Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Report of the Life Sciences Working Group

This is a report on the Cornell University Library activities in response to two linked faculty initiatives, the Cornell Genomics Initiative (CGI) and the New Life Sciences Initiative (NLSI). The Cornell University Library has always supported the life sciences, through instruction on the use of Medline and Biosis since before this incoming freshman class was born and through exemplary collections in the life sciences. This report describes how the Library responded to the two University initiatives in the life sciences that began in 1998. In Phase I (CGI) the activity was centered in Mann Library, in Phase II the activity was in transition, in Phase III (NLSI) the library participation expanded to all the science units

Asynchronous Delivery Format: Differential Effects On Performance And Engagement

In recent years, the number of online courses offered to students has increased. This increase brings the question of what format of online learning is the best for students. The purpose for this study was to investigate the effects of three asynchronous presentation formats on participant performance and engagement: slides with video-audio, slides with audio, and slides alone. We hypothesized significant differences among the formats in participant performance and engagement (best in video-audio format, worst in slides alone format). Participants (N=27) were randomly assigned to one of the three formats. Each format presented information about a fake island created by the research team. Participants completed a pre and post-exam on lesson content (performance measure) and an exit survey on engagement (engagement measure). ANOVA revealed significant differences in responses to two exit survey questions among formats, F(2,24)=4.0, p.\u3c.05. LSD test showed that this difference was between the audio and videoaudio formats. This result indicates that participants in the audio format were more engaged in lesson content than those in the video-audio format. ANOVA found a significant difference between pre and postexam scores (F(1,24)=258.5, p.\u3c.01), showing that the information presented effectively facilitated learning. No main effects for presentation formats or interaction with presentation formats were found, suggesting that the presentation formats were equally effective at facilitating learning. Overall, the results suggest that narration should either be included alone or not at all with textual presentations for maximized student performance and engagement. Additional results, implications, and limitations are discussed

Quadrivalent human papillomavirus vaccination in girls and the risk of autoimmune disorders: the Ontario Grade 8 HPV Vaccine Cohort Study

Background Despite demonstrated effectiveness in real-world settings, concerns persist regarding the safety of the quadrivalent human papillomavirus (HPV4) vaccine. We sought to assess the risk of autoimmune disorders following HPV4 vaccination among grade 8 girls eligible for Ontario's school-based HPV vaccination program. Methods We undertook a population-based retrospective cohort study using Ontario's administrative health and vaccination databases from 2007 to 2013. The self-controlled case series method was used to compare the rate of a composite end point of autoimmune disorders diagnosed during days 7-60 post-vaccination ("exposed" follow-up) to that at any other time ("unexposed"). The analysis was repeated to assess the effect of a history of immune-mediated diseases and time since vaccination. We also conducted an exploratory analysis of individual autoimmune disorders. Rate ratios and 95% confidence intervals (CIs) were estimated using conditional Poisson regression, adjusted for age, seasonality, concomitant vaccinations and infections. Results The study cohort consisted of 290 939 girls aged 12-17 years who were eligible for vaccination between 2007 and 2013. There was no significant risk for developing an autoimmune disorder following HPV4 vaccination (n = 681; rate ratio 1.12, 95% CI 0.85-1.47), and the association was unchanged by a history of immune-mediated disorders and time since vaccination. Exploratory analyses of individual autoimmune disorders found no significant risks, including for Bell palsy ( n = 65; rate ratio 1.73, 95% CI 0.77-3.89), optic neuritis (n = 67; rate ratio 1.57, 95% CI 0.74-3.33) and Graves disease (n = 47; rate ratio 1.55, 95% CI 0.92-2.63). Interpretation We did not observe an increased risk of autoimmune disorders following HPV4 vaccination among teenaged girls. These findings should reassure parents and health care providers

Effects Of Online Presentation Format On Student Stress

The number of online courses has increased in recent years which calls into question the presentation format of online learning that is most beneficial for students. The purpose for this study was to measure stress levels as a function of different asynchronous online presentation formats: slides only, slides with audio of a male instructor and slides with video and audio of the same instructor. Temperature, oxygen levels, GSR, blood pressure, heart rate were measured because prior studies have indicated a relation between stress and these physiological variables. Participants (N=27) were randomly assigned to one of three formats. Each format presented information about a fictitious island. Participants were administered a personality assessment, stress survey, and pre-exam. These were followed by the presentation about the island. At the end of the presentation questions about the island were asked (post-exam). The physiological measures were taken after the pre-exam, after the presentation, and after the post-exam. We hypothesized that stress levels would fluctuate throughout the course of the session with peak stress levels occurring prior to the postexam. Repeated measures ANOVAs revealed no significant effects of the presentation formats on the physiological stress measures (p\u3e. 05). We also predicted that the highest stress levels would be achieved during the slides-only format condition. The results did not support this hypothesis either (p\u3e.05). These nonsignificant results could be because of low power. Overall, the results suggest that the type of presentation format used for asynchronous learning does not have a significant impact on student stress

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.https://doi.org/10.3390/ijerph19191209