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In the presence of health threats, precision public health approaches aim to provide targeted, timely, and population-specific interventions. Accurate surveillance methodologies that can estimate infectious disease activity ahead of official healthcare-based reports, at relevant spatial resolutions, are important for achieving this goal. Here we introduce a methodological framework which dynamically combines two distinct influenza tracking techniques, using an ensemble machine learning approach, to achieve improved state-level influenza activity estimates in the United States. The two predictive techniques behind the ensemble utilize (1) a self-correcting statistical method combining influenza-related Google search frequencies, information from electronic health records, and historical flu trends within each state, and (2) a network-based approach leveraging spatio-temporal synchronicities observed in historical influenza activity across states. The ensemble considerably outperforms each component method in addition to previously proposed state-specific methods for influenza tracking, with higher correlations and lower prediction errors. Real-time disease surveillance can aid mitigation of outbreaks. Here, Lu et al. combine an approach using Google search and EHR data with an approach leveraging spatiotemporal synchronicities of influenza activity across states to improve state-level influenza activity estimates in the US.

The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

Recurrent and chronic major depressive disorder (MDD) accounts for a substantial part of the disease burden because this course is most prevalent and typically requires long-term treatment. We associated blood DNA methylation profiles from 581 MDD patients at baseline with MDD status 6 years later. A resampling approach showed a highly significant association between methylation profiles in blood at baseline and future disease status (P = 2.0 × 10−16). Top MWAS results were enriched specific pathways, overlapped with genes found in GWAS of MDD disease status, autoimmune disease and inflammation, and co-localized with eQTLS and (genic enhancers of) of transcription sites in brain and blood. Many of these findings remained significant after correction for multiple testing. The major themes emerging were cellular responses to stress and signaling mechanisms linked to immune cell migration and inflammation. This suggests that an immune signature of treatment-resistant depression is already present at baseline. We also created a methylation risk score (MRS) to predict MDD status 6 years later. The AUC of our MRS was 0.724 and higher than risk scores created using a set of five putative MDD biomarkers, genome-wide SNP data, and 27 clinical, demographic and lifestyle variables. Although further studies are needed to examine the generalizability to different patient populations, these results suggest that methylation profiles in blood may present a promising avenue to support clinical decision making by providing empirical information about the likelihood MDD is chronic or will recur in the future.

Numerous strategies have been proposed for the detection and prevention of non‐technical electricity losses due to fraudulent activities. Among these, machine learning algorithms and data‐driven techniques have gained prominence over traditional methodologies due to their superior performance, leading to a trend of increasing adoption in recent years. A novel two‐step process is presented for detecting fraudulent Non‐technical losses (NTLs) in smart grids. The first step involves transforming the time‐series data with additional extracted features derived from the publicly available State Grid Corporation of China (SGCC) dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto‐Regressive Integrated Moving Average model, and the Holt‐Winters model. Following this, five distinct classification models are used to train and evaluate a fraud detection model using the SGCC dataset. The evaluation results indicate that the most effective model among the five is the Gradient Boosting Machine. This two‐step approach enables the classification models to surpass previously reported high‐performing methods in terms of accuracy, F1‐score, and other relevant metrics for non‐technical loss detection. A novel two‐step process is presented for detecting fraudulent NTLs in smart grids. The first step involves transforming the time‐series data with additional extracted features derived from the publicly available SGCC dataset. The features are extracted after identifying abrupt changes in electricity consumption patterns using the sum of finite differences, the Auto‐Regressive Integrated Moving Average (ARIMA) model, and the Holt‐Winters model. The evaluation results indicate that the Gradient Boosting Machine (GBM) surpasses previously reported high‐performing methods in terms of accuracy, F1‐score, and other relevant metrics for NTL detection.

This study compiles, reviews, and discusses the relevant history, present status, and growing trends in wireless electric vehicle charging. Various reported concepts, technologies, and available literature are discussed in this paper. The literature can be divided into two main groups: those that discuss the technical aspects and those that discuss the operations and systems involved in wireless electric vehicle charging systems. There may be an overlap of discussion in some studies. However, there is no single study that combines all the relevant topics into a guide for researchers, policymakers, and government entities. With the growing interest in wireless charging in the electric vehicle industry, this study aims to promote efforts to realize wireless power transfer in electric vehicles.

Based on an extensive simulation study, McGregor and colleagues recently recommended the use of surrogate variable analysis (SVA) to control for the confounding effects of cell-type heterogeneity in DNA methylation association studies in scenarios where no cell-type proportions are available. As their recommendation was mainly based on simulated data, we sought to replicate findings in two large-scale empirical studies. In our empirical data, SVA did not fully correct for cell-type effects, its performance was somewhat unstable, and it carried a risk of missing true signals caused by removing variation that might be linked to actual disease processes. By contrast, a reference-based correction method performed well and did not show these limitations. A disadvantage of this approach is that if reference methylomes are not (publicly) available, they will need to be generated once for a small set of samples. However, given the notable risk we observed for cell-type confounding, we argue that, to avoid introducing false-positive findings into the literature, it could be well worth making this investment. Please see related Correspondence article: https://genomebiology.biomedcentral.com/articles/10/1186/s13059-017-1149-7 and related Research article: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0935-y

