Browse Articles

Flight densities of adult black flies (Diptera: Simuliidae) and control strategies in the Betsumata area, upper Ukawa River, Niigata Prefecture, Japan

Evaluation of performance properties in woven Terry towel by multi response optimization

Measurement model of credit risk for unlisted agricultural enterprises

This paper aims to measure credit risks of unlisted agricultural enterprises by using the KMV model integrating a CNN-BiLSTM neural network. Initially, the expected default frequencies (EDF) for each listed agricultural enterprise are computed using the Black-Scholes option pricing formula within the KMV framework. We apply the neural network model trained by listed agricultural enterprises to the credit risk analysis of unlisted agricultural enterprises. The EDF and financial data of listed agricultural enterprises undergo Z-score standardization and comparison using CNN-BiLSTM neural networks. Model parameters are then experimented with to determine the optimal CNN-BiLSTM model. This selected optimal CNN-BiLSTM model is applied to standardized financial data of unlisted agricultural enterprises to derive corresponding EDF. Based on the EDF of the listed agricultural enterprises, corresponding rating intervals are determined for unlisted agricultural enterprises. We use unlisted companies in China as an example in empirical analysis. The results demonstrate the effective assessment of credit ratings for unlisted agricultural enterprises using this model, generally aligning with institutional rating outcomes. Given differences in rating systems, the model helps identify hidden credit risks that are challenging to detect through conventional rating methods. It highlights the nonlinear relationship between enterprise credit risks and financial indicators, including debt repayment capacity, operational capability, growth potential, profitability, and debt structure.

Studies on zoonotic onchocerciasis in Japan

Perceived health related quality of life among cancer patients based on insights from Kamuzu central hospital patients, caregivers and staff

Multimodal analysis investigating the shared pathogenic mechanisms of osteoporosis and osteoarthritis with an initial exploration of the role of ferroptosis

Background Osteoarthritis (OA) and osteoporosis (OP) are prevalent conditions with overlapping molecular mechanisms. Recent studies have brought attention to ferroptosis, a type of cell death that relies on iron, as a possible connection between these diseases. Methods We performed a comprehensive analysis with datasets obtained from the Gene Expression Omnibus (GEO), focusing on differentially expressed genes (DEGs) in OA and OP. Functional enrichment and integrated analyses identified ferroptosis-related pathways. The role of ferroptosis in OP was further explored through in vivo studies using an ovariectomy-induced OP mouse model. Results The analysis revealed significant overlaps in DEGs related to ferroptosis pathways in both OA and OP. Key genes like TXNIP and SLC2A3 were implicated in the regulation of ferroptosis and associated with disease mechanisms. In vivo results confirmed increased ferroptosis markers in bone marrow stromal cells (BMSCs) from OP mice, supporting the hypothesis that ferroptosis contributes to bone density reduction and structural deterioration. Conclusion Our findings highlight ferroptosis as a critical pathway in the pathogenesis of both OA and OP. Targeting ferroptosis-related genes and pathways could provide new therapeutic opportunities for managing these musculoskeletal diseases.

Advancing real-time validation of automotive software systems via continuous integration and intelligent failure analysis

Abstract In the automotive industry, a rigorous testing process based on ISO 26,262 is carried out at various stages of the V-model to ensure the quality of software systems. Conventional validation of embedded electronic control units (ECUs) using hardware-in- the-loop (HIL) testing is performed in the late stages using the big bang integration style, resulting in delayed feedback, lack of scalability, and insufficient fault diagnosis. Furthermore, test recording analysis is performed manually based on expert knowledge to identify the nature of the failure occurring. This, in turn, resulted in higher development costs and effort, delays fault detection, and hinders agile collaboration. To address these gaps, this article proposes a novel continuous integration (CI)-enabled HIL testing framework to facilitate continuous software development through iterative cycles. Furthermore, based on a representative critical faults dataset, intelligent data-driven ML-assisted Fault Detection and Diagnosis (FDD) models are developed, including LSTM and K-means for the diagnosis of known and unknown sensor-related faults as classification and clustering problems, respectively. The novel aspect of the robust models lies in the integration of a denoising autoencoder (DAE) for the extraction of representative features before the classification and clustering process, considering the noise conditions. The evaluation outcomes illustrate the superiority of the proposed model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low mean squared error (MSE) and Davies–Bouldin index (DBI), i.e., 0.044 and 0.68, respectively. It has been demonstrated that the proposed methodology facilitates more efficient, automated, and accurate fault analysis within the framework of automotive software validation workflows. Consequently, this approach enhances both safety and efficiency in comparison to conventional methodologies.

