledge of mental health and disorders, (2) recovery-oriented skills, (3) physical as well as mental health skills, (4) therapeutic strategies, (5) resilience and self-development and (6) advanced knowledge and skills. The education of comprehensive nursing education needs to be reviewed as a matter of priority to ensure graduates with the attributes required to provide high-quality care for consumers of mental health services. A skilled and knowledgeable workforce is an essential component of high-quality mental health services. Research highlighting the current deficits and issues is therefore of the highest priority. © 2014 John Wiley & Sons Ltd.

  15. Learn the Lagrangian: A Vector-Valued RKHS Approach to Identifying Lagrangian Systems.

    Science.gov (United States)

    Cheng, Ching-An; Huang, Han-Pang

    2016-12-01

    We study the modeling of Lagrangian systems with multiple degrees of freedom. Based on system dynamics, canonical parametric models require ad hoc derivations and sometimes simplification for a computable solution; on the other hand, due to the lack of prior knowledge in the system's structure, modern nonparametric models in machine learning face the curse of dimensionality, especially in learning large systems. In this paper, we bridge this gap by unifying the theories of Lagrangian systems and vector-valued reproducing kernel Hilbert space. We reformulate Lagrangian systems with kernels that embed the governing Euler-Lagrange equation-the Lagrangian kernels-and show that these kernels span a subspace capturing the Lagrangian's projection as inverse dynamics. By such property, our model uses only inputs and outputs as in machine learning and inherits the structured form as in system dynamics, thereby removing the need for the mundane derivations for new systems as well as the generalization problem in learning from scratches. In effect, it learns the system's Lagrangian, a simpler task than directly learning the dynamics. To demonstrate, we applied the proposed kernel to identify the robot inverse dynamics in simulations and experiments. Our results present a competitive novel approach to identifying Lagrangian systems, despite using only inputs and outputs.

  16. Investigating Essential Factors on Students' Perceived Accomplishment and Enjoyment and Intention to Learn in Web Development

    Science.gov (United States)

    Zhang, Yulei; Dang, Yan

    2015-01-01

    Web development is an important component in the curriculum of computer science and information systems areas. However, it is generally considered difficult to learn among students. In this study,we examined factors that could influence students' perceptions of accomplishment and enjoyment and their intention to learn in the web development…

  17. Linking Essential Learning Outcomes and Interprofessional Collaborative Practice Competency in Health Science Undergraduates

    Science.gov (United States)

    Reed, Carole-Rae; Garcia, Luis Ivan; Slusser, Margaret M.; Konowitz, Sharon; Yep, Jewelry

    2017-01-01

    Assessing student learning outcomes and determining achievement of the Interprofessional Collaborative Practice (IPCEP) Core Competency of Values/Ethics in a generic pre-professional Bachelor of Science in Health Science (BSHS) program is challenging. A course level Student Learning Outcome (SLO) is: "….articulate the impact of personal…

  18. Learning to Identify Local Flora with Human Feedback (Author’s Manuscript)

    Science.gov (United States)

    2014-06-23

    cally tag images with species names of flora or fauna to sup- port content-based retrieval [10]. Detecting and identifying species could help to infer...Learning to Identify Local Flora with Human Feedback Stefan Lee and David Crandall School of Informatics and Computing Indiana University {steflee...applications that use consumer pho- tos to track the distribution of natural phenomena [8]. But flora identification is a very difficult problem, both

  19. Alanine Scanning Mutagenesis Identifies an Asparagine–Arginine–Lysine Triad Essential to Assembly of the Shell of the Pdu Microcompartment

    Energy Technology Data Exchange (ETDEWEB)

    Sinha, Sharmistha; Cheng, Shouqiang; Sung, Yea Won; McNamara, Dan E.; Sawaya, Michael R.; Yeates, Todd O.; Bobik, Thomas A.

    2014-06-01

    Bacterial microcompartments (MCPs) are the simplest organelles known. They function to enhance metabolic pathways by confining several related enzymes inside an all-protein envelope called the shell. In this study, we investigated the factors that govern MCP assembly by performing scanning mutagenesis on the surface residues of PduA, a major shell protein of the MCP used for 1,2-propanediol degradation. Biochemical, genetic, and structural analysis of 20 mutants allowed us to determine that PduA K26, N29, and R79 are crucial residues that stabilize the shell of the 1,2-propanediol MCP. In addition, we identify two PduA mutants (K37A and K55A) that impair MCP function most likely by altering the permeability of its protein shell. These are the first studies to examine the phenotypic effects of shell protein structural mutations in an MCP system. The findings reported here may be applicable to engineering protein containers with improved stability for biotechnology applications.

  20. Hepatitis A Virus: Essential Knowledge and a Novel Identify-Isolate-Inform Tool for Frontline Healthcare Providers

    Directory of Open Access Journals (Sweden)

    Kristi L. Koenig

    2017-10-01

    Full Text Available Infection with hepatitis A virus (HAV causes a highly contagious illness that can lead to serious morbidity and occasional mortality. Although the overall incidence of HAV has been declining since the introduction of the HAV vaccine, there have been an increasing number of outbreaks within the United States and elsewhere between 2016 and 2017. These outbreaks have had far-reaching consequences, with a large number of patients requiring hospitalization and several deaths. Accordingly, HAV is proving to present a renewed public health challenge. Through use of the “Identify-Isolate-Inform” tool as adapted for HAV, emergency physicians can become more familiar with the identification and management of patients presenting to the emergency department (ED with exposure, infection, or risk of contracting disease. While it can be asymptomatic, HAV typically presents with a prodrome of fever, nausea/vomiting, and abdominal pain followed by jaundice. Healthcare providers should maintain strict standard precautions for all patients suspected of having HAV infection as well as contact precautions in special cases. Hand hygiene with soap and warm water should be emphasized, and affected patients should be counseled to avoid food preparation and close contact with vulnerable populations. Additionally, ED providers should offer post-exposure prophylaxis to exposed contacts and encourage vaccination as well as other preventive measures for at-risk individuals. ED personnel should inform local public health departments of any suspected case.

  1. Competence Description for Personal Recommendations: The Importance of Identifying the Complexity of Learning and Performance Situations

    Science.gov (United States)

    Prins, Frans J.; Nadolski, Rob J.; Berlanga, Adriana J.; Drachsler, Hendrik; Hummel, Hans G. K.; Koper, Rob

    2008-01-01

    For competences development of learners and professionals, target competences and corresponding competence development opportunities have to be identified. Personal Recommender Systems (PRS) provide personal recommendations for learners aimed at finding and selecting learning activities that best match their needs. This article argues that a…

  2. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    Science.gov (United States)

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  3. Moderated mediation to identify the knowledge stocks, learning flows and barriers at a Dutch telecom operator

    NARCIS (Netherlands)

    de Schryver, Tom; Rosendaal, Bas

    2013-01-01

    Drawing on the 4I-model of Crossan et al. (1999), we have identified the knowledge stocks, learning flows and barriers at a Dutch telecom operator by means of moderated mediation. In this company, the strategic relevant knowledge stocks move in the same direction and many processes support their

  4. Learning to Promote Health at an Emergency Care Department: Identifying Expansive and Restrictive Conditions

    Science.gov (United States)

    Gustavsson, Maria; Ekberg, Kerstin

    2015-01-01

    This article reports on the findings of a planned workplace health promotion intervention, and the aim is to identify conditions that facilitated or restricted the learning to promote health at an emergency care department in a Swedish hospital. The study had a longitudinal design, with interviews before and after the intervention and follow-up…

  5. The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement

    Science.gov (United States)

    Bodily, Robert; Nyland, Rob; Wiley, David

    2017-01-01

    The RISE (Resource Inspection, Selection, and Enhancement) Framework is a framework supporting the continuous improvement of open educational resources (OER). The framework is an automated process that identifies learning resources that should be evaluated and either eliminated or improved. This is particularly useful in OER contexts where the…

  6. Identifying Core Mobile Learning Faculty Competencies Based Integrated Approach: A Delphi Study

    Science.gov (United States)

    Elbarbary, Rafik Said

    2015-01-01

    This study is based on the integrated approach as a concept framework to identify, categorize, and rank a key component of mobile learning core competencies for Egyptian faculty members in higher education. The field investigation framework used four rounds Delphi technique to determine the importance rate of each component of core competencies…

  7. Using Discrete Trial Training to Identify Specific Learning Impairments in Boys with Fragile X Syndrome

    Science.gov (United States)

    Hall, Scott S.; Hustyi, Kristin M.; Hammond, Jennifer L.; Hirt, Melissa; Reiss, Allan L.

    2014-01-01

    We examined whether "discrete trial training" (DTT) could be used to identify learning impairments in mathematical reasoning in boys with fragile X syndrome (FXS). Boys with FXS, aged 10-23 years, and age and IQ-matched controls, were trained to match fractions to pie-charts and pie-charts to decimals either on a computer or with a…

  8. Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa.

    Science.gov (United States)

    Artini, Marco; Patsilinakos, Alexandros; Papa, Rosanna; Božović, Mijat; Sabatino, Manuela; Garzoli, Stefania; Vrenna, Gianluca; Tilotta, Marco; Pepi, Federico; Ragno, Rino; Selan, Laura

    2018-02-23

    Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity against P. aeruginosa . Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity on P. aeruginosa , the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils as P. aeruginosa anti-biofilm. Many samples inhibited P. aeruginosa biofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms.

  9. Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa

    Directory of Open Access Journals (Sweden)

    Marco Artini

    2018-02-01

    Full Text Available Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity against P. aeruginosa. Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity on P. aeruginosa, the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity–composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils as P. aeruginosa anti-biofilm. Many samples inhibited P. aeruginosa biofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms.

  10. Identifying potential misfit items in cognitive process of learning engineering mathematics based on Rasch model

    International Nuclear Information System (INIS)

    Ataei, Sh; Mahmud, Z; Khalid, M N

    2014-01-01

    The students learning outcomes clarify what students should know and be able to demonstrate after completing their course. So, one of the issues on the process of teaching and learning is how to assess students' learning. This paper describes an application of the dichotomous Rasch measurement model in measuring the cognitive process of engineering students' learning of mathematics. This study provides insights into the perspective of 54 engineering students' cognitive ability in learning Calculus III based on Bloom's Taxonomy on 31 items. The results denote that some of the examination questions are either too difficult or too easy for the majority of the students. This analysis yields FIT statistics which are able to identify if there is data departure from the Rasch theoretical model. The study has identified some potential misfit items based on the measurement of ZSTD where the removal misfit item was accomplished based on the MNSQ outfit of above 1.3 or less than 0.7 logit. Therefore, it is recommended that these items be reviewed or revised to better match the range of students' ability in the respective course.

  11. Utilizing constructivism learning theory in collaborative testing as a creative strategy to promote essential nursing skills.

    Science.gov (United States)

    Duane, Barbara T; Satre, Maria E

    2014-01-01

    In nursing education, students participate in individual learner testing. This process follows the instructionist learning theory of a system model. However, in the practice of nursing, success depends upon collaboration with numerous people in different capacities, critical thinking, clinical reasoning, and the ability to communicate with others. Research has shown that collaborative testing, a constructivism learning activity and a form of collaborative learning, enhances students' abilities to master these areas. Collaborative testing is a clear, creative strategy which constructivists would say supports the socio-linguistic base of their learning theory. The test becomes an active implementation of peer-mediated learning where individual knowledge is enhanced through problem solving or defense of an individual position with the collaborative method. There is criticism for the testing method's potential of grade inflation and for students to receive grade benefits with little effort. After a review of various collaborative testing methods, this nursing faculty implemented a collaborative testing format that addresses both the positive and negative aspects of the process. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. Essentials of University Strategy Development in the Field of Lifelong Learning

    Directory of Open Access Journals (Sweden)

    Alina Irina POPESCU

    2012-06-01

    Full Text Available The process of strategy development reflects, in any organisation, the clarity of the purpose of the organisation’s mere existence. Although many organisations may decide ‘to go with the flow’, in the current economic context it is advisable that organisations, including higher education institutions, go through a thorough strategy development process. The lifelong learning approach brings a shift in the paradigm of education, and was considered to be the manner in which individuals get educated in the knowledge-based society. The most active players in the higher education market embraced this approach by developing lifelong learning strategies, either separated or incorporated in the overall university strategy. In this context, the study presents guidelines for the development of strategies in universities, and attempts to investigate to which extent three public universities representative for different regions of Romania have embraced the lifelong learning approach in their university strategies so far. The investigation uses the framework of the principles of university lifelong learning presented in the Universities‘ Charter on Lifelong Learning (2008.

  13. Novel Active Learning Experiences for Students to Identify Barriers to Independent Living for People with Disabilities.

    Science.gov (United States)

    McArthur, Polly; Burch, Lillian; Moore, Katherine; Hodges, Mary Sue

    2016-07-01

    This article describes interactive learning about independent living for people with disabilities and features the partnership of the College of Nursing and a Center for Independent Living (CIL). Using qualitative descriptive approach, students' written reflections were analyzed. Through "Xtreme Challenge," 82 undergraduate nursing students participated in aspects of independent living as well as identifying barriers. Students were engaged and learned to consider the person before the disability. Moreover, students valued the activity leaders' openness, which facilitated understanding the point of view of a person with disability. The value of partnership was evident as it allowed students to participate in active learning, which led to growth in the affective domain. Students became aware of potential education resources through the CIL. This article will guide educators in designing experiences that teach nursing care at the individual, family, and community level for people living with disabilities. © 2015 Association of Rehabilitation Nurses.

  14. Citation Indexing and Threshold Concepts: An Essential Ah-Ha in Student Learning

    Science.gov (United States)

    McLaughlin, Jeremy L.; Tucker, Virginia M.

    2017-01-01

    Understanding information organization is a key component to navigating digital library environments as an information professional. While traditionally thought of within the areas of assessment and evaluation, citation indexing is another form of organization and navigation, and learning about it can transform one's knowledge of the information…

  15. Identifying key areas for active interprofessional learning partnerships: A facilitated dialogue.

    Science.gov (United States)

    Steven, Kathryn; Angus, Allyson; Breckenridge, Jenna; Davey, Peter; Tully, Vicki; Muir, Fiona

    2016-11-01

    Student and service user involvement is recognised as an important factor in creating interprofessional education (IPE) opportunities. We used a team-based learning approach to bring together undergraduate health professional students, early career professionals (ECPs), public partners, volunteers, and carers to explore learning partnerships. Influenced by evaluative inquiry, this qualitative study used a free text response to allow participants to give their own opinion. A total of 153 participants (50 public partners and 103 students and professionals representing 11 healthcare professions) took part. Participants were divided into mixed groups of six (n = 25) and asked to identify areas where students, professionals, and public could work together to improve health professional education. Each group documented their discussions by summarising agreed areas and next steps. Responses were collected and transcribed for inductive content analysis. Seven key themes (areas for joint working) were identified: communication, public as partners, standards of conduct, IPE, quality improvement, education, and learning environments. The team-based learning format enabled undergraduate and postgraduate health professionals to achieve consensus with public partners on areas for IPE and collaboration. Some of our results may be context-specific but the approach is generalisable to other areas.

  16. Identifying the Components of Effective Learning Environments Based on Health Students\\' Perception

    Directory of Open Access Journals (Sweden)

    Yousefi Afrashteh M

    2015-08-01

    Full Text Available Aims: Effective learning environment can lead to establish and strengthen the appropriate conditions of learning in higher education. This study aimed to identify and define the factors associated with effective learning environment in the field of health education. Participants & Methods: This qualitative study with content analysis approach was conducted in 2013. Participants were 9 graduate and 7 undergraduate students of health majors that were selected using purposive sampling method. Data were recorded by interview and were analyzed using qualitative content analysis. Findings: Analysis of the data revealed 4 themes and 13 classes active and interactive teaching (participating viewpoints of students in educational planning, engaging students in class discussions, providing practical examples to understand the content, relaxing about expressed thoughts, the possibility of constructive criticism master plan of activities and according to the conditions and individual differences between students, Joyful atmosphere (academic motivation, the joy of learning and attendance, a sense of acceptance and respect from teachers and classroom dynamics and vitality and fatigue, relation of courses with professional needs (knowledge of the needs of the job in training course content and related training to the needs of job opportunities and professors’ scientific and power and expert (expertise and scientific capabilities in the field of teaching. Conclusion: 4 major themes and their characteristics can help to organize the learning environment in medical education.

  17. Comparative genomics in Chlamydomonas and Plasmodium identifies an ancient nuclear envelope protein family essential for sexual reproduction in protists, fungi, plants, and vertebrates.

    Science.gov (United States)

    Ning, Jue; Otto, Thomas D; Pfander, Claudia; Schwach, Frank; Brochet, Mathieu; Bushell, Ellen; Goulding, David; Sanders, Mandy; Lefebvre, Paul A; Pei, Jimin; Grishin, Nick V; Vanderlaan, Gary; Billker, Oliver; Snell, William J

    2013-05-15

    Fertilization is a crucial yet poorly characterized event in eukaryotes. Our previous discovery that the broadly conserved protein HAP2 (GCS1) functioned in gamete membrane fusion in the unicellular green alga Chlamydomonas and the malaria pathogen Plasmodium led us to exploit the rare biological phenomenon of isogamy in Chlamydomonas in a comparative transcriptomics strategy to uncover additional conserved sexual reproduction genes. All previously identified Chlamydomonas fertilization-essential genes fell into related clusters based on their expression patterns. Out of several conserved genes in a minus gamete cluster, we focused on Cre06.g280600, an ortholog of the fertilization-related Arabidopsis GEX1. Gene disruption, cell biological, and immunolocalization studies show that CrGEX1 functions in nuclear fusion in Chlamydomonas. Moreover, CrGEX1 and its Plasmodium ortholog, PBANKA_113980, are essential for production of viable meiotic progeny in both organisms and thus for mosquito transmission of malaria. Remarkably, we discovered that the genes are members of a large, previously unrecognized family whose first-characterized member, KAR5, is essential for nuclear fusion during yeast sexual reproduction. Our comparative transcriptomics approach provides a new resource for studying sexual development and demonstrates that exploiting the data can lead to the discovery of novel biology that is conserved across distant taxa.

  18. Metabolomics identifies perturbations in amino acid metabolism in the prefrontal cortex of the learned helplessness rat model of depression.

    Science.gov (United States)

    Zhou, Xinyu; Liu, Lanxiang; Zhang, Yuqing; Pu, Juncai; Yang, Lining; Zhou, Chanjuan; Yuan, Shuai; Zhang, Hanping; Xie, Peng

    2017-02-20

    Major depressive disorder is a serious psychiatric condition associated with high rates of suicide and is a leading cause of health burden worldwide. However, the underlying molecular mechanisms of major depression are still essentially unclear. In our study, a non-targeted gas chromatography-mass spectrometry-based metabolomics approach was used to investigate metabolic changes in the prefrontal cortex of the learned helplessness (LH) rat model of depression. Body-weight measurements and behavioral tests including the active escape test, sucrose preference test, forced swimming test, elevated plus-maze and open field test were used to assess changes in the behavioral spectrum after inescapable footshock stress. Rats in the stress group exhibited significant learned helpless and depression-like behaviors, while without any significant change in anxiety-like behaviors. Using multivariate and univariate statistical analysis, a total of 18 differential metabolites were identified after the footshock stress protocol. Ingenuity Pathways Analysis and MetaboAnalyst were applied for predicted pathways and biological functions analysis. "Amino Acid Metabolism, Molecule Transport, Small Molecule Biochemistry" was the most significantly altered network in the LH model. Amino acid metabolism, particularly glutamate metabolism, cysteine and methionine metabolism, arginine and proline metabolism, was significantly perturbed in the prefrontal cortex of LH rats. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

  19. Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

    Science.gov (United States)

    Cubuk, E. D.; Schoenholz, S. S.; Rieser, J. M.; Malone, B. D.; Rottler, J.; Durian, D. J.; Kaxiras, E.; Liu, A. J.

    2015-03-01

    We use machine-learning methods on local structure to identify flow defects—or particles susceptible to rearrangement—in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.

  20. Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value.

    Science.gov (United States)

    Elahian, Bahareh; Yeasin, Mohammed; Mudigoudar, Basanagoud; Wheless, James W; Babajani-Feremi, Abbas

    2017-10-01

    Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  1. Identifying and evaluating electronic learning resources for use in adult-gerontology nurse practitioner education.

    Science.gov (United States)

    Thompson, Hilaire J; Belza, Basia; Baker, Margaret; Christianson, Phyllis; Doorenbos, Ardith; Nguyen, Huong

    2014-01-01

    Enhancing existing curricula to meet newly published adult-gerontology advanced practice registered nurse (APRN) competencies in an efficient manner presents a challenge to nurse educators. Incorporating shared, published electronic learning resources (ELRs) in existing or new courses may be appropriate in order to assist students in achieving competencies. The purposes of this project were to (a) identify relevant available ELR for use in enhancing geriatric APRN education and (b) to evaluate the educational utility of identified ELRs based on established criteria. A multilevel search strategy was used. Two independent team members reviewed identified ELR against established criteria to ensure utility. Only resources meeting all criteria were retained. Resources were found for each of the competency areas and included formats such as podcasts, Web casts, case studies, and teaching videos. In many cases, resources were identified using supplemental strategies and not through traditional search or search of existing geriatric repositories. Resources identified have been useful to advanced practice educators in improving lecture and seminar content in a particular topic area and providing students and preceptors with additional self-learning resources. Addressing sustainability within geriatric APRN education is critical for sharing of best practices among educators and for sustainability of teaching and related resources. © 2014.

  2. Identifying and describing patients' learning experiences towards self-management of bipolar disorders: a phenomenological study.

    Science.gov (United States)

    Van den Heuvel, S C G H; Goossens, P J J; Terlouw, C; Van Achterberg, T; Schoonhoven, L

    2015-12-01

    -to-face, in-depth interviews, guided by a topic list, along service users with BD I or II (n = 16) in three specialised community care clinics across the Netherlands. Interviews were digitally recorded and transcribed verbatim prior to analysis in Atlas.ti 7. Unlike existing studies, which suggest that individual abilities of service users determine outcomes in self-management of BD, the current study found that self-management of BD is a learning process that takes place in a collaborative network. We identified five categories: acknowledgment of having BD, processing the information load, illness management, reflecting on living with BD, and self-management of BD. The success of self-management depends on the acknowledgment of individual limitations in learning to cope with BD and willingness to use a social network as a back-up instead. Especially, the dormant fear of a recurrent episode is a hampering factor in this learning process. © 2015 John Wiley & Sons Ltd.

  3. Prezi essentials

    CERN Document Server

    Sinclair, Domi

    2014-01-01

    If you want to learn Prezi, and specifically design within Prezi, this is the book for you. Perhaps you already know a bit about Prezi but have never used it, or perhaps you have used Prezi before but want to learn how to incorporate your own custom design elements. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic design concepts and the use of Prezi, but prior experience is not essential.

  4. The Essential Guide to HTML5 Using Games to Learn HTML5 and JavaScript

    CERN Document Server

    Meyer, Jeanine

    2010-01-01

    HTML5 opens up a plethora of new avenues for application and game development on the web. Games can now be created and interacted with directly within HTML with no need for users to download extra plugins, or for developers to learn new languages. Important new features such as the Canvas tag enable drawing directly onto the web page, the Audio tag allows sounds to be triggered and played from within your HTML code, the web sockets API brings the facility for real-time communication, and the local storage API enables data such as high scores or game preferences to be kept on a user's computer

  5. Identifying Features of Bodily Expression As Indicators of Emotional Experience during Multimedia Learning

    Directory of Open Access Journals (Sweden)

    Valentin Riemer

    2017-07-01

    Full Text Available The importance of emotions experienced by learners during their interaction with multimedia learning systems, such as serious games, underscores the need to identify sources of information that allow the recognition of learners’ emotional experience without interrupting the learning process. Bodily expression is gaining in attention as one of these sources of information. However, to date, the question of how bodily expression can convey different emotions has largely been addressed in research relying on acted emotion displays. Following a more contextualized approach, the present study aims to identify features of bodily expression (i.e., posture and activity of the upper body and the head that relate to genuine emotional experience during interaction with a serious game. In a multimethod approach, 70 undergraduates played a serious game relating to financial education while their bodily expression was captured using an off-the-shelf depth-image sensor (Microsoft Kinect. In addition, self-reports of experienced enjoyment, boredom, and frustration were collected repeatedly during gameplay, to address the dynamic changes in emotions occurring in educational tasks. Results showed that, firstly, the intensities of all emotions indeed changed significantly over the course of the game. Secondly, by using generalized estimating equations, distinct features of bodily expression could be identified as significant indicators for each emotion under investigation. A participant keeping their head more turned to the right was positively related to frustration being experienced, whereas keeping their head more turned to the left was positively related to enjoyment. Furthermore, having their upper body positioned more closely to the gaming screen was also positively related to frustration. Finally, increased activity of a participant’s head emerged as a significant indicator of boredom being experienced. These results confirm the value of bodily

  6. Identifying Opportunities for Peer Learning: An Observational Study of Medical Students on Clinical Placements.

    Science.gov (United States)

    Tai, Joanna H; Canny, Benedict J; Haines, Terry P; Molloy, Elizabeth K

    2017-01-01

    Phenomenon: Peer assisted learning (PAL) is frequently employed and researched in preclinical medical education. Fewer studies have examined PAL in the clinical context: These have focused mainly on the accuracy of peer assessment and potential benefits to learner communication and teamwork skills. Research has also examined the positive and negative effects of formal, structured PAL activities in the clinical setting. Given the prevalence of PAL activities during preclinical years, and the unstructured nature of clinical placements, it is likely that nonformal PAL activities are also undertaken. How PAL happens formally and informally and why students find PAL useful in this clinical setting remain poorly understood. This study aimed to describe PAL activities within the context of clinical placement learning and to explore students' perceptions of these activities. An ethnographic study was conducted to gather empirical data on engagement in clinical placement learning activities, including observations and interviews with students in their 1st clinical year, along with their supervising clinicians. Thematic analysis was used to interrogate the data. On average, students used PAL for 5.19 hours per week in a range of activities, of a total of 29.29 hours undertaking placements. PAL was recognized as a means of vicarious learning and had greater perceived value when an educator was present to guide or moderate the learning. Trust between students was seen as a requirement for PAL to be effective. Students found passive observation a barrier to PAL and were able to identify ways to adopt an active stance when observing peers interacting with patients. For example, learners reported that the expectation that they had to provide feedback to peers after task observation, resulted in them taking on a more critical gaze where they were encouraged to consider notions of good practice. Insights: Students use PAL in formal (i.e., tutorial) and nonformal (e.g., peer

  7. A machine learning-based framework to identify type 2 diabetes through electronic health records.

    Science.gov (United States)

    Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You

    2017-01-01

    To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature

  8. An array of Escherichia coli clones over-expressing essential proteins: A new strategy of identifying cellular targets of potent antibacterial compounds

    International Nuclear Information System (INIS)

    Xu, H. Howard; Real, Lilian; Bailey, Melissa Wu

    2006-01-01

    With the advancement of high throughput screening, it has become easier and faster to discover hit compounds that inhibit proliferation of bacterial cells. However, development in technologies used to identify cellular targets of potent antibacterial inhibitors has lagged behind. Here, we describe a novel strategy of target identification for antibacterial inhibitors using an array of Escherichia coli clones each over-expressing one essential protein. In a proof-of-concept study, eight essential genes were cloned into pLex5BA vector under the control of an inducible promoter. Over-expression of target proteins was confirmed. For two clones, one over-expressing FabI and the other over-expressing MurA enzymes, the host cells became 17- and 139-fold more resistant to the specific inhibitors triclosan and phosphomycin, respectively, while the susceptibility of other clones towards these inhibitors remained unchanged after induction of gene expression. Target identification via target protein over-expression was demonstrated using both mixed clone and individual clone assay formats

  9. Haploid genetic screens identify an essential role for PLP2 in the downregulation of novel plasma membrane targets by viral E3 ubiquitin ligases.

    Directory of Open Access Journals (Sweden)

    Richard T Timms

    Full Text Available The Kaposi's sarcoma-associated herpesvirus gene products K3 and K5 are viral ubiquitin E3 ligases which downregulate MHC-I and additional cell surface immunoreceptors. To identify novel cellular genes required for K5 function we performed a forward genetic screen in near-haploid human KBM7 cells. The screen identified proteolipid protein 2 (PLP2, a MARVEL domain protein of unknown function, as essential for K5 activity. Genetic loss of PLP2 traps the viral ligase in the endoplasmic reticulum, where it is unable to ubiquitinate and degrade its substrates. Subsequent analysis of the plasma membrane proteome of K5-expressing KBM7 cells in the presence and absence of PLP2 revealed a wide range of novel K5 targets, all of which required PLP2 for their K5-mediated downregulation. This work ascribes a critical function to PLP2 for viral ligase activity and underlines the power of non-lethal haploid genetic screens in human cells to identify the genes involved in pathogen manipulation of the host immune system.

  10. STEM-based science learning implementation to identify student’s personal intelligences profiles

    Science.gov (United States)

    Wiguna, B. J. P. K.; Suwarma, I. R.; Liliawati, W.

    2018-05-01

    Science and technology are rapidly developing needs to be balanced with the human resources that have the qualified ability. Not only cognitive ability, but also have the soft skills that support 21st century skills. Science, Technology, Engineering, and Mathematics (STEM) Education is a solution to improve the quality of learning and prepare students may be able to trained 21st century skills. This study aims to analyse the implementation of STEM-based science learning on Newton’s law of motion by identifying the personal intelligences profile junior high school students. The method used in this research is pre experiment with the design of the study one group pre-test post-test. Samples in this study were 26 junior high school students taken using Convenience Sampling. Students personal intelligences profile after learning STEM-based science uses two instruments, self-assessment and peer assessment. Intrapersonal intelligence profile based self-assessment and peer assessment are respectively 69.38; and 64.08. As for interpersonal intelligence for self-assessment instrument is 73 and the peer assessment is 60.23.

  11. Identifying Green Infrastructure from Social Media and Crowdsourcing- An Image Based Machine-Learning Approach.

    Science.gov (United States)

    Rai, A.; Minsker, B. S.

    2016-12-01

    In this work we introduce a novel dataset GRID: GReen Infrastructure Detection Dataset and a framework for identifying urban green storm water infrastructure (GI) designs (wetlands/ponds, urban trees, and rain gardens/bioswales) from social media and satellite aerial images using computer vision and machine learning methods. Along with the hydrologic benefits of GI, such as reducing runoff volumes and urban heat islands, GI also provides important socio-economic benefits such as stress recovery and community cohesion. However, GI is installed by many different parties and cities typically do not know where GI is located, making study of its impacts or siting new GI difficult. We use object recognition learning methods (template matching, sliding window approach, and Random Hough Forest method) and supervised machine learning algorithms (e.g., support vector machines) as initial screening approaches to detect potential GI sites, which can then be investigated in more detail using on-site surveys. Training data were collected from GPS locations of Flickr and Instagram image postings and Amazon Mechanical Turk identification of each GI type. Sliding window method outperformed other methods and achieved an average F measure, which is combined metric for precision and recall performance measure of 0.78.

  12. Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission

    Science.gov (United States)

    Xu, Duo; Offner, Stella S. R.

    2017-12-01

    Stellar feedback created by radiation and winds from massive stars plays a significant role in both physical and chemical evolution of molecular clouds. This energy and momentum leaves an identifiable signature (“bubbles”) that affects the dynamics and structure of the cloud. Most bubble searches are performed “by eye,” which is usually time-consuming, subjective, and difficult to calibrate. Automatic classifications based on machine learning make it possible to perform systematic, quantifiable, and repeatable searches for bubbles. We employ a previously developed machine learning algorithm, Brut, and quantitatively evaluate its performance in identifying bubbles using synthetic dust observations. We adopt magnetohydrodynamics simulations, which model stellar winds launching within turbulent molecular clouds, as an input to generate synthetic images. We use a publicly available three-dimensional dust continuum Monte Carlo radiative transfer code, HYPERION, to generate synthetic images of bubbles in three Spitzer bands (4.5, 8, and 24 μm). We designate half of our synthetic bubbles as a training set, which we use to train Brut along with citizen-science data from the Milky Way Project (MWP). We then assess Brut’s accuracy using the remaining synthetic observations. We find that Brut’s performance after retraining increases significantly, and it is able to identify yellow bubbles, which are likely associated with B-type stars. Brut continues to perform well on previously identified high-score bubbles, and over 10% of the MWP bubbles are reclassified as high-confidence bubbles, which were previously marginal or ambiguous detections in the MWP data. We also investigate the influence of the size of the training set, dust model, evolutionary stage, and background noise on bubble identification.

