137 results
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2. Ten simple rules for reading a scientific paper
- Author
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William A. Petri, Kevin L Steiner, and Maureen A. Carey
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Science and Technology Workforce ,Economics ,Social Sciences ,Scientific literature ,Careers in Research ,Key (music) ,Habits ,Learning and Memory ,Sociology ,Reading (process) ,Psychology ,Biology (General) ,media_common ,Simple (philosophy) ,Textbooks ,Ecology ,Library card ,Careers ,Publications ,Research Assessment ,Professions ,Editorial ,Computational Theory and Mathematics ,Modeling and Simulation ,Research Reporting Guidelines ,Educational Status ,Periodicals as Topic ,Employment ,QH301-705.5 ,Science Policy ,media_common.quotation_subject ,Research and Analysis Methods ,Education ,Cellular and Molecular Neuroscience ,Level of Effort ,Human Learning ,Genetics ,Mathematics education ,Learning ,Early career ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Publishing ,Behavior ,Research ,Cognitive Psychology ,Biology and Life Sciences ,Advice (programming) ,Reading ,Labor Economics ,People and Places ,Scientists ,Cognitive Science ,Population Groupings ,Undergraduates ,Neuroscience - Abstract
“There is no problem that a library card can't solve” according to author Eleanor Brown [1]. This advice is sound, probably for both life and science, but even the best tool (like the library) is most effective when accompanied by instructions and a basic understanding of how and when to use it. For many budding scientists, the first day in a new lab setting often involves a stack of papers, an email full of links to pertinent articles, or some promise of a richer understanding so long as one reads enough of the scientific literature. However, the purpose and approach to reading a scientific article is unlike that of reading a news story, novel, or even a textbook and can initially seem unapproachable. Having good habits for reading scientific literature is key to setting oneself up for success, identifying new research questions, and filling in the gaps in one’s current understanding; developing these good habits is the first crucial step. Advice typically centers around two main tips: read actively and read often. However, active reading, or reading with an intent to understand, is both a learned skill and a level of effort. Although there is no one best way to do this, we present 10 simple rules, relevant to novices and seasoned scientists alike, to teach our strategy for active reading based on our experience as readers and as mentors of undergraduate and graduate researchers, medical students, fellows, and early career faculty. Rules 1–5 are big picture recommendations. Rules 6–8 relate to philosophy of reading. Rules 9–10 guide the “now what?” questions one should ask after reading and how to integrate what was learned into one’s own science.
- Published
- 2020
3. Women are underrepresented in computational biology: An analysis of the scholarly literature in biology, computer science and computational biology.
- Author
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Bonham, Kevin S. and Stefan, Melanie I.
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STEM education ,COMPUTATIONAL biology ,BIBLIOMETRICS ,SCIENCE publishing ,SCIENCE & state - Abstract
While women are generally underrepresented in STEM fields, there are noticeable differences between fields. For instance, the gender ratio in biology is more balanced than in computer science. We were interested in how this difference is reflected in the interdisciplinary field of computational/quantitative biology. To this end, we examined the proportion of female authors in publications from the PubMed and arXiv databases. There are fewer female authors on research papers in computational biology, as compared to biology in general. This is true across authorship position, year, and journal impact factor. A comparison with arXiv shows that quantitative biology papers have a higher ratio of female authors than computer science papers, placing computational biology in between its two parent fields in terms of gender representation. Both in biology and in computational biology, a female last author increases the probability of other authors on the paper being female, pointing to a potential role of female PIs in influencing the gender balance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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4. Ten simple rules for developing good reading habits during graduate school and beyond.
- Author
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Méndez, Marcos
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READING ,HABIT formation ,COMPREHENSION ,HISTORY ,PERIODICALS ,BIBLIOGRAPHICAL citations - Abstract
The author talks about several rules that a person can follow to develop good reading habits in graduate school and beyond. Topics discussed include the importance of developing the habit of reading on a daily basis; the need to develop comprehension skills; and the need to study the history of one's discipline. Also mentioned are the importance of creating a list of relevant journals, the need to read books, and the benefits of using a reference manager.
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- 2018
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5. Ten simple rules to consider regarding preprint submission.
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Bourne, Philip E., Polka, Jessica K., Vale, Ronald D., and Kiley, Robert
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PREPRINTS ,DATA mining ,LICENSES ,COPYRIGHT - Abstract
The article discusses rules for considering regarding preprint submission of scientific work in journals. Topics include analysis of published papers undertaken by cell biologist Stephen Royle for estimating the average time from first submission to publication, data mining of the written content for making it better utilizing the knowledge, and encouraging authors to licenses and formats for facilitating reuse while retaining copyright to their work.
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- 2017
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6. Even a good influenza forecasting model can benefit from internet-based nowcasts, but those benefits are limited.
- Author
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Osthus, Dave, Daughton, Ashlynn R., and Priedhorsky, Reid
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INFLUENZA ,RESPIRATORY infections ,PUBLIC health ,MATHEMATICAL models of forecasting - Abstract
The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches. [ABSTRACT FROM AUTHOR]
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- 2019
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7. Ten Simple Rules for a Bioinformatics Journal Club.
- Author
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Lonsdale, Andrew, Sietsma Penington, Jocelyn, Rice, Timothy, Walker, Michael, and Dashnow, Harriet
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BIOINFORMATICS ,INFORMATION science ,COMPUTATIONAL biology ,CLUBS ,SCIENTIFIC literature ,SOCIETIES - Abstract
The article outlines the rules for a bioinformatics journal club which, according to the authors, is a great way to take in the scientific literature, keep up with developments in their field, and hone their communication and analytical skills. The rules include holding a journal club at eight in the morning, finding good articles for discussion, and expanding the roster of leaders as people join the journal club.
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- 2016
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8. The life history of learning: Demographic structure changes cultural outcomes.
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Fogarty, Laurel, Creanza, Nicole, and Feldman, Marcus W.
