Coordinated rhythmic movements are ubiquitous in animal behavior. In many organisms, chains of neural oscillators underlie the generation of these rhythms. In C. elegans, locomotor wave generation has been poorly understood; in particular, it is unclear where in the circuit rhythms are generated, and whether there exists more than one such generator. We used optogenetic and ablation experiments to probe the nature of rhythm generation in the locomotor circuit. We found that multiple sections of forward locomotor circuitry are capable of independently generating rhythms. By perturbing different components of the motor circuit, we localize the source of secondary rhythms to cholinergic motor neurons in the midbody. Using rhythmic optogenetic perturbation, we demonstrate bidirectional entrainment of oscillations between different body regions. These results show that, as in many other vertebrates and invertebrates, the C. elegans motor circuit contains multiple oscillators that coordinate activity to generate behavior.

Background Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Although approximately one in five Medicare beneficiaries are discharged from hospital acute care to postacute care at skilled nursing facilities (SNFs), little is known about access to timely medical care for these patients after they are admitted to a SNF. Our analysis of 2,392,753 such discharges from hospitals under fee-for-service Medicare in the period January 2012-October 2014 indicated that first visits by a physician or advanced practitioner (a nurse practitioner or physician assistant) for initial medical assessment occurred within four days of SNF admission in 71.5 percent of the stays. However, there was considerable variation in days to first visit at the regional, facility, and patient levels. We estimated that in 10.4 percent of stays there was no physician or advanced practitioner visit. Understanding the underlying reasons for, and consequences of, variability in timing and receipt of initial medical assessment after admission to a SNF for postacute care may prove important for improving patient outcomes and particularly relevant to current efforts to promote value-based purchasing in postacute care. As US health care moves toward value-based payment, hospitals are being held accountable for patient outcomes after discharge. Medicare's Hospital Readmissions Reduction Program, for example, made reducing hospital readmissions a national priority by applying financial penalties to hospitals with excess readmission rates. Skilled nursing facilities (SNFs), which provide postacute care for one in five Medicare beneficiaries, 1,2 represent an important discharge destination for patients who require rehabilitation or skilled nursing after an acute hospital stay. These patients are medically complex and at high risk of poor outcomes, with