  13. Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

    Science.gov (United States)

    Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

    2016-03-01

    Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

  14. Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning.

    Science.gov (United States)

    LeMoyne, Robert; Tomycz, Nestor; Mastroianni, Timothy; McCandless, Cyrus; Cozza, Michael; Peduto, David

    2015-01-01

    Essential tremor (ET) is a highly prevalent movement disorder. Patients with ET exhibit a complex progressive and disabling tremor, and medical management often fails. Deep brain stimulation (DBS) has been successfully applied to this disorder, however there has been no quantifiable way to measure tremor severity or treatment efficacy in this patient population. The quantified amelioration of kinetic tremor via DBS is herein demonstrated through the application of a smartphone (iPhone) as a wireless accelerometer platform. The recorded acceleration signal can be obtained at a setting of the subject's convenience and conveyed by wireless transmission through the Internet for post-processing anywhere in the world. Further post-processing of the acceleration signal can be classified through a machine learning application, such as the support vector machine. Preliminary application of deep brain stimulation with a smartphone for acquisition of a feature set and machine learning for classification has been successfully applied. The support vector machine achieved 100% classification between deep brain stimulation in `on' and `off' mode based on the recording of an accelerometer signal through a smartphone as a wireless accelerometer platform.

  15. Preservation of Essential Odor-Guided Behaviors and Odor-Based Reversal Learning after Targeting Adult Brain Serotonin Synthesis.

    Science.gov (United States)

    Carlson, Kaitlin S; Whitney, Meredith S; Gadziola, Marie A; Deneris, Evan S; Wesson, Daniel W

    2016-01-01

    The neurotransmitter serotonin (5-HT) is considered a powerful modulator of sensory system organization and function in a wide range of animals. The olfactory system is innervated by midbrain 5-HT neurons into both its primary and secondary odor-processing stages. Facilitated by this circuitry, 5-HT and its receptors modulate olfactory system function, including odor information input to the olfactory bulb. It is unknown, however, whether the olfactory system requires 5-HT for even its most basic behavioral functions. To address this question, we established a conditional genetic approach to specifically target adult brain tryptophan hydroxylase 2 ( Tph2 ), encoding the rate-limiting enzyme in brain 5-HT synthesis, and nearly eliminate 5-HT from the mouse forebrain. Using this novel model, we investigated the behavior of 5-HT-depleted mice during performance in an olfactory go/no-go task. Surprisingly, the near elimination of 5-HT from the forebrain, including the olfactory bulbs, had no detectable effect on the ability of mice to perform the odor-based task. Tph2 -targeted mice not only were able to learn the task, but also had levels of odor acuity similar to those of control mice when performing coarse odor discrimination. Both groups of mice spent similar amounts of time sampling odors during decision-making. Furthermore, odor reversal learning was identical between 5-HT-depleted and control mice. These results suggest that 5-HT neurotransmission is not necessary for the most essential aspects of olfaction, including odor learning, discrimination, and certain forms of cognitive flexibility.

  16. Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

    Science.gov (United States)

    Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

    2016-05-18

    Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

  17. Effect of Modifying Intervention Set Size with Acquisition Rate Data among Students Identified with a Learning Disability

    Science.gov (United States)

    Haegele, Katherine; Burns, Matthew K.

    2015-01-01

    The amount of information that students can successfully learn and recall at least 1 day later is called an acquisition rate (AR) and is unique to the individual student. The current study extended previous drill rehearsal research with word recognition by (a) using students identified with a learning disability in reading, (b) assessing set sizes…

  18. The map: An essential tool in the teaching-learning process of the Marxism-Leninism and History curriculum

    Directory of Open Access Journals (Sweden)

    Montero, Martiza Isabel

    2012-05-01

    Full Text Available This paper evaluates the use of maps in teaching Geography by a sample of professor at “José Marti” College of Education. A systematic use of maps constitutes one of the major problems in the teaching-learning process in the Marxism-Leninism and History Curriculum. Likewise, it has been identify as a shortcoming in graduates and in-service trainees. It would be recommendable to highlight the value and importance of maps in teaching, consequently a number of suggestions are given to lead, reflection and discussion by the teacher’s.

  19. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity.

    Science.gov (United States)

    Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B; Bringas-Vega, Maria L; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A

    2017-01-01

    In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  20. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS Severity

    Directory of Open Access Journals (Sweden)

    Jorge Bosch-Bayard

    2018-01-01

    Full Text Available In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia, Mathematics (Dyscalculia, or Writing (Dysgraphia. By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  1. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence.

    Science.gov (United States)

    Ahn, Woo-Young; Vassileva, Jasmin

    2016-04-01

    Recent animal and human studies reveal distinct cognitive and neurobiological differences between opiate and stimulant addictions; however, our understanding of the common and specific effects of these two classes of drugs remains limited due to the high rates of polysubstance-dependence among drug users. The goal of the current study was to identify multivariate substance-specific markers classifying heroin dependence (HD) and amphetamine dependence (AD), by using machine-learning approaches. Participants included 39 amphetamine mono-dependent, 44 heroin mono-dependent, 58 polysubstance dependent, and 81 non-substance dependent individuals. The majority of substance dependent participants were in protracted abstinence. We used demographic, personality (trait impulsivity, trait psychopathy, aggression, sensation seeking), psychiatric (attention deficit hyperactivity disorder, conduct disorder, antisocial personality disorder, psychopathy, anxiety, depression), and neurocognitive impulsivity measures (Delay Discounting, Go/No-Go, Stop Signal, Immediate Memory, Balloon Analogue Risk, Cambridge Gambling, and Iowa Gambling tasks) as predictors in a machine-learning algorithm. The machine-learning approach revealed substance-specific multivariate profiles that classified HD and AD in new samples with high degree of accuracy. Out of 54 predictors, psychopathy was the only classifier common to both types of addiction. Important dissociations emerged between factors classifying HD and AD, which often showed opposite patterns among individuals with HD and AD. These results suggest that different mechanisms may underlie HD and AD, challenging the unitary account of drug addiction. This line of work may shed light on the development of standardized and cost-efficient clinical diagnostic tests and facilitate the development of individualized prevention and intervention programs for HD and AD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  2. Factors enabling and inhibiting facilitator development: lessons learned from Essentials of Care in South Eastern Sydney Local Health District

    Directory of Open Access Journals (Sweden)

    Tamera Watling

    2015-11-01

    . Conclusion: Facilitators need to be enabled to access training, practise their skills and learn from more experienced facilitators. There are parallels between the enablers of progress of the implementation of Essentials of Care and those to promote the development of facilitation capacity. Organisational leaders have a significant role in supporting both; it is critical they ensure there is a team of facilitators to share the workload, that time is allocated for facilitators to be released from clinical duties for development opportunities, and that there is time for teams to engage in programme activities. Implications for practice: •\tFindings suggest a relationship between facilitation capacity, context and the progression of practice development-based work. This evaluation offers practical examples that inform how these elements may be enhanced for the successful pursuit of person-centred healthcare practice •\tManagers and those in leadership positions have a key role in overcoming the contextual factors that inhibit facilitator development and programme implementation •\tOrganisational planning and accountability to manage staffing, ensure optimal workloads and promote practice development work as a priority supports the engagement and motivation of staff to participate in programme activities, and therefore the capacity of teams to progress practice development work and transform culture and practice •\tThe engagement of managers and those in leadership positions to clarify roles and responsibilities and establish agreed mechanisms for support of individuals and teams should precede the implementation of practice development programmes •\tTime is a significant resource in the successful advancement of facilitator development and programmes underpinned by practice development. In healthcare contexts, where staff feel time poor in the presence of the increasing demands of patient care, the pressure of multiple improvement programmes and other professional

  3. Identifying Students' Difficulties When Learning Technical Skills via a Wireless Sensor Network

    Science.gov (United States)

    Wang, Jingying; Wen, Ming-Lee; Jou, Min

    2016-01-01

    Practical training and actual application of acquired knowledge and techniques are crucial for the learning of technical skills. We established a wireless sensor network system (WSNS) based on the 5E learning cycle in a practical learning environment to improve students' reflective abilities and to reduce difficulties for the learning of technical…

  4. Identifying the Learning Styles and Instructional Tool Preferences of Beginning Food Science and Human Nutrition Majors

    Science.gov (United States)

    Bohn, D. M.; Rasmussen, C. N.; Schmidt, S. J.

    2004-01-01

    Learning styles vary among individuals, and understanding which instructional tools certain learning styles prefer can be utilized to enhance student learning. Students in the introductory Food Science and Human Nutrition course (FSHN 101), taught at the Univ. of Illinois at Urbana-Champaign, were asked to complete Gregorc's Learning Style…

  5. Using natural language processing and machine learning to identify gout flares from electronic clinical notes.

    Science.gov (United States)

    Zheng, Chengyi; Rashid, Nazia; Wu, Yi-Lin; Koblick, River; Lin, Antony T; Levy, Gerald D; Cheetham, T Craig

    2014-11-01

    Gout flares are not well documented by diagnosis codes, making it difficult to conduct accurate database studies. We implemented a computer-based method to automatically identify gout flares using natural language processing (NLP) and machine learning (ML) from electronic clinical notes. Of 16,519 patients, 1,264 and 1,192 clinical notes from 2 separate sets of 100 patients were selected as the training and evaluation data sets, respectively, which were reviewed by rheumatologists. We created separate NLP searches to capture different aspects of gout flares. For each note, the NLP search outputs became the ML system inputs, which provided the final classification decisions. The note-level classifications were grouped into patient-level gout flares. Our NLP+ML results were validated using a gold standard data set and compared with the claims-based method used by prior literatures. For 16,519 patients with a diagnosis of gout and a prescription for a urate-lowering therapy, we identified 18,869 clinical notes as gout flare positive (sensitivity 82.1%, specificity 91.5%): 1,402 patients with ≥3 flares (sensitivity 93.5%, specificity 84.6%), 5,954 with 1 or 2 flares, and 9,163 with no flare (sensitivity 98.5%, specificity 96.4%). Our method identified more flare cases (18,869 versus 7,861) and patients with ≥3 flares (1,402 versus 516) when compared to the claims-based method. We developed a computer-based method (NLP and ML) to identify gout flares from the clinical notes. Our method was validated as an accurate tool for identifying gout flares with higher sensitivity and specificity compared to previous studies. Copyright © 2014 by the American College of Rheumatology.

  6. Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

    Science.gov (United States)

    Zheng, Wu; Blake, Catherine

    2015-10-01

    Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. Identifying Human Phenotype Terms by Combining Machine Learning and Validation Rules

    Directory of Open Access Journals (Sweden)

    Manuel Lobo

    2017-01-01

    Full Text Available Named-Entity Recognition is commonly used to identify biological entities such as proteins, genes, and chemical compounds found in scientific articles. The Human Phenotype Ontology (HPO is an ontology that provides a standardized vocabulary for phenotypic abnormalities found in human diseases. This article presents the Identifying Human Phenotypes (IHP system, tuned to recognize HPO entities in unstructured text. IHP uses Stanford CoreNLP for text processing and applies Conditional Random Fields trained with a rich feature set, which includes linguistic, orthographic, morphologic, lexical, and context features created for the machine learning-based classifier. However, the main novelty of IHP is its validation step based on a set of carefully crafted manual rules, such as the negative connotation analysis, that combined with a dictionary can filter incorrectly identified entities, find missed entities, and combine adjacent entities. The performance of IHP was evaluated using the recently published HPO Gold Standardized Corpora (GSC, where the system Bio-LarK CR obtained the best F-measure of 0.56. IHP achieved an F-measure of 0.65 on the GSC. Due to inconsistencies found in the GSC, an extended version of the GSC was created, adding 881 entities and modifying 4 entities. IHP achieved an F-measure of 0.863 on the new GSC.

  8. Identifying Cassini's Magnetospheric Location Using Magnetospheric Imaging Instrument (MIMI) Data and Machine Learning

    Science.gov (United States)

    Vandegriff, J. D.; Smith, G. L.; Edenbaum, H.; Peachey, J. M.; Mitchell, D. G.

    2017-12-01

    We analyzed data from Cassini's Magnetospheric Imaging Instrument (MIMI) and Magnetometer (MAG) and attempted to identify the region of Saturn's magnetosphere that Cassini was in at a given time using machine learning. MIMI data are from the Charge-Energy-Mass Spectrometer (CHEMS) instrument and the Low-Energy Magnetospheric Measurement System (LEMMS). We trained on data where the region is known based on a previous analysis of Cassini Plasma Spectrometer (CAPS) plasma data. Three magnetospheric regions are considered: Magnetosphere, Magnetosheath, and Solar Wind. MIMI particle intensities, magnetic field values, and spacecraft position are used as input attributes, and the output is the CAPS-based region, which is available from 2004 to 2012. We then use the trained classifier to identify Cassini's magnetospheric regions for times after 2012, when CAPS data is no longer available. Training accuracy is evaluated by testing the classifier performance on a time range of known regions that the classifier has never seen. Preliminary results indicate a 68% accuracy on such test data. Other techniques are being tested that may increase this performance. We present the data and algorithms used, and will describe the latest results, including the magnetospheric regions post-2012 identified by the algorithm.

  9. Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

    Directory of Open Access Journals (Sweden)

    Sanchez-Vazquez Manuel J

    2012-08-01

    Full Text Available Abstract Background Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Results Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. Conclusions The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

  10. Identifying associations between pig pathologies using a multi-dimensional machine learning methodology.

    Science.gov (United States)

    Sanchez-Vazquez, Manuel J; Nielen, Mirjam; Edwards, Sandra A; Gunn, George J; Lewis, Fraser I

    2012-08-31

    Abattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously. Multi-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology. The application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

  11. Assessing Uncertainty in Deep Learning Techniques that Identify Atmospheric Rivers in Climate Simulations

    Science.gov (United States)

    Mahesh, A.; Mudigonda, M.; Kim, S. K.; Kashinath, K.; Kahou, S.; Michalski, V.; Williams, D. N.; Liu, Y.; Prabhat, M.; Loring, B.; O'Brien, T. A.; Collins, W. D.

    2017-12-01

    Atmospheric rivers (ARs) can be the difference between CA facing drought or hurricane-level storms. ARs are a form of extreme weather defined as long, narrow columns of moisture which transport water vapor outside the tropics. When they make landfall, they release the vapor as rain or snow. Convolutional neural networks (CNNs), a machine learning technique that uses filters to recognize features, are the leading computer vision mechanism for classifying multichannel images. CNNs have been proven to be effective in identifying extreme weather events in climate simulation output (Liu et. al. 2016, ABDA'16, http://bit.ly/2hlrFNV). Here, we compare three different CNN architectures, tuned with different hyperparameters and training schemes. We compare two-layer, three-layer, four-layer, and sixteen-layer CNNs' ability to recognize ARs in Community Atmospheric Model version 5 output, and we explore the ability of data augmentation and pre-trained models to increase the accuracy of the classifier. Because pre-training the model with regular images (i.e. benches, stoves, and dogs) yielded the highest accuracy rate, this strategy, also known as transfer learning, may be vital in future scientific CNNs, which likely will not have access to a large labelled training dataset. By choosing the most effective CNN architecture, climate scientists can build an accurate historical database of ARs, which can be used to develop a predictive understanding of these phenomena.

  12. Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

    Science.gov (United States)

    Poos, Alexandra M; Maicher, André; Dieckmann, Anna K; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer

    2016-06-02

    Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  13. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    Science.gov (United States)

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  14. Identifying tropical dry forests extent and succession via the use of machine learning techniques

    Science.gov (United States)

    Li, Wei; Cao, Sen; Campos-Vargas, Carlos; Sanchez-Azofeifa, Arturo

    2017-12-01

    Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.

  15. Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

    Science.gov (United States)

    Wallace, Byron C; Noel-Storr, Anna; Marshall, Iain J; Cohen, Aaron M; Smalheiser, Neil R; Thomas, James

    2017-11-01

    Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

  16. Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

    Science.gov (United States)

    Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

    2016-01-01

    A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

  17. Essential astrophysics

    CERN Document Server

    Lang, Kenneth R

    2013-01-01

    Essential Astrophysics is a book to learn or teach from, as well as a fundamental reference volume for anyone interested in astronomy and astrophysics. It presents astrophysics from basic principles without requiring any previous study of astronomy or astrophysics. It serves as a comprehensive introductory text, which takes the student through the field of astrophysics in lecture-sized chapters of basic physical principles applied to the cosmos. This one-semester overview will be enjoyed by undergraduate students with an interest in the physical sciences, such as astronomy, chemistry, engineering or physics, as well as by any curious student interested in learning about our celestial science. The mathematics required for understanding the text is on the level of simple algebra, for that is all that is needed to describe the fundamental principles. The text is of sufficient breadth and depth to prepare the interested student for more advanced specialized courses in the future. Astronomical examples are provide...

  18. Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

    Science.gov (United States)

    Olm, Matthew R.; Morowitz, Michael J.

    2018-01-01

    ABSTRACT Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We found that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes. Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism’s direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics treatment and predict how organisms in the gut microbiome will respond to antibiotic administration. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to

  19. Using appreciative inquiry to help students identify strategies to overcome handicaps of their learning styles.

    Science.gov (United States)

    Kumar, Latha Rajendra; Chacko, Thomas Vengail

    2012-01-01

    In India, as in some other neighboring Asian countries, students and teachers are generally unaware of the differences in the learning styles among learners, which can handicap students with learning styles alien to the common teaching/learning modality within the institution. This study aims to find out whether making students aware of their learning styles and then using the Appreciative Inquiry approach to help them discover learning strategies that worked for them and others with similar learning styles within the institution made them perceive that this experience improved their learning and performance in exams. The visual, auditory, read-write, and kinesthetic (VARK) inventory of learning styles questionnaire was administered to all 100 first-year medical students of the Father Muller's Medical College in Mangalore India to make them aware of their individual learning styles. An Appreciate Inquiry intervention was administered to 62 student volunteers who were counseled about the different learning styles and their adaptive strategies. Pre and post intervention change in student's perception about usefulness of knowing learning styles on their learning, learning behavior, and performance in examinations was collected from the students using a prevalidated questionnaire. Post intervention mean scores showed a significant change (P learning style and discovering strategies that worked within the institutional environment. There was agreement among students that the intervention helped them become more confident in learning (84%), facilitating learning in general (100%), and in understanding concepts (100%). However, only 29% of the students agreed that the intervention has brought about their capability improvement in application of learning and 31% felt it improved their performance in exams. Appreciate Inquiry was perceived as useful in helping students discover learning strategies that work for different individual learning styles and sharing them within

  20. Identifying Keys to Success in Innovative Teaching: Student Engagement and Instructional Practices as Predictors of Student Learning in a Course Using a Team-Based Learning Approach

    Directory of Open Access Journals (Sweden)

    Rosa M. Alvarez-Bell

    2017-09-01

    Full Text Available When implementing innovative teaching techniques, instructors often seek to gauge the success of their methods. Proposing one approach to assessing classroom innovation, this study examines the ability of students’ ratings of engagement and instructional practices to predict their learning in a cooperative (team-based framework. After identifying the factor structures underlying measures of student engagement and instructional practices, these factors were used as predictors of self-reported student learning in a general chemistry course delivered using a team-based learning approach. Exploratory factor analyses showed a four-factor structure of engagement: teamwork involvement, investment in the learning process, feelings about team-based learning, level of academic challenge; and a three-factor structure of instructional practices: instructional guidance, fostering self-directed learning skills, and cognitive level. Multiple linear regression revealed that feelings about team-based learning and perceptions of instructional guidance had significant effects on learning, beyond other predictors, while controlling gender, GPA, class level, number of credit hours, whether students began college at their current institution, expected highest level of education, racial or ethnic identification, and parental level of education. These results yield insight into student perceptions about team-based learning, and how to measure learning in a team-based learning framework, with implications for how to evaluate innovative instructional methods.

  1. Na+/K+-ATPase α1 identified as an abundant protein in the blood-labyrinth barrier that plays an essential role in the barrier integrity.

    Directory of Open Access Journals (Sweden)

    Yue Yang

    2011-01-01

    Full Text Available The endothelial-blood/tissue barrier is critical for maintaining tissue homeostasis. The ear harbors a unique endothelial-blood/tissue barrier which we term "blood-labyrinth-barrier". This barrier is critical for maintaining inner ear homeostasis. Disruption of the blood-labyrinth-barrier is closely associated with a number of hearing disorders. Many proteins of the blood-brain-barrier and blood-retinal-barrier have been identified, leading to significant advances in understanding their tissue specific functions. In contrast, capillaries in the ear are small in volume and anatomically complex. This presents a challenge for protein analysis studies, which has resulted in limited knowledge of the molecular and functional components of the blood-labyrinth-barrier. In this study, we developed a novel method for isolation of the stria vascularis capillary from CBA/CaJ mouse cochlea and provided the first database of protein components in the blood-labyrinth barrier as well as evidence that the interaction of Na(+/K(+-ATPase α1 (ATP1A1 with protein kinase C eta (PKCη and occludin is one of the mechanisms of loud sound-induced vascular permeability increase.Using a mass-spectrometry, shotgun-proteomics approach combined with a novel "sandwich-dissociation" method, more than 600 proteins from isolated stria vascularis capillaries were identified from adult CBA/CaJ mouse cochlea. The ion transporter ATP1A1 was the most abundant protein in the blood-labyrinth barrier. Pharmacological inhibition of ATP1A1 activity resulted in hyperphosphorylation of tight junction proteins such as occludin which increased the blood-labyrinth-barrier permeability. PKCη directly interacted with ATP1A1 and was an essential mediator of ATP1A1-initiated occludin phosphorylation. Moreover, this identified signaling pathway was involved in the breakdown of the blood-labyrinth-barrier resulting from loud sound trauma.The results presented here provide a novel method for

  2. The Value of Identifying and Recovering Lost GN&C Lessons Learned: Aeronautical, Spacecraft, and Launch Vehicle Examples

    Science.gov (United States)

    Dennehy, Cornelius J.; Labbe, Steve; Lebsock, Kenneth L.

    2010-01-01

    Within the broad aerospace community the importance of identifying, documenting and widely sharing lessons learned during system development, flight test, operational or research programs/projects is broadly acknowledged. Documenting and sharing lessons learned helps managers and engineers to minimize project risk and improve performance of their systems. Often significant lessons learned on a project fail to get captured even though they are well known 'tribal knowledge' amongst the project team members. The physical act of actually writing down and documenting these lessons learned for the next generation of NASA GN&C engineers fails to happen on some projects for various reasons. In this paper we will first review the importance of capturing lessons learned and then will discuss reasons why some lessons are not documented. A simple proven approach called 'Pause and Learn' will be highlighted as a proven low-impact method of organizational learning that could foster the timely capture of critical lessons learned. Lastly some examples of 'lost' GN&C lessons learned from the aeronautics, spacecraft and launch vehicle domains are briefly highlighted. In the context of this paper 'lost' refers to lessons that have not achieved broad visibility within the NASA-wide GN&C CoP because they are either undocumented, masked or poorly documented in the NASA Lessons Learned Information System (LLIS).

  3. Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

    Science.gov (United States)

    Bowd, Christopher; Weinreb, Robert N; Balasubramanian, Madhusudhanan; Lee, Intae; Jang, Giljin; Yousefi, Siamak; Zangwill, Linda M; Medeiros, Felipe A; Girkin, Christopher A; Liebmann, Jeffrey M; Goldbaum, Michael H

    2014-01-01

    The variational Bayesian independent component analysis-mixture model (VIM), an unsupervised machine-learning classifier, was used to automatically separate Matrix Frequency Doubling Technology (FDT) perimetry data into clusters of healthy and glaucomatous eyes, and to identify axes representing statistically independent patterns of defect in the glaucoma clusters. FDT measurements were obtained from 1,190 eyes with normal FDT results and 786 eyes with abnormal FDT results from the UCSD-based Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES). For all eyes, VIM input was 52 threshold test points from the 24-2 test pattern, plus age. FDT mean deviation was -1.00 dB (S.D. = 2.80 dB) and -5.57 dB (S.D. = 5.09 dB) in FDT-normal eyes and FDT-abnormal eyes, respectively (p<0.001). VIM identified meaningful clusters of FDT data and positioned a set of statistically independent axes through the mean of each cluster. The optimal VIM model separated the FDT fields into 3 clusters. Cluster N contained primarily normal fields (1109/1190, specificity 93.1%) and clusters G1 and G2 combined, contained primarily abnormal fields (651/786, sensitivity 82.8%). For clusters G1 and G2 the optimal number of axes were 2 and 5, respectively. Patterns automatically generated along axes within the glaucoma clusters were similar to those known to be indicative of glaucoma. Fields located farther from the normal mean on each glaucoma axis showed increasing field defect severity. VIM successfully separated FDT fields from healthy and glaucoma eyes without a priori information about class membership, and identified familiar glaucomatous patterns of loss.

  4. A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials.

    Science.gov (United States)

    Zilcha-Mano, Sigal; Roose, Steven P; Brown, Patrick J; Rutherford, Bret R

    2018-01-11

    Despite efforts to identify characteristics associated with medication-placebo differences in antidepressant trials, few consistent findings have emerged to guide participant selection in drug development settings and differential therapeutics in clinical practice. Limitations in the methodologies used, particularly searching for a single moderator while treating all other variables as noise, may partially explain the failure to generate consistent results. The present study tested whether interactions between pretreatment patient characteristics, rather than a single-variable solution, may better predict who is most likely to benefit from placebo versus medication. Data were analyzed from 174 patients aged 75 years and older with unipolar depression who were randomly assigned to citalopram or placebo. Model-based recursive partitioning analysis was conducted to identify the most robust significant moderators of placebo versus citalopram response. The greatest signal detection between medication and placebo in favor of medication was among patients with fewer years of education (≤12) who suffered from a longer duration of depression since their first episode (>3.47 years) (B = 2.53, t(32) = 3.01, p = 0.004). Compared with medication, placebo had the greatest response for those who were more educated (>12 years), to the point where placebo almost outperformed medication (B = -0.57, t(96) = -1.90, p = 0.06). Machine learning approaches capable of evaluating the contributions of multiple predictor variables may be a promising methodology for identifying placebo versus medication responders. Duration of depression and education should be considered in the efforts to modulate placebo magnitude in drug development settings and in clinical practice. Copyright © 2018 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  5. An Analysis of the Relationship between English Language Arts and Mathematics Achievement and Essential Learning Mastery in Grades 3 and 4. Executive Summary

    Science.gov (United States)

    Haystead, Mark W.

    2016-01-01

    Over several years, Clark Pleasant Community School Corporation (CPCSC) schools have dedicated significant professional development hours and time to develop Essential Learnings (ELs) along with proficiency scales that could guide the content of classroom assessments used to determine student mastery. This executive summary highlights key findings…

  6. Using machine learning to identify structural breaks in single-group interrupted time series designs.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

  7. A study looking at the effectiveness of developmental screening in identifying learning disabilities in early childhood.

    Science.gov (United States)

    Flanagan, O; Nualláin, S O

    2001-05-01

    This is a retrospective study of children under six years of age referred to the Brothers of Charity Early Intervention Services in County Galway, a service that caters for children under 6 years with learning disabilities. The aim in doing this study was to assess the value of routine developmental screening in identifying children with learning difficulties. This study also investigates the patterns and sources of referral to the remedial services provided by the Brothers of Charity and highlights possible avoidable delays in referral. The results showed that many children were referred for remedial services late. The reasons for late referral included late identification of some children with problems, insufficient co-ordination of community-based services and a lack of awareness of the importance of early intervention in some cases. As some communication disorders such as autism, autistic spectrum disorders and specific language delay may not express themselves until the later part of the second year of life, the 18-24 month developmental assessment is of vital importance. However identification of these disorders can present difficulties and may call for additional training for professionals involved in the developmental screening of children in that age group. The interval between initial identification and referral for remedial care in many cases was more than twelve months. We propose that, in order to minimize this time, children requiring a more in-depth assessment should be assessed by a community-based multidisciplinary team, enabling integrated assessment by the different disciplines and thus speedier referral to remedial services.

  8. Learning and Teaching Styles in Management Education: Identifying, Analyzing, and Facilitating

    Science.gov (United States)

    Provitera, Michael J.; Esendal, Esin

    2008-01-01

    Drawing on the learning theory of the Felder-Silverman model (2002), and the work of A.F. Grasha, this paper provides a brief review of teaching and learning styles used in management education. Professors, like students, demonstrate a number of learning styles and a professor has some responsibility to organize and present a course to satisfy…

  9. Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

    Science.gov (United States)

    Kinnebrew, John S.; Biswas, Gautam

    2012-01-01

    Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

  10. Machine Learning Analysis Identifies Drosophila Grunge/Atrophin as an Important Learning and Memory Gene Required for Memory Retention and Social Learning.

    Science.gov (United States)

    Kacsoh, Balint Z; Greene, Casey S; Bosco, Giovanni

    2017-11-06

    High-throughput experiments are becoming increasingly common, and scientists must balance hypothesis-driven experiments with genome-wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin ( Gug/Atro ), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction. Copyright © 2017 Kacsoh et al.

  11. Data fusion and machine learning to identify threat vectors for the Zika virus and classify vulnerability

    Science.gov (United States)

    Gentle, J. N., Jr.; Kahn, A.; Pierce, S. A.; Wang, S.; Wade, C.; Moran, S.

    2016-12-01

    With the continued spread of the zika virus in the United States in both Florida and Virginia, increased public awareness, prevention and targeted prediction is necessary to effectively mitigate further infection and propagation of the virus throughout the human population. The goal of this project is to utilize publicly accessible data and HPC resources coupled with machine learning algorithms to identify potential threat vectors for the spread of the zika virus in Texas, the United States and globally by correlating available zika case data collected from incident reports in medical databases (e.g., CDC, Florida Department of Health) with known bodies of water in various earth science databases (e.g., USGS NAQWA Data, NASA ASTER Data, TWDB Data) and by using known mosquito population centers as a proxy for trends in population distribution (e.g., WHO, European CDC, Texas Data) while correlating historical trends in the spread of other mosquito borne diseases (e.g., chikungunya, malaria, dengue, yellow fever, west nile, etc.). The resulting analysis should refine the identification of the specific threat vectors for the spread of the virus which will correspondingly increase the effectiveness of the limited resources allocated towards combating the disease through better strategic implementation of defense measures. The minimal outcome of this research is a better understanding of the factors involved in the spread of the zika virus, with the greater potential to save additional lives through more effective resource utilization and public outreach.

  12. A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning.

    Science.gov (United States)

    Macedo, Maysa M G; Guimarães, Welingson V N; Galon, Micheli Z; Takimura, Celso K; Lemos, Pedro A; Gutierrez, Marco Antonio

    2015-12-01

    Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 μm. Recent studies in IV-OCT have demonstrated the importance of the bifurcation regions. Therefore, the development of an automated tool to classify hundreds of coronary OCT frames as bifurcation or nonbifurcation can be an important step to improve automated methods for atherosclerotic plaques quantification, stent analysis and co-registration between different modalities. This paper describes a fully automated method to identify IV-OCT frames in bifurcation regions. The method is divided into lumen detection; feature extraction; and classification, providing a lumen area quantification, geometrical features of the cross-sectional lumen and labeled slices. This classification method is a combination of supervised machine learning algorithms and feature selection using orthogonal least squares methods. Training and tests were performed in sets with a maximum of 1460 human coronary OCT frames. The lumen segmentation achieved a mean difference of lumen area of 0.11 mm(2) compared with manual segmentation, and the AdaBoost classifier presented the best result reaching a F-measure score of 97.5% using 104 features. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Per-service supervised learning for identifying desired WoT apps from user requests in natural language.

    Directory of Open Access Journals (Sweden)

    Young Yoon

    Full Text Available Web of Things (WoT platforms are growing fast so as the needs for composing WoT apps more easily and efficiently. We have recently commenced the campaign to develop an interface where users can issue requests for WoT apps entirely in natural language. This requires an effort to build a system that can learn to identify relevant WoT functions that fulfill user's requests. In our preceding work, we trained a supervised learning system with thousands of publicly-available IFTTT app recipes based on conditional random fields (CRF. However, the sub-par accuracy and excessive training time motivated us to devise a better approach. In this paper, we present a novel solution that creates a separate learning engine for each trigger service. With this approach, parallel and incremental learning becomes possible. For inference, our system first identifies the most relevant trigger service for a given user request by using an information retrieval technique. Then, the learning engine associated with the trigger service predicts the most likely pair of trigger and action functions. We expect that such two-phase inference method given parallel learning engines would improve the accuracy of identifying related WoT functions. We verify our new solution through the empirical evaluation with training and test sets sampled from a pool of refined IFTTT app recipes. We also meticulously analyze the characteristics of the recipes to find future research directions.

