Search Results (405)

Publication bias has been a problem facing meta-analysts. Methods adjusting for publication bias have been proposed in the literature. Comparative studies for methods adjusting for publication bias are found in the literature, but these studies are limited. We investigated and compared the performance of five methods adjusting for publication bias for the case of continuous outcomes. Three measures of continuous treatment effect, the mean difference, Cohen’s d and Hedges’ g, were considered. The methods studied were Copas, PET-PEESE, p-uniform, Trim and Fill and the limit meta-analysis. In addition, the performance of the random-effects meta-analysis using the DerSimonian estimator was also investigated. The analysis was conducted using a case study and an extensive simulation study including different scenarios. In general, the Copas and the PET-PEESE were found to be the least biased methods adjusting for publication bias. However, the Copas method, like other likelihood-based methods, can have convergence issues. In addition, the PET-PEESE method is robust in case of heteroscedasticity, making the PET-PEESE method a preferable technique to adjust for publication bias. Full article

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, characterized primarily by motor impairments due to the loss of dopaminergic neurons. Despite extensive research, the precise causes of PD remain unknown, and reliable non-invasive biomarkers are still lacking. This study aimed to explore gene expression profiles in peripheral blood to identify potential biomarkers for PD using machine learning approaches. We analyzed microarray-based gene expression data from 105 individuals (50 PD patients, 33 with other neurodegenerative diseases, and 22 healthy controls) obtained from the GEO database (GSE6613). Preprocessing was performed using the “affy” package in R with Expresso normalization. Feature selection and classification were conducted using a decision tree approach (C4.5/J48 algorithm in WEKA), and model performance was evaluated with 10-fold cross-validation. Additional classifiers such as Support Vector Machine (SVM), the Naive Bayes classifier and Multilayer Perceptron Neural Network (MLP) were used for comparison. ROC curve analysis and Gene Ontology (GO) enrichment analysis were applied to the selected genes. A nine-gene decision tree model (TMEM104, TRIM33, GJB3, SPON2, SNAP25, TRAK2, SHPK, PIEZO1, RPL37) achieved 86.71% accuracy, 88% sensitivity, and 87% specificity. The model significantly outperformed other classifiers (SVM, Naive Bayes, MLP) in terms of overall predictive accuracy. ROC analysis showed moderate discrimination for some genes (e.g., TRAK2, TRIM33, PIEZO1), and GO enrichment revealed associations with synaptic processes, inflammation, mitochondrial transport, and stress response pathways. Our decision tree model based on blood gene expression profiles effectively discriminates between PD, other neurodegenerative conditions, and healthy controls, offering a non-invasive method for potential early diagnosis. Notably, TMEM104, TRIM33, and SNAP25 emerged as promising candidate biomarkers, warranting further investigation in larger and synthetic datasets to validate their clinical relevance. Full article

This paper presents a comparative evaluation of three distinct methodologies for estimating the parameters of the Inverse Weibull distribution under Type-I right censoring: trimmed linear moments (using Type-AT and Type-BT variants), direct linear moments (using Type-AD and Type-BD variants), and maximum likelihood estimation. The performance of these methods is assessed through Monte Carlo simulations, focusing on estimation accuracy, relative absolute bias, and root mean square error to identify the most appropriate approach. The practical applicability of these techniques is demonstrated through a real-world dataset analysis. Full article