Framework for analyzing MAE-derived immunopeptidomes from cell lines with shared HLA haplotypes

Background The goal in vaccinology is to identify candidate antigens for clinical trials that will elicit an immune response for a significant portion of the target population. Unfortunately, promising data generated at the preclinical level often cannot be replicated in larger sample sizes. The goal of this project was to develop a methodology for processing MAE-generated data to identify MHC epitopes, minimize non-specific contaminants, find binding motifs, and utilize genetic connections among donors to determine which peptides were presented by specific MHC alleles. Results Our approach demonstrated that mild acid elution of peptides from seven consanguineous B-lymphocyte lines accurately reflects the HLA genotypes within family members, highlighting the specificity of MAE. Additionally, the data successfully reproduced known MHC binding motifs and partially deconvoluted the originating HLA alleles of the epitopes. Conclusions These findings suggest that our approach could be applied to numerous cell lines globally to evaluate a wide array of HLA haplotypes. This may help to reveal candidate vaccine antigens that induce immune protection for a wider population.

Lens opacity as a predictor of retinal vasculature change following cataract surgery

Abstract Cataract surgery, one of the most common surgical procedures worldwide, significantly improves visual acuity and quality of life for patients. However, recent studies suggest that it may have broader implications for ocular health, including changes in retinal perfusion. This prospective clinical study investigates the relationship between preoperative lens opacity and postoperative changes in macular perfusion using optical coherence tomography in 46 patients. Objective metrics were assessed automatically using a custom computer script. The analysis revealed significant increases in vessel density, diameter, and complexity across the superficial, intermediate, and deep retinal vascular plexuses, with the most pronounced changes occurring within the first postoperative week. A strong correlation was observed between preoperative nuclear lens opacity and the increase in macular perfusion, suggesting that reduced light transmission through dense cataracts may drive postoperative functional hyperemia. In contrast, surgical parameters such as phacoemulsification energy showed no significant association, and intraocular pressure reduction correlated only with subtle vascular perimeter changes. These findings indicate that enhanced light exposure following cataract removal—rather than just inflammation or mechanical factors—likely stimulates adaptive retinal metabolic responses. Clinically, this highlights the importance of preoperative lens opacity assessment as a predictor of vascular remodeling, potentially informing strategies to mitigate complications in the short and long term.

Blood flow-restricted resistance training modulates miRNAs to improve early hypertensive cardiac function

Objective The aim of this study was to explore the differentially expressed miRNAs in the hearts of rats protected from early spontaneous hypertension by blood flow-restricted resistance training and to elucidate the effects of blood flow-restricted resistance training on the expression of these genes. Methods Four-week-old SHRs and WKY rats were used and randomly divided into five groups: the normal group (WKY), SHR control group (SHR-SED), high-intensity resistance training group (HIRT), medium-intensity resistance training group (MIRT), and blood flow-restricted medium-intensity resistance training group (BFRT). During the experiment, the body weight, cardiac function and hemodynamic parameters of the rats were measured. After training, total RNA was extracted from the left ventricular myocardium of rats in the SHR-SED group and the BFRT group, miRNAs were sequenced, followed by GO enrichment and KEGG pathway analyses, and the differentially expressed miRNAs were subsequently validated via qRT‒PCR. Results 1) Hemodynamic tests revealed that the blood pressure of SHRs in the BFRT decreased significantly and that the blood pressure level of SHRs in the BFRT decreased more significantly than that of the simple resistance training groups did (P < 0.05). 2) Cardiac function tests revealed that the EF, FS, and MV E/A of SHRs in the BFRT significantly increased, whereas the HR, IVSd, IVSs, LVIDd, LIVDs, LVPWd, LVPWs and LV mass significantly decreased (P < 0.05). 3) Transcriptome sequencing revealed 9 differentially expressed miRNAs in the BFRT group compared with the SHR-SED group (2 miRNAs were significantly upregulated, and 7 miRNAs were significantly downregulated), with P < 0.05 and |log2FoldChange| ≥ 1 used as the criteria for differential significance. The most prominent differentially expressed miRNA was miR-200b-3p (P = 0.00, |log2FoldChange| = 2.45). 4) The miRNA validation results revealed that BFRT significantly reduced the expression of miR-200a-3p, miR-200b-3p, miR-342-3p, miR-350, miR-429, miR-1249, miR-1949 in SHR myocardium, and increased the expression of miR-31a-5p and miR-224-5p (P < 0.01). Conclusion Eight weeks of blood flow-restricted medium-intensity resistance training could lower SHR blood pressure, and it might also improve early SHR cardiac function by regulating the expression of miR-224-5p, miR-31a-5p, miR-200b-3p, miR-200a-3p, miR-342-3p, miR-429, miR-1949, miR-1249, and miR-350, with the differential expression of miR-200b-3p being particularly significant.

