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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
Geomagnetic storms caused by solar wind might be more extreme than previously thought
Mediation analysis of fundamental movement skills, physical activity, fitness, and BMI in primary school children
Abstract This study aims to investigate the relationship between fundamental movement skills (FMS) and moderate-to-vigorous physical activity (MVPA) in children, with physical fitness (PF) and body mass index (BMI) as potential mediating factors. The sample included 1014 Portuguese children (540 girls) aged 6–10 years from the REACT project. MVPA was measured using wrist-worn accelerometers over 7 days. BMI z-scores were calculated. PF assessment included: standing long jump, handgrip strength, shuttle-run, 50-yard dash, and pacer; all test scores were standardized and summed to generate an overall PF score. The Meu Educativo platform was used to assess five object control FMS. FMS were factor analyzed and derived factor scores were standardized to generate an overall FMS score. A serial mediation model was tested. Showed a significant total effect of FMS on MVPA (b = 10.574, SE = 1.059, p < 0.001). PF was the principal specific mediator of the FMS-MVPA relationship (b = 2.748, SE = 0.559, p < 0.001). BMI did not mediate this association independently (b=-0.181, SE = 0.139, p > 0.05). However, the serial pathway through PF followed by BMI (FMS -> PF -> BMI -> MVPA) was statistically significant, though smaller in magnitude (b = 0.395, SE = 0.134, p < 0.05). Together, PF and BMI mediated 28.0% of the FMS-MVPA relationship. FMS was associated with MVPA directly and indirectly, primarily through PF, with BMI playing a role only within the sequential PF-to-BMI pathway.
A radical approach to making C-glycoside molecules
Correction: Transcription factor CEBPA suppresses sepsis induced lung injury by activating SIRT4 transcription
Insulator-free topological photonic multi-lane highways
Numerical investigation of pore-scale heat conduction in rock matrix blocks subject to time-dependent fracture thermal conditions
A vector-based strategy for olfactory navigation in Drosophila
Abstract For many species, odours serve as key navigational cues 1,2 . Although tracking an odour plume has been modelled as a reflexive process 3–5 , it remains unclear whether animals can use memories of their past odour encounters to infer the spatial structure of their chemical environment or their location within it. Here we developed a virtual-reality olfactory paradigm that allows head-fixed Drosophila to explore structured chemical landscapes, offering insight into how memory mechanisms shape their navigational strategies. We found that flies track an appetitive odour corridor by following its boundary, alternating between rapid counter-turns to exit the plume and directed returns to its edge. Using a combination of behavioural modelling, functional calcium imaging and neural perturbations, we show that this ‘edge tracking’ strategy relies on vector-based computations within the Drosophila central complex, in which flies store and dynamically update memories of the direction to return to the plume’s boundary. Consistent with this, we find that FC2 neurons within the fan-shaped body, which encode a fly’s navigational goal 6 , signal the direction back to the odour boundary when flies are outside the plume. Plume tracking thus engages components of a conserved navigational toolkit, in which flies can use directional memories to navigate through complex and shifting chemical landscapes.