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On the origin of acoustic emission in the stress-induced martensite regime of shape memory alloys
Abstract The superelastic (SE) deformation behavior of Heusler-type Co-Ni-Ga shape memory alloy (SMA) single crystals is investigated employing complementary in situ techniques. Findings obtained by optical microscopy, neutron diffraction and acoustic emission (AE) provide deep insights into the microstructural events taking place during compressive loading. In addition to the martensitic forward and reverse transformation, unloading of the stress-induced martensite is found to be accompanied by the emission of acoustic signals. In the unloading martensite regime, neutron diffraction gives clear evidence for a change in the volume fractions of martensite domain variants upon unloading, i.e. reorientation of martensite by the growth of one martensite domain variant at the expense of the other one. Hence, it is shown that the origin of the AE signals occurring in that unloading martensite regime can be ascribed to twin boundary motion.
Novel multiple-image encryption with ultrafast light springs
Elevating thermal-deicing efficiency via regulating interfacial-ice induced by microstructure under flow condition
Simulated microgravity induces a NOX-sensitive oxidative response that is attenuated by resveratrol in human endothelial cells
End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithms
Abstract Elucidating molecular structures from spectroscopic data remains one of chemistry’s most fundamental challenges, typically requiring extensive expert knowledge and manual interpretation of multiple analytical techniques. This is because the structure elucidation problem often has degenerate solutions for a limited set of experimental data. Existing computational approaches are limited to single spectroscopic modalities, require extensive manual preprocessing, and lack the confidence estimates and context necessary for practical application. Here we present , a framework that combines contrastive learning with evolutionary algorithms to automate structure elucidation directly from raw, multimodal spectroscopic data. By aligning embeddings across NMR, infrared, and mass spectrometry, mimics how experts use multiple spectroscopic lenses while providing calibrated confidence scores and relevant database context. On challenging molecular identification tasks, matches expert chemist performance in head-to-head comparisons in a pilot study. The system successfully identifies incorrect structure assignments in published literature and adapts to new chemical domains without retraining by updating its reference database. Our approach demonstrates how synergistic combination of machine learning paradigms can solve analytical bottlenecks that have constrained chemical discovery.
Experimental evolution under colistin selection increases colistin minimum inhibitory concentration (MIC) without detectable collateral MIC shifts in Escherichia coli ATCC 25922
Abstract Colistin is a last-resort antimicrobial in human and veterinary medicine, yet it remains unclear whether prolonged exposure can drive collateral resistance beyond polymyxins. Here, we used a spatial experimental-evolution platform, the Microbial Evolution and Growth Arena (MEGA)-plate, to examine adaptation of Escherichia coli ATCC 25922 to stepwise colistin selection in three independent runs. Endpoint isolates from all three runs ( n = 3 isolates per exposure zone) were phenotyped, whereas one randomly selected representative isolate per exposure zone (0× , 1× , 10× , 100× , and 1000×) was sequenced because MIC profiles were identical across runs. Colistin MIC increased from 0.5 to 64 μg/mL across the selection gradient, while MICs for the other tested agents remained unchanged. Across the sequenced representatives, no plasmid-mediated colistin resistance determinants were detected and the overall set of CARD strict hits remained unchanged. Comparative genomics identified only limited coding changes with potential relevance to envelope-associated adaptation, notably an ftsI missense variant in the 100× and 1000× representatives and an ompC synonymous variant of uncertain functional significance in the 1× representative. Relative to the untreated representative, exposed isolates differed by only a small number of baseline-unique high-confidence annotated coding variants. These data support a compound-restricted phenotypic response in this single-strain model under the conditions tested. However, because genomic sampling was limited to one endpoint representative per exposure zone and no transcriptomic or functional assays were performed, broader regulatory adaptation or efflux-related responses cannot be excluded.
