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Mechanism of small heat shock protein client sequestration and induced polydispersity
Effective polyclonal antibodies against the virulence-associated protein D (VapD) of Helicobacter pylori, obtained from recombinant VapD
Helicobacter pylori is a microorganism associated with serious gastric pathologies. This bacterium presents specific genes that encode for different virulence factors associated with the development of gastric disease. The VapD protein has rarely been studied, although it has been previously demonstrated its participation in the protection of Helicobacter pylori within gastric cells. In the present work, we document the protocols developed to generate the VapD recombinant protein and the subsequent production of polyclonal antibodies. Our research group faced several problems throughout the trials; however, all of them were successfully solved.
Impact of high temperature in 2023 and 2024 on spring leaf flush phenology in Japan derived by GCOM-C satellite
Maintenance of the great late Ediacaran ice age
Isolation of a potentially arsenic-resistant Halomonas elongata strain (ml10562) from hypersaline systems in the Peruvian Andes, Cusco
Halomonas elongata strain ml10562, was isolated from hypersaline that was collected from Acos Peru. Average Nucleotide Identity (ANI) and dDDH (digital DNA-DNA Hybridization) values between strain ml10562 and type strains of Halomonas elongata species were 71.0–78.4% and 18.8–21.5%, respectively. The draft genome, spanning 4,075,440 base pairs, has a GC content of 64.2% and contains 3,912 genes. Functional characterization revealed the strain’s ability to tolerate and resist increasing concentrations of sodium arsenate, with a minimum inhibitory concentration of 25 mM. Bioinformatic analysis revealed the presence of two operons, arsR-arsH-arsB and arsJ-gapdh-arsC, in the genome of strain ml10562, which could play a crucial role in arsenic resistance through transporter-mediated mechanisms. Overall, these results emphasize the potential adaptability of H. elongata ml10562 to arsenic-containing environments and extend our understanding of bacterial arsenic resistance mechanisms, allowing promising applications in bioremediation.
Prediction of barberry witches’ broom rust disease using artificial intelligence models: a case study in South Khorasan, Iran
Abstract The South Khorasan Province in Iran is the main producer of seedless barberry, accounting for 98% of the country’s production. This has led to significant economic growth in the region. However, the cultivation of barberry is threatened by the rust fungus Puccinia arrhenatheri, which causes witches’ brooms on Berberis vulgaris L. var. asperma. Our research aims to detect infected leaves containing this fungal pathogen using deep learning (DL)-based artificial intelligence (AI) techniques on an available dataset. We captured healthy and infected barberry foliage images and used conventional laboratory methods to label them. We developed a convolutional neural network (CNN) deep learning model using TensorFlow’s Keras API to detect and classify barberry broom rust disease. A cross-validation technique is used to check the robustness of the proposed model. The results imply that the proposed model successfully distinguished between healthy specimens and those affected by broom rust disease. The model achieved an impressive accuracy rate of 98% in automatically identifying the disease type and its severity. This interdisciplinary research demonstrates the practical application of AI in agriculture, providing timely intervention strategies to protect crop yields and maintain economic viability in the face of plant diseases.
Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery
Optimizing lipocalin sequence classification with ensemble deep learning models
Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To address these challenges, we present EnsembleDL-Lipo, an innovative ensemble deep learning framework that combines Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to enhance the identification of lipocalin sequences. Lipocalins are multifunctional extracellular proteins involved in various diseases and stress responses, and their low sequence similarity and occurrence in the ‘twilight zone’ of sequence alignment present significant hurdles for accurate classification. These challenges necessitate efficient computational methods to complement traditional, labor-intensive experimental approaches. EnsembleDL-Lipo overcomes these issues by leveraging a set of PSSM-based features to train a large ensemble of deep learning models. The framework integrates multiple feature representations derived from position-specific scoring matrices (PSSMs), optimizing classification performance across diverse sequence patterns. The model achieved superior results on the training dataset, with an accuracy (ACC) of 97.65%, recall of 97.10%, Matthews correlation coefficient (MCC) of 0.95, and area under the curve (AUC) of 0.99. Validation on an independent test set further confirmed the robustness of the model, yielding an ACC of 95.79%, recall of 90.48%, MCC of 0.92, and AUC of 0.97. These results demonstrate that EnsembleDL-Lipo is a highly effective and computationally efficient tool for lipocalin sequence identification, significantly outperforming existing methods and offering strong potential for applications in biomarker discovery.
