Browse Articles

Genetic background sets the trajectory of experimental cancer evolution

Abstract Human cancers are heterogeneous 1 . Dissecting how germline genetic variation and environmental factors shape tumour evolution using human datasets is limited by inherent diversity in genetic backgrounds 2 and environmental exposures 3–5 . Here, to overcome these limitations, we re-ran early tumour evolution hundreds of times in diverged inbred mouse strains, generating matched histology and whole-genome and transcriptome sequences. The sex, environment and carcinogenic exposures were all controlled, and the study design allowed us to capture genetic variation comparable with that observed across human populations while exploiting the nested hierarchical structure of strain–litter–animal–tumour relationships. Our analyses reveal that epistatic interactions between genetic background and acquired somatic mutations result in population-specific disease progression, including choice of driver mutations, occurrence of whole-genome duplication and subclonal selection dynamics that mirror both cancer susceptibility and tumour growth rate. Even modest genetic divergence, comparable with that found across human ancestry groups, can strikingly alter selection pressures during cancer development to shape both cancer risk and the trajectory of tumour evolution.

Rainfall isotopic variability in northern Central America driven by regionally-organized deep atmospheric convection and airmass rainout

Electronic-resonance enhanced molecule for perovskite solar cells

A crossover strategy integrated dung beetle optimization for global optimization problems and feature selection problems

Abstract Dung Beetle Optimization (DBO) is a recent swarm intelligence optimizer inspired by the rolling, dancing, foraging, breeding, and stealing behaviors of dung beetles. Although DBO has shown promising search capability, it may suffer from diversity loss, ineffective inferior-solution utilization, and boundary-induced stagnation when solving complex optimization problems. To address these issues, this paper proposes a Crossover Strategy Integrated Dung Beetle Optimization algorithm, named CSIDBO. The proposed method incorporates three mechanisms: a horizontal crossover strategy for dimension-level information exchange, a precise elimination mechanism for replacing low-quality individuals, and a global-best-guided boundary handling strategy for correcting infeasible solutions. Moreover, a transfer-function-based binary version of CSIDBO is developed for wrapper feature selection. The performance of CSIDBO is evaluated on CEC2017, CEC2020, and CEC2022 benchmark functions and sixteen feature selection datasets. Comparative experiments, ablation studies, convergence curves, exploration–exploitation analysis, runtime comparison, and statistical tests are conducted to provide a comprehensive evaluation. Experimental results show that CSIDBO achieves competitive optimization accuracy and feature selection performance compared with classical algorithms and recent DBO variants. Nevertheless, the results also indicate that CSIDBO is not universally optimal on all functions, and its additional operators introduce extra function evaluations and runtime overhead. These limitations are discussed to provide a balanced assessment of the proposed method.

Efficient and precise programmable DNA knock-in without double-strand breaks

Acoustic feature selection and clinical information integration for machine learning based dialysis shunt function prediction

Background genetics and new mutations interact to direct cancer evolution

Effects of accelerated DLPFC-rTMS on cognition, EEG activity, and blood biomarkers in Alzheimer’s disease

Exceptional brain and ecological diversity in the earliest snakes

CRISPR/Cas9-mediated UBC knockout impairs CD44 expression and reduces the metastatic potential of 23132/87 gastric cancer cells

RNA synthesis and substrate analog inhibition in the CCHFV polymerase

Modeling climate change impacts on coastal land use and vegetation dynamics

Here’s what’s next for the Artemis II astronauts

Novel psychological perception distance measures in a multigranular linguistic term set environment

Abstract Distance measures are widely used in multi-criteria decision-making problems, but classical measures generally rely on transformed objective evaluation values and may not fully reflect decision-makers’ psychological reference points or heterogeneous linguistic expressions. To address this issue, this study proposes psychological perception distance measures in a multigranular linguistic term set environment by combining classical Euclidean and Hamming distance structures with a prospect-theory value function. The proposed measures transform evaluations relative to individual psychological reference points and then calculate perceived distances between decision-makers or alternatives. Numerical examples show that the proposed measures can produce rankings that differ from those obtained using classical Euclidean and Hamming distances when psychological reference points and multigranular linguistic expressions are considered. An improved TOPSIS procedure is further developed and applied to a hospital service-quality evaluation problem. The results illustrate that incorporating psychological expectations into distance measurement can affect alternative ranking and provide an additional perspective for decision-making under linguistic uncertainty.

How do quantum computers work?

Temporal deep learning framework for multi condition fault classification of three-phase induction motors

Abstract Induction Motors (IMs) are a very important group of electrical appliances used in industry, due to their strength, versatility, high reliability, and cost-effectiveness. However, due to various faults, there can be many issues, such as production downtime, energy losses, and increased maintenance costs. This paper employs Deep learning (DL) frameworks, such as Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for the detection and categorization of IM. The novelty of the proposed work lies in the unified multi-sensor, multi-class, and multi-model DL framework with comprehensive cross-architecture evaluation for robust IM fault diagnosis under eight different operating scenarios, including startup transients and phase-removal faults, load-based faulty and healthy conditions. The results show that the DL is effective in electrical fault identification, using the classification accuracies of RNN—85.14%, LSTM −90.19%, BiLSTM—92.24%, and GRU—94.19%. The proposed approach can be used to differentiate between healthy and faulty operating conditions, which aim to highlight the effectiveness of DL for intelligent condition monitoring in induction motor drive-based machines.

