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Author Correction: Bioinformatics approach for the construction of multiple epitope vaccine against omicron variant of SARS-CoV-2

Pesticides and neurodevelopment of children in low and middle-income countries: A systematic review

Background Pesticides are increasingly common in low- and middle-income countries (LMICs), where weaker regulations and multiple risk factors for poor neurodevelopment exist. Due to biological and behavioral factors, children are vulnerable to chronic pesticide exposure at a time when brain development is critical. The objective of this study is to systematically review studies assessing pesticides use with child neurodevelopment in LMICs. Methods Using terms developed by a medical librarian, a search was performed in June 2023 across online databases, including OVID MEDLINE and EMBASE. For inclusion, studies required a measurement of pesticide exposure and neurodevelopmental outcomes using a standardized tool and study participants ≤18 years within an LMIC, as determined by World Bank criteria. Descriptive analyses were performed using extracted data, including published outcomes of significance. Results were assessed for internal validity and reported by the method of exposure measurement (biomarkers or questionnaires/region of residence). Results A total of 31 studies spanning 11 LMICs met the inclusion criteria. An adverse association was found between pesticide exposure and at least one domain of neurodevelopment in 23 studies, including 12 studies with child-level measurements, 10 studies with maternal measurements in pregnancy, and one questionnaire-based study. Exposure to organochlorines, carbamates, chlorpyrifos, and fungicides were consistently associated with worse outcomes for neurodevelopment, specifically executive functioning, cognition, motor development, and behavior. Few studies found adverse associations with urine/serum organophosphate levels. Due to the heterogeneity of existing data, we were unable to quantify the relationship between pesticide exposure and neurodevelopment. Conclusions While studies suggest that some domains of neurodevelopment may be negatively associated with pesticide exposure, extrapolation is limited due to the challenges in measuring pesticide exposure within these contexts and differing study designs. Several research gaps must be addressed to develop policy and regulations that protect children from potential neurodevelopmental deficits associated with pesticide exposure.

Monitoring the rate and variability of somatic genomic alterations using long-read sequencing

Correction to “Electrocatalytic Oxidation of Ammonia by (Salen)ruthenium(III) Ammine Complexes: Direct Evidence for a Ruthenium(VI) Nitrido Active Intermediate”

Global burden of knee osteoarthritis from 1990 to 2021: Trends, inequalities, and projections to 2035

Introduction To present the global, regional, and national burden of knee osteoarthritis (KOA) and its causative factors, categorized by age, gender, and sociodemographic indices from 1990 to 2021, and to project future trends to 2035. Methods A comprehensive analysis of KOA epidemiology was conducted using data from the 2021 Global Burden of Disease Study (GBD). The study examined change trends in KOA burden between 1990 and 2021, including prevalence, incidence, disability-adjusted life years (DALYs), and associated risk factors. Health inequality analyses were performed using slope index of inequality (SII) and concentration index (CI). Decomposition analysis was conducted to understand the contributions of population growth, aging, and epidemiological changes to the increasing burden. Future projections were made for global, Chinese, and Indian trends to 2035. Results In 2021, the global prevalence of KOA was 374.7 million cases, with an annual incidence of 3.0846 million cases, totaling 12.01 million DALYs. Age-standardized rates for prevalence, incidence, and DALYs increased by 8.3%, 7.1%, and 8.2% respectively since 1990. Health inequality analyses revealed widening disparities across SDI levels, with SII for crude incidence rates increasing from 251 to 400 per 100,000 between 1990 and 2021. Decomposition analysis showed population growth as the primary driver of increased burden globally (75.07% for DALYs), with variations across SDI regions. Projections to 2035 indicate substantial increases in global burden, with incidence expected to rise by 33.6%, prevalence by 43.8%, and DALYs by 41.4%. China and India show differing patterns in projected burden increases. Conclusion KOA remains a significant public health concern with increasing burden and widening health inequalities. The projected increases highlight the need for targeted interventions, especially in rapidly growing populations. Preventive measures should focus on reducing high BMI, implementing gender-specific treatments, and addressing regional disparities to mitigate the future burden of KOA.

