Using Prior Knowledge and Student Engagement to Understand Student Performance in an Undergraduate Learning-to-Learn Course

This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective under-graduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement (Reeve, 2013). Hierarchical logistic regression using both prior knowledge and log data from the course revealed: (a) the weak-grades group demonstrated less behavioral engagement than the good-grades group, (b) the good-grades group demonstrated less agentic engagement than the excellent-grades group, and (c) models composed of both prior knowledge and engagement measures were more accurate than models composed of only engagement measures. Findings demonstrate students performing at different grade-levels may experience different challenges in their course engagement. This study informs our own instructional strategies and interventions to increase student success in the course and provides recommendations for other instructors to support student success

Identification of oral clefts as a risk factor for hearing loss during newborn hearing screening

Objective: This study assessed whether children with oral clefts are appropriately classified as at-risk for hearing loss at the time of newborn hearing screening and describes their screening and diagnostic results. Design: Birth certificates were used to identify children with cleft lip and palate or isolated cleft palate born in Washington State from 2008–2013. These were cross-referenced with the state’s Early Hearing Detection, Diagnosis and Intervention (EHDDI) database. Multivariate logistic regression was used to examine associations. Results: Birth records identified 235 children with cleft lip and palate and 116 with isolated cleft palate. Six children were listed as having both diagnoses. Only 138 (39%) of these children were designated as having a craniofacial anomaly in the EHDDI database. Children who were misclassified were less likely to have referred on initial hearing screening, OR 0.3, 95% CI [0.2, 0.5]. Misclassification of risk factor status was also associated with delayed hearing screening past 30 days of age or unknown age at screening, OR 4.4, 95% CI [1.5, 13.3], p = 0.008. Of 50 children with diagnostic results; 25 (50%) had hearing loss: 18 conductive, 2 mixed, and 5 unspecified. Conclusion: A majority of children with oral clefts were misclassified regarding risk factor for hearing loss in the EHDDI database

Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction

<p>Abstract</p> <p>Background</p> <p>Multifactor Dimensionality Reduction (MDR) has been introduced previously as a non-parametric statistical method for detecting gene-gene interactions. MDR performs a dimensional reduction by assigning multi-locus genotypes to either high- or low-risk groups and measuring the percentage of cases and controls incorrectly labelled by this classification – the classification error. The combination of variables that produces the lowest classification error is selected as the best or most fit model. The correctly and incorrectly labelled cases and controls can be expressed as a two-way contingency table. We sought to improve the ability of MDR to detect gene-gene interactions by replacing classification error with a different measure to score model quality.</p> <p>Results</p> <p>In this study, we compare the detection and power of MDR using a variety of measures for two-way contingency table analysis. We simulated 40 genetic models, varying the number of disease loci in the model (2 – 5), allele frequencies of the disease loci (.2/.8 or .4/.6) and the broad-sense heritability of the model (.05 – .3). Overall, detection using NMI was 65.36% across all models, and specific detection was 59.4% versus detection using classification error at 62% and specific detection was 52.2%.</p> <p>Conclusion</p> <p>Of the 10 measures evaluated, the likelihood ratio and normalized mutual information (NMI) are measures that consistently improve the detection and power of MDR in simulated data over using classification error. These measures also reduce the inclusion of spurious variables in a multi-locus model. Thus, MDR, which has already been demonstrated as a powerful tool for detecting gene-gene interactions, can be improved with the use of alternative fitness functions.</p

Large-Scale Multi-Omics Studies Provide New Insights into Blood Pressure Regulation

Recent genome-wide association studies uncovered part of blood pressure’s heritability. However, there is still a vast gap between genetics and biology that needs to be bridged. Here, we followed up blood pressure genome-wide summary statistics of over 750,000 individuals, leveraging comprehensive epigenomic and transcriptomic data from blood with a follow-up in cardiovascular tissues to prioritise likely causal genes and underlying blood pressure mechanisms. We first prioritised genes based on coding consequences, multilayer molecular associations, blood pressure-associated expression levels, and coregulation evidence. Next, we followed up the prioritised genes in multilayer studies of genomics, epigenomics, and transcriptomics, functional enrichment, and their potential suitability as drug targets. Our analyses yielded 1880 likely causal genes for blood pressure, tens of which are targets of the available licensed drugs. We identified 34 novel genes for blood pressure, supported by more than one source of biological evidence. Twenty-eight (82%) of these new genes were successfully replicated by transcriptome-wide association analyses in a large independent cohort (n = ~220,000). We also found a substantial mediating role for epigenetic regulation of the prioritised genes. Our results provide new insights into genetic regulation of blood pressure in terms of likely causal genes and involved biological pathways offering opportunities for future translation into clinical practice

CD4 intragenic SNPs associate with HIV-2 plasma viral load and CD4 count in a community-based study from Guinea-Bissau, West Africa.

OBJECTIVES: The human genetics of HIV-2 infection and disease progression is understudied. Therefore, we studied the effect of variation in 2 genes that encode products critical to HIV pathogenesis and disease progression: CD4 and CD209. DESIGN: This cross-sectional study consisted of 143 HIV-2, 30 HIV-1 + HIV-2 and 29 HIV-1-infected subjects and 194 uninfected controls recruited from rural Guinea-Bissau. METHODS: We genotyped 14 CD4 and 4 CD209 single nucleotide polymorphisms (SNPs) that were tested for association with HIV infection, HIV-2 plasma viral load (high vs. low), and CD4 T-cell count (high vs. low). RESULTS: The most significant association was between a CD4 haplotype rs11575097-rs10849523 and high viral load [odds ratio (OR): = 2.37, 95% confidence interval (CI): 1.35 to 4.19, P = 0.001, corrected for multiple testing], suggesting increased genetic susceptibility to HIV-2 disease progression for individuals carrying the high-risk haplotype. Significant associations were also observed at a CD4 SNP (rs2255301) with HIV-2 infection (OR: = 2.36, 95% CI: 1.19 to 4.65, P = 0.01) and any HIV infection (OR: = 2.50, 95% CI: 1.34 to 4.69, P = 0.004). CONCLUSIONS: Our results support a role of CD4 polymorphisms in HIV-2 infection, in agreement with recent data showing that CD4 gene variants increase risk to HIV-1 in Kenyan female sex workers. These findings indicate at least some commonality in HIV-1 and HIV-2 susceptibility

Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had PmetaHLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available

Space Environments and Spacecraft Effects Organization Concept

The National Aeronautics and Space Administration (NASA) is embarking on a course to expand human presence beyond Low Earth Orbit (LEO) while also expanding its mission to explore the solar system. Destinations such as Near Earth Asteroids (NEA), Mars and its moons, and the outer planets are but a few of the mission targets. Each new destination presents an opportunity to increase our knowledge of the solar system and the unique environments for each mission target. NASA has multiple technical and science discipline areas specializing in specific space environments disciplines that will help serve to enable these missions. To complement these existing discipline areas, a concept is presented focusing on the development of a space environments and spacecraft effects (SENSE) organization. This SENSE organization includes disciplines such as space climate, space weather, natural and induced space environments, effects on spacecraft materials and systems and the transition of research information into application. This space environment and spacecraft effects organization will be composed of Technical Working Groups (TWG). These technical working groups will survey customers and users, generate products, and provide knowledge supporting four functional areas: design environments, engineering effects, operational support, and programmatic support. The four functional areas align with phases in the program mission lifecycle and are briefly described below. Design environments are used primarily in the mission concept and design phases of a program. Engineering effects focuses on the material, component, sub-system and system-level selection and the testing to verify design and operational performance. Operational support provides products based on real time or near real time space weather to mission operators to aid in real time and near-term decision-making. The programmatic support function maintains an interface with the numerous programs within NASA, other federal government agencies, and the commercial sector to ensure that communications are well established and the needs of the programs are being met. The programmatic support function also includes working in coordination with the program in anomaly resolution and generation of lessons learned documentation. The goal of this space environment and spacecraft effects organization is to develop decision-making tools and engineering products to support all mission phases from mission concept through operations by focusing on transitioning research to application. Products generated by this space environments and effects application are suitable for use in anomaly investigations. This paper will describe the scope of the TWGs and their relationship to the functional areas, and discuss an organizational structure for this space environments and spacecraft effects organization

Assessment of Unabated Facility Emission Potentials for Evaluating Airborne Radionuclide Monitoring Requirements at Pacific Northwest National Laboratory - 2003

Assessments were performed to evaluate compliance with the airborne radionuclide emission monitoring requirements in the National Emission Standards for Hazardous Air Pollutants (NESHAP - U.S. Code of Federal Regulations, Title 40, Part 61, Subpart H) and Washington Administrative Code (WAC) 246-247: Radiation Protection - Air Emissions. In these assessments, potential unabated offsite doses were evaluated for emission locations at facilities owned by the U.S. Department of Energy and operated by Pacific Northwest National Laboratory (PNNL) on the Hanford Site. This report describes the inventory-based methods and provides the results for the assessment performed in 2003

A Rare Novel Deletion of the Tyrosine Hydroxylase Gene in Parkinson Disease

Tyrosine hydroxylase (TH) enzyme is a rate limiting enzyme in dopamine biosynthesis. Missense mutation in both alleles of the TH gene is known to cause dopamine-related phenotypes, including dystonia and infantile Parkinsonism. However, it is not clear if single allele mutation in TH modifies the susceptibility to the adult form of Parkinson disease (PD). We reported a novel deletion of entire TH gene in an adult with PD. The deletion was first identified by copy number variation (CNV) analysis in a genome-wide association study using Illumina Infinium BeadChips. After screening 635 cases and 642 controls, the deletion was found in one PD case but not in any control. The deletion was confirmed by multiple quantitative PCR (qPCR) assays. There is no additional exonic single nucleotide variant in the one copy of TH gene of the patient. The patient has an age-at-onset of 54 years, no evidence for dystonia, and was responsive to L-DOPA. This case supports the importance of the TH gene in PD pathogenesis and raises more attention to rare variants in candidate genes being a risk factor for Parkinson disease. © 2010 Wiley-Liss, Inc

Using Mendelian randomisation to identify opportunities for type 2 diabetes prevention by repurposing medications used for lipid management

Background: Maintaining a healthy lifestyle to reduce type 2 diabetes (T2D) risk is challenging and additional strategies for T2D prevention are needed. We evaluated several lipid control medications as potential therapeutic options for T2D prevention using tissue-specific predicted gene expression summary statistics in a two-sample Mendelian randomisation (MR) design. Methods: Large-scale European genome-wide summary statistics for lipids and T2D were leveraged in our multi-stage analysis to estimate changes in either lipid levels or T2D risk driven by tissue-specific predicted gene expression. We incorporated tissue-specific predicted gene expression summary statistics to proxy therapeutic effects of three lipid control medications [i.e., statins, icosapent ethyl (IPE), and proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK-9i)] on T2D susceptibility using two-sample Mendelian randomisation (MR). Findings: IPE, as proxied via increased FADS1 expression, was predicted to lower triglycerides and was associated with a 53% reduced risk of T2D. Statins and PCSK-9i, as proxied by reduced HMGCR and PCSK9 expression, respectively, were predicted to lower LDL-C levels but were not associated with T2D susceptibility. Interpretation: Triglyceride lowering via IPE may reduce the risk of developing T2D in populations of European ancestry. However, experimental validation using animal models is needed to substantiate our results and to motivate randomized control trials (RCTs) for IPE as putative treatment for T2D prevention. Funding: Only summary statistics were used in this analysis. Funding information is detailed under Acknowledgments. © 2022Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]