ABSTRACT Inspired by the drople dynamics drag reduction properties of dragonfly wing surfaces, the biomimetic drag‐reducing functional surface cylinders are prepared. Their drag reduction performance and the mechanism of bubble‐induced drag reduction under dynamic volume and pressure conditions were studied. The influence of key factors such as texture types and coating categories on drag reduction performance is systematically analysed by a constant‐volume variable‐pressure interstitial flow system (single‐channel) and a recirculating variable‐volume experimental system (dual‐channel). The experimental results show that all shapes of cylindrical biomimetic textured surfaces have air bubbles aggregated in the textured cavities when falling. The air bubbles reduce the friction at the solid–liquid interface and have an obvious drag reduction effect, with the square texture having the best effect. Furthermore, the hydrophobic state surface cylinders under the composite treatment of coating and texture have the largest drag reduction rate of 18.7%. Based on the theoretical model of two‐phase flow, the mechanism of drag reduction by low‐speed vortex flow has been investigated through numerical simulation of liquid interstitial flow characteristics on the textured surface. The results demonstrate that the low‐speed vortices present inside the texture interact with the high‐speed water flow outside the texture; the frictional resistance at the solid–liquid interface is effectively reduced. The shear stress on the cylindrical surface is significantly suppressed by the texture.

Strata structure is an important factor affecting floor heave of deep soft rock roadway, however, the mechanism of floor heave induced by the rock structure has not been fully understood, so it is still necessary to study the floor heave of deep-buried roadway under different rock structure conditions.Thus providing further theoretical basis and scientific basis for the control of surrounding rock in mine tunnels. In order to study the floor heave mechanism of soft rock roadway deep-buried roadway excavated in soft rock of gently inclined thin strata, a physical model experiment based on the engineering geological conditions of typical mine roadway was carried out. During the experiment, CCD camera and strain acquisition system were employed to collect image data and strain data. Based on the analysis of the experimental results, the following conclusions are drawn: (1) For the soft rock roadway located in deep-buried thin rock stratum, horizontal stress is an important factor affecting the floor heave of roadway; (2) When the floor heave occurs in the roadway, the strain distribution of floor of roadway is funnel-shaped, the max VME strain value of the floor is near 0.19, the affected range of floor strata is about half of the roadway width; (3) The surrounding rock within one time of the tunnel diameter is in the state of tensile stress after the process of roadway floor heave failure, and the areas of tensile stress and compressive stress in the surrounding rock alternates around the roadway; The failure mechanism of floor heave of the roadway is the fracture and uplift of the floor of roadway. Besides, a numerical model was conducted to verify the results of the physical model, the results of numerical simulation are in good agreement with those of physical model.

Abstract A plenoptic camera was coupled into a self-aligned focusing schlieren (SAFS) system, allowing for the instantaneous capture of focused schlieren images at multiple planes from a single snapshot. Each focused schlieren image can be refocused to an arbitrary depth, within limits discussed herein, revealing density gradients at different spatial locations in the flow. A description of the plenoptic SAFS system is presented, along with a characterization of the experimental system. This characterization details the two depths-of-field associated with a plenoptic camera; the so-called reduced aperture depth-of-field, which gives the refocusing range, and the full aperture depth-of-field, which determines the depth-of-field of the final refocused image. Further, the spatial resolution of the plenoptic SAFS system was determined via a modulation transfer function analysis using a slant-edge target. Finally, results of an experiment consisting of two helium jets in both side-by-side and inline, vertically-opposed configurations are presented, as well as results from a wind tunnel experiment.

Antimicrobial resistance poses a significant global health threat. Experimental evolution studies are crucial in understanding resistance mechanisms and thereby informing strategies to preserve antibiotic efficacy. We developed a novel continuous experimental evolution system enabling uninterrupted medium exchange with a rising antibiotic gradient, using standard laboratory equipment. We applied this system to three Enterobacter cloacae complex strains isolated from urinary tract infections in Germany between 1990 and 1992, which therefore had no prior exposure to cefepime, a fourth-generation cephalosporin approved in Germany in 2004. After four days of exposure to a cefepime gradient, resistant mutants emerged in all three strains. Notably, one mutant exhibited cross-resistance to the novel antibiotics cefiderocol and ceftazidime-avibactam, due to a single missense mutation in the β-lactamase gene blaMIR-11. Our study demonstrates the effectiveness of this novel approach for investigating antimicrobial resistance development and cross-resistance mechanisms, as well as identifying and characterizing a mutation attributed to major cross-resistance.

