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The international epidemiology of Hepatitis Delta Virus (HDV) is challenging to accurately estimate due to limited active surveillance for this rare infectious disease. Prior HDV epidemiological studies have relied on meta-analysis of aggregated and static datasets. These limitations restrict the capacity to actively detect low-level and/or geographically dispersed changes in the incidence of HDV diagnoses. This study was designed to provide a resource to track and analyze the international HDV epidemiology. Datasets analyzed collectively consisted of >700,000 HBV and >9,000 HDV reported cases ranging between 1999-2020. Datasets mined from government publications were identified for Argentina, Australia, Austria, Brazil, Bulgaria, Canada, Finland, Germany, Macao, Netherlands, New Zealand, Norway, Sweden, Taiwan, Thailand, United Kingdom, and United States. Time series analyses, including Mann-Kendall (MK) trend test, Bayesian Information Criterion (BIC), and hierarchal clustering, were performed to characterize trends in the HDV timelines. An aggregated prevalence of 2,560 HDV/HBV100,000 cases (95% CI 180-4940) or 2.56% HDV/HBV cases was identified, ranging from 0.26% in Canada to 20% in the United States. Structural breaks in the timeline of HDV incidence were identified in 2002, 2012, and 2017, with a significant increase occurring between 2013-2017. Significant increasing trends in reported HDV and HBV cases were observed in 47% and 24% of datasets, respectively. Analyses of the HDV incidence timeline identified four distinct temporal clusters, including Cluster I (Macao, Taiwan), Cluster II (Argentina, Brazil, Germany, Thailand), Cluster III (Bulgaria, Netherlands, New Zealand, United Kingdom, United States) and Cluster IV (Australia, Austria, Canada, Finland, Norway, Sweden). Tracking of HDV and HBV cases on an international scale is essential in defining the global impact of viral hepatitis. Significant disruptions of HDV and HBV epidemiology have been identified. Increased surveillance of HDV is warranted to further define the etiology of the recent breakpoints in the international HDV incidence.

Deep mutational scanning provides new insights into how mutations alter the expression and activity of the potassium ion channel Kir2.1, which is associated with many diseases.

Abstract Background Dry mouth currently affects roughly 20% of the population and is a condition characterized by chronic hyposalivation and/or subjective reports of xerostomia. Low saliva flow can be indicative of other undiagnosed diseases, such as primary Sjogren’s syndrome, and may contribute to difficulty chewing, increased caries susceptibility and infection. The passive drool test (PDT) is the primary method used to evaluate patients for hyposalivation but it is time-consuming and inconvenient. New methodology is needed to facilitate increased testing for hyposalivation in the dental clinic. The aim of this study was to evaluate an alternative method to measure salivary flow in dental offices. Methods In this study, we tested a new biomedical device, the BokaFlo™, to measure salivary flow in subjects in comparison to the current PDT standard. Participants completed an oral health questionnaire and saliva flow was evaluated by the PDT and the BokaFlo™ system. Results Saliva flow as measured by the BokaFlo™ positively correlated with the saliva flow measured by the PDT methodology (r = 0.22, p

Urinary tract infection (UTI) is an extremely common infectious condition affecting people throughout the world. Increasing antibiotic resistance in pathogens causing UTI threatens our ability to continue to treat patients in the clinics.

Background This study assessed the epidemiology of hepatitis delta virus (HDV) within the University of Utah UHealth health care system (2000–2021). Methods Analysis of HDV/HBV testing, diagnostic codes, liver enzymes, and comorbidities was performed. Results Among the 1962 HBV patients, only 22.2% underwent HDV testing, revealing an 8.3% positivity rate for HDV coinfections. This study observed a consistent increase in HBV and HDV cases, with higher HDV detection rates linked to increased testing. Limited HDV testing and potential screening biases were evident. Discussion Improved HDV testing and surveillance are crucial for early detection and implementation of targeted therapies. [ABSTRACT FROM AUTHOR]

Mutations in GDP-mannose pyrophosphorylase B (GMPPB), a catalyst for the formation of the sugar donor GDP-mannose, were recently identified as a cause of muscular dystrophy resulting from abnormal glycosylation of α-dystroglycan. In this series, we report nine unrelated individuals with GMPPB-associated dystroglycanopathy. The most mildly affected subject has normal strength at 25 years, whereas three severely affected children presented in infancy with intellectual disability and epilepsy. Muscle biopsies of all subjects are dystrophic with abnormal immunostaining for glycosylated α-dystroglycan. This cohort, together with previously published cases, allows preliminary genotype-phenotype correlations to be made for the emerging GMPPB common variants c.79G>C (p.D27H) and c.860G>A (p.R287Q). We observe that c.79G>C (p.D27H) is associated with a mild limb-girdle muscular dystrophy phenotype, whereas c.860G>A (p.R287Q) is associated with a relatively severe congenital muscular dystrophy typically involving brain development. Sixty-six percent of GMPPB families to date have one of these common variants.

