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Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions
Convergent evolution, or convergence, refers to repeated, independent emergences of the same trait in two or more lineages of species during evolution, often indicating functional adaptation to specific environmental factors. Many computational methods have been proposed to investigate the genetic basis for organismal functional convergence, as an important way to decode the complex sequence–function map of proteins. These methods mostly focus on the convergence of amino acid states at the level of individual sites in functionally related proteins. However, even without site-level sequence similarity, protein function similarity may also stem from convergence of high-order protein features, which cannot be captured by the conventional methods. To fill this gap, we first derived numerical embeddings from protein sequences by pretrained protein language models (PLM). In four previously reported cases, we found that functionally convergent proteins have similar embeddings despite no site-level convergence, indicating that PLM embeddings can reflect convergence of high-order protein features. We then designed a pipeline to detect Adaptive Convergence by Embedding of Protein (ACEP). ACEP tests were significant on known and additional candidate genes with putative adaptive convergence like echolocation and crassulacean acid metabolism. Genome-wide application showed that the ACEP framework can effectively enrich such candidates. Relations between convergences of PLM embeddings and specific protein physicochemical features were further examined. In conclusion, PLM embeddings can indicate adaptive convergence of high-order protein features beyond site identities, demonstrating the power of deep learning tools for investigating the complex mapping between molecular sequences and functions.
Polyaniline-functionalized Nb2CTx Mxene composites for effective Cr(VI) adsorption from wastewater
Research on multi-objective emergency resource scheduling optimization in chemical industrial parks
The high concentration of hazardous sources in chemical parks, which is prone to cause chain accidents, puts forward the demand for dynamic cooperative optimization of emergency resource scheduling. Aiming at the deficiencies of existing studies in the adaptability of dynamic multi-hazard scenarios and the quantification of resource allocation fairness, this paper constructs a three-objective mixed-integer planning model that integrates time efficiency, demand coverage and allocation fairness. Fairness is innovatively quantified as an independent optimization objective, and a standard deviation-based dynamic resource allocation balance index is proposed, which combines multi-warehouse collaborative supply and multi-resource coupling constraint mechanism to systematically solve the problem of trade-offs between timeliness, adequacy and fairness in emergency dispatching in chemical accidents. The improved NSGA-II algorithm is used to solve the Pareto front efficiently, and the search efficiency is improved by the elite reservation strategy and the congestion adaptive adjustment mechanism. In the case study, comparative experiments with the weighted method and the MOGWO algorithm demonstrate that NSGA-II performs superiorly in key metrics, exhibiting excellent convergence, diversity, and stability. Based on this, a case study is conducted using a chemical industrial park in China as an example, generating 41 sets of weights covering extreme preferences, two-objective balance, and three-objective balance. Decision-makers screen solutions based on loss tolerance thresholds and select the optimal solution using a composite score of comprehensive weighted losses. The study further reveals that improvements in demand satisfaction rates are often accompanied by significant increases in transportation time, while pursuing optimal fairness may weaken overall demand satisfaction levels. Sensitivity analysis confirms that resource demand is the key driver determining the number of feasible solutions, while fairness, as an independent optimization objective, holds irreplaceable importance in emergency scheduling decisions.
In situ structures of the <i>Legionella</i> Dot/Icm T4SS identify the DotA–IcmX complex as the gatekeeper for effector translocation
The Dot/Icm machine of Legionella pneumophila is among the most versatile type IV secretion systems (T4SSs), capable of translocating more than 330 distinct effector proteins across the bacterial envelope into host cells. Assembly and function of the system require at least 27 Dot and Icm proteins, yet its architecture and activation mechanism remain poorly understood at the molecular level. Here, we deploy in situ single-particle cryoelectron microscopy to determine near-atomic structures of the Dot/Icm machine and its intimate association with three distinct outer membrane porins in intact bacteria. Notably, two essential yet enigmatic components, DotA and IcmX, form a pentameric protochannel in an inactive state at the central axis of the Dot/Icm machine. Upon Dot/Icm activation with host lysate, this protochannel undergoes extensive rearrangements to generate an extended transenvelope conduit, as visualized by cryoelectron tomography (cryo-ET) and subtomogram averaging. Furthermore, a combination of cryo-ET and cryo-FIB milling of macrophages infected with L. pneumophila reveals tethering of the Dot/Icm machine to the host membrane, suggesting direct translocation of effector proteins from the bacterial cytoplasm into the host. Together, our studies identify the DotA–IcmX complex as a gatekeeper for effector translocation and provide a molecular framework for understanding the assembly and activation of the elaborate Dot/Icm T4SS.
