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Factors influencing uptake of COVID-19 diagnostics in Sub-Saharan Africa: a rapid scoping review

Background Diagnostics are critical for preventing COVID-19 transmission, enabling disease management and engagement with care. However, COVID-19 testing uptake remained low in low- and middle- income countries in Sub-Saharan Africa (SSA) during the recent pandemic, due to issues of supply, access and acceptability. Early studies conducted outside of the region provide insight into uptake of COVID-19 testing, however there has been no systematic research within the region. The aim of this scoping review is to investigate factors influencing uptake of COVID-19 testing in different settings across SSA. Materials and methods Inclusion criteria was any study employing qualitative or mixed methodologies, addressing uptake of COVID-19 testing conducted in SSA. MEDLINE, PubMed, Google Scholar, Web of Science, and Africa-Wide Information were searched. Thematic content analysis was conducted across all included articles until saturation was attained. Results In total 2994 articles were identified and fourteen reviewed. Structural, social, epidemiological, informational, and political elements affected how the public interacted with COVID-19 testing. Coverage was limited by insufficient diagnostic capabilities caused by a shortage of laboratory resources and trained personnel. False information spread through social media led to testing misperceptions and apprehension. Testing hesitancy was ascribed to fear of restrictive measures and the possibility of social harms if positive. Facility-based testing was physically inaccessible and perceived as lacking privacy, whereas self-testing distributed by the community removed lengthy distances and prevented stigma. Perceptions that COVID-19 was not severe and low numbers of confirmed cases in comparison to other settings undermined public urgency for testing. Low testing frequency led to low-rate assumptions, which in turn generated denial and othering narratives. Politicians’ acceptance or denial of COVID-19 affected the mobilization of the health system, and their model actions—such as testing openly—promoted public confidence and involvement in interventions. Conclusions This review emphasizes the necessity of strong political commitments to enhancing health systems for future pandemic preparedness. Response plans should consider contextual elements that affect how people react to interventions and perceive health emergencies. Community-driven self-testing distribution could enhance the uptake of diagnostics through addressing socio-economic constraints impacting facility-delivered testing.

Intraspecific variability of social structure and linked foraging behavior in females of a widespread bat species (Phyllostomus hastatus)

Intraspecific variation in morphology and behavior is widespread, especially in species with large distribution ranges. This includes foraging which can vary according to the local resource landscape. How this may be linked to differences in social structure, especially in socially foraging species is less known. Greater spear-nosed bats are well known for their large repertoire of often highly complex social behaviors. In Trinidad, they form stable groups of unrelated females that recruit other members to temporally unpredictable flowering balsa trees. We compared these findings with a dataset of capture data, GPS tracks, and observations collected over six years in a colony in Panamá. We found profound differences in the foraging behavior and group stability of Phyllostomus hastatus during the dry season where social behaviors were expected. Female bats did not coordinate commutes to exploit distinct foraging resources as a group. Instead, females commuted individually to very distant foraging areas which overlapped between groups. Linked to this we found groups to be unstable in size over the short and long term. Our findings highlight the large intraspecific variation and indicate a strong influence of the local resource landscape and associated benefits of social foraging on the social structure in these bats and possibly many other animals.

Modelling the mitigation of anti-vaccine opinion propagation to suppress epidemic spread: A computational approach

Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions.

Using a Bayesian network to classify time to return to sport based on football injury epidemiological data

The return-to-sport (RTS) process is multifaceted and complex, as multiple variables may interact and influence the time to RTS. These variables include intrinsic factors related the player, such as anthropometrics and playing position, or extrinsic factors, such as competitive pressure. Providing an individualised estimation of time to return to play is often challenging, and clinical decision support tools are not common in sports medicine. This study uses epidemiological data to demonstrate a Bayesian Network (BN). We applied a BN that integrated clinical, non-clinical factors, and expert knowledge to classify time day to RTS and injury severity (minimal, mild, moderate and severe) for individual players. Retrospective injury data of 3374 player seasons and 6143 time-loss injuries from seven seasons of the professional German football league (Bundesliga, 2014/2015 through 2020/2021) were collected from public databases and media resources. A total of twelve variables from three categories (player’s characteristics and anthropometrics, match information and injury information) were included. The response variables were 1) days to RTS (1–3, 4–7, 8–14, 15–28, 29–60, > 60, and 2) injury severity (minimal, mild, moderate, and severe). The sensitivity of the model for days to RTS was 0.24–0.97, while for severity categories it was 0.73–1.00. The user’s accuracy of the model for days to RTS was 0.52–0.83, while for severity categories, it was 0.67–1.00. The BN can help to integrate different data types to model the probability of an outcome, such as days to return to sport. In our study, the BN may support coaches and players in 1) predicting days to RTS given an injury, 2) team planning via assessment of scenarios based on players’ characteristics and injury risk, and 3) understanding the relationships between injury risk factors and RTS. This study demonstrates the how a Bayesian network may aid clinical decision making for RTS.

