Search Results (251,947)

Viticulture in the Iberian Peninsula is increasingly threatened by climate change, particularly rising temperatures and prolonged droughts. This study evaluates the ability of the De Martonne Index (DMI), a simple climatic aridity index based solely on temperature and precipitation, to serve as a proxy for vineyard health over a 30-year period (1993–2022). Vineyard health was assessed using the Vegetation Health Index (VHI), derived from satellite remote sensing data. DMI values were computed from bias-corrected ERA5-Land data, and VHI composites were generated from NOAA satellite imagery. Vineyard-specific outputs were isolated using land cover datasets, and a contingency analysis compared drought classifications from both indices. Results show a strong spatio-temporal correspondence between low DMI values and reduced VHI, with agreement rates for severe/extreme drought conditions reaching up to 56% under the most restrictive DMI thresholds. In the analyzed period, years such as 1995, 1997, 2005, 2009, and 2012, showed over 20% of vineyard areas affected by moderate-to-severe/extreme drought. The spatial analysis revealed that northern and northwestern regions of the peninsula experienced less drought stress, while central and southern areas were more frequently affected. This approach demonstrates that the DMI alone can provide a reliable assessment of vineyard health, potentially enabling its direct use with seasonal forecasts, which are generally available for temperature and precipitation, to anticipate drought impacts and support adaptation in viticulture. The proposed methodology is scalable and transferable to other crops and regions, serving as a tool for climate adaptation strategies in viticulture. Full article

Background/Objectives: The World Health Organization (WHO) recommends the use of attenuated Sabin strains for the production of inactivated poliovirus vaccine (IPV), offering improved biosafety while retaining immunogenicity. To better characterize the antigenic composition of Sabin strain-based IPV (sIPV), including both the protective D-antigen and the non-protective H-antigen forms, we developed a method for purifying D- and H-antigens forms. Methods: D- and H-antigens of poliovirus Sabin strains types 1, 2, and 3 were purified using gradient ultracentrifugation and used to generate antigen-specific polyclonal antibodies. Results: The generated polyclonal antibodies demonstrated high specificity with neutralizing titers of antibodies against Sabin type 1 poliovirus—1:2048, against Sabin type 2 poliovirus—more than 1:2048, against Sabin type 3 poloivirus —1:2048. Conclusions: This antigen-specific antibody approach provides a valuable tool for routine quality control in sIPV manufacturing, enabling accurate quantification of immunogenic components and detection of potentially immunogenic degradation products during vaccine storage and distribution. Antibodies to the D-antigen allow assessment of immunogenic, neutralizing epitopes, while antibodies to the H-antigen provide a tool for detecting non-neutralizing components. This antigen-specific antibody approach offers a valuable tool for studying the antigenic structure of sIPV and for improving the accuracy of ELISA-based antigen quantification. Full article

Background: Obesity is a well-established risk factor for numerous chronic diseases, including heart disease and type 2 diabetes, and its impact is particularly acute among Black/African American men. According to the Centers for Disease Control and Prevention (CDC), 70.9% of Black men aged 20 and older are overweight or obese. Despite this alarming prevalence, there remains a limited number of studies that specifically investigate the root causes of obesity in this population. Addressing this gap is critical to developing culturally relevant interventions that promote health equity. The purpose of this study was to assess dietary patterns, that are associated with overweight/obesity, in Black men aged 18–65 across the United States of America, to gain an in-depth understanding of variables influencing BMI in Black men. Methods: This study utilized a quantitative approach to collect information from participants. A survey questionnaire was developed and administered via Qualtrics to participants using a web link. The survey collected information across 18 dietary variables. Data was exported to Microsoft Excel for statistical analysis. A simple linear regression was used to determine dietary variables correlation strength and significance with BMI. A significance level of p < 0.05 was used to determine if a variable was statistically significant. Variables were then organized based on significance vs. non significance and correlation strength. Result: The study sample consisted of 466 Black men aged 18 to 65 years. The mean BMI was 30.21. Approximately 19% (n = 87) had a BMI within the healthy range (18.5–24.9), 41% (n = 190) were categorized as overweight (BMI 25.0–29.9), and another 41% (n = 189) were classified as obese (BMI ≥ 30.0). The findings revealed that fruit and vegetable consumption and whole grain cereal consumption were significantly and positively correlated with BMI. Other variables, such as fried foods, processed foods, and sugary drinks, though historically associated with obesity, did not show statistical significance in this population. Conclusions: Results suggest that while multiple dietary factors influence BMI, fruit, vegetable, and whole grain consumption are significantly correlated with BMI in Black men living in America. The findings from this study serve as a foundational step for designing targeted, culturally sensitive interventions aimed at reducing obesity-related health disparities. Future research should further explore how tailored public health messaging and community-based programming can address the specific needs of this population. Full article