Adolescent suicide behaviors associate with accelerated reductions in cortical gray matter volume and slower decay of behavioral activation Fun-Seeking scores

Abstract Distinguishing those at risk of making a suicide attempt from those who experience only suicidal ideations remains a significant clinical challenge. Longitudinal studies during early adolescence may provide insight into altered brain and behavioral developmental trajectories among those who develop suicide behaviors (SB). Here, we applied linear mixed effects regression models to several global brain volumes and psychiatric/behavioral measures from the Adolescent Brain Cognitive Development (ABCD) Study. We analyzed data from baseline up until the two-year follow-up, when participants were roughly 10 to 12 years of age. Individuals who had either ever endorsed or developed SB exhibited the greatest reductions in cortical gray brain matter volume. Those who developed SB exhibited the greatest increase in DSM5-depression scores and were the only group that maintained their levels of Behavioral Activation System (BAS) Fun-Seeking behaviors. Finally, we applied a Cross-Lagged Panel Modelling approach to the whole ABCD sample and found that baseline total cortical gray matter structure significantly predicted variation in BAS Fun-Seeking behaviors at the two-year follow-up, providing evidence supportive of a potential causal relationship between these two measures. Altogether, our findings suggest that differences in total cortical gray matter volume at 9–10 years of age may impact the development of behavioral approach systems. Altered development of behavioral approach systems and depressive symptoms distinguish youth who developed suicide behaviors during early adolescence.

Collaborative representation based on enhanced tensor robust PCA for hyperspectral anomaly detection

This paper presents a novel hyperspectral anomaly detection (HAD) method, ETRPCA-CRD, which integrates enhanced tensor robust principal component analysis (ETRPCA) with collaborative representation detection (CRD) to effectively separate anomalous targets from background data. The key novelty lies in the use of weighted tensor Schatten-p norm minimization (WTSNM) within the ETRPCA framework, which assigns distinct weights to different singular values to preserve important information while eliminating noise. The ETRPCA problem is efficiently solved by Fourier transform, generalized soft-thresholding (GST), and T-singular value decomposition (SVD) methods. This approach significantly improves detection accuracy by fully utilizing the spectral-spatial information of hyperspectral images (HSIs) represented as tensors. The low-rank tensor obtained from ETRPCA serves as the background data for CRD, further enhancing detection performance. Experiments on three real hyperspectral datasets and one simulated dataset demonstrate that ETRPCA-CRD outperforms several state-of-the-art algorithms, achieving superior detection accuracy and robustness. The proposed method’s ability to effectively distinguish anomalies from background data while preserving salient signals makes it a powerful tool for hyperspectral anomaly detection.

