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Arsenic-free Ge-Te-based ovonic threshold switching material with reduced leakage current

In vitro evaluation and phytochemical analysis of Curcuma aeruginosa Roxb. against human coronavirus OC43

Technoeconomic and environmental analysis of cryogenic and MQL-assisted machining of Hastelloy X

Abstract The growing significance of superalloys like Hastelloy X, particularly in critical engineering sectors such as aerospace, chemical processing, and selective biomedical equipment (e.g., surgical instruments and medical tooling), underscores the need for advancements in their manufacturing processes. In today’s era of advanced manufacturing, it is crucial to develop machining systems that are both environmentally sustainable and cost-effective. To bridge the existing gap between economic, technological, and sustainability aspects in the machining of Hastelloy X, the present research aims to shed light on this critical interplay. Experimental investigations were conducted to evaluate the performance of various cooling techniques, including dry machining, minimum quantity lubrication (MQL), and cryogenic cooling using liquid nitrogen (LN₂) and carbon dioxide (CO₂). The results revealed that cryogenic cooling with LN₂ demonstrated superior performance across technological, sustainability, and economic metrics, outperforming other methods. Specifically, LN₂ cooling during the turning of Hastelloy X led to a reduction in tool wear and surface roughness by 21.11% and 25%, respectively, over dry machining conditions. These findings highlight the potential of advanced lubrication and cooling techniques to enhance sustainable manufacturing practices, reducing resource consumption while improving machining performance, particularly for industries involving difficult-to-machine superalloys.

Diminished angiogenic capacity in the hippocampus compared to the cortex indicates regional vulnerability

Genetic contributions to brain criticality and its relationship with human cognitive functions

Recently, extensive evidence has demonstrated that the brain operates close to a critical state, characterized by dynamic patterns known as neuronal avalanches. The critical state, reflecting the delicate balance between neural excitation and inhibition, offers numerous advantages in information processing. However, the role of genetics in shaping brain criticality is not fully understood. Whether there is any shared genetic factor influencing the critical state and cognitive functions remains elusive. Here, we aimed to address these questions by examining the heritability of brain criticality and its relation to cognitive function by analyzing resting-state functional magnetic resonance imaging (rs-fMRI) in 250 monozygotic twins, 142 dizygotic twins, and 437 Not-twin subjects. We found that genetic factors substantially influenced brain criticality across various scales, encompassing brain regions, functional networks, and the whole brain. These genetic influences exhibited heterogeneity, with the criticality of the primary sensory cortex being more strongly influenced by genetic factors compared to that of the association cortex. Furthermore, we combined rs-fMRI data with transcriptional microarray data from the Allen Brain Atlas: Human Brain (ABHB) dataset and found that the organization of regional critical dynamics was highly explained by a specific gene expression profile. Finally, our results showed that the critical state was correlated with total cognition and had a genetic link with it. These findings provide empirical evidence that brain criticality is a biological phenotype and suggest a shared genetic foundation underlying brain criticality and cognitive functions. Our results pave the way toward revealing specific biological mechanisms contributing to critical dynamics and their associations with brain function and dysfunction.

Lactate promotes invasive Klebsiella pneumoniae liver abscess syndrome by increasing capsular polysaccharide biosynthesis via the PTS-CRP axis

Heterogeneity of IL-15-expressing mesenchymal stromal cells controls natural killer cell development and immune cell homeostasis

Abstract Bone marrow (BM) mesenchymal stromal cells (MSC) provide microenvironmental niches that support hematopoietic stem cells and regulate hematopoiesis. Whether functional heterogeneity among BM MSCs contributes to the development and survival of distinct immune cell lineages remains incompletely understood. Here, we use an Il15 knockin reporter and multiple conditional deletion mouse models to show distinct differences in IL-15 expression between BM MSC subtypes. Conditional deletion of Il15 in Osx + stromal cells results in decreased natural killer (NK) cell precursors, memory CD8+ T cells and NKT cells but not mature NK cells. Lepr + stromal cells support the survival of mature NK cells and memory CD8+ T cells in the BM of older mice, while endothelial cells support mature NK cells and memory CD8+ T cells in the blood but not in the BM. Thus, our data suggest that MSC subtypes differentially regulate the development and survival of IL-15-dependent immune cell lineages in the BM.

Muscle Rev-erb controls time-dependent adaptations to chronic exercise in mice

A Carboxylate‐based Hydrophilic Organic Photovoltaic Catalyst with a Large Molecular Dipole Moment for High‐Performance Photocatalytic Hydrogen Evolution

Abstract Achieving ultrafast dissociation of photogenerated excitons and efficient charge transport within the photocatalyst is a fundamental issue. Additionally, enhancing the interaction between semiconductors and water is crucial for efficient photocatalytic water splitting. Herein, we synthesized a carboxylate‐based hydrophilic polymer, hPTB7‐Th. Exposed carboxylates enhance semiconductor‐water interfacial compatibility, reducing contact resistance and accelerating charge transfer kinetics. Furthermore, the carboxylate substitution shifts polarity centers, amplifying the molecular dipole moment by 10‐fold. This induces a giant built‐in electric field, enabling ultrafast electron‐transfer process (ca. 0.31 ps) in the hPTB7‐Th:PCBM bulk heterojunction. Consequently, the hPTB7‐Th:PCBM‐based bulk heterojunction nanoparticles exhibit excellent photocatalytic activity, achieving an optimal hydrogen evolution rate of 111.5 mmol g −1 h −1 , four times over the ester‐based counterpart (PTB7‐Th:PCBM). Moreover, the electrostatic stability imparted by the carboxylates endows hPTB7‐Th:PCBM with outstanding operational stability, maintaining 81% of its initial hydrogen evolution rate after 100 h operation. This result places it among the state‐of‐the‐art organic photovoltaic bulk heterojunction photocatalysts in terms of stability. This work establishes a molecular engineering strategy for high‐performance bulk heterojunction photocatalysts, emphasizing synergistic optimization of hydrophilicity, dipole engineering, and interfacial dynamics.