Polyurethane foams (PUFs) utilised in the comfort industry generate substantial trim waste volumes requiring end-of-life management. Rebonding, one form of mechanical recycling, is a technique involving the mechanical breakdown and subsequent adhesion of PUF using polyurethane prepolymers yielding a recycled material. However, the limited investigation into the properties of rebond PUF constrains its potential for novel alternative uses, such as soilless plant-growing media. A laboratory-scale rebond production method has been developed, and a series of rebond PUFs produced to evaluate the influence of crumb size, density, prepolymer chemistry, and prepolymer loading on the properties of the rebond PUFs and their suitability as growing media. The results indicated that higher quality rebonds were obtained with larger crumb sizes (mixed or >7 mm), moderate amounts of prepolymer (4.5 to 7.5% by mass), and higher densities. Increasing density directly influenced plant growth-related properties, including reducing airflow, increasing water uptake through wicking, and increasing water retention through drainage alongside larger crumb sizes [>7 mm]. To demonstrate the method’s utility for rapid screening, a plant growth trial was conducted using density as the key variable. Eruca sativa plants grown in low-density rebonds exhibited comparable growth (leaf length, leaf width, and shoot fresh weight) to mineral wool, whereas medium- and high-density rebonds showed reduced growth. This study validates a lab-scale technique that enables the rapid optimisation of rebond PUFs for novel applications like soilless growing media. Full article

This paper presents an effective method for generating blending meshes by leveraging geodesic curves on triangular meshes. Depending on whether the input meshes intersect, the blending regions are automatically initialized using either minimum-distance points or intersection curves, while allowing users to intuitively adjust boundary curves directly on the mesh. Each blending region is parameterized via geodesic linear interpolation, and a reparameterization strategy is employed to establish optimal correspondences between boundary curves, ensuring smooth, twist-free connections. The resulting blending mesh is merged with the input meshes through subdivision, trimming, and co-refinement along the boundaries. The proposed method is applicable to both intersecting and non-intersecting meshes and offers flexible control over the shape and curvature of the blending region through various user-defined parameters, such as boundary radius, scaling factor, and blending function parameters. Experimental results demonstrate that the method produces stable and smooth transitions even for complex geometries, highlighting its robustness and practical applicability in diverse domains including digital fabrication, mechanical design, and 3D object modeling. Full article

This study investigates the effects of Ar ion irradiation on the mechanical properties and microstructure of SA508 Grade 3 Class 1 and Class 2 reactor pressure vessel steels. Three different fluence levels of Ar ion irradiation were applied to simulate accelerated irradiation damage conditions. Charpy impact and tensile tests conducted before and after irradiation showed no significant changes in bulk mechanical properties. Stopping and Range of Ions in Matter (SRIM) and Transport of Ions in Matter (TRIM) simulations revealed that Ar ion irradiation produces a shallow penetration depth of approximately 2.5 µm, highlighting the limitations of conventional macro-mechanical testing for evaluating irradiation effects in such a thin surface layer. To overcome this limitation, nano-indentation tests were performed, revealing a clear increase in indentation hardness after irradiation. Transmission electron microscopy (TEM) analysis using STEM–BF imaging confirmed a higher density of irradiation-induced defects in the irradiated specimens. The findings demonstrate that while macro-mechanical properties remain largely unaffected, micro-scale testing methods such as nano-indentation are essential for assessing irradiation-induced hardening in shallowly damaged layers, providing insight into the behavior of SA508 reactor pressure vessel steels under accelerated irradiation conditions. Full article

A precision bandgap reference (BGR) is an essential building block in modern analog and mixed-signal systems, as it provides stable and predictable current and voltage references required for reliable operation across process, voltage, and temperature variations. However, one of the key challenges in conventional BGR circuits is their sensitivity to resistance variations, which directly impacts the accuracy of bias currents. Even small changes in resistance can lead to significant current mismatch between the core branches of the circuit, thereby degrading output stability and limiting the precision of the overall system. This degradation is particularly problematic in high-performance applications such as data converters, oscillators, and low-power biasing networks, where robust current matching is critical. To address this limitation, this work proposes a resistance-compensated BGR architecture that incorporates an auxiliary trimming network and a compensation branch. The trimming network senses variations in resistance and generates a control bias proportional to the deviation, while the compensation branch injects a corrective current into the output stage. By dynamically balancing the mismatch introduced by resistor spread, the proposed architecture effectively restores current stability across process corners. This method achieves reduction in the current variation across resistance corners from 21% to 3% in worst-case corners (±3%). This approach offers enhancement of current mismatches in analog systems in which robust current is essential. Full article