CSW-YOLO: A traffic sign small target detection algorithm based on YOLOv8

In order to improve the real-time and feasibility of traffic sign detection for autonomous driving in complex traffic environments, this paper proposes a small target detection algorithm for traffic signs based on the YOLOv8 model. First, the bottleneck of the C2f module in the original yolov8 network is replaced with the residual Faster-Block module in FasterNet, and then the new channel mixer convolution GLU (CGLU) in TransNeXt is combined with it to construct the C2f-faster-CGLU module, reducing the number of model parameters and computational load; Secondly, the SPPF module is combined with the large separable kernel attention (LSKA) to construct the SPPF-LSKA module, which greatly enhances the feature extraction ability of the model; Then, by adding a small target detection layer, the accuracy of small target detection such as traffic signs is greatly improved; Finally, the Inner-IoU and MPDIoU loss functions are integrated to construct WISE-Inner-MPDIoU, which replaces the original CIoU loss function, thereby improving the calculation accuracy. The model has been validated on two datasets Tsinghua-Tencent 100K (TT100K) and CSUST Chinese Traffic Sign Detection Benchmark 2021 (CCTSDB 2021), achieving Map50 of 89.8% and 98.9% respectively. The model achieves precision on par with existing mainstream algorithms, while being simpler, significantly reducing computational requirements, and being more suitable for small target detection tasks. The source code and test results of the models used in this study are available at https://github.com/lyzzzzyy/CSW-YOLO.git.

High temporal variability not trend dominates Mediterranean precipitation

Correction: The intersection of finTech adoption, HR competency potential, service innovation, and firm growth in the banking sectors using Entropy and TOPSIS

Clinical characteristics, comorbidities, and correlation with advanced lipedema stages: A retrospective study from a Swiss referral centre

Introduction Lipedema is a chronic condition involving abnormal fat deposition in the lower limbs, often underdiagnosed, and poorly understood. We examined the epidemiological and clinical characteristics of a large patient cohort in Switzerland and their associations with disease severity. Methods We included women aged 18 and over with lipedema at Lausanne University Hospital (CHUV), Switzerland. Demographic and clinical data, including disease type, stage, symptoms, and comorbidities, were collected. Descriptive statistics were used to summarise the data, and logistic regression was employed for analysis. Results A total of 381 females (mean age 41.9 years) were included, mostly classified as type III (48.3%) and IV (30.2%) lipedema. In our population, 26.6% of patients were classified as stage 1, 44.5% as stage 2, and 28.9% as stages 3–4. Family history was reported in 49.9%, with symptoms often starting during adolescence (62.2%). Pain affected 87.9%, and quality of life (QoL) was significantly reduced, with 71.5% reporting low physical and 67.4% low mental well-being. Comorbidities were present in 92.1%, increasing with advanced disease stage, with chronic venous disease (86.2%) and obesity (51.7%) being the most common. Univariate analysis showed advanced lipedema was associated with age (OR: 1.07, 95% CI: 1.05–1.09), BMI (OR: 1.24, 95% CI: 1.19–1.29), and comorbidities (OR: 1.59, 95% CI: 1.39–1.81). Multivariate analysis confirmed age (OR: 1.06, 95% CI: 1.04–1.08) and BMI (OR: 1.22, 95% CI: 1.17–1.28) as correlates with disease stage. Conclusions Our study highlights frequent comorbidities in patients with lipedema, including chronic venous disease, obesity, and mental health conditions such as anxiety and depression. The distribution of comorbidities supports the need for tailored management. The correlation between disease stages, age, and BMI suggests potential progression, warranting confirmation through prospective studies.

