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Association between β-blocker use and outcomes in patients with heart failure and chronic obstructive pulmonary disease: a retrospective cohort study
Adaptive reinforcement learning for lithography optimization: a scalable AI-driven solution for next-generation semiconductor manufacturing
Abstract Semiconductor lithography, a pivotal process in integrated circuit (IC) fabrication, accounts for approximately 30% of production costs and faces significant challenges as feature sizes shrink to sub-nanometer scales. Optical diffraction and process-induced distortions complicate precise patterning, necessitating advanced techniques beyond traditional Optical Proximity Correction (OPC). Inverse Lithography Technology (ILT) offers a mathematically robust approach to enhance pattern fidelity, yet its high computational complexity limits scalability. We propose Adaptive Reinforcement Learning for Lithography Optimization (ARLO), a U-Net-based framework integrating self-attention mechanisms and reinforcement learning (RL) to iteratively optimize photomasks using real-time lithographic simulations. Evaluated on the LithoBench benchmark, ARLO achieves a 37.8% reduction in $$L_2$$ Loss and a 74.0% reduction in Process Variation Band (PVB) compared to GAN-OPC, alongside 14.7% and 9.1% $$L_2$$ Loss reductions and 51.3% and 37.1% PVB reductions versus Deep LithoNet (DLN) and RL-ILT, respectively. Despite a higher shot count (181.4% increase vs. GAN-OPC, 59.0% vs. DLN-1, 29.4% vs. RL-ILT), ARLO maintains a competitive runtime of 0.035 seconds per patch. These results position ARLO as a scalable, efficient solution for next-generation semiconductor manufacturing.
Modeling and optimization of performance and emissions in a gasoline-isopropanol SI engine: multi-model prediction and a PID-based search algorithm
Association of ALDH2 rs671 Polymorphism with chronic kidney disease incidence in a population-based Korean cohort
Abstract The Aldehyde dehydrogenase 2 ( ALDH2 ) rs671 polymorphism, a common variant that impairs aldehyde detoxification, has been linked to cardiovascular disease, but its role in chronic kidney disease (CKD) remains unclear. This study examined the association between the ALDH2 rs671 polymorphism and incident CKD in a population-based cohort, and whether alcohol consumption modifies this relationship. We analyzed 5,369 Korean adults aged 40–69 years without CKD at baseline from the community-based Korean Genome and Epidemiology Stud y , followed biennially for up to 18 years. The main exposures were ALDH2 genotype (GG vs. GA/AA) and categorized alcohol consumption (none, low, moderate, high). Incident CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² or new-onset proteinuria (≥ 1 + on dipstick). Cox proportional hazards models estimated adjusted hazard ratios (HRs). During a mean 11.7-year follow-up, 1,396 participants (26.0%) developed CKD. CKD risk did not differ significantly between genotypes, and alcohol intake was not associated with CKD incidence. These associations were consistent across genotype or sex. Overall, ALDH2 rs671 and alcohol intake showed limited relevance to CKD onset, suggesting that ALDH2 -related biological effects may be more pertinent to disease progression rather than initiation in the general population.
Highly sensitive electrochemical detection and quantification of opium derived morphine sulfate using cysteamine loaded MWCNTs@V2O5 telluride composite
The antibacterial effect of Tanacetum argyrophyllum essential oil on kanamycin-resistant Escherichia coli by disruption of energy metabolism and proton fluxes
Guest Edited Collection: “Ecohydraulics” in river and coastal restoration
Synergistic effects of HDAC inhibitor tucidinostat and ENT inhibitor dipyridamole in T-cell malignancies
Abstract Achieving both selectivity and cytotoxicity against cancer cells remains a major challenge in cancer therapy. Histone deacetylase (HDAC) inhibitors, such as tucidinostat, suppress tumor growth by epigenetically regulating tumor suppressor gene expression and are approved for the treatment of hematological malignancies. Despite their promising efficacy, frequent and severe adverse effects, particularly hematological toxicities, often limit long-term treatment, underscoring the urgent need for safer therapeutic strategies. In this study, we show ex vivo that combining tucidinostat with dipyridamole, an approved antiplatelet agent and equilibrative nucleoside transporter (ENT) inhibitor, reduces the effective dose of tucidinostat while maintaining its antitumor activity. In ex vivo models, this combination exerted specific and potent effects against T-cell lymphomas, including adult T-cell leukemia/lymphoma (ATL), inducing apoptosis beyond that achieved with either agent alone. Mechanistically, ENT inhibition appeared to enhance extracellular adenosine signaling, and activation of adenosine receptors, particularly A2b, contributed, at least in part, to the observed antitumor synergy. Moreover, tucidinostat-mediated HDAC inhibition was associated with upregulation of genes involved in adenosine signaling, including adenosine receptors as well as CD39 and CD73, suggesting a coordinated molecular mechanism underlying the enhanced efficacy. Collectively, these findings suggest that the combination of tucidinostat and dipyridamole may represent a promising therapeutic approach targeting both epigenetic and metabolic pathways to enhance cancer cell death in hematological malignancies.