With the rapid expansion of digital knowledge platforms and intelligent information systems, organizations and communities are producing a vast number of unstructured knowledge data, including annotated corpora, technical diagrams, collaborative whiteboard content, and domain-specific multimedia archives. However, knowledge sharing across institutions is hindered by privacy risks, high communication overhead, and fragmented ownership of data. Federated learning promises to overcome these barriers by enabling collaborative model training without exchanging raw knowledge artifacts, but its success depends on motivating data holders to undertake the additional computational and communication costs. Most existing incentive schemes, which are based on non-cooperative game formulations, neglect unstructured interactions and communication efficiency, thereby limiting their applicability in knowledge-driven scenarios. To address these challenges, we introduce SC-NBTI, a smart contract and Nash bargaining-based incentive framework for federated learning in knowledge collaboration environments. We cast the reward allocation problem as a cooperative game, devise a heuristic algorithm to approximate the NP-hard Nash bargaining solution, and integrate a probabilistic gradient sparsification method to trim communication costs while safeguarding privacy. Experiments on the FMNIST image classification task show that SC-NBTI requires fewer training rounds while achieving 5.89% higher accuracy than the DRL-Incentive baseline. Full article

White line disorders represent the most prevalent claw horn disruption lesion in dairy cattle. Recent studies have yielded new insights into the appropriate treatment modalities for these lesions. The aims of this study are to elucidate the pathogenesis of white line disorders and its associated claw lesions, such as toe tip necrosis, and to discuss practical treatment applications. In Western Europe, many herds are endemically infected with digital dermatitis. White line disorders in zone 3 and toe tip necrosis starting in zone 1—often beginning as axial white line lesions—frequently exhibit a suboptimal response to standard treatments, including corrective trimming, the application of a hoof block on the healthy claw and the administration of NSAIDs, due to secondary infections with Treponema spp. This study addresses the current perspectives on the aetiopathogenesis of white line disorders and the therapeutic challenges in promoting complete recovery and the correct use of antibiotics, along with preventive measures, such as good flooring. An important factor of its pathogenesis is a decrease in body condition around parturition, Correct diagnosis can be made via the use of regular locomotion scoring and good diagnostic tools, and thin soles by among others overtrimming should be prevented. Current therapeutic methods consist of the prompt application of a block and an NSAID and, in some circumstances, a parenteral injection with antibiotics when there is no good response to the applied therapies. Full article

Background/Objectives: Gastric cancer (GC), a prevalent global malignancy and a leading cause of cancer-related mortality, has a poorly understood prognosis related to TRIP13 expression. TRIP13 has a recognized part in driving tumor progression across different cancer types, yet its precise role in GC remains beyond our full comprehension. Our study aimed to explore TRIP13’s prognostic value and function in GC patients. Methods: We extensively explored TRIP13’s influence on GC prognosis, functionality, and immune response by examining various cancer-related databases like UALCAN, GEPIA, GEO, and TIMER. Immunohistochemistry (IHC) staining was also conducted to assess the link between TRIM13 and GC patient survival. Results: TRIP13 expression levels were found to be significantly elevated in GC tissues compared to normal tissues through analysis of mRNA data from multiple public databases. IHC analysis exposed elevated TRIP13 protein levels in GC tissues and connected it with tumor depth. Prognostic evaluation demonstrated that GC patients exhibiting heightened TRIP13 expression endured a diminished overall survival rate. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that genes related to TRIP13 are involved in processes such as the cell cycle and DNA repair. Additionally, TRIP13 expression was found to correlate with ferroptosis-related genes and may play a role in regulating ferroptosis. Immune cell infiltration analysis demonstrated that TRIP13 expression is negatively correlated with the infiltration of CD4⁺ T cells, CD8⁺ T cells, and B cells. Conclusions: TRIP13 emerges as a candidate independent prognostic indicator and a promising intervention point for GC treatment. Full article