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LEARNING ,CULTURAL pluralism ,POPULATION ,DEMOGRAPHIC characteristics ,SOCIAL evolution - Abstract
Human populations show rich cultural diversity. Underpinning this diversity of tools, rituals, and cultural norms are complex interactions between cultural evolutionary and demographic processes. Most models of cultural change assume that individuals use the same learning modes and methods throughout their lives. However, empirical data on ‘learning life histories’—the balance of dominant modes of learning (for example, learning from parents, peers, or unrelated elders) throughout an individual’s lifetime—suggest that age structure may play a crucial role in determining learning modes and cultural evolutionary trajectories. Thus, studied in isolation, demographic and cultural evolutionary models show only part of the picture. This paper describes a mathematical and computational framework that combines demographic and cultural evolutionary methods. Using this general framework, we examine interactions between the ways in which culture is spread throughout an individual’s lifetime and cultural change across generations. We show that including demographic structure alongside cultural dynamics can help to explain domain-specific patterns of cultural evolution that are a persistent feature of cultural data, and can shed new light on rare but significant demographic events. [ABSTRACT FROM AUTHOR]
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- 2019
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9. Chemical features mining provides new descriptive structure-odor relationships.
- Author
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Licon, Carmen C., Bosc, Guillaume, Sabri, Mohammed, Mantel, Marylou, Fournel, Arnaud, Bushdid, Caroline, Golebiowski, Jerome, Robardet, Celine, Plantevit, Marc, Kaytoue, Mehdi, and Bensafi, Moustafa
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ODORS ,COLOR vision ,PREDICTION models ,BIOLOGY ,ALGORITHMS - Abstract
An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling. [ABSTRACT FROM AUTHOR]
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- 2019
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10. Ten principles for machine-actionable data management plans.
- Author
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Miksa, Tomasz, Simms, Stephanie, Mietchen, Daniel, and Jones, Sarah
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DATA ,RESEARCH management ,MACHINERY ,PLANNING ,INFORMATION sharing ,RESEARCH personnel ,INVESTORS - Abstract
Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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11. Ten simple rules for scientists: Improving your writing productivity.
- Author
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Peterson, Todd C., Kleppner, Sofie R., and Botham, Crystal M.
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WORK environment ,SELF-talk - Abstract
An introduction to the journal is presented in which the editor discusses various reports in the issue on the topics including the importance of writing in science research, developing a working environment in workplace, and managing self talk about writing.
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- 2018
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12. Age density patterns in patients medical conditions: A clustering approach.
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Alhasoun, Fahad, Aleissa, Faisal, Alhazzani, May, Moyano, Luis G., Pinhanez, Claudio, and González, Marta C.
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HEALTH facilities ,POPULATION biology ,POPULATION density ,MEDICAL care ,COMMUNICABLE diseases ,CHICKENPOX - Abstract
This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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13. Ten simple rules for responsible referencing.
- Author
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Penders, Bart
- Subjects
CITATION indexes ,CROSS references (Information retrieval) ,BIBLIOGRAPHICAL citations ,TRUTHFULNESS & falsehood ,CITATION analysis - Abstract
The author discusses 10 simple rules for responsible referencing. Topics discussed include citations for content of the publication and reading the publications of citations for quality and credibility of work; importance of self-citation in discussions of citations and evaluations of citation-based metrics; and evaluating citations in their rhetorical context and accepting differences in citation cultures across boundaries.
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- 2018
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14. Ten simple rules for giving an effective academic job talk.
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Sura, Shayna A., Smith, Lauren L., Ambrose, Monique R., Amorim, C. Eduardo Guerra, Beichman, Annabel C., Gomez, Ana C. R., Juhn, Mark, Kandlikar, Gaurav S., Miller, Julie S., Mooney, Jazlyn, Mummah, Riley O., Lohmueller, Kirk E., and Lloyd-Smith, James O.
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JOB applications ,CAREER development ,EMPLOYMENT interviewing ,CONVERSATION ,RESEARCH grants ,JOB hunting - Published
- 2019
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15. Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks.
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Rule, Adam, Birmingham, Amanda, Zuniga, Cristal, Altintas, Ilkay, Huang, Shih-Cheng, Knight, Rob, Moshiri, Niema, Nguyen, Mai H., Rosenthal, Sara Brin, Pérez, Fernando, and Rose, Peter W.
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PDF (Computer file format) ,NOTEBOOKS ,HUMAN-computer interaction - Published
- 2019
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16. Ten simple rules when considering retirement.
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Bourne, Philip E.
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RETIREMENT ,COMPUTATIONAL biology ,BIOCOMPATIBILITY ,STEM education ,AUTOBIOGRAPHY - Abstract
The article presents author's comments on the rules that should be considered during retirement. It focuses on the retirement in computational biology. It offers information on autobiographical perspective on biomedical data. It focuses on the need for expertise in science, technology, engineering, and medicine (STEM).
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- 2018
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17. Ten simple rules for drawing scientific comics.
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McDermott, Jason E., Partridge, Matthew, and Bromberg, Yana
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COMIC books, strips, etc. ,SCIENTIFIC terminology ,SCIENTIFIC communication ,SCIENCE journalism ,SCIENTIFIC community - Abstract
The article presents guidelines for drawing scientific comics for efficient communication of difficult ideas. Suggestions mentioned include finding the right shapes for use in conveying the appropriate message one is trying to make, making one's comics simple to convey a complicated idea easier to understand, and taking time to ensure that the details in one's comics are right.
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- 2018
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18. Ten simple rules for successfully completing a graduate degree in Latin America.
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Ruelas Inzunza, Ernesto, Salazar-Rivera, Gabriela I., Láinez, Magdiel, Ruiz-Gómez, María Guadalupe, Domínguez-Eusebio, Carlo A., Cristóbal-Sánchez, Griselda, Teodosio Faustino, Issaac A., Pérez-López, Edel, Campbell, Meagan L., Merfa, Marcus Vinicius, Latorre Beltrán, Ivonne Tatiana, Armas, Fernanda, and Mota-Vargas, Claudio
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GRADUATE education ,LIFE science education ,LIFE sciences ,SCHOLARSHIPS ,EDUCATION - Abstract
This article offers tips for students to complete a graduate degree in biological sciences in Latin America. Topics discussed include the features that underlie the performance of graduate programs around the world, the importance of investigating the graduate program and the adviser, scholarship programs for graduate students, and the need to follow administrative procedures from admission to graduation.
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- 2017
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19. Ten simple rules for getting the most out of a summer laboratory internship.
- Author
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Aicher, Toby P., Barabási, Dániel L., Harris, Benjamin D., Nadig, Ajay, and Williams, Kaitlin L.