Response of high-speed railway tunnel-track system under strike-slip fault dislocation-Part 2: Experimental observations and system behavior

Gsx2 regulates oligodendrocyte precursor formation in the zebrafish spinal cord.

Experimental analysis and system design of a novel autothermal moderate-temperature chemical looping hydrogen production process with steam enhancement

Hypoxia is a physiologically relevant microenvironment in both normal and diseased tissues and has emerged as a potent modulator of cellular senescence and organismal longevity. This review synthesizes evidence that hypoxia delays senescence across diverse experimental systems and species, and highlights mechanisms by which hypoxia rewires chromatin states during senescence-associated transitions. We focus on oxygen- and α-ketoglutarate-dependent epigenetic regulators, particularly histone lysine demethylases, whose catalytic activities are limited under hypoxia. Consequently, histone methylation increases and higher-order chromatin organization is stabilized. Using oncogene-induced senescence as an experimentally tractable framework, we discuss recent findings showing that hypoxia suppresses senescence-associated histone clipping, preserves nuclear lamina integrity, and restrains large-scale heterochromatin reorganization while leaving canonical cell-cycle arrest largely intact. We further consider emerging links among DNA damage, epigenetic instability, and aging phenotypes, and propose that senescence can be viewed as a breakdown of coordinated epigenetic homeostasis. By integrating these concepts, we position hypoxia and hypoxia-mimetic interventions as promising strategies to modulate aging-associated cellular states and to explore therapeutic opportunities in age-related pathologies.

Cellular organelles are not just static structures; they are highly dynamic and directly linked to cellular functions. Changes in their morphology can be early indicators of diseases. Recent advancements in light microscopy techniques have transformed organelle research from qualitative descriptions to precise, quantitative measurements, enabling nanoscale resolution, high-throughput image analysis, and live-cell compatibility. This enables accurate measurement of organelle morphology, dynamics, and spatial organization using modern imaging and analysis techniques. By quantifying organelles, we go beyond simply visualizing to measuring and statistically comparing cellular features across different samples. This article addresses a wide range of cellular organelles across all major experimental systems, specifically mentioning mitochondria, myofibers, actin filaments, endoplasmic reticulum, and Golgi apparatus, by integrating experimental design, optimized sample preparation, high-resolution imaging, and validated Fiji/ImageJ-based analysis workflows. For each organelle, step-by-step protocols specify reagents, equipment, acquisition parameters, and expected results. Although recent advances, such as expansion microscopy, correlative light-electron microscopy, and AI-powered segmentation, offer gains in throughput and resolution, this workflow demonstrates that Fiji-based analysis remains fully capable of delivering high-precision organelle quantification. The entire workflow can be completed within 2-4 weeks, from initial design through validation and the production of measurements suitable for cross-study comparisons. Overall, these protocols establish a flexible approach to standardizing organelle quantification so as to understand multiple organelles simultaneously in their cellular contexts. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Mitochondrial quantification Basic Protocol 2: Lipid droplet identification and image processing Basic Protocol 3: Myofibril quantification Basic Protocol 4: Golgi apparatus morphometry Basic Protocol 5: Endoplasmic reticulum network analysis Alternate Protocol: Super-resolution imaging protocol.

Myelination is essential for rapid axonal conduction and neuronal integrity, and its loss in demyelinating diseases such as multiple sclerosis (MS) leads to progressive neurological impairment. Despite advances in immunomodulatory therapies, effective strategies that promote remyelination remain limited. Here, we identify Morusin, a prenylated flavonoid natural compound, as a potent enhancer of oligodendrocyte (OL) differentiation and myelination-associated outcomes. Using a fluorescence-based screen of diverse flavonoids in primary rat oligodendrocyte progenitor cells (OPCs), it was found that Morusin markedly increased myelin basic protein (MBP) expression. To enable cross-species validation, we established a SOX10-inducible human OPC differentiation system, which shortened differentiation time and allowed functional screening in human cells. In this platform, Morusin enhanced OL maturation and induced a transcriptional profile enriched for myelination- and axon ensheathment-related genes, including MBP, PLP1, MAG, and SIRT2. Furthermore, in myelin oligodendrocyte glycoprotein (MOG)35–55-induced experimental autoimmune encephalomyelitis (EAE) mice, Morusin improved myelination-associated histological features and functional recovery, comparable to the benchmark compound Benztropine. Collectively, these findings identify Morusin as a promising natural compound with pro-myelinating activity across multiple experimental systems and highlight the potential of rationally guided natural compound screening for regenerative therapy in demyelinating diseases.