Enhancing security in electromagnetic radiation therapy using fuzzy graph theory
Abstract This research investigates the application of fuzzy graph theory to address critical security challenges in electromagnetic radiation therapy systems. Through comprehensive theoretical analysis and experimental validation, we introduce novel approaches leveraging fuzzy cognitive maps and fuzzy graph-based architectures for access control, intrusion detection, secure communication, and risk assessment. The study demonstrates significant improvements over traditional security measures across multiple performance metrics. The fuzzy graph-based access control model achieved a 2.5% false acceptance rate compared to 7.8% in traditional systems, while intrusion detection accuracy improved to 95% with only 3% false positives. Secure communication protocols demonstrated 98% confidentiality and 96% integrity rates, surpassing conventional methods. Risk assessment coverage increased to 92% with reduced false positives. The system maintained linear scaling in processing time from 180 ms at 1000 to 320 ms at 100,000 records, with CPU utilization remaining between 65 and 72%. These findings underscore the immense potential of fuzzy graph theory in strengthening the safety and privacy of electromagnetic radiation therapy systems, providing a foundation for future research and clinical adoption. The study also identifies key directions for future research, including machine learning integration, blockchain implementation, and scalability optimization.
Reverse filling approach to mixed matrix covalent organic framework membranes for gas separation
Acute contact toxicity of insecticides for the chemical control of the invasive yellow-legged hornet Vespa velutina nigrithorax (Hymenoptera: Vespidae)
The yellow-legged hornet, Vespa velutina subs. nigrithorax Buysson, 1905, originally from Southeast Asia, has become an invasive species in Europe since its introduction in France around 2004. Its rapid proliferation and voracious predatory behavior pose a significant threat to native insects, particularly honeybees and other pollinators, impacting agricultural production, biodiversity, and human safety. Eradication in Europe seems now impossible, and the control efforts are hindered by the lack of standardized application protocols, including for insecticide use, leading to potential indiscriminate pesticide application and, consequently, environmental damages. Our study evaluated the acute contact toxicity on V. v. nigrithorax workers of four commercially available formulations containing acetamiprid, cypermethrin, a mix of natural pyrethrins, and Spinosad as active ingredients. These tests were performed in laboratory conditions, offering novel data for the chemical control of this invasive species. Our results suggest acetamiprid and spinosad as promising candidates for the yellow-legged hornet control. Further research is needed to validate their efficacy under field conditions and assess ecological impacts of these pesticides on non-target organisms. Integrated pest management strategies should prioritize insecticides with low non-target toxicity and minimal environmental persistence to mitigate resistance development and ensure effective pest control. Comprehensive assessments considering multiple factors beyond mortality are essential for informing sustainable pest control strategies.
Temporal and spatial distributions of calanoid copepod eggs in intertidal sediment of a tropical coastal lagoon
Food taboo practices among pregnant women in Deder town, Eastern Ethiopia, 2024
Background Maternal nutrition during pregnancy is influenced by food taboo practices, which vary across cultural contexts. Food taboos during pregnancy significantly affect fetal outcomes by impacting maternal nutrition. Understanding these practices in Eastern Ethiopia is crucial for designing culturally appropriate interventions. This could contribute to a better understanding of food taboos practices and inform the development of culturally sensitive interventions to promote optimal nutrition during pregnancy. Therefore, the study aimed to assess the extent of food taboo practices among pregnant women in Deder town, Eastern Ethiopia. Method An institutional-based cross-sectional study was conducted among 418 pregnant women. The study participants were selected by systematic random sampling. The data were collected using a structured interviewer-administered questionnaire. Data were entered into Epi data version 3.02 and then exported to SPSS version 25 for analysis. Binary logistic regression was fitted to identify factors associated with food taboo practices. P-value < 0.05 was used as a cut-off point for statistical significance. Results The study showed that 56% (95% CI: 51.2, 60.8%) of pregnant women practiced food taboos. Pregnant women who were unable to read and write (AOR=3.36, 95%CI: 1.24, 9.16), did not have antenatal care (ANC) follow-up (AOR=2.04, 95%CI: 1.27, 3.29), food aversion (AOR=2.04, 95%CI: 1.31, 3.18), no additional meal practice (AOR=1.77, 95%CI: 1.14, 2.76), poor knowledge level (AOR=1.96, 95%CI: 1.24, 3.09), and unfavorable attitude (AOR=1.91, 95%CI: 1.22, 2.99) were significantly associated with food taboos practice. Conclusion More than half of pregnant women practiced food taboos, indicating a significant public health concern. Culturally sensitive nutrition education and awareness programs at health facilities are necessary to address these practices and improve maternal nutrition outcomes.
