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Whole-genome transcriptomic profiling reveals distinct sex-specific responses to heat stroke.
No full textHeat-related mortality remains health challenges exacerbated by climate change, with sex-based differences in outcomes, yet underlying mechanisms remain poorly understood. This study examined transcriptomic responses to heat exposure in peripheral blood mononuclear cells from 19 heat stroke patients (8 males, mean age 64.8 ± 6.6 years; 11 females, mean age 49.7 ± 11 years) and 19 controls (11 males, mean age 48.9 ± 9.6 years; 8 females, mean age 44.9 ± 11.8 years). At admission, gene expression revealed upregulation of heat shock protein genes and pathway analysis, demonstrated activation of heat shock and unfolded protein responses across both sexes consistent with proteotoxic stress. However, distinct metabolic, oxidative stress, cell cycle control and immune responses were observed within each sex. Females displayed inhibition of protein synthesis, oxidative phosphorylation, and metabolic pathways, including glucose metabolism, indicative of a hypometabolic state. Males maintained metabolic activity pre-cooling, and enhanced ATP production post-cooling. Females activated NRF2-mediated oxidative stress responses and inhibited DNA replication and mitosis, potentially mitigating genomic instability, while these pathways showed limited regulation in males. Females promoted innate immunity via IL-6, inflammasome, and TREM1 signaling, whereas, males showed suppression of both innate and adaptive immunity, including IL-12, Th1, and T-cell receptor pathways. Upstream analysis identified over 100 transcription factors in both sexes. Males primarily relied on transcriptional mechanisms, whereas females also exhibited translational regulation via LARP1, FMR1, IGF2BP1, and EIF6. These findings suggest distinct, sex-specific molecular adaptations to heat stroke, underscoring the need for targeted therapeutic strategies to mitigate heat-induced morbidity and mortality
Grazing Modulates the Multiscale Spatial Structure of Dryland Vegetation
No full textPlants can facilitate their local environment and create a two-phase spatial structure of vegetation and bare soil in drylands, which largely influences ecosystem functioning. Although an increasing number of studies have examined how global change drivers like aridity influence vegetation spatial structure in drylands (e.g., the patch size distribution), it remains unclear how grazing impacts differ from those of climatic gradients, how these effects vary with herbivore feeding habits, and which plant-level traits—such as size and life form—mediate these spatial responses. Here, we coupled spatial vegetation pattern analyses of ecosystem images with field data analyses of the size distribution and dominant life forms of plants from 326 plots sampled across 25 countries and six continents to explore the effects of herbivores on the spatial structure of dryland vegetation. The effects of herbivores on vegetation spatial structure were opposite to the effects of aridity. Specifically, vegetation in grazed areas was clustered into larger patches, with fewer small patches, which skewed the patch-size distribution towards larger patches. These effects differed between browsing and grazing herbivores. Grazing effects were partially explained by the fact that grazing reduced average plant size, increased shrub density, and promoted facilitation among species of contrasting sizes. Similar effects were also confirmed by using model simulations that accounted for positive plant interactions. By linking remotely sensed images, a global field survey, and a mathematical model, our study uncovers the species-level mechanisms by which herbivores shape ecosystem-level spatial patterns and provides insights into the consequence of herbivory pressure on the resilience of drylands.This research benefited from the support of the Chair Modelisation Mathématique et Biodiversite VEOLIA~Ecole Polytechnique~MNHN~F-X. TheBIODESERT global survey was funded by the European Research Council (ERC Grant agreement 647038). FTM acknowledges support by the King AbdullahUniversity of Science and Technology (KAUST) and the KAUST Climate and Livability Initiative. D.E. is supported by the Hermon Slade Foundation
Capturing of Exhaust Gases with the Purpose of Treatment Media Modification
No full textHydraulic fracturing and acid treatments have become essential tools in reservoir engineering. These processes require high-pressure pumps, typically powered with internal combustion engines, which generate significant emissions. A potential solution is to capture these emissions and use them to create foam or energized fluids for injection, reducing environmental impact and optimizing operations. This article outlines five methods for capturing exhaust gases generated during hydraulic fracturing and acid treatments. By directly injecting these gases, without prior separation, into the formation as a part of treatment slurry, we can create energized or foamed fluids without the need for additional gas processing infrastructure. This approach not only eliminates the requirement for a separate gas separation facility but also sequesters all types of gaseous emissions. Ultimately, this integrated strategy significantly reduces the environmental impact of these essential reservoir stimulation techniques.
