Search Results (21,898)

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model (SDM). Luciola unmunsana is an endemic species that lives only in South Korea, and because its females do not have genus wings and are less fluid, it is difficult to collect, so research related to its distribution and restoration is relatively understudied. Therefore, this study predicted the potential habitats of Luciola unmunsana across South Korea using the single model Maximum Entropy (MaxEnt) and a multi-model ensemble model to prepare basic data necessary for a conservation and habitat restoration plan for the species. A total of 39 points of occurrence were built based on public data and prior research from the Jeonbuk Green Environment Support Center (JGESC), the Global Biodiversity Information Facility (GBIF), and the National Institute of Biological Resources (NIBR). Among the input variables, climate variables were based on the shared socioeconomic pathway (SSP) scenario-based ecological climate index, while nonclimate variables were based on topography, land cover maps, and the Enhanced Vegetation Index (EVI). The main findings of this study are summarized below. First, in predicting Luciola unmunsana potential habitats, the EVI, water network analysis, land cover, and annual precipitation (Bio12) were identified as good predictors in both models. Accordingly, areas with high vegetation activity in their forests, adjacent to water resources, and stable humidity were predicted as potential habitats. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within their forests, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, literature surveys used to predict potential habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeollabuk-do, Mudeungsan Mountain, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant in that it is the first to develop a regional SDM for Luciola unmunsana, whose population is declining due to urbanization. In addition, by applying various environmental variables that reflect ecological characteristics, it contributes to more accurate predictions of the potential habitats of this species. The predicted results can be used as basic data for the future conservation of Luciola unmunsana and the establishment of habitat restoration strategies. Full article

This study investigates how lighting conditions influence emotional and neural responses in a standardized, simulated museum environment. A multimodal evaluation framework combining subjective and objective measures was used. Thirty-two international students assessed their viewing experiences using 14 semantic differential descriptors, while real-time EEG signals were recorded via the EMOTIV EPOC X device. Spectral energy analyses of the α, β, and θ frequency bands were conducted, and a θα energy ratio combined with γ coefficients was used to model attention and comfort levels. The results indicated that high illuminance (300 lx) and high correlated color temperature (4000 K) significantly enhanced both attention and comfort. Art majors showed higher attention levels than engineering majors during short-term viewing. Among four regression models, the backpropagation (BP) neural network achieved the highest predictive accuracy (R² = 88.65%). These findings provide empirical support for designing culturally inclusive museum lighting and offer neuroscience-informed strategies for promoting the global dissemination of traditional Chinese culture, further supported by retrospective interview insights. Full article

Background: The accurate prediction of pathological complete response (pCR) following total neoadjuvant therapy (TNT) is crucial for optimising treatment protocols in locally advanced rectal cancer (LARC). Although conventional imaging techniques such as MRI show limitations in assessing treatment response, metabolic imaging utilising 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET-CT) provides distinctive information by quantifying tumour glycolytic activity. This study investigates the predictive value of sequential 18F-FDG PET-CT parameters, focusing on Total Lesion Glycolysis (TLG), in predicting pCR after TNT. Methods: We conducted a retrospective analysis of 33 LARC patients (T3–4/N0–1) treated with TNT (neoadjuvant-chemoradiation followed by consolidation FOLFOX chemotherapy). Sequential PET-CT scans were performed at baseline, interim (after 4 cycles of FOLFOX), and post-TNT. Metabolic parameters, including maximum standardised uptake value (SUVmax) and TLG, were measured. Receiver operating characteristic (ROC) analysis assessed the predictive performance of these parameters for pCR. Results: The pCR rate was 21.2% (7/33). Post-TNT TLG ≤ 10 demonstrated excellent predictive accuracy for pCR (AUC 0.887, 92.3% sensitivity, 85.7% specificity, and 96.0% PPV), outperforming SUVmax (AUC 0.843). Interim TLG ≤ 10 also showed a strong predictive value (AUC 0.824, 100% sensitivity, and 71.4% specificity). Conclusions: TLG may serve as a reliable metabolic biomarker for predicting pathologic complete response (pCR) after total neoadjuvant therapy (TNT) in locally advanced rectal cancer (LARC). Its inclusion in clinical decision-making could improve patient selection for organ preservation strategies, thereby reducing the need for unnecessary surgeries in the future. However, given that the study is based on a small retrospective design, the findings should be interpreted with caution and used alongside other decision-making tools until more comprehensive data are collected from larger studies. Full article

