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Characterization and genetic diversity analysis of lesser-known goat population of northern hills of Chhattisgarh
A Pragmatic Trial of Glucocorticoids for Community-Acquired Pneumonia
KCTD10 is a sensor for co-directional transcription–replication conflicts
Boosting the energy storage performance with human hair-derived reduced keratin
Once-Monthly Maridebart Cafraglutide in Obesity — A Phase 2 Trial
An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD
Small Steps, Big Ventricles: Idiopathic Normal-Pressure Hydrocephalus
Diagnostic ultrasound-microbubble therapy promotes wound healing through miR-26b-5p-mediated GSK3β suppression
From Bandwidth to Bedside — Bringing AI-Enabled Care to Rural America
Bayesian spatio-temporal modeling of COVID-19 incidence in Algerian provinces using integrated nested Laplace approximations
Abstract The COVID-19 pandemic in Algeria dissplayed significant spatial and temporal heterogeneity, especially during the severe summer 2021 wave driven by the Delta variant. Standard national-level statistics often obscure this critical local variation, creating a need for advanced modeling to inform precise public health interventions. This study aimed to perform a high-resolution spatio-temporal analysis of COVID-19 incidence across Algeria’s 48 provinces (Wilayas) to identify persistent high-risk areas and track the dynamics of viral spread. A spatio-temporal analysis was conducted on COVID-19 case data from all 48 Wilayas during epidemiological weeks 26-37 of 2021. We employed a Bayesian hierarchical model fitted using the Integrated Nested Laplace Approximation (INLA). The model incorporated structured spatial (Leroux prior), temporal (random walk of order 1), and spatio-temporal (Type IV interaction) random effects. Model selection was performed using the Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criterion (DIC) The spatio-temporally structured interaction model provided the best fit. Spatial heterogeneity was the dominant driver of transmission risk, accounting for 83.4% of the explained variance. Northeastern Wilayas, including Constantine and Tebessa, exhibited persistently high relative risks. The national temporal trend showed a sharp peak in early August 2021. The spatio-temporal interaction term (16.5% of variance) captured the progressive westward spread of the virus along the northern coast throughout the study period. This analysis demonstrates the critical utility of Bayesian spatio-temporal models in moving beyond national averages to identify specific high-risk areas and understand the evolving dynamics of an epidemic. The findings provide a valuable evidence base for designing targeted public health strategies. While this foundational study establishes the spatio-temporal risk patterns, future work incorporating socio-economic and environmental covariates will be essential to elucidate the underlying drivers of transmission.
Measurable Residual Disease–Guided Therapy for Chronic Lymphocytic Leukemia
Structural insights into AVR-Rmg8 recognition mechanisms by the wheat blast resistance gene Rmg8
Abstract Wheat blast disease, caused by the Triticum pathotype of Magnaporthe oryzae (MoT), poses a significant threat to global food security. The blast resistance gene Rmg8 , recently isolated from a hexaploid wheat cultivar, strongly confers resistance to all Bangladeshi and Zambian MoT isolates that carry the eI type of AVR-Rmg8. However, the molecular interactions underlying this recognition at the protein level remain poorly understood. In this study, we elucidated the structural and biological characteristics of RMG8 proteins and their recognition of the AVR-Rmg8 effector proteins using computational biology approaches. Amino acid sequence comparison of four AVR-Rmg8 types revealed that only three amino acid residues distinguish the eI type of AVR-Rmg8, which induces a higher level of resistance conferred by RMG8. The most intriguing finding of this study is that only the eI type effector interacts with ATP through the Pro26 residue, a feature not present in the other AVR-Rmg8 types. We identified that the Protein Kinase C (PKC) domain of RMG8, where proline dependency mediates the phosphorylation of a serine residue, is involved in the strong recognition of the eI type of AVR-Rmg8. Phylogenetic analyses indicated that RMG8 might have evolved from proteins closely associated with plant signaling pathways. Although Rmg8 is an atypical resistance gene, our data suggest that it may function as a hub in the plant defense network, as it is a type of nuclear membrane protein, specifically a calcium-dependent multiple C2 domain protein with transmembrane regions (MCTP) kinase, which integrates signaling for effector recognition. Taken together, our study provides detailed insights into the molecular recognition mechanism between AVR-Rmg8 and RMG8, which is expected to aid in wheat blast resistance breeding. Future studies involving the purification and structural characterization of MoT effector proteins and Rmg8 gene products are necessary to validate these findings.