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Application of varied datum in time-to-depth conversion for high resolution coal seam micromorphology characterization
Diene-Capped Carbene: Pathways to Cycloaddition, Copolymerization, and Catalysis
SputOMICs identifies common and distinct markers in cystic fibrosis and chronic obstructive pulmonary disease
Abstract Cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) are muco-obstructive lung diseases. Knowledge of molecular processes has much improved therapeutic options in CF, whereas much less is known for COPD, a disease affecting an increasing number of patients. Here, we report a multilayer workflow integrating microbiome, inflammation and proteome profiling with clinical data to identify disease specific characteristics in sputum. Our proof-of-concept study shows that CF sputum is dominated by Pseudomonas and Staphylococcus , exhibits heightened neutrophilic inflammation, and a severe protease-antiprotease imbalance. In contrast, COPD displays heterogeneous microbiome composition, eosinophilic inflammation, and altered extracellular matrix remodeling. Proteome-based cellular deconvolution identifies disease-specific immune cell signatures, underscoring the complexity, especially in COPD. Multi-omics factor analysis suggests that matrisome and nucleotide metabolism changes may act as disease discriminators, though future confirmation in larger cohorts is needed. These findings highlight the potential of our integrated approach to uncover sputum biomarkers as tools for patient stratification and personalized therapeutic strategies in CF and COPD.
Mixed Ionic–Electronic Transport in Metal–Organic Frameworks
Advanced prediction of combustion phasing in methane homogeneous engines via in-cylinder ion current profiling and a novel ionic kinetics framework
Single-Electron Transfer Stabilizes Metastable Alane in a Bipyridine-Functionalized MOF Nanopore
LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty
Abstract Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.
Unplugging Asymmetric Synthesis with a Wireless, Self-Pumping Electrochemical Reactor
An efficient reparameterized small object detection transformer for thermal infrared images
Steric Hindrance-Driven Closed-Loop Conversion of Acceptor Enables Long-Life and High-Capacity Fluoride-Ion Batteries
Risk stratification system for sentinel lymph node metastasis in clinically node-negative early breast cancer with sonographically abnormal but cytologically negative axilla
Structural Mimics of Hydrocarbon Intermediates Reveal Counterclockwise Cyclization Pathways in the Sesquiterpene Synthases TmS and NcECS
RandMScan: accelerating parallel scan via matrix computation and random-jump strategy
Abstract Parallel scan is a fundamental primitive widely used in a broad range of workloads, including parallel sorting, graph algorithms, and sampling in large language model inference. Although GPU-optimized parallel scan algorithms have been extensively studied, their reliance on vector units makes them inefficient on modern AI accelerators, which typically lack such units but incorporate abundant processing element (PE) arrays tailored for matrix operations. Existing matrix-based approaches, however, suffer from excessive bandwidth consumption due to auxiliary matrix loading as well as costly inter-block communication, thereby limiting their scalability in practical deployments. In this paper, we propose RandMScan, a two-stage parallel scan framework specifically designed for AI accelerators equipped with PE arrays, to address the aforementioned challenges. The first stage employs an efficient matrix-based local chunk scan algorithm that fully exploits fine-grained parallelism within PE arrays, while the second stage introduces a lightweight Random-Jump strategy to coordinate global aggregation with reduced synchronization overhead. Together, these techniques enable scalable execution over long sequences while effectively mitigating the substantial communication overhead that typically arises in prior solutions. Extensive evaluations on state-of-the-art AI accelerators demonstrate that our method achieves more than 80% speedup compared to existing matrix-based implementations for long input sequences, and can reduce the end-to-end latency by 15%–26% in representative downstream applications of scan.
Reaction-Induced Post-Activated Nanotrap Strategy for Leakage-Resistant Immobilization of Radioactive Organic Iodide
Evaluating postoperative quality of life and satisfaction in physicians undergoing small incision lenticule extraction
The [4Fe-4S] Cluster of HydF Is Essential for [FeFe]-Hydrogenase Maturation
SS_CASE_UNet: an attention-enhanced semi-supervised framework for fetal cerebellum segmentation in ultrasound images
Abstract Accurate segmentation of the fetal cerebellum in ultrasound images is crucial for assessing fetal development and detecting prenatal abnormalities. However, this task remains challenging due to factors such as image noise, complex anatomical structures, and limited availability of annotated data, which is further compounded by the high cost and effort required for manual labeling. To address these challenges, we propose SS_CASE_UNet, a novel semi-supervised segmentation framework that enhances U-Net with attention mechanisms to better manage image noise and anatomical complexity. Additionally, a multi-stage semi-supervised training strategy effectively mitigates the scarcity of annotated data. The architecture integrates Squeeze-and-Excitation blocks for dynamic channel-wise feature recalibration and a Coordinate Attention block at the bottleneck to capture precise spatial and long-range dependencies. Our multi-stage training pipeline leverages both labeled and unlabeled data through iterative pseudo-label and re-training, improving generalization in low-annotation scenarios. Experimental results demonstrate that SS_CASE_UNet outperforms existing methods, achieving a Dice Similarity Coefficient (DSC) of 87.65%, along with high accuracy (99.08%), precision (93.49%), recall (82.34%), and Jaccard Similarity (81.78%). Despite incorporating advanced attention mechanisms, our model maintains a balanced complexity-performance trade-off. These results highlight SS_CASE_UNet as a robust and clinically practical solution for automated segmentation of the fetal cerebellum in ultrasound images.
A Conceptual Framework for the Crystallizability of Organic Compounds
Sustainable application of edible solute to control reservoir evaporation loss
Abstract Recently, water preservation globally, particularly in Indian cities, has been prominently featured in newspaper headlines, underscoring its importance. This research explores the innovative use of edible solutes to tackle the challenges of evaporation in reservoirs, including water loss, increased salinity, and ecological disruptions. Traditional methods for controlling evaporation often have environmental drawbacks and high operational costs. By evaluating the environmental impact, cost-effectiveness, and feasibility of edible solutes such as mustard oil, neem oil, til oil, castor oil, cetyl alcohol, and stearyl alcohol, this study investigated their sustainable application in eight reservoirs across Andhra Pradesh and Telangana states in India. Through break-even analysis, the economic viability of edible solutes is compared to that of conventional methods over the lifespan of a reservoir. These findings suggest that edible solutes offer a promising and environmentally friendly alternative, reducing evaporation rates while minimizing the adverse effects on water quality and ecosystems. Despite the initial investment costs, the long-term savings and environmental benefits surpass those of the conventional approaches. This study estimated evaporation rates for eight reservoirs across Andhra Pradesh and Telangana in India (3049 mcm of water/year), showing a significant reduction when cetyl alcohol was used as a solute. Cetyl and stearyl alcohols are highlighted as practical and cost-effective evaporation retardants. Considering the cost of water at one paise per five litres of saved water, the break-even point (BEP) analysis for the adopted scenarios reveals that BEP is achieved for 30%, 10%, and 5% reduction in evaporation within one, two, and three months, respectively. Similarly, for scenario II (one paisa per one litre of saved water), the BEP was achieved at the beginning, 1.5 months, and 2.5 months, considering evaporation reduction by 30%, 10%, and 5%, respectively. Future research should validate the efficacy of microfilms in mitigating evaporation using time-resolved interferometry techniques. This study advocates sustainable water management practices and provides valuable insights for policymakers, water resource managers, and stakeholders seeking efficient solutions for evaporation control in reservoirs.