Multi-Disciplinary Investigation Identifies Increased Potency of Ethyl-Parathion Inhaled Within a Soil-Dust Matrix to Cause Acetylcholinesterase-Dependent Molecular Impacts.

Neurotoxicity investigations of inhaled organophosphorus pesticide (OP), ethyl-parathion (EP), were conducted in Sprague Dawley rats comparing exposures to EP volatilized at 0, 1, 10, and 20mg/m versus EP incorporated into soil dust (5mg/m) at 0, 0.0095, 0.09, and 0.

Analyzing diversity trends in dermatology: A comprehensive overview.

Background: Dermatology lags behind other medical specialties in workforce diversity, particularly regarding gender, race, and ethnicity. This study aims to analyze the current demographics of dermatology physicians in the United States, comparing them with other medical specialties, the overall population of practicing U.S.

A scalable convolutional neural network approach to fluid flow prediction in complex environments.

We evaluate the capability of convolutional neural networks (CNNs) to predict a velocity field as it relates to fluid flow around various arrangements of obstacles within a two-dimensional, rectangular channel. We base our network architecture on a gated residual U-Net template and train it on velocity fields generated from computational fluid dynamics (CFD) simulations. We then assess the extent to which our model can accurately and efficiently predict steady flows in terms of velocity fields associated with inlet speeds and obstacle configurations not included in our training set.

Probing Asymmetric Interactions with Time-Separated Mutual Information: A Case Study Using Golden Shiners.

Leader-follower modalities and other asymmetric interactions that drive the collective motion of organisms are often quantified using information theory metrics like transfer or causation entropy. These metrics are difficult to accurately evaluate without a much larger number of data than is typically available from a time series of animal trajectories collected in the field or from experiments. In this paper, we use a generalized leader-follower model to argue that the time-separated mutual information between two organism positions can serve as an alternative metric for capturing asymmetric correlations that is much less data intensive and more accurately estimated by popular -nearest neighbor algorithms than transfer entropy.

The Dynamic Spatial Structure of Flocks.

Studies of collective motion have heretofore been dominated by a thermodynamic perspective in which the emergent "flocked" phases are analyzed in terms of their time-averaged orientational and spatial properties. Studies that attempt to scrutinize the dynamical processes that spontaneously drive the formation of these flocks from initially random configurations are far more rare, perhaps owing to the fact that said processes occur far from the eventual long-time steady state of the system and thus lie outside the scope of traditional statistical mechanics. For systems whose dynamics are simulated numerically, the nonstationary distribution of system configurations can be sampled at different time points, and the time evolution of the average structural properties of the system can be quantified.

Comprehensive Factors for Predicting the Complications of DiabetesMellitus: A Systematic Review.

Background: This article focuses on extracting a standard feature set for predicting the complications of diabetes mellitus by systematically reviewing the literature. It is conducted and reported by following the guidelines of PRISMA, a well-known systematic review and meta-analysis method. The research articles included in this study are extracted using the search engine "Web of Science" over eight years.

Predicting ophthalmic clinic non-attendance using machine learning: Development and validation of models using nationwide data.

Background: Ophthalmic clinic non-attendance in New Zealand is associated with poorer health outcomes, marked inequities and costs NZD$30 million per annum. Initiatives to improve attendance typically involve expensive and ineffective brute-force strategies. The aim was to develop machine learning models to accurately predict ophthalmic clinic non-attendance.

Toxicokinetic Modeling of Per- and Polyfluoroalkyl Substance Concentrations within Developing Zebrafish () Populations.

Per- and polyfluoroalkyl substances (PFAS) are pervasive environmental contaminants, and their relative stability and high bioaccumulation potential create a challenging risk assessment problem. Zebrafish () data, in principle, can be synthesized within a quantitative adverse outcome pathway (qAOP) framework to link molecular activity with individual or population level hazards. However, even as qAOP models are still in their infancy, there is a need to link internal dose and toxicity endpoints in a more rigorous way to further not only qAOP models but adverse outcome pathway frameworks in general.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.

Experiments in cross-domain few-shot learning for image classification.

Cross-domain few-shot learning has many practical applications. This paper attempts to shed light on suitable configurations of feature exactors and 'shallow' classifiers in this machine learning setting. We apply ResNet-based feature extractors pretrained on two versions of the ImageNet dataset to five target domains with different degrees of similarity to ImageNet, varying the feature extractor size, the network stage at which features are extracted, and the learning algorithm applied to the extracted features.

Predicting glucose level with an adapted branch predictor.

Background And Objective: Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone.

COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications.

The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of "social distancing" policy, but infections have continued to spread despite these efforts. Absent a vaccine, authorities have few other tools by which to mitigate further spread of the virus.

Magnetic induction inspires a schematic theory for crosstalk-driven relaxation dynamics in cells.