Concentrating Solar Power (CSP) mirrors must have high slope accuracy in order to achieve high solar concentration. Deflectometry is an established method for measuring high-resolution, high-accuracy maps of mirror slope. In this paper we describe improvements to Sandia’s deflectometry system, SOFAST, which enable it to be accurately calibrated even in difficult conditions of poor access to system components, imperfect components, and varying ambient light. These extensions improve the robustness of the system and expand the range of problems it can solve.

Concentrating solar mirrors need to accurately focus sunlight onto a receiver. Since mirror surface normal determines reflected light direction, reflection-based metrology methods that measure surface normal have the advantage of directly measuring the mirror’s intended functional performance characteristic. Several methods have been reported that produce high-resolution measurements of mirror surface normal of concentrating solar mirrors [1-8]; these systems typically employ an optical target, a camera, and sometimes an external light source. These elements must be placed in a configuration that satisfies multiple feasibility constraints, including mechanical and optical visibility and reflection constraints. For complex problems or installations in cluttered environments, it can be difficult to analyze all of these constraints manually. We have created a computer automated design (CAD) tool that aids in visualization of reflection-based metrology systems, making it possible to determine layouts for complex metrology design problems. The tool is implemented SolidWorks, a commercial CAD system [9]. By exploiting the SoidWorks constraint satisfaction mechanism, the tool is able to present interactive visualization of reflection and field of view constraints in real time without any additional code or macros. The tool supports ease of use and multiple design configurations through an ordinary spreadsheet interface. The tool can be used to address a variety of reflection-based metrology design problems, and is available to the public as open source.

This paper outlines the design and error analysis for a flux characterization device (i.e. flux wand) which traverses the aperture of a falling particle receiver cavity providing the incident power measurement needed for the calculation of receiver efficiency without significantly disrupting receiver output. The design features a linear actuator, water cooled extension arm, and refractory bar. The refractory bar/flux wand is a diffusely reflective surface which reflects light as it is translated across the receiver aperture to provide a light intensity distribution that is correlated to a measured flux. The component sizing, constrained by a typical 30 frame per second camera and a 4-point average of the measured reflectance, is presented. Simulation reveals the error that results from a 5% variation in flux wand reflectance is within 4% of the true value.

Links between proglacial lakes and glacier dynamics are poorly understood but are necessary to predict how mountain glaciers will react to a warmer, wetter climate, where such lakes are expected to increase both in number and volume. Here, we examine a long-term (~120 year) record of terminus retreat, thinning and surface velocities from in-situ and remote sensing observations at the terminus of Kaskawulsh Glacier, Yukon, Canada, and determine the impact of a local proglacial hydrological reorganisation on glacier dynamics. After an initial deceleration during the late 1990s, terminus velocities increased at a rate of 3 m a−2 from 2000–12, while proglacial Slims Lake area increased simultaneously. The rapid drainage of the lake in May 2016 substantially altered the velocity profile, decreasing annual velocities by 48% within 3 km of the terminus between 2015 and 2021, at an average rate of ~ 12.5 m a−2. A key cause of the rapid drop in glacier motion was a reduction in flotation of the lower part of the glacier terminus after lake drainage. This has important implications for glacier dynamics and provides one of the first assessments of the impacts of a rapid proglacial lake drainage event on local terminus velocities.

Concentrating Solar Power (CSP) systems use mirorrs to focus light onto a receiver, and high optical slope accuracy is required to achieve high concentration. Deflectometry is a technique for measuring optical slope directly, at fine spatial resolution across a miror surface. In this paper we report techniques for making an existing deflectometry system, SOFAST, more flexible and easier to deploy across a wide range of application scenarios. Example applications include mirror prototype development, high-volume manufacturing, outdoor in situ heliostat inspection, and education. We report using the resulting system to measure mirrors ranging in diameter from 0.13 m to 9.3 m, a range of nearly two orders of magnitude. Hardware implementations vary from a simple laptop computer to a system combining a computer, projector, screen, and camera. These efforts pursue a goal of making deflectometry easier to use, applicable to a wider range of problems, and widely available to the CSP community.