To investigate how dynamic fluctuations in oxygen concentration—induced by air leakage flow in the goaf—affect the oxidation and spontaneous combustion behavior of residual coal along the airflow path, particularly considering the catalytic and inhibitory roles of CO and CO2 generated during coal oxidation, a series-connected dual coal sample tank experimental system was developed. Experiments were conducted under controlled thermal conditions: isothermal operation in the upstream coal sample tank and programmed temperature ramping in the downstream tank. Coal oxidation indicators—including O2 consumption rate, CO/CO2 generation profiles, heat release rate, and apparent activation energy—were systematically quantified under dynamically varying atmospheric conditions and benchmarked against those obtained under fresh air and fixed-O2 reference conditions. The results reveal that under dynamic atmospheres—characterized by declining O2 concentration coupled with accumulating CO and CO2—coal oxidation deviates markedly from behavior observed under stable, high-O2 conditions. Crucially, CO and CO2 are not merely passive oxidation products; they actively modulate reaction kinetics. Specifically, they suppress the dominant chain-propagation reactions of low-temperature oxidation, thereby reducing both oxygen consumption and heat release rates relative to fixed-O2 controls at equivalent initial O2 levels. Concurrently, they accelerate the CO-producing pathway, resulting in disproportionately elevated CO yields, even under thermally mild conditions. This decoupling between thermal activity and gaseous hazard implies a heightened risk of CO poisoning and combustible gas accumulation, potentially preceding detectable temperature rise. Accordingly, conventional single-parameter risk assessment frameworks—especially those relying solely on temperature or O2 depletion—are insufficient for early hazard identification in such complex, transient airflow environments. We recommend integrating real-time CO concentration monitoring as a critical, proactive parameter in spontaneous combustion early-warning systems.

ABSTRACT Quartz, ceramics, and other insulating brittle materials exhibit outstanding physical and chemical properties, playing an indispensable role in the fields of aerospace and weaponry equipment. Due to their high hardness, brittleness, and non-conductive characteristics, conventional grinding cannot achieve efficient and damage-free machining of these materials. In this research, an electrochemical discharge assisted grinding (ECDAG) process is proposed by utilizing the localized high temperature generated by the discharges to soften the material, thereby facilitating plastic domain grinding to improve machining performance. The influence mechanism of key factors was analyzed, including the localization of electrochemical discharges, the abrasive grain rate of grinding wheels, and the energy matching between electrochemical discharges and grinding. Furthermore, an experimental system was developed for the ECDAG process, and comparative experiments between ECDAG and conventional grinding (CG) were conducted on quartz glass workpieces by machining grooves. The results demonstrate the feasibility and capability of ECDAG for machining insulating brittle materials. Compared with CG, ECDAG with plastic grinding characteristics can achieve superior surface and subsurface quality, greater groove depth, and lower grinding force.

Against the backdrop of addressing climate change and reducing carbon emissions, carbon dioxide capture technology has gained increasing significance as a key approach toward achieving global carbon neutrality. However, in the development of phase change absorbents, current screening methods for phase separation agents (PSAs) predominantly rely on static empirical parameters, neglecting electronic effects and phase equilibrium mechanisms, which somewhat restrict the improvement of screening efficiency and accuracy. This study innovatively proposes a novel paradigm integrating quantum chemistry and machine learning. A total of 434 experimental systems with amine, PSA, and water at a 1:2:2 ratio were constructed, from which 358 valid data sets were selected. A three-dimensional quantum chemical descriptor system encompassing 48 dynamic electronic parameters was established, accompanied by an innovative anion-centered molecular characterization method for the reaction states. A two-stage machine learning framework was employed: the random forest algorithm evaluated three types of descriptors and identified 34 key features, with particular emphasis on anion-related descriptors such as Orbital Discrete Index and Electrostatic Potential. After the six machine-learning models were compared, CatBoost was identified as the optimal classifier, achieving a test accuracy of 89.7% and a phase change recall of 96.3%. Interpretive analysis based on SHAP revealed that PSAs with lower mean Orbital Discrete Index values facilitate phase separation by weakening the hydrogen bond network, while the geometric feature (longest interatomic distance) and electronic property (minimum electrostatic potential) of organic amine anions synergistically drive the reconstruction of the phase interface. Validation in a 3-(dimethylamino)propylamine system demonstrated a prediction accuracy of 83.3%, significantly reducing experimental costs. This study provides an accurate and interpretable solution for the rational design of PSAs, strongly promoting the development of high-efficiency carbon dioxide capture materials.

