Unabridged phase diagram for single-phased FeSexTe1-x thin films

A complete phase diagram and its corresponding physical properties are essential prerequisites to understand the underlying mechanism of iron based superconductivity. For the structurally simplest 11 (FeSeTe) system, earlier attempts using bulk samples have not been able to do so due to the fabrication difficulties. Here, thin FeSexTe1-x films with the Se content covering the full range were fabricated by using pulsed laser deposition method. Crystal structure analysis shows that all films retain the tetragonal structure in room temperature. Significantly, the highest superconducting transition temperature (TC = 20 K) occurs in the newly discovered domain, 0.6 - 0.8. The single-phased superconducting dome for the full Se doping range is the first of its kind in iron chalcogenide superconductors. Our results present a new avenue to explore novel physics as well as to optimize superconductors

Artificial Coal: Facile and Green Production Method via Low-Temperature Hydrothermal Carbonization of Lignocellulose

A new concept is proposed for the production of artificial coal under HTC conditions using Mg(NO3)2 as an oxidant in a short time, which is found to enhance the coalification degree of hydrochar from lignocellulosic materials. Pressure promotes decarboxylation reactions of lignocellulose to form hollow smooth-faced regular spherical particles, avoiding the agglomeration of hydrochar particles. In parallel, oxidation can break down the biopolymer structure to form low-molecular-weight compounds, which is found to be a key step during artificial coal formation. The artificial coal synthesized has a high degree of coalification

Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer

Background: Ulcerative colitis (UC) is a chronic inflammatory condition that predisposes patients to colorectal cancer (CRC) through mechanisms that remain largely undefined. Given the pivotal role of cellular senescence in both chronic inflammation and tumorigenesis, we integrated machine learning and bioinformatics approaches to identify senescence-related biomarkers and potential therapeutic targets involved in the progression from UC to CRC. Methods: Gene expression profiles from six GEO datasets were analyzed to identify differentially expressed genes (DEGs) using the limma package in R. Weighted gene co-expression network analysis (WGCNA) was employed to delineate modules significantly associated with UC and CRC, and the intersection of DEGs, key module genes, and senescence-related genes from the CellAge database yielded 112 candidate genes. An integrated machine learning (IML) model—utilizing 12 algorithms with 10-fold cross-validation—was constructed to pinpoint key diagnostic biomarkers. The diagnostic performance of the candidate genes was evaluated using receiver operating characteristic (ROC) analyses in both training and validation cohorts. In addition, immune cell infiltration, protein–protein interaction (PPI) networks, and drug enrichment analyses—including molecular docking—were performed to further elucidate the biological functions and therapeutic potentials of the identified genes. Results: Our analysis revealed significant transcriptomic alterations in UC and CRC tissues, with the turquoise module demonstrating the strongest association with disease traits. The IML approach identified five pivotal genes (ABCB1, CXCL1, TACC3, TGFβI, and VDR) that individually exhibited AUC values > 0.7, while their combined diagnostic model achieved an AUC of 0.989. Immune infiltration analyses uncovered distinct immune profiles correlating with these biomarkers, and the PPI network confirmed robust interactions among them. Furthermore, drug enrichment and molecular docking studies identified several promising therapeutic candidates targeting these senescence-related genes. Conclusion: This study provides novel insights into the molecular interplay between cellular senescence and the UC-to-CRC transition. The identified biomarkers not only offer strong diagnostic potential but also represent promising targets for therapeutic intervention, paving the way for improved clinical management of UC-associated CRC

System Simulation Study on Performance of Non-Supplementary Combustion Liquid Compressed Air Energy Storage System

ObjectivesCompressed air energy storage is a type of energy storage technology with large capacity, long cycle, low cost and high efficiency. Due to the strict requirements of gas storage chambers, gaseous compressed air energy storage cannot be widely promoted and applied in multiple scenarios and on a large scale. Therefore, a non-supplementary combustion liquid compressed air energy storage system was proposed.MethodsA theoretical calculation model was constructed to conduct sensitivity analysis on key parameters such as compressor interstage temperature, number of compressor stages, and turbine inlet temperature within the system. The results were compared with those of a non-supplementary combustion gaseous compressed air energy storage system.ResultsToo low or too high interstage temperature in compressors will restrict the improvement of electric-electric conversion efficiency of the system. The number of compressor stages is positively correlated with compressor power consumption, and negatively correlated with the turbine power generation. Under the same inlet pressure, the higher the inlet air temperature of the turbine is, the larger the power generation is, and the higher the electric-electric conversion efficiency is. Compared with the non-supplementary combustion gaseous energy storage system, the density of non-supplementary combustion liquid energy storage system is increased by 3.7 times, and the volume of the storage chamber is decreased by 9/10.ConclusionsThe non-supplementary combustion liquid compressed air energy storage system effectively solves the problem of gas storage chambers, enabling compressed air energy storage technology to be promoted and applied in multiple scenarios and on a large scale. It is of great significance for deep peak shaving of thermal power units and large-scale energy storage in power grids

Screening of rootstocks with resistance to chilling and continuous cropping but without compromising fruit quality for protected watermelon production

Chilling stress and continuous cropping obstacles limit sustainable production of watermelons under controlled environments. Grafting of watermelon scions onto resistant rootstocks is an effective strategy currently used to overcome these environment limitations. However, currently used commercial rootstocks adversely affect watermelon fruit quality. The chilling tolerance and Fusarium oxysporum f. sp. niveum race 1 (FON1) resistance of seven Cucurbit germplasms, including four watermelon germplasms (M08, TC, YL, and MY), two muskmelon cultivars (JT1 and JSM), and one commercial Cucurbita rootstock (QZ1) of watermelon, were explored in the current study. The effects of the rootstocks of these germplasms on watermelon resistance to chilling stress and continuous cropping obstacles were evaluated. TC rootstock showed the highest chilling tolerance and increased chilling tolerance of watermelon scion. All Cucurbit germplasms showed higher resistance to FON1 than watermelon cultivar N5 (control). Watermelons grafted onto QZ1 showed the lowest wilt incidence and highest fruit yield but had the worst fruit quality after planting on soils continuously cropped for 11 years. Watermelons grafted onto TC showed higher resistance and yield and the best fruit quality. These findings indicate that TC has a large potential for use in grafting watermelon planted in continuously cropped soils (< 10 years). TC can also be used as breeding rootstocks to improve watermelon resistance against continuous cropping obstacles without compromising fruit quality

NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results

This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQChallenge-CVPR-NTIRE2024.Comment: Accepted by CVPR2024 Workshop. The challenge report for CVPR NTIRE2024 Short-form UGC Video Quality Assessment Challeng

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification

This paper proposes a novel spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. Current deep learning-based methods have limitations in spectral and spatial feature representation of hyperspectral images, and these shortcomings will severely restrict the hyperspectral image classification performance. The proposed spectral-spatial multi-layer perceptron network exclusively utilizes multi-layer perceptron to represent and classify hyperspectral images. Specifically, the spectral multi-layer perceptron is investigated to model the long-range dependencies along the spectral dimension, because all diagnostic spectral bands contribute to classification performance. Then, we exploit the spatial multi-layer perceptron to extract local spatial features from hyperspectral data, which are also crucial for land cover classification. Furthermore, global spectral characteristics and local spatial features are integrated to perform the hyperspectral image spectral-spatial classification. Three benchmark hyperspectral datasets are employed for comparative classification experiments and ablation study, and experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy