PAGER-CoV (http://discovery.informatics.uab.edu/PAGER-CoV/) is a new web-based database that can help biomedical researchers interpret coronavirus-related functional genomic study results in the context of curated knowledge of host viral infection, inflammatory response, organ damage, and tissue repair. The new database consists of 11 835 PAGs (Pathways, Annotated gene-lists, or Gene signatures) from 33 public data sources. Through the web user interface, users can search by a query gene or a query term and retrieve significantly matched PAGs with all the curated information. Users can navigate from a PAG of interest to other related PAGs through either shared PAG-to-PAG co-membership relationships or PAG-to-PAG regulatory relationships, totaling 19 996 993. Users can also retrieve enriched PAGs from an input list of COVID-19 functional study result genes, customize the search data sources, and export all results for subsequent offline data analysis. In a case study, we performed a gene set enrichment analysis (GSEA) of a COVID-19 RNA-seq data set from the Gene Expression Omnibus database. Compared with the results using the standard PAGER database, PAGER-CoV allows for more sensitive matching of known immune-related gene signatures. We expect PAGER-CoV to be invaluable for biomedical researchers to find molecular biology mechanisms and tailored therapeutics to treat COVID-19 patients.

Objective: To use machine learning to predict AIS scores for newly injured SCI patients at hospital discharge time from hospital admission data. Additionally, to analyze the best model for feature importance in order to validate the criticality of AIS score and highlight relevant demographic details.Design: Data used for training machine learning models was from the NSCISC database of United States SCI patient details. 18 real features were used from 417 provided ones, which mapped to 53 machine learning features after processing. 8 models were tuned on the dataset to predict AIS scores and Shapely analysis was performed to extract the most important of the 53 features.Participants: Patients within the NSCISC database who sustained injuries between 1972 and 2016 after data cleaning (n = 20,790).Outcome Measures: Test set multi-class and aggregated Shapely score magnitudes.Results: Ridge Classifier was the best performer with 73.6% test set accuracy. AIS scores and neurologic category at admission time were the best predictors of recovery. Demographically, features were less important but age, sex, marital status, and race stood out. AIS scores on admission are highly predictive of patient outcomes when combined with patient demographic data.Conclusion: Promising results in terms of predicting recovery were seen and Shapely analysis allowed for the machine learning model to be probed as whole, giving insight into overall feature trends.SignificanceThe research is intended to introduce the use of machine learning to enhance predictive capabilities of spinal cord injury recovery, to validate previous motor-sensory classification work, and to extract important deciders of recovery from constructed models.

Labor market theory suggests that a tax cut proportional to earned income will increase labor force participation rates. We formulate our research question: How does receiving the EITC change the number of hours that a single mother will work? We conclude that we have not found evidence that receiving the EITC has an impact on the total number of hours worked per week for a single mother.

If the opponent-process theory remains the best explanation for the pursuit of exercise, then practicing exercise routines is crucial to developing an active lifestyle. An individual seeking to make the transition from a sedentary to an active lifestyle should keep in mind that the emotional benefits of exercise will increase over time. Once established, exercise routines are self-perpetuating, as long as the individual follows the same routine. A different routine, however, will eliminate the relief stimuli and reset the opponent process, threatening recently active individuals with the risk of relapse.