This is the first systematic review and meta-analysis on the prevalence and risk factors of M. hyopneumoniae infection in swine farms across mainland China from 2003 to 2024. A total of 54 eligible cross-sectional studies were analyzed by stratifying farms as subclinically or clinically infected. The overall pooled prevalence of M. hyopneumoniae was estimated as 33.4%, with clinical infection farms showing a significantly higher prevalence (52.9%) than subclinical farms (11.5%). Subgroup analyses revealed significant variations in infection rates based on age, sampling year, geographic region, farming scale, season, sampling type, and diagnostic method. Small-scale farms, farms with breeding swine, and farms in the Northwest region showed the highest infection rates. Diagnostic methods and sampling types also significantly influenced detection rates. Sensitivity analyses confirmed the robustness of the results, while publication bias was addressed using the Trim-and-Fill method. To effectively reduce the burden of M. hyopneumoniae in the swine industry in mainland China, future efforts should prioritize enhanced biosecurity, improved diagnostic accuracy, and region-specific vaccination and management strategies. Full article

Background/Objectives: This meta-analytical review assesses the relationship between effect size and replication success in genetic studies of athletic performance, focusing on the ACTN3 and ACE polymorphisms across power- and endurance-based sports. The analysis revealed substantial heterogeneity in reported effect sizes (overall I² = 72.3%), indicating considerable variability between studies, likely influenced by differences in population genetics, study design, and sample size. Methods: For ACTN3, the pooled effect sizes were 1.40 (95% CI: 1.18–1.65) for power sports and 1.35 (95% CI: 1.12–1.58) for endurance sports. Although the difference between these estimates is small, it reached statistical significance (p = 0.0237), reflecting the large sample size, but it remains of limited practical and clinical significance. For the ACE polymorphism, effect sizes were similar in both endurance (ES = 1.22, 95% CI: 1.05–1.41) and power sports (ES = 1.20, 95% CI: 1.03–1.43), with overlapping confidence intervals, indicating no meaningful difference in association strength between sport types. Effect sizes were calculated as odds ratios (OR) with 95% confidence intervals for case–control designs, with standardized conversion protocols applied for alternative study designs reporting standardized mean differences or regression coefficients. Results: Publication bias was detected, particularly in smaller studies on ACTN3 and power sports (Egger’s test p = 0.007). The pooled effect of ACTN3 in power sports (OR 1.40, 95% CI: 1.18–1.65, 95% PI: 0.89–2.20) was adjusted to OR 1.32 (95% CI: 1.15–1.51) following trim-and-fill publication bias correction. The high degree of heterogeneity (I² = 72.3%) cautions against overgeneralization of the pooled results and highlights the need for careful interpretation, robust replication studies, and standardized methodologies. Conclusions: The findings emphasize that, while genetic markers such as ACTN3 and ACE are statistically associated with athletic performance, the magnitude of these associations is modest and should be interpreted conservatively. Methodological differences and publication bias continue to limit the reliability of the evidence. Future research should prioritize large, well-powered, and methodologically consistent studies—ideally genome-wide approaches—to better account for the polygenic and multifactorial nature of elite athletic ability. Full article

