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We present a comprehensive review of keV-scale sterile neutrino Dark Matter, collecting views and insights from all disciplines involved - cosmology, astrophysics, nuclear, and particle physics - in each case viewed from both theoretical and experimental/observational perspectives. After reviewing the role of active neutrinos in particle physics, astrophysics, and cosmology, we focus on sterile neutrinos in the context of the Dark Matter puzzle. Here, we first review the physics motivation for sterile neutrino Dark Matter, based on challenges and tensions in purely cold Dark Matter scenarios. We then round out the discussion by critically summarizing all known constraints on sterile neutrino Dark Matter arising from astrophysical observations, laboratory experiments, and theoretical considerations. In this context, we provide a balanced discourse on the possibly positive signal from X-ray observations. Another focus of the paper concerns the construction of particle physics models, aiming to explain how sterile neutrinos of keV-scale masses could arise in concrete settings beyond the Standard Model of elementary particle physics. The paper ends with an extensive review of current and future astrophysical and laboratory searches, highlighting new ideas and their experimental challenges, as well as future perspectives for the discovery of sterile neutrinos.

We present a comprehensive review of keV-scale sterile neutrino Dark Matter, collecting views and insights from all disciplines involved - cosmology, astrophysics, nuclear, and particle physics - in each case viewed from both theoretical and experimental/observational perspectives. After reviewing the role of active neutrinos in particle physics, astrophysics, and cosmology, we focus on sterile neutrinos in the context of the Dark Matter puzzle. Here, we first review the physics motivation for sterile neutrino Dark Matter, based on challenges and tensions in purely cold Dark Matter scenarios. We then round out the discussion by critically summarizing all known constraints on sterile neutrino Dark Matter arising from astrophysical observations, laboratory experiments, and theoretical considerations. In this context, we provide a balanced discourse on the possibly positive signal from X-ray observations. Another focus of the paper concerns the construction of particle physics models, aiming to explain how sterile neutrinos of keV-scale masses could arise in concrete settings beyond the Standard Model of elementary particle physics. The paper ends with an extensive review of current and future astrophysical and laboratory searches, highlighting new ideas and their experimental challenges, as well as future perspectives for the discovery of sterile neutrinos.

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers.

The Medicare Improvements for Patients and Providers Act passed by Congress in 2008 has changed prescribing practices in the United States. Electronic prescriptions (e-prescriptions) have now become the most widely used form of prescriptions. The government in fact financially discourages the use of the older more traditional paper prescriptions. Many emergency medicine physicians fear that this blanket policy is not in the best interests of their unique patient population. It is the belief of many of these physicians that emergency patients are more likely to fill paper prescriptions than e-prescriptions. This theory is predicated on the knowledge that many emergency patients are less established in the system and their visits are frequently rushed, chaotic, and unplanned. For these reasons, the e-prescription system is not ideal for them and the theorized consequence is that many e-prescriptions go unfilled, leaving patients to go untreated. A retrospective analysis was conducted at the emergency department of the University of California, Irvine Medical Center to identify insured adult patients who were given a non-controlled substance prescription in either the paper or electronic form. Pharmacy claim data to insurances was used to determine whether these prescriptions were filled. 405 encounters were included, 218 of which included e-prescriptions and 187 of which included paper prescriptions. Our findings showed that paper prescriptions are filled at the same rate as electronic prescriptions (58.3% versus 57.8% p=1). These results were surprising as they contradicted what many physicians believe is the situation. More studies are needed in order to be able to broaden these results to the entire emergency medicine patient population, but these results may begin to alter prescription practices in emergency medicine.

Introduction: Many teachers adopt instructional methods based on assumptions of best practices without attention to or knowledge of supporting education theory. Familiarity with a variety of theories informs education that is efficient, strategic, and evidence-based. As part of the Academic Life in Emergency Medicine Faculty Incubator Program a list of key education theories for junior faculty was developed. Methods: A list of key papers on theories relevant to medical education was generated using an expert panel, a virtual community of practice synthetic discussion, and a social media call for resources. A three-round, Delphi-informed voting methodology including novice and expert educators produced a rank order of the top papers. Results: Thirty-four unique papers were identified. Eleven papers described general theories, while 23 papers focused on a specific theory. The top three ranked general education theories and top five ranked specific education theory papers are summarized. The relevance of each paper for junior faculty and faculty developers is also presented. Conclusion: This paper presents a reading list of key papers for junior faculty in medical education roles. Three papers about general education theories and five papers about specific educational theories are identified and annotated. These papers may help provide foundational knowledge in education theory to inform junior faculty teaching practice.