Transformer-based prediction of radiotherapy couch shift risk in prostate cancer based on rectal volume
Abstract Accurate tumor targeting is vital in prostate cancer (PCa) radiotherapy for precise dose delivery and minimizing healthy tissue exposure. Currently, no artificial intelligence (AI) tool exists to predict setup degradation risk considering rectal volume (RV) changes pre-treatment. Our study aimed to assess this risk and identify a critical RV cutoff. We retrospectively analyzed 498 cone-beam computed tomography (CBCT) scans from 38 PCa patients. Rectal organs at risk were contoured, and 3D couch shifts were calculated. A novel unsupervised 2-stage transformer-based encoder processed longitudinal RV and displacement data. K-means clustering grouped patients, visualized with T-distributed Stochastic Neighbor Embedding (t-SNE). Statistical analysis, including ROC AUC, identified an optimal planning CT RV cutoff. Three distinct patient clusters were observed in CBCT scans. A significant correlation ( p < 0.001) was found between initial RVs and these clusters. The ROC AUC was 0.93, establishing an optimal cutoff of 81.62 cm³ for planning CT RV, effectively distinguishing patients prone to significant positional variability. Our study developed an AI-driven model predicting patient couch shifts in PCa radiotherapy, identifying a crucial RV threshold. These findings may advance positioning accuracy, enabling proactive adjustments and optimizing therapy.
A comprehensive evaluation on the bioactivity, antibacterial efficacy and cell viability of PVA-PVP-chitosan electrospun scaffolds reinforced with calcium/zinc silicate
Generic logic block based on bias-gated 2D MoS2 transistors
Synthetic aptamer mechanoreceptors enable cell-specific force sensing and temporal control via DNA circuits
Abstract Cells interpret mechanical cues from their microenvironment with spatiotemporal precision to guide adaptive behaviors. However, engineering synthetic mechanosensing systems with both cell-specificity and programmability remains challenging, especially when targeting ubiquitous classical mechanoreceptors. Here, we introduce an all-DNA mechanosensing platform based on aptamers that transmit force through noncanonical surface receptors. Aptamer–receptor recognition acts as a molecular gate for force transduction, enabling the design of mechanoprobes with cell-type selectivity. These probes interpret diverse mechanical inputs via distinct mechanisms, including actomyosin-driven contractility and membrane ruffling during macropinocytosis. By integrating aptamer mechanoprobes with upstream DNA reaction networks, we achieve reversible and temporally programmable mechanoresponses. This modular, all-nucleic-acid system offers a general framework for constructing tunable mechanotransduction circuits. It expands the design space for synthetic mechanobiology and provides opportunities for autonomous, multi-layered mechanical–biochemical regulation in tissue engineering, morphogenesis, and dynamic cell programming.
Both genome instability and replicative senescence stem from the shortest telomere in telomerase-negative cells
Abstract In the absence of telomerase, telomere shortening triggers replicative senescence, a tumor suppressor mechanism that is also associated with oncogenic genomic instability. Yet, the precise mechanism that connects these seemingly opposing forces remains poorly understood. To directly study the complex interplay between senescence, telomere dynamics, and genomic instability, we develop a system in Saccharomyces cerevisiae to generate and track telomeres of precise length in the absence of telomerase. Using single-telomere and single-cell analyses combined with mathematical modeling, we identify a threshold length at which telomeres switch into dysfunction. A single shortest telomere below the threshold length is necessary and sufficient to trigger the onset of replicative senescence in a majority of cells. At population level, fluctuation assays establish that rare genomic instability arises predominantly in cis to the shortest telomere as Pol32-dependent non-reciprocal translocations that result in re-elongation of the shortest telomere and likely transient escape from senescence. The switch of the shortest telomere into dysfunction and subsequent processing in telomerase-negative cells thus serves as the mechanistic link between replicative senescence onset, genomic instability and the initiation of post-senescence survival.