Measuring political polarization through visible interactions between religious and non-religious citizens
Effect of short-term exercise with different programs on prevention of sarcopenia in postmenopausal women: A Quasi-Randomized Controlled Trial
Background Physical exercise is an effective measure to prevent sarcopenia. However, the effects of Nordic walking based on high-intensity interval training (HIIT NW) and conventional strength training (ST) on the parameters related to sarcopenia in postmenopausal women remain unclear. Therefore, this study aims to evaluate the effects of 12-week HIIT NW and ST on body composition and physical function performance in postmenopausal women. Method The participants were 71 women aged between 60 and 79 years old without sarcopenia. Participants were randomly assigned to the HIIT NW group (12-week Nordic walking training, 3 × /week), the ST group (12-week strength training, 3 × /week), and the control group. The body composition was determined by using Otupole InBody 720. Test the hand grip strength with a digital hand force gauge. The strength of the extensor and flexor muscles of the knee joint was measured using Biodex System 4 Pro™. This study also employed common methods for measuring functional performance and conducted two measurements of blood creatinine and creatine kinase. Result Compared with the control group, significant improvements were observed in parameters such as Time Up and Go (TUG) and knee joint flexor strength (KFS) in both the HIIT NW group and the ST group. In the ST group alone, significant enhancements were noted in parameters including walking speed (GS) and hand strength on the left side (HS-L). Following the intervention, the HIIT NW group exhibited a marked increase in limb lean mass, which led to a significant rise in the skeletal muscle index (SMI) (p < 0.001). However, the body fat mass (BFM) and body mass index (BMI) decreased significantly in the ST group (p < 0.001 and p = 0.005, respectively). No significant changes were observed in the control group. Conclusion Both HIIT NW and ST interventions can effectively prevent sarcopenia in postmenopausal women. The former focuses on improving lower limb strength, while the latter focuses on improving upper limb strength. In the short term, the HIIT NW intervention model is more beneficial for postmenopausal women with normal weight, while the conventional ST intervention model is more conducive to the overweight population.
A precise metallicity and carbon-to-oxygen ratio for a warm giant exoplanet from its panchromatic JWST emission spectrum
WASP-80 b, a warm sub-Jovian (equilibrium temperature ∼ 820 K, 0.5 Jupiter masses), presents an opportunity to characterize a rare gas giant exoplanet around a low-mass star. In addition, its moderate temperature enables its atmosphere to host a range of carbon and oxygen species (H 2 O, CH 4 , CO, CO 2 , NH 3 ). In this paper, we present a panchromatic emission spectrum of WASP-80 b, the first gas giant around a late K/early M-dwarf star and the coolest planet for which the James Webb Space Telescope has obtained a complete emission spectrum spanning 2.4 to 12 μ m, including NIRCam F322W2 (2.4 to 4 μ m) and F444W (4 to 5 μ m), and MIRI LRS (5 to 12 μ m). We report confident detections of H 2 O, CH 4 , CO, and CO 2 , and a tentative detection of NH 3 . We estimate WASP-80 b’s atmospheric metallicity and carbon-to-oxygen ratio and compare them with estimates for other gas giants. Despite the relative rarity of giant planets around low-mass stars, we find that WASP-80 b’s composition is consistent with other hot gas giants, suggesting that the formation pathway of WASP-80 b may not be dissimilar from hot gas giants around higher-mass stars.
Network analysis of gene expression reveals regulators of cell viscosity and mechanical phenotype
Abstract Cell mechanical properties, such as cell stiffness and viscous behavior, have been proposed as biomarkers of cell disease states. Moreover, the molecular pathways that modify cell mechanics may also be potential novel targets for managing lethal diseases, such as cancer, by specifically changing the mechanical phenotype of cells along with the associated functional phenotype. This study explores the relationship between the viscosity and stiffness of cells and the underlying molecular mechanisms. We used a large linked molecular dataset to explore the correlations between gene expression, cell migration, and cell mechanical properties, which were quantified by two viscous rate constants from a standard linear solid viscoelasticity model and apparent Young’s modulus from a Hertzian contact mechanics model. Using a causal network analysis built on known relationships curated from literature in Qiagen’s Ingenuity Pathway Analysis package, we identified potential molecular control nodes that could modify the expression of multiple genes correlated with cell mechanics. We investigated the up- and down-regulation of expression by two predicted potential small molecule regulators (lacidipine and AG879) and four predicted potential gene regulators (AKT2, ITGB6, mir-183, and CD82) through small molecule inhibition, RNA interference, and introduction of microRNAs. The effects of modulation of these regulators were measured on both cell mechanical properties and gene expression in three ovarian cancer cell types. We identified several regulators that change the viscosity and stiffness of the cell with a corresponding change to the functional migratory ability in a cell-type specific manner.
