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A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system. 

Abstract With declining response rates and challenges of using RDD sampling for telephone surveys, collecting data from address-based samples has become more attractive. Two approaches are doing telephone interviews at telephone numbers matched to addresses and asking those at sampled addresses to call into an Interactive Voice Response (IVR) system to answer questions. This study used in-person interviewing to evaluate the effects of nonresponse and problems matching telephone numbers when telephone and IVR were used as the initial modes of data collection. The survey questions were selected from major US federal surveys covering a variety of topics. Both nonresponse and, for telephone, inability to find matches result in important nonresponse error for nearly half the measures across all topics, even after adjustments to fit the known demographic characteristics of the residents. Producing credible estimates requires using supplemental data collection strategies to reduce error from nonresponse. 

The gut is a well-established route of infection and target for viral damage by SARSCoV-2. This is supported by the clinical observation that about half of COVID-19 patients exhibit gastrointestinal (GI) symptoms. We asked whether the analysis of plasma could provide insight into gut barrier dysfunction in patients with COVID-19 infection. Plasma samples of COVID-19 patients (n=30) and healthy control (n=16) were collected during hospitalization. Plasma microbiome was analyzed using 16S rRNA sequencing, metatranscriptomic analysis, and gut permeability markers including FABP-2, PGN and LPS in both patient cohorts. Almost 65% (9 out 14) COVID-19 patients showed abnormal presence of gut microbes in their bloodstream. Plasma samples contained predominately Proteobacteria, Firmicutes, and Actinobacteria. The abundance of gram-negative bacteria (Acinetobacter, Nitrospirillum, Cupriavidus, Pseudomonas, Aquabacterium, Burkholderia, Caballeronia, Parabhurkholderia, Bravibacterium, and Sphingomonas) was higher than the gram-positive bacteria (Staphylococcus and Lactobacillus) in COVID-19 subjects. The levels of plasma gut permeability markers FABP2 (1282±199.6 vs 838.1±91.33; p=0.0757), PGN (34.64±3.178 vs 17.53±2.12; p<0.0001), and LPS (405.5±48.37 vs 249.6±17.06; p=0.0049) were higher in COVID-19 patients compared to healthy subjects. These findings support that the intestine may represent a source for bacteremia and may contribute to worsening COVID-19 outcomes. Therapies targeting the gut and prevention of gut barrier defects may represent a strategy to improve outcomes in COVID19 patients. 

ObjectiveThe objective of this study was to assess nonresponse error in telephone health survey data based on an address‐based sample. Data SourcesTelephone and in‐person interviews in Greater Boston. Study Design/Data CollectionInterviewers attempted telephone interviews at addresses that were matched to telephone numbers using questions drawn from federal health surveys. In‐person household interviews were carried out with telephone nonrespondents and at addresses without matching telephone numbers. Principal FindingsAfter adjusting for demographic differences, only eight of 15 estimates based on the telephone interviews lay within two standard errors of the estimates when data from all three groups were included. ConclusionsFor health surveys of address‐based samples, many estimates based on telephone respondents differ from the total population in ways that cannot be corrected with simple demographic adjustments.