  14. Incorporating deep learning with convolutional neural networks and position specific scoring matrices for identifying electron transport proteins.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2017-09-05

    In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  15. Per-service supervised learning for identifying desired WoT apps from user requests in natural language.

    Science.gov (United States)

    Yoon, Young

    2017-01-01

    Web of Things (WoT) platforms are growing fast so as the needs for composing WoT apps more easily and efficiently. We have recently commenced the campaign to develop an interface where users can issue requests for WoT apps entirely in natural language. This requires an effort to build a system that can learn to identify relevant WoT functions that fulfill user's requests. In our preceding work, we trained a supervised learning system with thousands of publicly-available IFTTT app recipes based on conditional random fields (CRF). However, the sub-par accuracy and excessive training time motivated us to devise a better approach. In this paper, we present a novel solution that creates a separate learning engine for each trigger service. With this approach, parallel and incremental learning becomes possible. For inference, our system first identifies the most relevant trigger service for a given user request by using an information retrieval technique. Then, the learning engine associated with the trigger service predicts the most likely pair of trigger and action functions. We expect that such two-phase inference method given parallel learning engines would improve the accuracy of identifying related WoT functions. We verify our new solution through the empirical evaluation with training and test sets sampled from a pool of refined IFTTT app recipes. We also meticulously analyze the characteristics of the recipes to find future research directions.

  16. Don't forget the learner: an essential aspect for developing effective hypermedia online learning in continuing medical education.

    Science.gov (United States)

    Sandars, John; Homer, Matthew; Walsh, Kieran; Rutherford, Alaster

    2012-03-01

    There is increasing use of hypermedia online learning in continuing medical education (CME) that presents the learner with a wide range of different learning resources, requiring the learner to use self-regulated learning (SRL) skills. This study is the first to apply an SRL perspective to understand how learners engage with hypermedia online learning in CME. We found that the main SRL skills used by learners were use of strategies and monitoring. The increasing use of strategies was associated with increasing interest in the topic and with increasing satisfaction with the learning experience. Further research is recommended to understand SRL processes and its impact on learning in other aspects of hypermedia online learning across the different phases of medical education. Research is also recommended to implement and evaluate the learning impact of a variety of approaches to develop the SRL skills of hypermedia online learners in CME.

  17. Generalized query-based active learning to identify differentially methylated regions in DNA.

    Science.gov (United States)

    Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

    2013-01-01

    Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

  18. A Comparison of Educational Statistics and Data Mining Approaches to Identify Characteristics That Impact Online Learning

    Science.gov (United States)

    Miller, L. Dee; Soh, Leen-Kiat; Samal, Ashok; Kupzyk, Kevin; Nugent, Gwen

    2015-01-01

    Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying…

  19. Identify the Motivational Factors to Affect the Higher Education Students to Learn Using Technology

    Science.gov (United States)

    Yau, Hon Keung; Cheng, Alison Lai Fong; Ho, Wing Man

    2015-01-01

    The purpose of this study is twofold. Firstly, engineering students' motivation in using technology for learning in one of Hong Kong universities is investigated. Secondly, new research model about students' perception in using technology for learning is developed. Survey was employed and the questionnaires were distributed to targeted university…

  20. Identifying the Individual Differences among Students during Learning and Teaching Process by Science Teachers

    Science.gov (United States)

    Kubat, Ulas

    2018-01-01

    It is important for teachers to know variables such as physical characteristics, intelligence, perception, gender, ability, learning styles, which are individual differences of the learners. An effective and productive learning-teaching process can be planned by considering these individual differences of the students. Since the learners' own…

  1. Identifying Instructional Strategies Used to Design Mobile Learning in a Corporate Setting

    Science.gov (United States)

    Jackson-Butler, Uletta

    2016-01-01

    The purpose of this qualitative embedded multiple case study was to describe what instructional strategies corporate instructional designers were using to design mobile learning and to understand from their experiences which instructional strategies they believed enhance learning. Participants were five instructional designers who were actively…

  2. Can Pre-Service Physical Education Majors Identify Learning Standards during Authentic Teaching Episodes?

    Science.gov (United States)

    Kniffin, Mike; Foley, John; MacDonald, Lynn Couturier; Howarth, Kath

    2014-01-01

    Only a handful of research studies have been conducted to determine whether or not physical educators or pre-service physical education teachers are utilizing learning standards in their teaching. While pre-service teachers are typically required to align lesson objectives and content, their extent of their understanding of how learning standards…

  3. Identifying Learning Patterns of Children at Risk for Specific Reading Disability

    Science.gov (United States)

    Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

    2016-01-01

    Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural…

  4. Pre-Service Teachers Identify Connections between Teaching-Learning and Literacy Strategies

    Science.gov (United States)

    Liu, Kimy; Robinson, Quintin; Braun-Monegan, Jenelle

    2016-01-01

    This study explores the transformation of pre-service teachers in their attainment of effective teaching skills. Pre-service teachers learn about the learning-to-read process and implementations of component skills of teaching reading within the practicum. More importantly, pre-service teachers achieve a meaningful understanding of the…

  5. An Achievement Degree Analysis Approach to Identifying Learning Problems in Object-Oriented Programming

    Science.gov (United States)

    Allinjawi, Arwa A.; Al-Nuaim, Hana A.; Krause, Paul

    2014-01-01

    Students often face difficulties while learning object-oriented programming (OOP) concepts. Many papers have presented various assessment methods for diagnosing learning problems to improve the teaching of programming in computer science (CS) higher education. The research presented in this article illustrates that although max-min composition is…

  6. Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners

    Science.gov (United States)

    Novak, Joseph D.

    2002-07-01

    The construction and reconstruction of meanings by learners requires that they actively seek to integrate new knowledge with knowledge already in their cognitive structure. Ausubel's assimilation theory of cognitive learning has been shown to be effective in guiding research and instructional design to facilitate meaningful learning (Ausubel, The psychology of meaningful verbal learning, New York: Grune and Stratton, 1963; Educational psychology: A cognitive view, New York: Holt, Rinehart and Winston, 1968; The acquisition and retention of knowledge, Dordrecht: Kluwer, 2000). Gowin's Vee heuristic has been employed effectively to aid teachers and students in understanding the constructed nature of knowledge (Gowin, Educating, Ithaca, NY: Cornell University Press, 1981). Situated learning occurs when learning is by rote or at a lower level of meaningful learning. Concept mapping has been used effectively to aid meaningful learning with resulting modification of student's knowledge structures. When these knowledge structures are limited or faulty in some way, they may be referred to as Limited or Inappropriate Propositional Hierarchies (LIPH's). Conceptual change, or more accurately conceptual reconstrution, requires meaningful learning to modify LIPH's. Collaborative group learning facilitates meaningful learning and new knowledge construction. World-wide economic changes are forcing major changes in business and industry placing a premium on the power and value of knowledge and new knowledge production. These changes require changes in school and university education that centers on the nature and power of meaningful learning. New computer tools are available to facilitate teaching activities targeted at modifying LIPH's, and aiding meaningful learning in general.

  7. Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

    Directory of Open Access Journals (Sweden)

    Kuchin Yan

    2017-12-01

    Full Text Available The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

  8. Learning in context: identifying gaps in research on the transfer of medical communication skills to the clinical workplace.

    NARCIS (Netherlands)

    Eertwegh, V. van den; Dulmen, S. van; Dalen, J. van; Scherpbier, A.J.J.A.; Vleuten, C.P.M. van der

    2013-01-01

    Objective: In order to reduce the inconsistencies of findings and the apparent low transfer of communication skills from training to medical practice, this narrative review identifies some main gaps in research on medical communication skills training and presents insights from theories on learning

  9. Learning in context: identifying gaps in research on the transfer of medical communication skills to the clinical workplace

    NARCIS (Netherlands)

    Eertwegh, V. van den; Dulmen, S. van; Dalen, J. Van; Scherpbier, A.J.J.A.; Vleuten, C.P.M. van der

    2013-01-01

    OBJECTIVE: In order to reduce the inconsistencies of findings and the apparent low transfer of communication skills from training to medical practice, this narrative review identifies some main gaps in research on medical communication skills training and presents insights from theories on learning

  10. Identifying Stages in a Learning Hierarchy for Use in Formative Assessment--The Example of Line Graphs

    Science.gov (United States)

    Stacey, Kaye; Price, Beth; Steinle, Vicki

    2012-01-01

    This paper discusses issues arising in the design of questions to use in an on-line computer-based formative assessment system, focussing on how best to identify the stages of a learning hierarchy for reporting to teachers. Data from several hundred students is used to illustrate how design decisions have been made for a test on interpreting line…

  11. Towards identifying nurse educator competencies required for simulation-based learning: A systemised rapid review and synthesis

    DEFF Research Database (Denmark)

    Bøje, Rikke Buus; Topping, Annie; Rekola, Leena

    2015-01-01

    Objectives: This paper presents the results of a systemised rapid reviewand synthesis of the literature undertaken to identify competencies required by nurse educators to facilitate simulation-based learning (SBL). Design: An international collaboration undertook a protocol-based search, retrieva...... further development as a model for educators delivering SBL as part of nursing curricula....

  12. A Canine Audience: The Effect of Animal-Assisted Therapy on Reading Progress among Students Identified with Learning Disabilities

    Science.gov (United States)

    Griess, Julie Omodio

    2010-01-01

    This study explored the use of animal-assisted therapy with students identified with a learning disability and limited reading success. Initially, reading progress was defined as the participants' comprehension rate obtained from an oral Informal Reading Inventory (IRI) passage. The nature of the Informal Reading Inventory requires the…

  13. Identifying College Students at Risk for Learning Disabilities: Evidence for Use of the Learning Difficulties Assessment in Postsecondary Settings

    Science.gov (United States)

    Kane, Steven T.; Roy, Soma; Medina, Steffanie

    2013-01-01

    This article describes research supporting the use of the Learning Difficulties Assessment (LDA), a normed and no-cost, web-based survey that assesses difficulties with reading, writing, spelling, mathematics, listening, concentration, memory, organizational skills, sense of control, and anxiety in college students. Previous research has supported…

  14. Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

    Science.gov (United States)

    Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

  15. The Leadership Factor: Identifying  Leadership Skills and Characteristics Essential For Student Achievement in High Poverty Elementary Schools in the Commonwealth of Virginia

    OpenAIRE

    Owens, Anita Michelle

    2016-01-01

    The success of a school is primarily dependent upon leadership (Marzano, McNulty, and Waters, 2005). A principal's skills greatly impact teaching and learning; thus, the degree to which a school is successful depends on an effective leader with a vision for transforming a school. Research from the early 2000s until now suggests that a challenge exists for schools as they seek to decrease the achievement gap and attain success for all students, especially those in low-income areas (Brock and ...

  16. Defining the Undefinable: Operationalization of Methods to Identify Specific Learning Disabilities among Practicing School Psychologists

    Science.gov (United States)

    Cottrell, Joseph M.; Barrett, Courtenay A.

    2016-01-01

    Accurate and consistent identification of students with specific learning disabilities (SLDs) is crucial; however, state and district guidelines regarding identification methods lack operationalization and are inconsistent throughout the United States. In the current study, the authors surveyed 471 school psychologists about "school" SLD…

  17. IDENTIFYING FACTORS THAT CONTRIBUTE TO THE SATISFACTION OF STUDENTS IN E-LEARNING

    Directory of Open Access Journals (Sweden)

    Levent CALLI,

    2013-01-01

    Full Text Available There has been an increasing interest in the application of e-learning through the enhancement of internet and computer technologies. Satisfaction has appeared as a key factor in order to develop efficient course content in line with students’ demands and expectations. Thus, a lot of research has been conducted on the concept of satisfaction in electronic environments. Satisfaction has been seen to be the most significant variable on loyalty and usage intention in marketing and information science terms, which can also be highly related to academic success. In this regard, this study set out to investigate the effects of several variables on the learning processes of 930 e-learning students in the Sakarya University distance learning program. The findings of the research indicated that factors perceived playfulness, perceived ease of use and multimedia content effectiveness had a significant effect on perceived usefulness. Furthermore, it was concluded that satisfaction was affected by perceived usefulness, perceived playfulness and multimedia content effectivenes

  18. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Geert Dewulf; Theo van der Voordt; Ronald Beckers

    2015-01-01

    Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed

  19. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Beckers, R; van der Voordt, Theo; Dewulf, G

    2015-01-01

    Purpose - The purpose of this paper is to explore the spatial implications of new learning theories and the use of Information and Communication Technologies (ICT) in higher education.
    Design/methodology/approach - Based on a review of literature, a theoretical framework has been developed that

  20. A conceptual framework to identify spatial implications of new ways of learning in higher education

    NARCIS (Netherlands)

    Beckers, Ronald; van der Voordt, Theo; Dewulf, Geert P.M.R.

    2015-01-01

    Purpose – The purpose of this paper is to explore the spatial implications of new learning theories and the use of information and communication technologies (ICT) in higher education. Design/methodology/approach – Based on a review of the literature, a theoretical framework has been developed that

  1. Identifying beneficial task relations for multi-task learning in deep neural networks

    DEFF Research Database (Denmark)

    Bingel, Joachim; Søgaard, Anders

    2017-01-01

    Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP...

  2. Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities

    Science.gov (United States)

    Crippa, Alessandro; Salvatore, Christian; Perego, Paolo; Forti, Sara; Nobile, Maria; Molteni, Massimo; Castiglioni, Isabella

    2015-01-01

    In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children…

  3. Using Virtual Worlds to Identify Multidimensional Student Engagement in High School Foreign Language Learning Classrooms

    Science.gov (United States)

    Jacob, Laura Beth

    2012-01-01

    Virtual world environments have evolved from object-oriented, text-based online games to complex three-dimensional immersive social spaces where the lines between reality and computer-generated begin to blur. Educators use virtual worlds to create engaging three-dimensional learning spaces for students, but the impact of virtual worlds in…

  4. Training School Psychologists to Identify Specific Learning Disabilities: A Content Analysis of Syllabi

    Science.gov (United States)

    Barrett, Courtenay A.; Cottrell, Joseph M.; Newman, Daniel S.; Pierce, Benjamin G.; Anderson, Alisha

    2015-01-01

    Approximately 2.4 million children receive special education services for specific learning disabilities (SLDs), and school psychologists are key contributors to the SLD eligibility decision-making process. The Individuals with Disabilities Education Act (2004) enabled local education agencies to use response to intervention (RTI) instead of the…

  5. Touching Mercury in Community Media: Identifying Multiple Literacy Learning through Digital Arts Production

    Science.gov (United States)

    Arndt, Angela E.

    2011-01-01

    Educational paradigm shifts call for 21st century learners to possess the knowledge, skills, abilities, values, and experiences associated with multiple forms of literacy in a participatory learning culture. Contemporary educational systems are slow to adapt. Outside of school, people have to be self-motivated and have access to resources in order…

  6. Identifying and responding to weak signals to improve learning from experiences in high-risk industry

    NARCIS (Netherlands)

    Guillaume, E.G.

    2011-01-01

    1. Context This thesis forms part of an extended study funded by FonCSI (Fondation pour une Culture de Sécurité Industrielle) about learning systems of major hazard companies. All French industrial sites running a risky activity – e.g. petrochemicals, steel making plants – must put a Safety

  7. Issues and Challenges Identified in the Development of a Broad Multidisciplinary Work Integrated Learning Package

    Science.gov (United States)

    Sutherland, Karen; Symmons, Mark

    2013-01-01

    Work integrated learning (WIL) units can be discipline specific and constructed for majors or degrees with a strong vocational orientation. This paper describes an undergraduate unit with its genesis in a public relations internship. The original unit enjoyed strong support from industry partners and was instrumental in many graduates securing…

  8. Identifying key features of effective active learning: the effects of writing and peer discussion.

    Science.gov (United States)

    Linton, Debra L; Pangle, Wiline M; Wyatt, Kevin H; Powell, Karli N; Sherwood, Rachel E

    2014-01-01

    We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. © 2014 D. L. Linton et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

  9. Emory University: MEDICI (Mining Essentiality Data to Identify Critical Interactions) for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has developed a computational methodology to combine high-throughput knockdown data with known protein network topologies to infer the importance of protein-protein interactions (PPIs) for the survival of cancer cells.  Applying these data to the Achilles shRNA results, the CCLE cell line characterizations, and known and newly identified PPIs provides novel insights for potential new drug targets for cancer therapies and identifies important PPI hubs.

  10. Learning in context: identifying gaps in research on the transfer of medical communication skills to the clinical workplace.

    Science.gov (United States)

    van den Eertwegh, Valerie; van Dulmen, Sandra; van Dalen, Jan; Scherpbier, Albert J J A; van der Vleuten, Cees P M

    2013-02-01

    In order to reduce the inconsistencies of findings and the apparent low transfer of communication skills from training to medical practice, this narrative review identifies some main gaps in research on medical communication skills training and presents insights from theories on learning and transfer to broaden the view for future research. Relevant literature was identified using Pubmed, GoogleScholar, Cochrane database, and Web of Science; and analyzed using an iterative procedure. Research findings on the effectiveness of medical communication training still show inconsistencies and variability. Contemporary theories on learning based on a constructivist paradigm offer the following insights: acquisition of knowledge and skills should be viewed as an ongoing process of exchange between the learner and his environment, so called lifelong learning. This process can neither be atomized nor separated from the context in which it occurs. Four contemporary approaches are presented as examples. The following shift in focus for future research is proposed: beyond isolated single factor effectiveness studies toward constructivist, non-reductionistic studies integrating the context. Future research should investigate how constructivist approaches can be used in the medical context to increase effective learning and transition of communication skills. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  11. Authoring Tool for Identifying Learning Styles, Using Self-Organizing Maps on Mobile Devices

    Directory of Open Access Journals (Sweden)

    Ramón Zatarain Cabada

    2011-05-01

    Full Text Available This work explores a methodological proposal whose main objective is the identification of learning styles using a method of self-organizing maps designed to work, for the most part, on mobile devices. These maps can work in real time and without direct student interaction, which implies the absence of prior information. The results generated are an authoring tool for adaptive courses in Web 2.0 environments.

  12. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Directory of Open Access Journals (Sweden)

    Ahmad Karim

    Full Text Available Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS, disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  13. Identifying different methods for creating knowledge from lessons learned in project oriented organizations

    Directory of Open Access Journals (Sweden)

    Ahmad Norang

    2016-01-01

    Full Text Available Nowadays, the increase in competition has increased the relative importance of innovation for most firms and many managers believe a good innovation must be knowledge oriented. This paper has tried to determine different methods for creating knowledge in project oriented organizations. The study designs a questionnaire in Likert scale and distributes it among 32 experts who were well informed about different methods of knowledge creation and lessons learned. Cronbach alphas for all components of the survey were well above the desirable level. The study has detected 11 methods for knowledge creation and lessons learned. In terms of preliminary assessment, business transactions has received the highest impact while knowledge team has received the highest effect in terms of necessary assessment. The results of this survey have indicated that although there are several methods for detecting knowledge within organizations, in most cases, it is not easy to gain value added knowledge within an organization, quickly. The people who participated in our survey have indicated that organizational commitment, brainstorming, Delphi and storytelling also have played important role for creation of knowledge. The results have also shown that brainstorming, knowledge brokers, map knowledge and work experience were easier to use for knowledge creation and lessons learned compared with other forms of knowledge creation.

  14. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  15. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

  16. Shifting the Focus to Student Learning: Characteristics of Effective Teaching Practice As Identified by Experienced Pre-service Faculty Advisors

    Directory of Open Access Journals (Sweden)

    Nancy Maynes

    2012-11-01

    Full Text Available Cochrane-Smith and Power identify trends in teacher education programs with some relating to heightened teacher accountability for students’ learning. In this paper we provide a model that identifies characteristics believed to be critical elements related to a teacher’s conceptual focus shifting from an emphasis on their teaching to their students’ learning and we have grounded these characteristics in current educational research. Through focus group inquiry, we have identified those teacher characteristics thought to account for effective teaching practice. These characteristics include: a professional growth perspective, passion and enthusiasm for the  content, pedagogical content knowledge, a rich instructional repertoire of strategies, awareness of assessment for, as, and of learning, ability to read the body language  of the learner, caring classroom management strategies, and instructional efforts (e.g., social justice. Our research data provide a conceptual framework for further study.

  17. Family Literacy and the New Canadian: Formal, Non-Formal and Informal Learning: The Case of Literacy, Essential Skills and Language Learning in Canada

    Science.gov (United States)

    Eaton, Sarah Elaine

    2011-01-01

    This paper examines literacy and language learning across the lifespan within the context of immigrants in the Canadian context. It explores the process of improving literacy skills and acquiring second or third language skills through the systems of formal, non-formal and informal learning, as defined by the OECD [Organisation for Economic…

  18. Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

    Directory of Open Access Journals (Sweden)

    Krzysztof Rataj

    2018-05-01

    Full Text Available The identification of subtype-selective GPCR (G-protein coupled receptor ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP. This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivities.

  19. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning.

    Science.gov (United States)

    Kermany, Daniel S; Goldbaum, Michael; Cai, Wenjia; Valentim, Carolina C S; Liang, Huiying; Baxter, Sally L; McKeown, Alex; Yang, Ge; Wu, Xiaokang; Yan, Fangbing; Dong, Justin; Prasadha, Made K; Pei, Jacqueline; Ting, Magdalene Y L; Zhu, Jie; Li, Christina; Hewett, Sierra; Dong, Jason; Ziyar, Ian; Shi, Alexander; Zhang, Runze; Zheng, Lianghong; Hou, Rui; Shi, William; Fu, Xin; Duan, Yaou; Huu, Viet A N; Wen, Cindy; Zhang, Edward D; Zhang, Charlotte L; Li, Oulan; Wang, Xiaobo; Singer, Michael A; Sun, Xiaodong; Xu, Jie; Tafreshi, Ali; Lewis, M Anthony; Xia, Huimin; Zhang, Kang

    2018-02-22

    The implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. Here, we establish a diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases. Our framework utilizes transfer learning, which trains a neural network with a fraction of the data of conventional approaches. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. We further demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. This tool may ultimately aid in expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, resulting in improved clinical outcomes. VIDEO ABSTRACT. Copyright © 2018 Elsevier Inc. All rights reserved.

  20. Stacking machine learning classifiers to identify Higgs bosons at the LHC

    International Nuclear Information System (INIS)

    Alves, A.

    2017-01-01

    Machine learning (ML) algorithms have been employed in the problem of classifying signal and background events with high accuracy in particle physics. In this paper, we compare the performance of a widespread ML technique, namely, stacked generalization , against the results of two state-of-art algorithms: (1) a deep neural network (DNN) in the task of discovering a new neutral Higgs boson and (2) a scalable machine learning system for tree boosting, in the Standard Model Higgs to tau leptons channel, both at the 8 TeV LHC. In a cut-and-count analysis, stacking three algorithms performed around 16% worse than DNN but demanding far less computation efforts, however, the same stacking outperforms boosted decision trees. Using the stacked classifiers in a multivariate statistical analysis (MVA), on the other hand, significantly enhances the statistical significance compared to cut-and-count in both Higgs processes, suggesting that combining an ensemble of simpler and faster ML algorithms with MVA tools is a better approach than building a complex state-of-art algorithm for cut-and-count.

  1. DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

    Science.gov (United States)

    Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

    2017-09-01

    Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

  2. Deep learning for constructing microblog behavior representation to identify social media user’s personality

    Directory of Open Access Journals (Sweden)

    Xiaoqian Liu

    2016-09-01

    Full Text Available Due to the rapid development of information technology, the Internet has gradually become a part of everyday life. People would like to communicate with friends to share their opinions on social networks. The diverse behavior on socials networks is an ideal reflection of users’ personality traits. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic analysis. Although they work fairly well, they are hard to extend and maintain. In this paper, we utilize a deep learning algorithm to build a feature learning model for personality prediction, which could perform an unsupervised extraction of the Linguistic Representation Feature Vector (LRFV activity without supervision from text actively published on the Sina microblog. Compared with other feature extractsion methods, LRFV, as an abstract representation of microblog content, could describe a user’s semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using a linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of the prediction model, respectively. The results show that LRFV performs better in microblog behavior descriptions, and improves the performance of the personality prediction model.

  3. Intuitive Mathematical Knowledge as an Essential Aspect of Contemporary Adult Learning: A case of women street vendors in the city of Gaborone

    Directory of Open Access Journals (Sweden)

    Rebecca Nthogo Lekoko

    2006-04-01

    Full Text Available The findings of a phenomenological interview study with women street vendors showed a strong link between participants’ perceptions of everyday use of mathematical literacy and the speculations that mathematical use arose spontaneously in response to a practical need. The concept of intuitive mathematics as used indicates that mathematical thinking is an indispensable element of everyday conversation. Although the study finds that intuition and spontaneity are essential principles of lifelong learning, it concludes with recommendations for an empowerment curriculum that interweaves participants’ experiences and intuition with formal/academic mathematical literacy and psychosocial skills necessary for success in a highly competitive business world.

  4. Essential Conditions for Technology-Supported, Student-Centered Learning: An Analysis of Student Experiences with Math Out Loud Using the ISTE Standards for Students

    Science.gov (United States)

    Dondlinger, Mary Jo; McLeod, Julie; Vasinda, Sheri

    2016-01-01

    This article explores links between student experiences with technology-rich mathematics instruction and the ISTE Standards for Students. Research methods applied constructivist grounded theory to analyze data from student interviews against the ISTE Standards for Students to identify which elements of the design of this learning environment…

  5. The social essentials of learning: an experimental investigation of collaborative problem solving and knowledge construction in mathematics classrooms in Australia and China

    Science.gov (United States)

    Chan, Man Ching Esther; Clarke, David; Cao, Yiming

    2018-03-01

    Interactive problem solving and learning are priorities in contemporary education, but these complex processes have proved difficult to research. This project addresses the question "How do we optimise social interaction for the promotion of learning in a mathematics classroom?" Employing the logic of multi-theoretic research design, this project uses the newly built Science of Learning Research Classroom (ARC-SR120300015) at The University of Melbourne and equivalent facilities in China to investigate classroom learning and social interactions, focusing on collaborative small group problem solving as a way to make the social aspects of learning visible. In Australia and China, intact classes of local year 7 students with their usual teacher will be brought into the research classroom facilities with built-in video cameras and audio recording equipment to participate in purposefully designed activities in mathematics. The students will undertake a sequence of tasks in the social units of individual, pair, small group (typically four students) and whole class. The conditions for student collaborative problem solving and learning will be manipulated so that student and teacher contributions to that learning process can be distinguished. Parallel and comparative analyses will identify culture-specific interactive patterns and provide the basis for hypotheses about the learning characteristics underlying collaborative problem solving performance documented in the research classrooms in each country. The ultimate goals of the project are to generate, develop and test more sophisticated hypotheses for the optimisation of social interaction in the mathematics classroom in the interest of improving learning and, particularly, student collaborative problem solving.

  6. Identifying students’ learning performance as a way to determine the admission process in physical education field

    Science.gov (United States)

    Prihanto, J. B.; Kartiko, D. C.; Wijaya, A.

    2018-01-01

    The interest in the physical education field has been rising in the past ten years. It can be seen that registrants of the physical education program in several universities increase. This research is meant to analyze students’ admission process and its relation to their performance in the learning activities in the department of physical education at Universitas Negeri Surabaya. The design of this study was quantitative data analysis. The research was conducted by collecting students’ admission data and their transcripts. The result showed that the most influential factor of admission in physical education program was the student’ field of study in high school. In addition, their achievements in sports competitions and family welfare are not likely to be important factors. These results give a recommendation for the next admission process which related to the quality of graduates.

  7. The Outwardly Rectifying Current of Layer 5 Neocortical Neurons that was Originally Identified as "Non-Specific Cationic" Is Essentially a Potassium Current.

    Directory of Open Access Journals (Sweden)

    Omer Revah

    Full Text Available In whole-cell patch clamp recordings from layer 5 neocortical neurons, blockade of voltage gated sodium and calcium channels leaves a cesium current that is outward rectifying. This current was originally identified as a "non-specific cationic current", and subsequently it was hypothesized that it is mediated by TRP channels. In order to test this hypothesis, we used fluorescence imaging of intracellular sodium and calcium indicators, and found no evidence to suggest that it is associated with influx of either of these ions to the cell body or dendrites. Moreover, the current is still prominent in neurons from TRPC1-/- and TRPC5-/- mice. The effects on the current of various blocking agents, and especially its sensitivity to intracellular tetraethylammonium, suggest that it is not a non-specific cationic current, but rather that it is generated by cesium-permeable delayed rectifier potassium channels.

  8. Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

    International Nuclear Information System (INIS)

    Stingone, Jeanette A.; Pandey, Om P.; Claudio, Luz; Pandey, Gaurav

    2017-01-01

    Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was −1.19 points (95% CI −1.94, −0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be

  9. Office of Elementary and Secondary Education Webcast Introduction: Identifying, Recognizing, and Learning From Effective Schools

    Science.gov (United States)

    Simon, Ray; Jung, Britt; Johnson, Joseph; Wallinger, Linda; Bamberg, Wanda

    2004-01-01

    The purpose of this series of webcasts is to communicate directly with state educational agency (SEA) and local educational agency (LEA) staff - those who guide and support the work of schools - on issues related to the implementation of NCLB. The goal of this webcast is to prompt SEAs and LEAs to think about how to identify the qualities of…

  10. Exploring the relationship between fractal features and bacterial essential genes

    International Nuclear Information System (INIS)

    Yu Yong-Ming; Yang Li-Cai; Zhao Lu-Lu; Liu Zhi-Ping; Zhou Qian

    2016-01-01

    Essential genes are indispensable for the survival of an organism in optimal conditions. Rapid and accurate identifications of new essential genes are of great theoretical and practical significance. Exploring features with predictive power is fundamental for this. Here, we calculate six fractal features from primary gene and protein sequences and then explore their relationship with gene essentiality by statistical analysis and machine learning-based methods. The models are applied to all the currently available identified genes in 27 bacteria from the database of essential genes (DEG). It is found that the fractal features of essential genes generally differ from those of non-essential genes. The fractal features are used to ascertain the parameters of two machine learning classifiers: Naïve Bayes and Random Forest. The area under the curve (AUC) of both classifiers show that each fractal feature is satisfactorily discriminative between essential genes and non-essential genes individually. And, although significant correlations exist among fractal features, gene essentiality can also be reliably predicted by various combinations of them. Thus, the fractal features analyzed in our study can be used not only to construct a good essentiality classifier alone, but also to be significant contributors for computational tools identifying essential genes. (paper)

  11. NLTK essentials

    CERN Document Server

    Hardeniya, Nitin

    2015-01-01

    If you are an NLP or machine learning enthusiast with some or no experience in text processing, then this book is for you. This book is also ideal for expert Python programmers who want to learn NLTK quickly.

  12. A Genome-wide CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) Screen Identifies NEK7 as an Essential Component of NLRP3 Inflammasome Activation.

    Science.gov (United States)

    Schmid-Burgk, Jonathan L; Chauhan, Dhruv; Schmidt, Tobias; Ebert, Thomas S; Reinhardt, Julia; Endl, Elmar; Hornung, Veit

    2016-01-01

    Inflammasomes are high molecular weight protein complexes that assemble in the cytosol upon pathogen encounter. This results in caspase-1-dependent pro-inflammatory cytokine maturation, as well as a special type of cell death, known as pyroptosis. The Nlrp3 inflammasome plays a pivotal role in pathogen defense, but at the same time, its activity has also been implicated in many common sterile inflammatory conditions. To this effect, several studies have identified Nlrp3 inflammasome engagement in a number of common human diseases such as atherosclerosis, type 2 diabetes, Alzheimer disease, or gout. Although it has been shown that known Nlrp3 stimuli converge on potassium ion efflux upstream of Nlrp3 activation, the exact molecular mechanism of Nlrp3 activation remains elusive. Here, we describe a genome-wide CRISPR/Cas9 screen in immortalized mouse macrophages aiming at the unbiased identification of gene products involved in Nlrp3 inflammasome activation. We employed a FACS-based screen for Nlrp3-dependent cell death, using the ionophoric compound nigericin as a potassium efflux-inducing stimulus. Using a genome-wide guide RNA (gRNA) library, we found that targeting Nek7 rescued macrophages from nigericin-induced lethality. Subsequent studies revealed that murine macrophages deficient in Nek7 displayed a largely blunted Nlrp3 inflammasome response, whereas Aim2-mediated inflammasome activation proved to be fully intact. Although the mechanism of Nek7 functioning upstream of Nlrp3 yet remains elusive, these studies provide a first genetic handle of a component that specifically functions upstream of Nlrp3. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  13. Essential Tremor

    Science.gov (United States)

    ... Treatment There is no definitive cure for essential tremor. Symptomatic drug therapy may include propranolol or other beta blockers and primidone, an anticonvulsant drug. Eliminating tremor "triggers" ...

