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Neuroinflammation represents a central pathogenic driver in a spectrum of central nervous system disorders, predominantly mediated by microglial activation and the ensuing release of inflammatory cytokines. While the E3 ubiquitin ligase TRIM31 is implicated in peripheral immunity, its precise function within neuroinflammation defies precise delineation. In this study, we define the role of TRIM31 in microglia-driven neuroinflammation and clarify its molecular mechanism. Utilizing both cellular and murine models of lipopolysaccharide-induced neuroinflammation, we detected a marked induction of TRIM31 expression with LPS stimulation. Genetic knockdown of TRIM31 exacerbated the LPS-triggered upregulation of pro-inflammatory cytokines, including IL-6, TNF-α, and IL-1β. Conversely, TRIM31 overexpression potently suppressed cytokines release and attenuated neuroinflammatory responses in vitro and in vivo. Mechanistic investigations combining transcriptomic profiling and immunoblotting manifested that TRIM31 directly interacts with TAK1, catalyzing its K48-linked polyubiquitination and subsequent proteasomal degradation. This action provokes the downregulation of the NF-κB activation cascade. Collectively, our findings identify TRIM31 as a critical negative regulator of neuroinflammation and underscore its therapeutic potential for treating neuroinflammatory diseases via targeted degradation of TAK1.

Population phylogenomics uses sampled genomes to jointly infer population genetic processes (ancestral and contemporary population sizes, historical gene flow) and a phylogenetic tree relating species or populations including species split times. This challenging problem has been tackled most successfully in the Bayesian framework under the multispecies coalescent (MSC) model via Markov chain Monte Carlo (MCMC) computational algorithms. However, MCMC methods suffer from two serious problems: (i) mixing difficulties due to the high-dimensional state space with complex constraints, and (ii) the intrinsically serial nature of MCMC algorithms that defies parallelisation. To deal with both issues, we develop a new method, called Virtual Dimension Reduction allowing Parallelisation (VDRoP), that achieves the same MCMC mixing efficiency as dimension reduction through analytical integration of parameters, but without sacrificing parallel computation and without the restriction to conjugate priors. We implement the new method in the Bayesian program BPP and apply it to genomic datasets from Adansonia baobab trees, Anopheles mosquitoes, and Heliconius butterflies. The new algorithms reduce the run-time of MCMC analyses by 3 to 8 fold and improve the mixing efficiency by up to 50 fold for representative empirical datasets.

Reconfigurable electronic states in insulating materials enable metal-like transport while preserving the intrinsic robustness and functional versatility of insulating hosts, thereby redefining materials beyond the conventional metal-insulator dichotomy. However, obtaining such states remains extremely challenging owing to strong electronic localization inherent in insulating materials. We demonstrate a universal surface single-atom engineering strategy for linear and deep programming of electronic transport in insulating oxides and nitrides, including SiO2, Al2O3, and BN, by selectively inducing local symmetry breaking, effective bandgap compression, and impurity-band percolation. Consequently, this strategy continuously narrows the bandgap and ultimately yields metallic transport characteristics with anomalously minimal temperature dependence. Furthermore, we apply single-atom-anchored SiO2, an intrinsically electromagnetic wave-transparent material, to shielding with a record-high effectiveness of 98.6% for an ultrathin 80 µm film that also maintains stable performance over a temperature range of 300-800 K. This counterintuitive performance defies the conventional paradigm, demonstrating that an intrinsically insulating material can achieve electromagnetic shielding comparable to state-of-the-art metals while avoiding the temperature-induced performance degradation of metallic shielding materials. Overall, we believe that this study establishes single-atom band engineering as a general strategy for programming electronic transport in insulating materials, with broad implications for advanced electronics and unconventional functionalities.

Predicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape. Yet, over the past decade, groundbreaking advancements have emerged. Epigenetic clocks, derived from DNA methylation patterns, have established themselves as powerful aging biomarkers, capable of estimating biological age and assessing aging rates across diverse tissues with remarkable precision. These clocks provide predictive insights into mortality and age-related disease risks, effectively distinguishing biological age from chronological age and illuminating enduring questions in gerontology. Despite significant progress in epigenetic clock development, substantial challenges remain, underscoring the need for continued investigation to fully unlock their potential in the science of aging.

Fast coherent state transport is essential to quantum computation and quantum information processing. While an adiabatic transport of atomic qubits guarantees a high fidelity of the state preparation, it requires a long timescale that defies efficient quantum operations. Here, we propose an adaptable and fast bang-bang-bang protocol, utilizing a combination of forward- and backward-moving trap potentials, to expedite the coherent state transport. We further showcase the advantage of applying squeezed coherent state evolution under a deeper potential followed by a weaker one, where a design of symmetric squeezing potential transports promotes an even shorter timescale for genuine state preparation. Our protocols outperform conventional forward-moving-only methods, providing new insights and opportunities for rapid state transport and preparation, ultimately advancing the capabilities of quantum control and quantum operations.

