Search Results (326)

The fungal pathogen Puccinia striiformis f. sp. tritici, which causes yellow rust disease, poses a significant economic threat to wheat production not only in Uzbekistan but also globally, leading to substantial reductions in grain yield. This study aimed to develop yellow rust-resistance wheat lines by introgressing Yr10 and Yr15 genes into high-yielding cultivar Grom using the marker-assisted backcrossing (MABC) method. Grom was crossed with donor genotypes Yr10/6*Avocet S and Yr15/6*Avocet S, resulting in the development of F₁ generations. In the following years, the F₁ hybrids were advanced to the BC₂F₁ and BC₂F₂ generations using the MABC approach. Foreground and background selection using microsatellite markers (Xpsp3000 and Barc008) were employed to identify homozygous Yr10- and Yr15-containing genotypes. The resulting BC₂F₂ lines, designated as Grom-Yr10 and Grom-Yr15, retained key agronomic traits of the recurrent parent cv. Grom, such as spike length (13.0–11.9 cm) and spike weight (3.23–2.92 g). Under artificial infection conditions, the selected lines showed complete resistance to yellow rust (infection type 0). The most promising BC₂F₂ plants were subsequently advanced to homozygous BC₂F₃ lines harboring the introgressed resistance genes through marker-assisted selection. This study demonstrates the effectiveness of integrating molecular marker-assisted selection with conventional breeding methods to enhance disease resistance while preserving high-yielding traits. The newly developed lines offer valuable material for future wheat improvement and contribute to sustainable agriculture and food security. Full article

Irritable bowel syndrome (IBS) is a gut–brain axis chronic disorder, characterized by recurrent abdominal pain and altered bowel habits in the absence of organic pathology. Nutrition plays a central role in symptom management, yet no single dietary strategy has demonstrated universal effectiveness. This narrative review critically evaluates current nutritional approaches to IBS. The low-Fermentable Oligo-, Di-, Mono-saccharides and Polyols (FODMAP) diet is the most extensively studied and provides short-term symptom relief, but its long-term effects on microbiota diversity remain concerning. The Mediterranean diet, due to its anti-inflammatory and prebiotic properties, offers a sustainable, microbiota-friendly option; however, it has specific limitations in the context of IBS, particularly due to the adverse effects of certain FODMAP-rich foods. A gluten-free diet may benefit individuals with suspected non-celiac gluten sensitivity, although improvements are often attributed to fructan restriction and placebo and nocebo effects. Lactose-free diets are effective in patients with documented lactose intolerance, while a high-soluble-fiber diet is beneficial for constipation-predominant IBS. IgG-based elimination diets are emerging but remain controversial and require further validation. In this review, we present the 10 dietary commandments for IBS, pragmatic and easily retained recommendations. It advocates a personalized, flexible, and multidisciplinary management approach, avoiding rigidity and standardized protocols, with the aim of optimizing adherence, symptom mitigation, and health-related quality of life. Future research should aim to evaluate, in real-world clinical settings, the impact and applicability of the 10 dietary commandments for IBS in terms of symptom improvement and quality of life Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article

Hernia is a physiological condition that significantly impacts patients’ quality of life. Surgical treatment for hernias often involves the use of specialized meshes to support the abdominal wall. While this method is highly effective, it frequently leads to complications such as pain, infections, inflammation, adhesions, and even the need for revision surgeries. According to the Food and Drug Administration (FDA), hernia recurrence rates can reach up to 11%, surgical site infections occur in up to 21% of cases, and chronic pain incidence ranges from 0.3% to 68%. These statistics highlight the urgent need to improve mesh technologies to minimize such complications. The design and material composition of meshes are critical in reducing postoperative complications. Moreover, integrating drug-eluting properties into the meshes could address issues like infections and inflammation by enabling localized delivery of antibiotics and anti-inflammatory agents. Mesh design is equally important, with innovative structures like auxetic designs offering enhanced mechanical properties, flexibility, and tissue integration. These advanced designs can distribute stress more evenly, reduce fatigue, and improve performance in areas subjected to high pressures, such as during intense coughing, sneezing, or heavy lifting. Technological advancements, such as 3D printing, enable the precise fabrication of meshes with tailored designs and properties, providing new opportunities for innovation. By addressing these challenges, the development of next-generation mesh implants has the potential to reduce complications, improve patient outcomes, and significantly enhance quality of life for individuals undergoing hernia repair. Full article