Introduction Team collaboration is an essential part of success both within academics and the clinical environment. Often, team collaboration is not taught during medical school or even residency and must be learned during one’s postgraduate career. In this article, we aim to summarize five key papers about team collaboration for early career clinician educators. Methods We conducted a consensus building process amongst the writing team to generate a list of key papers that describe the importance or significance of team collaboration, seeking input from social media sources. The authors then used a three-round voting methodology akin to a Delphi study to determine the most important papers from the general list. Results The five most important papers on the topic of team collaboration, as determined by this mixed group of junior faculty members and faculty developers, is presented in this paper. For each included publication, a summary was provided along with its relevance to junior faculty members and faculty developers. Conclusion Five key papers about team collaboration are presented in this paper. These papers provide a foundational background to help junior faculty members with collaborating in teams both clinically and academically. This list may also inform senior faculty and faculty developers about the needs of junior faculty members.

Introduction: A proper understanding of study design is essential to creating successful studies. This is also important when reading or peer reviewing publications. In this article, we aimed to identify and summarize key papers that would be helpful for faculty members interested in learning more about study design in medical education research.Methods: The online discussions of the 2016-2017 Academic Life in Emergency Medicine Faculty Incubator program included a robust and vigorous discussion about education study design, which highlighted a number of papers on that topic. We augmented this list of papers with further suggestions by expert mentors. Via this process, we created a list of 29 papers in total on the topic of medical education study design. After gathering these papers, our authorship group engaged in a modified Delphi approach to build consensus on the papers that were most valuable for the understanding of proper study design in medical education.Results: We selected the top five most highly rated papers on the topic domain of study design as determined by our study group. We subsequently summarized these papers with respect to their relevance to junior faculty members and to faculty developers.Conclusion: This article summarizes five key papers addressing study design in medical education with discussions and applications for junior faculty members and faculty developers. These papers provide a basis upon which junior faculty members might build for developing and analyzing studies.

Introduction: Clinician educators are often asked to perform consultations for colleagues. Invitations to consult and advise others on local problems can help foster great collaborations between centres, and allows for an exchange of ideas between programs. In this article, the authors identify and summarize several key papers to assist emerging clinician educators with the consultation process. Methods: A consensus building process was used to generate a list of key papers that describe the importance and significance of educational consulting, informed by social media sources. A three-round voting methodology, akin to a Delphi study, determinedthe most impactful papers from the larger list. Results: Summaries of the five most highly rated papers on education consultation are presented in this paper. These papers were determined by a mixed group of junior and senior faculty members, who have summarized these papers with respect to their relevance for their peer groups. Conclusion: Five key papers on the educational consultation process are presented in this paper. These papers offer background and perspective to help junior faculty gain a grasp of consultation processes.

Introduction: Competency-based medical education (CBME) presents a paradigm shift in medicaltraining. This outcome-based education movement has triggered substantive changes across the globe.Since this transition is only beginning, many faculty members may not have experience with CBMEnor a solid foundation in the grounding literature. We identify and summarize key papers to help facultymembers learn more about CBME.Methods: Based on the online discussions of the 2016-2017 ALiEM Faculty Incubator program, a seriesof papers on the topic of CBME was developed. Augmenting this list with suggestions by a guest expertand by an open call on Twitter for other important papers, we were able to generate a list of 21 papers intotal. Subsequently, we used a modified Delphi study methodology to narrow the list to key papers thatdescribe the importance and significance for educators interested in learning about CBME. To determinethe most impactful papers, the mixed junior and senior faculty authorship group used three-round votingmethodology based upon the Delphi method.Results: Summaries of the five most highly rated papers on the topic of CBME, as determined by thismodified Delphi approach, are presented in this paper. Major themes include a definition of core CBMEthemes, CBME principles to consider in the design of curricula, a history of the development of the CBMEmovement, and a rationale for changes to accreditation with CBME. The application of the study findingsto junior faculty and faculty developers is discussed.Conclusion: We present five key papers on CBME that junior faculty members and faculty expertsidentified as essential to faculty development. These papers are a mix of foundational and explanatorypapers that may provide a basis from which junior faculty members may build upon as they help toimplement CBME programs.

