Single neurons in the human medial temporal lobe flexibly shift representations across spatial and memory tasks.

Investigations into how individual neurons encode behavioral variables of interest have revealed specific representations in single neurons, such as place and object cells, as well as a wide range of cells with conjunctive encodings or mixed selectivity. However, as most experiments examine neural activity within individual tasks, it is currently unclear if and how neural representations change across different task contexts. Within this discussion, the medial temporal lobe is particularly salient, as it is known to be important for multiple behaviors including spatial navigation and memory, however the relationship between these functions is currently unclear.

Precision Detection and Assessment of Ash Death and Decline Caused by the Emerald Ash Borer Using Drones and Deep Learning.

Emerald ash borer () is an invasive pest that has killed millions of ash trees ( spp.) in the USA since its first detection in 2002. Although the current methods for trapping emerald ash borers (e.

A revisit of the amygdala theory of autism: Twenty years after.

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD.

Lane-specific speed analysis in urban work zones with computer vision.

Objective: Work zone speed is one of the most important factors in road construction safety management. This work presents a computer vision based technique designed to measure lane-specific individual vehicle speed using existing traffic monitoring cameras and computers. The resulted speeds support the influence analysis of factors including traffic control, lane positions, and construction activity.

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers.

Thin Film Piezoelectric Nanogenerator Based on (100)-Oriented Nanocrystalline AlN Grown by Pulsed Laser Deposition at Room Temperature.

In wearable or implantable biomedical devices that typically rely on battery power for diagnostics or operation, the development of flexible piezoelectric nanogenerators (NGs) that enable mechanical-to-electrical energy harvesting is finding promising applications. Here, we present the construction of a flexible piezoelectric nanogenerator using a thin film of room temperature deposited nanocrystalline aluminium nitride (AlN). On a thin layer of aluminium (Al), the AlN thin film was grown using pulsed laser deposition (PLD).

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets.

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.

Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks.

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients.

GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery.

Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper proposes a hybrid solution that takes advantage of both traditional feature engineering and modern deep learning techniques to overcome the issue of as encountered in the SWIR band. Firstly, the paper revisits the theory of measurement levels.

Ultrasonic Texture Features for Assessing Cardiac Remodeling and Dysfunction.

Background: Changes in cardiac size, myocardial mass, cardiomyocyte appearance, and, ultimately, the function of the entire organ are interrelated features of cardiac remodeling that profoundly affect patient outcomes.

Objectives: This study proposes that the application of radiomics for extracting cardiac ultrasonic textural features (ultrasomics) can aid rapid, automated assessment of left ventricular (LV) structure and function without requiring manual measurements.

Methods: This study developed machine-learning models using cardiac ultrasound images from 1,915 subjects in 3 clinical cohorts: 1) an expert-annotated cardiac point-of-care-ultrasound (POCUS) registry (n = 943, 80% training/testing and 20% internal validation); 2) a prospective POCUS cohort for external validation (n = 275); and 3) a prospective external validation on high-end ultrasound systems (n = 484).

Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia.

Background: Glaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia.

Strongly enhanced THz generation enabled by a graphene hot-carrier fast lane.

Semiconductor photoconductive switches are useful and versatile emitters of terahertz (THz) radiation with a broad range of applications in THz imaging and time-domain spectroscopy. One fundamental challenge for achieving efficient ultrafast switching, however, is the relatively long carrier lifetime in most common semiconductors. To obtain picosecond ultrafast pulses, especially when coupled with waveguides/transmission lines, semiconductors are typically engineered with high defect density to reduce the carrier lifetimes, which in turn lowers the overall power output of the photoconductive switches.

Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images.

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened.

NanoMi: An open source electron microscope hardware and software platform.

We outline a public license (open source) electron microscopy platform, referred to as NanoMi. NanoMi offers a modular, flexible electron microscope platform that can be utilized for a variety of applications, such as microscopy education and development of proof-of-principle experiments, and can be used to complement an existing experimental apparatus. All components are ultra-high vacuum compatible and the electron optics elements are independent from the vacuum envelope.

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations.

COVID-19 Vaccination and Mental Stress within Diverse Sociodemographic Groups.

In this study, we surveyed 635 participants to determine: (a) major causes of mental stress during the pandemic and its future impacts, and (b) diversity in public perception of the COVID-19 vaccination and its acceptance (specifically for children). Statistical results and intelligent clustering outcomes indicate significant associations between sociodemographic diversity, mental stress causes, and vaccination perception. For instance, statistical results indicate significant dependence between gender (we will use term 'sex' in the rest of the manuscript) and mental stress due to COVID-19 infection ( = 1.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes.

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States.

Single cell metabolism: current and future trends.

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification.

A novel design of a passive variable stiffness soft robotic gripper.

Inspired by the twisting and hanging phenomenon of vines, this paper proposes and designs a passive variable stiffness soft robotic gripper to grasp an object in a simple and robust manner using the principle of jamming. This method has the characteristics of high reliability and good stability, which can achieve soft grasping and rigid load-bearing of the object. Firstly, according to two key issues, the design model of the gripper is proposed, the principle of the proposed gripper is analyzed, and the relationship between the stiffness of the gripper and the stiffness of the object is revealed.

Redox imbalance in COVID-19 pathophysiology.

Background: The pathophysiologic significance of redox imbalance is unquestionable as numerous reports and topic reviews indicate alterations in redox parameters during corona virus disease 2019 (COVID-19). However, a more comprehensive understanding of redox-related parameters in the context of COVID-19-mediated inflammation and pathophysiology is required.

Methods: COVID-19 subjects (n = 64) and control subjects (n = 19) were enrolled, and blood was drawn within 72 h of diagnosis.

Spatiotemporal Correlation Analysis of Hydraulic Fracturing and Stroke in the United States.

Hydraulic fracturing or fracking has led to a rapid growth of oil and gas production in the United States, but the impact of fracking on public health is an important but underresearched topic. We designed a methodology to study spatiotemporal correlations between the risk of fracking and stroke mortality. An annualized loss expectancy (ALE) model is applied to quantify the risk of fracking.

Peppers in Diet: Genome-Wide Transcriptome and Metabolome Changes in .

The habanero pepper () is an increasingly important spice and vegetable crop worldwide because of its high capsaicin content and pungent flavor. Diets supplemented with the phytochemicals found in habanero peppers might cause shifts in an organism's metabolism and gene expression. Thus, understanding how these interactions occur can reveal the potential health effects associated with such changes.

Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017-2019 health outcomes of approximately one-third of the population living in Utah.