Many transportation structures collapse or sustain severe damage as a result of natural disasters such as earthquakes, floods, wars, and similar attacks. These collapsed or severely damaged structures must be rebuilt and returned to service as quickly as possible. Water is used in the mix for cement-bound concrete roads. It is known that drought problems are emerging due to climate change and that water resources are rapidly depleting. Significant amounts of water are used in concrete production, further depleting water resources. In order to contribute to the elimination of these two problems, the usability of polyurethane resin binder in road pavement construction was investigated. Polyurethane resin binder road pavement is a new type of pavement that does not contain cement or bitumen as binders and does not contain water in its mixture. This new type of road pavement can be opened to traffic within 5–15 min. After determining the aggregate and binder mixture ratios, four different curing methods were applied to the created samples. After the curing, the samples were subjected to compression test, flexural test, Bohme abrasion test, freeze–thaw test, bond strength by pull-off test, ultrasonic pulse velocity (UPV) test, SEM-EDX analysis, XRD analysis, and FT-IR analysis. The new type of road pavement created within the scope of this study exhibited a compression strength of 41.22 MPa, a flexural strength of 25.32 MPa, a Bohme abrasion value of 0.99 cm³/50 cm², a freeze–thaw test mass loss per unit area of 0.77 kg/m², and an average bond strength by pull-off value of 4.63 MPa. It was observed that these values ensured the road pavement specification limits. Full article

Background/Objectives: Ketone bodies are increasingly studied for their potential therapeutic effects, particularly through exogenous ketosis, in a variety of diseases. This systematic review aimed to rigorously assess the clinical efficacy of exogenous ketosis in adults with medical conditions. Methods: Following PRISMA guidelines, we systematically searched MEDLINE and Scopus databases. Our inclusion criteria were defined according to the PICOS framework, focusing on studies involving exogenous ketosis in adult patients with specific diseases. The study is registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42023492846). Results: After a stringent selection process, fifty-one studies were analyzed. Twenty-two studies focused on neurological disorders, one on psychiatric disorders, twenty-two on metabolic disorders, five on cardiovascular disorders, and one on an inflammatory disorder. Exogenous ketosis demonstrated potential benefits across multiple conditions, including Alzheimer’s disease, mild cognitive impairment, McArdle’s disease, various forms of heart failure, cardiogenic shock, pulmonary hypertension, and COVID-19-related acute respiratory distress syndrome, although evidence is mostly limited to surrogate endpoints with insufficient hard outcome data. Subtherapeutic ketone concentrations induced by medium-chain triglycerides and limited follow-up periods often precluded firm conclusions regarding clinically meaningful outcomes. Conclusions: Exogenous ketosis shows potential in neurological, metabolic, and cardiovascular disorders, while evidence in psychiatric and inflammatory conditions remains scarce and preliminary. Ketone esters appear preferable for effective and tolerable ketosis. Future research should focus on identifying responsive patient populations, optimizing treatment regimens, and conducting long-term clinical trials with hard endpoints to validate these findings. Full article