The eutherian-specific histone H3.4 promotes germ cell development and reproductive fitness
Abstract Many genes encoding chromatin proteins are subject to evolutionary selection driving reproductive fitness. In mice and men, the H3f4 / H3-4 gene encoding the histone H3.4 variant (formerly known as H3t) is essential to spermatogenesis. Here we define the evolutionary origin and molecular-physiological roles of sequence variation in H3f4 for male germ cell development in mice. Our phylogenetic analyses indicate that eutherian H3f4 orthologs originate from an ancestral H3.2 gene existing prior to the divergence of eutherian and marsupial mammals over 100 million years ago. Positioned in small histone gene clusters, eutherian H3f4 orthologs show increased non-synonymous and synonymous substitution rates compared to orthologous marsupial H3.2 loci located in prototypal large histone clusters. To determine the impact of sequence divergence on reproductive fitness, we revert non-synonymously substituted residues in H3.4 to those present in canonical H3.1 ( H3f4 V24A , H3f4 H42R , H3f4 S98A ). Expression of such a triply reverted H3f4 H3.1 allele on a H3f4 -deficiency background causes an >40% reduction in testis weight associated with impaired meiotic progression, death of pachytene spermatocytes, impaired differentiation of spermatids and aberrant expression of thousands of genes during spermatid elongation. Hemizygous expression of individual residue substitution alleles reveals residues V24 and H42 of H3.4 to promote spermatogenesis, while residue S98 is neutral. Together, our study shows that H3f4 has been subject to positive evolutionary selection, promoting male reproductive fitness.
Automated fruit maturity grading using deep learning with feature fusion
Abstract Fruits are valued for their nutritional benefits, providing essential carbohydrates, vitamins, and dietary fibre. However, assessing fruit ripeness remains challenging, as it is governed by complex physiological processes and environmental factors that are not always reflected in external appearance. This difficulty is exemplified by Nam Dok Mai Si Tong (NDMST) mangoes, which retain a largely uniform yellow skin during ripening, making visual maturity grading unreliable. To address this limitation, we propose AFMG-DLFF (Automated Fruit Maturity Grading using Deep Learning with Feature Fusion), a multimodal deep learning framework that integrates external RGB image features with internal biochemical attributes. Visual features are extracted using DenseNet201, Inception–ResNetV2, and EfficientNetV2, while intrinsic traits such as total soluble solids, titratable acidity, and BrimA are encoded via a dedicated neural network. These complementary feature spaces are fused and optimised using Glowworm Swarm Optimization (GSO) for hyperparameter tuning. The model is trained with an 80:20 train–test split and early-stopping-based validation, achieving a classification accuracy of 97.86% for NDMST mango maturity stages. The results demonstrate that AFMG-DLFF achieves strong performance relative to the evaluated deep-learning baselines and remains competitive with selected literature-reported fruit ripeness classification methods, while relying only on accessible RGB imaging and standard biochemical measurements. This highlights its potential as a practical, non-destructive, and cost-effective solution for automated fruit maturity grading in real-world supply chains.
Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow
Efficiency of indirect selection in sorghum breeding for target production environments in moisture deficit areas of Ethiopia
A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions
Lindera obtusiloba extract attenuates cytokine-mediated epidermal inflammation in keratinocytes and an MC903-induced AD-like mouse model
Development of LRRC15-binding disulfide-constrained peptides for PET imaging of cancer-associated fibroblasts
Field performance of inclined H-beam strength composite piles as internal bracing in a deep soft soil excavation
Real-time electrical monitoring of enzymatic catalytic dynamics at the single-molecule level
Abstract Monitoring enzyme structural dynamics is essential for elucidating catalytic mechanisms, yet transient conformational fluctuations on microsecond-to-millisecond timescales remain challenging to resolve with conventional techniques. Here, we investigate the catalytic dynamics of cytochrome P450 1A1 (CYP1A1) during benzo[a]pyrene (BaP) metabolism by measuring single-molecule protein conductance. We show that catalysis-induced α-helix structural rearrangements, together with redox transitions of the heme center, modulate charge-transport efficiency. A negative correlation between BaP concentration and conductance enables construction of a kinetic model, yielding an apparent Michaelis constant of 24.2-43.2 μM. Real-time conductance measurements resolve four distinct conductance states associated with catalytic intermediates, which are further assigned using metabolic intermediates as substrates. These results provide insight into competing detoxification and activation pathways of BaP metabolism. This work establishes protein conductance as a generalizable platform for probing transient enzymatic dynamics and kinetics at the single-molecule level.