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INTERNSHIP programs ,LEARNING ,COMMUNICATION ,MENTORS ,SOCIAL interaction - Abstract
The author offers rules for getting the most out of a summer laboratory internship. The rules discussed include summer internships afford no such luxury which is important to optimize learning by working diligently and efficiently, productive stream of communication with mentor and internship has a programming course which can start learning computational skills. It mentions satisfying project, a supportive group, and unique social interactions.
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- 2017
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20. PopART-IBM, a highly efficient stochastic individual-based simulation model of generalised HIV epidemics developed in the context of the HPTN 071 (PopART) trial
- Author
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Peter Bock, Estelle Piwowar-Manning, Richard J. Hayes, Sarah Fidler, Robert Hinch, Anne Cori, Rafael Sauter, Hptn (PopART) Study Team, Sian Floyd, Michael Pickles, Deborah Donnell, Christophe Fraser, Ethan Wilson, Helen Ayles, William J. M. Probert, and Medical Research Council (MRC)
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RNA viruses ,Male ,Epidemiology ,Computer science ,Human immunodeficiency virus (HIV) ,HIV Infections ,Pathology and Laboratory Medicine ,medicine.disease_cause ,Geographical Locations ,Immunodeficiency Viruses ,Antiretroviral Therapy, Highly Active ,Prevalence ,Econometrics ,Public and Occupational Health ,Viral suppression ,Biology (General) ,Hiv transmission ,Virus Testing ,Ecology ,Simulation and Modeling ,Incidence ,virus diseases ,Middle Aged ,Vaccination and Immunization ,Computational Theory and Mathematics ,HIV epidemiology ,Medical Microbiology ,Viral Pathogens ,Modeling and Simulation ,Viruses ,Disease Progression ,Female ,Pathogens ,Algorithms ,Research Article ,Adult ,Sexual network ,Adolescent ,Bioinformatics ,QH301-705.5 ,Immunology ,Antiretroviral Therapy ,Zambia ,Context (language use) ,Research and Analysis Methods ,HPTN 071 (PopART) Study Team ,Bayesian inference ,Microbiology ,Young Adult ,Cellular and Molecular Neuroscience ,Individual based ,Antiviral Therapy ,Diagnostic Medicine ,Retroviruses ,Genetics ,medicine ,Humans ,Computer Simulation ,IBM ,Microbial Pathogens ,Molecular Biology ,01 Mathematical Sciences ,Ecology, Evolution, Behavior and Systematics ,Aged ,Medicine and health sciences ,Stochastic Processes ,Models, Statistical ,Lentivirus ,Organisms ,Hiv epidemiology ,Biology and Life Sciences ,HIV ,Reproducibility of Results ,06 Biological Sciences ,Age Groups ,Medical Risk Factors ,People and Places ,Africa ,Population Groupings ,Preventive Medicine ,08 Information and Computing Sciences - Abstract
Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention., Author summary In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.
- Published
- 2021
21. bigPint: A Bioconductor visualization package that makes big data pint-sized
- Author
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Dianne Cook and Lindsay Rutter
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Big Data ,0301 basic medicine ,Science and Technology Workforce ,Source code ,Computer science ,Big data ,Datasets as Topic ,Gene Expression ,Molecular biology assays and analysis techniques ,Careers in Research ,Bioconductor ,Mathematical and Statistical Techniques ,0302 clinical medicine ,Computer graphics (images) ,Cluster Analysis ,Graphics ,Biology (General) ,Pseudocode ,media_common ,Nucleic acid analysis ,Ecology ,Software Engineering ,Genomics ,Research Assessment ,RNA analysis ,Reproducibility ,Professions ,Computational Theory and Mathematics ,Modeling and Simulation ,Engineering and Technology ,Research Article ,Computer and Information Sciences ,Science Policy ,QH301-705.5 ,media_common.quotation_subject ,Research and Analysis Methods ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Data visualization ,Interactivity ,Computer Graphics ,Genetics ,Hierarchical Clustering ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Sequence Analysis, RNA ,Software Tools ,business.industry ,Data Visualization ,Computational Biology ,Biology and Life Sciences ,Genome Analysis ,Visualization ,Molecular biology techniques ,030104 developmental biology ,People and Places ,Scientists ,Population Groupings ,business ,Software ,030217 neurology & neurosurgery - Abstract
Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks., Author summary Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis.
- Published
- 2020
22. Inference on dengue epidemics with Bayesian regime switching models
- Author
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Borame Sue Dickens, Ng Lee Ching, Alex R. Cook, Sun Haoyang, and Jue Tao Lim
- Subjects
0301 basic medicine ,RNA viruses ,Atmospheric Science ,Epidemiology ,Inference ,Dengue virus ,medicine.disease_cause ,Pathology and Laboratory Medicine ,Dengue fever ,Disease Outbreaks ,Dengue ,Geographical Locations ,Machine Learning ,Bayes' theorem ,0302 clinical medicine ,Statistics ,Statistical inference ,Medicine and Health Sciences ,Biology (General) ,Singapore ,Ecology ,Regression ,Infectious Diseases ,Computational Theory and Mathematics ,Medical Microbiology ,Modeling and Simulation ,Viral Pathogens ,Viruses ,Pathogens ,Research Article ,Computer and Information Sciences ,Asia ,Infectious Disease Control ,QH301-705.5 ,Bayesian probability ,Biology ,Models, Biological ,Microbiology ,Infectious Disease Epidemiology ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Meteorology ,Artificial Intelligence ,Genetics ,medicine ,Humans ,Disease Dynamics ,Molecular Biology ,Microbial Pathogens ,Ecology, Evolution, Behavior and Systematics ,Biology and life sciences ,Flaviviruses ,Organisms ,Outbreak ,Bayes Theorem ,Humidity ,Dengue Virus ,medicine.disease ,030104 developmental biology ,People and Places ,Earth Sciences ,030217 neurology & neurosurgery - Abstract
Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected., Author summary Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Assessments against various baseline showed that BRS performs better in characterizing dengue transmissions. The dengue regimes estimated by BRS are characterized by their persistent nature. Next, climate analysis showed no short nor long term associations between classified regimes with climate. Lastly, fitting BRS to simulated disease data generated from a mechanistic model, we showed links between disease infectivity and regimes classified using BRS. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.
