Is Dropping out of High School More Likely after Stressful Life Events?

High school dropout is typically viewed as the result of long-held vulnerabilities such as learning problems. This research brief, by PRC visiting scholars Véronique Dupéré and Eric Dion and PRC faculty research associate Robert Crosnoe and colleagues, shows that recent stressful life events can lead to a student dropping out.Population Research Cente

Faculty Perceptions of Using Synchronous Video-Based Communication Technology

Online learning has traditionally relied on asynchronous text-based communication. The COVID-19 pandemic, though, has provided many faculty members with new and/or additional experience using synchronous video-based communication. Questions remain, though, about how this experience will shape online teaching and learning in the future. We conducted a mixed method study to investigate faculty perceptions of using synchronous video-based communication technology. In this paper, we present the results of our inquiry and implications for future research and practice

tRNASec is transcribed by RNA polymerase II in Trypanosoma brucei but not in humans

Nuclear-encoded tRNAs are universally transcribed by RNA polymerase III (Pol-III) and contain intragenic promoters. Transcription of vertebrate tRNASec however requires extragenic promoters similar to Pol-III transcribed U6 snRNA. Here, we present a comparative analysis of tRNASec transcription in humans and the parasitic protozoa Trypanosoma brucei, two evolutionary highly diverged eukaryotes. RNAi-mediated ablation of Pol-II and Pol-III as well as oligo-dT induced transcription termination show that the human tRNASec is a Pol-III transcript. In T. brucei protein-coding genes are polycistronically transcribed by Pol-II and processed by trans-splicing and polyadenylation. tRNA genes are generally clustered in between polycistrons. However, the trypanosomal tRNASec genes are embedded within a polycistron. Their transcription is sensitive to α-amanitin and RNAi-mediated ablation of Pol-II, but not of Pol-III. Ectopic expression of the tRNASec outside but not inside a polycistron requires an added external promoter. These experiments demonstrate that trypanosomal tRNASec, in contrast to its human counterpart, is transcribed by Pol-II. Synteny analysis shows that in trypanosomatids the tRNASec gene can be found in two different polycistrons, suggesting that it has evolved twice independently. Moreover, intron-encoded tRNAs are present in a number of eukaryotic genomes indicating that Pol-II transcription of tRNAs may not be restricted to trypanosomatids

Green Edge ice camp campaigns : understanding the processes controlling the under-ice Arctic phytoplankton spring bloom

The Green Edge initiative was developed to investigate the processes controlling the primary productivity and fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797∘ N, 63.7895∘ W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea-ice cover from the surface to the bottom (at 360 m depth) to better understand the factors driving the PSB. Key variables, such as conservative temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured at the ice camp. Meteorological and snow-relevant variables were also monitored. Here, we present the results of a joint effort to tidy and standardize the collected datasets, which will facilitate their reuse in other Arctic studies

The imperative for controlled mechanical stresses in unraveling cellular mechanisms of mechanotransduction

BACKGROUND: In vitro mechanotransduction studies are designed to elucidate cell behavior in response to a well-defined mechanical signal that is imparted to cultured cells, e.g. through fluid flow. Typically, flow rates are calculated based on a parallel plate flow assumption, to achieve a targeted cellular shear stress. This study evaluates the performance of specific flow/perfusion chambers in imparting the targeted stress at the cellular level. METHODS: To evaluate how well actual flow chambers meet their target stresses (set for 1 and 10 dyn/cm(2 )for this study) at a cellular level, computational models were developed to calculate flow velocity components and imparted shear stresses for a given pressure gradient. Computational predictions were validated with micro-particle image velocimetry (μPIV) experiments. RESULTS: Based on these computational and experimental studies, as few as 66% of cells seeded along the midplane of commonly implemented flow/perfusion chambers are subjected to stresses within ±10% of the target stress. In addition, flow velocities and shear stresses imparted through fluid drag vary as a function of location within each chamber. Hence, not only a limited number of cells are exposed to target stress levels within each chamber, but also neighboring cells may experience different flow regimes. Finally, flow regimes are highly dependent on flow chamber geometry, resulting in significant variation in magnitudes and spatial distributions of stress between chambers. CONCLUSION: The results of this study challenge the basic premise of in vitro mechanotransduction studies, i.e. that a controlled flow regime is applied to impart a defined mechanical stimulus to cells. These results also underscore the fact that data from studies in which different chambers are utilized can not be compared, even if the target stress regimes are comparable