Introduction of “MAPS” wound healing index and its correlation with guided bone regeneration outcome

This study aims to introduce a new index that could become a framework for future modification and improvement, and retrospectively test the predictability of this index collectively and individually for final bone changes by using existing research data pertinent to guided bone regeneration (GBR). Methods The MAPS score was introduced to evaluate the bioMechanical, Aesthetic/Anatomical, Pathophysiologic, and Subject-related parameters for the healing assessment of 20 patients who underwent GBR in the posterior mandible retrospectively. Intraoral photography was taken at 3-, 10-, 21 days, and 5 months, resulting in 80 follow-up visits. Two independent examiners evaluated the photos giving scores for each timepoint and tested against horizontal bone gain (CBCT) for predictability. Results Cohen’s Kappa values showed high intra- and inter-examiner agreement. Pearson’s correlation showed an inverse correlation between baseline bone width and bone changes at a 3 mm level (R2 = 0.23). The higher M, A, and P values at any time point were associated with higher bone gain. The 10-day MAPS score turns out the most predictive of bone gain (RMSE 1.32, R2 0.75). In addition, increasing the average P score by 1 point at 10 days is associated with an increase in bone gain of 1.23 (p=.057). Conclusion The MAPS score improves consistently over the 5-month healing period. However, no statistically significant difference is observed between the scores at 21 days and 5 months, reflecting the clinical healing pattern for GBR. The overall MAPS score correlated with bone changes after GBR procedures, indicating its potential for estimating hard tissue regenerative outcomes.

Extreme heat prediction through deep learning and explainable AI

Extreme heat waves are causing widespread concern for comprehensive studies on their ecological and societal implications. With the ongoing rise in global temperatures, precise forecasting of heatwaves becomes increasingly crucial for proactive planning and ensuring safety. This study investigates the efficacy of deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), using five years of meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance the interpretability of models. Although Weather forecasting has advanced in predicting sunshine, rain, clouds, and general weather patterns, the study of extreme heat, particularly using advanced computer models, remains largely unexplored, overlooking this gap risks significant disruptions in daily life. Our study addresses this gap by collecting five years of weather dataset and developing a comprehensive framework integrating DL and XAI models for extreme heat prediction. Key variables such as temperature, pressure, humidity, wind, and precipitation are examined. Our findings demonstrate that the LSTM model outperforms others with a lead time of 1–3 days and minimal error metrics, achieving an accuracy of 96.2%. Through the utilization of SHAP and LIME XAI methods, we elucidate the significance of humidity and maximum temperature in accurately predicting extreme heat events. Overall, this study emphasizes how important it is to investigate intricate DL models that integrate XAI for the prediction of extreme heat. Making these models understood allows us to identify important parameters, improving heatwave forecasting accuracy and guiding risk-reduction strategies.

Correction: Post-COVID-19 syndrome: Physical capacity, fatigue and quality of life

Understanding non-nutritive oral behaviors in dairy calves (Bos taurus): A systematic review protocol

This protocol outlines the proposed aims, rationale, study design, methods, and dissemination plan for a systematic review focusing on non-nutritive oral behaviors in dairy calves. The primary outcome measures of interest are the occurrence, frequency, and duration of these behaviors, with a specific focus on cross-sucking of pen mates. The review assesses how farm management interventions affect the occurrence, frequency, and duration of non-nutritive oral behaviors in dairy calves and informs recommendations regarding management practices that mitigate cross-sucking and other non-nutritive oral behaviors. Where applicable, PRISMA-P guidelines are followed, and all data will be made publicly available at the time of publication.