Botulism is a severe muscle paralysis disease mediated by the botulinum toxin. Here, we reported a foodborne botulism case caused by Clostridium botulinum subtype A5(b3) from self-packaged vacuum spicy rabbit heads. Treatment for this case was delayed due to misdiagnosis and insufficient diagnostic capacity in three hospitals, which resulted in progressive clinical deterioration, and eventually, the patient was transferred to Shandong Public Health Clinical Center for specialized therapy. The case was suspected as foodborne botulism by the Qilu Medical-Prevention Innovation Integration pathway and multi-disciplinary consultation. An epidemiological investigation and laboratory confirmation revealed that the botulinum neurotoxin originated from vacuum-packaged spicy rabbit heads distributed via interprovincial cold chain logistics. After treatment with botulism antiserum, the patient’s condition significantly improved, and they were discharged after recovery. We revealed that this foodborne botulism outbreak was caused by the Clostridium botulinum A5(b3) subtype from food by whole-genome sequencing and SNP typing. All the strains belonged to Group I carrying the botulinum neurotoxin gene classified as the ha cluster. Toxin A was confirmed by MBA and other methods, while toxin B was non-functional due to the truncated bont/B gene. Other virulence genes and antibiotic resistance genes were also detected. Our findings indicate that self-packaged vacuum meat products represent an emerging risk factor for botulism transmission when stored improperly. Importantly, the recurrent misdiagnosis in this case underscored the urgent need to enhance the training of healthcare professionals in medical institutions to improve the diagnostic accuracy and clinical management of botulism. Full article

Maize is a vital global crop, serving as a fundamental component of global food security. To support sustainable maize production, the accurate classification of maize seeds—particularly distinguishing haploid from diploid types—is essential for enhancing breeding efficiency. Conventional methods relying on manual inspection or simple machine learning are prone to errors and unsuitable for large-scale data. To overcome these limitations, we propose Slot-Maize, a novel deep learning architecture that integrates Convolutional Neural Networks (CNN), Slot Attention, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) layers. The Slot-Maize model was evaluated using two datasets: the Maize Seed Dataset and the Maize Variety Dataset. The Slot Attention module improves feature representation by focusing on object-centric regions within seed images. The GRU captures short-term sequential patterns in extracted features, while the LSTM models long-range dependencies, enhancing temporal understanding. Furthermore, Grad-CAM was utilized as an explainable AI technique to enhance the interpretability of the model’s decisions. The model demonstrated an accuracy of 96.97% on the Maize Seed Dataset and 92.30% on the Maize Variety Dataset, outperforming existing methods in both cases. These results demonstrate the model’s robustness, generalizability, and potential to accelerate automated maize breeding workflows. In conclusion, the Slot-Maize model provides a robust and interpretable solution for automated maize seed classification, representing a significant advancement in agricultural technology. By combining accuracy with explainability, Slot-Maize provides a reliable tool for precision agriculture. Full article