Radiologic error remains an enduring challenge in diagnostic medicine. Despite study of radiologic error since the mid-twentieth century, interpretive discrepancy rates have remained remarkably stable across modalities, institutions, and technologic eras. Moreover, despite compelling evidence that diagnostic performance is shaped by workload, feedback delays, information quality, and tradeoffs under pressure, radiology remains governed predominantly by discrepancy counting, individual remediation, and retrospective attribution. This persistence defies reductionist explanations narrowly centered on individual fallibility and highlights radiology's structural properties as a complex sociotechnical system. This Perspective reconceptualizes radiologic error through the lens of complex-adaptive systems theory whereby safety is understood as an emergent property of dynamic interactions rather than absence of individual failure. The article describes how radiology has not kept pace with epistemologic shifts in understanding error and proposes a reframing of radiologic safety grounded in adaptive capacity, resilience, and systems learning. The impact of artificial intelligence in reshaping system behavior and thereby introducing new challenges is considered. Drawing on the evolution of safety science from linear human-centric models to contemporary resilience-oriented frameworks, the analysis integrates empiric evidence on interpretive variability with theory from systems engineering, cognitive science, and organizational safety to identify conditions under which accurate diagnoses are routinely achieved.

Aortic dissection (AD) represents a life-threatening cardiovascular emergency. The condition is traditionally classified according to the Stanford system: type A involves the ascending aorta, while type B is confined to the descending aorta. However, a small subset of cases defies this binary classification and is categorized as non-A non-B aortic dissection. These atypical presentations frequently involve the aortic arch or exhibit complex morphological features, such as retrograde extension or multi-territorial involvement, distinguishing them from classical type A or B dissections. The optimal management strategy for non-A non-B aortic dissection remains controversial. Current therapeutic approaches encompass open surgical repair, thoracic endovascular aortic repair (TEVAR), hybrid procedures, and conservative medical management. This review synthesizes the contemporary evidence regarding the epidemiology, pathophysiology, diagnostic challenges, and treatment modalities for non-A non-B aortic dissection, underscoring the critical importance of individualized management in therapeutic decision-making.

Conventional additive manufacturing (AM) of metallic materials demands costly high-vacuum or ultra-pure inert atmospheres to suppress impurity-induced embrittlement. Here, we overturn this paradigm by demonstrating that ambient trace O and N in an inert atmosphere can be turned into potent in-situ alloying species so that the strength and ductility of the material can be simultaneously enhanced. In a Ti56Zr30Nb14 medium-entropy alloy (MEA) additively manufactured with optimized air doping, the yield strength rises by 67% to ≈1 GPa and the tensile ductility increases by 64% to ≈18%, achieving a simultaneous gain that defies the classical strength-ductility trade-off. Atom-probe tomography, enhanced by a machine-learning workflow, identifies two distinct families of nanoscale ordered interstitial complexes (OICs): O-rich OIC1 (O-Zr-Ti) and N-rich OIC2 (N-Zr-Ti). These complexes act as potent dislocation-pinning sites while promoting extensive cross-slip of dislocations and activating Frank-Read sources during plastic deformation. The resultant wavy slip and sustained work-hardening capacity give rise to exceptional strength-ductility synergy. Eliminating the need for high-purity inert gas, this air-alloying route delivers a low-cost, scalable pathway to strong-yet-ductile AM metallic materials.

Proteins operate in dynamic environments where ions, lipids and temperature collectively define their properties, yet most studies rely on simplified conditions that overlook these intrinsic variables. Here we show two such factors-temperature and Ca2+-remodel the function and pharmacology of TRPM4, an ion channel implicated in cardiac conduction, immune regulation, cancer and intestinal-fluid homeostasis. At physiological temperature and Ca2+, TPPO-previously considered a selective TRPM5 inhibitor inactive toward TRPM4-potently activates TRPM4, revealing strong synergy among temperature, Ca2+ and ligand binding. By contrast, Necrocide-1, a necroptotic activator targeting the same binding pocket, defies this logic: it opens TRPM4 without Ca2+ but is antagonized by Ca2+. Meanwhile, the inhibitors NBA and CBA engage a nearby pocket, locking the channel in a non-conductive pre-open state. Our findings highlight that even rigid binding pockets can exhibit temperature-dependent ligand recognition, revealing hidden pharmacology and informing selective, environment-aware therapeutic strategies.