Author Correction: Association of hepatic steatosis and liver fibrosis with chronic obstructive pulmonary disease among adults

Evidence of Heteroepitaxy and Solid Solutions in Lattice Matched Ternary Covalent Organic Framework Systems

A novel approach to group decision-making using generalized bipolar neutrosophic sets

This study introduces operational laws for Aczél-Alsina aggregation within the framework of generalized bipolar neutrosophic sets (GBNS), tailored for group decision-making scenarios. Novel aggregation operators, including the Generalized Bipolar Neutrosophic Aczél-Alsina Weighted Average (GBNAAWA), Generalized Bipolar Neutrosophic Aczél-Alsina Ordered Weighted Average (GBNAAOWA), Generalized Bipolar Neutrosophic Aczél-Alsina Hybrid Weighted Average (GBNAAHWA), Generalized Bipolar Neutrosophic Aczél-Alsina Weighted Geometric (GBNAAWG), Generalized Bipolar Neutrosophic Aczél-Alsina Ordered Weighted Geometric (GBNAAOWG), and Generalized Bipolar Neutrosophic Aczél-Alsina Hybrid Weighted Geometric (GBNAAHWG), are proposed to address complex decision-making processes under uncertainty. The methodology is demonstrated through a case study and an illustrative example to validate its practical applicability. Comparative and sensitivity analyses highlight the robustness and adaptability of the proposed operators in various decision contexts. Key findings, discussions, and limitations are presented to provide insights into the method’s effectiveness and areas for future research. This work contributes to advancing decision-making models by integrating Aczél-Alsina aggregation with bipolar neutrosophic theory, offering a novel approach to handling ambiguity and conflicting information.

Taiwan telecommunication AI industry ESG disclosure

Factors that contribute to loss to follow-up in the medium term after initiation of anti-vascular endothelial growth factor therapy for neovascular age-related macular degeneration in Japanese patients

Purpose To identify time-specific factors associated with loss to follow-up (LTFU) in the early to medium term after initiating anti-vascular endothelial growth factor (VEGF) treatment in patients with neovascular age-related macular degeneration (nAMD) in Japan. Methods The study had a retrospective multicenter case–control design and was performed across 16 specialist retinal facilities in Japan. Patients diagnosed with nAMD at their initial visit who initiated treatment between January 2017 and December 2020 were included. Patient characteristics were analyzed to identify factors associated with LTFU within 3 months (very early), 3 months to 1 year (early), and 1 year to 2 years (medium term) after starting treatment. Results Data for 2389 patients with nAMD were analyzed. The very early, early, and medium-term LTFU rates were 6.8%, 13.8%, and 21.2%, respectively. Stepwise regression analysis identified factors that were significantly associated with LTFU at a very early stage to be greater central retinal thickness at baseline and a prior treatment history, those associated with early LTFU to be worse baseline best-corrected visual acuity (BCVA), anti-VEGF treatment combined with photodynamic therapy, and a follow-up period that overlapped with the COVID-19 pandemic, and that associated with medium-term LTFU to be worse BCVA at 3 months. LTFU in any period within 3 months to 2 years was more likely in patients aged >80 years, and LTFU very early within 3 months was more likely in those aged <60 years. A poor baseline BCVA (logMAR) of >1 was a risk factor for LTFU within 3 months and 1 year, whereas LTFU was significantly less likely in patients with good baseline BCVA (<0.1). Conclusion The LTFU rate in patients with nAMD increased over time. Factors contributing to LTFU vary depending on the time since initiation of treatment.