- Published
- 2019
23. Time varying methods to infer extremes in dengue transmission dynamics
- Author
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Alex R. Cook, Yi Ting Han, Jue Tao Lim, Borame L Dickens, and Lee Ching Ng
- Subjects
0301 basic medicine ,Viral Diseases ,Atmospheric Science ,Time Factors ,Epidemiology ,Disease Outbreaks ,Dengue Fever ,Dengue fever ,Dengue ,Geographical Locations ,Medical Conditions ,0302 clinical medicine ,Tropical climate ,Statistics ,Medicine and Health Sciences ,Dengue transmission ,Credible interval ,Public and Occupational Health ,Biology (General) ,Singapore ,Ecology ,Infectious Diseases ,Geography ,Computational Theory and Mathematics ,Modeling and Simulation ,Physical Sciences ,Research Article ,Neglected Tropical Diseases ,Healthcare system ,Asia ,QH301-705.5 ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Meteorology ,Genetics ,medicine ,Humans ,Weather ,Pareto Distribution ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Models, Statistical ,Extreme events ,Computational Biology ,Outbreak ,Humidity ,Tropical Diseases ,Probability Theory ,Probability Distribution ,medicine.disease ,030104 developmental biology ,People and Places ,Earth Sciences ,Mathematics ,030217 neurology & neurosurgery ,Quantile - Abstract
Dengue is an arbovirus affecting global populations. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue to transmit. Little work has, however, quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non-extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. This approach permits inference of differences in climatic forcing across non-extreme and extreme periods of dengue case counts, quantification of their temporal dependence as well as estimation of thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non-extreme periods, but that it has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a threshold at the 70th (95% credible interval 41.1, 83.8) quantile is optimal, with extreme events of 526.6, 1052.2 and 1183.6 weekly case counts expected at return periods of 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. The tvEM approach can provide valuable inference on the extremes of time series, which in the case of infectious disease notifications, allows public health officials to understand the likely scale of outbreaks in the long run., Author summary Dengue is an arbovirus affecting populations across much of the globe. Frequent outbreaks occur, especially in equatorial cities such as Singapore, where the year-round tropical climate, large daily influx of travelers and population density provide the ideal conditions for dengue transmission. Little work has however quantified the peaks of dengue outbreaks, when health systems are likely to be most stretched. Nor have methods been developed to infer differences in exogenous factors which lead to the rise and fall of dengue case counts across extreme and non extreme periods. In this paper, we developed time varying extreme mixture (tvEM) methods to account for the temporal dependence of dengue case counts across extreme and non-extreme periods. tvEM is able to infer differences in climatic forcing across non-extreme and extreme periods of dengue case counts, their temporal dependence as well as estimate suitable thresholds with associated uncertainties to determine dengue case count extremities. Using tvEM, we found no evidence that weather affects dengue case counts in the near term for non extreme periods, but has non-linear and mixed signals in influencing dengue through tvEM parameters in the extreme periods. Using the most appropriate tvEM specification, we found that a high percentile threshold is estimated, with dengue outbreak events far larger than currently observed to be expected in 5, 50 and 75 years. Weather parameters at a 1% scaled increase was found to decrease the long-run expected case counts, but larger increases would lead to a drastic expected rise from the baseline correspondingly. tvEM can provide valuable inference on the extremes of time series, which in the case of infectious disease data, allows public health officials to understand factors and the likely scale of infectious disease outbreaks in the long run.
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- 2020
24. IL6-mediated HCoV-host interactome regulatory network and GO/Pathway enrichment analysis
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Alfredo Benso and Gianfranco Politano
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RNA viruses ,Proteomics ,0301 basic medicine ,Science and Technology Workforce ,Genes, Viral ,Coronaviruses ,Interaction Networks ,Ontology (information science) ,Betacoronavirus ,Coronavirus Infections ,Humans ,Interleukin-6 ,Pandemics ,Pneumonia, Viral ,Viral Proteins ,Gene Ontology ,Host-Pathogen Interactions ,Careers in Research ,Biochemistry ,Interactome ,0302 clinical medicine ,Medicine and Health Sciences ,Drug Interactions ,Viral ,Biology (General) ,Pathology and laboratory medicine ,Ecology ,Gene Ontologies ,Genomics ,Medical microbiology ,Nucleic acids ,Professions ,Computational Theory and Mathematics ,Modeling and Simulation ,Viruses ,Protein Interaction Networks ,SARS CoV 2 ,Pathogens ,Host (network) ,Network Analysis ,Research Article ,Computer and Information Sciences ,SARS coronavirus ,Coronavirus disease 2019 (COVID-19) ,Science Policy ,QH301-705.5 ,Systems biology ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Computational biology ,Biology ,Research and Analysis Methods ,Microbiology ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Genetics ,Molecular Biology Techniques ,Non-coding RNA ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Life Scientists ,Pharmacology ,Natural antisense transcripts ,SARS-CoV-2 ,Gene Mapping ,Organisms ,Viral pathogens ,COVID-19 ,Biology and Life Sciences ,Computational Biology ,Pneumonia ,Pathway enrichment ,Genome Analysis ,Gene regulation ,Microbial pathogens ,MicroRNAs ,030104 developmental biology ,Genes ,People and Places ,RNA ,Scientists ,Population Groupings ,Gene expression ,030217 neurology & neurosurgery - Abstract
During these days of global emergency for the COVID-19 disease outbreak, there is an urgency to share reliable information able to help worldwide life scientists to get better insights and make sense of the large amount of data currently available. In this study we used the results presented in [1] to perform two different Systems Biology analyses on the HCoV-host interactome. In the first one, we reconstructed the interactome of the HCoV-host proteins, integrating it with highly reliable miRNA and drug interactions information. We then added the IL-6 gene, identified in recent publications [2] as heavily involved in the COVID-19 progression and, interestingly, we identified several interactions with the reconstructed interactome. In the second analysis, we performed a Gene Ontology and a Pathways enrichment analysis on the full set of the HCoV-host interactome proteins and on the ones belonging to a significantly dense cluster of interacting proteins identified in the first analysis. Results of the two analyses provide a compact but comprehensive glance on some of the current state-of-the-art regulations, GO, and pathways involved in the HCoV-host interactome, and that could support all scientists currently focusing on SARS-CoV-2 research., Author summary In this paper we provide data about the HCoV-host interactome that can be extracted from the integration of several public available databases. We used the initial interactome published by Zhou et al. and analyzed if there are already known and validated interactions. We also looked into possible known miRNAs and drugs interactions to suggest possible biomarker candidates and treatment options. We also performed a Gene Ontology and a Pathways enrichment analysis to understand which are the pathways most likely involved in the proteins targeted by SARS-CoV-2. This paper not only provides a set of validated and reliable data that could help researchers in their fight against the COVID-19 disease outbreak, but also demonstrates how Systems Biology can be effectively used to quickly gather preliminary but still significant data without resorting only to expensive lab experiments.