Botanical insecticides have gained interest due to a rising demand for environmentally friendly pest control methods for stored-product protection. The insecticidal effectiveness of the essential oil (EO) obtained from the oleo-gum-resin of myrrh (Commiphora myrrha (Nees) Engl.), against Prostephanus truncatus (Horn) and Sitophilus zeamais Motschulsky, and the metabolic shifts of the two species, were investigated in this work. A thorough gas chromatography-mass spectrometry (GC-MS) investigation showed that the composition of this EO was dominated by furanosesquiterpenes, specifically, furanoeudesma-1,3-diene and curzerene. Commiphora myrrha EO treatments, especially at 1000 ppm, resulted in high adult mortality for P. truncatus (up to 85.6%), while S. zeamais showed only moderate mortality (up to 25.6%). To investigate the different species-specific effectiveness of the EO, untargeted GC-MS metabolomic profiling was conducted to elucidate the impact of the EO on the metabolism of the insects, with subsequent data analysis employing multivariate, univariate, and network methods. Each species reacts differently to the treatments (myrrh EO versus the synthetic insecticide pirimiphos-methyl (PM)), according to the analysis results. In particular, myrrh EO caused distinct shifts in metabolic pathways that varied between P. truncatus and S. zeamais. Overall, C. myrrha EO exhibits potential as a botanical insecticide, especially against P. truncatus, and it causes metabolic disturbances specific to the species. The results demonstrate the significance of metabolomic technologies in assessing bioinsecticide mechanisms and lend credence to their possible incorporation in integrated pest management methodologies or their contribution to the creation of diagnostic indicators of insecticidal exposure. Full article

Background: Outcome prediction in patients undergoing lung transplantation (LUTX) for end-stage pulmonary disease can be challenging. We examined the prognostic value for mortality of respiratory system compliance (C_RS) and mechanical power of ventilation (MP) at end of surgery in patients undergoing LUTX for end-stage pulmonary disease. Methods: In this single-center retrospective study, we included 755 patients undergoing LUTX between 2014 and 2023. The primary endpoint of this study was 1-year mortality, with 30-day mortality serving as a secondary endpoint. We conducted both univariate and multivariate analyses and constructed Receiver Operating Characteristic curves. Results: Of 755 patients, 1.9% and 12.2% patients died within 30 days and 1 year after LUTX. Fifteen-point four percent of all patients required extracorporeal membrane oxygenation (ECMO) prolongation into the early postoperative period. C_RS, but not MP was higher in 1-year survivors compared to non-survivors [median 25.8 mL/cmH₂O (20.1, 32.1) and 22.5 mL/cmH₂O (15.2, 28.4); p < 0.001] and [median 10.0 J/min (7.8, 12.0) and 9.3 J/min (6.2, 13.1); p = 0.329]. Moreover, low C_RS < 25.1 mL/cmH₂O remained an independent factor for increased 1–year mortality after LUTX. Additionally, increased MP and C_RS were predictive for 30–day survival with an acceptable area under the curve of 0.758 (95% CI: 0.6–0.8; p < 0.001) and 0.735 (95% CI: 0.5–0.9; p = 0.003), and a sensitivity and specificity of 51% and 75.5% for MP and 50% and 85% for C_RS, respectively. Conclusions: Postoperative C_RS serves as a significant independent predictor for short and long-term outcome in patients undergoing LUTX with and without ECMO prolongation into the early postoperative period. Full article

Background: The high-precision analysis of multimode fibers (MMFs) is a critical task in numerous applications, including remote sensing, medical imaging, and environmental monitoring. In this study, we propose a novel deep interpretable network approach to reconstruct spectral images captured using CCD sensors. Methods: Our model leverages a Tiny-YOLO-inspired convolutional neural network architecture, specifically designed for spectral wavelength prediction tasks. A total of 1880 CCD interference images were acquired across a broad near-infrared range from 1527.7 to 1565.3 nm. To ensure precise predictions, we introduce a dynamic factor α and design a dynamic adaptive loss function based on Huber loss and Log-Cosh loss. Results: Experimental evaluation with five-fold cross-validation demonstrates the robustness of the proposed method, achieving an average validation MSE of 0.0149, an R² score of 0.9994, and a normalized error (μ) of 0.0005 in single MMF wavelength prediction, confirming its strong generalization capability across unseen data. The reconstructed outputs are further visualized as smooth spectral curves, providing interpretable insights into the model’s decision-making process. Conclusions: This study highlights the potential of deep learning-based interpretable networks in reconstructing high-fidelity spectral images from CCD sensors, paving the way for advancements in spectral imaging technology. Full article