  14. Identifying and Remediating Student Misconceptions in Introductory Biology via Writing-to-Learn Assignments and Peer Review.

    Science.gov (United States)

    Halim, Audrey S; Finkenstaedt-Quinn, Solaire A; Olsen, Laura J; Gere, Anne Ruggles; Shultz, Ginger V

    2018-06-01

    Student misconceptions are an obstacle in science, technology, engineering, and mathematics courses and unless remediated may continue causing difficulties in learning as students advance in their studies. Writing-to-learn assignments (WTL) are characterized by their ability to promote in-depth conceptual learning by allowing students to explore their understanding of a topic. This study sought to determine whether and what types of misconceptions are elicited by WTL assignments and how the process of peer review and revision leads to remediation or propagation of misconceptions. We examined four WTL assignments in an introductory biology course in which students first wrote about content by applying it to a realistic scenario, then participated in a peer-review process before revising their work. Misconceptions were identified in all four assignments, with the greatest number pertaining to protein structure and function. Additionally, in certain contexts, students used scientific terminology incorrectly. Analysis of the drafts and peer-review comments generated six profiles by which misconceptions were addressed through the peer-review process. The prevalent mode of remediation arose through directed peer-review comments followed by correction during revision. It was also observed that additional misconceptions were elicited as students revised their writing in response to general peer-review suggestions.

  15. Moodle administration essentials

    CERN Document Server

    Henrick, Gavin

    2015-01-01

    If you are an experienced system administrator and know how to manage servers and set up web environments but now want to explore Moodle, this book is perfect for you. You'll get to grips with the basics and learn to manage Moodle quickly, focusing on essential tasks. Having prior knowledge of virtual learning environments would be beneficial, but is not mandatory to make the most of this book.

  16. KNIME essentials

    CERN Document Server

    Bakos, Gábor

    2013-01-01

    KNIME Essentials is a practical guide aimed at getting the results you want, as quickly as possible.""Knime Essentials"" is written for data analysts looking to quickly get up to speed using the market leader in data processing tools, KNIME. No knowledge of KNIME is required, but we will assume that you have some background in data processing.

  17. Lessons learned from England's Health Checks Programme: using qualitative research to identify and share best practice.

    Science.gov (United States)

    Ismail, Hanif; Kelly, Shona

    2015-10-20

    This study aimed to explore the challenges and barriers faced by staff involved in the delivery of the National Health Service (NHS) Health Check, a systematic cardiovascular disease (CVD) risk assessment and management program in primary care. Data have been derived from three qualitative evaluations that were conducted in 25 General Practices and involved in depth interviews with 58 staff involved all levels of the delivery of the Health Checks. Analysis of the data was undertaken using the framework approach and findings are reported within the context of research and practice considerations. Findings indicated that there is no 'one size fits all' blueprint for maximising uptake although success factors were identified: evolution of the programme over time in response to local needs to suit the particular characteristics of the patient population; individual staff characteristics such as being proactive, enthusiastic and having specific responsibility; a supportive team. Training was clearly identified as an area that needed addressing and practitioners would benefit from CVD specific baseline training and refresher courses to keep them up to date with recent developments in the area. However there were other external factors that impinged on an individual's ability to provide an effective service, some of these were outside the control of individuals and included cutbacks in referral services, insufficient space to run clinics or general awareness of the Health Checks amongst patients. The everyday experiences of practitioners who participated in this study suggest that overall, Health Check is perceived as a worthwhile exercise. But, organisational and structural barriers need to be addressed. We also recommend that clear referral pathways be in place so staff can refer patients to appropriate services (healthy eating sessions, smoking cessation, and exercise referrals). Local authorities need to support initiatives that enable data sharing and linkage so that

  18. A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov.

    Science.gov (United States)

    de la Iglesia, Diana; García-Remesal, Miguel; Anguita, Alberto; Muñoz-Mármol, Miguel; Kulikowski, Casimir; Maojo, Víctor

    2014-01-01

    Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is

  19. Towards identifying nurse educator competencies required for simulation-based learning: A systemised rapid review and synthesis.

    Science.gov (United States)

    Topping, Anne; Bøje, Rikke Buus; Rekola, Leena; Hartvigsen, Tina; Prescott, Stephen; Bland, Andrew; Hope, Angela; Haho, Paivi; Hannula, Leena

    2015-11-01

    This paper presents the results of a systemised rapid review and synthesis of the literature undertaken to identify competencies required by nurse educators to facilitate simulation-based learning (SBL). An international collaboration undertook a protocol-based search, retrieval and critical review. Web of Science, PubMed, CINAHL Plus, PsycInfo, ERIC, the Cochrane Library and Science Direct. The search was limited to articles published in English, 2002-2012. The search terms used: nurse*, learn*, facilitator, simula*, lecturer, competence, skill*, qualificat*, educator, health care, "patient simulation", "nursing education" and "faculty". The search yielded 2156 "hits", following a review of the abstracts, 72 full-text articles were extracted. These were screened against predetermined inclusion/exclusion criteria and nine articles were retained. Following critical appraisal, the articles were analyzed using an inductive approach to extract statements for categorization and synthesis as competency statements. This review confirmed that there was a modest amount of empirical evidence on which to base a competency framework. Those papers that provided descriptions of educator preparation identified simulation-based workshops, or experiential training, as the most common approaches for enhancing skills. SBL was not associated with any one theoretical perspective. Delivery of SBL appeared to demand competencies associated with planning and designing simulations, facilitating learning in "safe" environments, expert nursing knowledge based on credible clinical realism, reference to evidence-based knowledge and demonstration of professional values and identity. This review derived a preliminary competency framework. This needs further development as a model for educators delivering SBL as part of nursing curricula. Copyright © 2015. Published by Elsevier Ltd.

  20. Astronomy essentials

    CERN Document Server

    Brass, Charles O

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Astronomy includes the historical perspective of astronomy, sky basics and the celestial coordinate systems, a model and the origin of the solar system, the sun, the planets, Kepler'

  1. Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine.

    Science.gov (United States)

    Parodi, Stefano; Dosi, Corrado; Zambon, Antonella; Ferrari, Enrico; Muselli, Marco

    2017-12-01

    Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.

  2. Neural correlates of olfactory learning paradigms in an identified neuron in the honeybee brain.

    Science.gov (United States)

    Mauelshagen, J

    1993-02-01

    1. Sensitization and classical odor conditioning of the proboscis extension reflex were functionally analyzed by repeated intracellular recordings from a single identified neuron (PE1-neuron) in the central bee brain. This neuron belongs to the class of "extrinsic cells" arising from the pedunculus of the mushroom bodies and has extensive arborizations in the median and lateral protocerebrum. The recordings were performed on isolated bee heads. 2. Two different series of physiological experiments were carried out with the use of a similar temporal succession of stimuli as in previous behavioral experiments. In the first series, one group of animals was used for a single conditioning trial [conditioned stimulus (CS), carnation; unconditioned stimulus (US), sucrose solution to the antennae and proboscis), a second group was used for sensitization (sensitizing stimulus, sucrose solution to the antennae and/or proboscis), and the third group served as control (no sucrose stimulation). In the second series, a differential conditioning paradigm (paired odor CS+, carnation; unpaired odor CS-, orange blossom) was applied to test the associative nature of the conditioning effect. 3. The PE1-neuron showed a characteristic burstlike odor response before the training procedures. The treatments resulted in different spike-frequency modulations of this response, which were specific for the nonassociative and associative stimulus paradigms applied. During differential conditioning, there are dynamic up and down modulations of spike frequencies and of the DC potentials underlying the responses to the CS+. Overall, only transient changes in the minute range were observed. 4. The results of the sensitization procedures suggest two qualitatively different US pathways. The comparison between sensitization and one-trial conditioning shows differential effects of nonassociative and associative stimulus paradigms on the response behavior of the PE1-neuron. The results of the differential

  3. Beaglebone essentials

    CERN Document Server

    Giometti, Rodolfo

    2015-01-01

    If you are a developer with some hardware or electrical engineering experience who wants to learn how to use embedded machine-learning capabilities and get access to a GNU/Linux device driver to collect data from a peripheral or to control a device, this is the book for you.

  4. Evaluation of toxic metals and essential elements in children with learning disabilities from a rural area of southern Brazil.

    Science.gov (United States)

    do Nascimento, Sabrina Nunes; Charão, Mariele Feiffer; Moro, Angela Maria; Roehrs, Miguel; Paniz, Clovis; Baierle, Marília; Brucker, Natália; Gioda, Adriana; Barbosa, Fernando; Bohrer, Denise; Ávila, Daiana Silva; Garcia, Solange Cristina

    2014-10-17

    Children's exposure to metals can result in adverse effects such as cognitive function impairments. This study aimed to evaluate some toxic metals and levels of essential trace elements in blood, hair, and drinking water in children from a rural area of Southern Brazil. Cognitive ability and δ-aminolevulinate dehydratase (ALA-D) activity were evaluated. Oxidative stress was evaluated as a main mechanism of metal toxicity, through the quantification of malondialdehyde (MDA) levels. This study included 20 children from a rural area and 20 children from an urban area. Our findings demonstrated increase in blood lead (Pb) levels (BLLs). Also, increased levels of nickel (Ni) in blood and increase of aluminum (Al) levels in hair and drinking water in rural children were found. Deficiency in selenium (Se) levels was observed in rural children as well. Rural children with visual-motor immaturity presented Pb levels in hair significantly increased in relation to rural children without visual-motor immaturity (p < 0.05). Negative correlations between BLLs and ALA-D activity and positive correlations between BLLs and ALA-RE activity were observed. MDA was significantly higher in rural compared to urban children (p < 0.05). Our findings suggest that rural children were co-exposed to toxic metals, especially Al, Pb and Ni. Moreover, a slight deficiency of Se was observed. Low performance on cognitive ability tests and ALA-D inhibition can be related to metal exposure in rural children. Oxidative stress was suggested as a main toxicological mechanism involved in metal exposure.

  5. Evaluation of Toxic Metals and Essential Elements in Children with Learning Disabilities from a Rural Area of Southern Brazil

    Directory of Open Access Journals (Sweden)

    Sabrina Nunes do Nascimento

    2014-10-01

    Full Text Available Children’s exposure to metals can result in adverse effects such as cognitive function impairments. This study aimed to evaluate some toxic metals and levels of essential trace elements in blood, hair, and drinking water in children from a rural area of Southern Brazil. Cognitive ability and δ-aminolevulinate dehydratase (ALA-D activity were evaluated. Oxidative stress was evaluated as a main mechanism of metal toxicity, through the quantification of malondialdehyde (MDA levels. This study included 20 children from a rural area and 20 children from an urban area. Our findings demonstrated increase in blood lead (Pb levels (BLLs. Also, increased levels of nickel (Ni in blood and increase of aluminum (Al levels in hair and drinking water in rural children were found. Deficiency in selenium (Se levels was observed in rural children as well. Rural children with visual-motor immaturity presented Pb levels in hair significantly increased in relation to rural children without visual-motor immaturity (p < 0.05. Negative correlations between BLLs and ALA-D activity and positive correlations between BLLs and ALA-RE activity were observed. MDA was significantly higher in rural compared to urban children (p < 0.05. Our findings suggest that rural children were co-exposed to toxic metals, especially Al, Pb and Ni. Moreover, a slight deficiency of Se was observed. Low performance on cognitive ability tests and ALA-D inhibition can be related to metal exposure in rural children. Oxidative stress was suggested as a main toxicological mechanism involved in metal exposure.

  6. Identifying and Supporting English Learner Students with Learning Disabilities: Key Issues in the Literature and State Practice. REL 2015-086

    Science.gov (United States)

    Burr, Elizabeth; Haas, Eric; Ferriere, Karen

    2015-01-01

    While the literature on learning disabilities and on second-language acquisition is relatively extensive within the field of education, less is known about the specific characteristics and representation of English learner students with learning disabilities. Because there are no definitive resources and processes for identifying and determining…

  7. Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

    Science.gov (United States)

    Huertas-Company, M.; Primack, J. R.; Dekel, A.; Koo, D. C.; Lapiner, S.; Ceverino, D.; Simons, R. C.; Snyder, G. F.; Bernardi, M.; Chen, Z.; Domínguez-Sánchez, H.; Lee, C. T.; Margalef-Bentabol, B.; Tuccillo, D.

    2018-05-01

    We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock “observed” images of simulated galaxies at three phases of evolution— pre-BN, BN, and post-BN—and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ∼0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at z = 1–3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2–10.3 M ⊙ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.

  8. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

    Science.gov (United States)

    Passos, Ives Cavalcante; Mwangi, Benson; Cao, Bo; Hamilton, Jane E; Wu, Mon-Ju; Zhang, Xiang Yang; Zunta-Soares, Giovana B; Quevedo, Joao; Kauer-Sant'Anna, Marcia; Kapczinski, Flávio; Soares, Jair C

    2016-03-15

    A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (pdisorder (PTSD) comorbidity. Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Dart essentials

    CERN Document Server

    Sikora, Martin

    2015-01-01

    This book is targeted at expert programmers in JavaScript who want to learn Dart quickly. Some previous experience with OOP programming in other languages and a good knowledge of JavaScript are assumed.

  10. Machine learning models identify molecules active against the Ebola virus in vitro [version 3; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2017-01-01

    Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

  11. Machine learning models identify molecules active against the Ebola virus in vitro [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2015-10-01

    Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

  12. Machine learning models identify molecules active against the Ebola virus in vitro [version 2; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2016-01-01

    Full Text Available The search for small molecule inhibitors of Ebola virus (EBOV has led to several high throughput screens over the past 3 years. These have identified a range of FDA-approved active pharmaceutical ingredients (APIs with anti-EBOV activity in vitro and several of which are also active in a mouse infection model. There are millions of additional commercially-available molecules that could be screened for potential activities as anti-EBOV compounds. One way to prioritize compounds for testing is to generate computational models based on the high throughput screening data and then virtually screen compound libraries. In the current study, we have generated Bayesian machine learning models with viral pseudotype entry assay and the EBOV replication assay data. We have validated the models internally and externally. We have also used these models to computationally score the MicroSource library of drugs to select those likely to be potential inhibitors. Three of the highest scoring molecules that were not in the model training sets, quinacrine, pyronaridine and tilorone, were tested in vitro and had EC50 values of 350, 420 and 230 nM, respectively. Pyronaridine is a component of a combination therapy for malaria that was recently approved by the European Medicines Agency, which may make it more readily accessible for clinical testing. Like other known antimalarial drugs active against EBOV, it shares the 4-aminoquinoline scaffold. Tilorone, is an investigational antiviral agent that has shown a broad array of biological activities including cell growth inhibition in cancer cells, antifibrotic properties, α7 nicotinic receptor agonist activity, radioprotective activity and activation of hypoxia inducible factor-1. Quinacrine is an antimalarial but also has use as an anthelmintic. Our results suggest data sets with less than 1,000 molecules can produce validated machine learning models that can in turn be utilized to identify novel EBOV inhibitors in

  13. A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov.

    Directory of Open Access Journals (Sweden)

    Diana de la Iglesia

    Full Text Available Clinical Trials (CTs are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano, and CTs that do not involve nanotechnology (non-nano. Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results.We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i extraction and manual annotation of CTs as nano vs. non-nano, ii pre-processing and automatic classification, and iii performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset.The performance of the best automated classifier closely matches that of experts (AUC over 0.95, suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice

  14. The learning environment and learning styles: a guide for mentors.

    Science.gov (United States)

    Vinales, James Jude

    The learning environment provides crucial exposure for the pre-registration nursing student. It is during this time that the student nurse develops his or her repertoire of skills, knowledge, attitudes and behaviour in order to meet competencies and gain registration with the Nursing and Midwifery Council. The role of the mentor is vital within the learning environment for aspiring nurses. The learning environment is a fundamental platform for student learning, with mentors key to identifying what is conducive to learning. This article will consider the learning environment and learning styles, and how these two essential elements guide the mentor in making sure they are conducive to learning.

  15. Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

    Science.gov (United States)

    Uddin, Raihan; Singh, Shiva M

    2017-01-01

    As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

  16. Essential AOP

    DEFF Research Database (Denmark)

    De Fraine, Bruno; Ernst, Erik; Südholt, Mario

    2010-01-01

    Aspect-oriented programming (AOP) has produced interesting language designs, but also ad hoc semantics that needs clarification. We contribute to this clarification with a calculus that models essential AOP, both simpler and more general than existing formalizations. In AOP, advice may intercept...

  17. Highcharts essentials

    CERN Document Server

    Shahid, Bilal

    2014-01-01

    If you are a web developer with a basic knowledge of HTML, CSS, and JavaScript and want to quickly get started with this web charting technology, this is the book for you. This book will also serve as an essential guide to those who have probably used a similar library and are now looking at migrating to Highcharts.

  18. Swift essentials

    CERN Document Server

    Blewitt, Alex

    2014-01-01

    Whether you are a seasoned Objective-C developer or new to the Xcode platform, Swift Essentials will provide you with all you need to know to get started with the language. Prior experience with iOS development is not necessary, but will be helpful to get the most out of the book.

  19. Assessing the Ability of Vegetation Indices to Identify Shallow Subsurface Water Flow Pathways from Hyperspectral Imagery Using Machine Learning: Methodology

    Science.gov (United States)

    Byers, J. M.; Doctor, K.

    2017-12-01

    A common application of the satellite and airborne acquired hyperspectral imagery in the visible and NIR spectrum is the assessment of vegetation. Various absorption features of plants related to both water and chlorophyll content can be used to measure the vigor and access to underlying water sources of the vegetation. The typical strategy is to form hand-crafted features from the hyperspectral data cube by selecting two wavelengths to form difference or ratio images in the pixel space. The new image attempts to provide greater contrast for some feature of the vegetation. The Normalized Difference Vegetation Index (NDVI) is a widely used example formed from the ratio of differences and sums at two different wavelengths. There are dozens of these indices that are ostensibly formed using insights about the underlying physics of the spectral absorption with claims to efficacy in representing various properties of vegetation. In the language of machine learning these vegetation indices are features that can be used as a useful data representation within an algorithm. In this work we use a powerful approach from machine learning, probabilistic graphical models (PGM), to balance the competing needs of using existing hydrological classifications of terrain while finding statistically reliable features within hyperspectral data for identifying the generative process of the data. The algorithm in its simplest form is called a Naïve Bayes (NB) classifier and can be constructed in a data-driven estimation procedure of the conditional probability distributions that form the PGM. The Naïve Bayes model assumes that all vegetation indices (VI) are independent of one another given the hydrological class label. We seek to test its validity in a pilot study of detecting subsurface water flow pathways from VI. A more sophisticated PGM will also be explored called a tree-augmented NB that accounts for the probabilistic dependence between VI features. This methodology provides a

  20. Splunk essentials

    CERN Document Server

    Sigman, Betsy Page

    2015-01-01

    This book is intended for a business person, analyst, or student who wants to quickly learn how to use Splunk to manage data. It would be helpful to have a bit of familiarity with basic computer concepts, but no prior experience of Splunk is required.

  1. Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

    Science.gov (United States)

    Stingone, Jeanette A; Pandey, Om P; Claudio, Luz; Pandey, Gaurav

    2017-11-01

    Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other

  2. Essentials of cloud computing

    CERN Document Server

    Chandrasekaran, K

    2014-01-01

    ForewordPrefaceComputing ParadigmsLearning ObjectivesPreambleHigh-Performance ComputingParallel ComputingDistributed ComputingCluster ComputingGrid ComputingCloud ComputingBiocomputingMobile ComputingQuantum ComputingOptical ComputingNanocomputingNetwork ComputingSummaryReview PointsReview QuestionsFurther ReadingCloud Computing FundamentalsLearning ObjectivesPreambleMotivation for Cloud ComputingThe Need for Cloud ComputingDefining Cloud ComputingNIST Definition of Cloud ComputingCloud Computing Is a ServiceCloud Computing Is a Platform5-4-3 Principles of Cloud computingFive Essential Charact

  3. Identifying Information Behavior in Information Search and Retrieval through Learning Activities Using an E-learning Platform Case: Interamerican School of Library and Information Science at the University of Antioquia (Medellin-Colombia)

    Science.gov (United States)

    Tirado, Alejandro Uribe; Munoz, Wilson Castano

    2011-01-01

    This text presents the future of librarian education as exemplified by the Interamerican School of Library and Information Science at the University of Antioquia (Medellin-Colombia), using an online learning platform-LMS (Moodle) and through different personalized and collaborative learning activities and tools that help students identify their…

  4. Learning a novel technique to identify possible melanomas: are Australian general practitioners better than their U.K. colleagues?

    Directory of Open Access Journals (Sweden)

    Watson Tony

    2009-04-01

    Full Text Available Abstract Background Spectrophotometric intracutaneous analysis (SIAscopy™ is a multispectral imaging technique that is used to identify 'suspicious' (i.e. potentially malignant pigmented skin lesions for further investigation. The MoleMate™ system is a hand-held scanner that captures SIAscopy™ images that are then classified by the clinician using a computerized diagnostic algorithm designed for the primary health care setting. The objectives of this study were to test the effectiveness of a computer program designed to train health care workers to identify the diagnostic features of SIAscopy™ images and compare the results of a group of Australian and a group of English general practitioners (GPs. Methods Thirty GPs recruited from the Perth (Western Australia metropolitan area completed the training program at a workshop held in March 2008. The accuracy and speed of their pre- and post-test scores were then compared with those of a group of 18 GPs (including 10 GP registrars who completed a similar program at two workshops held in Cambridge (U.K. in March and April, 2007. Results The median test score of the Australian GPs improved from 79.5% to 86.5% (median increase 5.5%; p Conclusion Most of the SIAscopy™ features can be learnt to a reasonable degree of accuracy with this brief computer training program. Although the Australian GPs scored higher in the pre-test, both groups had similar levels of accuracy and speed in interpreting the SIAscopy™ features after completing the program. Scores were not affected by previous dermoscopy experience or dermatology training, which suggests that the MoleMate™ system is relatively easy to learn.

  5. Essential SQLAlchemy

    CERN Document Server

    Copeland, Rick

    2008-01-01

    Essential SQLAlchemy introduces a high-level open-source code library that makes it easier for Python programmers to access relational databases such as Oracle, DB2, MySQL, PostgreSQL, and SQLite. SQLAlchemy has become increasingly popular since its release, but it still lacks good offline documentation. This practical book fills the gap, and because a developer wrote it, you get an objective look at SQLAlchemy's tools rather than an advocate's description of all the "cool" features. SQLAlchemy includes both a database server-independent SQL expression language and an object-relational mappe

  6. PHPUnit essentials

    CERN Document Server

    Machek, Zdenek

    2014-01-01

    This book is a practical guide featuring a step-by-step approach that aims to help PHP developers who want to learn or improve their software testing skills. It also takes you through many real-life examples encountered by PHP developers to help you avoid common pitfalls.This book is for developers who have experience with PHP and who want to take their coding skills to another level. Developers who have previous experience with PHPUnit will find interesting chapters concerning PHPUnit in the context of web application development.

  7. Identifying Effective Design Features of Technology-Infused Inquiry Learning Modules: A Two-Year Study of Students' Inquiry Abilities

    Science.gov (United States)

    Hsu, Ying-Shao; Fang, Su-Chi; Zhang, Wen-Xin; Hsin-Kai, Wu; Wu, Pai-Hsing; Hwang, Fu-Kwun

    2016-01-01

    The two-year study aimed to explore how students' development of different inquiry abilities actually benefited from the design of technology-infused learning modules. Three learning modules on the topics of seasons, environmental issues and air pollution were developed to facilitate students' inquiry abilities: questioning, planning, analyzing,…

  8. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function.

    Science.gov (United States)

    Zhang, Li; Ai, Hai-Xin; Li, Shi-Meng; Qi, Meng-Yuan; Zhao, Jian; Zhao, Qi; Liu, Hong-Sheng

    2017-10-10

    In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson's correlation coefficient of 0.707, and Spearman's rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking-rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

  9. Assessing the Ability of Vegetation Indices to Identify Shallow Subsurface Water Flow Pathways from Hyperspectral Imagery Using Machine Learning: Application

    Science.gov (United States)

    Doctor, K.; Byers, J. M.

    2017-12-01

    Shallow underground water flow pathways expressed as slight depressions are common in the land surface. Under conditions of saturated overland flow, such as during heavy rain or snow melt, these areas of preferential flow might appear on the surface as very shallow flowing streams. When there is no water flowing in these ephemeral channels it can be difficult to identify them. It is especially difficult to discern the slight depressions above the subsurface water flow pathways (SWFP) when the area is covered by vegetation. Since the soil moisture content in these SWFP is often greater than the surrounding area, the vegetation growing on top of these channels shows different vigor and moisture content than the vegetation growing above the non-SWFP area. Vegetation indices (VI) are used in visible and near infrared (VNIR) hyperspectral imagery to enhance biophysical properties of vegetation, and so the brightness values between vegetation atop SWFP and the surrounding vegetation were highlighted. We performed supervised machine learning using ground-truth class labels to determine the conditional probability of a SWFP at a given pixel given either the spectral distribution or VI at that pixel. The training data estimates the probability distributions to a determined finite sampling accuracy for a binary Naïve Bayes classifier between SWFP and non-SWFP. The ground-truth data provides a test bed for understanding the ability to build SWFP classifiers using hyperspectral imagery. SWFP were distinguishable in the imagery within corn and grass fields and in areas with low-lying vegetation. However, the training data is limited to particular types of terrain and vegetation cover in the Shenandoah Valley, Virginia and this would limit the resulting classifier. Further training data could extend its use to other environments.

  10. Essential SQLAlchemy

    CERN Document Server

    Myers, Jason

    2016-01-01

    Dive into SQLAlchemy, the popular, open-source code library that helps Python programmers work with relational databases such as Oracle, MySQL, PostgresSQL, and SQLite. Using real-world examples, this practical guide shows you how to build a simple database application with SQLAlchemy, and how to connect to multiple databases simultaneously with the same metadata. SQL is a powerful language for querying and manipulating data, but it's tough to integrate it with your application. SQLAlchemy helps you map Python objects to database tables without substantially changing your existing Python code. If you're an intermediate Python developer with knowledge of basic SQL syntax and relational theory, this book serves as both a learning tool and a handy reference.

  11. Innovation in pediatric clinical education: application of the essential competencies.

    Science.gov (United States)

    Kenyon, Lisa K; Birkmeier, Marisa; Anderson, Deborah K; Martin, Kathy

    2015-01-01

    At the Section on Pediatrics Education Summit in July 2012, consensus was achieved on 5 essential core competencies (ECCs) that represent a knowledge base essential to all graduates of professional physical therapist education programs. This article offers suggestions for how clinical instructors (CIs) might use the ECCs to identify student needs and guide student learning during a pediatric clinical education experience. Pediatric CIs potentially might choose to use the ECCs as a reference tool in clinical education to help (1) organize and develop general, clinic-specific clinical education objectives, (2) develop and plan individualized student learning experiences, (3) identify student needs, and (4) show progression of student learning from beginner to intermediate to entry level. The ECCs may offer CIs insights into the role of pediatric clinical education in professional physical therapist education.

  12. The Delphi Technique in Identifying Learning Objectives for the Development of Science, Technology and Society Modules for Palestinian Ninth Grade Science Curriculum

    Science.gov (United States)

    Abualrob, Marwan M. A.; Daniel, Esther Gnanamalar Sarojini

    2013-01-01

    This article outlines how learning objectives based upon science, technology and society (STS) elements for Palestinian ninth grade science textbooks were identified, which was part of a bigger study to establish an STS foundation in the ninth grade science curriculum in Palestine. First, an initial list of STS elements was determined. Second,…

  13. Animal-Assisted Literacy Instruction for Students with Identified Learning Disabilities: Examining the Effects of Incorporating a Therapy Dog into Guided Oral Reading Sessions

    Science.gov (United States)

    Treat, Wendy Abigail

    2013-01-01

    Literacy acquisition is imperative to successful academic progress and to successful participation in our society. Students with identified learning disabilities are often among those who struggle to acquire literacy skills. The following dissertation shares the results of a reading intervention study in which nine students with identified…

  14. Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle.

    Science.gov (United States)

    Sharifi, Somayeh; Pakdel, Abbas; Ebrahimi, Mansour; Reecy, James M; Fazeli Farsani, Samaneh; Ebrahimie, Esmaeil

    2018-01-01

    Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as

  15. Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity

    Directory of Open Access Journals (Sweden)

    Rosaleena Mohanty

    2018-05-01

    Full Text Available Interventional therapy using brain-computer interface (BCI technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI scans were collected at four time points: pre-, mid-, post-, and 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation used by a linear binary support vector machine (SVM classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke

  16. Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media.

    Science.gov (United States)

    Comfort, Shaun; Perera, Sujan; Hudson, Zoe; Dorrell, Darren; Meireis, Shawman; Nagarajan, Meenakshi; Ramakrishnan, Cartic; Fine, Jennifer

    2018-06-01

    There is increasing interest in social digital media (SDM) as a data source for pharmacovigilance activities; however, SDM is considered a low information content data source for safety data. Given that pharmacovigilance itself operates in a high-noise, lower-validity environment without objective 'gold standards' beyond process definitions, the introduction of large volumes of SDM into the pharmacovigilance workflow has the potential to exacerbate issues with limited manual resources to perform adverse event identification and processing. Recent advances in medical informatics have resulted in methods for developing programs which can assist human experts in the detection of valid individual case safety reports (ICSRs) within SDM. In this study, we developed rule-based and machine learning (ML) models for classifying ICSRs from SDM and compared their performance with that of human pharmacovigilance experts. We used a random sampling from a collection of 311,189 SDM posts that mentioned Roche products and brands in combination with common medical and scientific terms sourced from Twitter, Tumblr, Facebook, and a spectrum of news media blogs to develop and evaluate three iterations of an automated ICSR classifier. The ICSR classifier models consisted of sub-components to annotate the relevant ICSR elements and a component to make the final decision on the validity of the ICSR. Agreement with human pharmacovigilance experts was chosen as the preferred performance metric and was evaluated by calculating the Gwet AC1 statistic (gKappa). The best performing model was tested against the Roche global pharmacovigilance expert using a blind dataset and put through a time test of the full 311,189-post dataset. During this effort, the initial strict rule-based approach to ICSR classification resulted in a model with an accuracy of 65% and a gKappa of 46%. Adding an ML-based adverse event annotator improved the accuracy to 74% and gKappa to 60%. This was further improved by

  17. Short report The DeDiMa battery: a tool for identifying students’ mathematical learning profiles

    Directory of Open Access Journals (Sweden)

    Giannis Karagiannakis

    2014-10-01

    Full Text Available Background The DeDiMa battery is designed for assessing students’ mathematical learning profiles, and it has been used to validate a 4-dimensional model for classifying mathematical learning difficulties. The model arises from existing hypotheses in the cognitive psychology and neuroscience literature, while the DeDiMa battery provides a reliable set of mathematical tasks that help to match characteristics of students’ mathematical performances to their more basic learning difficulties. Participants and procedure In this report we address the question of how these tools can help sketch out a student’s mathematical learning profile. The participants are 5th and 6th grade students. Results We compare the emerging profiles of two students with mathematical learning difficulties (MLD matched for age, performance on a standardized test, non-verbal IQ, and educational experiences. The profiles are very different. Conclusions We believe that this approach can inform the design of individualized remedial interventions for MLD students.

  18. Autodesk Maya 2014 essentials

    CERN Document Server

    Naas, Paul

    2013-01-01

    The premiere book on getting started with Maya 2014 Whether you're just beginning, or migrating from another 3D application, this step-by-step guide is what you need to get a good working knowledge of Autodesk Maya 2014. Beautifully illustrated with full-color examples and screenshots, Autodesk Maya 2014 Essentials explains the basics of Maya as well as modeling, texturing, animating, setting a scene, and creating visual effects. You'll absorb important concepts and techniques, and learn how to confidently use Maya tools the way professionals do. Each chapter includes fun and cha

  19. Research Relating to the Learning of Children Identified as Having Experienced Malnutrition and/or Heavy Metal Poisoning. Final Report.

