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Microbiome analysis in individuals with human papillomavirus oral infection
Optimal network sizes for most robust Turing patterns
Abstract Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. Broadly speaking, this optimal size emerges from a trade-off between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.
Structural basis for the conformational protection of nitrogenase from O2
Cloud radiative effect dominates variabilities of surface energy budget in the dark Arctic
Genetic structure and divergence of marginal populations of black poplar (Populus nigra L.) in Poland
Abstract Genetic diversity is crucial to secure the survival and sustainability of ecosystems. Given anthropogenic pressure, as well as the projected alterations connected with the level and circulation of water, riparian forests are of particular concern. In this paper, we assessed the genetic variation of black poplar – one of the keystone tree species of riverine forests. The natural habitats of black poplar have been severely transformed leading to a significant decline of its population size. Using a set of 18 nuclear microsatellites and geographic location data, we studied 26 remnant populations (1,261 trees) located along the biggest river valleys in Poland. Our main goal was to assess the overall genetic variation and to verify if range fragmentation and habitat transformation have disrupted gene exchange among populations. Genotyping revealed that 261 trees were clones. The level of clonality was higher in more transformed river sections. All populations have probably gone through a drastic genetic bottleneck in the distant past, and most of them have low effective population sizes. Still, the overall level of genetic variation remains high, but certain populations require attention due to their lower genetic variation, higher clonality and strong spatial genetic structure. Genetic differentiation was low, yet Bayesian clustering supported the existence of 11 genetic clusters. According to the results, gene exchange is most prevalent between adjacent populations. Relatively free gene flow occurs only along the Vistula, particularly in its middle section which is characterized by the highest genetic variation. Noticeable genetic structuring was observed along the Oder. Populations located at the range margin showed signs of genetic divergence and reduction of variation. We conclude that human activities have impacted the gene pool of black poplar in Poland by disrupting landscape connectivity and preventing the species from generative reproduction. The study provides practical guidelines on how to develop and implement the conservation program for the gene pool of black poplar in Poland. It also presents a strong case favoring river renaturation and genetic monitoring, particularly concerning keystone species.
Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework
Abstract Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, with increasing global incidence and have poor prognosis. Tumour-infiltrating lymphocytes (TILs) are recognized as a key prognostic indicator and play a vital role in OSCC grading. However, current methods for TILs quantification are based on subjective visual assessments, leading to inter-observer variability and inconsistent diagnostic reproducibility. Only a few studies have been conducted in automating TILs quantification for OSCC, existing methods use score-based systems that focus only on tissue-level spatial analysis, overlooking essential cellular-level information and do not provide TILs infiltration subcategories required for determining OSCC grading. To address these limitations, we propose OralTILs-ViT, a novel joint representation learning framework that integrates cellular and tissue-level information. Our model employs two parallel encoders: one extracts cellular features from cellular density maps, while the other processes tissue features from H&E-stained tissue images. This dual-encoder approach enables OralTILs-ViT to capture complex tissue-cellular interactions, classifying TILs infiltration categories consistent with Broders’ grading system-“Moderate to Marked”, “Slight” and “None to Very Less.” This approach reflects pathology practices and increases TILs classification accuracy. To generate cellular density maps, we introduce TILSeg-MobileViT, a multiclass segmentation model trained using a weakly supervised method, minimizing the need for manual annotation of cellular masks and overcoming the limitations of previous TILs assessment techniques. An extensive evaluation of our methodology demonstrates that OralTILs-ViT with the configuration (Adam, $$\alpha$$ α = 0.001) outperforms existing approaches, achieving 96.37% accuracy, 96.34% precision, 96.37% recall, and a 96.35% F1 score. Furthermore, TOPSIS analysis confirms that our method ranks first across all TILs infiltration categories. In summary, our proposed methodology outperforms single modality-representation learning approaches for accurate and automated TILs classification.
Increased overwintering temperature reduces reproductive success of the solitary bee species Osmia bicornis
Multilayer entropy-weighted TOPSIS method and its decision-making in ecological operation during the subsidence period of the Three Gorges Reservoir
Predictors associated with time to default for HIV/AIDS patients under HAART at Debre Tabor Referral Hospital: a Cox regression model
Distributed nash equilibrium seeking for heterogeneous second-order nonlinear noncooperative games with communication delays
How a small but mighty protein protects a life-sustaining enzyme
Study on the mechanics and self-sensing properties of ultrahigh-performance shotcrete containing waste glass aggregates
Multicooperation of Turtle-inspired amphibious spherical robots
Identification of candidate genes involved in Zika virus-induced reversible paralysis of mice
Abstract Zika virus (ZIKV) causes a variety of peripheral and central nervous system complications leading to neurological symptoms such as limb weakness. We used a mouse model to identify candidate genes potentially involved in causation or recovery from ZIKV-induced acute flaccid paralysis. Using Zikv and Chat chromogenic and fluorescence in situ RNA hybridization, electron microscopy, immunohistochemistry, and ZIKV RT-qPCR, we determined that some paralyzed mice had infected motor neurons, but motor neurons are not reduced in number and the infection was not present in all paralyzed mice; hence infection of motor neurons were not strongly correlated with paralysis. Consequently, paralysis was probably caused by by-stander effects. To address this, we performed bioinformatics analysis on spinal cord RNA to identify 2058 differentially expressed genes (DEGs) that were altered during paralysis and then normalized after paralysis. Of these “biphasic” DEGs, 951 were up-regulated and 1107 were down-regulated during paralysis, followed by recovery. To refine the search for candidate DEGs we used gene ontology analysis and RT-qPCR to select 3 DEGs that could be involved with the node of Ranvier function and 5 DEGs that could be involved with synaptic function. Among these, SparcL1:Sparc DEG ratios were identified to be inversely correlated with ZIKV-induced paralysis, which is consistent with the known function of SPARC protein to antagonize the synaptogenesis of SPARCL1. Ank3, Sptbn1, and Epb41l3 affecting the structures at and near the nodes of Ranvier were significantly downregulated during ZIKV-induced paralysis. The primary contribution is the identification of 8 candidate genes that may be involved in the causation or recovery of ZIKV-induced paralysis.
Magmatic evolution of the Kikai caldera revealed by zircon triple dating and its chemistry
Abstract Reconstructing the volcanic history of the Kikai caldera, a large active volcano that produced a ~ 160 km3 eruption at 7.3 ka off the southern coast of Kyushu Island (southwest Japan), is crucial to assess potential future volcanic hazards at both regional and global scales. However, revealing its volcanic history before the 7.3 ka eruption has been challenging due to the caldera being mostly submerged. In this study, we present evidence that the Kikai caldera erupted a geochemically distinct silicic lava at ~ 250 ka by using zircon triple (U-Pb, Th-Pb, U-Th) dating and its chemistry. The presence of 1.5–1.0 Ma zircons in the 7.3 ka eruption deposits suggests that zircon crystallization in the Kikai caldera began during this period. We further infer large eruptions occurred around 0.7–0.6 Ma, suggesting that the Kikai caldera may have experienced at least 5 major eruptions during its 1.0–1.5-million-year magmatic evolution.