Marine heatwaves (MHWs) and Marine cold spells (MCSs) are oceanic events characterized by prolonged periods of anomalously warm or cold sea surface temperatures, which pose significant ecological and socio-economic threats on a global scale. These extreme temperature events exhibit an asymmetric trend under ongoing climate change in recent decades: MHWs have increased markedly in both frequency and intensity, whereas MCSs have shown an overall decline. Among the potential drivers, mesoscale eddies play a critical role in modulating sea surface temperature anomalies (SSTAs). Anticyclonic eddies (AEs) promote downwelling, generating positive SSTAs that potentially favor MHWs, while cyclonic eddies (CEs) enhance upwelling and negative anomalies that are potentially related to MCSs. In this paper, we investigate the relationship between mesoscale eddies and MHWs/MCSs using global satellite-derived datasets from 2010 to 2019. By analyzing the spatial overlap and intensity correlation between eddies and MHWs/MCSs, it is found that 12.2% of MHWs are accompanied by AEs, and 13.4% of MCSs by CEs, with a high degree of spatial containment where approximately 90.2% of MHW events are found within the mean eddy contour of AEs, and about 93.1% of MCS events fall inside the mean eddy contour of CEs. Stronger eddies tend to be associated with more intense MHWs/MCSs. This study provides new insights into the role of mesoscale eddies in regulating extreme oceanic temperature events, offering valuable information for future predictions in the context of climate change. Full article

Background: Women with systemic autoimmune rheumatic disease (SARD) are at higher risk of developing infection-related complications, anxiety, and depression. Using the example of the COVID-19 pandemic, we aimed to explore the impact of this external stressor on symptoms of depression, anxiety, and stress in a sample of women with SARD in a cross-sectional study design. Methods: Females aged 18–50 with SARD were enrolled from 04/2021 to 04/2022 at the Medical University of Vienna or through an online self-help group, while snowball sampling was used to recruit an age-matched healthy control group. Participants completed questionnaires including: (1) demographic information, medical history, and access to healthcare; (2) the Depression, Anxiety, and Stress Scale (DASS-21); and (3) the Coronavirus Anxiety Scale (CAS). Parameters were compared between groups using Chi-squared, Fisher’s exact, and Mann–Whitney U tests. Linear regression analysis was used to investigate which individual factors predicted the DASS-21 in women with SARD. Results: The study sample consisted of 226 women (n = 99 with SARD and n = 127 healthy controls). Women with SARD reported lower DASS-21 stress (p = 0.008) and CAS scores (p = 0.057) than the control group. There were no significant differences in DASS-21 anxiety or depression scores. Among women with SARD, a linear regression model identified the most important predictors of DASS-21 as access to rheumatological care (p = 0.002) and recent disease activity (p = 0.028). Conclusions: Despite the pandemic, women with SARD reported mental health outcomes equal to or better than those of the healthy control group. Continued access to rheumatological care may serve as an important protective factor for their mental health during large-scale crises like pandemics. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article

Objectives: The aim of the study was to assess the utility of contrast-enhanced ultrasound (CEUS), using both qualitative and quantitative perfusion analysis, in differentiating subtypes of renal cell carcinoma (RCC). Methods: This prospective, single-center study includes 91 patients with histologically confirmed RCC. We performed a CEUS within one week prior to nephrectomy. Qualitative parameters (enhancement pattern, heterogeneity, pseudocapsule) and quantitative perfusion metrics were assessed. Logistic regression models were developed to evaluate the diagnostic performance of CEUS in differentiating high-grade (clear cell RCC) from low-grade RCC (papillary and chromophobe). Results: Qualitative CEUS findings showed that hyperenhancement and isoenhancement were significantly associated with high-grade RCC (OR = 38.3 and OR = 7.8, respectively; p < 0.001 and p = 0.014). Hypoenhancement was predominant in low-grade RCC (80.0%). Quantitative parameters, including peak enhancement and wash-in/wash-out area under the curve, significantly differed between tumor grades (p < 0.001). A model using qualitative parameters alone achieved an AUC of 0.847 and 81.9% accuracy. Adding quantitative metrics marginally improved performance (AUC 0.912, accuracy 86.2%), though not significantly. Conclusions: CEUS provides valuable diagnostic information in differentiating RCC subtypes, with qualitative parameters alone demonstrating strong predictive power. While quantitative analysis slightly enhances diagnostic accuracy, its added value may be limited by technical challenges. Full article

