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Ultrafast control of currents on the nanoscale is essential for future innovations in nanoelectronics. Recently it was experimentally demonstrated that strong non-resonant few-cycle 4 fs laser pulses can be used to induce phase-controllable currents along gold–silica–gold nanojunctions in the absence of a bias voltage. However, since the effect depends on a highly non-equilibrium state of matter, its microscopic origin is unclear and the subject of recent controversy. Here we present atomistically detailed (time-dependent non-equilibrium Green’s function) electronic transport simulations that recover the main experimental observations and offer a simple intuitive picture of the effect. The photoinduced currents are seen to arise due to a difference in effective silica-metal coupling for negative and positive field amplitudes induced by lasers with low temporal symmetry. These insights can be employed to interpret related experiments, and advance our ability to control electrons in matter using lasers. Strong non-resonant few-cycle laser pulses can be used to induce ultrafast phase-controllable currents along nanojunctions but the microscopic origin is unclear. Here, the authors present time-dependent quantum transport simulations that recover the experimental observations and offer an intuitive picture of the effect.

In complex acoustic environments, both humans and animals are frequently exposed to sounds from multiple sources. The detection threshold for a target sound (or probe) can be elevated by interference sounds (masker) originating from various locations. This masking effect is reduced when the probe and masker are spatially separated compared to when they are colocalized, thereby improving the perception of the probe. This phenomenon is known as spatial release from masking. Currently, the neuronal and synaptic mechanisms underlying spatial release from masking in the auditory cortex are not fully understood. Here we employed single-unit recording and in vivo whole-cell patch-clamp recording techniques to examine how maskers from different spatial locations influence the detection thresholds of rat primary auditory cortex (A1) neurons in response to probe stimuli. At the cortical neuronal level, the masked detection thresholds of most A1 neurons in response to probes were significantly decreased when maskers were displaced from azimuths colocalized with the probe to other separated azimuths ipsilateral to the recording site. Similarly, at the cortical synaptic level, the masked detection thresholds of A1 neurons, as determined from the amplitude of evoked excitatory postsynaptic currents in response to probes presented at azimuth locations within the contralateral hemifield, were also decreased when maskers were shifted from azimuth locations in the contralteral hemifield to those in the ipsilateral hemifield. This study provides neuronal and synaptic evidences for spatial release from masking in the auditory cortex, advancing our understanding of the mechanisms involved in auditory signal processing in noisy environments.

Standard protocols for meta-analysis of association studies are inadequate for microbiome data due to their complex compositional structure, leading to inaccurate and unstable microbial signature selection. To address this issue, we introduce Melody, a framework that generates, harmonizes, and combines study-specific summary association statistics to powerfully and robustly identify microbial signatures in meta-analysis. Comprehensive and realistic simulations demonstrate that Melody substantially outperforms existing approaches in prioritizing true signatures. In the meta-analyses of five studies on colorectal cancer and eight studies on the gut metabolome, we showcase the superior stability, reliability, and predictive performance of Melody-identified signatures.

Microbiome data from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator challenges the validity of standard mediation analyses. We propose a phylogeny-based mediation analysis method called PhyloMed to address this challenge. Unlike existing methods that directly identify individual mediating taxa, PhyloMed discovers mediation signals by analyzing subcompositions defined on the phylogenic tree. PhyloMed produces well-calibrated mediation test p -values and yields substantially higher discovery power than existing methods.

Long-range interactions are essential determinants of chemical system behavior across diverse environments. We present a foundation framework that integrates explicit polarizable long-range physics with an equivariant graph neural network potential. It employs a physically motivated polarizable charge equilibration scheme that directly optimizes electrostatic interaction energies rather than partial charges. The foundation model, trained across the periodic table up to Pu, demonstrates strong performance across key materials modeling challenges. It effectively captures long-range interactions that are challenging for traditional message-passing mechanisms and accurately reproduces polarization effects under external electric fields. We have applied the model to mechanical properties, ionic diffusivity in solid-state electrolytes, ferroelectric phase transitions, and reactive dynamics at electrode-electrolyte interfaces, highlighting the model’s capacity to balance accuracy and computational efficiency. Furthermore, we show that as a foundation model, it can be efficiently finetuned to achieve high-level accuracy for specific challenging systems. Long-range interactions are challenging for machine learning interatomic potentials (MLIPs). Here, authors show that, by just learning from energies and forces, MLIPs can accurately capture electrostatics and predict atomic charges.

Background: Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. Objective: The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. Methods: US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. Results: We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). Conclusions: The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.

The ubiquitous daily rhythms in mammalian physiology are guided by progression of the circadian clock. In mice, systemic disruption of the clock can promote tumor growth. In vitro , multiple oncogenes can disrupt the clock. However, due to the difficulties of studying circadian rhythms in solid tissues in humans, whether the clock is disrupted within human tumors has remained unknown. We sought to determine the state of the circadian clock in human cancer using publicly available transcriptome data. We developed a method, called the clock correlation distance (CCD), to infer circadian clock progression in a group of samples based on the co-expression of 12 clock genes. Our method can be applied to modestly sized datasets in which samples are not labeled with time of day and coverage of the circadian cycle is incomplete. We used the method to define a signature of clock gene co-expression in healthy mouse organs, then validated the signature in healthy human tissues. By then comparing human tumor and non-tumor samples from twenty datasets of a range of cancer types, we discovered that clock gene co-expression in tumors is consistently perturbed. Subsequent analysis of data from clock gene knockouts in mice suggested that perturbed clock gene co-expression in human cancer is not caused solely by the inactivation of clock genes. Furthermore, focusing on lung cancer, we found that human lung tumors showed systematic changes in expression in a large set of genes previously inferred to be rhythmic in healthy lung. Our findings suggest that clock progression is dysregulated in many solid human cancers and that this dysregulation could have broad effects on circadian physiology within tumors. In addition, our approach opens the door to using publicly available data to infer circadian clock progression in a multitude of human phenotypes.