    Science.gov (United States)

    Snowdon, Charles T.

    Described was research on the behavioral and learning effects of lead poisoning or malnutrition in rats. It is explained that approximately 200 rats (either weanling, adult, pregnant, or nursing) were injected with various amounts of lead. It was found that symtomatic levels of lead in weanling or adult rats produced no obvious behavioral or…

  20. Identifying Configurations of Perceived Teacher Autonomy Support and Structure: Associations with Self-Regulated Learning, Motivation and Problem Behavior

    Science.gov (United States)

    Vansteenkiste, Maarten; Sierens, Eline; Goossens, Luc; Soenens, Bart; Dochy, Filip; Mouratidis, Athanasios; Aelterman, Nathalie; Haerens, Leen; Beyers, Wim

    2012-01-01

    Grounded in self-determination theory, the aim of this study was (a) to examine naturally occurring configurations of perceived teacher autonomy support and clear expectations (i.e., a central aspect of teacher structure), and (b) to investigate associations with academic motivation, self-regulated learning, and problem behavior. Based on…

  1. Identifying Subtypes among Children with Developmental Coordination Disorder and Mathematical Learning Disabilities, Using Model-Based Clustering

    Science.gov (United States)

    Pieters, Stefanie; Roeyers, Herbert; Rosseel, Yves; Van Waelvelde, Hilde; Desoete, Annemie

    2015-01-01

    A relationship between motor and mathematical skills has been shown by previous research. However, the question of whether subtypes can be differentiated within developmental coordination disorder (DCD) and/or mathematical learning disability (MLD) remains unresolved. In a sample of children with and without DCD and/or MLD, a data-driven…

  2. A Spreadsheet-Based Visualized Mindtool for Improving Students' Learning Performance in Identifying Relationships between Numerical Variables

    Science.gov (United States)

    Lai, Chiu-Lin; Hwang, Gwo-Jen

    2015-01-01

    In this study, a spreadsheet-based visualized Mindtool was developed for improving students' learning performance when finding relationships between numerical variables by engaging them in reasoning and decision-making activities. To evaluate the effectiveness of the proposed approach, an experiment was conducted on the "phenomena of climate…

  3. Automotive Mechanics. Student Learning Guides.

    Science.gov (United States)

    Ridge Vocational-Technical Center, Winter Haven, FL.

    These 33 learning guides are self-instructional packets for 33 tasks identified as essential for performance on an entry-level job in automotive mechanics. Each guide is based on a terminal performance objective (task) and 1-9 enabling objectives. For each enabliing objective, some or all of these materials may be presented: learning steps…

  4. The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

    Science.gov (United States)

    Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

    2017-06-15

    Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. The Social Essentials of Learning: An Experimental Investigation of Collaborative Problem Solving and Knowledge Construction in Mathematics Classrooms in Australia and China

    Science.gov (United States)

    Chan, Man Ching Esther; Clarke, David; Cao, Yiming

    2018-01-01

    Interactive problem solving and learning are priorities in contemporary education, but these complex processes have proved difficult to research. This project addresses the question "How do we optimise social interaction for the promotion of learning in a mathematics classroom?" Employing the logic of multi-theoretic research design,…

  6. Characterization equipment essential drawing plan

    International Nuclear Information System (INIS)

    WILSON, G.W.

    1999-01-01

    The purpose of this document is to list the Characterization equipment drawings that are classified as Essential Drawings. Essential Drawings: Are those drawings identified by the facility staff as necessary to directly support the safe operation of the facility or equipment (HNF 1997a). The Characterization equipment drawings identified in this report are deemed essential drawings as defined in HNF-PRO-242, Engineering Drawing Requirements (HNF 1997a). These drawings will be prepared, revised, and maintained per HNF-PRO-440, Engineering Document Change Control (HNF 1997b). All other Characterization equipment drawings not identified in this document will be considered Support drawings until the Characterization Equipment Drawing Evaluation Report is completed

  7. Identifying antigenicity associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning

    OpenAIRE

    Cai, Zhipeng; Ducatez, Mariette F.; Yang, Jialiang; Zhang, Tong; Long, Li-Ping; Boon, Adrianus C.; Webby, Richard J.; Wan, Xiu-Feng

    2012-01-01

    Since the isolation of A/goose/Guangdong/1/1996 (H5N1) in farmed geese in southern China, highly pathogenic H5N1 avian influenza viruses have posed a continuous threat to both public and animal health. The non-synonymous mutation of the H5 hemagglutinin gene has resulted in antigenic drift, leading to difficulties in both clinical diagnosis and vaccine strain selection. Characterizing H5N1’s antigenic profiles would help resolve these problems. In this study, a novel sparse learning method wa...

  8. Identifying antigenicity-associated sites in highly pathogenic H5N1 influenza virus hemagglutinin by using sparse learning.

    OpenAIRE

    Cai, Zhipeng; Yang, Jialiang; Zhang, Tong; Long, Li-Ping; Boon, Adrianus C; Webby, Richard J; Wan, Xiu-Feng

    2012-01-01

    Since the isolation of A/goose/Guangdong/1/1996 (H5N1) in farmed geese in southern China, highly pathogenic H5N1 avian influenza viruses have posed a continuous threat to both public and animal health. The non-synonymous mutation of the H5 hemagglutinin (HA) gene has resulted in antigenic drift, leading to difficulties in both clinical diagnosis and vaccine strain selection. Characterizing H5N1's antigenic profiles would help resolve these problems. In this study, a novel sparse learning meth...

  9. Using c-Jun to identify fear extinction learning-specific patterns of neural activity that are affected by single prolonged stress.

    Science.gov (United States)

    Knox, Dayan; Stanfield, Briana R; Staib, Jennifer M; David, Nina P; DePietro, Thomas; Chamness, Marisa; Schneider, Elizabeth K; Keller, Samantha M; Lawless, Caroline

    2018-04-02

    Neural circuits via which stress leads to disruptions in fear extinction is often explored in animal stress models. Using the single prolonged stress (SPS) model of post traumatic stress disorder and the immediate early gene (IEG) c-Fos as a measure of neural activity, we previously identified patterns of neural activity through which SPS disrupts extinction retention. However, none of these stress effects were specific to fear or extinction learning and memory. C-Jun is another IEG that is sometimes regulated in a different manner to c-Fos and could be used to identify emotional learning/memory specific patterns of neural activity that are sensitive to SPS. Animals were either fear conditioned (CS-fear) or presented with CSs only (CS-only) then subjected to extinction training and testing. C-Jun was then assayed within neural substrates critical for extinction memory. Inhibited c-Jun levels in the hippocampus (Hipp) and enhanced functional connectivity between the ventromedial prefrontal cortex (vmPFC) and basolateral amygdala (BLA) during extinction training was disrupted by SPS in the CS-fear group only. As a result, these effects were specific to emotional learning/memory. SPS also disrupted inhibited Hipp c-Jun levels, enhanced BLA c-Jun levels, and altered functional connectivity among the vmPFC, BLA, and Hipp during extinction testing in SPS rats in the CS-fear and CS-only groups. As a result, these effects were not specific to emotional learning/memory. Our findings suggest that SPS disrupts neural activity specific to extinction memory, but may also disrupt the retention of fear extinction by mechanisms that do not involve emotional learning/memory. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. How do postgraduate GP trainees regulate their learning and what helps and hinders them? A qualitative study

    NARCIS (Netherlands)

    Sagasser, M.H.; Kramer, A.W.M.; Vleuten, C.P.M. van der

    2012-01-01

    ABSTRACT: BACKGROUND: Self-regulation is essential for professional development. It involves monitoring of performance, identifying domains for improvement, undertaking learning activities, applying newly learned knowledge and skills and self-assessing performance. Since self-assessment alone is

  11. Student Modeling and Machine Learning

    OpenAIRE

    Sison , Raymund; Shimura , Masamichi

    1998-01-01

    After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine le...

  12. Identifiability and Accessibility in Learning Definite Article Usages: A Quasi-Experimental Study with Japanese Learners of English

    Science.gov (United States)

    Hinenoya, Kimiko; Lyster, Roy

    2015-01-01

    The present study investigated the effects of instruction on the use of the definite article "the" by Japanese learners of English by implementing two instructional treatments that varied in the extent to which they emphasized identifiability and accessibility. One instructional treatment, referred to as the traditional (TR) treatment,…

  13. Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns

    International Nuclear Information System (INIS)

    Chou, Jui-Sheng; Ngo, Ngoc-Tri

    2016-01-01

    Highlights: • This study develops a novel time-series sliding window forecast system. • The system integrates metaheuristics, machine learning and time-series models. • Site experiment of smart grid infrastructure is installed to retrieve real-time data. • The proposed system accurately predicts energy consumption in residential buildings. • The forecasting system can help users minimize their electricity usage. - Abstract: Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based machine learning system for predicting real-time building energy consumption data collected by a smart grid. The proposed system integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed system fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed system. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting system. The prediction system yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026 kW h. Notably, the system demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially

  14. Essential French grammar

    CERN Document Server

    Thacker, Mike

    2014-01-01

    Essential French Grammar is an innovative reference grammar and workbook for intermediate and advanced undergraduate students of French (CEFR levels B2 to C1). Its clear explanations of grammar are supported by contemporary examples and lively cartoon drawings.  Each chapter contains: * real-life language examples in French, with English translations * a 'key points' box and tables that summarise grammar concepts * a variety of exercises to reinforce learning * a contemporary primary source or literary extract to illustrate grammar in context. To aid your understanding, this book also contains a glossary of grammatical terms in French and English, useful verb tables and a key to the exercises. Together, these features all help you to grasp complex points of grammar and develop your French language skills.

  15. Identifying ecological "sweet spots" underlying cyanobacteria functional group dynamics from long-term observations using a statistical machine learning approach

    Science.gov (United States)

    Nelson, N.; Munoz-Carpena, R.; Phlips, E. J.

    2017-12-01

    Diversity in the eco-physiological adaptations of cyanobacteria genera creates challenges for water managers who are tasked with developing appropriate actions for controlling not only the intensity and frequency of cyanobacteria blooms, but also reducing the potential for blooms of harmful taxa (e.g., toxin producers, N2 fixers). Compounding these challenges, the efficacy of nutrient management strategies (phosphorus-only versus nitrogen-and-phosphorus) for cyanobacteria bloom abatement is the subject of an ongoing debate, which increases uncertainty associated with bloom mitigation decision-making. In this work, we analyze a unique long-term (17-year) dataset composed of monthly observations of cyanobacteria genera abundances, zooplankton abundances, water quality, and flow from Lake George, a bloom-impacted flow-through lake of the St. Johns River (FL, USA). Using the Random Forests machine learning algorithm, an assumption-free ensemble modeling approach, the dataset was evaluated to quantify and characterize relationships between environmental conditions and seven cyanobacteria groupings: five genera (Anabaena, Cylindrospermopsis, Lyngbya, Microcystis, and Oscillatoria) and two functional groups (N2 fixers and non-fixers). Results highlight the selectivity of nitrogen in describing genera and functional group dynamics, and potential for physical effects to limit the efficacy of nutrient management as a mechanism for cyanobacteria bloom mitigation.

  16. Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions.

    Science.gov (United States)

    Agarwal, Shashank; Liu, Feifan; Yu, Hong

    2011-10-03

    Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles. Simple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.

  17. Comparisons and Lessons Learned

    NARCIS (Netherlands)

    Jensen, PA; van der Voordt, Theo; Coenen, C; Sarasoja, AL; van der Voordt, DJM; Jensen, PA; Coenen, C

    2012-01-01

    Purpose: To create an overview and evaluation of the achievements of the contributions in this book by identifying, summarising and discussing cross-cutting themes and essential learning points across the former chapters.
    Methodology: Based on a purposeful reading of all chapters comparisons are

  18. The effect of essential oils of sweet fennel and pignut on mortality and learning in africanized honeybees (Apis mellifera L.) (Hymenoptera: Apidae)

    Energy Technology Data Exchange (ETDEWEB)

    Abramson, Charles I.; Michaluk, Lynnette M. [Oklahoma State University, Stillwater, OK (United States). Depts. of Psychology and Zoology. Lab. Comparative Psychology and Behavioral Biology]. E-mail: charles.abramson@okstate.edu; Wanderley, Paulo A.; Wanderley, Maria J.A.; Silva, Jose C.R. [Universidade Federal da Paraiba (UFPB), Bananeiras, PB (Brazil). Dept. de Agricultura

    2007-11-15

    It was recently discovered that exposure to small concentrations of the essential oils of sweet fennel (Foeniculum vulgare Mill) or pignut [Hyptis suaveolens (L.) Poit] can be used to control aphids. What is not known is whether these oils also influence honeybee behavior. Experiments using both harnessed and free-flying foragers at concentrations used to control aphids showed that bees readily associated the odors with a reward, discriminated between them, and were not repelled. Honeybees, however, would not consume the oils when mixed with sucrose to create an unconditioned stimulus. An experiment in which harnessed bees consumed various concentrations showed that concentrations greater than 50% were detrimental. The experiments reported here provide further evidence supporting the use of conditioning techniques to evaluate the use of essential oils on honey bee behavior. (author)

  19. The effect of essential oils of sweet fennel and pignut on mortality and learning in africanized honeybees (Apis mellifera L.) (Hymenoptera: Apidae)

    International Nuclear Information System (INIS)

    Abramson, Charles I.; Michaluk, Lynnette M.; Wanderley, Paulo A.; Wanderley, Maria J.A.; Silva, Jose C.R.

    2007-01-01

    It was recently discovered that exposure to small concentrations of the essential oils of sweet fennel (Foeniculum vulgare Mill) or pignut [Hyptis suaveolens (L.) Poit] can be used to control aphids. What is not known is whether these oils also influence honeybee behavior. Experiments using both harnessed and free-flying foragers at concentrations used to control aphids showed that bees readily associated the odors with a reward, discriminated between them, and were not repelled. Honeybees, however, would not consume the oils when mixed with sucrose to create an unconditioned stimulus. An experiment in which harnessed bees consumed various concentrations showed that concentrations greater than 50% were detrimental. The experiments reported here provide further evidence supporting the use of conditioning techniques to evaluate the use of essential oils on honey bee behavior. (author)

  20. Intuitive Mathematical Knowledge as an Essential Aspect of Contemporary Adult Learning: A case of women street vendors in the city of Gaborone

    OpenAIRE

    Nthogo Lekoko, Rebecca; Getrude Garegae, Kgomotso

    2006-01-01

    The findings of a phenomenological interview study with women street vendors showed a strong link between participants’ perceptions of everyday use of mathematical literacy and the speculations that mathematical use arose spontaneously in response to a practical need. The concept of intuitive mathematics as used indicates that mathematical thinking is an indispensable element of everyday conversation. Although the study finds that intuition and spontaneity are essential principles of lifelong...

  1. Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial.

    Science.gov (United States)

    Shao, Weixiang; Adams, Clive E; Cohen, Aaron M; Davis, John M; McDonagh, Marian S; Thakurta, Sujata; Yu, Philip S; Smalheiser, Neil R

    2015-03-01

    It is important to identify separate publications that report outcomes from the same underlying clinical trial, in order to avoid over-counting these as independent pieces of evidence. We created positive and negative training sets (comprised of pairs of articles reporting on the same condition and intervention) that were, or were not, linked to the same clinicaltrials.gov trial registry number. Features were extracted from MEDLINE and PubMed metadata; pairwise similarity scores were modeled using logistic regression. Article pairs from the same trial were identified with high accuracy (F1 score=0.843). We also created a clustering tool, Aggregator, that takes as input a PubMed user query for RCTs on a given topic, and returns article clusters predicted to arise from the same clinical trial. Although painstaking examination of full-text may be needed to be conclusive, metadata are surprisingly accurate in predicting when two articles derive from the same underlying clinical trial. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. French essentials for dummies

    CERN Document Server

    Lawless, Laura K

    2011-01-01

    Just the core concepts you need to write and speak French correctly If you have some knowledge of French and want to polish your skills, French Essentials For Dummies focuses on just the core concepts you need to communicate effectively. From conjugating verbs to understanding tenses, this easy-to-follow guide lets you skip the suffering and score high at exam time. French 101 - get the lowdown on the basics, from expressing dates and times to identifying parts of speech Gender matters - see how a noun's gender determines the articles, adjectives, and pronouns y

  3. TH-B-BRC-00: How to Identify and Resolve Potential Clinical Errors Before They Impact Patients Treatment: Lessons Learned

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2016-06-15

    Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803.

  4. TH-B-BRC-00: How to Identify and Resolve Potential Clinical Errors Before They Impact Patients Treatment: Lessons Learned

    International Nuclear Information System (INIS)

    2016-01-01

    Radiation treatment consists of a chain of events influenced by the quality of machine operation, beam data commissioning, machine calibration, patient specific data, simulation, treatment planning, imaging and treatment delivery. There is always a chance that the clinical medical physicist may make or fail to detect an error in one of the events that may impact on the patient’s treatment. In the clinical scenario, errors may be systematic and, without peer review, may have a low detectability because they are not part of routine QA procedures. During treatment, there might be errors on machine that needs attention. External reviews of some of the treatment delivery components by independent reviewers, like IROC, can detect errors, but may not be timely. The goal of this session is to help junior clinical physicists identify potential errors as well as the approach of quality assurance to perform a root cause analysis to find and eliminate an error and to continually monitor for errors. A compilation of potential errors will be presented by examples of the thought process required to spot the error and determine the root cause. Examples may include unusual machine operation, erratic electrometer reading, consistent lower electron output, variation in photon output, body parts inadvertently left in beam, unusual treatment plan, poor normalization, hot spots etc. Awareness of the possibility and detection of error in any link of the treatment process chain will help improve the safe and accurate delivery of radiation to patients. Four experts will discuss how to identify errors in four areas of clinical treatment. D. Followill, NIH grant CA 180803

  5. A Collaborative Approach to Identifying Social Media Markers of Schizophrenia by Employing Machine Learning and Clinical Appraisals.

    Science.gov (United States)

    Birnbaum, Michael L; Ernala, Sindhu Kiranmai; Rizvi, Asra F; De Choudhury, Munmun; Kane, John M

    2017-08-14

    Linguistic analysis of publicly available Twitter feeds have achieved success in differentiating individuals who self-disclose online as having schizophrenia from healthy controls. To date, limited efforts have included expert input to evaluate the authenticity of diagnostic self-disclosures. This study aims to move from noisy self-reports of schizophrenia on social media to more accurate identification of diagnoses by exploring a human-machine partnered approach, wherein computational linguistic analysis of shared content is combined with clinical appraisals. Twitter timeline data, extracted from 671 users with self-disclosed diagnoses of schizophrenia, was appraised for authenticity by expert clinicians. Data from disclosures deemed true were used to build a classifier aiming to distinguish users with schizophrenia from healthy controls. Results from the classifier were compared to expert appraisals on new, unseen Twitter users. Significant linguistic differences were identified in the schizophrenia group including greater use of interpersonal pronouns (P<.001), decreased emphasis on friendship (P<.001), and greater emphasis on biological processes (P<.001). The resulting classifier distinguished users with disclosures of schizophrenia deemed genuine from control users with a mean accuracy of 88% using linguistic data alone. Compared to clinicians on new, unseen users, the classifier's precision, recall, and accuracy measures were 0.27, 0.77, and 0.59, respectively. These data reinforce the need for ongoing collaborations integrating expertise from multiple fields to strengthen our ability to accurately identify and effectively engage individuals with mental illness online. These collaborations are crucial to overcome some of mental illnesses' biggest challenges by using digital technology. ©Michael L Birnbaum, Sindhu Kiranmai Ernala, Asra F Rizvi, Munmun De Choudhury, John M Kane. Originally published in the Journal of Medical Internet Research (http

  6. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

    Directory of Open Access Journals (Sweden)

    Jeffrey J Nirschl

    Full Text Available Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.

  7. The Past as a Puzzle: How Essential Questions Can Piece Together a Meaningful Investigation of History

    Science.gov (United States)

    Obenchain, Kathryn M.; Orr, Angela; Davis, Susan H.

    2011-01-01

    This article details a professional development program focused on the use of essential questions in reframing U.S. history learning experiences in elementary, middle, and high schools. Teachers identified four problems in designing and teaching engaging, relevant, and challenging U.S. history lessons. Each problem was addressed through the…

  8. Transition-Focused Professional Development: An Annotated Bibliography of Essential Elements and Features of Professional Development

    Science.gov (United States)

    Holzberg, Debra G.; Clark, Kelly A.; Morningstar, Mary E.

    2018-01-01

    Transition professional development (PD) has been identified as a way to improve transition services; however, there is a dearth of literature on transition-focused PD. To learn more about the essential features of effective PD, 73 published articles were evaluated in the area of PD in both secondary transition and special education. Articles were…

  9. Lifelong learning in nursing: a Delphi study.

    Science.gov (United States)

    Davis, Lisa; Taylor, Heidi; Reyes, Helen

    2014-03-01

    In order to foster a culture of lifelong learning in nursing, it is important to identify what the concept means in the nursing profession as well as the characteristics of a lifelong learner. The purpose of this Delphi study was to conceptualize lifelong learning from the perspective of nursing, and to identify characteristics and essential elements of lifelong learning. A Delphi Study technique in three phases was completed using an online survey tool. Data were analyzed for conceptual description, ratings of characteristics and attributes, and expert consensus in these three phases. An online survey tool was used in this study. Recognized experts in nursing education, administration and public policy participated in this study. Lifelong learning in nursing is defined as a dynamic process, which encompasses both personal and professional life. This learning process is also both formal and informal. Lifelong learning involves seeking and appreciating new worlds or ideas in order to gain a new perspective as well as questioning one's environment, knowledge, skills and interactions. The most essential characteristics of a lifelong learner are reflection, questioning, enjoying learning, understanding the dynamic nature of knowledge, and engaging in learning by actively seeking learning opportunities. Keeping the mind active is essential to both lifelong learning and being able to translate knowledge into the capacity to deliver high quality nursing care. It is hoped that a clearer understanding of lifelong learning in nursing will foster more discussion and research about intentional, active inclusion of lifelong learning behaviors in nursing curricula. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

    Science.gov (United States)

    Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

    2018-03-28

    To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

  11. Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network.

    Science.gov (United States)

    Woodhams, Victoria; de Lusignan, Simon; Mughal, Shakeel; Head, Graham; Debar, Safia; Desombre, Terry; Hilton, Sean; Al Sharifi, Houda

    2012-06-10

    Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare. We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN). We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops. We identified three types of intervention: pre-hospital; within the emergency department (ED); and post-admission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR - Patients at risk of readmission and ACG - Adjusted Clinical Groups) sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP) within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission. Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don't change.

  12. Short-term delayed recall of auditory verbal learning test is equivalent to long-term delayed recall for identifying amnestic mild cognitive impairment.

    Directory of Open Access Journals (Sweden)

    Qianhua Zhao

    Full Text Available Delayed recall of words in a verbal learning test is a sensitive measure for the diagnosis of amnestic mild cognitive impairment (aMCI and early Alzheimer's disease (AD. The relative validity of different retention intervals of delayed recall has not been well characterized. Using the Auditory Verbal Learning Test-Huashan version, we compared the differentiating value of short-term delayed recall (AVL-SR, that is, a 3- to 5-minute delay time and long-term delayed recall (AVL-LR, that is, a 20-minute delay time in distinguishing patients with aMCI (n = 897 and mild AD (n = 530 from the healthy elderly (n = 1215. In patients with aMCI, the correlation between AVL-SR and AVL-LR was very high (r = 0.94, and the difference between the two indicators was less than 0.5 points. There was no difference between AVL-SR and AVL-LR in the frequency of zero scores. In the receiver operating characteristic curves analysis, although the area under the curve (AUC of AVL-SR and AVL-LR for diagnosing aMCI was significantly different, the cut-off scores of the two indicators were identical. In the subgroup of ages 80 to 89, the AUC of the two indicators showed no significant difference. Therefore, we concluded that AVL-SR could substitute for AVL-LR in identifying aMCI, especially for the oldest patients.

  13. An essential role for UBE2A/HR6A in learning and memory and mGLUR-dependent long-term depression.

    Science.gov (United States)

    Bruinsma, Caroline F; Savelberg, Sanne M C; Kool, Martijn J; Jolfaei, Mehrnoush Aghadavoud; Van Woerden, Geeske M; Baarends, Willy M; Elgersma, Ype

    2016-01-01

    UBE2A deficiency syndrome (also known as X-linked intellectual disability type Nascimento) is an intellectual disability syndrome characterized by prominent dysmorphic features, impaired speech and often epilepsy. The syndrome is caused by Xq24 deletions encompassing the UBE2A (HR6A) gene or by intragenic UBE2A mutations. UBE2A encodes an E2 ubiquitin-conjugating enzyme involved in DNA repair and female fertility. A recent study in Drosophila showed that dUBE2A binds to the E3 ligase Parkin, which is required for mitochondrial function and responsible for juvenile Parkinson's disease. In addition, these studies showed impairments in synaptic transmission in dUBE2A mutant flies. However, a causal role of UBE2A in of cognitive deficits has not yet been established. Here, we show that Ube2a knockout mice have a major deficit in spatial learning tasks, whereas other tested phenotypes, including epilepsy and motor coordination, were normal. Results from electrophysiological measurements in the hippocampus showed no deficits in synaptic transmission nor in the ability to induce long-term synaptic potentiation. However, a small but significant deficit was observed in mGLUR-dependent long-term depression, a pathway previously implied in several other mouse models for neurodevelopmental disorders. Our results indicate a causal role of UBE2A in learning and mGLUR-dependent long-term depression, and further indicate that the Ube2a knockout mouse is a good model to study the molecular mechanisms underlying UBE2A deficiency syndrome. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  14. Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

    Science.gov (United States)

    Shallue, Christopher J.; Vanderburg, Andrew

    2018-02-01

    NASA’s Kepler Space Telescope was designed to determine the frequency of Earth-sized planets orbiting Sun-like stars, but these planets are on the very edge of the mission’s detection sensitivity. Accurately determining the occurrence rate of these planets will require automatically and accurately assessing the likelihood that individual candidates are indeed planets, even at low signal-to-noise ratios. We present a method for classifying potential planet signals using deep learning, a class of machine learning algorithms that have recently become state-of-the-art in a wide variety of tasks. We train a deep convolutional neural network to predict whether a given signal is a transiting exoplanet or a false positive caused by astrophysical or instrumental phenomena. Our model is highly effective at ranking individual candidates by the likelihood that they are indeed planets: 98.8% of the time it ranks plausible planet signals higher than false-positive signals in our test set. We apply our model to a new set of candidate signals that we identified in a search of known Kepler multi-planet systems. We statistically validate two new planets that are identified with high confidence by our model. One of these planets is part of a five-planet resonant chain around Kepler-80, with an orbital period closely matching the prediction by three-body Laplace relations. The other planet orbits Kepler-90, a star that was previously known to host seven transiting planets. Our discovery of an eighth planet brings Kepler-90 into a tie with our Sun as the star known to host the most planets.

  15. Essentials for successful English language teaching

    CERN Document Server

    Farrell, Thomas S C

    2010-01-01

    Essentials For Successful English Language Teaching is about how we teach English Language Learners (ELLs) and how our ELLs learn. Farrell and Jacobs encourage those involved in teaching English to develop, maintain and rediscover the reasons that led them to take up the profession. They focus on the essentials in teaching the English language that teachers can implement in their instruction so that their students can excel in their learning: Encourage learner autonomy Emphasize the social nature of learning Develop curricular integration, focus on meaning Celebrate diversity Expand thinking s

  16. Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.

    Science.gov (United States)

    Ahmad, Tariq; Lund, Lars H; Rao, Pooja; Ghosh, Rohit; Warier, Prashant; Vaccaro, Benjamin; Dahlström, Ulf; O'Connor, Christopher M; Felker, G Michael; Desai, Nihar R

    2018-04-12

    Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response. The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1-year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity-matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C-statistic=0.83) whereas left ventricular ejection fraction did not (C-statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1-year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1-year survival. There were significant interactions between propensity-matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin-converting enzyme inhibitors, β-blockers, and nitrates, P <0.001, all). Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in

  17. Mobile NBM - Android medical mobile application designed to help in learning how to identify the different regions of interest in the brain's white matter.

    Science.gov (United States)

    Sánchez-Rola, Iskander; Zapirain, Begoña García

    2014-07-18

    One of the most critical tasks when conducting neurological studies is identifying the different regions of interest in the brain's white matter. Currently few programs or applications are available that serve as an interactive guide in this process. This is why a mobile application has been designed and developed in order to teach users how to identify the referred regions of the brain. It also enables users to share the results obtained and take an examination on the knowledge thus learnt. In order to provide direct user-user or user-developer contact, the project includes a website and a Twitter account. An application has been designed with a basic, minimalist look, which anyone can access easily in order to learn to identify a specific region in the brain's white matter. A survey has also been conducted on people who have used it, which has shown that the application is attractive both in the student (final mean satisfaction of 4.2/5) and in the professional (final mean satisfaction of 4.3/5) environment. The response obtained in the online part of the project reflects the high practical value and quality of the application, as shown by the fact that the website has seen a large number of visitors (over 1000 visitors) and the Twitter account has a high number of followers (over 280 followers). Mobile NBM is the first mobile application to be used as a guide in the process of identifying a region of interest in the brain's white matter. Although initially not many areas are available in the application, new ones can be added as required by users in their respective studies. Apart from the application itself, the online resources provided (website and Twitter account) significantly enhance users' experience.

  18. A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers.

    Science.gov (United States)

    Romo-Bucheli, David; Janowczyk, Andrew; Gilmore, Hannah; Romero, Eduardo; Madabhushi, Anant

    2017-06-01

    The treatment and management of early stage estrogen receptor positive (ER+) breast cancer is hindered by the difficulty in identifying patients who require adjuvant chemotherapy in contrast to those that will respond to hormonal therapy. To distinguish between the more and less aggressive breast tumors, which is a fundamental criterion for the selection of an appropriate treatment plan, Oncotype DX (ODX) and other gene expression tests are typically employed. While informative, these gene expression tests are expensive, tissue destructive, and require specialized facilities. Bloom-Richardson (BR) grade, the common scheme employed in breast cancer grading, has been shown to be correlated with the Oncotype DX risk score. Unfortunately, studies have also shown that the BR grade determined experiences notable inter-observer variability. One of the constituent categories in BR grading is the mitotic index. The goal of this study was to develop a deep learning (DL) classifier to identify mitotic figures from whole slides images of ER+ breast cancer, the hypothesis being that the number of mitoses identified by the DL classifier would correlate with the corresponding Oncotype DX risk categories. The mitosis detector yielded an average F-score of 0.556 in the AMIDA mitosis dataset using a 6-fold validation setup. For a cohort of 174 whole slide images with early stage ER+ breast cancer for which the corresponding Oncotype DX score was available, the distributions of the number of mitoses identified by the DL classifier was found to be significantly different between the high vs low Oncotype DX risk groups (P machine classifier trained to separate low/high Oncotype DX risk categories using the mitotic count determined by the DL classifier yielded a 83.19% classification accuracy. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

  19. Essential learning tools for continuing medical education for physicians, geneticists, nurses, allied health professionals, mental health professionals, business administration professionals, and reproductive endocrinology and infertility (REI) fellows: the Midwest Reproductive Symposium International.

    Science.gov (United States)

    Collins, Gretchen G; Jeelani, Roohi; Beltsos, Angeline; Kearns, William G

    2018-04-01

    Essential learning tools for continuing medical education are a challenge in today's rapidly evolving field of reproductive medicine. The Midwest Reproductive Symposium International (MRSi) is a yearly conference held in Chicago, IL. The conference is targeted toward physicians, geneticists, nurses, allied health professionals, mental health professionals, business administration professionals, and reproductive endocrinology and infertility (REI) fellows engaged in the practice of reproductive medicine. In addition to the scientific conference agenda, there are specific sessions for nurses, mental health professionals, and REI fellows. Unique to the MRSi conference, there is also a separate "Business Minds" session to provide education on business acumen as it is an important element to running a department, division, or private clinic.

  20. Mastering machine learning with scikit-learn

    CERN Document Server

    Hackeling, Gavin

    2014-01-01

    If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.

