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When the angle of the mirror ranging from 30° to 40°, the focal depth can change from 39 mm to 140 mm. LY333531 Furthermore, the focus can be controlled in a sector with an angle of 60°. The "acoustic projector" demonstrates simple but precise control of acoustic fields and may broaden their applicability. In order to show its imaging ability, the three groups of target balls at different positions were imaged and given their position information by scanning the mirrors in simulation.3D neural networks are widely used in real-world applications (e.g., AR/VR headsets, self-driving cars). They are required to be fast and accurate; however, limited hardware resources on edge devices make these requirements rather challenging. Previous work processes 3D data using either voxel-based or point-based neural networks, but both types of 3D models are not hardware-efficient due to the large memory footprint and random memory access. In this paper, we study 3D deep learning from the efficiency perspective. We first systematically analyze the bottlenecks of previous 3D methods. We then combine the best from point-based and voxel-based models together and propose a novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We further enhance this primitive with the sparse convolution to make it more effective in processing large (outdoor) scenes. Based on our designed 3D primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the best 3D network architecture given a resource constraint. We evaluate our proposed method on six representative benchmark datasets, achieving state-of-the-art performance with 1.8-23.7x measured speedup. Furthermore, our method has been deployed to the autonomous racing vehicle of MIT Driverless, achieving larger detection range, higher accuracy and lower latency.Semantic parsing, edge detection and pose estimation of human are three closely-related tasks. They present human characteristics from three complementary aspects. Compared to learning them individually, solving these tasks jointly can explore the interaction of their contextual cues. However, prior works usually study the fusion of two of them, e.g., parsing and pose, parsing and edge. In this paper, we explore how pixel-level semantics, human boundaries and joint locations can be effectively learned in a unified model. Specifically, we propose an end-to-end trainable Human Task Correlation Machine (HTCorrM) to implement the three tasks. It is asymmetric in that it supports a main task using the other two as auxiliary tasks. We also introduce a Heterogeneous Non-Local module (HNL) to discover the correlations of the three heterogeneous domains. HNL fully explores the global dependency among tasks between any two positions in the feature map. Experimental results on human parsing, pose estimation and body edge detection demonstrate that HTCorrM achieves competitive performance. We show that when designated as the main task, the accuracy of each of the three tasks is improved. Importantly, comparative studies confirm the advantages of our proposed feature correlation strategy over the traditional feature concatenation or post processing.

The objective of this work was to develop and experimentally validate a bioimpedance-based framework to identify tissues in contact with the surgical instrument during cataract surgery.

This work introduces an integrated hardware and software solution based on the unique bioimpedance of different intraocular tissues. The developed hardware can be readily integrated with commonly used surgical instruments. The proposed software framework, which encompasses data acquisition and a machine-learning classifier, is fast enough to be deployed in real-time surgical interventions. The experimental protocol included bioimpedance data collected from 31 ex vivo pig eyes targeting four intraocular tissues Iris, Cornea, Lens, and Vitreous.

A classifier based on a support vector machine exhibited an overall accuracy of 91% across all trials. The algorithm provided substantial performance in detecting the intraocular tissues with 100% reliability and 95% sensitivity for the lens, along with 88% reliability and 94% sensitivity for the vitreous.

The developed impedance-based framework demonstrated successful intraocular tissue identification.

Clinical implications include the ability to ensure safe operations by detecting posterior capsule rapture with 94% probability and improving surgical efficacy through lens detection with 100% reliability.

Clinical implications include the ability to ensure safe operations by detecting posterior capsule rapture with 94% probability and improving surgical efficacy through lens detection with 100% reliability.

Current treatment of type 1 diabetes by closed-loop approaches depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore glucose homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurement. This work aims at investigating the performance of the biosensor in a blood glucose control loop, to establish an in silico proof-of-concept.

Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inner regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Then, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients.

With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as performing as standard treatments, and outperformed them under challenging conditions.

This work validates the concept of using pancreatic islets algorithms in an artificial pancreas in silico.

Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.The Abbott BinaxNOW rapid antigen test is cheaper and faster than real-time reverse transcription PCR (rRT-PCR) for detecting severe acute respiratory syndrome coronavirus 2. We compared BinaxNOW with rRT-PCR in 769 paired specimens from 342 persons during a coronavirus disease outbreak among horse racetrack workers in California, USA. We found positive percent agreement was 43.3% (95% CI 34.6%-52.4%), negative percent agreement 100% (95% CI 99.4%-100%), positive predictive value 100% (95% CI 93.5%-100%), and negative predictive value 89.9% (95% CI 87.5%-92.0%). Among 127 rRT-PCR-positive specimens, the 55 with paired BinaxNOW-positive results had a lower mean cycle threshold than the 72 with paired BinaxNOW-negative results (17.8 vs. 28.5; p less then 0.001). Of 100 specimens with cycle threshold less then 30, a total of 51 resulted in positive virus isolation; 45 (88.2%) of those were BinaxNOW-positive. Our comparison supports immediate isolation for BinaxNOW-positive persons and confirmatory testing for negative persons.Although coronavirus disease (COVID-19) outbreaks have been relatively well controlled in Hong Kong, containment remains challenging among socioeconomically disadvantaged persons. They are at higher risk for widespread COVID-19 transmission through sizable clustering, probably because of exposure to social settings in which existing mitigation policies had differential socioeconomic effects.We aimed to generate an unbiased estimate of the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 4 urban counties in Utah, USA. We used a multistage sampling design to randomly select community-representative participants >12 years of age. During May 4-June 30, 2020, we collected serum samples and survey responses from 8,108 persons belonging to 5,125 households. We used a qualitative chemiluminescent microparticle immunoassay to detect SARS-CoV-2 IgG in serum samples. We estimated the overall seroprevalence to be 0.8%. The estimated seroprevalence-to-case count ratio was 2.5, corresponding to a detection fraction of 40%. Only 0.2% of participants from whom we collected nasopharyngeal swab samples had SARS-CoV-2-positive reverse transcription PCR results. SARS-CoV-2 antibody prevalence during the study was low, and prevalence of PCR-positive cases was even lower. The comparatively high SARS-CoV-2 detection rate (40%) demonstrates the effectiveness of Utah's testing strategy and public health response.Recovery-oriented cross-sectoral collaboration is a cornerstone of the debate concerning health professionals and users of mental health services and constitutes an objective in government health policy in Scandinavia and other Western countries. Users do not find that professionals communicate with each other across specific sectors regarding plans that have been prepared. They often experience that they have to start over again every time they switch between treatment locations. The aim of this study is to develop a recovery-oriented model for network meetings. Health professionals and users with experience from mental health services participated in three workshops to discuss and achieve a plan for recovery-oriented network meetings. Knowledge was generated in dynamic research cycles that were experiential, presentational, propositional, and practical. Themes were developed and framed by a content analysis.Recommendations are presented as a narrative from all the participants involved. The overall theme was 'more focus on personal recovery' with subthemes such as 'CHIME as a recovery-oriented approach'. In addition, other themes were generated such as 'open dialogical meetings', with subthemes such as 'meeting structures' and 'open dialogues'. This study concludes recommendations to promote a recovery-oriented approach in cross-sectoral network meetings inspired by theoretical perspectives along with the experiences and knowledge of co-researchers.Introduction Stigma affects all aspects of transgender peoples' health. The purpose of this systematic review is to summarize the quantitative findings from the literature focused on the health impact of stigma resulting from discrimination, prejudice, and bias experienced by transgender people.Method To better understand the current state of the health impact of transgender stigma, the author conducted a search that included stigma, discrimination, prejudice, bias, health, and transgender people.Results A total of 15 studies met inclusion criteria for review. Results indicate that transgender people experience discrimination, prejudice, and bias at high levels. When internalized, this victimization leads to decreased psychological health, including increased harmful behaviors such as substance abuse and eating disorders, reduced relationship quality, ineffective coping and lower levels of self-esteem, and increased risk of attempted suicide. Internalized stigma also leads to decreased physical health outcomes stemming from healthcare avoidance, reduced healthcare utilization, decreased screenings, and delayed treatment.