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Abstract The study explores the dynamic and complex nexus between ICTs and economic performance by applying a meta-analysis with data from 65 typical studies at the country level. The overwhelmingly significant positive effects of ICTs on economic performance are confirmed through the vote count, funnel plot, precision-effect test, and meta-regression analysis. Additionally, differential influences of ICTs on developed and developing countries are investigated. Overall, developed countries gain more benefits from ICTs, while mobile technology tends to have more potential for economic growth. Moreover, the sample size and time period the data covered affect the ICTs—economic performance effect. In contrast, the type of data does not appear to significantly influence this effect. To better understand the impacts of ICTs, the continued skepticism and periodical revisit of this topic are required. The survey provides an updated and comprehensive examination of the state of the literature and also points out that although the generally favorable influences of ICTs are emphasized, the detrimental effects of ICTs on economic performance, especially for developing countries, cannot be ignored. With insights from this review, the study suggests directions for future research.

Abstract. Gallbladder polypoid lesions (GPLs) refer to any elevated lesion of the mucosal surface of the gallbladder wall, and the prevalence is estimated to be between 0.9% and 12.1%. GPLs include benign polyps and malignant polyps. Benign polyps are further classified as non-neoplastic polyps and neoplastic polyps. Cholesterol polyps are the most common benign polyps and adenocarcinoma is the main type of malignant polyp. Hepatitis B virus infection, liver function abnormalities, dyslipidemia, and obesity are the main risk factors for GPLs. Studies of biological mechanisms have focused on malignant gallbladder polyps, the development of which is regulated by hormone levels in vivo, gut microbiota, inflammation, oxidative stress, Salmonella typhimurium, and related molecules. Diagnostic modalities include chemical examination and imaging examination, with imaging examination currently being the mainstay. Treatment of patients with GPLs is based on the presence or absence of symptoms, age, size of the polyps, tendency of the polyp to increase, and risk factors for symptomatic malignancy to determine whether surgery should be performed.

The objectives of this study were to evaluate and compare the application of freehand transperineal prostate biopsy (FTPB) under local anesthesia (LA) versus systematic transperineal prostate biopsy (STPB) in an outpatient setting without antibiotics. A total of 218 biopsy-naive patients with suspicion of prostate cancer (PCa) based on multiparametric magnetic resonance imaging (mpMRI) were retrospectively analyzed. Suspicious areas on mpMRI were graded using the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1). FTPB was performed for each suspicious lesion, followed by a 12-core STPB under LA. Histopathological outcomes, complications, and tolerability were recorded. The results showed that PCa was detected in 129 (59.17%) patients, with 104 (47.71%) being clinically significant prostate cancer (csPCa). There was agreement between FTPB and STPB in 94 (72.88%) patients. FTPB diagnosed fewer PCa cases but more csPCa cases than STPB. LA-FTPB was well-tolerated with no severe complications. The conclusions suggest that FTPB under LA in an outpatient setting without antibiotics is safe, feasible, and efficient.

Abstract Background There is a high prevalence of childhood maltreatment among Chinese children and adolescents, but little is known about its impact on alcohol and tobacco use trajectories and how positive school and neighborhood environments moderate the associations. The objective of this study was to assess the association between multiple forms of childhood maltreatment and longitudinal alcohol and tobacco use trajectories, and to assess the possibility that perceived connections to school and neighborhood moderate these associations. Methods This longitudinal cohort study included 2594 adolescents (9 to 13 years) from a low-income rural area in China. Childhood exposure to abuse and neglect was assessed using the Childhood Trauma Questionnaire. Participants reported past-month alcohol and tobacco use at three time points over 1 year. Results Growth curve models revealed that childhood sexual abuse was associated with a higher risk of past-month drinking (OR = 1.53, 95% CI 1.19–2.03, p

Background Breast cancer has become the most common malignant tumor in women worldwide. Mitotic catastrophe (MC) is a way of cell death that plays an important role in the development of tumors. However, the exact relationship between MC-related genes (MCRGs) and the development of breast cancer is still unclear, and further research is needed to elucidate this complexity. Methods Transcriptome data and clinical data of breast cancer were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We identified differential expression of MCRGs by comparing tumor tissue with normal tissue. Subsequently, we used COX regression analysis and LASSO regression analysis to construct the prognosis risk model of MCRGs. Kaplan–Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive ability of prognostic model. Moreover, the clinical relevance, gene set enrichment analysis (GSEA), immune landscape, tumor mutation burden (TMB), and immunotherapy and drug sensitivity analysis between high-risk and low-risk groups were systematically investigated. Finally, we validated the expression levels of genes involved in constructing the prognostic model through real-time quantitative polymerase chain reaction (RT-qPCR) at the cellular and tissue levels. Results We identified 12 prognostic associated MCRGs, four of which were selected to construct prognostic model. The Kaplan-Meier analysis suggested that patients in the high-risk group had a shorter overall survival (OS). The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of breast cancer patients. Mechanistically, a remarkable difference was observed in clinical relevance, GSEA, immune landscape, TMB, immunotherapy response, and drug sensitivity analysis. RT-qPCR results showed that genes involved in constructing the prognostic model showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results. Conclusions We established a prognosis risk model based on four MCRGs that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for breast cancer.