Medical education is the foundation of knowledge among medical students. This study aims to investigate the knowledge of medical students at Al-Balqa Applied University, exploring their awareness of five communicable diseases, namely, leishmaniasis, hepatitis B, tuberculosis, measles, and cholera. This cross-sectional survey included 271 participants who answered a structured validated questionnaire with varying questions on causes, symptoms, complications, transmission routes, and preventive measures for each disease. Knowledge of all five communicable diseases was low. Leishmaniasis knowledge was notably low (mean=6.07, SD=1.43), with participants grappling with misconceptions about transmission modes, symptoms, and preventability. Hepatitis B knowledge was also low (mean=10.46, SD=1.67), especially regarding transmission modes, revealing that 76% of students were unaware of how the virus spreads. Tuberculosis knowledge unveiled gaps (mean=7.007, SD=1.90), particularly in recognizing the causes, symptoms, and transmission routes. Measles knowledge (mean=9.56, SD=1.92) indicated a robust understanding of symptoms but unveiled misconceptions about complications and transmission routes. For cholera (mean=14.50, SD=1.98), a knowledge of symptoms was demonstrated, but confusion about causative agents, transmission routes, and preventive measures was highlighted. The findings of the study emphasize the critical need for enhanced educational strategies including curriculum revisions, increased practical exposure, engaging awareness campaigns, and the integration of interactive learning methods to increase knowledge about communicable diseases.

Background In 1984, we developed a private practice joint replacement registry (JRR) to prospectively follow patients undergoing THA and TKA to assess clinical and radiographic outcomes, complications, and implant survival. Little has been reported in the literature regarding management of this type of database, and it is unclear whether and how the information can be useful for addressing longer-term questions. Questions/purposes We answered the following questions: (1) What is the rate of followup for THA and TKA in our JRR? (2) What factors affect followup? (3) How successful is this JRR model in capturing data and what areas of improvement are identified? And (4) what costs are associated with maintaining this JRR? Methods We collected clinical data on all 12,047 patients having primary THA and TKA since 1984. Clinical and radiographic data were collected at routine followup intervals and entered into a prospective database. We searched this database to assess the rate of successful followup and data collection and to compare the effect of patient variables on followup. Costs related to database management were evaluated. Results Followup was poor at every time interval after surgery, with a tendency for worsening over time. Patients with a complication and those younger than 70 years tended to followup with greater frequency. There were difficulties with data capture and substantial expenses related to managing the database. Conclusions Our findings highlight the difficulties in managing a JRR. Followup is poor and data collection is often incomplete. Newer technologies that allow easier tracking of patients and facilitate data capture may streamline this process and control costs.

Retinal imaging is a non-invasive technique used to scan the back of the eye, enabling the extraction of potential biomarkers like the artery and vein ratio (AVR). This ratio is known for its association with various diseases, such as hypertensive retinopathy (HR) or diabetic retinopathy, and is crucial in assessing retinal health. HR refers to the morphological changes in retinal vessels caused by persistent high blood pressure. Timely identification of these alterations is crucial for preventing blindness and reducing the risk of stroke-related fatalities. The main objective of this paper is to propose a new method for assessing one of the morphological changes in the fundus through morphometric analysis of retinal images. The proposed method in this paper introduces a novel approach called the arteriovenous length ratio (AVLR), which has not been utilized in previous studies. Unlike commonly used measures such as the arteriovenous width ratio or tortuosity, AVLR focuses on assessing the relative length of arteries and veins in the retinal vasculature. The initial step involves segmenting the retinal blood vessels and distinguishing between arteries and veins; AVLR is calculated based on artery and vein caliber measurements for both eyes. Nine equations are used, and the length of both arteries and veins is measured in the region of interest (ROI) covering the optic disc for each eye. Using the AV-Classification dataset, the efficiency of the iterative AVLR assessment is evalutaed. The results show that the proposed approach performs better than the existing methods. By introducing AVLR as a diagnostic feature, this paper contributes to advancing retinal imaging analysis. It provides a valuable tool for the timely diagnosis of HR and other eye-related conditions and represents a novel diagnostic-feature-based method that can be integrated to serve as a clinical decision support system.

Recently, mobility prediction researches attracted increasing interests, especially for mobile networks where nodes are free to move in the three-dimensional space. Accurate mobility prediction leads to an efficient data delivery for real time applications and enables the network to plan for future tasks such as route planning and data transmission in an adequate time and a suitable space. In this paper, we proposed, tested and validated an algorithm that predicts the future mobility of mobile networks in three-dimensional space. The prediction technique uses polynomial regression to model the spatial relation of a set of points along the mobile node’s path and then provides a time-space mapping for each of the three components of the node’s location coordinates along the trajectory of the node. The proposed algorithm was tested and validated in MATLAB simulation platform using real and computer generated location data. The algorithm achieved an accurate mobility prediction with minimal error and provides promising results for many applications.