A crop yield prediction is critical to increase agricultural sustainability because it allows for the more effective use of natural resources, including water, fertilizers, and soil. Accurate yield estimates enable farmers and governments to more accurately manage resources, decreasing waste and minimizing adverse environmental effects such as the degradation of soil and water quality issues. In addition, predictive models serve to alleviate the consequences of climate change by promoting adaptable farming techniques and improving the availability of food by means of early decision-making. Thus, including a crop yield prediction into farming practices is critical for combining productivity and sustainability. In contrast to conventional machine learning models, which frequently require long feature engineering, deep learning may obtain complicated yield-related characteristics directly from initial or merely preprocessed data from different sources. This research paper aims to demonstrate the strength of deep learning models and their ensembles in agricultural yield prediction in Saudi Arabia, where agriculture faces issues such as scarce water resources and harsh climate conditions. We first define and evaluate a Multilayer Perceptron (MLP), a Gated Recurrent Unit (GRU), and a Convolutional Neural Network (CNN) as baseline deep models for the crop yield prediction. Then, we investigate combining these three models based on stacking, blending, and boosting ensemble methods. Finally, we study the uncertainty quantification for the proposed models, which involves a discussion of many enhancements’ techniques. As a result, this research shows that, by applying the right architectures with strong parametrization and optimization techniques, we obtain models that can explain 96% of the variance in the crop yield with a very low uncertainty rate (reaching an MPIW of 0.60), which proves the reliability and trustworthiness of the prediction. Full article

Against the backdrop of global food security challenges, precise pre-harvest yield estimation of large-scale soybean crops is crucial for optimizing agricultural resource allocation and ensuring stable food supplies. This study developed an integrated prediction model for county-level soybean yield forecasting, which combines multi-source remote-sensing data with advanced deep learning techniques. The ant colony optimization-convolutional neural network with gated recurrent units and multi-head attention (ACGM) model showcases remarkable predictive prowess, as evidenced by a coefficient of determination (R²) of 0.74, a root mean square error (RMSE) of 123.94 kg/ha, and a mean absolute error (MAE) of 105.39 kg/ha. When pitted against other models, including the random forest regression (RFR), support vector regression (SVR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, the ACGM model clearly emerges as the superior performer. This study identifies August as the optimal period for early soybean yield prediction, with the model performing best when combining environmental and photosynthetic parameters (ED + PP). The ACGM model demonstrates a good accuracy and generalization ability, providing a practical approach for refined agricultural management. By integrating deep learning with open-source remote-sensing data, this research opens up new avenues for enhancing agricultural decision-making and safeguarding food security. Full article

Ocean ecosystem services provisioning is driven by phytoplankton, which form the base of the ocean food chain in aquatic ecosystems and play a critical role as the Earth‘s carbon sink. Phytoplankton is highly sensitive to temperature, making it vulnerable to the effects of temperature variations. The aim of this research was to develop and test a workflow analysis to monitor the impact of sea surface temperature (SST) on phytoplankton biomass and primary production by combining field and remote sensing data of Chl-a and net primary production (NPP) (as proxies of phytoplankton biomass). The tropical zone was used as a case study to test the procedure. Firstly, machine learning algorithms were applied to the field data of SST, Chl-a and NPP, showing that the Random Forest was the most effective in capturing the dataset’s patterns. Secondly, the Random Forest algorithm was applied to MODIS SST images to build Chl-a and NPP time series. The time series analysis showed a significant increase in SST which corresponded to a significant negative trend in Chl-a concentrations and NPP variation. The recurrence plot of the time series revealed significant disruptions in Chl-a and NPP evolutions, potentially linked to El Niño–Southern Oscillation (ENSO) events. Therefore, the analysis can help to highlight the effects of temperature variation on Chl-a and NPP, such as the long-term evolution of the trend and short perturbation events. The methodology, starting from local studies, can support broader spatial–temporal-scale studies and provide insights into future scenarios. Full article

