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Engineering electro-crystallization orientation and surface activation in wide-temperature zinc ion supercapacitors
Abstract Matching the capacity of the anode and cathode is essential for maximizing electrochemical cell performance. This study presents two strategies to balance the electrode utilization in zinc ion supercapacitors, by decreasing dendritic loss in the zinc anode while increasing the capacity of the activated carbon cathode. The anode current collector was modified with copper nanoparticles to direct zinc plating orientation and minimize dendrite formation, improving the Coulombic efficiency and cycle life. The cathode was activated by an electrolyte reaction to increase its porosity and gravimetric capacity. The full cell delivered a specific energy of 192 ± 0.56 Wh kg−1 at a specific power of 1.4 kW kg−1, maintaining 84% capacity after 50,000 full charge-discharge cycles up to 2 V. With a cumulative capacity of 19.8 Ah cm−2 surpassing zinc ion batteries, this device design is particularly promising for high-endurance applications, including un-interruptible power supplies and energy-harvesting systems that demand frequent cycling.
Taurine is a potential therapy for rheumatoid arthritis via targeting FOXO3 through cellular senescence and autophagy
Background Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease closely related to aging with unclear pathogenic mechanisms. This study aims to identify the biomarkers in RA, aging and autophagy using bioinformatics and machine learning and explore the binding stability of taurine to target utilizing computer-aided drug design (CADD). Methods We identified differentially expressed genes (DEGs) for RA, then crossed with gene libraries for aging and autophagy to identify common genes (Co-genes). We performed Gene Ontology (GO), Kyoto Encyclopedia of the Genome (KEGG), and ClueGO analysis for Co-genes. The Co-genes were subjected to support vector machine-recursive feature elimination (SVM-RFE), Degree, and Betweenness algorithms to get hub genes, then verified by an artificial neural network (ANN). After continuing to perform least absolute shrinkage and selection operator (LASSO) and weighted gene co-expression network analysis (WGCNA) on Co-genes, the results were crossed with hub genes to obtain genes, which were imported into various validation sets for receiver operating characteristics (ROC) to identify key genes. We analyzed the microRNA/TF network, enriched pathways, and immune cell infiltration for key genes. The binding stability of taurine with the target protein was verified by CADD. Finally, we used Western blot for in vitro experimental verification. Results We obtained 74 Co-genes enriched in RA, cellular senescence, and regulation of programmed cell death. The model prediction of hub genes works well in ANN. The key genes (MMP9, CXCL10, IL15, FOXO3) were tested in ROC with excellent efficacy. In RA, FOXO3 expression was down-regulated while MMP9, CXCL10, and IL15 expression were upregulated, and FOXO3 was negatively correlated with MMP9, CXCL10, and IL15. Two miRNAs (hsa-mir-21-5p, hsa-mir-129-2-3p) and four TFs (CTCF, KLF, FOXC1, TP53) were associated with key genes. The immune cells positively correlated with MMP9, CXCL10, and IL15 expression and negatively correlated with FOXO3 expression were Plasma cells, CD8 T cells, memory-activated CD4 T cells, and follicular helper T cells, aggregating in RA. The binding stability of taurine with FOXO3 was verified by molecular docking and molecular dynamics simulation. In vitro experiments have indicated that taurine can upregulate the expression of FOXO3 and treat RA through the FOXO3-Parkin signaling pathway. Conclusions MMP9, CXCL10, IL15, and FOXO3 are biomarkers of RA, cellular senescence, and autophagy. Taurine might be a promising drug against RA via targeting cellular senescence and autophagy through FOXO3.
Blind and endmember guided autoencoder model for unmixing the absorbance spectra of phytoplankton pigments
Abstract Hyperspectral sensing of phytoplankton, free-living microscopic photosynthetic organisms, offers a comprehensive and scalable method for assessing water quality and monitoring changes in aquatic ecosystems. However, unmixing the intrinsic optical properties of phytoplankton from hyperspectral data is a complex challenge. This research addresses the problem of non-linear unmixing hyperspectral absorbance data of concentrated water samples using Blind (BAE) and Endmember Guided Autoencoder (EGAE). We show that spectral unmixing using the EGAE model with different objective functions can effectively estimate the abundance of different optical components in spectral data. The EGAE model demonstrated a higher correlation between unmixed endmember abundances and ground truth for chlorophyll-a (chl-a) and fucoxanthin (fx) biomarker pigment concentrations compared to the BAE model, effectively unmixed the absorbance spectrum of cyanobacterial pigment phycocyanin (pc) and was robust to changes in network architecture. It can adaptively unmix various endmembers without impacting the abundance estimates of other pigments. Our results demonstrate that EGAE provided stable abundance estimates and improved the accuracy and reliability of identifying and quantifying pigments, allowing for more precise unmixing of hyperspectral data into their constituent endmembers. We anticipate that our study will serve as a starting point for targeted unmixing of specific photosynthetic pigments using EGAE.
The Role of Striatum in Controlling Waiting during Reactive and Self-Timed Behaviors
The ability to wait before responding is crucial for many cognitive functions, including reaction time (RT) tasks, where one must resist premature actions before the stimulus and respond quickly once it is presented. However, the brain regions governing waiting remain unclear. Using localized excitotoxic lesions, we investigated the roles of the motor cortex (MO) and sensorimotor dorsolateral striatum (DLS) in male rats performing a conditioned lever-release task with variable delays. Neural activity in both MO and DLS showed similar firing patterns during waiting and responding periods. However, only bilateral DLS lesions caused a sustained increase in premature (anticipatory) responses, whereas bilateral MO lesions primarily prolonged RTs. In a self-timing version of the task, where rats held a lever for a fixed delay before releasing it, DLS lesions caused a leftward shift in response timing, leading to persistently greater premature responses. These waiting deficits were accompanied by reduced motor vigor, such as slower reward-orienting locomotion. Our findings underscore the critical role of the sensorimotor striatum in regulating waiting behavior in timing-related tasks.
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.