Introduction: Peer review, a cornerstone of academia, promotes rigor and relevance in scientificpublishing. As educators are encouraged to adopt a more scholarly approach to medical education,peer review is becoming increasingly important. Junior educators both receive the reviews of theirpeers, and are also asked to participate as reviewers themselves. As such, it is imperative for juniorclinician educators to be well-versed in the art of peer reviewing their colleagues’ work. In thisarticle, our goal was to identify and summarize key papers that may be helpful for faculty membersinterested in learning more about the peer-review process and how to improve their reviewing skills.Methods: The online discussions of the 2016-17 Academic Life in Emergency Medicine (ALiEM)Faculty Incubator program included a robust discussion about peer review, which highlighted anumber of papers on that topic. We sought to augment this list with further suggestions by guestexperts and by an open call on Twitter for other important papers. Via this process, we created a listof 24 total papers on the topic of peer review. After gathering these papers, our authorship groupengaged in a consensus-building process incorporating Delphi methods to identify the papers thatbest described peer review, and also highlighted important tips for new reviewers.Results: We found and reviewed 24 papers. In our results section, we present our authorshipgroup’s top five most highly rated papers on the topic of peer review. We also summarize thesepapers with respect to their relevance to junior faculty members and to faculty developers.Conclusion: We present five key papers on peer review that can be used for faculty development fornovice writers and reviewers. These papers represent a mix of foundational and explanatory papersthat may provide some basis from which junior faculty members might build upon as they both undergothe peer-review process and act as reviewers in turn.

Introduction: Modern learners have immediate, unlimited access to a wide variety of onlineresources. To appeal to this current generation of learners, educators must embrace the useof technology. However, educators must balance newer, novel technologies with traditionalmethods to achieve the best learning outcomes. Therefore, we aimed to review several papersuseful for faculty members wishing to incorporate technology into instructional design.Methods: We identified a broad list of papers relevant to teaching and lea rning with technologywithin the online discussions of the Academic Life in Emergency Medicine (ALiEM) FacultyIncubator. This list was augmented with suggestions by a guest expert (BT) and an open callon Twitter (tagged with the #meded and #FOAMed hashtags) yielding 24 papers. We thenconducted a modified three-round Delphi process within the autho rship group, including juniorand senior faculty members, to identify the most impactful papers.Results: We pared the list of 24 papers to five that were most highly rate d. Two were researchpapers and three were commentaries or editorials. The authorship group reviewed andsummarized these papers with specific consideration to their val ue to junior educators andfaculty developers.Conclusion: This is a key reading list for junior faculty members and faculty developersinterested in teaching with technology. The commentary contextualizes the importance of thesepapers for medical educators, to optimize use of technology in their teaching or incorporate intofaculty development.

The first paper of three uses the nationally representative Education Longitudinal Study of 2002 to detail the relationship between student ability, enrollment in math or English remediation, and the probability of student transfer to a four-year institution. Using fixed effects logistic regressions, this paper finds three important findings: first, students are most likely placed into remediation due to their domain-specific ability (math, reading) but certain demographics also predict placement into remediation, such as student race, independent of ability and high school quality; second, students placed into English remediation are less likely to transfer to a four-year, especially if they are under-placed into these courses; third, summer enrollment has both a powerful positive association with vertical transfer and is particularly strong for students enrolled in remediation and with subject deficits. The second paper of three synthesizes six national databases over the span of twenty years to test the impacts of state articulation policies as they are established by examining the change in vertical transfer rates before and after a policy is enacted. After a series of careful robustness and falsification tests, there are several lessons from this analysis. First, cross-sectional analyses that utilized by these national databases may vary considerably from one database to another, although in the aggregate they support a positive association between state policy and vertical transfer. Second, longitudinal data suggests that the implementation of any articulation policy is associated with increases in student transfer rates within state, especially if analysis provides a three year grace period for the policy to take effect. Third, the strongest policy appears to be associate degree transfer agreements between two-year institutions and four-year institutions, as other policy components do not have a particularly strong impact on student transfer rates. The third

This dissertation investigates the interplay between second and fourth graders’ use of rulers and paper strips to solve number line measurement problems. Drawing upon and extending Vygotsky’s (1978, 1986) approach to mediation in problem solving, and Saxe’s (2012) treatment of microgenesis of representational activity, I analyzed 36 second and 39 fourth graders’ measurement activity as they participated in videotaped semi-structured interviews. At each grade, students were randomly assigned to ruler, short paper strip, or long paper strip conditions, and solved five number line problems first without the tool available and then five similar problems with the tool available. For each problem, two numbers were labeled (e.g., 4 and 6) and students were asked to place a third number appropriately (e.g., 9). Problems and tools were designed to elicit unitizing strategies (splitting, iterating, counting) as students coordinated tool with target to produce a linear measure. I report findings related to the precision of students’ estimates contrasting tool absent and present conditions, and findings related to students’ adaptation of tools to serve measurement functions. Analyses of students’ precision revealed that with or without the support of tools, fourth graders were more precise than second graders, though the utility of tool varied with tool and problem type. The long paper strip was particularly difficult for second graders, and at both grades, many students rejected using the long paper strip. Students generally became precise with tools when a solving a problem type that displayed a linear unit of 1 instead of a larger unit, though this varied with tool type. On other problem types, tools interfered with precision, and on still other problem types, tools interfered with the precision of second, but supported the precision of fourth graders. Analyses of strategies revealed three levels in students’ ability to coordinate a unitization of tool and target as they tri