The Huang-Huai-Hai Plain is one of China’s primary winter wheat production regions, making accurate yield estimation critical for agricultural decision-making and national food security. In this study, a yield estimation framework was developed by integrating Sentinel-2 and Landsat-8 satellite data with the WOFOST crop growth model and deep learning techniques. Initially, a multi-scenario sample dataset was constructed using historical meteorological and agronomic data through the WOFOST model. Leaf Area Index (LAI) values were then derived from Landsat-8 and Sentinel-2 imagery, and a GRU (Gated Recurrent Unit) neural network was trained on the simulation samples to establish a relationship between LAI and yield. This trained model was applied to the remote sensing-derived LAI to generate initial yield estimates. To enhance accuracy, the results were further corrected using county-level statistical data, producing a spatially explicit winter wheat yield dataset for the Huang-Huai-Hai Plain from 2014 to 2022. Validation against statistical yearbook data at the county level demonstrated a correlation coefficient (r) of 0.659, a root mean square error (RMSE) of 578.34 kg/ha, and a mean relative error (MRE) of 6.63%. These results indicate that the dataset provides reliable regional-scale yield estimates, offering valuable support for agricultural planning and policy development. Full article

With the rapid development of the Internet, businesses in the traditional catering industry are increasingly shifting toward the Online-to-Offline mode, as on-demand food delivery platforms continue to grow rapidly. Within these takeout systems, riders have a role throughout the order fulfillment process. Their behaviors involve multiple key time points, and accurately predicting these critical moments in advance is essential for enhancing both user retention and operational efficiency on such platforms. This paper first proposes a time chain simulation method, which simulates the order fulfillment in segments with an incremental process by combining dynamic and static information in the data. Subsequently, a GRU-Transformer architecture is presented, which is based on the Transformer incorporating the advantages of the Gated Recurrent Unit, thus working in concert with the time chain simulation and enabling efficient parallel prediction before order creation. Extensive experiments conducted on a real-world takeout food order dataset demonstrate that the Mean Squared Error of the prediction results of GRU-Transformer with time chain simulation is reduced by about 9.78% compared to the Transformer. Finally, according to the temporal inconsistency analysis, it can be seen that GRU-Transformer with time chain simulation still has a stable performance during peak periods, which is valuable for the intelligent takeout system. Full article

The wine industry faces increasing challenges related to authenticity, safety, and sustainability due to recurrent fraud, shifting consumer preferences, and environmental concerns. In this study, as part of the B.I.O.C.E.R.T.O project, we integrated blockchain technology with ultrasonic spectroscopy and soil quality data by using the arthropod-based Soil Biological Quality Index (QBS-ar) to enhance traceability, ensure wine quality, and certify sustainable vineyard practices. Four representative wines from the Marche region (Sangiovese, Maceratino, and two Verdicchio PDO varieties) were analyzed across two vintages (2021 and 2022). Ultrasound spectroscopy demonstrated high sensitivity in distinguishing wines based on ethanol and sugar content, comparably to conventional viscosity-based methods. The QBS-ar index was applied to investigate the soil biodiversity status according to the agricultural management practices applied in each vineyard, reinforcing consumer confidence in environmentally responsible viticulture. By recording these data on a public blockchain, we developed a secure, transparent, and immutable certification system to verify the geographical origin of wines along with their unique characteristics. This is the first study to integrate advanced analytical techniques with blockchain technology for wine traceability, simultaneously addressing counterfeiting, consumer demand for transparency, and biodiversity preservation. Our findings support the applicability of this model to other agri-food sectors, with potential for expansion through additional analytical techniques, such as isotopic analysis and further agroecosystem sustainability indicators. Full article