Author Correction: Single-element ultrasound system for high-resolution jugular venous pulse contour detection

A Molecule Assembly Route to Simultaneously Detoxify Platinum Sites and Disentangle Reactant Transport Paths in Proton Exchange Membrane Fuel Cells

Input-output efficiency, productivity dynamics, and determinants in western China’s higher education: A three-stage DEA, global Malmquist index, and Tobit model approach

Amid rapid globalization and the expansion of the knowledge economy, enhancing input-output efficiency of higher education is essential for regional development and national competitiveness. In resource-constrained regions like western China, it is of utmost significance to obtain efficiency gains by accurately assessing this efficiency and identifying its determinants, which are crucial for balanced regional development and educational equity. This study employs a comprehensive research methodology, combining a three-stage Data Envelopment Analysis, the global Malmquist productivity index, and a Tobit model to systematically evaluate input-output efficiency, productivity dynamics, and factors influencing technical efficiency in higher education across 12 provinces in western China from 2010 to 2022. The findings reveal that environmental factors and random errors significantly affect the input-output efficiency of higher education in western China, resulting in an overestimation of overall efficiency. Adjusted values for technical efficiency, pure technical efficiency, and scale efficiency are 0.8510, 0.9761, and 0.8707, respectively. Although most western provinces demonstrate relatively high pure technical efficiency, deficiencies in scale efficiency reduce overall technical efficiency, particularly in Tibet, Qinghai, and Ningxia. Total factor productivity exhibits a modest annual growth rate of 1.05%, driven by both technical efficiency advancements and technological progress, with technical efficiency change—primarily via enhanced scale efficiency—acting as the main contributor. Moreover, human capital structures and educational funding allocations significantly impact technical efficiency. Specifically, the proportion of full-time faculty with senior or associate senior academic titles and per-student education expenditure have a significantly negative influence on technical efficiency, whereas the share of operating expenses in total higher education expenditure and the proportion of employees with at least a college education are positively correlated with technical efficiency. This study offers empirical evidence to inform the formulation of targeted policies for the development of higher education in western China.

Pushing the Boundaries of Resolution in Solid-State Nuclear Magnetic Resonance of Biomolecules with 160 kHz Magic-Angle Spinning

A novel approach combining YOLO and DeepSORT for detecting and counting live fish in natural environments through video

Applying Artificial Intelligence (AI) to the monitoring of live fish in natural environments represents a promising approach to the sustainable management of aquatic resources. Detecting and counting fish in water through video analysis is crucial for fish population statistics. This study employs AI algorithms, specifically YOLOv10 (You Only Look Once version 10) for identifying the presence fish in video frames, combined with the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm to count the number of fish individual moving across the frames. A total of 9,002 frames were extracted from 13 videos recorded in five different environments: areas with submerged tree roots, shallow marine regions, coral reefs, bleached coral reefs and seagrass meadows. To train the recognition model, the dataset was divided into training, validation and testing sets in 8:1:1 ratio. The results demonstrated that the model achieved an accuracy of 89.5%, with processing times of 6.2ms for preprocessing, 387.0ms for inference and 0.9ms for postprocessing per image. The combination of YOLO and DeepSORT enhances the accuracy of tracking objects in aquatic environments, showing great potential for the monitoring of fishery resources.

In-depth exploration of software defects and self-admitted technical debt through cutting-edge deep learning techniques

Most previous research focuses on finding Self-Admitted Technical Debt (SATD) or detecting bugs alone, rather to addressing the concurrent identification of both issues. These study investigations solely identify and classify the SATD or faults, without identifying or categorising bugs based on SATD. Furthermore, the majority of current methodologies do not incorporate contemporary deep learning techniques. This work presents an innovative method utilising deep learning techniques to discover and classify Self-Admitted Technical Debt (SATD) and to find defects in software comments associated with SATD. The proposed approach detects this issue and classifies and enhances the understanding and localization of defects. The methodology involves developing a deep learning model using diverse data from repositories, including Apache, Mozilla Firefox, and Eclipse. The chosen data set comprises projects, designated SATD examples, and bug instances, facilitating thorough model training and evaluation. The methodology comprises data analysis, preprocessing, and model training utilising deep learning architectures such as LSTM, BI-LSTM, GRU, and BI-GRU, with Transformer models like BERT and GPT-3, in conjunction with machine learning methods. The performance evaluation criteria, such as precision, recall, accuracy, and F1 score, illustrate the efficacy of the suggested method. Comparative assessment with existing methodologies underscores notable improvements, while cross-validation ensures model resilience. All deep learning models achieved an accuracy and precision of 0.98, and transformer models achieved slightly higher metrics. The GPT-3 achieved an overall accuracy of 0.984. We see that using the transfer learning approach the transformer model (GPT-3) outperformed the other as it achieved an overall accuracy of 0.96 and F1-Score of 0.96, precision of 0.96, and recall of 0.96, and deep learning models (LSTM, GRU) also give significant performance, but their accuracy is slightly lower than baseline model (Naive Bayes). The research has significant implications for software engineering, providing a comprehensive method for software quality assessment and maintenance. It enhances software architecture technical debt (SATD) and knowledge of bugs, as well as prioritization and resource allocation for software maintenance and evolution. The research’s ramifications go beyond academia; it has a direct impact on business procedures and makes it easier to create software systems that are reliable and long-lasting.

