Forecasting severe respiratory disease hospitalizations using machine learning algorithms.

Background: Forecasting models predicting trends in hospitalization rates have the potential to inform hospital management during seasonal epidemics of respiratory diseases and the associated surges caused by acute hospital admissions. Hospital bed requirements for elective surgery could be better planned if it were possible to foresee upcoming peaks in severe respiratory illness admissions. Forecasting models can also guide the use of intervention strategies to decrease the spread of respiratory pathogens and thus prevent local health system overload.

Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath.

enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system.

Subclinical maternal autoimmune disease leading to congenital high degree atrioventricular block: Case report and review of the literature.

Maternal autoimmune disease is the most common cause of congenital heart block (CHB), a rare illness characterized by fibrosis and calcification of the fetal atrioventricular (AV) node due to maternal autoantibodies anti-SSA/Ro and anti-SSB/La. We report the full autopsy and clinical information on a female neonate with high degree AV block and calcification in the AV node, atrial approaches to the AV node, and both right and left bundle branches, born to a 27-year-old female with subclinical autoimmune disease.

AdductHunter: identifying protein-metal complex adducts in mass spectra.

Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis.

Stellar scintillation statistics and the impact of aperture averaging on space-to-ground optical communications.

Statistical probability distributions characterizing received optical power fluctuations, or scintillation, enable performance predictions of space-to-ground optical communication systems. In this paper, we present measurements of stellar scintillation over a wide range of elevation angles and turbulence conditions collected simultaneously with a 5 cm and 40 cm telescope aperture, which allows a comparison between minimal and significant aperture averaging conditions. The measured data is compared to a reasonable set of candidate probability distribution functions (PDFs), including lognormal, which is most often cited in the literature for weak to moderate scintillation.