H Index Scholar: the h-index for Spanish public universities' professors of humanities and social sciences

[EN] The H-Index Scholar is a bibliometric index that measures the productivity and scientific impact of the academic production in humanities and social sciences by professors and researchers at public Spanish universities. The methodology consisted of counting their publications and citations received in Google Scholar. The main features and characteristics of the index are explained. Despite technical and methodological problems that Google Scholar might have as a source of information, the authors estimate that they do not affect substantially the calculated h and g indexes, probably being the error lower than 10%. The total population analyzed was 40,993 researchers, but data are displayed only for 13,518 researchers, the ones located in the first tertile of their respective areas.[ES] H Index Scholar es un índice bibliométrico sobre la productividad e impacto científico de la producción académica de los profesores e investigadores de universidades públicas españolas en humanidades y ciencias sociales. Se realiza mediante el recuento de sus publicaciones y de las citas bibliográficas que han recibido en Google Scholar. Se describen las principales funciones y características del producto. A pesar de los problemas técnicos y metodológicos que pueda presentar Google Scholar como fuente de información, los autores estiman que no afectan en lo sustancial a los índices h y g ofrecidos, estando dentro de una tasa de error del 10%. La población total analizada ha sido de 40.993 profesores, de los que se visualiza un total de 13.518 situados en el primer tercil de sus respectivas áreas.Trabajo financiado con cargo al proyecto HAR2011-30383-C02-02 de la Dirección General de Investigación y Gestión del Plan Nacional de I+D+I. Ministerio de Economía y Competitividad.Delgado López, E.; Orduña Malea, E.; Jimenez Contreras, E.; Ruiz Pérez, R. (2014). H Index Scholar: el índice H de los profesores de las universidades públicas españolas en humanidades y ciencias sociales. El Profesional de la Información. 23(1):87-94. https://doi.org/10.3145/epi.2014.ene.11S8794231Aguillo, I. F., Ortega, J. L., Fernández, M., & Utrilla, A. M. (2010). Indicators for a webometric ranking of open access repositories. Scientometrics, 82(3), 477-486. doi:10.1007/s11192-010-0183-yArchambault, Eric; Larivière, Vincent (2010). "The limits of bibliometrics for the analysis of the social sciences and humanities literature". World social science report: competing in the knowledge society. Unesco, pp. 251-254.Archambault, É., Vignola-Gagné, É., Côté, G., Larivière, V., & Gingrasb, Y. (2006). Benchmarking scientific output in the social sciences and humanities: The limits of existing databases. Scientometrics, 68(3), 329-342. doi:10.1007/s11192-006-0115-zArdanuy, J. (2013). Sixty years of citation analysis studies in the humanities (1951-2010). Journal of the American Society for Information Science and Technology, 64(8), 1751-1755. doi:10.1002/asi.22835Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152. doi:10.1007/s11192-006-0144-7Giménez-Toledo, E., & Román-Román, A. (2009). Assessment of humanities and social sciences monographs through their publishers: a review and a study towards a model of evaluation. Research Evaluation, 18(3), 201-213. doi:10.3152/095820209x471986Gorraiz, J., Purnell, P. J., & Glänzel, W. (2013). Opportunities for and limitations of the Book Citation Index. Journal of the American Society for Information Science and Technology, 64(7), 1388-1398. doi:10.1002/asi.22875Hicks, Diana M.; Wang, Jian (2009). "Towards a bibliometric database for the social sciences and humanities" [report]. http://works.bepress.com/diana_hicks/18Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569-16572. doi:10.1073/pnas.0507655102Jacsó, P. (2008). Google Scholar revisited. Online Information Review, 32(1), 102-114. doi:10.1108/14684520810866010Jacso´, P. (2008). The pros and cons of computing the h‐index using Google Scholar. Online Information Review, 32(3), 437-452. doi:10.1108/14684520810889718Jacsó, P. (2012). Using Google Scholar for journal impact factors and the h‐index in nationwide publishing assessments in academia – siren songs and air‐raid sirens. Online Information Review, 36(3), 462-478. doi:10.1108/14684521211241503Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google Web/URL citations: A multi-discipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065. doi:10.1002/asi.20584Kousha, K., & Thelwall, M. (2007). Sources of Google Scholar citations outside the Science Citation Index: A comparison between four science disciplines. Scientometrics, 74(2), 273-294. doi:10.1007/s11192-008-0217-xKousha, K., Thelwall, M., & Rezaie, S. (2011). Assessing the citation impact of books: The role of Google Books, Google Scholar, and Scopus. Journal of the American Society for Information Science and Technology, 62(11), 2147-2164. doi:10.1002/asi.21608Leydesdorff, L., & Felt, U. (2012). Edited volumes, monographs and book chapters in the Book Citation Index (BKCI) and Science Citation Index (SCI, SoSCI, A&HCI). Journal of Scientometric Research, 1(1), 28-34. doi:10.5530/jscires.2012.1.7Nederhof, A. J. (2006). Bibliometric monitoring of research performance in the Social Sciences and the Humanities: A Review. Scientometrics, 66(1), 81-100. doi:10.1007/s11192-006-0007-2Orduña-Malea, Enrique (2012). Propuesta de un modelo de análisis redinformétrico multinivel para el estudio sistémico de las universidades españolas. Valencia: Universidad Politécnica de Valencia [tesis doctoral]Orduña-Malea, E., & Ontalba-Ruipérez, J.-A. (2012). Proposal for a multilevel university cybermetric analysis model. Scientometrics, 95(3), 863-884. doi:10.1007/s11192-012-0868-5Orduña-Malea, E., Serrano-Cobos, J., & Lloret-Romero, N. (2009). Las universidades públicas españolas en Google Scholar: presencia y evolución de su publicación académica web. El Profesional de la Informacion, 18(5), 493-500. doi:10.3145/epi.2009.sep.02Thelwall, M. (2002). Research dissemination and invocation on the Web. Online Information Review, 26(6), 413-420. doi:10.1108/14684520210452745Torres-Salinas, D., Ruiz-Pérez, R., & Delgado-López-Cózar, E. (2009). Google Scholar como herramienta para la evaluación científica. El Profesional de la Informacion, 18(5), 501-510. doi:10.3145/epi.2009.sep.03White, H. D., Boell, S. K., Yu, H., Davis, M., Wilson, C. S., & Cole, F. T. H. (2009). Libcitations: A measure for comparative assessment of book publications in the humanities and social sciences. Journal of the American Society for Information Science and Technology, 60(6), 1083-1096. doi:10.1002/asi.2104