Factors associated with antibiotic use patterns in Thailand after COVID-19
A qualitative study of Chinese teacher’s perceptions and practices of meritocracy
China has a long history of meritocracy, but as social inequality grows, people are increasingly questioning whether hard work promises a better life, even as national policies and mainstream media spare no effort to promote meritocratic narratives. In response, how do people interpret their lives and act within the conflict context between social realities and political forces? On the basis of semi-structured interviews with teachers in different types of schools, this paper explores how teachers interpret meritocracy and navigate it in their teaching practices. The results indicate that teachers show a dual attitude toward meritocracy. On the one hand, they believe that effort and ability are crucial to occupational and educational success, yet on the other hand, they also acknowledge the influence of guanxi on employment and the noticeable educational disparities caused by family background. Teachers have different approaches to balancing meritocratic and nonmeritocratic factors in their teaching. Teachers who limit their responsibilities regarding student growth offer verbal advice. The majority of teachers guide students to focus on working to redress the gap derived from nonmeritocratic factors while also warning students not to place too much hope on agency. Teachers’ practices inevitably contribute to social inequality. This paper underscores that in an environment lacking redistribution mechanisms, meritocracy for teachers is more of a pragmatic calculation than a belief.
Spatiotemporal assessment of ecological quality and driving mechanisms in the Beijing metropolitan area
Unlocking innovation: the dynamics of user engagement in an open-source 3D printer community
User innovation lies at the core of a growing body of research, with a predominant focus on how individual user attributes can be associated with observed contributions to innovation. However, framing user innovation in terms of individual attributes risks neglecting important changes in behavior at the individual and community level over time. Here, our focus is on how the time that individual users spend in that community relates to innovation outcomes. We study innovation in a firm-hosted online user community for a 3D printer company, marked by their commitment to open-source hardware. We analyze a unique longitudinal dataset of 38,277 observations recording user posts to an open-access user forum from October 2011 to March 2015. We find that time spent in a user community is positively associated with contributions to innovation, as users migrate from identifying problems and needs, to contributing innovation-related modifications and hacks. Our finding emphasizes the value of user retention in open-source communities as our analysis shows that typical users can add value to firms’ innovation activities over time. Our focus on open-hardware complements many existing studies on open-source software and advances our understanding of open innovation more generally.
Associations between oral health measures and cognitive function in middle aged and older Indian adults
Courses of depressive symptoms and diabetes incidence among middle-aged and older adults: A prospective study
Elevated depressive symptoms are a risk factor for diabetes. Although depressive symptoms can remit or emerge over time, little work has considered if courses of depressive symptoms are associated with incident diabetes. The purpose of this study was to explore associations between courses of depressive symptoms and incident diabetes. Data came from the English Longitudinal Study of Ageing (n = 4,978), which is an ongoing, cohort study of adults aged 50 years and older residing in private households in England. Depressive symptoms were measured biennially from 2002 to 2008. Participants were categorized into one of six groups: no depressive symptoms, remitted depressive symptoms, incident depressive symptoms with remission, incident depressive symptoms without remission, chronic depressive symptoms, and variable course. Diabetes status was self-reported biennially from 2010 to 2018. After adjusting for covariates, remitted depressive symptoms (HR = 1.52, 95% CI [1.06, 2.22]) and variable course depressive symptoms (HR = 1.83, 95% CI [1.19, 2.81]) remained associated with incident diabetes. In sensitivity analyses, which lowered the cut-off score for depressive symptoms, variable course depressive symptoms (HR = 1.61, 95% CI [1.11, 2.33]) remained associated with incident diabetes. Specific courses of depressive symptoms, including variable course depressive symptoms, were associated with diabetes incidence. Continuing to examine the link between patterns of depressive symptoms over time and incident diabetes may lead to the development of more targeted interventions.