Improved artificial hummingbird algorithm for electric vehicle charging station fast and slow charging location determination method
Electric vehicle (EV) charging infrastructure is rapidly improving. To address high site selection costs from unbalanced fast-slow charging ratios and multi-party cost allocation issues, we propose a four-objective optimization model based on a three-party cost game (suppliers, users, power grid). The model minimizes: (1) construction/operation costs, (2) user time loss costs, (3) grid power loss costs, and (4) voltage deviation costs under different charging modes. An improved artificial hummingbird algorithm solves the model. Results show the approach improves economic efficiency, provides valuable reference for EV charging station siting, and demonstrates strong algorithm robustness and generalization.
Disentangling metabolic and neurovascular timescales supporting cognitive processes
The balance between neural excitation and inhibition (EIB), governed by glutamatergic and GABAergic neurotransmission, is an essential mechanism supporting cognitive processes. Yet, little is understood about how EIB shifts with engaging cognitive load and its impact on functional connectivity dynamics. Here, we combined time-resolved functional magnetic resonance spectroscopy and magnetic resonance imaging to investigate temporal profiles of the reciprocal modulation between EIB and functional network dynamics during working memory tasks in healthy subjects. We quantified EIB kinetics by measuring excitatory (Glx) and inhibitory (GABA+) levels and assessed their temporal coupling with dynamic functional connectivity states, revealing a dependence that scales with cognitive load. Notably, we found that as cognitive challenges intensify, brain networks transition toward more specialized and enduring connectivity configurations, accompanied by imbalances that favor excitation, changes that may impact cognitive adaptability. Fluctuations in EIB and functional connectivity occurred on comparable timescales and were both associated with individual differences in cognitive performance. Importantly, this experimental approach demonstrates a close synergy between EIB kinetics, brain network dynamics, and cognitive performance, defining the groundwork for exploring healthy and aberrant cognitive states.
Novel approach to determine components size in a total ankle replacement
Evaluating liver type fatty acid binding protein as a diagnostic and prognostic biomarker in metabolic dysfunction-associated steatotic liver disease in pediatric patients
Background Metabolic dysfunction-associated steatotic liver disease (MASLD), is a common liver disorder, predicted to increase globally. Currently, non-invasive methods are proposed for the assessment of (MASLD). This study aimed to investigate the use of serum Liver Type Fatty Acid Binding Protein (L-FABP) concentration as a diagnostic and prognostic biomarker in pediatric MASLD patients and to evaluate its relationship with steatosis and fibrosis. Methods An observational, cross-sectional study on paediatric MASLD patients. Serum levels of L-FABP and hepatic biochemical markers were measured and analyzed. Statistical analyses evaluated the association between L-FABP and other markers, while logistic regression and ROC curves assessed its diagnostic accuracy. Results Serum L-FABP levels showed a significant difference between the MASLD and control groups (P < 0.0001). The results of logistic regression revealed that each one-unit elevation of L-FABP level was associated with 144.5% higher odds of being MASLD (95% CI: 129.3% − 167.8%). A receiver operating characteristics (ROC) curve was constructed to assess the diagnostic accuracy of L-FABP. The resulted area under the ROC curve (AUC) was 0.885. The cutoff value was 5.7 ng/ mL, with sensitivity of 72.73%, and a specificity of 93.62%. The results also showed that the odds ratio of progressing from stage F2 to F3 increases by 108.9% (95% CI: 87.2% − 141.4%) for each one-unit increase in serum L-FABP level. Conclusion L-FABP shows promise as a non-invasive biomarker for diagnosing and monitoring MASLD in pediatric patients. Its association with disease stages suggests its utility in assessing disease progression, particularly in advanced stages.
Functional spectrum of USP7 pathogenic variants in Hao–Fountain syndrome: Insights into the enzyme’s activity, stability, and allosteric modulation
Hao–Fountain syndrome is a rare neurodevelopmental disorder caused by mutations in the deubiquitinating enzyme Ubiquitin-Specific Protease 7 (USP7). Due to the novelty of the disease and its poorly understood molecular mechanisms, treatments for the syndrome are currently lacking. This study examines the effects of 11 patient-derived variants located within the catalytic domain of USP7, focusing on their impact on the enzyme’s activity, thermodynamic stability, and substrate recognition. Our findings reveal a spectrum of functional consequences, ranging from complete inactivation to hyperactivation of USP7. Notably, we identify a specific subset of pathogenic variants whose catalytic activity can be significantly boosted using an allosteric activator, MS-8. These results provide insight into USP7 malfunction in Hao–Fountain syndrome–linked variants and pave the way for improved prognostic approaches and targeted treatments in the future.