Introduction Team collaboration is an essential part of success both within academics and the clinical environment. Often, team collaboration is not taught during medical school or even residency and must be learned during one’s postgraduate career. In this article, we aim to summarize five key papers about team collaboration for early career clinician educators. Methods We conducted a consensus building process amongst the writing team to generate a list of key papers that describe the importance or significance of team collaboration, seeking input from social media sources. The authors then used a three-round voting methodology akin to a Delphi study to determine the most important papers from the general list. Results The five most important papers on the topic of team collaboration, as determined by this mixed group of junior faculty members and faculty developers, is presented in this paper. For each included publication, a summary was provided along with its relevance to junior faculty members and faculty developers. Conclusion Five key papers about team collaboration are presented in this paper. These papers provide a foundational background to help junior faculty members with collaborating in teams both clinically and academically. This list may also inform senior faculty and faculty developers about the needs of junior faculty members.

Transportation is arguably experiencing its most transformative revolution since the introduction of the automobile. Concerns over climate change and equity are converging with dramatic technological advances. Although these changes – including shared mobility and automation – are rapidly altering the mobility landscape, predictions about the future of transportation are complex, nuanced, and widely debated. California is required by law to renew the California Transportation Plan (CTP), updating its models and policy considerations to reflect industry changes every five years. This document is envisioned as a reference for modelers and decision makers. We aggregate current information and research on the state of key trends and emerging technologies/services, documented impacts on California’s transportation ecosystem, and future growth projections (as appropriate). During 2017, we reviewed an expanded list of 20 topics by referencing state agency publications, peer-reviewed journal articles, and forecast reports from consulting firms and think tanks. We followed transportation newsletters and media sources to track industry developments, and interviewed six experts to explore their opinions on the future of transportation. We consulted an advisory committee of over 50 representatives from local and state transportation agencies, who provided input throughout the project’s evolution. We also obtained feedback on our draft report from a panel of U.S. experts.

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Aqueous two-phase systems (ATPSs) have been widely utilized for liquid-liquid extraction and purification of biomolecules, with some studies also demonstrating their capacity as a biomarker concentration technique for use in diagnostic settings. As the limited polarity range of conventional polymer-based ATPSs can restrict their use, ionic liquid (IL)-based ATPSs have been recently proposed as a promising alternative to polymer-based ATPSs, since ILs are regarded as tunable solvents with excellent solvation capabilities for a variety of natural compounds and proteins. This study demonstrates the first application of IL ATPSs to point-of-care diagnostics. ATPSs consisting of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) and sodium phosphate salt were utilized to quickly concentrate biomarkers prior to detection using the lateral-flow immunoassay (LFA). We found the phase separation speed of the IL ATPS to be very rapid and a significant improvement upon the separation speed of both polymer-salt and micellar ATPSs. This system was successfully applied to both sandwich and competitive LFA formats and enhanced the detection of both Escherichia coli bacteria and the transferrin protein up to 8- and 20-fold, respectively. This system's compatibility with a broad range of biomolecules, rapid phase separation speed, and tunability suggest wide applicability for a large range of different antigens and biomarkers.

The Wide-Field Infrared Survey Telescope (WFIRST) is expected to launch in the mid-2020s. With its wide-field near-infrared (NIR) camera, it will survey the sky to unprecedented detail. As part of normal operations and as the result of multiple expected dedicated surveys, WFIRST will produce several relatively wide-field (tens of square degrees) deep (limiting magnitude of 28 or fainter) fields. In particular, a planned supernova survey is expected to image 3 deep fields in the LSST footprint roughly every 5 days over 2 years. Stacking all data, this survey will produce, over all WFIRST supernova fields in the LSST footprint, ~12-25 deg^2 and ~5-15 deg^2 regions to depths of ~28 mag and ~29 mag, respectively. We suggest LSST undertake mini-surveys that will match the WFIRST cadence and simultaneously observe the supernova survey fields during the 2-year WFIRST supernova survey, achieving a stacked depth similar to that of the WFIRST data. We also suggest additional observations of these same regions throughout the LSST survey to get deep images earlier, have long-term monitoring in the fields, and produce deeper images overall. These fields will provide a legacy for cosmology, extragalactic, and transient/variable science.

