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Modulation of Pt electron transfer via engineered ultra-thin TiO2-Al2O3 interfaces for coke-resistant methane dry reforming
Interference-aware frequency-agile onboard processor using fine-grained multilevel analysis–synthesis filter-bank channelization
CryoEM structure of mGlu6 captures receptor activation prior to G protein coupling
The BSRA framework for dual sparse parameter efficient fine tuning with block structured gating and rank adaptation
Single-cell genomics highlight MYC-associated metabolic activation and altered cell interactions in T-prolymphocytic leukemia progression
Abstract T-prolymphocytic leukemia (T-PLL) typically presents with rapidly progressing tumor burden. However, 15–25% of cases are diagnosed at an indolent stage with asymptomatic and stable low-level blood lymphocytosis over up to 2-3 years before advancing to active-stage disease. To define the molecular changes underlying this transition, we perform single-cell RNA sequencing of 28 treatment-naïve samples including 11 longitudinally acquired indolent/active pairs, paralleled by longitudinal whole genome sequencing. This reveals both patient-specific lesions and common global alterations of gene expression. Strong upregulations of MYC-target gene signatures in active T-PLL samples associated with enhanced energy metabolism implicate acquired autonomy from energetic restrictions. Recurrent downregulation of genes of the T-cell-receptor signaling cascade and reduced interactions of the T-PLL cell with non-leukemic cell types further indicate progressive independence from regulatory survival signals and escape from micromilieu-mediated control. This single-cell and disease-stage resolved genomic analysis of T-PLL provides insights into shared mechanisms of tumor evolution, which have to prove their amenability as targetable lesions.
Green synthesis and enhanced photocatalytic activity of ZnSe nanoparticles capped with Artemisia herba-alba and calligonum plants extracts
CXCR4-tropic HIV-1 infection in an immunocompetent monkey model
High-resolution regional climate projections and tourism impacts in the Macaronesian archipelagos
Abstract Understanding the impacts of climate change in the North Atlantic Macaronesian archipelagos of the Azores, Madeira, the Canary Islands, and Cabo Verde is critical due to their economic reliance on tourism. In this study, a high-resolution Regional Climate Model (RCM) approach was applied, using the Weather Research and Forecasting (WRF) model driven by CMIP6 data through the pseudo-global warming (PGW) method. Focusing on the Holiday Climate Index for Beach tourism (HCIB), changes are projected for the mid-century (2030–2059) and end-of-century (2070–2099) against a recent past baseline (1990–2019) under both low-emissions (SSP1-2.6) and high-emissions (SSP5-8.5) scenarios. Projections demonstrate pronounced latitudinal and seasonal gradients: the northern archipelagos (the Azores and Madeira) reveal increased summer suitability, while southern archipelagos (the Canary Islands and Cabo Verde) indicate enhanced winter conditions, suggesting a seasonal shift in peak tourism conditions. Thermal comfort changes predominantly drive these trends, except in Cabo Verde, where aesthetic factors (cloud cover) are more influential. These findings provide valuable insights for developing location-specific climate adaptation strategies to support sustainable tourism in these vulnerable island regions.
NPM3 functions as a lactyltransferase to promote necroptosis in male diabetic cardiomyopathy mice models via FASN transcription modulation
Geometry quantification for growth assessment of abdominal aortic aneurysms under surveillance
Water mass specific genes dominate the Southern Ocean microbiome
Abstract The Southern Ocean (SO) plays a key role in regulating global biogeochemical cycles and climate, yet microbial genes sustaining its biological activity remain poorly characterized. We introduce a microbial genes collection from 218 metagenomes sampled during the Antarctic Circumnavigation Expedition, the majority of which are missing from functional databases. 38% even lack homologs in current reference marine gene catalogs, defining a singular genetic seascape. We show that SO gene assemblages exhibit a common polar signature with the Arctic Ocean while being structured by water masses at the SO-scale. We analyze genomic markers of diverse SO biomes, focusing on dimethylsulphoniopropionate (DMSP) cleavage by polar-adapted bacteria, organic matter consumption in the blooming Mertz polynya and adaptation to polar conditions in the ubiquitous bacteria Pelagibacter. Our work takes a step towards a comprehensive understanding of SO’s plankton ecology and evolution, capturing the current state of the unique microbial diversity in this rapidly changing Ocean.
Optimizing renewable energy investments using artificial intelligence-based multi-facet fuzzy decision models
Epilepsy-associated FOXJ3 variants link a transcriptional program of the PTEN-mTOR pathway to neuronal specification and cortical lamination
A multicenter cross-sectional study on perceptions and peer-reported prevalence of research misconduct among Chinese medical postgraduates
Transcriptional regulation of the pneumococcal capsule can dictate serotype-specific infection
Comparative analysis of ANFIS, ANN, and BBD for enhanced prediction of methyl orange adsorption in water treatment
Abstract This work focused on the enhanced prediction of methyl orange removal (MO) from water by activated carbon synthesized from banana peels. Characterization was done using powder X-ray diffraction (PXRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET). Modeling and prediction of process variables, pH (5–9), time (3–60 min), and temperature (25–50 °C), was carried out using Box-Behnken design (BBD), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Performance metrics of R 2 , Adjusted R 2 , Pearson’s r, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) were used to evaluate the models. The regression coefficients from the modeling and prediction showed that BBD (R 2 = 0.9849), ANFIS (R 2 = 0.9934), and ANN (R 2 = 0.9921), which describes the high prediction capacity of the three models. The performance metrics showed that ANFIS had superior capacity in data modeling and prediction compared to BBD and ANN when analyzing complex non-linear relationships. The Elovich, pseudo-first-order, intraparticle diffusion, and pseudo-second-order kinetic models had high R 2 values. The data obtained showed that the pseudo-second-order fitted the data well; as such, chemisorption was the most dominant mechanism. In addition, the isotherm models of Freundlich, Temkin, Langmuir, and Dubinin-Radushkevich were determined. The Freundlich model shows the highest R 2 , as such adsorption occurs on heterogeneous multilayer surfaces. This study therefore shows the efficiency of ANFIS, ANN, and BBD in the prediction of dye removal by activated carbon synthesized from banana peels.
Directional dynamics in the entorhinal cortex of male mice driven by behavioral constraints
Release dynamics and plant availability of POLY4 fertilizer nutrients in tropical acidic soils
Molecular mapping in DNA-PAINT via modified Gaussian Mixture Modeling
Abstract Super-resolution fluorescence microscopy, and specifically DNA-PAINT, provides localization precision down to ~2 nm enabling molecular-resolution imaging. To produce molecular maps of single biomolecules, their positions must be inferred from localizations stemming from single fluorescent molecules. Current clustering methods fail to exploit the full potential of the imaging method. Here, we introduce G5M, a modified Gaussian Mixture Modeling algorithm tailored to DNA-PAINT data. By incorporating prior knowledge of localization precision, spatial constraints, and DNA hybridization kinetics, G5M accurately infers true molecular positions while avoiding overfitting. In realistic simulations of dimers, G5M resolves molecules at the Rayleigh limit with a 27-fold higher recovery rate than current methods and <0.1% false positives. Applied to experimental datasets, G5M recovers full nuclear pore complex structures and detects higher-order CD20 oligomers induced by antibody treatment, outperforming conventional DNA-PAINT analysis. G5M is implemented in the open-source Picasso platform, offering an accessible solution for high-resolution, high-accuracy molecular mapping in super-resolution microscopy.