Risk of tuberculosis and hepatitis B reactivation during adalimumab use in the treatment of hidradenitis suppurativa
Toward a unified taxonomy of information dynamics via Integrated Information Decomposition
Our ability to understand and control complex systems of many interacting parts remains limited. A key challenge is that we still do not know how best to describe—and quantify—the many-to-many dynamical interactions that characterize their complexity. To address this limitation, we introduce the mathematical framework of Integrated Information Decomposition, or Φ ID. Φ ID provides a comprehensive framework to disentangle and characterize the information dynamics of complex multivariate systems. On the theoretical side, Φ ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer, information storage, and dynamical complexity as aggregates of these modes, thereby overcoming some of their known theoretical shortcomings. On the empirical side, we validate our theoretical results with computational models and examples from over 1,000 biological, social, physical, and synthetic dynamical systems. Altogether, Φ ID improves our understanding of the behavior of widely used measures for characterizing complex systems across disciplines and leads to new more refined analyses of dynamical complexity.
Bridging the gender gap in mobile payment services: insights from consumer of Bangladesh
Riverine heat waves on the rise, outpacing air heat waves
While air heat waves often grab headlines, riverine heat waves have gone quietly unnoticed because rivers are commonly perceived as cool refuges. Analysis of riverine heat waves has been hindered by fragmented datasets, despite a proliferation in water-temperature monitoring with sensors and satellites. Here, we analyze riverine heat wave events by training one single deep learning (long short-term memory) model and reconstructing consistent and continuous daily water temperatures (WT) in 1471 sites in the Contiguous United States (1980–2022). We show that riverine heat waves occur at about half the frequency (2.3 versus 4.6 events/year), a third intensity (2.6 versus 7.7 °C/event), but almost double the duration (7.2 versus 4.0 d/event) of air heat waves. Riverine heat wave events have increased at double to quadruple rates of air heat wave events, amounting to an additional 1.8 events/year in frequency, 0.43 °C/event in intensity, 3.4 d/event in duration, and 7 to 15 additional thermal stress days for aquatic ecosystems in 2022 compared to 1980. Rising riverine heat waves have outpaced those of air heat waves in 65 to 76% of the sites, particularly in regions experiencing accelerated warming (e.g., the Rockies). Riverine heat wave trends are driven predominantly by climate-induced changes such as warming and dwindling snowpacks and water flow. Human activities do play important roles: large dams elongate, whereas agriculture reduces heat waves. These results highlight anthropogenic climate change as the primary external driver, whereas human-induced structural changes as the secondary internal modulators of river response to heat disturbance. The widespread rise of riverine heat waves threatens aquatic ecosystems and water-energy-food security, underscoring the need for their global characterization and risk assessment.
Experimental investigation and parametric optimization of machinability and surface characteristics in wire EDM of Inconel 718
Motor origins of timbre in piano performance
Creativity in the arts, such as painting and musical performance, hinges on the ability to produce a wide spectrogram of perceptual experiences. In music, it has long been believed that the timbre of tones can be altered by nuanced movements of performers. Previous studies have described relationships between fundamental elements of auditory perceptions (e.g., loudness, tempo) and physical movements (e.g., force, speed), but it remains unknown whether and how delicate features of perceptual experiences such as tone timbre are manipulated through dexterous motor skills. Here, we bridge this gap using a twofold experimental approach. First, our listening test revealed that the timbral qualities pianists intended to express in piano playing were perceived as intended by both pianists and musically untrained individuals, with pianists showing a greater perceptual sensitivity to different timbres. Second, through a motor behavioral experiment using a noncontact, high-resolution sensing system, we identified five specific movement features in piano touch that were intricately linked to three categories of perceived timbre; weight, clarity, and brightness. Furthermore, the direct manipulation of a specific key movement feature resulted in systematic changes in perceived timbre, providing evidence for a causal relationship. The result indicates that pianists share common motor skills to modify perceived tone timbre by manipulating specific movement features. Our findings underscore the pivotal roles of subtle physical gestures in creating the rich timbral palette of piano tones, advancing our understanding of the intersection between motor control and artistic expression.