Abstract Background The interaction between cancer diagnoses and COVID‐19 infection and outcomes is unclear. We leveraged a state‐wide, multi‐institutional database to assess cancer‐related risk factors for poor COVID‐19 outcomes. Methods We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at 17 California medical centers. We identified adults tested for SARS‐CoV‐2 from 2/1/2020–12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS‐CoV‐2 test. Secondary outcomes were SARS‐CoV‐2 positivity and severe COVID‐19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer‐related factors associated with outcomes. Results We identified 409,462 patients undergoing SARS‐CoV‐2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67–0.74, p

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

In the domain of planetary science, novelty detection is gaining attention because of the operational opportunities it offers, including annotated data products and downlink prioritization. Using a variational autoencoder (VAE), this work improves upon state-of-the-art novelty detection performance in the context of Martian exploration by >7% (measured by the area under the receiver operating characteristic curve (ROC AUC)). Autoencoders, especially VAEs, perform well across all classes of novelties defined for Martian exploration. VAEs are shown to have high recall in the Martian context, making them particularly useful for on-ground processing. Convolutional autoencoders (CAEs), on the other hand, demonstrate high precision making them good candidates for onboard downlink prioritization. In our implementation adversarial autoencoders (AAEs) are also shown to perform on par with state-of-the-art. Dimensionality reduction is a key feature of autoencoders for novelty detection. In this study the impact of dimensionality reduction on detection quality is explored, showing that both VAEs and AAEs achieve comparable ROC AUCs to CAEs despite observably poorer (blurred) image reconstructions; this is observed both in Martian data and in lunar analogue data.

Abstract Background Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

Abstract Introduction Alzheimer's disease (AD) is characterized by neurotoxic immuno‐inflammation concomitant with cytotoxic oligomerization of amyloid beta (Aβ) and tau, culminating in concurrent, interdependent immunopathic and proteopathic pathogeneses. Methods We performed a comprehensive series of in silico, in vitro, and in vivo studies explicitly evaluating the atomistic–molecular mechanisms of cytokine‐mediated and Aβ‐mediated neurotoxicities in AD. Next, 471 new chemical entities were designed and synthesized to probe the pathways identified by these molecular mechanism studies and to provide prototypic starting points in the development of small‐molecule therapeutics for AD. Results In response to various stimuli (e.g., infection, trauma, ischemia, air pollution, depression), Aβ is released as an early responder immunopeptide triggering an innate immunity cascade in which Aβ exhibits both immunomodulatory and antimicrobial properties (whether bacteria are present, or not), resulting in a misdirected attack upon “self” neurons, arising from analogous electronegative surface topologies between neurons and bacteria, and rendering them similarly susceptible to membrane‐penetrating attack by antimicrobial peptides (AMPs) such as Aβ. After this self‐attack, the resulting necrotic (but not apoptotic) neuronal breakdown products diffuse to adjacent neurons eliciting further release of Aβ, leading to a chronic self‐perpetuating autoimmune cycle. AD thus emerges as a brain‐centric autoimmune disorder of innate immunity. Based upon the hypothesis that autoimmune processes are susceptible to endogenous regulatory processes, a subsequent comprehensive screening program of 1137 small molecules normally present in human brain identified tryptophan metabolism as a regulator of brain innate immunity and a source of potential endogenous anti‐AD molecules capable of chemical modification into multi‐site therapeutic modulators targeting AD's complex immunopathic–proteopathic pathogenesis. Discussion Conceptualizing AD as an autoimmune disease, identifying endogenous regulators of this autoimmunity, and designing small molecule drug‐like analogues of these endogenous regulators represents a novel therapeutic approach for AD.

BackgroundGiven the increasing use of photo-activated resins in dentistry, dentists and researchers need a user-friendly dental radiometer to measure the power output from dental light-curing units (LCUs).ObjectiveOur goal was to measure the accuracy of two brands of dental radiometers in reporting the power (mW) from twelve brands of contemporary LCUs compared to a 'gold standard' (GS) reference value obtained from an integrating sphere attached to a fiberoptic spectroradiometer.MethodsThe power output was measured from two units of 12 brands of LCUs, five times on the ''GS" system, five times on two Bluephase Meter II dental radiometers, and five times on two Mini Gig hand-held spectroradiometers. The emission spectrum was also recorded using the 'GS' integrating sphere. The power values reported by each meter were subjected to t-tests to compare the two examples of each LCU, and 3-way ANOVA followed by Bonferroni's post-hoc tests. Regression analyses were also performed to determine the relationship between the data from the hand-held radiometers and the 'GS' integrating sphere.ResultsThere was a large difference in the power values (mW) and the emission spectra from the 12 brands of LCUs on their standard-settings (p 98%).ConclusionWe concluded that the power values reported from both brands of dental radiometers we tested were accurate, provided that the light source did not emit wavelengths of light that were beyond the radiometer's detection limit.

In a lidar system, replacing moving components with solid-state devices is highly anticipated to make a reliable and compact lidar system, provided that a substantially large beam area with a large angular extent as well as high angular resolution is assured for the lidar transmitter and receiver. A new quasi-solid-state lidar optical architecture employs a transmitter with a two-dimensional MEMS mirror for fine beam steering at a fraction of the degree of the angular resolution and is combined with a digital micromirror device for wide FOV scanning over 37 degree while sustaining a large aperture area of 140 mm squared. In the receiver, a second digital micromirror device is synchronized to the transmitter DMD, which enables a large FOV receiver. An angular resolution of 0.57°(H) by 0.23° (V) was achieved with 0.588 fps for scanning 1344 points within the field of view.