Course-based undergraduate research experiences (CUREs) provide authentic research while promoting student engagement, persistence in STEM, and skill development. In response to the COVID-19 pandemic and growing demand for flexible, scalable educational tools in biology, we developed and implemented a digital CURE (DCURE) adapted from a live-plant manipulative experiment CURE. This DCURE engages students on research in Arabidopsis thaliana, utilizing images of experimental plants and digital platforms. By eliminating the need for during-semester in-lab/course plant cultivation, this module expands equitable access to research experiences, especially for institutions or students without greenhouse or growth chamber infrastructure. Implemented across community colleges and 4-year institutions in both foundational and more advanced biology courses, the DCURE promotes transferable skills in digital literacy, data analysis, and scientific communication. Students participated in authentic scientific processes-collecting and curating data, generating and refining hypotheses, analyzing results, and contributing findings to a public research database. Formal evaluation from over 1,300 students across 5 semesters revealed significant gains in technical proficiency, confidence in data analysis, and conceptual understanding of plant biology. Faculty also reported benefits including enhanced collaboration of students within courses and across institutions, improving instructional design through iterative feedback, and a stronger focus on helping students connect digital methods to biological theory. The DCURE serves as a flexible, accessible, and rigorous model for integrating digital research experiences into undergraduate education. It equips students with the computational and analytical skills essential for a modern biology workforce. This model is adaptable for use in other experimental systems across the life sciences.

CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

The zebrafish ( Danio rerio ) has emerged as a crucial vertebrate model for studying thyroid physiology and toxicology, owing to its high genomic homology with mammals, external embryogenesis, transparent development, and the availability of advanced genetic manipulation techniques. Moreover, this experimental system is widely used for toxicity testing of environmental chemical compounds that affect thyroid function, yielding well-characterized phenotypic and molecular responses. It is well known that thyroid hormones regulate embryonic development in vertebrates, particularly the development of the central nervous system. Moreover, thyroid hormones control energy metabolism, growth, cellular differentiation, and overall homeostasis, physiological processes conserved in both zebrafish and mammal models. Therefore, this mini-review provides a comprehensive analysis of the current literature regarding the use of zebrafish to investigate the effects of endocrine-disrupting chemicals on the hypothalamic-pituitary-thyroid axis and thyroid function, as well as the associated physiological and behavioral responses. This review also discusses the limitations of the zebrafish model, including the challenges of establishing exposure models that are realistic and comparable to those experienced by humans and other animals in the environment. Finally, it discusses the intra- and intergroup variability, technical challenges in molecular analyses at early developmental stages, anatomical differences relative to humans, and difficulties in experimental handling and reproducibility. Nevertheless, zebrafish is an undeniable, versatile, and powerful model for advancing research in thyroid endocrinology and toxicology, especially during critical developmental windows, which are more complex to assess in mammals.

The dissociation behavior of 1-methylpyrene (MP) was investigated by using the SWEET experimental system under electron-induced collisions to elucidate the fragmentation dynamics of polycyclic aromatic hydrocarbon (PAH) ions. Molecular interaction with 200 eV electrons initiated multiple ionization and fragmentation pathways, resulting in molecular cation production. The unfragmented monocation MP+ is identified as the most abundant one, the intact dication MP2+, the de-ethylated dication, and a stable trication MP3+ were also observed, demonstrating the formation and stability (>ms) of highly charged molecular ions. Among the smaller observed hydrocarbon fragments, neutral C2Hx0 and cationic C2Hx+ species were the predominant dissociation products. Experimental findings were compared with density functional theory-based tight-binding molecular dynamics simulations. Measurements of the cation signals as a function of the incident electron energy (17-35 eV) provided appearance energies for the dications and correlated cations. The higher experimental appearance energy values relative to simulated vertical ionization energies suggest the involvement of autoionization processes from initially populated electronically exited states of the molecule.