  1. Treatment of Essential Tremor

    Science.gov (United States)

    ... for PATIENTS and their FAMILIES TREATMENT OF ESSENTIAL TREMOR This fact sheet is provided to help you understand which therapies help treat essential tremor. Neurologists from the American Academy of Neurology are ...

  2. Triumph of hope over experience: learning from interventions to reduce avoidable hospital admissions identified through an Academic Health and Social Care Network

    Directory of Open Access Journals (Sweden)

    Woodhams Victoria

    2012-06-01

    Full Text Available Abstract Background Internationally health services are facing increasing demands due to new and more expensive health technologies and treatments, coupled with the needs of an ageing population. Reducing avoidable use of expensive secondary care services, especially high cost admissions where no procedure is carried out, has become a focus for the commissioners of healthcare. Method We set out to identify, evaluate and share learning about interventions to reduce avoidable hospital admission across a regional Academic Health and Social Care Network (AHSN. We conducted a service evaluation identifying initiatives that had taken place across the AHSN. This comprised a literature review, case studies, and two workshops. Results We identified three types of intervention: pre-hospital; within the emergency department (ED; and post-admission evaluation of appropriateness. Pre-hospital interventions included the use of predictive modelling tools (PARR – Patients at risk of readmission and ACG – Adjusted Clinical Groups sometimes supported by community matrons or virtual wards. GP-advisers and outreach nurses were employed within the ED. The principal post-hoc interventions were the audit of records in primary care or the application of the Appropriateness Evaluation Protocol (AEP within the admission ward. Overall there was a shortage of independent evaluation and limited evidence that each intervention had an impact on rates of admission. Conclusions Despite the frequency and cost of emergency admission there has been little independent evaluation of interventions to reduce avoidable admission. Commissioners of healthcare should consider interventions at all stages of the admission pathway, including regular audit, to ensure admission thresholds don’t change.

  3. Class and Home Problems. Identify-Solve-Broadcast Your Own Transport Phenomenon: Student-Created YouTube Videos to Foster Active Learning in Mass and Heat Transfer

    Science.gov (United States)

    Wen, Fei; Khera, Eshita

    2016-01-01

    Despite the instinctive perception of mass and heat transfer principles in daily life, productive learning in this course continues to be one of the greatest challenges for undergraduate students in chemical engineering. In an effort to enhance student learning in classroom, we initiated an innovative active-learning method titled…

  4. iPedagogy: Using Multimedia Learning Theory to iDentify Best Practices for MP3 Player Use in Higher Education

    Science.gov (United States)

    Downs, Edward; Boyson, Aaron R.; Alley, Hannah; Bloom, Nikki R.

    2011-01-01

    Some institutions of higher learning have invested considerable resources to diffuse iPods and MP3 devices though little is known about learning outcomes tied to their use. Dual-coding and multimedia learning theories guided the development of a typical college lecture so that it could be presented in a combination of audio and visual forms across…

  5. Genome-wide methylation profiling identifies an essential role of reactive oxygen species in pediatric glioblastoma multiforme and validates a methylome specific for H3 histone family 3A with absence of G-CIMP/isocitrate dehydrogenase 1 mutation.

    Science.gov (United States)

    Jha, Prerana; Pia Patric, Irene Rosita; Shukla, Sudhanshu; Pathak, Pankaj; Pal, Jagriti; Sharma, Vikas; Thinagararanjan, Sivaarumugam; Santosh, Vani; Suri, Vaishali; Sharma, Mehar Chand; Arivazhagan, Arimappamagan; Suri, Ashish; Gupta, Deepak; Somasundaram, Kumaravel; Sarkar, Chitra

    2014-12-01

    Pediatric glioblastoma multiforme (GBM) is rare, and there is a single study, a seminal discovery showing association of histone H3.3 and isocitrate dehydrogenase (IDH)1 mutation with a DNA methylation signature. The present study aims to validate these findings in an independent cohort of pediatric GBM, compare it with adult GBM, and evaluate the involvement of important functionally altered pathways. Genome-wide methylation profiling of 21 pediatric GBM cases was done and compared with adult GBM data (GSE22867). We performed gene mutation analysis of IDH1 and H3 histone family 3A (H3F3A), status evaluation of glioma cytosine-phosphate-guanine island methylator phenotype (G-CIMP), and Gene Ontology analysis. Experimental evaluation of reactive oxygen species (ROS) association was also done. Distinct differences were noted between methylomes of pediatric and adult GBM. Pediatric GBM was characterized by 94 hypermethylated and 1206 hypomethylated cytosine-phosphate-guanine (CpG) islands, with 3 distinct clusters, having a trend to prognostic correlation. Interestingly, none of the pediatric GBM cases showed G-CIMP/IDH1 mutation. Gene Ontology analysis identified ROS association in pediatric GBM, which was experimentally validated. H3F3A mutants (36.4%; all K27M) harbored distinct methylomes and showed enrichment of processes related to neuronal development, differentiation, and cell-fate commitment. Our study confirms that pediatric GBM has a distinct methylome compared with that of adults. Presence of distinct clusters and an H3F3A mutation-specific methylome indicate existence of epigenetic subgroups within pediatric GBM. Absence of IDH1/G-CIMP status further indicates that findings in adult GBM cannot be simply extrapolated to pediatric GBM and that there is a strong need for identification of separate prognostic markers. A possible role of ROS in pediatric GBM pathogenesis is demonstrated for the first time and needs further evaluation. © The Author(s) 2014

  6. N400 Response Indexes Word Learning from Linguistic Context in Children

    Science.gov (United States)

    Abel, Alyson D.; Schneider, Julie; Maguire, Mandy J

    2018-01-01

    Word learning from linguistic context is essential for vocabulary growth from grade school onward; however, little is known about the mechanisms underlying successful word learning in children. Current methods for studying word learning development require children to identify the meaning of the word after each exposure, a method that interacts…

  7. Exploring levers and barriers to accessing primary care for marginalised groups and identifying their priorities for primary care provision: a participatory learning and action research study.

    Science.gov (United States)

    O'Donnell, Patrick; Tierney, Edel; O'Carroll, Austin; Nurse, Diane; MacFarlane, Anne

    2016-12-03

    The involvement of patients and the public in healthcare has grown significantly in recent decades and is documented in health policy documents internationally. Many benefits of involving these groups in primary care planning have been reported. However, these benefits are rarely felt by those considered marginalised in society and they are often excluded from participating in the process of planning primary care. It has been recommended to employ suitable approaches, such as co-operative and participatory initiatives, to enable marginalised groups to highlight their priorities for care. This Participatory Learning and Action (PLA) research study involved 21 members of various marginalised groups who contributed their views about access to primary care. Using a series of PLA techniques for data generation and co-analysis, we explored barriers and facilitators to primary healthcare access from the perspective of migrants, Irish Travellers, homeless people, drug users, sex workers and people living in deprivation, and identified their priorities for action with regard to primary care provision. Four overarching themes were identified: the home environment, the effects of the 'two-tier' healthcare system on engagement, healthcare encounters, and the complex health needs of many in those groups. The study demonstrates that there are many complicated personal and structural barriers to accessing primary healthcare for marginalised groups. There were shared and differential experiences across the groups. Participants also expressed shared priorities for action in the planning and running of primary care services. Members of marginalised groups have shared priorities for action to improve their access to primary care. If steps are taken to address these, there is scope to impact on more than one marginalised group and to address the existing health inequities.

  8. Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes.

    Science.gov (United States)

    Dehghan, Azad; Kovacevic, Aleksandar; Karystianis, George; Keane, John A; Nenadic, Goran

    2017-11-01

    De-identification of clinical narratives is one of the main obstacles to making healthcare free text available for research. In this paper we describe our experience in expanding and tailoring two existing tools as part of the 2016 CEGS N-GRID Shared Tasks Track 1, which evaluated de-identification methods on a set of psychiatric evaluation notes for up to 25 different types of Protected Health Information (PHI). The methods we used rely on machine learning on either a large or small feature space, with additional strategies, including two-pass tagging and multi-class models, which both proved to be beneficial. The results show that the integration of the proposed methods can identify Health Information Portability and Accountability Act (HIPAA) defined PHIs with overall F 1 -scores of ∼90% and above. Yet, some classes (Profession, Organization) proved again to be challenging given the variability of expressions used to reference given information. Copyright © 2017. Published by Elsevier Inc.

  9. Electric circuits essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Electric Circuits I includes units, notation, resistive circuits, experimental laws, transient circuits, network theorems, techniques of circuit analysis, sinusoidal analysis, polyph

  10. Calculus III essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Calculus III includes vector analysis, real valued functions, partial differentiation, multiple integrations, vector fields, and infinite series.

  11. Statistics I essentials

    CERN Document Server

    Milewski, Emil G

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Statistics I covers include frequency distributions, numerical methods of describing data, measures of variability, parameters of distributions, probability theory, and distributions.

  12. Pre-calculus essentials

    CERN Document Server

    Woodward, Ernest

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Pre-Calculus reviews sets, numbers, operations and properties, coordinate geometry, fundamental algebraic topics, solving equations and inequalities, functions, trigonometry, exponents

  13. Transport phenomena II essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Transport Phenomena II covers forced convention, temperature distribution, free convection, diffusitivity and the mechanism of mass transfer, convective mass transfer, concentration

  14. Differential equations I essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Differential Equations I covers first- and second-order equations, series solutions, higher-order linear equations, and the Laplace transform.

  15. Heat transfer II essentials

    CERN Document Server

    REA, The Editors of

    1988-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Heat Transfer II reviews correlations for forced convection, free convection, heat exchangers, radiation heat transfer, and boiling and condensation.

  16. Numerical analysis II essentials

    CERN Document Server

    REA, The Editors of; Staff of Research Education Association

    1989-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Numerical Analysis II covers simultaneous linear systems and matrix methods, differential equations, Fourier transformations, partial differential equations, and Monte Carlo methods.

  17. Algebra & trigonometry II essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Algebra & Trigonometry II includes logarithms, sequences and series, permutations, combinations and probability, vectors, matrices, determinants and systems of equations, mathematica

  18. Modern algebra essentials

    CERN Document Server

    Lutfiyya, Lutfi A

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Modern Algebra includes set theory, operations, relations, basic properties of the integers, group theory, and ring theory.

  19. Business statistics I essentials

    CERN Document Server

    Clark, Louise

    2014-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Business Statistics I includes descriptive statistics, introduction to probability, probability distributions, sampling and sampling distributions, interval estimation, and hypothesis t

  20. Computer science I essentials

    CERN Document Server

    Raus, Randall

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Computer Science I includes fundamental computer concepts, number representations, Boolean algebra, switching circuits, and computer architecture.

  1. Blended Learning

    Science.gov (United States)

    Imbriale, Ryan

    2013-01-01

    Teachers always have been and always will be the essential element in the classroom. They can create magic inside four walls, but they have never been able to create learning environments outside the classroom like they can today, thanks to blended learning. Blended learning allows students and teachers to break free of the isolation of the…

  2. The Effects of Using the Essential Skills Inventory on Teacher Perception of High-Quality Classroom Instruction

    Science.gov (United States)

    Sornson, Bob

    2015-01-01

    This study explores the efficacy of using the Essential Skill Inventories (ESI) to increase high-quality instruction in the early learning years. Kindergarten and first- and second-grade teachers, who were identified as using the ESI with fidelity, assessed their own teaching skills and behaviors, reflecting on these before and after use of the…

  3. Essential Medicines in National Constitutions

    Science.gov (United States)

    Toebes, Brigit; Hogerzeil, Hans

    2016-01-01

    Abstract A constitutional guarantee of access to essential medicines has been identified as an important indicator of government commitment to the progressive realization of the right to the highest attainable standard of health. The objective of this study was to evaluate provisions on access to essential medicines in national constitutions, to identify comprehensive examples of constitutional text on medicines that can be used as a model for other countries, and to evaluate the evolution of constitutional medicines-related rights since 2008. Relevant articles were selected from an inventory of constitutional texts from WHO member states. References to states’ legal obligations under international human rights law were evaluated. Twenty-two constitutions worldwide now oblige governments to protect and/or to fulfill accessibility of, availability of, and/or quality of medicines. Since 2008, state responsibilities to fulfill access to essential medicines have expanded in five constitutions, been maintained in four constitutions, and have regressed in one constitution. Government commitments to essential medicines are an important foundation of health system equity and are included increasingly in state constitutions. PMID:27781006

  4. Essential Bacillus subtilis genes

    DEFF Research Database (Denmark)

    Kobayashi, K.; Ehrlich, S.D.; Albertini, A.

    2003-01-01

    To estimate the minimal gene set required to sustain bacterial life in nutritious conditions, we carried out a systematic inactivation of Bacillus subtilis genes. Among approximate to4,100 genes of the organism, only 192 were shown to be indispensable by this or previous work. Another 79 genes were...... predicted to be essential. The vast majority of essential genes were categorized in relatively few domains of cell metabolism, with about half involved in information processing, one-fifth involved in the synthesis of cell envelope and the determination of cell shape and division, and one-tenth related...... to cell energetics. Only 4% of essential genes encode unknown functions. Most essential genes are present throughout a wide range of Bacteria, and almost 70% can also be found in Archaea and Eucarya. However, essential genes related to cell envelope, shape, division, and respiration tend to be lost from...

  5. Physics I essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Physics I includes vectors and scalars, one-dimensional motion, plane motion, dynamics of a particle, work and energy, conservation of energy, dynamics of systems of particles, rotation

  6. Electronics II essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Electronics II covers operational amplifiers, feedback and frequency compensation of OP amps, multivibrators, logic gates and families, Boolean algebra, registers, counters, arithmet

  7. Thermodynamics I essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Thermodynamics I includes review of properties and states of a pure substance, work and heat, energy and the first law of thermodynamics, entropy and the second law of thermodynamics

  8. C programming language essentials

    CERN Document Server

    Ackermann, Ernest C

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. C Programming Language discusses fundamental notions, data types and objects, expressions, statements, declarations, function and program structure, the preprocessor, and the standar

  9. Electronics I essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Electronics I covers fundamentals of semiconductor devices, junction diodes, bipolar junction transistors, power supplies, multitransistor circuits, small signals, low-frequency anal

  10. Thermodynamics II essentials

    CERN Document Server

    REA, The Editors of

    2013-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Thermodynamics II includes review of thermodynamic relations, power and refrigeration cycles, mixtures and solutions, chemical reactions, chemical equilibrium, and flow through nozzl

  11. Group theory I essentials

    CERN Document Server

    Milewski, Emil G

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Group Theory I includes sets and mapping, groupoids and semi-groups, groups, isomorphisms and homomorphisms, cyclic groups, the Sylow theorems, and finite p-groups.

  12. Boolean algebra essentials

    CERN Document Server

    Solomon, Alan D

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Boolean Algebra includes set theory, sentential calculus, fundamental ideas of Boolean algebras, lattices, rings and Boolean algebras, the structure of a Boolean algebra, and Boolean

  13. Laplace transforms essentials

    CERN Document Server

    Shafii-Mousavi, Morteza

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Laplace Transforms includes the Laplace transform, the inverse Laplace transform, special functions and properties, applications to ordinary linear differential equations, Fourier tr

  14. Physical chemistry II essentials

    CERN Document Server

    REA, The Editors of

    1992-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Physical Chemistry II includes reaction mechanisms, theoretical approaches to chemical kinetics, gravitational work, electrical and magnetic work, surface work, kinetic theory, collisional and transport properties of gases, statistical mechanics, matter and waves, quantum mechanics, and rotations and vibrations of atoms and molecules.

  15. Statistics II essentials

    CERN Document Server

    Milewski, Emil G

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Statistics II discusses sampling theory, statistical inference, independent and dependent variables, correlation theory, experimental design, count data, chi-square test, and time se

  16. Algebra & trigonometry I essentials

    CERN Document Server

    REA, Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Algebra & Trigonometry I includes sets and set operations, number systems and fundamental algebraic laws and operations, exponents and radicals, polynomials and rational expressions, eq

  17. Geometry I essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Geometry I includes methods of proof, points, lines, planes, angles, congruent angles and line segments, triangles, parallelism, quadrilaterals, geometric inequalities, and geometric

  18. Transport phenomena I essentials

    CERN Document Server

    REA, The Editors of

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Transport Phenomena I includes viscosity, flow of Newtonian fluids, velocity distribution in laminar flow, velocity distributions with more than one independent variable, thermal con

  19. Data structures II essentials

    CERN Document Server

    Smolarski, Dennis C

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures II includes sets, trees, advanced sorting, elementary graph theory, hashing, memory management and garbage collection, and appendices on recursion vs. iteration, alge

  20. Computer science II essentials

    CERN Document Server

    Raus, Randall

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Computer Science II includes organization of a computer, memory and input/output, coding, data structures, and program development. Also included is an overview of the most commonly

  1. Data structures I essentials

    CERN Document Server

    Smolarski, Dennis C

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Data Structures I includes scalar variables, arrays and records, elementary sorting, searching, linked lists, queues, and appendices of binary notation and subprogram parameter passi

  2. Set theory essentials

    CERN Document Server

    Milewski, Emil G

    2012-01-01

    REA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Set Theory includes elementary logic, sets, relations, functions, denumerable and non-denumerable sets, cardinal numbers, Cantor's theorem, axiom of choice, and order relations.

  3. Identifying emerging trends for implementing learning technology in special education: a state-of-the-art review of selected articles published in 2008-2012.

    Science.gov (United States)

    Liu, Gi-Zen; Wu, No-Wei; Chen, Yi-Wen

    2013-10-01

    As electronic learning (e-learning) becomes increasingly popular in education worldwide, learning technology (LT) has been applied in various learning environments and activities to promote meaningful, efficient, and effective learning. LT has also been adopted by researchers and teacher-practitioners in the field of special education, but as yet little review-based research has been published. This review research thus carefully examined the trends of LT implementations in special education, providing a comprehensive analysis of 26 studies published in indexed journals in the past five years (2008-2012). Two research questions were addressed: (a) What are the major research aims, methodologies, and outcomes in these studies of implementing LT in the field of special education? and (b) What types of LT are mainly used with special education students, and for what kinds of students? Major findings include that examining the learning effectiveness of LT using was the most common research purpose (75%); researchers primarily relied on experimental studies (46%, 12 studies), followed by interviews and questionnaires (19%, 5 studies). Moreover, the most common use of LT was computer-assisted technology (such as web-based mentoring, educational computer games, laptop computers) in special education; studies investigating the use of LT with mentally disabled students were more than those with physically disabled ones. It is expected that the findings of this work and their implications will serve as valuable references with regard to the use of LT with special education students. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. Centos system administration essentials

    CERN Document Server

    Mallett, Andrew

    2014-01-01

    If you are a Linux administrator who is looking to gain knowledge that differentiates yourself from the crowd, then this is the book for you. Beginners who have a keen interest to learn more about Linux administration will also progress quickly with this resourceful learning guide.

  5. Roitt's essential immunology

    National Research Council Canada - National Science Library

    Delves, Peter J; Roitt, Ivan M

    2011-01-01

    ... of the immune system, the hallmark easy-reading style of Roitt's Essential Immunology clearly explains the key principles needed by medical and health sciences students, from the basis of immunity to clinical applications...

  6. Benign Essential Blepharospasm

    Science.gov (United States)

    ... the same for many years; and, in rare cases, improve spontaneously. Clinical Trials Throughout the U.S. and Worldwide NINDS Clinical Trials Related ... Definition Benign essential blepharospasm (BEB) is a progressive neurological ...

  7. Marketingmanagement : De essentie

    NARCIS (Netherlands)

    Kotler, P.J.; Keller, K.; Robben, H.S.J.

    2007-01-01

    'Marketingmanagement, de essentie' biedt een volledige introductie in modern marketingmanagement. De nieuwste concepten en onderzoeksresultaten komen aan bod. Zo wordt veel aandacht besteed aan holistische marketing en is de impact van technologische ontwikkelingen op hedendaagse marketing in deze

  8. Marketing management : De essentie

    NARCIS (Netherlands)

    Kotler, P.J.; Keller, K.; Robben, H.S.J.

    2010-01-01

    'Marketingmanagement, de essentie' biedt een volledige introductie in modern marketingmanagement. De nieuwste concepten en onderzoeksresultaten komen aan bod. Zo wordt veel aandacht besteed aan holistische marketing en is de impact van technologische ontwikkelingen op hedendaagse marketing in deze

  9. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  10. North Dakota Native American Essential Understandings

    Science.gov (United States)

    North Dakota Department of Public Instruction, 2015

    2015-01-01

    In the spring of 2015, the North Dakota Department of Public Instruction brought together tribal Elders from across North Dakota to share stories, memories, songs, and wisdom in order to develop the North Dakota Native American Essential Understandings (NDNAEU) to guide the learning of both Native and non-Native students across the state. They…

  11. Islam Is Essential for General Education

    Science.gov (United States)

    Meacham, Jack

    2015-01-01

    The religion of Islam is often portrayed with false and negative stereotypes. If we expect our students to understand and participate in the global world and to be informed and engaged citizens in a democratic America, then it is essential that they develop a basic and sound understanding of Islam. Furthermore, learning about Islam can facilitate…

  12. Fostering Topic Knowledge: Essential for Academic Writing

    Science.gov (United States)

    Proske, Antje; Kapp, Felix

    2013-01-01

    Several researchers emphasize the role of the writer's topic knowledge for writing. In academic writing topic knowledge is often constructed by studying source texts. One possibility to support that essential phase of the writing process is to provide interactive learning questions which facilitate the construction of an adequate situation…

  13. Essential software architecture

    CERN Document Server

    Gorton, Ian

    2011-01-01

    Job titles like ""Technical Architect"" and ""Chief Architect"" nowadays abound in software industry, yet many people suspect that ""architecture"" is one of the most overused and least understood terms in professional software development. Gorton's book tries to resolve this dilemma. It concisely describes the essential elements of knowledge and key skills required to be a software architect. The explanations encompass the essentials of architecture thinking, practices, and supporting technologies. They range from a general understanding of structure and quality attributes through technical i

  14. Strategic leadership: the essential skills.

    Science.gov (United States)

    Schoemaker, Paul J H; Krupp, Steve; Howland, Samantha

    2013-01-01

    The more uncertain your environment, the greater the opportunity--if you have the leadership skills to capitalize on it. Research at the Wharton school and at the authors' consulting firm, involving more than 20,000 executives to date, has identified six skills that, when mastered and used in concert, allow leaders to think strategically and navigate the unknown effectively. They are the abilities to anticipate, challenge, interpret, decide, align, and learn. This article describes the six skills in detail and includes a self-assessment that will enable you to identify the ones that most need your attention. The authors have found that strength in one skill cannot easily compensate for a deficit in another. An adaptive strategic leader has learned to apply all six at once.

  15. Raspberry Pi Server essentials

    CERN Document Server

    Kula, Piotr

    2014-01-01

    The book is an example based, hands-on guide where you will learn how to make a game from scratch, and learn how to develop games on the iOS platform.If you have great ideas for games and want to learn iOS game development, then this book is the right choice for you. Being familiar with iOS development is a plus, but is not mandatory. You will gradually get to grips with the new Sprite Kit framework with the help of this book.

  16. Apache Mahout essentials

    CERN Document Server

    Withanawasam, Jayani

    2015-01-01

    If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.

  17. Python essential reference

    CERN Document Server

    Beazley, David M

    2009-01-01

    Python Essential Reference is the definitive reference guide to the Python programming language — the one authoritative handbook that reliably untangles and explains both the core Python language and the most essential parts of the Python library. Designed for the professional programmer, the book is concise, to the point, and highly accessible. It also includes detailed information on the Python library and many advanced subjects that is not available in either the official Python documentation or any other single reference source. Thoroughly updated to reflect the significant new programming language features and library modules that have been introduced in Python 2.6 and Python 3, the fourth edition of Python Essential Reference is the definitive guide for programmers who need to modernize existing Python code or who are planning an eventual migration to Python 3. Programmers starting a new Python project will find detailed coverage of contemporary Python programming idioms.

  18. Essential travel medicine

    CERN Document Server

    Zuckerman, Jane N; Leggat, Peter

    2015-01-01

    This 1st edition of Essential Travel Medicine provides an excellent concise introduction to the specialty of Travel Medicine. This core text will enable health care practitioners particularly those new to the clinical practice of Travel Medicine, to gain a fundamental understanding of the diverse and complex issues which can potentially affect the health of the many millions of people who undertake international travel. Jane N Zuckerman is joined by Gary W Brunette from CDC and Peter A Leggat from Australia as Editors. Leading international specialists in their fields have contributed authoritative chapters reflecting current knowledge to facilitate best clinical practice in the different aspects of travel medicine. The aim of Essential Travel Medicine is to provide a comprehensive guide to Travel Medicine as well as a fundamental knowledge base to support international undergraduate and postgraduate specialty training programmes in the discipline of Travel Medicine. The 1st edition of Essential Travel ...

  19. Autodesk Robot Structural Analysis Professional 2016 essentials

    CERN Document Server

    Marsh, Ken

    2016-01-01

    Autodesk Robot Structural Analysis Professional 2016 - Essentials is an excellent introduction to the essential features, functions, and workflows of Autodesk Robot Structural Analysis Professional. Master the tools you will need to make Robot work for you: Go from zero to proficiency with this thorough and detailed introduction to the essential concepts and workflows of Robot Structural Analysis Professional 2016. - Demystify the interface - Manipulate and manage Robot tables like a pro - Learn how to use Robot's modeling tools - Master loading techniques - Harness Robot automated load combinations - Decipher simplified seismic loading - Discover workflows for steel and concrete design - Gain insights to help troubleshoot issues Guided exercises are provided to help cement fundamental concepts in Robot Structural Analysis and drive home key functions. Get up to speed quickly with this essential text and add Robot Structural Analysis Professional 2016 to your analysis and design toolbox. New in 2016: AWC-NDS ...

  20. Physics Essentials For Dummies

    CERN Document Server

    Holzner, Steven

    2010-01-01

    For students who just need to know the vital concepts of physics, whether as a refresher, for exam prep, or as a reference, Physics Essentials For Dummies is a must-have guide. Free of ramp-up and ancillary material, Physics Essentials For Dummies contains content focused on key topics only. It provides discrete explanations of critical concepts taught in an introductory physics course, from force and motion to momentum and kinetics. This guide is also a perfect reference for parents who need to review critical physics concepts as they help high school students with homework assignments, as we

  1. Essentials of Computational Electromagnetics

    CERN Document Server

    Sheng, Xin-Qing

    2012-01-01

    Essentials of Computational Electromagnetics provides an in-depth introduction of the three main full-wave numerical methods in computational electromagnetics (CEM); namely, the method of moment (MoM), the finite element method (FEM), and the finite-difference time-domain (FDTD) method. Numerous monographs can be found addressing one of the above three methods. However, few give a broad general overview of essentials embodied in these methods, or were published too early to include recent advances. Furthermore, many existing monographs only present the final numerical results without specifyin

  2. ESSENTIAL DYNAMICS OF PROTEINS

    NARCIS (Netherlands)

    AMADEI, A; LINSSEN, ABM; BERENDSEN, HJC

    1993-01-01

    Analysis of extended molecular dynamics (MD) simulations of lysozyme in vacuo and in aqueous solution reveals that it is possible to separate the configurational space into two subspaces: (1) an ''essential'' subspace containing only a few degrees of freedom in which anharmonic motion occurs that

  3. Essential Palatal Myoclonus

    Directory of Open Access Journals (Sweden)

    Bhuwan Raj Pandey

    2017-06-01

    Full Text Available Introduction: Palatal myoclonus is a rare condition presenting with clicking sound in ear or muscle tremor in pharynx. There are two varieties: essential and symptomatic. Various treatment options exists ranging from watchful observation to botulinum toxin injection. We have not found any reported case of palatal myoclonus from our country. Here we present a case of essential palatal myoclonus managed with clonazepam. Case report: A young female presented in Ear Nose and Throat clinic with complain of auditory click and spontaneous rhythmic movement of throat muscles for eight months. On examination, there was involuntary, rhythmic contraction of bilateral soft-palate, uvula, and base of tongue. Neurological, eye, and peripheral examination were normal. A diagnosis of essential palatal myoclonus was made. It was managed successfully with clonazepam; patient was still on low dose clonazepam at the time of making this report. Conclusion: Essential palatal myoclonus can be clinically diagnosed and managed even in settings where MRI is not available or affordable.

  4. The 2003 essential. AREVA

    International Nuclear Information System (INIS)

    2004-07-01

    This document presents the essential activities of the Areva Group, a world nuclear industry leader. This group proposes technological solutions to produce the nuclear energy and to transport the electric power. It develops connection systems for the telecommunication, the computers and the automotive industry. Key data on the program management, the sustainable development activities and the different divisions are provided. (A.L.B.)

  5. Essentials of Risk Theory

    NARCIS (Netherlands)

    Roeser, S.; Hillerbrand, R.; Sandin, P.; Peterson, M.B.

    2012-01-01

    Risk has become one of the main topics in fields as diverse as engineering, medicine and economics, and it is also studied by social scientists, psychologists and legal scholars. This Springer Essentials version offers an overview of the in-depth handbook and highlights some of the main points

  6. Essential trichomegaly: case report

    Directory of Open Access Journals (Sweden)

    Julia Dutra Rossetto

    2013-02-01

    Full Text Available The present study reports two cases of symptomatic essential trichomegaly. Trichomegaly may develop in various diseases, including anorexia nervosa, hypothyroidism, pregnancy, pretibial myxedema, systemic lupus erythematosus, vernal keratoconjunctivitis, and uveitis. The exact incidence trichomegaly is unknown, and the condition remains sporadically reported. Two cases of symptomatic trichomegaly without any associated systemic disorder are presented in this paper.

  7. Developing, implementing and evaluating a simulation learning ...

    African Journals Online (AJOL)

    Background: The training of undergraduate midwifery students to identify and manage post-partum haemorrhage, is an essential skill in midwifery. Aim: The aim of this study was to develop, implement and evaluate a simulation learning package (SLP) on post-partum haemorrhage for undergraduate midwifery students ...

  8. Predicting Contextual Informativeness for Vocabulary Learning

    Science.gov (United States)

    Kapelner, Adam; Soterwood, Jeanine; Nessaiver, Shalev; Adlof, Suzanne

    2018-01-01

    Vocabulary knowledge is essential to educational progress. High quality vocabulary instruction requires supportive contextual examples to teach word meaning and proper usage. Identifying such contexts by hand for a large number of words can be difficult. In this work, we take a statistical learning approach to engineer a system that predicts…

  9. Autodesk Maya 2013 Essentials

    CERN Document Server

    Naas, Paul

    2012-01-01

    Recommnded text for those preparing for the Maya Associate exam Maya, the industry-leading 3D animation and effects software used in movies, games, cartoons, and commercials, is challenging to learn. This full-color guide features approachable, hands-on exercises and additional task-based tutorials that allow new users to quickly become productive with the program and familiar with its workflow in a professional environment. You'll learn the basics of modeling, texturing, animating, and lighting; explore different parts of the production pipeline; and practice on some real-world projects. Ma

  10. Exploring the Optimal Strategy to Predict Essential Genes in Microbes

    Directory of Open Access Journals (Sweden)

    Yao Lu

    2011-12-01

    Full Text Available Accurately predicting essential genes is important in many aspects of biology, medicine and bioengineering. In previous research, we have developed a machine learning based integrative algorithm to predict essential genes in bacterial species. This algorithm lends itself to two approaches for predicting essential genes: learning the traits from known essential genes in the target organism, or transferring essential gene annotations from a closely related model organism. However, for an understudied microbe, each approach has its potential limitations. The first is constricted by the often small number of known essential genes. The second is limited by the availability of model organisms and by evolutionary distance. In this study, we aim to determine the optimal strategy for predicting essential genes by examining four microbes with well-characterized essential genes. Our results suggest that, unless the known essential genes are few, learning from the known essential genes in the target organism usually outperforms transferring essential gene annotations from a related model organism. In fact, the required number of known essential genes is surprisingly small to make accurate predictions. In prokaryotes, when the number of known essential genes is greater than 2% of total genes, this approach already comes close to its optimal performance. In eukaryotes, achieving the same best performance requires over 4% of total genes, reflecting the increased complexity of eukaryotic organisms. Combining the two approaches resulted in an increased performance when the known essential genes are few. Our investigation thus provides key information on accurately predicting essential genes and will greatly facilitate annotations of microbial genomes.