Giant ZT enhancement in rhombohedral GeTe-based thermoelectric materials
Extensive enhancer crosstalk controls PPARG2 activation during adipogenesis
Learning data-efficient coarse-grained molecular dynamics from forces and noise
Abstract Molecular dynamics (MD) simulations are essential for elucidating biomolecular function, yet the computational cost of all-atom models often limits their reach. Machine-learned coarse-grained (MLCG) models offer a solution by simplifying the representation while maintaining near-atomistic accuracy. However, the training of MLCG models currently requires vast amounts of force-labeled sample conformations from reference atomistic MD. Here, we overcome this limitation by unifying the training of MLCG models with the principles of generative diffusion models. We demonstrate that accurate high-dimensional distributions of molecular ensembles can be recovered by integrating traditional force-matching with denoising objectives. This framework enables the construction of physically consistent and stable force fields while reducing atomistic data requirements by up to two orders of magnitude. Validated across diverse protein folds and scales, our work establishes a bridge between molecular dynamics simulation and modern generative learning, substantially lowering the computational cost of constructing accurate MLCG models and broadening their applicability to large biomolecular systems.
A topographical organization in the primary olfactory cortex
tRNA-derived RNA processing in sperm transmits non-genetically inherited phenotypes to offspring in C. elegans
Abstract The environment encountered by an organism can modulate epigenetic information in gametes to transmit non-genetically inherited phenotypes to offspring. In mouse models, the diet of males regulates specific tRNA-derived RNAs (tDRs) in sperm. After fertilization, tDRs regulate embryonic gene expression and generate metabolic phenotypes in adult offspring through uncharacterized changes during development. Here we demonstrate that in C. elegans , tDRs also accumulate in sperm and similarly transmit epigenetically inherited phenotypes to offspring. We identify the RNaseT2 enzyme, rnst-2 , as a regulator of C. elegans tDR accumulation. RNST-2 processes or degrades tRNA-halves, to short <30 nt fragments. This rnst-2 dependent regulation of tDR length distribution modulates specific tDRs in sperm which, after fertilization, regulate early embryonic and developmental gene expression, leading to adaptive phenotypes in progeny. Our findings establish tDRs as a conserved carrier of intergenerational epigenetic information and position the worm as a model for dissecting paternal non-genetic inheritance mechanistically.
Modifying muscle metabolic dysregulation in inclusion body myositis with pioglitazone: a single-arm trial
Abstract This single-arm, open-label phase 1 trial evaluated the PPARγ agonist pioglitazone in patients with inclusion body myositis (IBM). After a 16-week observation (lead-in) period, participants received pioglitazone 45 mg daily for 32 weeks. The primary outcome was the change in PPARGC1A expression and related metabolic pathways in muscle after 16 weeks of treatment compared with the lead-in period. Of the 16 enrolled participants, 13 initiated pioglitazone and completed at least one on-treatment assessment; the trial was terminated early due to the COVID-19 pandemic. At baseline, muscle metabolomics revealed broad metabolic abnormalities compared with controls. Pioglitazone reversed elements of this signature, increasing PPARGC1A expression ( p = 0.099) and modulating downstream pathways in muscle, including enhanced oxidative phosphorylation. Clinical outcomes were unchanged overall, but a subset with favorable metabolic responses showed slower decline in the IBM-Functional Rating Score (IBM-FRS) and Modified Timed Up and Go (m-TUG). Reported adverse effects included myalgia and heart failure exacerbation. As a phase 1 trial with a limited cohort, these findings provide preliminary evidence that pioglitazone modulates muscle metabolism and warrants further investigation in IBM. This study was supported by the Ira T. Discovery Fund and the Peter and Carmen Lucia Buck Foundation Myositis Discovery Fund. Clinical Trials Registration: NCT03440034.