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

The Wide-Field Infrared Survey Telescope (WFIRST) is expected to launch in the mid-2020s. With its wide-field near-infrared (NIR) camera, it will survey the sky to unprecedented detail. As part of normal operations and as the result of multiple expected dedicated surveys, WFIRST will produce several relatively wide-field (tens of square degrees) deep (limiting magnitude of 28 or fainter) fields. In particular, a planned supernova survey is expected to image 3 deep fields in the LSST footprint roughly every 5 days over 2 years. Stacking all data, this survey will produce, over all WFIRST supernova fields in the LSST footprint, ~12-25 deg^2 and ~5-15 deg^2 regions to depths of ~28 mag and ~29 mag, respectively. We suggest LSST undertake mini-surveys that will match the WFIRST cadence and simultaneously observe the supernova survey fields during the 2-year WFIRST supernova survey, achieving a stacked depth similar to that of the WFIRST data. We also suggest additional observations of these same regions throughout the LSST survey to get deep images earlier, have long-term monitoring in the fields, and produce deeper images overall. These fields will provide a legacy for cosmology, extragalactic, and transient/variable science.

Aqueous two-phase systems (ATPSs) have been widely utilized for liquid-liquid extraction and purification of biomolecules, with some studies also demonstrating their capacity as a biomarker concentration technique for use in diagnostic settings. As the limited polarity range of conventional polymer-based ATPSs can restrict their use, ionic liquid (IL)-based ATPSs have been recently proposed as a promising alternative to polymer-based ATPSs, since ILs are regarded as tunable solvents with excellent solvation capabilities for a variety of natural compounds and proteins. This study demonstrates the first application of IL ATPSs to point-of-care diagnostics. ATPSs consisting of 1-butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) and sodium phosphate salt were utilized to quickly concentrate biomarkers prior to detection using the lateral-flow immunoassay (LFA). We found the phase separation speed of the IL ATPS to be very rapid and a significant improvement upon the separation speed of both polymer-salt and micellar ATPSs. This system was successfully applied to both sandwich and competitive LFA formats and enhanced the detection of both Escherichia coli bacteria and the transferrin protein up to 8- and 20-fold, respectively. This system's compatibility with a broad range of biomolecules, rapid phase separation speed, and tunability suggest wide applicability for a large range of different antigens and biomarkers.

The Liver Imaging and Reporting Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging with the overarching goal of improving communication, clinical care, education, and research relating to patients at risk for or diagnosed with hepatocellular carcinoma (HCC). In 2018, the American Association for the Study of Liver Diseases (AASLD) integrated LI-RADS into its clinical practice guidance for the imaging-based diagnosis of HCC. The harmonization between the AASLD and LI-RADS diagnostic imaging criteria required minor modifications to the recently released LI-RADS v2017 guidelines, necessitating a LI-RADS v2018 update. This article provides an overview of the key changes included in LI-RADS v2018 as well as a look at the LI-RADS v2018 diagnostic algorithm and criteria, technical recommendations, and management suggestions. Substantive changes in LI-RADS v2018 are the removal of the requirement for visibility on antecedent surveillance ultrasound for LI-RADS 5 (LR-5) categorization of 10-19 mm observations with nonrim arterial phase hyper-enhancement and nonperipheral "washout", and adoption of the Organ Procurement and Transplantation Network definition of threshold growth (≥ 50% size increase of a mass in ≤ 6 months). Nomenclatural changes in LI-RADS v2018 are the removal of -us and -g as LR-5 qualifiers.

Transportation is arguably experiencing its most transformative revolution since the introduction of the automobile. Concerns over climate change and equity are converging with dramatic technological advances. Although these changes – including shared mobility and automation – are rapidly altering the mobility landscape, predictions about the future of transportation are complex, nuanced, and widely debated. California is required by law to renew the California Transportation Plan (CTP), updating its models and policy considerations to reflect industry changes every five years. This document is envisioned as a reference for modelers and decision makers. We aggregate current information and research on the state of key trends and emerging technologies/services, documented impacts on California’s transportation ecosystem, and future growth projections (as appropriate). During 2017, we reviewed an expanded list of 20 topics by referencing state agency publications, peer-reviewed journal articles, and forecast reports from consulting firms and think tanks. We followed transportation newsletters and media sources to track industry developments, and interviewed six experts to explore their opinions on the future of transportation. We consulted an advisory committee of over 50 representatives from local and state transportation agencies, who provided input throughout the project’s evolution. We also obtained feedback on our draft report from a panel of U.S. experts.