Experimental investigation of influence of age hardening temperature and cooling medium on tribological behaviour of aluminium/tungsten carbide metal matrix composite

Structural and mechanical properties of humidity-responsive Geraniaceae awns

Abstract Hygroscopic deformations in plants are passive movements within specialized structures triggered by changes in environmental humidity. In the Geraniaceae, the sterile extension of the mericarp, called awn, facilitates seed dispersal by actuating hygroscopic coiling. Notably, the morphological characteristics and regional distribution of awns vary significantly among the family species, suggesting different mechanisms at the base of dispersion. Despite these variations, no prior investigation has solely focused on examining the combination of the structural and mechanical properties of the awn. Thus, this study fills the gap by conducting an in-depth comparative analysis of the awns from two Geraniaceae species, Pelargonium appendiculatum (L.f.) Willd. and Erodium gruinum (L.) L’Her., which exhibit similar coiling behavior but possess distinct structural features. Through an interdisciplinary approach, we have identified key internal structural characteristics that profoundly impact the awn’s mechanical properties and hygroscopic response, directly influencing its movement. Our comprehensive findings highlight distinct dispersion mechanisms tailored to each species, providing new insights into the functional role of awn structures. This study not only advances our understanding of plant biomechanics but also highlights the intricate relationship between structure and function.

Comparative analysis of machine learning approaches for heatwave event prediction in India

Abstract Heatwaves, are identified as prolonged durations of unusually high temperatures, which pose significant threats to human health, animal health and agriculture. With the increasing frequency and intensity of heatwaves driven by climate change, accurate and early prediction of these extreme weather events is crucial for effective mitigation and adaptation. This research paper conducts a comparative analysis of various machine learning models for heatwave event classification using a time series dataset from a weather station in the equatorial region of India, specifically Chennai, Tamil Nadu. The study evaluates the performance of models including Random Forest, Convolutional Neural Networks, LightGBM, Long Short-Term Memory Networks, Transformer Networks, Support Vector Machines, Graph Neural Networks, Extreme Gradient Boosting and Autoencoders for Anomaly Detection in heatwave. The challenges posed by class imbalance and the limitations of traditional oversampling techniques are discussed, with insights into effective strategies for improving prediction accuracy. Accurate prediction of heatwaves enables mitigation plans to protect humans, animal and plants.

Reducing plastic waste from cable ties in food packaging through microbiological testing and behavioral intervention informed by stakeholder perspectives

Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning

The effect of pre-transplant dialysis duration on the relationship between delayed graft function and recipient death

The impact of green spaces, urban settings, seasonal changes, and pollutants on dissemination of antimicrobial genes in air

Zone adaptive fuel mapping for high resolution wildfire spread forecasting

Abstract Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map.

Immune cell profiling reveals diverse niches of immune residents of the enteric nervous system and potential neuroimmune interactions

Gastrointestinal (GI) neuroimmune interactions are crucial sensors and regulators of tissue homeostasis. Most enteric neurons reside within the myenteric plexus of the enteric nervous system in the muscular region, forming a structure called the muscularis externa . Despite established interactions between muscularis macrophages and neurons, the presence and function of other immune cell types remains poorly characterized. Here, we mapped the muscularis immune cell landscape, revealing that diverse cell types are present within distinct locations of the GI tract, and they lie in proximity to neuronal cell bodies and their axons. Using a hypothesis-free computational approach, we identify putative ligand–receptor interactions from publicly available single-cell RNA datasets and further validate one of these (App-CD74). This study provides a valuable reference to encourage new avenues of research underpinning enteric neuroimmune interactions as key contributors to GI homeostasis and diseases.

Enhancing energetic disorder in all-organic composite dielectrics for high-temperature capacitive energy storage

Abstract The urgent demand for capacitive energy storage at elevated temperatures is limited by significant leakage currents in existing polymer dielectrics, which lead to excessive heat generation and increase the risk of thermal runaway. Here we demonstrate a strategy to mitigate conduction loss by modulating energetic disorder within the polymer matrix. Incorporation of high-polarity organic molecules into polyetherimide enhances dipole-dipole interactions, increasing energetic disorder and thereby decreasing charge carrier mobility. Experimental measurements and computational simulations reveal that disorder-induced energy fluctuations broaden the energy separation between transport states, effectively suppressing charge transport. The resulting composite delivers an energy density of 6.45 J cm−3 with a charge-discharge efficiency of 90% at 200 °C, and exhibits stable performance over 100,000 cycles under an applied field of 400 MV m−1. The observed uniformity and quality of the all-organic composite films address the challenges of scalable manufacturing for dielectric films, offering a practical pathway for the development of high-temperature dielectric materials.

Bridging cell morphological behaviors and molecular dynamics in multi-modal spatial omics with MorphLink