Establishing formal mathematical analogies between disparate physical systems can be a powerful tool, allowing for the well studied behavior of one system to be directly translated into predictions about the behavior of another that may be harder to probe. In this paper we lay the foundation for such an analogy between the macroscale electrodynamics of simple magnetic circuits and the microscale chemical kinetics of transcriptional regulation in cells. By artificially allowing the inductor coils of the former to elastically expand under the action of their Lorentz pressure, we introduce nonlinearities into the system that we interpret through the lens of our analogy as a schematic model for the impact of crosstalk on the rates of gene expression near steady state.

Devil in the details: Mechanistic variations impact information transfer across models of transcriptional cascades.

The transcriptional network determines a cell's internal state by regulating protein expression in response to changes in the local environment. Due to the interconnected nature of this network, information encoded in the abundance of various proteins will often propagate across chains of noisy intermediate signaling events. The data-processing inequality (DPI) leads us to expect that this intracellular game of "telephone" should degrade this type of signal, with longer chains losing successively more information to noise.

Modeling a synthetic aptamer-based riboswitch biosensor sensitive to low hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) concentrations.

RNA aptamers are relatively short nucleic acid sequences that bind targets with high affinity, and when combined with a riboswitch that initiates translation of a fluorescent reporter protein, can be used as a biosensor for chemical detection in various types of media. These processes span target binding at the molecular scale to fluorescence detection at the macroscale, which involves a number of intermediate rate-limiting physical (e.g.

Modeling the impacts of a simple meiotic gene drive on small, homeostatic populations.

Gene drives offer unprecedented control over the fate of natural ecosystems by leveraging non-Mendelian inheritance mechanisms to proliferate synthetic genes across wild populations. However, these benefits are offset by a need to avoid the potentially disastrous consequences of unintended ecological interactions. The efficacy of many gene-editing drives has been brought into question due to predictions that they will inevitably be thwarted by the emergence of drive-resistant mutations, but these predictions derive largely from models of large or infinite populations that cannot be driven to extinction faster than mutations can fixate.

Stochastically modeling multiscale stationary biological processes.

Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive (multiscale stochastic models), fail to capture the effects of noise across a system (chemical kinetic models), or fail to provide sufficient biological fidelity because of broad simplifying assumptions (stochastic differential equations). We use a new approach to modeling multiscale stationary biological processes that embraces the noise found in experimental data to provide estimates of the parameter uncertainties and the potential mis-specification of models.

Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine.

Digital innovations have led to an explosion of data in healthcare, driving processes of democratization and foreshadowing the end of the paternalistic era of medicine and the inception of a new epoch characterized by patient-centered care. We illustrate that the "do it yourself" (DIY) automated insulin delivery (AID) innovation of diabetes is a leading example of democratization of medicine as evidenced by its application to the three pillars of democratization in healthcare (intelligent computing; sharing of information; and privacy, security, and safety) outlined by Stanford but also within a broader context of democratization. The heuristic algorithms integral to DIY AID have been developed and refined by human intelligence and demonstrate intelligent computing.

Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g.

A survey of neural network-based cancer prediction models from microarray data.

Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identified articles published between 2013-2018 in scientific databases using keywords such as cancer classification, cancer analysis, cancer prediction, cancer clustering and microarray data.

Detection of hexahydro-1,3-5-trinitro-1,3,5-triazine (RDX) with a microbial sensor.

Explosives such as hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) are common contaminants found in soil and groundwater at military facilities worldwide, but large-scale monitoring of these contaminants at low concentrations is difficult. Biosensors that incorporate aptamers with high affinity and specificity for a target are a novel way of detecting these compounds. This work describes novel riboswitch-based biosensors for detecting RDX.

Privacy protection for health information research in New Zealand district health boards.

Aim: To examine the practices used by New Zealand's 20 district health boards (DHBs) to protect patient privacy when patient information is used for research, and particularly practices for de-identifying information.

Method: An e-mailed questionnaire survey, using New Zealand's Official Information Act to request information on the policies and practices of each DHB.

Results: 19/20 DHBs (95%) responded to the survey, one of which reported that it did not provide patient information for research.

Statistical relationship between metabolic decomposition and chemical uptake predicts bioconcentration factor data for diverse chemical exposures.

Background: A challenge of in vitro to in vivo extrapolation (IVIVE) is to predict the physical state of organisms exposed to chemicals in the environment from in vitro exposure assay data. Although toxicokinetic modeling approaches promise to bridge in vitro screening data with in vivo effects, they are often encumbered by a need for redesign or re-parameterization when applied to different tissues or chemicals.

Results: We demonstrate a parameterization of reverse toxicokinetic (rTK) models developed for the adult zebrafish (Danio rerio) based upon particle swarm optimizations (PSO) of the chemical uptake and degradation rates that predict bioconcentration factors (BCF) for a broad range of chemicals.