Transportation is arguably experiencing its most transformative revolution since the introduction of the automobile. Concerns over climate change and equity are converging with dramatic technological advances. Although these changes – including shared mobility and automation – are rapidly altering the mobility landscape, predictions about the future of transportation are complex, nuanced, and widely debated. California is required by law to renew the California Transportation Plan (CTP), updating its models and policy considerations to reflect industry changes every five years. This document is envisioned as a reference for modelers and decision makers. We aggregate current information and research on the state of key trends and emerging technologies/services, documented impacts on California’s transportation ecosystem, and future growth projections (as appropriate). During 2017, we reviewed an expanded list of 20 topics by referencing state agency publications, peer-reviewed journal articles, and forecast reports from consulting firms and think tanks. We followed transportation newsletters and media sources to track industry developments, and interviewed six experts to explore their opinions on the future of transportation. We consulted an advisory committee of over 50 representatives from local and state transportation agencies, who provided input throughout the project’s evolution. We also obtained feedback on our draft report from a panel of U.S. experts.

We demonstrate the use of patterned aerosol adhesives to construct both planar and nonplanar 3D paper microfluidic devices. By spraying an aerosol adhesive through a metal stencil, the overall amount of adhesive used in assembling paper microfluidic devices can be significantly reduced. We show on a simple 4-layer planar paper microfluidic device that the optimal adhesive application technique and device construction style depends heavily on desired performance characteristics. By moderately increasing the overall area of a device, it is possible to dramatically decrease the wicking time and increase device success rates while also reducing the amount of adhesive required to keep the device together. Such adhesive application also causes the adhesive to form semi-permanent bonds instead of permanent bonds between paper layers, enabling single-use devices to be non-destructively disassembled after use. Nonplanar 3D origami devices also benefit from the semi-permanent bonds during folding, as it reduces the likelihood that unrelated faces may accidently stick together. Like planar devices, nonplanar structures see reduced wicking times with patterned adhesive application vs uniformly applied adhesive.

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.

Toward the goal of maximizing the impact of computer modeling on the design of future particle accelerators and the development of new accelerator techniques & technologies, this white paper presents the rationale for: (a) strengthening and expanding programmatic activities in accelerator modeling science within the Department of Energy (DOE) Office of High Energy Physics (HEP) and (b) increasing the community-wide coordination and integration of code development.

Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.

Infectious diseases are a significant problem, accounting for 1 in every 4 deaths worldwide. The field of bioengineering is constantly innovating and advancing diagnostic technology; however, more often than not, these innovations are accessible only to communities with privileged resources. This has led to a growing focus on point-of-care (POC) diagnostics. Due to their ease of use, speed, and low cost, POC diagnostics can effectively test patients in resource-poor settings. One of the most well known POC technologies is the lateral-flow immunoassay (LFA). Most easily recognized for its use in pregnancy tests, LFA is a paper-based diagnostic that produces visually interpreted results using a colorimetric indicator decorated with antibodies specific to the target. Recently, we have seen rapid advancement in the fields of paper fluidics and paper diagnostics, which can have a tremendous impact on the future of LFA technology. In light of this, we focused our work in 3 distinct directions, which involves development of quantitative experimental methodologies, mathematical modeling, and improving the ease-of-use of the advanced LFA technology.Development of new paper-based devices would benefit significantly from being able to quantitatively assess the effects of engineering the device on important LFA parameters. For example, it would be useful to know the effects of manipulating the gold nanoprobes and test strips on the forward (kf,s) and reverse (kr,s) rate constants for the probe binding to and dissociating from the test line, respectively. We discuss our novel approach for determining these rate constants and the volumetric flow rate by using mathematical modeling and radioactive iodine-125 (125I). Moreover, we demonstrate how radioactivity and paper strips can also be used to determine the volume of fluid in and before the test line, the concentration of gold nanoprobes, and the number of antibodies per gold nanoprobe. As the field of paper-based diagnostics con

Point-of-care technologies provide innovative solutions that improve treatment. Healthcare systems including some low-resource settings have begun implementing these technologies providing the convenience and reduction of large laboratory set-ups. Low-cost is one of the main driving components when it comes to point-of-care diagnostics. Paper-based microfluidics has generated a great amount of interest for the development of low-cost diagnostic and self-contained analytical devices. Satisfying the World Health Organization’s (WHO) recommended ASSURED criteria; Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment free, Deliverable, paper-based microfluidics have made point-of-care testing more accessible. Applications range from healthcare, food safety, and environmental monitoring, among others. What has in part attracted attention is the low-cost, ease-of-use, and adaptability of these paper devices. Compared to conventional microfluidic devices, the paper-based counterparts are able to utilize paper’s inherent wicking property to eliminate the external pumping needed to drive the fluid. Channels are easily formed by either selectively removing sections of the paper substrate or by pattering channel boundaries with a hydrophobic material. In spite of the benefits and advantages described above, paper-based microfluidic technologies often lack the necessary sensitivity and sophistication available in conventional microfluidic devices. In order to be a competitive alternative, paper-based microfluidics require improvement and novel development of feasible detection methods. These methods will likely require increasingly complex chemistry and control of reagents. Thus, understanding imbibition as well as obtaining precise, accurate, and consistent fluid handling within the paper device will be crucial.Although considerable knowledge exists on techniques to manipulate fluid within the paper channel, what is lacking are studies on how non-laborato