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- 2020
25. Wisdom of crowds in computational biology.
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Papin, Jason A. and Mac Gabhann, Feilim
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MEDICAL publishing ,MACHINE learning ,ARTIFICIAL intelligence in medicine ,COMPUTATIONAL biology ,INDIVIDUALIZED medicine - Abstract
The authors comment on the breadth of research published in the journal at the intersection of machine learning and health and biology. Topics covered include the application of machine learning to health and biology, the hope that data-driven strategies will lead to a richer understanding of biological mechanisms, and cross-journal initiatives aimed at exploring how disciplines can be brought together to tackle problems in computational biology and precision medicine.
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- 2019
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26. Consensus and uncertainty in the geographic range of Aedes aegypti and Aedes albopictus in the contiguous United States: Multi-model assessment and synthesis.
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Monaghan, Andrew J., Eisen, Rebecca J., Eisen, Lars, McAllister, Janet, Savage, Harry M., Mutebi, John-Paul, and Johansson, Michael A.
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AEDES aegypti ,AEDES albopictus ,YELLOW fever ,MOSQUITO vectors ,ZIKA virus ,UNCERTAINTY ,VIRUS diseases - Abstract
Aedes (Stegomyia) aegypti (L.) and Ae. (Stegomyia) albopictus (Skuse) mosquitoes can transmit dengue, chikungunya, yellow fever, and Zika viruses. Limited surveillance has led to uncertainty regarding the geographic ranges of these vectors globally, and particularly in regions at the present-day margins of habitat suitability such as the contiguous United States. Empirical habitat suitability models based on environmental conditions can augment surveillance gaps to describe the estimated potential species ranges, but model accuracy is unclear. We identified previously published regional and global habitat suitability models for Ae. aegypti (n = 6) and Ae. albopictus (n = 8) for which adequate information was available to reproduce the models for the contiguous U.S. Using a training subset of recently updated county-level surveillance records of Ae. aegypti and Ae. albopictus and records of counties conducting surveillance, we constructed accuracy-weighted, probabilistic ensemble models from these base models. To assess accuracy and uncertainty we compared individual and ensemble model predictions of species presence or absence to both training and testing data. The ensemble models were among the most accurate and also provided calibrated probabilities of presence for each species. The quantitative probabilistic framework enabled identification of areas with high uncertainty and model bias across the U.S. where improved models or additional data could be most beneficial. The results may be of immediate utility for counties considering surveillance and control programs for Ae. aegypti and Ae. albopictus. Moreover, the assessment framework can drive future efforts to provide validated quantitative estimates to support these programs at local, national, and international scales. [ABSTRACT FROM AUTHOR]
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- 2019
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27. Ten simple rules for providing optimal administrative support to research teams.
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Garrido, Romina, Trowbridge, Casandra A., and Tamura, Nana
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RESEARCH teams ,ADMINISTRATIVE assistants ,SOCIAL learning ,PROFESSIONAL relationships ,SCIENTIFIC community ,CULTURAL pluralism ,HEBBIAN memory ,BUSINESS communication - Abstract
You can help your principal investigator on the oversight of grant rules and procedures, keeping protocols up to date, writing reports, and fulfilling requests from the Institutional Review Board (IRB). Regardless, what is important is for the administrator to be curious about science and make the effort to get a basic familiarity with the scientific discipline and academic working environment they are supporting. Although human resources, principal investigators, and lab managers are a large part of the onboarding process for a new employee, administrative staff are typically involved in the process as well. [Extracted from the article]
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- 2019
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28. Quantifying model evidence for yellow fever transmission routes in Africa.
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Gaythorpe, Katy A. M., Jean, Kévin, Cibrelus, Laurence, and Garske, Tini
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YELLOW fever ,MONTE Carlo method ,MARKOV chain Monte Carlo ,DISEASE vectors ,ENDEMIC diseases - Abstract
Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R
0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data. [ABSTRACT FROM AUTHOR]- Published
- 2019
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29. Fast and near-optimal monitoring for healthcare acquired infection outbreaks.
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Adhikari, Bijaya, Lewis, Bryan, Vullikanti, Anil, Jiménez, José Mauricio, and Prakash, B. Aditya
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MEDICAL personnel ,INFECTION ,PREVENTIVE medicine ,HOSPITAL patients - Abstract
According to the Centers for Disease Control and Prevention (CDC), one in twenty five hospital patients are infected with at least one healthcare acquired infection (HAI) on any given day. Early detection of possible HAI outbreaks help practitioners implement countermeasures before the infection spreads extensively. Here, we develop an efficient data and model driven method to detect outbreaks with high accuracy. We leverage mechanistic modeling of C. difficile infection, a major HAI disease, to simulate its spread in a hospital wing and design efficient near-optimal algorithms to select people and locations to monitor using an optimization formulation. Results show that our strategy detects up to 95% of “future” C. difficile outbreaks. We design our method by incorporating specific hospital practices (like swabbing for infections) as well. As a result, our method outperforms state-of-the-art algorithms for outbreak detection. Finally, a qualitative study of our result shows that the people and locations we select to monitor as sensors are intuitive and meaningful. [ABSTRACT FROM AUTHOR]
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- 2019
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30. EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology.
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Picault, Sébastien, Huang, Yu-Lin, Sicard, Vianney, Arnoux, Sandie, Beaunée, Gaël, and Ezanno, Pauline
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PLANT epidemiology ,FOOD emulsions ,MULTISCALE modeling ,STOCHASTIC models ,EMULSIONS ,KNOWLEDGE representation (Information theory) ,PLANT capacity - Abstract
Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: . [ABSTRACT FROM AUTHOR]
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- 2019
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31. Perspective: Dimensions of the scientific method.