  11. The essential guide to metadata for books

    CERN Document Server

    Register, Renee

    2013-01-01

    In The Essential Guide to Metadata for Books, you will learn exactly what you need to know to effectively generate, handle and disseminate metadata for books and ebooks. This comprehensive but digestible document will explain the life-cycle of book metadata, industry standards, XML, ONIX and the essential elements of metadata. It will also show you how effective, well-organized metadata can improve your efforts to sell a book, especially when it comes to marketing, discoverability and converting at the point of sale. This information-packed document also includes a glossary of terms

  12. Python data science essentials

    CERN Document Server

    Boschetti, Alberto

    2015-01-01

    If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.

  13. OpenStack essentials

    CERN Document Server

    Radez, Dan

    2015-01-01

    If you need to get started with OpenStack or want to learn more, then this book is your perfect companion. If you're comfortable with the Linux command line, you'll gain confidence in using OpenStack.

  14. IPython notebook essentials

    CERN Document Server

    Martins, L Felipe

    2014-01-01

    If you are a professional, student, or educator who wants to learn to use IPython Notebook as a tool for technical and scientific computing, visualization, and data analysis, this is the book for you. This book will prove valuable for anyone that needs to do computations in an agile environment.

  15. GameSalad essentials

    CERN Document Server

    DeQuadros, Miguel

    2015-01-01

    If you want to create your own game, but don't know where to start, this is the book for you. Whether you've used GameSalad before, or have prior game development experience or not you are sure to learn! Imaging software experience, such as Photoshop, is good to have, but art and assets are provided in the book's resources.

  16. AWS development essentials

    CERN Document Server

    Kuppusamy, Prabhakaran

    2014-01-01

    This book is intended for expert programmers and architects who want to learn how to migrate the existing infrastructure to AWS Cloud and start using AWS services in all application tiers. Basic knowledge of Java and competence in cloud computing will be needed to follow the examples in this book.

  17. Spring batch essentials

    CERN Document Server

    Rao, P Raja Malleswara

    2015-01-01

    If you are a Java developer with basic knowledge of Spring and some experience in the development of enterprise applications, and want to learn about batch application development in detail, then this book is ideal for you. This book will be perfect as your next step towards building simple yet powerful batch applications on a Java-based platform.

  18. Genetics Home Reference: essential tremor

    Science.gov (United States)

    ... Facebook Twitter Home Health Conditions Essential tremor Essential tremor Printable PDF Open All Close All Enable Javascript to view the expand/collapse boxes. Description Essential tremor is a movement disorder that causes involuntary, rhythmic ...

  19. Essential oil content and composition of aniseed

    Directory of Open Access Journals (Sweden)

    Aćimović Milica G.

    2015-01-01

    Full Text Available The field experiments were carried out during 2011 and 2012 in three localities in Vojvodina (Serbia with the application of six different fertilizer regimes aimed at determining the content and composition of the aniseed essential oil. It was found that the average essential oil content of aniseed, obtained by hydrodistillation, was 3.72%. The weather conditions during the year and the locality had a statistically significant effect on the essential oil content, while different source of fertilizers was not statistically significant for the essential oil content and its composition. Essential oil composition was determined using GC-MS technique, and a total of 15 compounds were identified. It was found that the major component was trans-anethole, 94.78% on the average, and the coefficient of variation was 2%. The second most abundant component was γ-himachalene with 2.53% (CV 28%. All other components were present in less than 1%.

  20. Essentials of artificial intelligence

    CERN Document Server

    Ginsberg, Matt

    1993-01-01

    Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergraduate levels. Based on the author'scourse at Stanford University, the book is an integrated, cohesiveintroduction to the field. The author has a fresh, entertaining writingstyle that combines clear presentations with humor and AI anecdotes. At thesame time, as an active AI researcher, he presents the materialauthoritatively and with insight that reflects a contemporary, first hand

  1. Essentials of Endodontic Microsurgery

    Science.gov (United States)

    2010-04-01

    Holtzman DJ, et al. Quality of root-end preparations using ultrasonic and rotary instrumentation in cadavers. J Endod 2000;26:281. 39. Peters CI...00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Essentials of Endodontic Microsurgery 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT... Endodontic Program,Harvard School of Dental Medicine,Boston,MA 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND

  2. Process Improvement Essentials

    CERN Document Server

    Persse, James R

    2006-01-01

    Process Improvement Essentials combines the foundation needed to understand process improvement theory with the best practices to help individuals implement process improvement initiatives in their organization. The three leading programs: ISO 9001:2000, CMMI, and Six Sigma--amidst the buzz and hype--tend to get lumped together under a common label. This book delivers a combined guide to all three programs, compares their applicability, and then sets the foundation for further exploration.

  3. IPv6 Essentials

    CERN Document Server

    Hagen, Silvia

    2006-01-01

    IPv6 Essentials, Second Edition provides a succinct, in-depth tour of all the new features and functions in IPv6. It guides you through everything you need to know to get started, including how to configure IPv6 on hosts and routers and which applications currently support IPv6. Aimed at system and network administrators, engineers, network designers, and IT managers, this book will help you understand, plan for, design, and integrate IPv6 into your current IPv4 infrastructure

  4. Android application security essentials

    CERN Document Server

    Rai, Pragati

    2013-01-01

    Android Application Security Essentials is packed with examples, screenshots, illustrations, and real world use cases to secure your apps the right way.If you are looking for guidance and detailed instructions on how to secure app data, then this book is for you. Developers, architects, managers, and technologists who wish to enhance their knowledge of Android security will find this book interesting. Some prior knowledge of development on the Android stack is desirable but not required.

  5. Rake task management essentials

    CERN Document Server

    Koleshko, Andrey

    2014-01-01

    A step-by-step and interactive approach explaining the Rake essentials along with code examples and advanced features. If you are a developer who is acquainted with the Ruby language and want to speed up writing the code concerned with files, then this book is for you. To start reading this book, basic Ruby knowledge is required; however, a huge amount of experience with the language is not necessary.

  6. Machine Learning-Assisted Network Inference Approach to Identify a New Class of Genes that Coordinate the Functionality of Cancer Networks.

    Science.gov (United States)

    Ghanat Bari, Mehrab; Ung, Choong Yong; Zhang, Cheng; Zhu, Shizhen; Li, Hu

    2017-08-01

    Emerging evidence indicates the existence of a new class of cancer genes that act as "signal linkers" coordinating oncogenic signals between mutated and differentially expressed genes. While frequently mutated oncogenes and differentially expressed genes, which we term Class I cancer genes, are readily detected by most analytical tools, the new class of cancer-related genes, i.e., Class II, escape detection because they are neither mutated nor differentially expressed. Given this hypothesis, we developed a Machine Learning-Assisted Network Inference (MALANI) algorithm, which assesses all genes regardless of expression or mutational status in the context of cancer etiology. We used 8807 expression arrays, corresponding to 9 cancer types, to build more than 2 × 10 8 Support Vector Machine (SVM) models for reconstructing a cancer network. We found that ~3% of ~19,000 not differentially expressed genes are Class II cancer gene candidates. Some Class II genes that we found, such as SLC19A1 and ATAD3B, have been recently reported to associate with cancer outcomes. To our knowledge, this is the first study that utilizes both machine learning and network biology approaches to uncover Class II cancer genes in coordinating functionality in cancer networks and will illuminate our understanding of how genes are modulated in a tissue-specific network contribute to tumorigenesis and therapy development.

  7. Digital collaborative learning: identifying what students value [v1; ref status: indexed, http://f1000r.es/55h

    Directory of Open Access Journals (Sweden)

    Claire Hemingway

    2015-03-01

    Full Text Available Digital technologies are changing the learning landscape and connecting classrooms to learning environments beyond the school walls.  Online collaborations among students, teachers, and scientists are new opportunities for authentic science experiences.  Here we present findings generated on PlantingScience (www.plantingscience.org, an online community where scientists from more than 14 scientific societies have mentored over 14,000 secondary school students as they design and think through their own team investigations on plant biology.  The core intervention is online discourse between student teams and scientist mentors to enhance classroom-based plant investigations.  We asked: (1 what attitudes about engaging in authentic science do students reveal, and (2 how do student attitudes relate to design principles of the program? Lexical analysis of open-ended survey questions revealed that students most highly value working with plants and scientists.  By examining student responses to this cognitive apprenticeship model, we provide new perspectives on the importance of the personal relationships students form with scientists and plants when working as members of a research community. These perspectives have implications for plant science instruction and e-mentoring programs.

  8. Urban High School Teachers' Beliefs Concerning Essential Science Teaching Dispositions

    Science.gov (United States)

    Miranda, Rommel

    2012-01-01

    This qualitative study addresses the link between urban high school science teachers' beliefs about essential teaching dispositions and student learning outcomes. The findings suggest that in order to help students to do well in science in urban school settings, science teachers should possess essential teaching dispositions which include…

  9. Modified Delphi Investigation of Motor Development and Learning in Physical Education Teacher Education

    Science.gov (United States)

    Ross, Susan; Metcalf, Amanda; Bulger, Sean M.; Housner, Lynn D.

    2014-01-01

    Purpose: As the scope of motor development and learning knowledge has successfully broadened over the years, there is an increased need to identify the content and learning experiences that are essential in preparing preservice physical educators. The purpose of this study was to generate expert consensus regarding the most critical motor…

  10. The GABAergic Anterior Paired Lateral Neurons Facilitate Olfactory Reversal Learning in "Drosophila"

    Science.gov (United States)

    Wu, Yanying; Ren, Qingzhong; Li, Hao; Guo, Aike

    2012-01-01

    Reversal learning has been widely used to probe the implementation of cognitive flexibility in the brain. Previous studies in monkeys identified an essential role of the orbitofrontal cortex (OFC) in reversal learning. However, the underlying circuits and molecular mechanisms are poorly understood. Here, we use the T-maze to investigate the neural…

  11. Neo4j essentials

    CERN Document Server

    Gupta, Sumit

    2015-01-01

    If you are an application developer or software architect who wants to dive into the Cypher language and learn the concepts of graph theory and graph-based data models, this is the book for you. Prior experience with a graph-based or NoSQL-based database is expected. Some knowledge of Java will be beneficial, as this will give you more insights into Neo4j's extensibility.

  12. KnockoutJS essentials

    CERN Document Server

    Ferrando, Jorge

    2015-01-01

    If you are a JavaScript developer who has been using DOM manipulation libraries such as Mootools or Scriptaculous, and you want go further in modern JavaScript development with a simple and well-documented library, then this book is for you. Learning how to use Knockout will be perfect as your next step towards building JavaScript applications that respond to user interaction.

  13. Python penetration testing essentials

    CERN Document Server

    Mohit

    2015-01-01

    If you are a Python programmer or a security researcher who has basic knowledge of Python programming and want to learn about penetration testing with the help of Python, this book is ideal for you. Even if you are new to the field of ethical hacking, this book can help you find the vulnerabilities in your system so that you are ready to tackle any kind of attack or intrusion.

  14. Samii's essentials in neurosurgery

    International Nuclear Information System (INIS)

    Ramina, Ricardo; Pontifical Catholic Univ. of Parana, Curitiba; Pires Aguiar, Paulo Henrique; Sao Paulo Univ.; Hospital Santa Paula, Sao Paulo; Tatagiba, Marcos

    2008-01-01

    'Samii's Essentials in Neurosurgery' contains selected papers written by internationally recognized contributors who were trained by Professor Madjid Samii in Hannover, Germany. The main topics deal with cutting-edge technology in neurosurgery, skull-base surgery, and specific peripheral nerve, spine, and vascular surgeries. The texts and a wealth of illustrations review and reinforce guidelines on the diagnosis and management of situations that readers are likely to encounter in everyday practice. This book will be of great interest to neurosurgeons, neurologists, ENT surgeons, neuroradiologists, and neurophysiotherapists. (orig.)

  15. Geometry essentials for dummies

    CERN Document Server

    Ryan, Mark

    2011-01-01

    Just the critical concepts you need to score high in geometry This practical, friendly guide focuses on critical concepts taught in a typical geometry course, from the properties of triangles, parallelograms, circles, and cylinders, to the skills and strategies you need to write geometry proofs. Geometry Essentials For Dummies is perfect for cramming or doing homework, or as a reference for parents helping kids study for exams. Get down to the basics - get a handle on the basics of geometry, from lines, segments, and angles, to vertices, altitudes, and diagonals Conque

  16. Essential dynamics and relativity

    CERN Document Server

    O'Donnell, Peter J

    2014-01-01

    Essential Dynamics & Relativity provides students with an introduction to the core aspects of dynamics and special relativity. The author reiterates important ideas and terms throughout and covers concepts that are often missing from other textbooks at this level. He also places each topic within the wider constructs of the theory, without jumping from topic to topic to illustrate a point.The first section of the book focuses on dynamics, discussing the basic aspects of single particle motion and analyzing the motion of multi-particle systems. The book also explains the dynamical behavior of b

  17. 3D Animation Essentials

    CERN Document Server

    Beane, Andy

    2012-01-01

    The essential fundamentals of 3D animation for aspiring 3D artists 3D is everywhere--video games, movie and television special effects, mobile devices, etc. Many aspiring artists and animators have grown up with 3D and computers, and naturally gravitate to this field as their area of interest. Bringing a blend of studio and classroom experience to offer you thorough coverage of the 3D animation industry, this must-have book shows you what it takes to create compelling and realistic 3D imagery. Serves as the first step to understanding the language of 3D and computer graphics (CG)Covers 3D anim

  18. Twisted network programming essentials

    CERN Document Server

    Fettig, Abe

    2005-01-01

    Twisted Network Programming Essentials from O'Reilly is a task-oriented look at this new open source, Python-based technology. The book begins with recommendations for various plug-ins and add-ons to enhance the basic package as installed. It then details Twisted's collection simple network protocols, and helper utilities. The book also includes projects that let you try out the Twisted framework for yourself. For example, you'll find examples of using Twisted to build web services applications using the REST architecture, using XML-RPC, and using SOAP. Written for developers who want to s

  19. The essential David Bohm

    CERN Document Server

    Nichol, Lee

    2002-01-01

    There are few scientists of the twentieth century whose life's work has created more excitement and controversy than that of physicist David Bohm (1917-1992). For the first time in a single volume, The Essential David Bohm offers a comprehensive overview of Bohm's original works from a non-technical perspective. Including three chapters of previously unpublished material, and a forward by the Dalai Lama, each reading has been selected to highlight some aspect of the implicate order process, and to provide an introduction to one of the most provocative thinkers of our time.

  20. Microsoft Windows Security Essentials

    CERN Document Server

    Gibson, Darril

    2011-01-01

    Windows security concepts and technologies for IT beginners IT security can be a complex topic, especially for those new to the field of IT. This full-color book, with a focus on the Microsoft Technology Associate (MTA) program, offers a clear and easy-to-understand approach to Windows security risks and attacks for newcomers to the world of IT. By paring down to just the essentials, beginners gain a solid foundation of security concepts upon which more advanced topics and technologies can be built. This straightforward guide begins each chapter by laying out a list of topics to be discussed,

  1. Microsoft Windows networking essentials

    CERN Document Server

    Gibson, Darril

    2011-01-01

    The core concepts and technologies of Windows networking Networking can be a complex topic, especially for those new to the field of IT. This focused, full-color book takes a unique approach to teaching Windows networking to beginners by stripping down a network to its bare basics, thereby making each topic clear and easy to understand. Focusing on the new Microsoft Technology Associate (MTA) program, this book pares down to just the essentials, showing beginners how to gain a solid foundation for understanding networking concepts upon which more advanced topics and technologies can be built.

  2. Cisco Networking Essentials

    CERN Document Server

    McMillan, Troy

    2011-01-01

    An engaging approach for anyone beginning a career in networking As the world leader of networking products and services, Cisco products are constantly growing in demand. Yet, few books are aimed at those who are beginning a career in IT--until now. Cisco Networking Essentials provides a solid foundation on the Cisco networking products and services with thorough coverage of fundamental networking concepts. Author Troy McMillan applies his years of classroom instruction to effectively present high-level topics in easy-to-understand terms for beginners. With this indispensable full-color resour

  3. Surface chemistry essentials

    CERN Document Server

    Birdi, K S

    2013-01-01

    Surface chemistry plays an important role in everyday life, as the basis for many phenomena as well as technological applications. Common examples range from soap bubbles, foam, and raindrops to cosmetics, paint, adhesives, and pharmaceuticals. Additional areas that rely on surface chemistry include modern nanotechnology, medical diagnostics, and drug delivery. There is extensive literature on this subject, but most chemistry books only devote one or two chapters to it. Surface Chemistry Essentials fills a need for a reference that brings together the fundamental aspects of surface chemistry w

  4. Cisco networking essentials

    CERN Document Server

    McMillan, Troy

    2015-01-01

    Start a career in networking Cisco Networking Essentials, 2nd Edition provides the latest for those beginning a career in networking. This book provides the fundamentals of networking and leads you through the concepts, processes, and skills you need to master fundamental networking concepts. Thinking of taking the CCENT Cisco Certified Entry Networking Technician ICND1 Exam 100-101? This book has you covered! With coverage of important topics and objectives, each chapter outlines main points and provides clear, engaging discussion that will give you a sound understanding of core topics and c

  5. RabbitMQ essentials

    CERN Document Server

    Dossot, David

    2014-01-01

    This book is a quick and concise introduction to RabbitMQ. Follow the unique case study of Clever Coney Media as they progressively discover how to fully utilize RabbitMQ, containing clever examples and detailed explanations.Whether you are someone who develops enterprise messaging products professionally or a hobbyist who is already familiar with open source Message Queuing software and you are looking for a new challenge, then this is the book for you. Although you should be familiar with Java, Ruby, and Python to get the most out of the examples, RabbitMQ Essentials will give you the push y

  6. Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

    Science.gov (United States)

    Meyers, Alysha R; Al-Tarawneh, Ibraheem S; Wurzelbacher, Steven J; Bushnell, P Timothy; Lampl, Michael P; Bell, Jennifer L; Bertke, Stephen J; Robins, David C; Tseng, Chih-Yu; Wei, Chia; Raudabaugh, Jill A; Schnorr, Teresa M

    2018-01-01

    This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry. Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers' Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions. On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days). This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.

  7. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

    Science.gov (United States)

    Bae, Sangwon; Chung, Tammy; Ferreira, Denzil; Dey, Anind K; Suffoletto, Brian

    2017-11-27

    Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical. To evaluate whether phone sensor data and machine learning models are useful to detect alcohol use events, and to discuss implications of these results for just-in-time mobile interventions. 38 non-treatment seeking young adult heavy drinkers downloaded AWARE app (which continuously collected mobile phone sensor data), and reported alcohol consumption (number of drinks, start/end time of prior day's drinking) for 28days. We tested various machine learning models using the 20 most informative sensor features to classify time periods as non-drinking, low-risk (1 to 3/4 drinks per occasion for women/men), and high-risk drinking (>4/5 drinks per occasion for women/men). Among 30 participants in the analyses, 207 non-drinking, 41 low-risk, and 45 high-risk drinking episodes were reported. A Random Forest model using 30-min windows with 1day of historical data performed best for detecting high-risk drinking, correctly classifying high-risk drinking windows 90.9% of the time. The most informative sensor features were related to time (i.e., day of week, time of day), movement (e.g., change in activities), device usage (e.g., screen duration), and communication (e.g., call duration, typing speed). Preliminary evidence suggests that sensor data captured from mobile phones of young adults is useful in building accurate models to detect periods of high-risk drinking. Interventions using mobile phone sensor features could trigger delivery of a range of interventions to potentially improve effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Characterization equipment essential/support drawing plan

    International Nuclear Information System (INIS)

    WILSON, G.W.

    1999-01-01

    The purpose of this document is to list the Characterization equipment drawings that are classified as Essential Drawings and Support Drawings. Essential Drawings: Are those drawings identified by the facility staff as necessary to directly support the safe operation of the facility or equipment (HNF 1997a). Support Drawings: Are those drawings identified by facility staff that further describe the design details of structures, systems, or components shown on essential drawings. (HNF 1997a) The Characterization equipment drawings identified in this report are deemed essential drawings as defined in HNF-PRO-242, Engineering Drawing Requirements (HNF 1997a). These drawings will be prepared, revised, and maintained per HNF-PRO-440, Engineering Document Change Control (HNF 1997b). All other Characterization equipment drawings not identified in this document will be considered General drawings until the Characterization Equipment Drawing Evaluation Report (Wilson 1998) is updated during fiscal year 1999. Trucks 1 and 2 drawings are not included in this revision of the essential drawing list due to uncertainty about future use

  9. Calcium, essential for health

    Science.gov (United States)

    Martínez de Victoria, Emilio

    2016-07-12

    Calcium (Ca) is the most abundant mineral element in our body. It accounts for about 2% of body weight. The functions of calcium are: a) functions skeletal and b) regulatory functions. Bone consists of a protein matrix that mineralizes mainly with calcium (the most abundant), phosphate and magnesium, for it is essential an adequate dietary intake of Ca, phosphorus and vitamin D. The ionic Ca (Ca2+) is essential to maintain and / or perform different specialized functions of, virtually, all body cells cellular. Because of its important functions Ca2+ must be closely regulated, keeping plasma concentrations within narrow ranges. For this reason there is an accurate response against hypocalcemia or hypercalcemia in which the parathormone, calcitriol, calcitonin and vitamin K are involved. Ca intakes in the Spanish population are low in a significant percentage of the older adult’s population, especially in women. The main source of Ca in the diet is milk and milk derivatives. Green leafy vegetables, fruits and legumes can be important sources of Ca in a Mediterranean dietary pattern. The bioavailability of dietary Ca depends on physiological and dietary factors. Physiological include age, physiological status (gestation and lactation) Ca and vitamin D status and disease. Several studies relate Ca intake in the diet and various diseases, such as osteoporosis, cancer, cardiovascular disease and obesity.

  10. Identifying the Behavior Patterns That Influence on Students' Achievement in Psychological Foundations of Learning and Development: A Case of Mekelle University, Ethiopia

    Science.gov (United States)

    Sekar, J. Master Arul; Eyasu, Mengesha

    2018-01-01

    Generally, the behavior patterns concerns a social significance of values. This paper highlights the various behavior patterns like planner behavior, solution oriented behavior, and prescriptive behavior patterns. The main objective of the present study is to identify the behavior patterns that influence on students' achievement in psychological…

  11. Journeys to School Leadership: How Action Learning Identified What Participants Valued in a Year-Long Australian Leadership Development Program Centered on Principles of Good Practice

    Science.gov (United States)

    McCulla, Norman; Degenhardt, Leoni

    2016-01-01

    The need to identify and suitably prepare teachers to undertake school leadership roles especially as principals is now well documented in the literature. Similarly documented is the general concern about the lack of suitable applicants willing to consider the role. This study raised the question of what might be learnt when a…

  12. Comparative analysis of the essential oils from normal and hairy ...

    African Journals Online (AJOL)

    The essential oils were extracted with steam distillation from normal and hairy roots of Panax japonicus C.A. Meyer. The constituents of essential oils were analyzed by gas chromatography mass spectrometry (GC-MS). The results showed that 40 and 46 kinds of compounds were identified from the essential oils of normal ...

  13. Apache Tomcat 7 Essentials

    CERN Document Server

    Khare, Tanuj

    2012-01-01

    This book is a step-by-step tutorial for anyone wanting to learn Apache Tomcat 7 from scratch. There are plenty of illustrations and examples to escalate you from a novice to an expert with minimal strain. If you are a J2EE administrator, migration administrator, technical architect, or a project manager for a web hosting domain, and are interested in Apache Tomcat 7, then this book is for you. If you are someone responsible for installation, configuration, and management of Tomcat 7, then too, this book will be of help to you.

  14. Source SDK development essentials

    CERN Document Server

    Bernier, Brett

    2014-01-01

    The Source Authoring Tools are the pieces of software used to create custom content for games made with Valve's Source engine. Creating mods and maps for your games without any programming knowledge can be time consuming. These tools allow you to create your own maps and levels without the need for any coding knowledge. All the tools that you need to start creating your own levels are built-in and ready to go! This book will teach you how to use the Authoring Tools provided with Source games and will guide you in creating your first maps and mods (modifications) using Source. You will learn ho

  15. Hudson 3 essentials

    CERN Document Server

    Meinholz, Lloyd

    2013-01-01

    A practical guide, packed with illustrations, that will help you become proficient with Hudson and able to utilize it how you want.If you are a Java developer or administrator who would to like automate some of the mundane work required to build and test software and improve software quality, this is the book for you. If you are a development manager or tester, you can also benefit from learning how Hudson works by gaining some insight into test results and historical trends.

  16. The essential research curriculum for doctor of pharmacy degree programs.

    Science.gov (United States)

    Lee, Mary W; Clay, Patrick G; Kennedy, W Klugh; Kennedy, Mary Jayne; Sifontis, Nicole M; Simonson, Dana; Sowinski, Kevin M; Taylor, William J; Teply, Robyn M; Vardeny, Orly; Welty, Timothy E

    2010-09-01

    In 2008, the American College of Clinical Pharmacy appointed the Task Force on Research in the Professional Curriculum to review and make recommendations on the essential research curriculum that should be part of doctor of pharmacy (Pharm.D.) degree programs. The essential research curriculum provides all students with critical and analytical thinking and lifelong learning skills, which will apply to current and future practice and stimulate some students to pursue a career in this field. Eight key curricular competencies are as follows: identifying relevant problems and gaps in pharmacotherapeutic knowledge; generating a research hypothesis; designing a study to test the hypothesis; analyzing data results using appropriate statistical tests; interpreting and applying the results of a research study to practice; effectively communicating research and clinical findings to pharmacy, medical, and basic science audiences; interpreting and effectively communicating research and clinical findings to patients and caregivers; and applying regulatory and ethical principles when conducting research or using research results. Faculty are encouraged to use research-related examples across the curriculum in nonresearch courses and to employ interactive teaching methods to promote student engagement. Examples of successful strategies used by Pharm.D. degree programs to integrate research content into the curriculum are provided. Current pharmacy school curricula allow variable amounts of time for instructional content in research, which may or may not include hands-on experiences for students to develop research-related skills. Therefore, an important opportunity exists for schools to incorporate the essential research curriculum. Despite the challenges of implementing these recommendations, the essential research curriculum will position pharmacy school graduates to understand the importance of research and its applications to practice. This perspective is provided as an aid

  17. Essential numerical computer methods

    CERN Document Server

    Johnson, Michael L

    2010-01-01

    The use of computers and computational methods has become ubiquitous in biological and biomedical research. During the last 2 decades most basic algorithms have not changed, but what has is the huge increase in computer speed and ease of use, along with the corresponding orders of magnitude decrease in cost. A general perception exists that the only applications of computers and computer methods in biological and biomedical research are either basic statistical analysis or the searching of DNA sequence data bases. While these are important applications they only scratch the surface of the current and potential applications of computers and computer methods in biomedical research. The various chapters within this volume include a wide variety of applications that extend far beyond this limited perception. As part of the Reliable Lab Solutions series, Essential Numerical Computer Methods brings together chapters from volumes 210, 240, 321, 383, 384, 454, and 467 of Methods in Enzymology. These chapters provide ...

  18. DSP Architecture Design Essentials

    CERN Document Server

    Marković, Dejan

    2012-01-01

    In DSP Architecture Design Essentials, authors Dejan Marković and Robert W. Brodersen cover a key subject for the successful realization of DSP algorithms for communications, multimedia, and healthcare applications. The book addresses the need for DSP architecture design that maps advanced DSP algorithms to hardware in the most power- and area-efficient way. The key feature of this text is a design methodology based on a high-level design model that leads to hardware implementation with minimum power and area. The methodology includes algorithm-level considerations such as automated word-length reduction and intrinsic data properties that can be leveraged to reduce hardware complexity. From a high-level data-flow graph model, an architecture exploration methodology based on linear programming is used to create an array of architectural solutions tailored to the underlying hardware technology. The book is supplemented with online material: bibliography, design examples, CAD tutorials and custom software.

  19. Are Archetypes Essential?

    Science.gov (United States)

    Colman, Warren

    2018-06-01

    This paper distinguishes between Jung's theoretical discourse regarding the archetypes and his phenomenological account of numinous experience. For this author, the initial attraction of 'my Jung' came from both the vivid Romanticism of his descriptions of the anima and the apparent 'ground of being' offered by his theory of archetypes. However, the essentialism inherent to archetypal theory in general and the anima in particular has necessitated a re-evaluation of Jung's theory in terms of emergence theory. My own version of this emphasises the role of symbols in the constitution of affect through collective human action in the world. In this reconfiguration, the visceral energy of numinous experience is retained while the problematic theory of archetypes is no longer needed. © 2018, The Society of Analytical Psychology.

  20. Essentials of nonlinear optics

    CERN Document Server

    Murti, Y V G S

    2014-01-01

    Current literature on Nonlinear Optics varies widely in terms of content, style, and coverage of specific topics, relative emphasis of areas and the depth of treatment. While most of these books are excellent resources for the researchers, there is a strong need for books appropriate for presenting the subject at the undergraduate or postgraduate levels in Universities. The need for such a book to serve as a textbook at the level of the bachelors and masters courses was felt by the authors while teaching courses on nonlinear optics to students of both science and engineering during the past two decades. This book has emerged from an attempt to address the requirement of presenting the subject at college level. A one-semester course covering the essentials can effectively be designed based on this.

  1. Essential real analysis

    CERN Document Server

    Field, Michael

    2017-01-01

    This book provides a rigorous introduction to the techniques and results of real analysis, metric spaces and multivariate differentiation, suitable for undergraduate courses. Starting from the very foundations of analysis, it offers a complete first course in real analysis, including topics rarely found in such detail in an undergraduate textbook such as the construction of non-analytic smooth functions, applications of the Euler-Maclaurin formula to estimates, and fractal geometry.  Drawing on the author’s extensive teaching and research experience, the exposition is guided by carefully chosen examples and counter-examples, with the emphasis placed on the key ideas underlying the theory. Much of the content is informed by its applicability: Fourier analysis is developed to the point where it can be rigorously applied to partial differential equations or computation, and the theory of metric spaces includes applications to ordinary differential equations and fractals. Essential Real Analysis will appeal t...

  2. TQM: the essential concepts.

    Science.gov (United States)

    Chambers, D W

    1998-01-01

    This is an introduction to the major concepts in total quality management, a loose collection of management approaches that focus on continuous improvement of processes, guided by routine data collection and adjustment of the processes. Customer focus and involvement of all members of an organization are also characteristics commonly found in TQM. The seventy-five-year history of the movement is sketched from its beginning in statistical work on quality assurance through the many improvements and redefinitions added by American and Japanese thinkers. Essential concepts covered include: control cycles, focus on the process rather than the defects, the GEAR model, importance of the customer, upstream quality, just-in-time, kaizen, and service quality.

  3. Essentials of stochastic processes

    CERN Document Server

    Durrett, Richard

    2016-01-01

    Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding. Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatm...

  4. Genetic algorithm essentials

    CERN Document Server

    Kramer, Oliver

    2017-01-01

    This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

  5. Project Management: Essential Skill of Nurse Informaticists.

    Science.gov (United States)

    Sipes, Carolyn

    2016-01-01

    With the evolution of nursing informatics (NI), the list of skills has advanced from the original definition that included 21 competencies to 168 basic competencies identified in the TIGER-based Assessment of Nursing Informatics Competencies (TANIC) and 178 advanced skills in the Nursing Informatics Competency Assessment (NICA) L3/L4 developed by Chamberlain College of Nursing, Nursing Informatics Research Team (NIRT). Of these competencies, project management is one of the most important essentials identified since it impacts all areas of NI skills and provides an organizing framework for processes and projects including skills such as design, planning, implementation, follow-up and evaluation. Examples of job roles that specifically require project management skills as an essential part of the NI functions include management, administration, leadership, faculty, graduate level master's and doctorate practicum courses. But first, better understanding of the NI essential skills is vital before adequate education and training programs can be developed.

  6. Seeking the Essential Superintendent.

    Science.gov (United States)

    Hawley, Willis D.

    1994-01-01

    Although typical school administration program deserves criticism, it would be impossible for a university-based preparation program to cover all topics identified in AASA's "Professional Standards for Superintendents." Universities understandably stress theory and cannot substitute for rich professional development program in school…

  7. Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.