Educational institutions face complex challenges when allocating limited teaching resources to specialized seminars, where budget, capacity, and balanced disciplinary representation must all be satisfied simultaneously. We address this for the first time in the educational domain by formulating the teacher seminar selection problem as a multi-dimensional knapsack variant with category-specific benefit multipliers. To solve it, we design a constraint-aware genetic algorithm that incorporates smart initialization, category-sensitive operators, adaptive penalties, and targeted repair mechanisms. In experiments on a realistic dataset representing multiple academic categories, our method achieved an 11.5% improvement in solution quality compared to the best constraint-aware greedy baseline while maintaining perfect constraint satisfaction (100% feasibility) vs. 0–30% for baseline methods. Statistical tests confirmed significant and practically meaningful advantages. For comprehensive benchmarking, we also implemented binary particle swarm optimization (PSO) and Tabu Search (TS) solvers with standard parameterizations. While PSO consistently produced feasible solutions with high budget utilization, its optimization quality was substantially lower than that of the GA. Notably, Tabu Search achieved the highest performance, with a mean fitness of 1557.3 compared to GA’s 1533.2, demonstrating that memory-based local search can be highly competitive for this problem structure. These findings show that metaheuristic approaches, particularly those integrating constraint-awareness into evolutionary or memory-based search, provide effective, scalable decision-support frameworks for complex, multi-constraint educational resource allocation. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Global warming poses a growing threat to winter wheat production in Henan Province, a critical region for China’s food security, necessitating a quantitative assessment of climate impacts. This study aimed to quantify the dominant climatic drivers of winter wheat yield and assess its spatiotemporal evolution and future risks under climate change, thereby providing a scientific basis for targeted adaptation strategies. Thus, the APSIM model in combination with the Geodetector method was applied to quantify the spatiotemporal response of winter wheat yield to climate change in Henan Province under historical (1957–2020) and SSP245 scenarios. The study results demonstrated significant trends in climatic factors during the winter wheat growing season: precipitation decreased by an average of 3.09 mm/decade, sunshine hours declined by 36 h/decade, wind speed reduced by 0.447 m/(s·decade), and evaporation decreased by 14.7 mm/decade. In contrast, the accumulated temperature ≥ 0 °C significantly increased by 70.9 °C·d/decade. Geodetector analysis further identified accumulated temperature as the dominant climatic driver (q = 0.548), followed by precipitation (q = 0.340) and sunshine hours (q = 0.261). Yield simulations from 1960 to 2018 indicated that most regions maintained stable or slightly increasing yields (<50 kg·ha⁻¹·decade⁻¹), though some areas experienced fluctuating declines. Under future scenarios, major production regions in Henan Province (Zhengzhou, Xinxiang, Luoyang) are projected to see substantial yield increases, with growth rates of 147.2–148.9 kg·ha⁻¹·decade⁻¹. Specifically, Xinxiang is expected to achieve yields of 6200 kg·ha⁻¹. The frequency of climate-induced negative yield years decreased by approximately 35% after 2003, highlighting the role of improved agricultural technologies in enhancing climate resilience. This study clarifies how multiple climatic factors jointly affect winter wheat yield, identifying rising accumulated temperature and water stress as key future constraints. It recommends optimizing varietal selection and cultivation practices according to regional climate patterns to improve policy relevance and local applicability. Full article

The growing demand for aquaculture has driven the search for sustainable practices and utilization of agro-industrial residues. Brown rice bran, an abundant and low-cost by-product, has emerged as a promising raw material. This dissertation aimed to evaluate solid-state fermentation (SSF) of rice bran using the fungus Rhizopus oryzae and the yeast Saccharomyces cerevisiae with the goal of improving its nutritional value for use in diets formulated for zebrafish (Danio rerio). Proximate composition analyses revealed the strong biotransformation potential of Rhizopus oryzae. Fermentation with this fungus resulted in a significant 36.45% increase in protein content, a 51.62% increase in total polyphenols, and a 13.7% reduction in lipid content. In an in vivo experiment, zebrafish fed a diet containing rice bran fermented by R. oryzae showed the best zootechnical performance, with higher weight gain, specific growth rate, and improved feed conversion. Gene expression analysis showed a significant difference only for glut6, which is related to glucose transport. In summary, the fermentation of brown rice bran with Rhizopus oryzae represents an effective strategy to enhance its nutritional profile, establishing it as a viable alternative for the formulation of more sustainable and efficient diets in aquaculture. Full article