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Voit, Eberhard O.
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SCIENTIFIC method ,SCIENTISTS ,SCIENCE & state ,MATHEMATICAL models ,KNOWLEDGE acquisition (Expert systems) ,GEOMETRIC shapes - Abstract
The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining–inspired, and “allochthonous” knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation. [ABSTRACT FROM AUTHOR]
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- 2019
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32. Bioinformatics in Jordan: Status, challenges, and future directions.
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Bani Baker, Qanita and Nuser, Maryam S.
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BIOINFORMATICS ,COMPUTATIONAL biology ,SEQUENCE alignment ,EDUCATION research ,SCIENCE & state - Abstract
Bioinformatics plays a key role in supporting the life sciences. In this work, we examine bioinformatics in Jordan, beginning with the current status of bioinformatics education and research, then exploring the challenges of advancing bioinformatics, and finally looking to the future for how Jordanian bioinformatics research may develop. [ABSTRACT FROM AUTHOR]
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- 2019
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33. Reappraising the utility of Google Flu Trends.
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Kandula, Sasikiran and Shaman, Jeffrey
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BOX-Jenkins forecasting ,INFLUENZA ,REGRESSION analysis ,VIRUS diseases ,PHYSICAL sciences ,RESPIRATORY infections - Abstract
Estimation of influenza-like illness (ILI) using search trends activity was intended to supplement traditional surveillance systems, and was a motivation behind the development of Google Flu Trends (GFT). However, several studies have previously reported large errors in GFT estimates of ILI in the US. Following recent release of time-stamped surveillance data, which better reflects real-time operational scenarios, we reanalyzed GFT errors. Using three data sources—GFT: an archive of weekly ILI estimates from Google Flu Trends; ILIf: fully-observed ILI rates from ILINet; and, ILIp: ILI rates available in real-time based on partial reporting—five influenza seasons were analyzed and mean square errors (MSE) of GFT and ILIp as estimates of ILIf were computed. To correct GFT errors, a random forest regression model was built with ILI and GFT rates from the previous three weeks as predictors. An overall reduction in error of 44% was observed and the errors of the corrected GFT are lower than those of ILIp. An 80% reduction in error during 2012/13, when GFT had large errors, shows that extreme failures of GFT could have been avoided. Using autoregressive integrated moving average (ARIMA) models, one- to four-week ahead forecasts were generated with two separate data streams: ILIp alone, and with both ILIp and corrected GFT. At all forecast targets and seasons, and for all but two regions, inclusion of GFT lowered MSE. Results from two alternative error measures, mean absolute error and mean absolute proportional error, were largely consistent with results from MSE. Taken together these findings provide an error profile of GFT in the US, establish strong evidence for the adoption of search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the utility of this data source in diverse domains. [ABSTRACT FROM AUTHOR]
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- 2019
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34. Information-theoretic analysis of multivariate single-cell signaling responses.
- Author
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Jetka, Tomasz, Nienałtowski, Karol, Winarski, Tomasz, Błoński, Sławomir, and Komorowski, Michał
- Abstract
Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI—statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single—cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory. [ABSTRACT FROM AUTHOR]
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- 2019
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35. Think: Theory for Africa.
- Author
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Currin, Christopher B., Khoza, Phumlani N., Antrobus, Alexander D., Latham, Peter E., Vogels, Tim P., and Raimondo, Joseph V.
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SCIENTIFIC knowledge ,SOCIAL sciences education ,TECHNOLOGY ,COMPUTATIONAL neuroscience ,SCIENCE & state - Abstract
The article offers information on the challenging scientific problems faced by humanity. Topics discussed include information on the efforts to empower Africans to join the global neuroscience community; discussions on the BRAIN initiative in the U.S. and the Human Brain Project in Europe; and the information on the limiting factor for computational neuroscience development in Africa.
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- 2019
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36. Modeling the temporal dynamics of the gut microbial community in adults and infants.
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Shenhav, Liat, Furman, Ori, Briscoe, Leah, Thompson, Mike, Silverman, Justin D., Mizrahi, Itzhak, and Halperin, Eran
- Subjects
ENTEROTYPES ,MICROBIAL communities ,INFANTS ,TIME series analysis ,HUMAN microbiota ,GUT microbiome - Abstract
Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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37. Problem-based learning in clinical bioinformatics education: Does it help to create communities of practice?
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Davies, Angela C., Harris, Diane, Banks-Gatenby, Amanda, and Brass, Andy
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PROBLEM-based learning ,FACE-to-face communication ,COMMUNITIES of practice ,CLINICAL education ,COURSEWARE ,SEMI-structured interviews ,VIRTUAL communities - Abstract
We have now reached the genomics era within medicine; genomics is being used to personalise treatment, make diagnoses, prognoses, and predict adverse outcomes resulting from treatment with certain drugs. Genomic data is now abundant in healthcare, and the newly created profession of clinical bioinformaticians are responsible for its analysis. In the United Kingdom, clinical bioinformaticians are trained within a 3-year programme, integrating a work-based placement with a part-time Master’s degree. As this profession is still developing, trainees can feel isolated from their peers whom are located in other hospitals and can find it difficult to gain the mentorship that they require to complete their training. Building strong networks or communities of practice (CoPs) and allowing sharing of knowledge and experiences is one solution to addressing this isolation. Within the Master’s delivered at the University of Manchester, we have integrated group-centred problem-based learning (PBL) using real clinical case studies worked on during each course unit. This approach is combined with a flipped style of teaching providing access to online content in our Virtual Learning Environment before the course. The face-to-face teaching is used to focus on the application of the students’ knowledge to clinical case studies. In this study, we conducted semistructured interviews with 8 students, spanning 3 cohorts of students. We evaluated the effectiveness of this style of teaching and whether it had contributed to the formation of CoPs between our students. Our findings demonstrated that this style of teaching was preferred by our students to a more traditional lecture-based format and that the problem-based learning approach enabled the formation of CoPs within these cohorts. These CoPs are valuable in the development of this new profession and assist with the production of new guidelines and policies that are helping to professionalise this new group of healthcare scientists. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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38. From trainees to trainers to instructors: Sustainably building a national capacity in bioinformatics training.
- Author
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McGrath, Annette, Champ, Katherine, Shang, Catherine A., van Dam, Ellen, Brooksbank, Cath, and Morgan, Sarah L.