Cumulative Burden of Colorectal Cancer-Associated Genetic Variants Is More Strongly Associated With Early-Onset vs Late-Onset Cancer.

BACKGROUND & AIMS: Early-onset colorectal cancer (CRC, in persons younger than 50 years old) is increasing in incidence; yet, in the absence of a family history of CRC, this population lacks harmonized recommendations for prevention. We aimed to determine whether a polygenic risk score (PRS) developed from 95 CRC-associated common genetic risk variants was associated with risk for early-onset CRC. METHODS: We studied risk for CRC associated with a weighted PRS in 12,197 participants younger than 50 years old vs 95,865 participants 50 years or older. PRS was calculated based on single nucleotide polymorphisms associated with CRC in a large-scale genome-wide association study as of January 2019. Participants were pooled from 3 large consortia that provided clinical and genotyping data: the Colon Cancer Family Registry, the Colorectal Transdisciplinary Study, and the Genetics and Epidemiology of Colorectal Cancer Consortium and were all of genetically defined European descent. Findings were replicated in an independent cohort of 72,573 participants. RESULTS: Overall associations with CRC per standard deviation of PRS were significant for early-onset cancer, and were stronger compared with late-onset cancer (P for interaction = .01); when we compared the highest PRS quartile with the lowest, risk increased 3.7-fold for early-onset CRC (95% CI 3.28-4.24) vs 2.9-fold for late-onset CRC (95% CI 2.80-3.04). This association was strongest for participants without a first-degree family history of CRC (P for interaction = 5.61 × 10-5). When we compared the highest with the lowest quartiles in this group, risk increased 4.3-fold for early-onset CRC (95% CI 3.61-5.01) vs 2.9-fold for late-onset CRC (95% CI 2.70-3.00). Sensitivity analyses were consistent with these findings. CONCLUSIONS: In an analysis of associations with CRC per standard deviation of PRS, we found the cumulative burden of CRC-associated common genetic variants to associate with early-onset cancer, and to be more strongly associated with early-onset than late-onset cancer, particularly in the absence of CRC family history. Analyses of PRS, along with environmental and lifestyle risk factors, might identify younger individuals who would benefit from preventive measures

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570