Platelets play critical roles in haemostasis and thrombosis. The platelet activation process is driven by agonist-induced rises in cytosolic [Ca²⁺]_i, where the patterns of Ca²⁺ responses are still incompletely understood. In this study, we developed a number of techniques to model the [Ca²⁺]_i curves of platelets from a single blood donor. Fura-2-loaded platelets were quasi-simultaneously stimulated with various agonists, i.e., thrombin, collagen, or CRP, in the presence or absence of extracellular Ca²⁺ entry, secondary mediator effects, or Ca²⁺ reuptake into intracellular stores. To understand the calibrated time curves of [Ca²⁺]_i rises, we developed two non-linear models, a multilayer perceptron (MLP) network and an autoregressive network with exogenous inputs (NARX). The trained networks accurately predicted the [Ca²⁺]_i curves for combinations of agonists and inhibitors, with the NARX model achieving an R² of 0.64 for the trend prediction of unforeseen data. In addition, we used the same dataset for the construction of a partial least square (PLS) linear regression model, which estimated the explained variance of each input. The NARX model demonstrated that good fits could be obtained for the nanomolar [Ca²⁺]_i curves modelled, whereas the PLS model gave useful interpretable information on the importance of each variable. These modelling results can be used for the development of novel platelet [Ca²⁺]_i-inhibiting drugs, such as the drug 2-aminomethyl diphenylborinate, blocking Ca²⁺ entry in platelets, or for the evaluation of general platelet signalling defects in patients with a bleeding disorder. Full article

Accurate forecasting of river water flow helps to warn of floods and droughts in advance, provides a basis for the rational allocation of water resources, and at the same time, offers important support for the safe operation of hydropower stations and water conservancy projects. Water flow is characterized by time series, but the existing models focus on the positive series when LSTM is applied, without considering the different contributions of the water flow series to the model at different moments. In order to solve this problem, this study proposes a river water flow prediction model, named AT-BiLSTM, which mainly consists of a bidirectional layer and an attention layer. The bidirectional layer is able to better capture the long-distance dependencies in the sequential data by combining the forward and backward information processing capabilities. In addition, the attention layer focuses on key parts and ignores irrelevant information when processing water flow data series. The effectiveness of the proposed method was validated against an actual dataset from the Shizuishan monitoring station on the Yellow River in China. The results confirmed that compared with the RNN model, the proposed model significantly reduced the MAE, MSE, and RMSE on the dataset by 27.16%, 42.01%, and 23.85%, respectively, providing the best predictive performance among the six compared models. Moreover, this attention mechanism enables the model to show good performance in 72 h (3 days) forecast, keeping the average prediction error below 6%. This implies that the proposed hybrid model could provide a decision base for river flow flood control and resource allocation. Full article

To solve the issue that existing yield prediction methods do not fully capture the interaction between multiple factors, we propose a winter wheat yield prediction framework with triple cross-attention for multi-source data fusion. This framework consists of three modules: a multi-source data processing module, a multi-source feature fusion module, and a yield prediction module. The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. The encoder part in the production prediction module adds a graph attention mechanism, forming a dual branch with the original multi-head self-attention mechanism to ensure the capture of global dependencies while enhancing the preservation of local feature information. The decoder section generates the final predicted output. The results show that: (1) Using 2021 and 2022 as test sets, the mean absolute error of our method is 385.99 kg/hm², and the root mean squared error is 501.94 kg/hm², which is lower than other methods. (2) It can be concluded that the jointing-heading stage (March to April) is the most crucial period affecting winter wheat production. (3) It is evident that our model has the ability to predict the final winter wheat yield nearly a month in advance. Full article

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to distribution shifts that a single model cannot easily generalize over. Additionally, conflicts between temporal domain-specific patterns and limited model capacity make it difficult to learn shared parameters that work universally. To address this challenge, we propose an ensemble learning framework that leverages multiple domain-specific models to improve temporal domain generalization for sensor data forecasting. We first segment the original sensor time series into distinct temporal tasks to better handle the distribution shifts inherent in sensor measurements. A meta-learning strategy is then applied to extract shared representations across these tasks. Specifically, during meta-training, a recurrent encoder combined with variational inference captures contextual information for each task, which is used to generate task-specific model parameters. Relationships among tasks are modeled via a self-attention mechanism. For each query, the prediction results are adaptively reweighted based on all previously learned models. At inference, predictions are directly generated through the learned ensemble mechanism without additional tuning. Extensive experiments on public sensor datasets demonstrate that our method significantly enhances the generalization performance in forecasting across unseen sensor segments. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