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LEAD ,TRAINING planes ,TRAINING of scientists ,MOLECULAR biology ,INDUSTRIAL research ,BIOINFORMATICS - Abstract
Demand for training life scientists in bioinformatics skills led to the development of a train-the-trainer collaboration between the European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI) and 2 Australian organisations, Bioplatforms Australia and Commonwealth Scientific and Industrial Research Organisation (CSIRO) in 2012. The goal of the collaboration was to establish a group of trained instructors who could develop and deliver short bioinformatics courses nationally. A train-the-trainer course introduces instructors to aspects of andragogy and evidence-based learning principles to help them better design, develop, and deliver high-quality training. Since then, both the number of trainers in the network and the course portfolio have grown. Best practises have been developed and shared between the Australian cohort and EMBL-EBI to address common challenges in bioinformatics training. The Australian trainer cohort undertook a train-the-trainer instructor course, again with EMBL-EBI, and subsequently successfully delivered train-the-trainer courses to interested bioinformatics trainers within Australia. We conclude that a train-the-trainer approach can help build national capacity and maintain a critical mass of trained instructors. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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39. Fostering bioinformatics education through skill development of professors: Big Genomic Data Skills Training for Professors.
- Author
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Zhan, Yingqian Ada, Wray, Charles Gregory, Namburi, Sandeep, Glantz, Spencer T., Laubenbacher, Reinhard, and Chuang, Jeffrey H.
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LIFE science education ,UNIVERSITIES & colleges ,BIG data ,COMPUTATIONAL biology ,ART colleges - Abstract
Bioinformatics has become an indispensable part of life science over the past 2 decades. However, bioinformatics education is not well integrated at the undergraduate level, especially in liberal arts colleges and regional universities in the United States. One significant obstacle pointed out by the Network for Integrating Bioinformatics into Life Sciences Education is the lack of faculty in the bioinformatics area. Most current life science professors did not acquire bioinformatics analysis skills during their own training. Consequently, a great number of undergraduate and graduate students do not get the chance to learn bioinformatics or computational biology skills within a structured curriculum during their education. To address this gap, we developed a module-based, week-long short course to train small college and regional university professors with essential bioinformatics skills. The bioinformatics modules were built to be adapted by the professor-trainees afterward and used in their own classes. All the course materials can be accessed at . [ABSTRACT FROM AUTHOR]
- Published
- 2019
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40. Models that learn how humans learn: The case of decision-making and its disorders.
- Author
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Dezfouli, Amir, Griffiths, Kristi, Ramos, Fabio, Dayan, Peter, and Balleine, Bernard W.
- Subjects
DECISION making ,RECURRENT neural networks ,REINFORCEMENT learning ,MENTAL health ,PREDICTION models ,COMPUTER simulation - Abstract
Popular computational models of decision-making make specific assumptions about learning processes that may cause them to underfit observed behaviours. Here we suggest an alternative method using recurrent neural networks (RNNs) to generate a flexible family of models that have sufficient capacity to represent the complex learning and decision- making strategies used by humans. In this approach, an RNN is trained to predict the next action that a subject will take in a decision-making task and, in this way, learns to imitate the processes underlying subjects’ choices and their learning abilities. We demonstrate the benefits of this approach using a new dataset drawn from patients with either unipolar (n = 34) or bipolar (n = 33) depression and matched healthy controls (n = 34) making decisions on a two-armed bandit task. The results indicate that this new approach is better than baseline reinforcement-learning methods in terms of overall performance and its capacity to predict subjects’ choices. We show that the model can be interpreted using off-policy simulations and thereby provides a novel clustering of subjects’ learning processes—something that often eludes traditional approaches to modelling and behavioural analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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41. Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings.
- Author
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Duval, Audrey, Obadia, Thomas, Boëlle, Pierre-Yves, Fleury, Eric, Herrmann, Jean-Louis, Guillemot, Didier, Temime, Laura, Opatowski, Lulla, and null, null
- Subjects
ENTEROBACTERIACEAE ,KLEBSIELLA pneumoniae ,DRUG resistance in bacteria ,NOSOCOMIAL infections ,ESCHERICHIA coli ,INFECTION prevention ,INFECTIOUS disease transmission ,ANTIBIOTICS - Abstract
Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC–and ESBL-KP–carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network’s ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination. [ABSTRACT FROM AUTHOR]
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- 2019
- Full Text
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42. Establishing a computational biology flipped classroom.
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Compeau, Phillip
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FLIPPED classrooms ,COMPUTATIONAL biology ,STUDENT-centered learning ,EFFECTIVE teaching ,BIOINFORMATICS - Abstract
In a flipped classroom, students complete automated modules to replace a traditional lecture, allowing the time devoted for the lecture to be devoted to constructive tasks reinforcing student knowledge. Yet although the flipped classroom offers a compelling approach for fostering a constructivist, student-centric learning environment, research on the efficacy of flipped classes has been mixed. For that matter, is it possible to successfully flip a classroom in an advanced, heavily specialized course like a bioinformatics algorithms course? Over the past several years, the author has implemented a flipped version of such a course and will discuss some of the successes and pitfalls encountered. [ABSTRACT FROM AUTHOR]
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- 2019
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43. Ten simple rules for providing a meaningful research experience to high school students.
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Lescak, Emily A., O’Neill, Kate M., Collu, Giovanna M., and Das, Subhamoy
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RESEARCH ,HIGH school students ,MENTORING ,STEM education ,STEM occupations - Abstract
An editorial is presented on rules for providing a meaningful research experience to high school students. Topics discussed include ten rules for becoming an effective research mentor for high school students and their training, research opportunities as undergraduates and career consideration in science, technology, engineering, and mathematics and bridging the gap between scientists and the general public on scientific research.
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- 2019
- Full Text
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44. Ten quick tips for creating an effective lesson.
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Wilson, Greg
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TEACHING ,COGNITIVE ability ,PSYCHOLOGY ,CLASSROOMS ,EMPIRICAL research - Abstract
We present 10 tips for building effective lessons that are grounded in empirical research on pedagogy and cognitive psychology and that we have found to be practically useful in both classroom and free-range settings [ABSTRACT FROM AUTHOR]
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- 2019
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45. Ten simple rules towards healthier research labs.