What counts as a moon? Huge ‘exosatellite’ sparks debate

Integrated serum lipidomics and selected immune profiling identify immunometabolic alterations associated with T2DM-TB comorbidity

Abstract Type 2 diabetes mellitus (T2DM) increases the risk of active tuberculosis (TB) and is associated with unfavorable clinical outcomes. Previous studies have shown that TB-DM comorbidity is accompanied by dyslipidemia and inflammatory alterations, but how selected cellular immune indicators relate to serum lipid species-level remodeling in newly diagnosed T2DM-TB remains incompletely characterized. This study aimed to explore selected immune-marker alterations and serum lipidomic remodeling in patients with T2DM-TB comorbidity. In this cross-sectional study, serum samples and peripheral blood immune data were collected from healthy controls (HC), patients with TB alone, patients with T2DM alone, and patients with T2DM-TB comorbidity. Peripheral T-cell subset percentages and selected cytokines were measured, and serum lipidomics was performed using liquid chromatography–tandem mass spectrometry. Immune indicators measured across all four groups were compared using analysis of covariance with adjustment for body mass index. IFN-γ and IL-2 were measured only in participants with active tuberculosis and were compared between the TB and T2DM-TB groups. IL-6 and TNF-α were not included in the original cytokine panel; therefore, the immune analysis should be interpreted as selected immune-marker profiling rather than comprehensive inflammatory profiling. Differential lipids were identified using predefined statistical and effect-size thresholds, and exploratory lipid–immune association analyses were performed. Patients with T2DM-TB showed alterations in selected immune indicators, characterized mainly by lower CD4 + T-cell percentages, a lower CD4 + /CD8 + ratio, and higher IL-10 levels compared with the other groups. In the TB-related subgroup, IFN-γ and IL-2 levels were lower in T2DM-TB than in TB alone. Serum lipidomics identified broad lipid remodeling in T2DM-TB, involving glycerophospholipids, glycerolipids, and sphingolipids. Compared with T2DM alone, the comorbid group showed prominent membrane phospholipid- and sphingolipid-related changes, whereas comparison with TB alone highlighted glycerolipid, glycerophospholipid, and lipid storage-related alterations. Exploratory correlation analyses identified several nominal lipid–immune associations, mainly along a CD4-negative axis and an IL-10-positive axis; however, none remained significant after Benjamini–Hochberg false discovery rate correction. T2DM-TB comorbidity was associated with alterations in selected immune indicators and broad serum lipid species-level remodeling relative to TB alone, T2DM alone, and healthy controls. These findings suggest a hypothesis-generating immunometabolic profile, rather than a validated diagnostic or mechanistic signature, involving T-cell subset changes, a regulatory cytokine shift, and remodeling of glycerophospholipid, glycerolipid, and sphingolipid pathways. However, the immune panel was limited, IL-6 and TNF-α were not measured, and lipid–immune associations did not survive false discovery rate correction. Larger independent cohorts with broader inflammatory profiling and mechanistic validation are needed.

Subnuclear genome compartmentalization controls bivalent chromatin activity

Abstract The nuclear genome is spatially organized into a three-dimensional architecture by physical association of large chromosomal domains with subnuclear compartments including the nuclear lamina at the radial periphery and nuclear speckles within the nucleoplasm 1–5 . However, how higher-order spatial genome architecture regulates human development has been overlooked, and the interplay between chromatin state and subnuclear genome compartmentalization is poorly understood. Here we generate high-resolution maps of genomic interactions with the lamina and speckles in cells of the neurogenic lineage isolated from mid-gestational human cortex, identifying an intimate association between subnuclear genome compartmentalization, chromatin state and transcription. During cortical neurogenesis, subnuclear genome compartmentalization is extensively remodelled, relocating hundreds of neuronal genes from the lamina to speckles, including key neurodevelopmental genes bivalent for trimethylation of histone H3 at Lys27 (H3K27me3) and Lys4 (H3K4me3). At the lamina, bivalent genes have exceptionally low expression, and relocation to speckles enhances resolution of bivalent chromatin to H3K4me3 monovalency and increases transcription more than eightfold. We further demonstrate that proximity to the nuclear periphery—not the presence of H3K27me3—maintains the lowly expressed, poised state of bivalent genes embedded in the lamina. We find that the repressive environment of the lamina is associated with spatial segregation of the transcriptional elongation machinery from the nuclear periphery. Our results establish a paradigm in which knowing the spatial location of a gene is necessary for understanding its epigenomic regulation.

Experimental evaluation, machine learning prediction, and life cycle assessment of sustainable self compacting concrete incorporating limestone powder and calcined clay