This innovative approach proposes a novel application for exhaust gases generated by internal combustion engines used in subterranean formation treatments. Applicable to a broad spectrum of operations, including hydraulic fracturing and acidizing, the method involves directly injecting these gases into the treatment fluid. This process can produce energized fluids with foam quality ranging from 20 to 50 percent, depending on pumping and in-situ conditions, without the need for additional liquid nitrogen or carbon dioxide. Moreover, the technique offers a promising solution for eliminating various gaseous pollutants, such as NOx, CO, and residual hydrocarbons, from the environment by sequestering them underground. While potential risks, including the impact of exhaust oxidants on treatment fluids, warrant careful consideration, the method holds significant promise for a wide range of applications, including geothermal, lithium, and water injection wells stimulation
Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions
No full textWhile interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and organize our discussion around a categorization we introduce, dividing network optimization problems into two primary formulations: one-shot optimization and Markov decision process (MDP). We first trace key works, including foundational contributions from the AI community, and categorize current efforts in network optimization. We also review frontier applications of GDMs and LPTMs in other networking tasks, providing additional context. Furthermore, we present theoretical generalization bounds for GDMs in both one-shot and MDP settings, offering insights into the fundamental factors affecting model performance. Most importantly, we reflect on the overestimated perception of GenAI's general capabilities and caution against the all-in-one illusion it may convey. We highlight critical limitations, including difficulties in constraint satisfying, limited concept understanding, and the inherent probabilistic nature of outputs. We also propose key future directions, such as bridging the gap between generation and optimization. Although they are increasingly integrated in implementations, they differ fundamentally in both objectives and underlying mechanisms, necessitating a deeper understanding of their theoretical connections. Ultimately, this survey aims to provide a structured overview and a deeper insight into the strengths, limitations, and potential of GenAI in network optimization
Terahertz Band UAV Base Stations for Post-Disaster Communication
No full textIn emergency situations, the demand for higher data rates significantly increases, yet the current disaster relief frequency bands are insufficient to meet this demand, and terrestrial base station infrastructure may become inoperable. Meanwhile, terahertz (THz) communication is gaining attention due to its massive available bandwidth, which enables extremely high data rates. Additionally, deploying unmanned aerial vehicle (UAV) base stations (BS) presents a promising solution for establishing reliable communication links during search and rescue operations. In this work, we propose and analyze the use of THz-band UAV BSs for disaster scenarios. This approach leverages the wideband capabilities of the unstandardized THz spectrum to provide sufficient capacity for high data rate applications in disaster relief efforts. The study evaluates the capacity performance and outage probability of THz-band UAV BSs for ground-to-UAV communication links, considering factors such as beam misalignment fading, turbulence fading, and the effects of rain and fog. The results highlight the potential of this approach compared to existing disaster relief bands. For large-scale disaster scenarios where terrestrial base stations are rendered inoperable, two alternative solutions are examined. The first involves a UAV-to-satellite communication link for the THz band, sub-6 GHz disaster relief band, and free-space optics, which reveals significant capacity reductions due to high link losses. The second scenario explores multi-hop UAV communication by deploying multiple UAVs to connect to the nearest operational terrestrial BS, demonstrating high capacity performance
High-Performance Statistical Computing (HPSC): Challenges, Opportunities, and Future Directions
No full textWe recognize the emergence of a statistical computing community focused on working with large computing platforms and producing software and applications that exemplify high-performance statistical computing (HPSC). The statistical computing (SC) community develops software that is widely used across disciplines. However, it remains largely absent from the high-performance computing (HPC) landscape, particularly on platforms such as those featured on the Top500 or Green500 lists. Many disciplines already participate in HPC, mostly centered around simulation science, although data-focused efforts under the artificial intelligence (AI) label are gaining popularity. Bridging this gap requires both community adaptation and technical innovation to align statistical methods with modern HPC technologies. We can accelerate progress in fast and scalable statistical applications by building strong connections between the SC and HPC communities. We present a brief history of SC, a vision for how its strengths can contribute to statistical science in the HPC environment (such as HPSC), the challenges that remain, and the opportunities currently available, culminating in a possible roadmap toward a thriving HPSC community
CCDC 2250199: Experimental Crystal Structure Determination :
No full textAn entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
No full textLarge multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning over a large number of images. Existing benchmarks for multi-image question-answering are limited in scope, each question is paired with only up to 30 images, which does not fully capture the demands of large-scale retrieval tasks encountered in the real-world usages. To reduce these gaps, we introduce two document haystack benchmarks, dubbed DocHaystack and InfoHaystack, designed to evaluate LMM performance on large-scale visual document retrieval and understanding. Additionally, we propose V-RAG, a novel, vision-centric retrieval-augmented generation (RAG) framework that leverages a suite of multimodal vision encoders, each optimized for specific strengths, and a dedicated question-document relevance module. V-RAG sets a new standard, with a 9% and 11% improvement in Recall@1 on the challenging DocHaystack-1000 and InfoHaystack-1000 benchmarks, respectively, compared to the previous best baseline models. Additionally, integrating V-RAG with LMMs enables them to efficiently operate across thousands of images, yielding significant improvements on our DocHaystack and InfoHaystack benchmarks. Our code and datasets are available at https://github.com/Vision-CAIR/dochaystack
Ignition of ultra-lean and highly-diluted hydrogen-air mixtures by bursts of nanosecond repetitively pulsed discharges
No full textAs a carbon-free fuel, hydrogen is being actively considered for various energy conversion systems. However, it is significantly more reactive than conventional fuels, necessitating fundamentally different combustion strategies. To mitigate its high reactivity, hydrogen can be burned in very lean or highly diluted environments. However, there are several challenges with burning very lean or highly diluted mixtures, including ignition and flame stability. Nanosecond Repetitively Pulsed (NRP) discharges have shown promising results in igniting lean or highly diluted mixtures of hydrocarbons. Yet, their efficiency on hydrogen ignition must be assessed. The main objective of this study is to comprehensively compare the ignition events of lean and nitrogen-diluted hydrogen-air mixtures by bursts of NRP discharges across a wide range of conditions. The details of energy deposition and plasma configuration are investigated by electrical measurement and optical visualization. Ignition probability, minimum ignition energy (MIE), and ignition delay are measured in a quiescent constant volume combustion chamber (CVCC). Validation experiments of the setup are conducted for well-studied conditions, i.e., for a lean hydrogen-air mixture at 0.2 equivalence ratio and atmospheric pressure, with pin-pin electrodes. The results match the MIE of capacitive spark discharge in hydrogen. Then, the pressure effect is compared with published study. The MIE results show that lean and diluted mixtures have different sensitivity to the pulse repetition frequency of the ignition sources. They also show that compared to the lean mixture, pressure has more influence on the ignition of N2-diluted mixture. The reasons for these results are analyzed with the support of Schlieren imaging.The paper is based upon work supported by Saudi Aramco Research and Development Center FUELCOM4 program under Master Research Agreement Number 6600024505/01. FUELCOM (Fuel Combustion for Advanced Engines) is collaborative research undertaking between Saudi Aramco and KAUST intended to address the fundamental aspects of fuel combustion in engines, and develop fuel/engine design tools suitable for advanced combustion modes
Unraveling cool-flame chemistry in tetrahydropyran oxidation: SVUV-PEPICO spectroscopy and computational insights into oxygenated heterocycle-dependent reactivity
No full textCyclic ethers, such as tetrahydropyran (THP), are promising biofuels derived from lignocellulosic-biomass and serve as key intermediates in the oxidation of both biofuels and fossil fuels, yet their low-temperatures oxidation mechanism—particularly the role of heterocycle conformation in chain-branching pathways—remains poorly understood. In this study, we employed synchrotron-based vacuum ultraviolet photoelectron photoion coincidence (SVUV-PEPICO) spectroscopy to investigate THP oxidation in a jet-stirred reactor (JSR). The unique sensitivity technique to molecular structure enabled the discrimination of isomers, specifically keto-hydroperoxides (KHPs) and alkenal-hydroperoxides (AnHPs), revealing how ring conformation influences the reactivity. Beyond isomer identification, we detected AnHP-derived decomposition products (e.g., dialdehydes and enals), linking their formation to heterocycle-specific chain-branching pathways. Quantitative analysis of hydroperoxide speciation, based on mass-selected threshold photoelectron spectroscopy (TPES) and total ion yield (TIY) measurements, provided experimental constraints for kinetic modeling. An updated THP oxidation mechanism was constructed to accurately reproduce both our experimental data and prior literature results, resolving discrepancies in predicted intermediate mole fractions. By combining isomer-resolved spectroscopy with kinetic modeling, this work advances the understanding of how heterocycle ring conformation governs low-temperature reactivity, a critical factor in biofuel combustion efficiency and cool-flame chemistry.We are grateful to the whole SOLEIL staff for smoothly running the facility and for the provision of synchrotron radiation under project 20230463. We thank the QUADMARTS International Research Network for promoting the collaboration. This work was performed using HPC resources from the EXPLOR center hosted by the University of Lorraine (Project: 2021EXTXX2356) and from KAUST Supercomputing Lab. This work was funded by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (Grant URF/1/435101–01 CRG 2020). Zhandong Wang acknowledges funding support from the National Key Research and Development Program of China (No. 2021YFA1601800)