Infectious diseases are a significant problem, accounting for 1 in every 4 deaths worldwide. The field of bioengineering is constantly innovating and advancing diagnostic technology; however, more often than not, these innovations are accessible only to communities with privileged resources. This has led to a growing focus on point-of-care (POC) diagnostics. Due to their ease of use, speed, and low cost, POC diagnostics can effectively test patients in resource-poor settings. One of the most well known POC technologies is the lateral-flow immunoassay (LFA). Most easily recognized for its use in pregnancy tests, LFA is a paper-based diagnostic that produces visually interpreted results using a colorimetric indicator decorated with antibodies specific to the target. Recently, we have seen rapid advancement in the fields of paper fluidics and paper diagnostics, which can have a tremendous impact on the future of LFA technology. In light of this, we focused our work in 3 distinct directions, which involves development of quantitative experimental methodologies, mathematical modeling, and improving the ease-of-use of the advanced LFA technology.Development of new paper-based devices would benefit significantly from being able to quantitatively assess the effects of engineering the device on important LFA parameters. For example, it would be useful to know the effects of manipulating the gold nanoprobes and test strips on the forward (kf,s) and reverse (kr,s) rate constants for the probe binding to and dissociating from the test line, respectively. We discuss our novel approach for determining these rate constants and the volumetric flow rate by using mathematical modeling and radioactive iodine-125 (125I). Moreover, we demonstrate how radioactivity and paper strips can also be used to determine the volume of fluid in and before the test line, the concentration of gold nanoprobes, and the number of antibodies per gold nanoprobe. As the field of paper-based diagnostics con

Point-of-care technologies provide innovative solutions that improve treatment. Healthcare systems including some low-resource settings have begun implementing these technologies providing the convenience and reduction of large laboratory set-ups. Low-cost is one of the main driving components when it comes to point-of-care diagnostics. Paper-based microfluidics has generated a great amount of interest for the development of low-cost diagnostic and self-contained analytical devices. Satisfying the World Health Organization’s (WHO) recommended ASSURED criteria; Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment free, Deliverable, paper-based microfluidics have made point-of-care testing more accessible. Applications range from healthcare, food safety, and environmental monitoring, among others. What has in part attracted attention is the low-cost, ease-of-use, and adaptability of these paper devices. Compared to conventional microfluidic devices, the paper-based counterparts are able to utilize paper’s inherent wicking property to eliminate the external pumping needed to drive the fluid. Channels are easily formed by either selectively removing sections of the paper substrate or by pattering channel boundaries with a hydrophobic material. In spite of the benefits and advantages described above, paper-based microfluidic technologies often lack the necessary sensitivity and sophistication available in conventional microfluidic devices. In order to be a competitive alternative, paper-based microfluidics require improvement and novel development of feasible detection methods. These methods will likely require increasingly complex chemistry and control of reagents. Thus, understanding imbibition as well as obtaining precise, accurate, and consistent fluid handling within the paper device will be crucial.Although considerable knowledge exists on techniques to manipulate fluid within the paper channel, what is lacking are studies on how non-laborato

Inspired by the success of convolutional neural networks in image classification, and other higher level vision tasks, we explore two applications of such deep convolutional neural networks to model tasks typically involving human assessment, viz. i) prediction of personality from social media images, and ii) prediction of citations from the visual elements of an academic paper. The aim in this context is to discover if there is any predictable and learnable signal in the input data. As an extension, we attempt to discover what aspects of the signal are indeed learnt that lead to the results presented. For instance, if personality can be predicted, what aspects of the image are causing that? Similarly if an academic paper is highly cited, what are the characteristic visual elements that cause this? We employ convolutional neural networks in order to understand what imputable attributes we may derive that are simpler to reason.

Introduction: Scholarship is an essential part of academic success. Junior faculty members are often unfamiliar with the grounding literature that defines educational scholarship. In this article, the authors aim to summarize five key papers outlining scholarship in the setting of academic contributions for emerging clinician educators. Methods: The authors conducted a consensus-building process to generate a list of key papers that describe the importance and significance of academic scholarship, informed by social media sources. We then used a three-round voting methodology, akin to a Delphi study, to determine the papers with the most impact. Results: A summary of the five most important papers on the topic domain of academic scholarship, as determined by this mixed group of junior faculty members and faculty developers, is presented in this paper. We subsequently wrote a summary of these five papers and their relevance to junior faculty members and faculty developers, as well. Conclusion: Five papers on educational scholarship, deemed essential by the authors’ consensus process, are presented in this paper. These papers may help provide the foundational background to help junior faculty members gain a grasp of the academic scholarly environment. This list may also inform senior faculty and faculty developers on the needs of junior faculty members in the nascent stages of their careers. [West JEmerg Med. 2016;17(5)519-526.]