Endometriosis is a chronic, hormone-dependent disorder characterized by an inflammatory response. The disease affects approximately 10% of the general female population, with prevalence rates reaching 30–40% in women with dysmenorrhea and 50–60% in those experiencing infertility. In addition to pelvic pain and reproductive issues, gastrointestinal symptoms, such as acute abdominal pain, constipation, diarrhea, or alternating bowel habits, are frequently reported and can be highly disabling. Emerging evidence indicates that dietary patterns may modulate the inflammatory environment associated with endometriosis, potentially influencing symptom severity by affecting oxidative stress, estrogen metabolism, and levels of sex hormone-binding globulin (SHBG). Diets rich in antioxidants, polyunsaturated fatty acids (PUFAs), and vitamins D, C, and E—alongside the avoidance of processed foods, red meat, and animal fats—may offer beneficial effects. This narrative review explores the relationship between nutrition and endometriosis, emphasizing the therapeutic potential of dietary interventions as a complementary strategy. Notably, dietary approaches may serve not only to alleviate pain and improve fertility outcomes but also to reduce lesion growth and recurrence, particularly in patients seeking pregnancy or those unable to undergo hormonal therapy due to contraindications. Furthermore, nutritional strategies may enhance postoperative recovery and act as a viable first-line therapy when conventional treatments are not applicable. A total of 250 studies were initially identified through PubMed and Scopus. After removing duplicates and non-relevant articles, 174 were included in this review. Our findings underscore the urgent need for further studies to develop evidence-based, personalized nutritional interventions for managing endometriosis-related symptoms. Full article

Background/Objectives: This study aims to fill gaps in the existing literature through a qualitative evaluation of stakeholders involved in Food Is Medicine (FIM) programs. The primary objective was to examine the structural components, implementation process, and perceived impact of the University of California Los Angeles (UCLA) and Venice Family Clinic (VFC) Emergency Prepared Meal Program (UCLA-VFC Food Program), developed as a collaboration between a university and Federally Qualified Health Center (FQHC) during a period of community crisis. A secondary objective was to compare this program with three other FIM initiatives, identifying convergences and divergences in design and effectiveness. Methods: The methodology involved semi-structured interviews conducted with stakeholders across all four programs. Participants were recruited based on their direct involvement with program ideation, design, or implementation. Interviews were conducted online between July and September 2022, with 11 stakeholders. A thematic analysis was applied to the transcribed responses using an inductive thematic analysis. Results: Key findings highlighted four recurrent themes: (1) the critical role of leadership and a collaborative culture; (2) the importance of community partnerships and health education; (3) challenges related to logistics, funding, and sustainability; and (4) the need for assessment and evaluation. These findings provide valuable insight into the design of future FIM interventions, particularly those embedded in healthcare settings. Conclusions: In conclusion, this study offers preliminary evidence supporting the unique potential of university–community partnerships to address food insecurity. Unlike previous research that emphasized clinical outcomes, our findings provide a contextualized understanding of programmatic implementation. While further quantitative evaluation is necessary, this work lays the groundwork for a collaborative model between various entities including universities, healthcare systems, clinics, and community health/food services aimed at addressing social determinants of health. Full article

Clostridioides difficile infection (CDI) appears mainly as nosocomial antibiotic-associated diarrhea, and community-acquired infection is increasingly being recognized. The threshold of asymptomatic colonization and the clinical manifestation of CDI need further elucidation. Community-acquired CDI (CA-CDI) should be considered when the disease commences within 48 h of admission to hospital or more than 12 weeks after discharge. Although CDI is not established as a food-borne or zoonotic disease, some data support that direction. The spores’ ability to survive standard cooking procedures and on abiotic surfaces, the formation of biofilms, and their survival within biofilms of other bacteria render even a low number of spores capable of food contamination and spread. Adequate enumeration methods for detecting a low number of spores in food have not been developed. Primary care physicians should take CA-CDI into consideration in the differential diagnosis of diarrhea, as there is a thin line between colonization and infection. In patients diagnosed with inflammatory bowel disease and other comorbidities, C. difficile can be the cause of recurrent disease and should be included in the estimation of diarrhea and worsening colitis symptoms. In the community setting, it is difficult to distinguish asymptomatic carriage from true infection. For asymptomatic carriage, antibiotic therapy is not suggested but contact isolation and hand-washing practices are required. Primary healthcare providers should be vigilant and implement infection control policies for the prevention of C. difficile spread. Full article