This paper introduces the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT), a state-of-the-art deep learning framework designed to enhance multi-variate time series forecasting. The MDSTFT diverges from traditional models by integrating Convolutional Neural Networks (CNNs), Attention-Enhanced Long Short-Term Memory (LSTM) networks, and advanced Transformer models into a cohesive architecture. This framework intricately captures the spatial-temporal dynamics in complex datasets, significantly improving forecasting accuracy. At its core, MDSTFT employs a multi-dimensional spatio-temporal encoder to meticulously unravel spatial correlations among variables, followed by the dynamic processing of temporal patterns through LSTM networks augmented with a sophisticated attention mechanism. This dual-phase processing is intricately designed to capture the essence of time series data, which is then refined through our novel Fusion Transformer Layers. These layers, incorporating advanced self-attention mechanisms, elevate the model’s capacity to discern long-range dependencies and subtle temporal nuances, thus offering a profound understanding of temporal sequences. Evaluated on the “Bank Central Asia Stock Historical Price” dataset, MDSTFT demonstrates unparalleled forecasting accuracy, significantly outperforming both traditional models and contemporary deep learning approaches. This empirical validation not only highlights MDSTFT’s efficacy in financial market forecasting but also its broader applicability across various domains requiring nuanced multi-variate time series analysis. Through integrating multiple deep learning paradigms, MDSTFT not only advances the field of time series forecasting but also sets a new benchmark for predictive analytics, promising enhanced decision-making capabilities in finance, environmental studies, supply chain management, and beyond.

Insects are one of the most widely distributed animals on earth, and their visual systems have strong adaptability, deeply affecting their survival, reproduction, diet, and environmental adaptation. With the sequencing of the entire genome of Drosophila and the development of modern biotechnology, research on visual proteins has made significant progress. Currently, a large amount of visual protein data has been obtained through conventional experimental methods and high-throughput analysis technology, but the functions of most visual proteins are still unannotated. In addition, the current research focus mostly on individual proteins or single interactions, and there has been no comprehensive analysis of visual proteins as a whole. Therefore, in this article we used network biology methods to construct the protein-protein interaction network of visual system in Drosophila and further determine the crucial proteins in the network. We have mainly obtained seed proteins by searching a large number of literature related to visual proteins in Drosophila, and used the STRING database to obtain information on protein-protein interactions to construct the protein-protein interaction network of visual system. The clusterProfiler software package was used for functional enrichment analysis. The protein-protein interaction network constructed using Cytoscape software included 248 proteins and 2948 interactions, and the crucial proteins in the network are Akt1, arm, Src42A, Pten, Rho1, norpA, and Egfr, etc. They are crucial visual proteins in Drosophila. These proteins play an important role in the network structure.The results showed that visual proteins play an important role in biological rhythms, morphogenesis, and basic metabolism of the insect.

Introduction: The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis. Methods: A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared. Results: A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively. Conclusion: The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

To investigate the effects of Altay lipid and its extract on liver lipid level and oxidative stress in mice, 84 Kunming mice were randomly divided into two batches of 20 days and 40 days, 7 groups each (n=6): blank group (basic feed feeding), rapeseed oil group, Altay sheep buttocks oil group, 2 ℃ solid oil group, 12 ℃ solid oil group, 22 ℃ solid oil group, 32 ℃ solid oil group, experimental oil group fed 0.15 mL/10 g of basic feed to mice, the liver index of mice was measured, pathology were observed, liver lipid level and oxidative stress index were determined according to the kit specification. The results showed that there was no significant effect on liver index of mice with Altay sheep buttocks fat and its extract (P>0.05), no pathological changes in liver tissue, compared with rapeseed oil, the Altay sheep buttocks fat extracts could effectively reduce the liver lipid level of mice, and the effect of 2 ℃ solid oil group was more significant (P

Temporal action localization (TAL) aims to predict action instance categories in videos and identify their start and end times. However, existing Transformer-based backbones focus only on global or local features, resulting in the loss of information. In addition, both global and local self-attention mechanisms tend to average embeddings, thereby reducing the preservation of critical features. To solve these two problems better, we propose two kinds of attention mechanisms, namely multi-headed local self-attention (MLSA) and max-average pooling attention (MA) to extract simultaneously local and global features. In MA, max-pooling is used to select the most critical information from local clip embeddings instead of averaging embeddings, and average-pooling is used to aggregate global features. We use MLSA for modeling local temporal context. In addition, to enhance collaboration between MA and MLSA, we propose the double attention block (DABlock), comprising MA and MLSA. Finally, we propose the final network double attention network (DANet), composed of DABlocks and other advanced blocks. To evaluate DANet’s performance, we conduct extensive experiments for the TAL task. Experimental results demonstrate that DANet outperforms the other state-of-the-art models on all datasets. Finally, ablation studies demonstrate the effectiveness of the proposed MLSA and MA. Compared with structures using backbone with convolution and global Transformer, DABlock consisting of MLSA and MA has a superior performance, achieving an 8% and 0.5% improvement on overall average mAP, respectively.

The intriguing physics of vanadium dioxide (VO2) makes it not only a fascinating object of study for fundamental research on solid-state physics but also an attractive means to actively modify the properties of integrated devices. In particular, the exceptionally large complex refractive index variation produced by the insulator-to-metal transition of this material opens up interesting opportunities to dynamically tune optical systems. This Perspective reviews some of the exciting work done on VO2 for nanophotonics in the last decade and suggests promising directions to explore for this burgeoning field.

The generalized l1 greedy algorithm was recently proposed and shown to outperform the standard reweighted l1-minimization and l1-greedy algorithms for image reconstruction in computed tomography (CT). Herein, this algorithm is extended as a semisoft generalized l1 greedy algorithm by adapting the wavelet technique of semisoft thresholding. The extended algorithm can also be applied to image reconstruction by incorporating it into the BCPCS framework, resulting in a semisoft generalized total variation minimization (SSGTV) algorithm for CT. Numerical tests indicate that the proposed SSGTV algorithm improves the image reconstruction for CT.