Ulcerative colitis (UC) is a chronic inflammatory disease that affects the colon, triggering persistent inflammation and ulceration, resulting in a severe impact on patients’ quality of life. Currently, the standard diagnostic methods for UC include invasive procedures such as colonoscopy and the use of non-specific inflammatory markers like C-reactive protein, which can be inconvenient or painful and lack specificity. This underscores the need for non-invasive and highly specific biomarkers for UC. MicroRNAs (miRNAs) are small non-coding RNAs, typically 22 nucleotides in length, which are well described as gene expression regulators. Several studies have reported their differential expression in various pathological conditions, including UC. Due to their role in gene regulation and stability in biological fluids, miRNAs present a promising opportunity as biomarkers. This systematic review explores the potential use of miRNAs as diagnostic biomarkers to distinguish between active and inactive ulcerative colitis. Following PRISMA guidelines and based on inclusion and exclusion criteria, seven studies, encompassing a total of 514 participants (181 with active UC and 116 with inactive UC), were included. Multiple miRNAs exhibiting differential expression between active and inactive UC were identified. Most notably, miR-21, miR-126, miR-146b-5p, and miR-223 exhibited consistent upregulation in active UC, suggesting their potential as diagnostic biomarkers. Supporting these findings is the fact that these miRNAs are involved in inflammatory pathways, further highlighting their relevance to the pathogenesis of UC. This review emphasizes the need for further validation studies with larger cohorts to confirm the utility of miRNAs as diagnostic tools for UC disease activity differentiation, which could enhance non-invasive disease monitoring and inform therapeutic decision-making. Future research should also evaluate the prognostic potential of these miRNAs for predicting treatment responses and long-term disease outcomes. Full article

Non-invasive ventilation (NIV) is a cornerstone in the management of acute and chronic respiratory failure, offering critical support without the risks of intubation. However, successful weaning from NIV remains a complex, high-stakes process. Poorly timed or improperly executed weaning significantly increases morbidity and mortality, yet current clinical practice often relies on subjective judgment rather than evidence-based protocols. This manuscript reviews the current landscape of NIV weaning, emphasizing structured approaches, objective monitoring, and predictors of weaning success or failure. It examines guideline-based indications, monitoring strategies, and various weaning techniques—gradual and abrupt—with evidence of their efficacy across different patient populations. Predictive tools such as the Rapid Shallow Breathing Index, Lung Ultrasound Score, Diaphragm Thickening Fraction, ROX index, and HACOR score are analyzed for their diagnostic value. Additionally, this review underscores the importance of care setting—ICU, step-down unit, or general ward—and how it influences outcomes. Finally, it highlights critical gaps in research, especially around weaning in non-ICU environments. By consolidating current evidence and identifying predictors and pitfalls, this article aims to support clinicians in making safe, timely, and patient-specific NIV weaning decisions. In the current literature, there are gaps regarding patient selection and lack of universal protocolization for initiation and de-escalation of NIV as the data has been scattered. This review aims to consolidate the relevant information to be utilized by clinicians throughout multiple levels of care in all hospital systems. Full article

Background: A growing body of evidence suggests that diet can modify Parkinson’s disease (PD) outcomes, although there is disagreement about what should be included and excluded in such a diet. Existing evidence suggests that adherence to the MIND and Mediterranean (MEDI) diets are associated with reduced PD symptoms, but only a few variables from the adherence scales are responsible for the statistically observed improvement. Objectives: The goal was to use patient-reported outcomes in a large cohort to identify the foods and dietary patterns (PRO diet) most strongly associated with the fewest PD symptoms over time, and to develop a composite adherence scale to enable comparisons between MEDI, MIND, and PRO. Methods: Data were obtained from the prospective longitudinal natural history study and from Modifiable Variables in Parkinsonism (MVP)—a study designed to identify behaviors associated with patient-reported outcomes (PRO-PD). Upon the completion of the binary and food frequency data collection, using various predictive models and considering congruence with historical data, the PRO diet was created via an iterative process. Our goal was to create a new scale and compare its performance to the existing MIND and MEDI scores. The comparison was made at baseline, using the regression models for PRO-PD and the different scales as the predictors. The models were compared via the Akaike Information Criterion (AIC). To examine whether baseline adherence levels predicted subsequent symptom trajectories, the baseline PRO diet adherence and subsequent slope of progression were evaluated. Results: Data from 2290 individuals with PD were available for this analysis. The Mediterranean and MIND diets showed almost identical effects. For both the diets, the effect they had on non-motor symptoms was about twice the effect on motor symptoms. The slopes for the total PRO-PD for MEDI, MIND, and PRO-21 were −64.20467, −64.04220, and −28.61995, respectively. The AIC value differences were substantial (>2), indicating meaningful improvements in the model fit for total PRO-PD, as follows: MEDI: 28,897.24, MIND: 28,793.08, and PRO-21: 27,500.71. The subset of individuals who were most adherent to the PRO-21 diet at baseline had the slowest subsequent progression, as measured by a 43% reduced PRO-PD slope, compared to the less adherent groups. Conclusions: The PRO-21 outperformed the MIND and MEDI diets in the model fit, overcoming the ceiling effects and showing orders of magnitude and superior explanatory power for variance in PD outcomes, despite the smaller per-unit effect sizes. However, its rigorous demands may introduce barriers related to cost, feasibility, and sustainability, underscoring the need for future intervention trials to assess real-world feasibility, adherence, side effects, and clinical impact. Full article