During prolonged storage of garlic scapes (Allium sativum L.), the proliferation of microorganisms, particularly fungi, frequently results in postharvest rot, which negatively impacts both product quality and market value. Carvacrol, a promising natural food preservative, exhibits broad-spectrum bioactivity against various microorganisms. In this study, a specific pathogenic fungal strain causing postharvest rot in garlic scapes, designated as HQ, was initially isolated from symptomatic garlic scapes. Based on a combination of physiological characteristics and molecular identification techniques, the HQ strain was identified as Aspergillus niger. Our findings further demonstrated that carvacrol exhibits significant in vitro inhibitory effects against Aspergillus niger with an EC₅₀ value of 75.99 μg/L. Moreover, scanning electron microscopy (SEM) observations revealed that carvacrol induces irreversible morphological and structural changes in the hyphae, resulting in deformation and rupture. Additionally, integrated transcriptomic and proteomic analyses indicated that carvacrol primarily targets the cell wall integrity (CWI) signaling pathway within the mitogen-activated protein kinase (MAPK) signaling pathway in Aspergillus niger, thereby compromising cell membrane integrity and stability, which ultimately suppresses fungal growth and proliferation. Full article

Background/Objectives: Tumor-associated antigens are not tumor-specific antigens but proteins that are overexpressed by tumor cells and also weakly expressed at the surface of healthy tissues. Therefore, some side effects are observed when targeted by therapeutic antibodies, a phenomenon named “on-target, off-tumor toxicity”. As tumors generate an acidic microenvironment, we investigated whether we could generate pH-dependent antibodies to increase their tumor specificity. For this proof-of-concept study, we selected the tyrosine kinase receptor AXL because we already developed several antibodies against this target. Methods: To generate a pH-dependent anti-AXL antibody, we performed classical panning of a single-chain variable fragment (scFv) library using phage display at an acidic pH throughout the process. Results: After the third round of panning, 9 scFvs, among the 96 picked clones, bound to AXL at acidic pH and showed very low binding at a neutral pH. After reformatting them into IgG, two clones were selected for further study due to their strong pH-sensitive binding. Using molecular docking and alanine scanning, we found that their binding strongly depended on two histidine residues present on AXL at positions 61 and 116. Conclusions: To conclude, we set-up an easy process to generate pH-dependent antibodies that may increase their tumor-binding specificity and potentially decrease toxicity towards healthy tissues. Full article

This work presents the development of a Z-depth system for Confocal Raman Spectroscopy (CRS), which allows for the acquisition of Raman spectra both at the surface and at depth profile in heterogeneous samples. The proposed CRS system consists of the coupling of a commercial 785 nm Raman Probe Bifurcated (RPB) with a 20x/0.40 infinity plan achromatic polarizing microscope objective, a Long Working Distance (LWD) of 1.2 cm, and a 50 $\mathsf{\mu }$m core-multimode optical fiber used as a pinhole filter. With this implementation, it is possible to achieve both a high spatial resolution of approximately 16.2 $\mathsf{\mu }$m and a spectral resolution of ∼14 cm${}^{-1}$, which is determined by the FWHM of the thin 1004 cm${}^{-1}$ Raman profile band. The system is configured to operate within 400–1800 cm${}^{-1}$ spectral windows. The implementation of a system of this nature offers a favorable cost–benefit ratio, as commercial CRS is typically found in high-cost environments such as cosmetics, pharmaceutical, and biological laboratories. The proposed system is low-cost and employs a minimal set of optical components to achieve functionality comparable to that of a confocal Raman microscope. High signal-to-noise ratio (SNR) Raman spectra (∼660.05 at 1447 cm${}^{-1}$) can be obtained with short integration times (∼25 s) and low laser power (30–35 mW) when analyzing biological samples such as in vivo human fingernails and fingertips. This power level is significantly lower than the exposure limits established by the American National Standards Institute (ANSI) for human laser experiments. Raman spectra were recorded from the surface of both the nails and fingertips of three volunteers, in order to characterize their biological samples at different depths. The measurements were performed in 50 $\mathsf{\mu }$m steps to obtain molecular structural information from both surface and subsurface tissue layers. The proposed CRS enables the identification of differences between two closely spaced, centered, and narrow Raman bands. Additionally, broad Raman bands observed at the skin surface can be deconvolved into at least three sub-bands, which can be quantitatively characterized in terms of intensity, peak position, and bandwidth, as the confocal plane advances in depth. Moreover, the CRS system enables the detection of subtle, low-intensity features that appear at the surface but disappear beyond specific depth layers. Full article