Cooperative Organosulfur/Photoredox Catalysis Enables Radical-Polar Crossover C(sp³)–N Coupling via Inner-Sphere Electron Shuttling

RETRACTED: A novel spectral transformation technique based on special functions for improved chest X-ray image classification

Chest X-ray image classification plays an important role in medical diagnostics. Machine learning algorithms enhanced the performance of these classification algorithms by introducing advance techniques. These classification algorithms often requires conversion of a medical data to another space in which the original data is reduced to important values or moments. We developed a mechanism which converts a given medical image to a spectral space which have a base set composed of special functions. In this study, we propose a chest X-ray image classification method based on spectral coefficients. The spectral coefficients are based on an orthogonal system of Legendre type smooth polynomials. We developed the mathematical theory to calculate spectral moment in Legendre polynomails space and use these moments to train traditional classifier like SVM and random forest for a classification task. The procedure is applied to a latest data set of X-Ray images. The data set is composed of X-Ray images of three different classes of patients, normal, Covid infected and pneumonia. The moments designed in this study, when used in SVM or random forest improves its ability to classify a given X-Ray image at a high accuracy. A parametric study of the proposed approach is presented. The performance of these spectral moments is checked in Support vector machine and Random forest algorithm. The efficiency and accuracy of the proposed method is presented in details. All our simulation is performed in computation softwares, Matlab and Python. The image pre processing and spectral moments generation is performed in Matlab and the implementation of the classifiers is performed with python. It is observed that the proposed approach works well and provides satisfactory results (0.975 accuracy), however further studies are required to establish a more accurate and fast version of this approach.

Divergent Mechanisms of SSZ-39 Crystallization Using Structurally Similar but Chemically Distinct Organic Structure-Directing Agents

Current applications and outcomes of AI-driven adaptive learning systems in physical rehabilitation science education: A scoping review protocol

Rationale Integrating artificial intelligence (AI) into education has introduced transformative possibilities, particularly through adaptive learning systems. Rehabilitation science education stands to benefit significantly from the integration of AI-driven adaptive learning systems. However, the application of these technologies remains underexplored. Understanding the current applications and outcomes of AI-driven adaptive learning in broader healthcare education can provide valuable insights into how these approaches can be effectively adapted to enhance multimodal case-based learning in Rehabilitation Science education. Methods The scoping review is based on the Joanne Briggs Institute (JBI) framework. It is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRIMSA-ScR). A comprehensive search strategy will be used to find relevant papers in Scopus, PubMed, CINAHL, Education Resources Information Center (ERIC), Association for Computing Machinery (ACM), ProQuest Education Journal, Web of Science, ProQuest Dissertations & Theses Global, and IEEE Digital Library. This review will include all types of studies that describe or evaluate our outcomes of interest: AI models used, learning and teaching methods, effective implementation, outcomes, and challenges of ALS’s in rehabilitation health science education. Data will be extracted using a pre-piloted data extraction sheet and synthesized narratively to identify themes and patterns. Discussion This scoping review will synthesize the applications of AI models in rehabilitation science education. It will provide evidence for educators, healthcare professionals, and policymakers to incorporate AI into educational curricula effectively. The protocol is registered on Open Science Framework registries at https://osf.io/e46s3.