ABSTRACT To investigate the combustion characteristics of spherical Al@C nanoparticles, an aluminum wire electrical explosion experimental system was constructed. A systematic investigation of the microstructural and morphological characteristics of Al@C nanoparticles was conducted, and comparative analyses were performed on their thermal decomposition properties and combustion behaviors using AP-based mixtures containing either the synthesized Al@C nanoparticles or the conventional nano-aluminum powder. Results demonstrate that the Al@C nanoparticles prepared via electrical explosion exhibit a distinct core-shell structure, with a carbon coating layer of approximately 10 nm thick, stable interfacial bonding between the carbon layer and aluminum core, forming an Al4C3 interface, and particle sizes ranging from 50 to200 nm (average particle size 99.50 nm, standard deviation ±34.00 nm). The carbon coating effectively isolates aluminum from oxygen and exhibits inherent thermal stability, with the Al@C nanoparticles showing a weak endothermic peak at 654.3°C corresponding to aluminum core melting. Compared with conventional nano-aluminum powder, the carbon-coated structure in Al@C nanoparticles offers the following advantages: reduces the AP decomposition temperature (exothermic peak shifted from 400.6°C to 331.27°C, a decrease of 69.33°C) and accelerates the AP decomposition reaction; maintains aluminum reactivity while significantly enhancing the combustion rate (burn duration reduced to 10% of that of conventional Al/AP systems, i.e. 90 ms for Al@C/AP vs. 900 ms for Al/AP); suppresses particle agglomeration during combustion; enhances energy release while stabilizing AP decomposition (the enhanced energy release capacity is reflected in a higher maximum combustion temperature of 2870.56°C and a stronger emission peak at 600 nm (8000 a.u.) compared with Al/AP (7450 a.u.)); promotes uniform heat diffusion during combustion, achieving higher core temperatures and more stable combustion wave propagation.

Transcriptional regulation lies at the heart of cellular identity and function, hinging on the precise binding of transcription factors (TFs) and cofactors to gene regulatory elements such as promoters and enhancers. Although it is relatively routine to profile genome-wide DNA binding landscapes of proteins, identifying the specific proteins that bind to, and regulate the transcription of, a particular gene of interest (GOI) remains a persistent experimental and conceptual challenge. This gene-centric question is complicated by the multilayered regulatory environment in which each gene resides, comprising 3D chromatin structure, enhancer-promoter looping, DNA accessibility, histone modifications, and cell state-dependent protein dynamics. In this review, we dissect the strengths, limitations, and biological relevance of current approaches for studying direct protein-DNA interactions, distinguishing between protein-centric and DNA-centric methodologies. We introduce a conceptual matrix of biological relevance, integrating the origin of DNA and protein elements (cis and trans) to evaluate false-positive and false-negative risks across experimental systems. Moreover, we explore how perturbation strategies-gain and loss of function-can complement steady-state profiling to establish causality in gene regulation. By critically examining both established tools and emerging techniques such as genome editing, synthetic chromosomes, and high-resolution imaging, we provide a practical framework for investigators seeking to uncover direct regulators of specific genes. Our goal is to guide the design of experiments that balance biological relevance, sensitivity, and interpretability to ultimately answer a deceptively simple question: What TFs directly regulate the expression of my GOI?

Cyclic voltammetry (CV) is a cornerstone of electrochemical analysis, yet the accurate determination of Faradaic peak heights is often compromised by overlapping signals and complex background currents. Traditional analysis relying on linear baseline subtraction is highly inaccurate, particularly for systems with multiple redox processes or interfering species. This work introduces a powerful and accessible automated fitting algorithm that uses semiderivative analysis to deconvolve complex voltammograms, suitable for linear diffusion controlled experiments conducted with planar working electrodes. The method employs flexible Pearson IV distributions to model a wide range of Faradaic peak shapes and introduces a novel piecewise function to accurately fit and subtract both capacitive and background electrolysis currents. The algorithm's efficacy is demonstrated on three challenging experimental systems: the reversible redox probe [Ru(NH3)6]Cl3 in the presence of interfering oxygen reduction, the sequential ligand reductions of [Ru(bpy)3](PF6)2 featuring heavily overlapping peaks, and the quantitative analysis of SO2 obscured by a large oxygen reduction signal. The results show a dramatic improvement in accuracy and signal deconvolution over the conventional methods. To promote broad adoption, a user-friendly program and its Python source code have been made freely available to the electrochemistry community.