Transwomen are among the most vulnerable populations in the United States and are disproportionately at risk for HIV and other negative health outcomes. Research with transwomen has been largely descriptive, documenting the high prevalence of various co-morbidities, ranging from substance use to negative mental health outcomes. In these studies, singular axes of identity have generally been analyzed. For example, studies have found that race is especially salient in the health of transwomen, finding that transwomen of color experience numerous structural inequalities that drive disparities in health. More intersectional research to examine social inequities in health and illness among transwomen is needed to better understand factors situated in the lived experiences of transwomen. This dissertation draws upon quantitative methods to examine disparities in HIV risk behavior and substance use among transwomen at the intersection of race and gender (Paper 1) and sexuality and gender (Paper 2). Qualitatively, this dissertation examines transmisogyny as a form of intersectional stress among transwomen (Paper 3). This first study is a cross-sectional analysis of data from a sample of 149 HIV-negative adult transwomen in San Francisco collected in 2013 from the TEACH 2 study. The second study is a cross-sectional analysis of data from a sample of 259 young transwomen in the San Francisco Bay Area collected in 2012-2014 from the SHINE study. The third study is a secondary analysis of qualitative, in-depth interview data from the Transgender Research Youth Project (TRYP), collected from 34 young transwomen in Chicago and Los Angeles in 2006. Altogether, these studies argue that future transgender research should increasingly take up intersectionality in order to understand the unique social location of transwomen.

This dissertation is comprised of three distinct papers covering topics in appliedmicroeconomics and applied econometrics. The first paper addresses a common problem faced by empirical researchers wishing to estimate Markov regime-switching models. For these models, testing for the possible presence of more than one regime requires the use of a non-standard test statistic. The analytic steps needed to implement the test of Markov regime-switching proposed by Cho & White (2007) are derived in detail in Carter & Steigerwald (2013). We summarize those implementation steps and address the computational issues that arise. A new Stata command to compute the regime-switching critical values, rscv, is introduced and presented in the context of empirical economic research. This paper is joint work with Douglas Steigerwald, and has previously appeared in the Stata Journal (Bostwick and Steigerwald, 2014).In the second paper, I address a question in the field of economics of education: that is,whether college students use their choice of major as a signal of unobserved productivityin the labor market. I propose a model of postsecondary education in which major fieldof study can be used by individuals to signal productivity to employers. Under thissignaling model, I show that geographic areas with high access to elite universities resultin fewer science, technology, engineering, and mathematics (STEM) majors among lower ability students at non-elite colleges. Using data from the National Center for Education Statistics' Baccalaureate and Beyond survey, I find evidence that is consistent with the signaling model prediction, specifically a 2.3-3.7 percentage point (or 16-25%) decrease in the probability of choosing a STEM major among lower ability students in areas with greater access to elite colleges. This paper has previously appeared in Economic Inquiry (Bostwick, 2016).In the third paper, I analyze an unexpected consequence of a highly debated educationpolicy. Many s

Several years ago, one of us, having noticed that inexperienced scientists tend to make largely the same mistakes while writing their first papers, was compelled to write a one-page note summarizing some dos and don'ts intended to help take care of common problems before they occur. Since these days the majority of research papers are written collaboratively by groups of co-authors, we are compelled to extend these recommendations to collaborative writing as we observe groups of co-authors falling into the same traps again and again.

Several years ago, one of us, having noticed that inexperienced scientists tend to make largely the same mistakes while writing their first papers, was compelled to write a one-page note summarizing some dos and don'ts intended to help take care of common problems before they occur. Since these days the majority of research papers are written collaboratively by groups of co-authors, we are compelled to extend these recommendations to collaborative writing as we observe groups of co-authors falling into the same traps again and again.

Inspired by the success of convolutional neural networks in image classification, and other higher level vision tasks, we explore two applications of such deep convolutional neural networks to model tasks typically involving human assessment, viz. i) prediction of personality from social media images, and ii) prediction of citations from the visual elements of an academic paper. The aim in this context is to discover if there is any predictable and learnable signal in the input data. As an extension, we attempt to discover what aspects of the signal are indeed learnt that lead to the results presented. For instance, if personality can be predicted, what aspects of the image are causing that? Similarly if an academic paper is highly cited, what are the characteristic visual elements that cause this? We employ convolutional neural networks in order to understand what imputable attributes we may derive that are simpler to reason.