- Author
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Maestre, Fernando T.
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SCHOLARS ,HEALTH ,PRIVATE investigators ,SCIENTISTS ,SCIENCE - Abstract
The negative effects of extremely competitive academic and research environments on the performance and health of researchers are well known and common worldwide. The prevalence of these effects, particularly among early career researchers, calls for a more humane and people-centered way of working within research labs. Although there is growing concern about the urgent need for a better life–work balance when doing science, there are not many examples about how this could be achieved in practice. In this article, I introduce 10 simple rules to make the working environment of research labs more nurturing, collaborative, and people-centered. These rules are directed towards existing and future principal investigators (PIs) but will be of interest to anyone working in a research lab and/or dealing with how to improve working conditions for scientists. [ABSTRACT FROM AUTHOR]
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- 2019
- Full Text
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46. Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.
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Kalimeri, Kyriaki, Delfino, Matteo, Cattuto, Ciro, Perrotta, Daniela, Colizza, Vittoria, Guerrisi, Caroline, Turbelin, Clement, Duggan, Jim, Edmunds, John, Obi, Chinelo, Pebody, Richard, Franco, Ana O., Moreno, Yamir, Meloni, Sandro, Koppeschaar, Carl, Kjelsø, Charlotte, Mexia, Ricardo, and Paolotti, Daniela
- Subjects
INFLUENZA ,PUBLIC health surveillance ,SELF-evaluation ,SYNDROMES ,INTERNET surveys ,ALGORITHMS ,PEARSON correlation (Statistics) - Abstract
Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries. [ABSTRACT FROM AUTHOR]
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- 2019
- Full Text
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47. Ten simple rules for organizing a webinar series.
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Fadlelmola, Faisal M., Panji, Sumir, Ahmed, Azza E., Ghouila, Amel, Akurugu, Wisdom A., Entfellner, Jean-Baka Domelevo, Souiai, Oussema, Mulder, Nicola, and null, null
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WEBINARS ,COMMUNITIES ,AUDIENCES ,EMAIL ,SOCIAL media - Abstract
The article presents ten simple rules for hosting a regular webinar series with particular emphasis on resource-constrained communities like many in Africa. Topics include aligning a webinar theme to the expectations of the audience; settling on a convenient and user friendly webinar platform; and announcing webinars through mailing lists and social media platform.
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- 2019
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48. Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.
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Bizzego, Andrea, Bussola, Nicole, Chierici, Marco, Maggio, Valerio, Francescatto, Margherita, Cima, Luca, Cristoforetti, Marco, Jurman, Giuseppe, and Furlanello, Cesare
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REPRODUCIBLE research ,ARTIFICIAL intelligence in medicine ,ALGORITHMS ,PATHOLOGY ,DEEP learning ,DATA analysis ,BIOMARKERS ,ARTIFICIAL intelligence ,COMPUTERS in biology - Abstract
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing results across trials. Deep learning features inferred from digital pathology scans can improve validity and robustness of current clinico-pathological features, up to identifying novel histological patterns, e.g., from tumor infiltrating lymphocytes. In this study, we examine the issue of evaluating accuracy of predictive models from deep learning features in digital pathology, as an hallmark of reproducibility. We introduce the DAPPER framework for validation based on a rigorous Data Analysis Plan derived from the FDA’s MAQC project, designed to analyze causes of variability in predictive biomarkers. We apply the framework on models that identify tissue of origin on 787 Whole Slide Images from the Genotype-Tissue Expression (GTEx) project. We test three different deep learning architectures (VGG, ResNet, Inception) as feature extractors and three classifiers (a fully connected multilayer, Support Vector Machine and Random Forests) and work with four datasets (5, 10, 20 or 30 classes), for a total of 53, 000 tiles at 512 × 512 resolution. We analyze accuracy and feature stability of the machine learning classifiers, also demonstrating the need for diagnostic tests (e.g., random labels) to identify selection bias and risks for reproducibility. Further, we use the deep features from the VGG model from GTEx on the KIMIA24 dataset for identification of slide of origin (24 classes) to train a classifier on 1, 060 annotated tiles and validated on 265 unseen ones. The DAPPER software, including its deep learning pipeline and the Histological Imaging—Newsy Tiles (HINT) benchmark dataset derived from GTEx, is released as a basis for standardization and validation initiatives in AI for digital pathology. [ABSTRACT FROM AUTHOR]
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- 2019
- Full Text
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49. A dynamic power-law sexual network model of gonorrhoea outbreaks.
- Author
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Whittles, Lilith K., White, Peter J., and Didelot, Xavier
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GONORRHEA ,SEX customs ,DRUG resistance in microorganisms ,COMPUTATIONAL biology ,DISEASE outbreaks - Abstract
Human networks of sexual contacts are dynamic by nature, with partnerships forming and breaking continuously over time. Sexual behaviours are also highly heterogeneous, so that the number of partners reported by individuals over a given period of time is typically distributed as a power-law. Both the dynamism and heterogeneity of sexual partnerships are likely to have an effect in the patterns of spread of sexually transmitted diseases. To represent these two fundamental properties of sexual networks, we developed a stochastic process of dynamic partnership formation and dissolution, which results in power-law numbers of partners over time. Model parameters can be set to produce realistic conditions in terms of the exponent of the power-law distribution, of the number of individuals without relationships and of the average duration of relationships. Using an outbreak of antibiotic resistant gonorrhoea amongst men have sex with men as a case study, we show that our realistic dynamic network exhibits different properties compared to the frequently used static networks or homogeneous mixing models. We also consider an approximation to our dynamic network model in terms of a much simpler branching process. We estimate the parameters of the generation time distribution and offspring distribution which can be used for example in the context of outbreak reconstruction based on genomic data. Finally, we investigate the impact of a range of interventions against gonorrhoea, including increased condom use, more frequent screening and immunisation, concluding that the latter shows great promise to reduce the burden of gonorrhoea, even if the vaccine was only partially effective or applied to only a random subset of the population. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
50. A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions.
- Author
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Engebretsen, Solveig, Engø-Monsen, Kenth, Frigessi, Arnoldo, and Freiesleben de Blasio, Birgitte
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IMMUNIZATION ,EPIDEMIOLOGY ,URBANIZATION ,VACCINATION ,POPULATION - Abstract
The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies—uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
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