Assessing wildfire extents in Siberian forests using machine learning

Abstract Wildfires significantly impact ecosystem dynamics and forest management strategies globally, including in Siberian forests. This study develops a machine learning (ML) framework to estimate wildfire size by integrating meteorological variables, forest composition, detection techniques, and historical fire records within the Krasnoyarsk Krai region of central Siberia. The dataset includes temperature, humidity, wind speed, precipitation, geospatial coordinates, and proximity to human settlements, which are used to train multiple predictive models, including XGBoost, Random Forest, K-Nearest Neighbors, Logistic Regression, and Decision Tree. XGBoost achieved the highest classification accuracy of 88.8%, outperforming other methods. Feature importance analysis highlights the influence of urban proximity, wind patterns, and meteorological conditions related to fuel moisture on fire size prediction. SHAP (SHapley Additive exPlanations) analysis indicates that smaller fires are associated with localized weather conditions, while extended dry periods correspond to larger fire events. While these results demonstrate the potential of ML for fire size classification in this specific region, the framework should be considered exploratory and region-specific. Future applications to other areas will require local data calibration.

Altered carnitine-acylcarnitine profiles in levothyroxine-treated congenital hypothyroid patients with fatigue: An LC-MS/MS-based study from Bangladesh

Congenital hypothyroidism (CH), characterized by insufficient thyroid hormone production at birth, is frequently associated with fatigue, particularly in cases with delayed diagnosis. This study employed liquid chromatography–tandem mass spectrometry (LC-MS/MS) to profile carnitine and acylcarnitines in late-diagnosed congenital hypothyroid patients receiving levothyroxine (LT4) therapy, with the aim of identifying metabolic alterations that may be associated with fatigue symptoms. A total of 56 late-diagnosed congenital hypothyroid patients and 107 age-, sex-, and BMI-matched healthy controls were enrolled. Blood samples were collected in EDTA tubes and as dried blood spots (DBS) on Whatman® 903 filter paper. LC-MS/MS was used to quantify free carnitine and 28 acylcarnitines, and plasma triglyceride (TG) levels were measured using a biochemical analyzer. Compared to healthy controls, congenital hypothyroid patients showed higher mean (±SD) concentrations of free carnitine (45.38 ± 12.61 vs. 41.54 ± 9.85 µmol/L; P = 0.049), total carnitines (67.33 ± 18.27 vs. 62.51 ± 14.13 µmol/L), and total acylcarnitines (21.95 ± 7.66 vs. 20.96 ± 5.61 µmol/L), although only free carnitine levels were statistically significant. Long-chain acylcarnitines were significantly lower in congenital hypothyroid patients (2.67 ± 0.87 µmol/L) compared to controls (3.15 ± 0.93 µmol/L; P = 0.0014). The β-oxidation ratio C0/(C16 + C18), a proxy for Carnitine Palmitoyltransferase I (CPT-I) activity, was significantly elevated in patients compared to healthy controls (34.55 ± 14.88 vs. 25.73 ± 6.87; P < 0.0001). Plasma TG levels were also significantly higher in patients (88.92 ± 59.54 mg/dL) than in controls (58.33 ± 15.79 mg/dL; P = 0.02). Metabolic profiling in congenital hypothyroid patients revealed impaired long-chain fatty acid oxidation and elevated triglyceride levels. These metabolic changes may contribute to fatigue symptoms and are potentially associated with reduced CPT-I activity, which is essential for mitochondrial β-oxidation. Additionally, mutations in the TPO and TSHR genes identified within this cohort may be linked to the observed metabolic alterations. Collectively, these findings suggest a possible interplay between genetic variants, disrupted lipid metabolism, and clinical features of congenital hypothyroidism.

New evidence reveals dispersal of pearl millet from West Africa to South Asia by 2500 BCE

Comparing Guillain-Barré syndrome outcomes between rural and urban hospitals in the United States: A retrospective cohort study