Pain is a prevalent clinical complaint that often defies explanation within conventional biomedical frameworks, particularly in chronic and idiopathic conditions, frequently leading to patient invalidation and inadequate care. We evaluate the potential of philosophy to expand the understanding of pain beyond biological reductionism by conceptualizing pain as a lived experience. This narrative review aims to integrate key philosophical perspectives with contemporary pain medicine and to examine their relevance for clinical practice. A narrative review of philosophical and medical literature was conducted, focusing on phenomenology, existential philosophy, philosophy of language, biopolitics, and neurophilosophy. These frameworks conceptualize pain as a disruption of embodied existence, a challenge to identity and autonomy, a phenomenon that resists full linguistic expression, a condition shaped by institutional and sociopolitical structures, and an inferential process influenced by prior experience and context. Together, these perspectives suggest that effective pain management requires more than symptom reduction and objective measurement. Attending to patients' lived experiences may strengthen therapeutic alliances, enhance clinical communication, and support more ethical, person-centered, and clinically meaningful approaches to pain care.

Achieving chemoselective hydrogenolysis of carbonyl compounds that defies the well-known electrophilicity order remains challenging yet promising for upgrading nitrogen-containing waste. Herein, we present density functional theory (DFT) calculations to elucidate the unclear mechanism of the highly chemoselective hydrogenolysis of urea to amide catalyzed by an Ir complex. Results show that the metal-ligand cooperative isocyanate pathway is more favorable than a conventional hemiaminal pathway, where the pyrrolyl moiety in the ligand serves as a proton transfer station by assisting N-H bond cleavage of urea. The unusual chemoselectivity favoring urea over amide counters carbonyl electrophilicity, arising from the fact that the isocyanate pathway is kinetically unfavorable for amide due to the endothermic generation of H2 as a byproduct. Interestingly, the Cs[IrIII(NP)(H)2(OtBu)(urea)]- ate complex generated in situ with CsOtBu exhibits superior proton transfer capability that effectively lowers the energy barrier of the isocyanate pathway. Consequently, Ir-catalyzed hydrogenolysis of carbamate is achieved, whereas it cannot succeed without CsOtBu. Furthermore, a positive correlation between the N-H bond dissociation energy of the substrate and the energy barrier was disclosed. These results provide fundamental insight into carbonyl hydrogenolysis reactivity and a strategy for precise conversion under mild conditions.

ConspectusFor centuries, the reductionist view that "the whole equals the sum of its parts" has guided scientific study, particularly materials design. Nature, however, often defies this logic: an aggregate (whole) can display emergent properties that are totally absent in its individual parts. Aggregation-induced emission (AIE) exemplifies this "anomaly": nonluminescent molecules become emissive upon aggregation, achieving a qualitative "0-to-1" leap that challenges the reductionist tenet and provides a unique lens through which to view the emergence of new properties.Since it was proposed as a concept in 2001, AIE has been mechanistically understood as arising from the restriction of molecular motion (RMM) in the excited state. In dilute solutions, molecular rotors and vibrators dissipate exciton energy through active motions, leading to nonradiative decay. Upon aggregation, these motions are physically restricted by molecular packing and noncovalent interactions, impeding nonradiative channels and opening radiative pathways. This mechanistic understanding has motivated extensive research into AIE and expanded the field into a diverse platform of aggregation-enabled luminescent systems, including clusteroluminescence (CL), room-temperature phosphorescence (RTP), and circularly polarized luminescence (CPL)─all absent in the isolated molecular constituents and emerging through aggregation.With accumulated knowledge in AIE, the attention has broadened toward the exploration of aggregation-generated function (AGF). From this perspective, molecular motions─previously viewed as energy "wasted" that reduced emission─can be harnessed to convert excited-state energy into heat through rotations and vibrations. By channeling the same exciton energy that underlies luminescence into nonradiative decay pathways, we can engineer aggregates to exhibit emergent photothermal (PT), photoacoustic (PA), and photocatalytic (PC) activities. These functions open new application avenues, including solar energy conversion, high-resolution deep-tissue imaging, and "intelligent" actuation.From the serendipitous encounter with AIE to the systematic study of AGF, advances in the field have shifted scientific attention from isolated molecules to complex aggregates. With the elucidation of principles governing emergent properties, it is becoming clear that a paradigm shift is needed─from molecularism to aggregatism or from molecular science to aggregate science (AS). Guided by emergentism, AS studies how molecules, through noncovalent interactions and hierarchical organization, give rise to macroscopic functions absent in their individual constituents. Notably, the materials we use and the life we see around us are all aggregates. This aggregate-level perspective enables the development of new systems with complex functionalities (e.g., advanced multimodal theranostics) and deepens our understanding of life─an archetypal multiary system in which the aggregation of nonliving biomolecular constituents yields a living organism.In this Account, we detail the intellectual trajectory from AIE to AGF and finally to AS. We distill the guiding principles and outline future directions, including transitions from unary to multiary systems, static structures to dynamic processes, and descriptive aggregate science to prescriptive aggregate engineering. A deeper understanding of AS will enable new scientific discoveries and technological innovations, inviting us to imagine a future designed not merely with matter but with the sophisticated organizational logic that endows it with "life-like" functions.