Background/Objectives: The diagnosis of prosthetic joint infection remains difficult. Microbiological cultures frequently have false-positive and false-negative results. This study investigates whether rapid nanopore sequencing can be used to aid the identification of bacteria causing prosthetic joint infection for more timely identification and treatment. Methods: Nineteen patients who had revision surgery following total joint arthroplasty were included in this study. Of these, 15 patients had an infected joint arthroplasty. All patients had joint fluid aspirated at the time of revision surgery. The DNA was extracted from these fluid aspirates, and rapid nanopore sequencing was performed using the MinION device from Oxford Nanopore Technologies. The sequencing data was trimmed to improve quality and filtered to remove human reads using bioinformatic tools. Genomic sequence classification was performed using the Basic Local Alignment Search Tool. The results were filtered by read length and sequence identity score. The European Bone and Joint Infection Society criteria were used as a standard to identify infected and not infected patients. Confusion tables were used to calculate accuracy and F1 score based on this criteria and the nanopore sequencing results. Results: Microbiological cultures and nanopore sequencing had an accuracy of 68% and 74%, respectively. However, combining both results predicted infection accurately in 94% of cases (F1 score 96%). Conclusions: Nanopore sequencing has the potential to aid identification of bacteria causing prosthetic joint infection and may be useful as a supplementary diagnostic tool. Full article

Background/Objectives: Previous meta-analyses on vitamin D (VitD) supplementation for fall prevention have mixed different populations and doses. This study aimed to evaluate whether VitD supplementation reduces fall risk in community-dwelling adults aged ≥65 years. Methods: Randomized clinical trials (RCTs) including adults ≥65 years living in the community and receiving supplemental VitD were identified through the MEDLINE and EMBASE databases (January 2005–July 2024), and independent reviewers selected studies reporting fall risk, extracted data, and assessed risk of bias. Outcomes were pooled using the inverse variance method. Heterogeneity and publication bias were assessed with I², Egger’s test, and the trim-and-fill method. Results: The analysis dataset was 23,211 participants from 10 RCTs. Most studies had minimal risk of bias. Overall, VitD supplementation was not associated with a reduction in fall risk, as no statistically significant or consistent trend was observed (OR = 0.99; 95%CI: 0.95–1.03; I² = 31%). In women, no significant association was found (OR = 0.97; 95%CI: 0.92–1.02; I² = 31.2%), and in men, no significant association was observed (OR = 1.08; 95%CI: 0.98–1.20; I² = 0%) when analyzed separately. Supplementation with doses ≤1000 IU/day showed no significant association with fall risk (OR = 0.96; 95%CI: 0.90–1.02; I² = 39.5%), as did durations ≤12 months (OR = 0.96; 95%CI: 0.90–1.02; I² = 56.2%) and daily administration (OR = 0.97; 95%CI: 0.92–1.03; I² = 37.2%). Doses >1000 IU/day, intermittent dosing (both OR = 1.02; 95%CI: 0.96–1.09; I² = 0%), and durations >12 months (OR = 1.01; 95%CI: 0.96–1.07; I² = 0%) all showed no significant association. Although evidence of publication bias was detected, adjustment did not alter the results. Conclusions: This meta-analysis shows that VitD supplementation has no effect on the risk of falls in community-dwelling adults ≥65 years, yet its main interest lies in ensuring population homogeneity found in previous analyses of mixed settings. Full article

Identifying robust differentially expressed genes (DEGs) in RNA-Seq data remains challenging under variable experimental conditions. To address this, we performed five independent RNA-Seq experiments using Drosophila melanogaster larvae treated with methyl lucidone—a putative juvenile hormone disruptor—and compared conventional normalization methods (relative log expression [RLE] via DESeq2 and trimmed mean of M-values [TMM] via edgeR) against our novel DEG-by-index ratio transformation (DiRT). DESeq2 identified two significant DEGs, while edgeR detected none; both methods showed limited validation across four additional independent experiments. In contrast, DiRT identified a distinct set of numerous DEGs with improved reproducibility and reliable validation. KEGG pathway analysis revealed that DiRT-derived DEGs were functionally enriched in pathways related to methyl lucidone detoxification, including the proteasome, drug metabolism, and xenobiotic metabolism mediated by cytochrome P450 and other enzymes. Although DESeq2 and edgeR remain widely used standard methods, DiRT offers a novel complementary approach to enhance DEG characterization in RNA-Seq studies affected by experimental variability. Full article