Background and purpose Rural-urban disparities in neurological care have been well documented, but limited data exist regarding Guillain-Barré Syndrome (GBS). This study examines differences in patient demographics, hospital characteristics, and outcomes among GBS admissions to rural versus urban hospitals in the United States. Methods Using the 2021 National Inpatient Sample, we conducted a retrospective cohort study of adult hospitalizations with a principal diagnosis of GBS. Hospitals were classified as rural or urban based on U.S. census designations. Multivariate logistic and linear regression models were used to assess associations between hospital location and outcomes, adjusting for demographic, clinical, and hospital-level factors. Results An estimated 10,035 weighted Guillain-Barré Syndrome hospitalizations were identified, of which 95.8% occurred in urban hospitals. Rural hospitalizations involved older individuals (mean age 56.8 years; 95% CI: 52.5–61.0) compared to urban hospitalizations (51.3 years; 95% CI: 50.3–52.2). Adjusted analyses showed no significant differences in in-hospital mortality (adjusted OR 2.00; 95% CI: 0.11–35.12) or length of stay (mean difference −1.85 days; 95% CI: −6.62 to 2.91). However, total hospital charges were significantly higher in urban hospitals, with an average difference of $39,474 (95% CI: $4,296–$74,651). Discharge disposition was comparable, with 40% of rural hospitalizations and 48.1% of urban hospitalizations discharged home, and 38.8% versus 43.3% discharged to skilled nursing facilities (all p > 0.05). Conclusions In this national analysis of over 10,000 Guillain-Barré Syndrome hospitalizations, rural and urban hospitals achieved comparable outcomes in terms of in-hospital mortality, length of stay, complications, and discharge disposition. Rural hospitalizations tended to involve older individuals from lower-income areas, whereas urban hospitals managed more cases with severe comorbidities and generated substantially higher costs. These findings suggest that rural hospitals are capable of delivering effective acute care for GBS, and highlight the need for future research on long-term functional outcomes across geographic settings.

Eco-labeled composts reduce microplastic contamination and mitigate heavy metal bioavailability in agricultural ecosystems

The decline of local wisdom in managing the Wain River protected forest near Indonesia’s new capital city buffer zone

The Wain River protected forest serves not only as a watershed but also holds a critical role in sustaining hydrological functions. The Sultan of Kutai Kertanegara Sultanate initiated ecological engineering efforts in 1934 by designating this area as a protected forest. However, rapid urbanization has led to a decline in local wisdom, posing a threat and intensifying pressure on the Wain River protected forest. The practices of local wisdom applied by the community within the Wain River protected forest area significantly impact the forest’s sustainability. Despite their diminishing influence, they still uphold ancestral guidance in forest conservation. Wain River is also a buffer zone forest for the new capital city of Indonesia at Penajam Paser Utara. Utilizing the Multidimensional Scaling (MDS) method, it was determined that the role of local wisdom in managing the Wain River protected forest falls under a category of weak sustainability, scoring 68.034 percent. Major influencing factors include land-use conversion and economic concerns, with the economic dimension scoring a sustainability rate of 62.83 percent. To foster the traditional agricultural economy, efforts are needed to maximize the utilization of Community Forests (CF) and capitalize on environmental services while ensuring the preservation of the Wain River protected forest.

Ecological sports tourism based on multi population evolutionary algorithm and entrepreneurship environment for sustainable development

Evaluating the diagnostic performance of OpenBioLLM in neurology: A case-based assessment of a medical large language model

In the evolving field of neurological healthcare, deep learning technologies are gaining recognition for their potential to enhance diagnostic accuracy. Transformer-based models, particularly large language models (LLMs) such as OpenBioLLM, have shown promise in processing large datasets typical of neurological assessments. This study evaluates the diagnostic capabilities of OpenBioLLM in the realm of neurological conditions. The primary aim of this research is to assess the diagnostic accuracy, comprehensiveness, supplementation, and fluency of OpenBioLLM when applied to complex neurological case studies. Twenty-five complex neurology cases were selected from “Clinical Cases in Neurology.” OpenBioLLM was used to generate diagnoses and rationales for each case. Two independent medical doctors evaluated the responses based on accuracy, comprehensiveness, supplementation, and fluency, with discrepancies resolved by a third assessor. Statistical analyses included one-way ANOVA, Bartlett’s test, and Spearman’s rank correlation. OpenBioLLM achieved a mean accuracy score of 38%, a comprehensiveness score of 52%, a supplementation score of 24%, and a fluency score of 100%. The model could localize neurological lesions but often struggled with identifying the correct pathophysiological causes. Accuracy scores did not significantly vary by neurological disorder type. While OpenBioLLM shows potential in diagnosing neurological conditions, its performance metrics suggest it is not yet a reliable standalone tool. Future research should focus on fine-tuning the model and improving its reasoning capabilities to enhance diagnostic accuracy.