    Science.gov (United States)

    Henneghan, Ashley M; Palesh, Oxana; Harrison, Michelle; Kesler, Shelli R

    2018-07-15

    The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric approach. Cross sectional data collection included completion of a survey, cognitive testing, and non-fasting blood from 66 participants. Data were analyzed using random forest regression to identify the most significant predictors for each of the cognitive test scores. A different cytokine profile predicted each cognitive test. Adjusted R 2 for each model ranged from 0.71-0.77 (p's < 9.50 -10 ). The relationships between all the cytokine predictors and cognitive test scores were non-linear. Our findings are unique to the field of CRCI and suggest non-linear cytokine specificity to neural networks underlying cognitive functions assessed in this study. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Interorganizational learning systems

    DEFF Research Database (Denmark)

    Hjalager, Anne-Mette

    1999-01-01

    The occurrence of organizational and interorganizational learning processes is not only the result of management endeavors. Industry structures and market related issues have substantial spill-over effects. The article reviews literature, and it establishes a learning model in which elements from...... organizational environments are included into a systematic conceptual framework. The model allows four types of learning to be identified: P-learning (professional/craft systems learning), T-learning (technology embedded learning), D-learning (dualistic learning systems, where part of the labor force is exclude...... from learning), and S-learning (learning in social networks or clans). The situation related to service industries illustrates the typology....

  9. Interim Stabilization Equipment Essential and Support Drawing Plan

    International Nuclear Information System (INIS)

    HORNER, T.M.

    2000-01-01

    The purpose of this document is to list the Interim Stabilization equipment drawings that are classified as Essential or Support drawings. Essential Drawings are those drawings identified by the facility staff as necessary to directly support the safe operation of the facility or equipment. [CHG 2000a]. Support Drawings are those drawings identified by the facility staff that further describe the design details of structures, systems or components shown on essential drawings. [CHG 2000a

  10. Interim Stabilization Equipment Essential and Support Drawing Plan

    International Nuclear Information System (INIS)

    KOCH, M.R.

    1999-01-01

    The purpose of this document is to list the Interim Stabilization equipment drawings that are classified as Essential or Support drawings. Essential Drawings: Those drawings identified by the facility staff as necessary to directly support the safe operation of the facility or equipment. Support Drawings: Those drawings identified by the facility staff that further describe the design details of structures, systems or components shown on essential drawings

  11. Rhetoric and Essentially Contested Arguments

    Science.gov (United States)

    Garver, Eugene

    1978-01-01

    Draws a connection between Gallie's essentially contested concepts and Aristotle's account of rhetorical argument by presenting a definition of Essentially Contested Argument which is used as the connecting term between rhetoric and essentially contested concepts and by demonstrating the value of making this connection. (JF)

  12. Linking Essential Tremor to the Cerebellum: Clinical Evidence.

    Science.gov (United States)

    Benito-León, Julián; Labiano-Fontcuberta, Andrés

    2016-06-01

    Essential tremor (ET) might be a family of diseases unified by the presence of kinetic tremor, but also showing etiological, pathological, and clinical heterogeneity. In this review, we will describe the most significant clinical evidence, which suggests that ET is linked to the cerebellum. Data for this review were identified by searching PUBMED (January 1966 to May 2015) crossing the terms "essential tremor" (ET) and "cerebellum," which yielded 201 entries, 11 of which included the term "cerebellum" in the article title. This was supplemented by articles in the author's files that pertained to this topic. The wide spectrum of clinical features of ET that suggest that it originates as a cerebellar or cerebellar outflow problem include the presence of intentional tremor, gait and balance abnormalities, subtle features of dysarthria, and oculomotor abnormalities, as well as deficits in eye-hand coordination, motor learning deficits, incoordination during spiral drawing task, abnormalities in motor timing and visual reaction time, impairment of social abilities, improvement in tremor after cerebellar stroke, efficacy of deep brain stimulation (which blocks cerebellar outflow), and cognitive dysfunction. It is unlikely, however, that cerebellar dysfunction, per se, fully explains ET-associated dementia, because the cognitive deficits that have been described in patients with cerebellar lesions are generally mild. Overall, a variety of clinical findings suggest that in at least a sizable proportion of patients with ET, there is an underlying abnormality of the cerebellum and/or its pathways.

  13. Integrating national community-based health worker programmes into health systems: a systematic review identifying lessons learned from low-and middle-income countries.

    Science.gov (United States)

    Zulu, Joseph Mumba; Kinsman, John; Michelo, Charles; Hurtig, Anna-Karin

    2014-09-22

    Despite the development of national community-based health worker (CBHW) programmes in several low- and middle-income countries, their integration into health systems has not been optimal. Studies have been conducted to investigate the factors influencing the integration processes, but systematic reviews to provide a more comprehensive understanding are lacking. We conducted a systematic review of published research to understand factors that may influence the integration of national CBHW programmes into health systems in low- and middle-income countries. To be included in the study, CBHW programmes should have been developed by the government and have standardised training, supervision and incentive structures. A conceptual framework on the integration of health innovations into health systems guided the review. We identified 3410 records, of which 36 were finally selected, and on which an analysis was conducted concerning the themes and pathways associated with different factors that may influence the integration process. Four programmes from Brazil, Ethiopia, India and Pakistan met the inclusion criteria. Different aspects of each of these programmes were integrated in different ways into their respective health systems. Factors that facilitated the integration process included the magnitude of countries' human resources for health problems and the associated discourses about how to address these problems; the perceived relative advantage of national CBHWs with regard to delivering health services over training and retaining highly skilled health workers; and the participation of some politicians and community members in programme processes, with the result that they viewed the programmes as legitimate, credible and relevant. Finally, integration of programmes within the existing health systems enhanced programme compatibility with the health systems' governance, financing and training functions. Factors that inhibited the integration process included a rapid

  14. Copyright for librarians the essential handbook

    CERN Document Server

    Berkman Center for Internet and Society

    2012-01-01

    "Copyright for Librarians" (CFL) is an online open curriculum on copyright law that was developed jointly with Harvard’s Berkman Center for Internet and Society. Re-designed as a brand new textbook, "Copyright for Librarians: the essential handbook" can be used as a stand-alone resource or as an adjunct to the online version which contains additional links and references for students who wish to pursue any topic in greater depth. Delve into copyright theory or explore enforcement. With a new index and a handy Glossary, the Handbook is essential reading for librarians who want to hone their skills in 2013, and for anyone learning about or teaching copyright law in the information field.

  15. Online Interactions and Social Presence in Online Learning

    Science.gov (United States)

    Lee, Sang Joon; Huang, Kun

    2018-01-01

    The community of inquiry framework identified three essential elements of cognitive, social, and teaching presences for a successful online learning experience. Among them, social presence is key for developing personal relationships and enhancing collaboration and critical discourse in online courses. This study examined whether providing more…

  16. Commercial Foods and Culinary Arts. Student Learning Guides.

    Science.gov (United States)

    Ridge Vocational-Technical Center, Winter Haven, FL.

    These 13 learning guides are self-instructional packets for 13 tasks identified as essential for performance on an entry-level job in commercial foods and culinary arts. Each guide is based on a terminal performance objective (task) and 1-4 enabling objectives. For each enabling objective, some or all of these materials may be presented: learning…

  17. Teaching and Learning Reflection in MPA Programs: Towards a Strategy

    Science.gov (United States)

    van der Meer, F. B.; Marks, P.

    2013-01-01

    Reflection is an essential ingredient of academic education in Public Administration, both for an academic and a professional career. Making a distinction between reflectivity and reflexivity we identify 30 foci of reflection. The main question of the article is how these forms of reflection can be taught and learned in PA programs, especially in…

  18. Essential Java for Scientists and Engineers

    CERN Document Server

    Hahn, Brian D; Malan, Katherine M

    2003-01-01

    Essential Java serves as an introduction to the programming language, Java, for scientists and engineers, and can also be used by experienced programmers wishing to learn Java as an additional language. The book focuses on how Java, and object-oriented programming, can be used to solve science and engineering problems. Many examples are included from a number of different scientific and engineering areas, as well as from business and everyday life. Pre-written packages of code are provided to help in such areas as input/output, matrix manipulation and scientific graphing. Java source code and

  19. SAS essentials mastering SAS for data analytics

    CERN Document Server

    Elliott, Alan C

    2015-01-01

    A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples. Keeping different experience levels in mind, the highly-qualified author team has developed the book over 20 years of teaching introductory SAS courses. Divided into two sections, the first part of the book provides an introduction to data manipulation, st

  20. Autodesk Roadway Design for Infraworks 360 essentials

    CERN Document Server

    Chappell, Eric

    2015-01-01

    Quickly master InfraWorks Roadway Design with hands-on tutorials Autodesk Roadway Design for InfraWorks 360 Essentials, 2nd Edition allows you to begin designing immediately as you learn the ins and outs of the roadway-specific InfraWorks module. Detailed explanations coupled with hands-on exercises help you get up to speed and quickly and become productive with the module's core features and functions. Compelling screenshots illustrate step-by-step tutorials, and the companion website provides downloadable starting and ending files so you can jump in at any point and compare your work to the

  1. Essential oils and anxiolytic aromatherapy.

    Science.gov (United States)

    Setzer, William N

    2009-09-01

    A number of essential oils are currently in use as aromatherapy agents to relieve anxiety, stress, and depression. Popular anxiolytic oils include lavender (Lavandula angustifolia), rose (Rosa damascena), orange (Citrus sinensis), bergamot (Citrus aurantium), lemon (Citrus limon), sandalwood (Santalum album), clary sage (Salvia sclarea), Roman chamomile (Anthemis nobilis), and rose-scented geranium (Pelargonium spp.). This review discusses the chemical constituents and CNS effects of these aromatherapeutic essential oils, as well as recent studies on additional essential oils with anxiolytic activities.

  2. Essential Oils and Antifungal Activity

    Science.gov (United States)

    Coppola, Raffaele; De Feo, Vincenzo

    2017-01-01

    Since ancient times, folk medicine and agro-food science have benefitted from the use of plant derivatives, such as essential oils, to combat different diseases, as well as to preserve food. In Nature, essential oils play a fundamental role in protecting the plant from biotic and abiotic attacks to which it may be subjected. Many researchers have analyzed in detail the modes of action of essential oils and most of their components. The purpose of this brief review is to describe the properties of essential oils, principally as antifungal agents, and their role in blocking cell communication mechanisms, fungal biofilm formation, and mycotoxin production. PMID:29099084

  3. Learning Spaces

    CERN Document Server

    Falmagne, Jean-Claude

    2011-01-01

    Learning spaces offer a rigorous mathematical foundation for practical systems of educational technology. Learning spaces generalize partially ordered sets and are special cases of knowledge spaces. The various structures are investigated from the standpoints of combinatorial properties and stochastic processes. Leaning spaces have become the essential structures to be used in assessing students' competence of various topics. A practical example is offered by ALEKS, a Web-based, artificially intelligent assessment and learning system in mathematics and other scholarly fields. At the heart of A

  4. Essential hypertension vs. secondary hypertension among children.

    Science.gov (United States)

    Gupta-Malhotra, Monesha; Banker, Ashish; Shete, Sanjay; Hashmi, Syed Sharukh; Tyson, John E; Barratt, Michelle S; Hecht, Jacqueline T; Milewicz, Diane M; Boerwinkle, Eric

    2015-01-01

    The aim was to determine the proportions and correlates of essential hypertension among children in a tertiary pediatric hypertension clinic. We evaluated 423 consecutive children and collected demographic and clinical history by retrospective chart review. We identified 275 (65%) hypertensive children (blood pressure >95th percentile per the "Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents") from 423 children referred to the clinic for history of elevated blood pressure. The remainder of the patients had normotension (11%), white coat hypertension (11%), prehypertension (10%), and pending diagnosis (3%). Among the 275 hypertensive children, 43% (n = 119; boys = 56%; median age = 12 years; range = 3-17 years) had essential hypertension and 57% (n = 156; boys = 66%; median age = 9 years; range = 0.08-19 years) had secondary hypertension. When compared with those with secondary hypertension, those with essential hypertension had a significantly older age at diagnosis (P = 0.0002), stronger family history of hypertension (94% vs. 68%; P secondary hypertension. The phenotype of essential hypertension can present as early as 3 years of age and is the predominant form of hypertension in children after age of 6 years. Among children with hypertension, those with essential hypertension present at an older age, have a stronger family history of hypertension, and have lower prevalence of preterm birth. © American Journal of Hypertension, Ltd 2014. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  5. Learning Ionic

    CERN Document Server

    Ravulavaru, Arvind

    2015-01-01

    This book is intended for those who want to learn how to build hybrid mobile applications using Ionic. It is also ideal for people who want to explore theming for Ionic apps. Prior knowledge of AngularJS is essential to complete this book successfully.

  6. Learning from Failed Decisions

    Science.gov (United States)

    Nutt, Paul C.

    2010-01-01

    The consequences and dilemmas posed by learning issues for decision making are discussed. Learning requires both awareness of barriers and a coping strategy. The motives to hold back information essential for learning stem from perverse incentives, obscure outcomes, and the hindsight bias. There is little awareness of perverse incentives that…

  7. Keeping Up in School? Identifying Learning Problems

    Science.gov (United States)

    ... of the benefits of healthy behaviors, such as exercise, and of health risks, such as obesity. This lack of knowledge ... help children use coping skills and build healthy attitudes about their ability to ... a disorder or to improve health in other ways. are provided, many of these ...

  8. Promoting ‘Learning’ Literacy through Picturebooks: Learning How to Learn

    Directory of Open Access Journals (Sweden)

    Gail Ellis

    2016-11-01

    Full Text Available Picturebooks provide a rich and motivating resource to develop children’s early language learning such as basic understanding, vocabulary and phrases related to the content of a story, but they can also be used to develop multiple literacies. These include visual, emotional, cultural, nature, digital, moving image literacy and ‘learning’ literacy, which is linked to learning how to learn and learner autonomy. ‘Learning’ literacy is described as an ethos, a culture and a way of life and involves being ready to develop learning capacities and the behaviours individuals need, including being willing to learn continuously, as competencies essential to thriving in a globally connected, digitally driven world. The Important Book (Brown & Weisgard, 1949 is used as an example of how learning literacy can be integrated into primary English language pedagogy by applying the Plan, Do, Review model of reflection. Working through the three stages of the Plan Do Review cycle, children are informed of the aims of the activity; they identify success criteria, draft and refine their own paragraphs about an important object, review what they did, what they learnt and how they learnt and then assess their performance to identify next steps. This process enables the teacher to create learning environments that develop learning literacy, by providing opportunities for systematic reflection and experimentation and the development of metacognitive and cognitive learning strategies.

  9. Adult Learning in Health Professions Education

    Science.gov (United States)

    Bierema, Laura L.

    2018-01-01

    This chapter focuses on the process of learning in health professions education (HPE) in terms of key issues that shape HPE learning and essential strategies for promoting and facilitating learning among professionals.

  10. Autodesk Inventor 2012 and Inventor LT 2012 Essentials

    CERN Document Server

    Tremblay, Thom

    2011-01-01

    Essential guide to learning Autodesk Inventor and Inventor LT The new Essentials books from Sybex are beautiful, task-based, full-color Autodesk Official Training Guides that help you get up to speed on Autodesk topics quickly and easily. Inventor Essentials thoroughly covers core features and functions of Autodesk's industry-leading 3D mechanical design software, teaching you what you need to become quickly productive with the software. By following the book's clear explanations, practical tutorials, and step-by-step exercises, you'll cover all the bases. Topics include drawing, modeling part

  11. Learning from nuclear regulatory self-assessment. International peer review of the CSN report on lessons learnt from the essential service water system degradation event at the Vandellos nuclear power plant

    International Nuclear Information System (INIS)

    2006-01-01

    Nuclear regulatory self-assessment together with the benchmarking of regulatory practices against those of other countries operating nuclear power plants are key elements in maintaining a high level of nuclear safety. In that light, the Spanish Consejo de Seguridad Nuclear (CSN) formally asked the OECD Nuclear Energy Agency (NEA) to establish an international peer review team to assess the CSN report on the lessons learnt as a result of the 2004 Vandellos II event involving essential service water system degradation. The International Review Team considers the CSN report prepared in follow-up to the Vandellos event to be a commendable effort in regulatory self-assessment. The report, complemented by this international peer review, should enable the CSN to take appropriate action to ensure that its regulatory supervision is in line with best international practice. (authors)

  12. [Chemical components from essential oil of Pandanus amaryllifolius leaves].

    Science.gov (United States)

    Chen, Xiao-Kai; Ge, Fa-Huan

    2014-04-01

    To analyze the chemical compositions of Pandanus amaryllifolius leaves essential oil extracted by steam distillation. The essential oil of Pandanus amaryllifolius leaves was analyzed by gas chromatography-mass spectrum, and the relative content of each component was determined by area normalization method. 128 peaks were separated and 95 compounds were identified, which weighed 97.75%. The main chemical components of the essential oil were phytol (42.15%), squalene (16.81%), what's more pentadecanal (6.17%), pentadecanoic acid (4.49%), 3, 7, 11, 15-tetramethyl-2-hexadecen-1-ol (3.83%), phytone (2.05%) and the other 74 chemical compositions were firstly identified from the essential oil of Pandanus amaryllifolius leaves. The chemical compositions of Pandanu samaryllifolius leaves essential oil was systematically, deeply isolated and identified for the first time. This experiment has provided scientific foundation for further utilization of Pandanus amaryllifolius leaves.

  13. A causal link between prediction errors, dopamine neurons and learning.

    Science.gov (United States)

    Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

    2013-07-01

    Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

  14. Essential Medicines in National Constitutions : Progress since 2008

    NARCIS (Netherlands)

    Perehudoff, S. Katrina; Toebes, Brigit; Hogerzeil, Hans

    A constitutional guarantee of access to essential medicines has been identified as an important indicator of government commitment to the progressive realization of the right to the highest attainable standard of health. The objective of this study was to evaluate provisions on access to essential

  15. Screening for Inhibitors of Essential Leishmania Glucose Transporters

    Science.gov (United States)

    2013-07-01

    Leishmania Glucose Transporters PRINCIPAL INVESTIGATOR: Scott M. Landfear, Ph.D. CONTRACTING ORGANIZATION: Oregon Health & Science...COVERED 1 July 2009- 30 June 2013 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Screening for Inhibitors of Essential Leishmania Glucose Transporters 5b...The objective of this project was to identify compounds that selectively inhibit the essential Leishmania glucose transporters and could hence serve

  16. Chemical composition of essential oil of exudates of Dryobalanops ...

    African Journals Online (AJOL)

    Purpose: To identify the chemical composition of essential oil from the exudates of Dryobalanops aromatica from Malaysia. Methods: Exudate was collected from D. aromatica and subjected to fractional distillation to obtain essential oil. Gas chromatography-mass spectrometry (GC-MS) was used to characterize the ...

  17. Chemical composition of essential oil of Psidium cattleianum var ...

    African Journals Online (AJOL)

    The aim of this study was to investigate the essential oil composition of Psidium cattleianum var. lucidum from South Africa. The essential oils were extracted by hydrodistillation and the components were identified by gas chromatography coupled to mass spectrometry (GC-MS) to determine the chemical composition of the ...

  18. Chemical composition and toxic activity of essential oil of ...

    African Journals Online (AJOL)

    During our screening program for new agrochemicals from Chinese medicinal herbs, essential oil of Caryopteris incana aerial parts was found to possess strong insecticidal activities against the maize weevil, Sitophilus zeamais. A total of 37 components of the essential oil were identified by GC and GC/MS. Estragole ...

  19. Chemical composition and antioxidant properties of the essential oil ...

    African Journals Online (AJOL)

    This study was designed to examine the in vitro antioxidant activities of the essential oil and methanol extracts of rhizoma Alpinia officinarum (small galanga) from China. The essential oil was analyzed by gas chromatography/ mass spectrometry (GC/MS) and 46 constituents were identified. Methanol extract from rhizoma A.

  20. A Framework for Identifying and Analyzing Major Issues in Implementing Big Data and Data Analytics in E-Learning: Introduction to Special Issue on Big Data and Data Analytics

    Science.gov (United States)

    Corbeil, Maria Elena; Corbeil, Joseph Rene; Khan, Badrul H.

    2017-01-01

    Due to rapid advancements in our ability to collect, process, and analyze massive amounts of data, it is now possible for educational institutions to gain new insights into how people learn (Kumar, 2013). E-learning has become an important part of education, and this form of learning is especially suited to the use of big data and data analysis,…

  1. Natural Variation in "Drosophila" Larval Reward Learning and Memory Due to a cGMP-Dependent Protein Kinase

    Science.gov (United States)

    Kaun, Karla R.; Hendel, Thomas; Gerber, Bertram; Sokolowski, Marla B.

    2007-01-01

    Animals must be able to find and evaluate food to ensure survival. The ability to associate a cue with the presence of food is advantageous because it allows an animal to quickly identify a situation associated with a good, bad, or even harmful food. Identifying genes underlying these natural learned responses is essential to understanding this…

  2. The 4-H Club Meeting: An Essential Youth Development Strategy

    Science.gov (United States)

    Cassels, Alicia; Post, Liz; Nestor, Patrick I.

    2015-01-01

    The club meeting has served as a key delivery method for 4-H programming across the United States throughout its history. A survey of WV 4-H community club members reinforces the body of evidence that the 4-H club meeting is an effective vehicle for delivering positive youth learning opportunities within the umbrella of the Essential Elements of…

  3. Feedback after continuous assessment: An essential element of ...

    African Journals Online (AJOL)

    Feedback after continuous assessment: An essential element of students' learning in medical education. Sir,. Regarding the philosophy and goals at all levels of education in Nigeria, section 1, paragraph 9(g) of the. National Policy on Education (revised 2004) stated that,. “educational assessment and evaluation shall be ...

  4. Essential idempotents and simplex codes

    Directory of Open Access Journals (Sweden)

    Gladys Chalom

    2017-01-01

    Full Text Available We define essential idempotents in group algebras and use them to prove that every mininmal abelian non-cyclic code is a repetition code. Also we use them to prove that every minimal abelian code is equivalent to a minimal cyclic code of the same length. Finally, we show that a binary cyclic code is simplex if and only if is of length of the form $n=2^k-1$ and is generated by an essential idempotent.

  5. Reflective Learning

    African Journals Online (AJOL)

    dell

    The main intent of this study was to identify the impact of using learning log as a learning strategy on the academic performance of university students. Second year psychology students were included as subjects of this study. In the beginning of the study, the students were divided into two: experimental group (N = 60) and ...

  6. Transformative Learning: Personal Empowerment in Learning Mathematics

    Science.gov (United States)

    Hassi, Marja-Liisa; Laursen, Sandra L.

    2015-01-01

    This article introduces the concept of personal empowerment as a form of transformative learning. It focuses on commonly ignored but enhancing elements of mathematics learning and argues that crucial personal resources can be essentially promoted by high engagement in mathematical problem solving, inquiry, and collaboration. This personal…

  7. iOS 5 Essentials

    CERN Document Server

    Daniel, Steven F

    2012-01-01

    Each chapter will take you through a new major feature of iOS 5. You will learn how to integrate each feature into your applications. If you ever wanted to learn about the latest features of iOS 5 and learn how to incorporate Twitter, iCloud and Core Image framework effects functionality into your applications, then this book is for you. You should have a good knowledge of programming experience with Objective-C, and have used Xcode 4. iPhone programming experience is not required.

  8. Learning Leadership

    DEFF Research Database (Denmark)

    Hertel, Frederik; Fast, Alf Michael

    2018-01-01

    Is leadership a result of inheritance or is it something one learns during formal learning in e.g. business schools? This is the essential question addressed in this article. The article is based on a case study involving a new leader in charge of a group of profession practitioners. The leader...... promotes his leadership as a profession comparable to the professions of practitioners. This promotion implies that leadership is something one can and probably must learn during formal learning. The practitioners on the other hand reject this comprehension of leadership and long for a fellow practitioner...... to lead the organization. While asked they are unable to describe how, where and when they think a practitioner develops leadership skills necessary for leading fellows. In the following we will start analysing the case in order to comprehend and discuss both the professional leaders and the practitioners...

  9. Learning about Learning: A Conundrum and a Possible Resolution

    Science.gov (United States)

    Barnett, Ronald

    2011-01-01

    What is it to learn in the modern world? We can identify four "learning epochs" through which our understanding of learning has passed: a metaphysical view; an empirical view; an experiential view; and, currently, a "learning-amid-contestation" view. In this last and current view, learning has its place in a world in which, the more one learns,…

  10. E-Learning Readiness in Public Secondary Schools in Kenya

    Science.gov (United States)

    Ouma, Gordon O.; Awuor, Fredrick M.; Kyambo, Benjamin

    2013-01-01

    As e-learning becomes useful to learning institutions worldwide, an assessment of e-learning readiness is essential for the successful implementation of e-learning as a platform for learning. Success in e-learning can be achieved by understanding the level of readiness of e-learning environments. To facilitate schools in Kenya to implement…

  11. Learning Apache Mahout classification

    CERN Document Server

    Gupta, Ashish

    2015-01-01

    If you are a data scientist who has some experience with the Hadoop ecosystem and machine learning methods and want to try out classification on large datasets using Mahout, this book is ideal for you. Knowledge of Java is essential.

  12. Levels of essential and non-essential elements in commercially ...

    African Journals Online (AJOL)

    The focus of this study was to assess the quality of commercially packaged moringa tea brands available in the retail markets in Nigerian cities on the basis of the essential and nonessential elemental content in their tissues. Four different brands of moringa tea comprising two locally processed teas and two imported teas ...

  13. Levels of Essential and Non-Essential Elements in Commercially ...

    African Journals Online (AJOL)

    Enebi Jasper

    INTRODUCTION. Plants have been ... plant metabolism and biosynthesis and act as cofactors for ... plant body. 3 . Some metals are essential nutrients (zinc, iron, copper, and chromium), ... non-destructive analysis, increased total speed, ... oleifera play both a curative and preventive ... maintenance of cardiac rhythm. 16.

  14. Essentials of Career Interest Assessment. Essentials of Psychological Assessment Series.

    Science.gov (United States)

    Prince, Jeffrey P.; Heiser, Lisa J.

    This book is a quick reference source to guide the career professional through the essentials of using the most popular career interest tools. It summarizes important technical aspects of each inventory, and offers step-by-step guidance in the interpretation and use of the various inventories. The chapters are: (1) "Overview"; (2)…

  15. Learning to Learn Together with CSCL Tools

    Science.gov (United States)

    Schwarz, Baruch B.; de Groot, Reuma; Mavrikis, Manolis; Dragon, Toby

    2015-01-01

    In this paper, we identify "Learning to Learn Together" (L2L2) as a new and important educational goal. Our view of L2L2 is a substantial extension of "Learning to Learn" (L2L): L2L2 consists of learning to collaborate to successfully face L2L challenges. It is inseparable from L2L, as it emerges when individuals face problems…

  16. Essential Medicines in a High Income Country: Essential to Whom?

    Science.gov (United States)

    Duong, Mai; Moles, Rebekah J; Chaar, Betty; Chen, Timothy F

    2015-01-01

    To explore the perspectives of a diverse group of stakeholders engaged in medicines decision making around what constitutes an "essential" medicine, and how the Essential Medicines List (EML) concept functions in a high income country context. In-depth qualitative semi-structured interviews were conducted with 32 Australian stakeholders, recognised as decision makers, leaders or advisors in the area of medicines reimbursement or supply chain management. Participants were recruited from government, pharmaceutical industry, pharmaceutical wholesale/distribution companies, medicines non-profit organisations, academic health disciplines, hospitals, and consumer groups. Perspectives on the definition and application of the EML concept in a high income country context were thematically analysed using grounded theory approach. Stakeholders found it challenging to describe the EML concept in the Australian context because many perceived it was generally used in resource scarce settings. Stakeholders were unable to distinguish whether nationally reimbursed medicines were essential medicines in Australia. Despite frequent generic drug shortages and high prices paid by consumers, many struggled to describe how the EML concept applied to Australia. Instead, broad inclusion of consumer needs, such as rare and high cost medicines, and consumer involvement in the decision making process, has led to expansive lists of nationally subsidised medicines. Therefore, improved communication and coordination is needed around shared interests between stakeholders regarding how medicines are prioritised and guaranteed in the supply chain. This study showed that decision-making in Australia around reimbursement of medicines has strayed from the fundamental utilitarian concept of essential medicines. Many stakeholders involved in medicine reimbursement decisions and management of the supply chain did not consider the EML concept in their approach. The wide range of views of what stakeholders

  17. Essential Medicines in a High Income Country: Essential to Whom?

    Directory of Open Access Journals (Sweden)

    Mai Duong

    Full Text Available To explore the perspectives of a diverse group of stakeholders engaged in medicines decision making around what constitutes an "essential" medicine, and how the Essential Medicines List (EML concept functions in a high income country context.In-depth qualitative semi-structured interviews were conducted with 32 Australian stakeholders, recognised as decision makers, leaders or advisors in the area of medicines reimbursement or supply chain management. Participants were recruited from government, pharmaceutical industry, pharmaceutical wholesale/distribution companies, medicines non-profit organisations, academic health disciplines, hospitals, and consumer groups. Perspectives on the definition and application of the EML concept in a high income country context were thematically analysed using grounded theory approach.Stakeholders found it challenging to describe the EML concept in the Australian context because many perceived it was generally used in resource scarce settings. Stakeholders were unable to distinguish whether nationally reimbursed medicines were essential medicines in Australia. Despite frequent generic drug shortages and high prices paid by consumers, many struggled to describe how the EML concept applied to Australia. Instead, broad inclusion of consumer needs, such as rare and high cost medicines, and consumer involvement in the decision making process, has led to expansive lists of nationally subsidised medicines. Therefore, improved communication and coordination is needed around shared interests between stakeholders regarding how medicines are prioritised and guaranteed in the supply chain.This study showed that decision-making in Australia around reimbursement of medicines has strayed from the fundamental utilitarian concept of essential medicines. Many stakeholders involved in medicine reimbursement decisions and management of the supply chain did not consider the EML concept in their approach. The wide range of views of

  18. The essential value of projects in faculty development.

    Science.gov (United States)

    Gusic, Maryellen E; Milner, Robert J; Tisdell, Elizabeth J; Taylor, Edward W; Quillen, David A; Thorndyke, Luanne E

    2010-09-01

    Projects--planned activities with specific goals and outcomes--have been used in faculty development programs to enhance participant learning and development. Projects have been employed most extensively in programs designed to develop faculty as educators. The authors review the literature and report the results of their 2008 study of the impact of projects within the Pennsylvania State University College of Medicine Junior Faculty Development Program, a comprehensive faculty development program. Using a mixed-methods approach, the products of project work, the academic productivity of program graduates, and the impact of projects on career development were analyzed. Faculty who achieved the most progress on their projects reported the highest number of academic products related to their project and the highest number of overall academic achievements. Faculty perceived that their project had three major effects on their professional development: production of a tangible outcome, development of a career focus, and development of relationships with mentors and peers. On the basis of these findings and a review of the literature, the authors conclude that projects are an essential element of a faculty development program. Projects provide a foundation for future academic success by enabling junior faculty to develop and hone knowledge and skills, identify a career focus and gain recognition within their community, generate scholarship, allocate time to academic work, and establish supportive relationships and collaborative networks. A list of best practices to successfully incorporate projects within faculty development programs is provided.

  19. Tank farms essential drawing plan

    International Nuclear Information System (INIS)

    Domnoske-Rauch, L.A.

    1998-01-01

    The purpose of this document is to define criteria for selecting Essential Drawings, Support Drawings, and Controlled Print File (CPF) drawings and documents for facilities that are part of East and West Tank Farms. Also, the drawings and documents that meet the criteria are compiled separate listings. The Essential Drawing list and the Support Drawing list establish a priority for updating technical baseline drawings. The CPF drawings, denoted by an asterisk (*), defined the drawings and documents that Operations is required to maintain per the TWRS Administration Manual. The Routing Boards in Buildings 272-WA and 272-AW are not part of the CPF

  20. Analgesic Potential of Essential Oils

    Directory of Open Access Journals (Sweden)

    José Ferreira Sarmento-Neto

    2015-12-01

    Full Text Available Pain is an unpleasant sensation associated with a wide range of injuries and diseases, and affects approximately 20% of adults in the world. The discovery of new and more effective drugs that can relieve pain is an important research goal in both the pharmaceutical industry and academia. This review describes studies involving antinociceptive activity of essential oils from 31 plant species. Botanical aspects of aromatic plants, mechanisms of action in pain models and chemical composition profiles of the essential oils are discussed. The data obtained in these studies demonstrate the analgesic potential of this group of natural products for therapeutic purposes.