Objective The purpose of the study is to develop a prognosis nomogram for esophageal squamous cell carcinoma (ESCC) patients with radical resection and to identify patients who may benefit from postoperative adjuvant radiotherapy/chemoradiotherapy through survival risk stratification. Methods We retrospectively enrolled patients who underwent esophagectomy in the First Affiliated Hospital of Nanjing Medical University from July 2015 to June 2017. Patients with stage I-III esophageal squamous cell carcinoma who received radical R0 resection with or without postoperative adjuvant radiotherapy/chemoradiotherapy were included. Further, patients were randomly allocated into two groups (training and validation cohorts) with a distribution ratio of 7:3. The prognosis nomogram was constructed based on independent factors determined by univariate and multivariate Cox analyses. The area under the receiver operating characteristic curve (AUC) and calibration curve were adopted to evaluate the discriminative ability and reliability of the nomogram. The accuracy and clinical practicability were respectively assessed by C-index values and decision curve analysis (DCA), and further contrasted the nomogram model and the eighth edition of the American Joint Committee on Cancer (AJCC) TNM staging system. In addition, survival risk stratification was further performed according to the nomogram, and the effect of postoperative adjuvant therapy on each risk group was appraised by the Kaplan-Meier survival analysis. Results A total of 399 patients with esophageal squamous cell carcinoma were recruited in this study, including the training cohort ( n  = 280) and the validation cohort ( n  = 119). The nomogram-related AUC values ​​for 1, 3, and 5-year OS were 0.900, 0.795, and 0.802, respectively, and 0.800, 0.865, 0.829 in the validation cohort, respectively. The slope of the calibration curve for both cohorts was close to 1, indicating good consistency. The C-index value of the nomogram was 0.769, which was higher than that of the AJCC 8th TNM staging system by 0.061 ( p  < 0.001). Based on the prognosis nomogram, patients were stratified into three risk groups (low, medium, and high), and there were obvious differences in prognosis among the groups ( p  < 0.001). Furthermore, postoperative adjuvant therapy has been shown to enhance the 5-year survival rate by over 15% among patients classified as medium- and high-risk. Conclusion The constructed nomogram as developed resulted in accurate and effective prediction performance in survival outcomes for patients with stage I-III esophageal squamous cell carcinoma who underwent radical R0 resection, which is superior to the AJCC 8th TNM staging system. The survival risk stratification had potential clinical application to guide further personalized adjuvant therapy.

The design of high‐entropy single‐atom catalysts (HESAC) with 5.2 times higher entropy compared to single‐atom catalysts (SAC) is proposed, by using four different metals (FeCoNiRu‐HESAC) for oxygen reduction reaction (ORR). Fe active sites with intermetallic distances of 6.1 Å exhibit a low ORR overpotential of 0.44 V, which originates from weakening the adsorption of OH intermediates. Based on density functional theory (DFT) findings, the FeCoNiRu‐HESAC with a nitrogen‐doped sample were synthesized. The atomic structures are confirmed with X‐ray photoelectron spectroscopy (XPS), X‐ray absorption (XAS), and scanning transmission electron microscopy (STEM). The predicted high catalytic activity is experimentally verified, finding that FeCoNiRu‐HESAC has overpotentials of 0.41 and 0.37 V with Tafel slopes of 101 and 210 mVdec−1 at the current density of 1 mA cm−2 and the kinetic current densities of 8.2 and 5.3 mA cm−2, respectively, in acidic and alkaline electrolytes. These results are comparable with Pt/C. The FeCoNiRu‐HESAC is used for Zinc–air battery applications with an open circuit potential of 1.39 V and power density of 0.16 W cm−2. Therefore, a strategy guided by DFT is provided for the rational design of HESAC which can be replaced with high‐cost Pt catalysts toward ORR and beyond. This study introduces high‐entropy single‐atom catalysts (HESAC) incorporating four metals in FeCoNiRu‐HESAC. The catalyst is predicted to have an ORR overpotential of 0.44 V, which is verified experimentally to be 0.41 V (acidic electrolyte) and 0.37 V (alkaline electrolyte). A Zinc–air battery based on this catalyst achieves an open circuit potential of 1.39 V and a power density of 0.16 W cm−2.

Fred Wright, Patrick Sullivan and colleagues present the results of a large expression QTL study of peripheral blood using a classic twin design with follow-up replication in independent samples. Their results enable a more precise estimate of the heritability of gene expression and provide a useful resource for exploring the genetic control of transcription. We assessed gene expression profiles in 2,752 twins, using a classic twin design to quantify expression heritability and quantitative trait loci (eQTLs) in peripheral blood. The most highly heritable genes (∼777) were grouped into distinct expression clusters, enriched in gene-poor regions, associated with specific gene function or ontology classes, and strongly associated with disease designation. The design enabled a comparison of twin-based heritability to estimates based on dizygotic identity-by-descent sharing and distant genetic relatedness. Consideration of sampling variation suggests that previous heritability estimates have been upwardly biased. Genotyping of 2,494 twins enabled powerful identification of eQTLs, which we further examined in a replication set of 1,895 unrelated subjects. A large number of non-redundant local eQTLs (6,756) met replication criteria, whereas a relatively small number of distant eQTLs (165) met quality control and replication standards. Our results provide a new resource toward understanding the genetic control of transcription.