Increasing hourly heavy rainfall in Austria reflected in flood changes

Associations with HIV preexposure prophylaxis use by cisgender female sex workers in two Ugandan cities

Background Sex workers of all genders have a high risk of HIV acquisition and are a priority population for HIV pre-exposure prophylaxis (PrEP). We aimed to assess current oral PrEP use and associated factors among cisgender female sex workers (FSW) in two Ugandan cities. Methods We administered a survey questionnaire to 236 HIV-negative FSW in the cities of Mbale and Mbarara from January to March 2020. The survey was nested in a quasi-experimental study to assess the effect of peer education and text message reminders on the uptake of regular sexually transmitted infection (STI) and HIV testing. Using interviewer-administered questionnaires, we obtained data on current self-reported tenofovir-based oral PrEP use. We used modified Poisson regression with robust standard errors to evaluate the factors associated with current oral PrEP usage. Results Nearly 70% of FSWs reported taking an HIV test during the past three months. Among the respondents, 33% (33/100) in Mbale and 67% (91/136) in Mbarara reported having ever heard of PrEP. However, only 9.7% (23/236) self-reported currently taking oral-PrEP. In Mbarara, FSWs were twice as likely to be aware of or use oral PrEP than those in Mbale (adjusted prevalence ratio [aPR] 2.33; 95% confidence interval (CI) 1.19–3.97; p = 0.01). Additionally, current use was positively associated with attainment of secondary (aPR 2.50; 95% CI: 1.14–5.45; p = 0.02) or tertiary education (aPR 3.12; 95% CI: 1.09–8.96; p = 0.03). Conclusion PrEP use in this cohort of FSWs was low and was associated with location and level of education. To increase PrEP uptake among FSWs, targeted educational campaigns and implementation studies are needed, particularly for those with lower levels of education.

How fast your brain ages is affected by these 64 genes

Voxel-based versus network-analysis of changes in brain states in patients with auditory verbal hallucinations using the Eriksen Flanker task

The present functional magnetic resonance imaging (fMRI) study investigated neural correlates of switching between task-processing and periods of rest in a conventional ON-OFF block-design in patients with auditory verbal hallucinations (AVHs) and healthy controls. It has been proposed that auditory hallucinations are a failure of top-down control of bottom-up perceptual processes which could be due to aberrant up- and down regulation of brain networks. A version of the Eriksen Flanker task was used to assess cognitive flexibility and conflict control. BOLD fMRI with alternating blocks of task engagement and rest was collected using a 3T MR scanner. The objective of the study was to explore how patients would dynamically modulate relevant brain networks in response to shifting environmental demands, while transitioning from a resting state to active task-processing. Analysis of performance data found significant behavioral effects between the groups, where AVH patients performed the Flanker task significantly less accurately and with longer reaction times (RTs) than the healthy control group, indicating that AVH patients displayed reduced top-down guided conflict control. A network connectivity analysis of the fMRI data showed that both groups recruited similar networks related to task-present and task-absent conditions. However, the controls displayed increased network variability across task-present and task-absent conditions. This would indicate that the controls were better at switching between networks and conditions when demands changed from task-present to task-absent, with the consequence that they would perform the Flanker task better than the AVH patients.

Scalable and rapid nearest neighbor particle search using adaptive disk sector

In this paper, we propose a framework for efficiently accelerating Nearest Neighbor Particle (NNP) calculations in a movable particle-based system by leveraging the dynamic changes in disk sectors. The NNP region based on particles and disk sectors is determined by the following three conditions: 1) The position of the disk resides within the range of neighbor particles. 2) The position of a neighbor particle exists within a disk sector. 3) A neighbor particle exists between the two vectors that form the disk sector. When all of these conditions are satisfied, we assume that there is a particle within the disk sector. In this paper, we automatically update the inspection range of NNP, which is the disk sector, based on the movement of particles. To calculate the dynamic changes in the disk sector, we control the direction, length, and angle of the disk based on the positions and velocities of particles. Ultimately, we accelerate the computation of NNP by utilizing the particles located within the calculated disk sector. The proposed acceleration method can be implemented simply, as it operates on the particles within the disk sector using closed-form expressions, without the explicit data structures like trees. Especially in the case of movable particles, unlike the conventional adaptive tree approach that requires continuous data structure updates, the proposed method can be efficiently utilized in applications requiring NNP. This is because it rapidly calculates collision areas using closed-form expressions that are adjusted according to the particles’ motion. Our method yielded results that were 2 to 20 times faster compared to Hash tables or K-d trees in experiments conducted across diverse scenes. Furthermore, its scalability was demonstrated through its application in various scenarios (particle-based fluids, splash and foam, isoline tracking, turbulent flow, collision handling).

Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8

With the increasing application of aluminum alloys in the industrial field, the defect of aluminum alloys significantly impacts the structural integrity and safety of products. However, state-of-the-art material defect detection methods have low detection accuracy and inaccurate defect target frame problems. Therefore, an enhanced YOLOv8-ALGP (aluminum, Ghost, P2) defect detection and classification method for 13 defects is proposed in this paper. Firstly, based on the AliCloud Tianchi dataset, 3 defects are added and an enhancement strategy is implemented to increase the diversity of the training dataset, which improves the generalization ability of the model. Secondly, an ALGC3 (aluminum, Ghost, Concentrated-Comprehensive Convolution Block (C3)) module is introduced to optimize the fusion of Ghost convolution and residual connectivity, resulting in a more lightweight model. Finally, the backbone network structure is reconstructed. Fine-grained adjustments and improvements are made to enhance neck network layers and the feature extraction capability. Defect features are extracted and analyzed more efficiently, and the network model better identifies defects such as jet, camouflage, etc. The average detection rate of all defects in the data set is improved. As a result, the average detection rate of all defects in the dataset is improved. Experimental results show that the proposed method performs effectively in target detection and classification. The number of model parameters is reduced from more than 300,000 to 160,000, significantly reducing the complexity of the model. In addition, the average detection accuracy is improved from 64.5% to 71.3% compared to the YOLOv8. In addition, the detection accuracies of effacement and jet defects, particularly, are improved from 21.6% and 38.5% to 32.2% and 60%, respectively. It shows that the proposed method can effectively identify the surface defects of aluminum alloys, which improves product performance in the aluminum industry.

Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning

Under the increasing pressure of global climate change, water conservation (WC) in semi-arid regions is experiencing unprecedented levels of stress. WC involves complex, nonlinear interactions among ecosystem components like vegetation, soil structure, and topography, complicating research. This study introduces a novel approach combining InVEST modeling, spatiotemporal transfer of Water Conservation Reserves (WCR), and deep learning to uncover regional WC patterns and driving mechanisms. The InVEST model evaluates Xiong’an New Area’s WC characteristics from 2000 to 2020, showing a 74% average increase in WC depth with an inverted “V” spatial distribution. Spatiotemporal analysis identifies temporal changes, spatial patterns of WCR and land use, and key protection areas, revealing that the WCR in Xiong’an New Area primarily shifts from the lowest WCR areas to lower WCR areas. The potential enhancement areas of WCR are concentrated in the northern region. Deep learning quantifies data complexity, highlighting critical factors like land use, precipitation, and drought influencing WC. This detailed approach enables the development of personalized WC zones and strategies, offering new insights into managing complex spatial and temporal WC data.

COVID-19 outbreak management in Western Sydney residential aged care homes: A mixed-methods Donabedian evaluation

Outbreaks of the novel respiratory viral disease, SARS-CoV-2 (COVID-19), have caused disproportionate morbidity and mortality for older people living in residential aged care homes. Between June 2021 and December 2022, the Delta and Omicron variants of COVID-19 were responsible for widespread outbreaks in homes across Western Sydney, New South Wales, Australia. To manage outbreaks in affected homes, a targeted response strategy was prepared and deployed in the form of outbreak management teams. This study utilised the Donabedian framework and a two-phase mixed methods design to evaluate the structures, processes and outcomes of the outbreak management teams at the level of the local health district. Phase 1 involved the descriptive analysis of outbreak data from Western Sydney aged care homes, created between June 2021 and December 2022. Phase 2 involved the completion of in-depth semi-structured interviews with 35 participants to explore the outbreak management team response from the perspective of its members and staff from residential aged care homes. Between June 2021 and December 2022, there were 281 outbreaks, 4113 resident cases, 346 hospitalisations and 127 deaths in residential aged care homes across Western Sydney. Structural factors that facilitated the outbreak management response and improved outcomes included smaller home sizes, the absence of shared rooms and bathrooms, adequate staffing and resources, suitable infrastructure, and the integration of the response with wider public health systems. Process facilitators included multi-disciplinary team membership, open communication channels, structured and streamlined procedures and roles, onsite infection control support and education, and long-term capability building. The lessons drawn from participants’ experiences aim to improve the outcomes and sustainability of current and future outbreak management strategies.