Temporally Multi-Scale Sparse Self-Attention for Physical Activity Data Imputation.

Wearable sensors enable health researchers to continuously collect data pertaining to the physiological state of individuals in real-world settings. However, such data can be subject to extensive missingness due to a complex combination of factors. In this work, we study the problem of imputation of missing step count data, one of the most ubiquitous forms of wearable sensor data.

Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach.

Objective: Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context.

Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care.

This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI-based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges.

Opposing, spatially-determined epigenetic forces impose restrictions on stochastic olfactory receptor choice.

Olfactory receptor (OR) choice represents an example of genetically hardwired stochasticity, where every olfactory neuron expresses one out of ~2000 OR alleles in the mouse genome in a probabilistic, yet stereotypic fashion. Here, we propose that topographic restrictions in OR expression are established in neuronal progenitors by two opposing forces: polygenic transcription and genomic silencing, both of which are influenced by dorsoventral gradients of transcription factors NFIA, B, and X. Polygenic transcription of OR genes may define spatially constrained OR repertoires, among which one OR allele is selected for singular expression later in development.

BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data.

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data.

Clinical guideline highlights for the hospitalist: Pediatric direct hospital admission.

Direct Admission to the Hospital for Children in the United States RELEASE DATE: March 3, 2023 PRIOR VERSION(S): n/a DEVELOPER: American Academy of Pediatrics, Committee on Hospital Care FUNDING SOURCE: American Academy of Pediatrics TARGET POPULATION: Children who are potential candidates for direct hospital admission.

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions.

Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies.

Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors.

Background: Our group has established the feasibility of using on-body electrocardiographic (ECG) sensors to detect cocaine use in the human laboratory. The purpose of the current study was to test whether ECG sensors and features are capable of discriminating cocaine use from other non-cocaine sympathomimetics.

Methods: Eleven subjects with cocaine use disorder wore the Zephyr BioHarness™ 3 chest band under six experimental (drug and non-drug) conditions, including 1) laboratory, intravenous cocaine self-administration, 2) after a single oral dose of methylphenidate, 3) during aerobic exercise, 4) during tobacco use (N=7 who smoked tobacco), and 5) during routine activities of daily inpatient living (unit activity).

Juneteenth in STEMM and the barriers to equitable science.

We are 52 Black scientists. Here, we establish the context of Juneteenth in STEMM and discuss the barriers Black scientists face, the struggles they endure, and the lack of recognition they receive. We review racism's history in science and provide institutional-level solutions to reduce the burdens on Black scientists.

Integration of olfactory and auditory cues eliciting parental behavior.

Parental care is crucial for the survival of all mammalian species. Given the evolutionary importance of parenting, this behavioral repertoire must be supported by circuitry that is innate but also capable of learning and flexibility - adjusting to changing environmental demands. In rodents, parental care is triggered by the perception of cues emitted by a pup.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts.

Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol.

Recent advances in mobile and wearable technologies have led to new forms of interventions, called "Just-in-Time Adaptive Interventions" (JITAI). JITAIs interact with the individual at the most appropriate time and provide the most appropriate support depending on the continuously acquired Intensive Longitudinal Data (ILD) on participant physiology, behavior, and contexts. These advances raise an important question: How do we model these data to better understand and intervene on health behaviors? The HeartSteps II study, described here, is a Micro-Randomized Trial (MRT) intended to advance both intervention development and theory-building enabled by the new generation of mobile and wearable technology.

Bianca Jones Marlin.

Bianca Jones Marlin draws inspiration for her work from people's stories. In an interview with Neuron, she talks about overcoming the challenges of the past year-opening a new lab, caring for a young family, and gaining the strength to speak her truth and create the inclusive community academia strives for.

Oxytocin neurons enable social transmission of maternal behaviour.

Maternal care, including by non-biological parents, is important for offspring survival. Oxytocin, which is released by the hypothalamic paraventricular nucleus (PVN), is a critical maternal hormone. In mice, oxytocin enables neuroplasticity in the auditory cortex for maternal recognition of pup distress.

New onset ANCA-associated vasculitis in an adolescent during an acute COVID-19 infection: a case report.

Background: SARS-CoV-2 has been found to be exquisitely adept at triggering autoimmunity and multiple new onset autoimmune diseases have been described as a post-infectious complication of COVID-19 infection in the adult population. Less has been described in the pediatric population, as infections are more likely to be asymptomatic and less severe. This case reports a previously healthy adolescent patient with new onset antineutrophil cytoplasmic autoantibody-associated vasculitis (AAV) diagnosed in the setting of acute COVID-19 infection.

Digitizing clinical trials.

Clinical trials are a fundamental tool used to evaluate the efficacy and safety of new drugs and medical devices and other health system interventions. The traditional clinical trials system acts as a quality funnel for the development and implementation of new drugs, devices and health system interventions. The concept of a "digital clinical trial" involves leveraging digital technology to improve participant access, engagement, trial-related measurements, and/or interventions, enable concealed randomized intervention allocation, and has the potential to transform clinical trials and to lower their cost.

The Next 50 Years of Neuroscience.

On the 50th anniversary of the Society for Neuroscience, we reflect on the remarkable progress that the field has made in understanding the nervous system, and look forward to the contributions of the next 50 years. We predict a substantial acceleration of our understanding of the nervous system that will drive the development of new therapeutic strategies to treat diseases over the course of the next five decades. We also see neuroscience at the nexus of many societal topics beyond medicine, including education, consumerism, and the justice system.

iLid: Low-power Sensing of Fatigue and Drowsiness Measures on a Computational Eyeglass.

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment.

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices.

In this paper, we present a new approach to learning cascaded classifiers for use in computing environments that involve networks of heterogeneous and resource-constrained, low-power embedded compute and sensing nodes. We present a generalization of the classical linear detection cascade to the case of tree-structured cascades where different branches of the tree execute on different physical compute nodes in the network. Different nodes have access to different features, as well as access to potentially different computation and energy resources.

Sex-Specific Differences in Oxytocin Receptor Expression and Function for Parental Behavior.

Parental care is among the most profound behavior expressed by humans and other animals. Despite intense interest in understanding the biological basis of parental behaviors, it remains unknown how much of parenting is encoded by the genome and which abilities instead are learned or can be refined by experience. One critical factor at the intersection between innate behaviors and experience-dependent learning is oxytocin, a neurohormone important for maternal physiology and neuroplasticity.

Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series.

This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time series. This is achieved by adding a spatio-temporal compactness term to the optimization objective of the embedding.

Learning Time Series Detection Models from Temporally Imprecise Labels.

In this paper, we consider the problem of learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels occur in areas like mobile health research when human annotators are tasked with labeling the occurrence of very short duration events.