Introduction: Emotional intelligence (EI) is increasingly acknowledged as a component that may influence nurses' job performance (JP), particularly in high-stress contexts. This study examined the relationship between emotional intelligence and job performance among critical care nurses at King Salman Specialist Hospital in Hail, Saudi Arabia. Design/Methods: The cross-sectional study included 50 registered nurses working in the critical care unit, following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Data were gathered using validated tools. The data were collected between October and December 2024. Point-biserial correlation (rpb), one-way ANOVA and simple linear regression were employed. Results: This study found that neither gender (rpb = 0.095, p = 0.514) nor age group (F = 0.945; p = 0.423) had a significant impact on EI or JP scores. Meanwhile, the linear regression model was highly significant (F [1, 48] = 45.829; p < 0.001), indicating that EI is a robust predictor of performance in this cohort. Contrary to common assumptions, a significant negative (inverse) relationship was identified. For every one-unit increase in EI, job performance decreased by 0.541 units (&#x3b2; = -0.699; t = -6.77; p < 0.001). Conclusions: This study confirms that EI serves as a notable inverse predictor of JP of critical care nurses. This shows that there could be high levels of emotional labor in the demanding clinical environment, which could hinder technical performance. This finding, irrespective of age or gender, defies the 'more is better' generalization of EI in the healthcare industry. Therefore, it is essential that there be available supportive mechanisms in the workplace to assist nurses with high EI in managing their emotional involvement with clinical work. This should be done to avoid a compromise in job performance.

Stabilizing metastable Cu(I) species during electrochemical CO2 reduction remains a fundamental challenge, as their rapid electroreduction into metallic Cu undermines C-C coupling and long-term selectivity. Here, we overturn this limitation through the interfacial engineering of Cu2O with hydroxy-terminated Ti3C2Tx MXene, creating an adaptive catalyst that sustains Cu(I) redox dynamics under strongly reducing conditions. In situ-grown Cu2O nanocubes leverage Ti3+ Lewis acid sites and surface -OH/F groups to establish a hydrophilic, locally oxidative microenvironment&#x2500;an unconventional stabilization regime that defies the typical reductive decay of oxide-derived Cu. This interfacial-driven approach delivers a 3-fold increase in activity and a 46% Faradaic efficiency toward C2 products, while maintaining stability beyond 70 h. Spectro-electrochemical Raman analyses, cyclic voltammetry, and real-time potential of zero charge analyses established that MXene-Cu coupling modulates and elevates local pH to enhance CO2 solubility, strengthens *CO adsorption, and uniquely stabilizes the rarely explored *C2H5O- intermediate, thereby providing an unexpected mechanistic pathway to selective multicarbon formation. By demonstrating that dynamic redox equilibria, rather than static oxidation states, govern efficient CO2-to-fuel conversion, this work redefines Cu-based electrocatalysis and establishes a new paradigm for designing resilient electrocatalysts through electronic and chemical environment control.

Mass spectrometry-based glycoproteomics is a critical platform for understanding the complex roles of protein glycosylation in biological systems, yet visualizing multidimensional glycoproteomics datasets remains a significant bottleneck in data interpretation and communication. Glycan microheterogeneity, i.e., the potential for a glycosite to be modified by multiple glycans, defies the binary presence-absence logic used in analyses of other post-translational modifications. Instead, glycoproteomics necessitates intentionally designed data structures and visualizations that are glycoform-centric, not just site-centric. Additionally, there is a need for complementary degrees of data analysis that alternate between glycoproteome-scale patterns and glycosite-specific regulation. Several bespoke frameworks for visualizing glycoproteomics data have emerged, but they often require advanced programming expertise and are designed for a single study rather than broad application. Here, we present our efforts to harmonize post-search data analysis of glycoproteomics through a modular R framework called GlycoDiveR. This platform streamlines import, transformation, and curation of qualitative and quantitative glycopeptide identifications, including support for raw output from multiple search engines. GlycoDiveR is designed to integrate seamlessly into existing analysis workflows by enabling fast, flexible exploration of highly dimensional glycoproteomics datasets via a consistently formatted data architecture. Our goal is to offer a customizable set of glycosylation-specific visualizations with minimal coding, while keeping data accessible to users who wish to further customize their characterization strategies. It also maintains a modular design that supports the continual addition of visualizations, analyses, and export functions. Ultimately, GlycoDiveR is meant to improve accessibility of glycoproteomic-specific analyses and lower the barrier to exploring biological narratives embedded in rich glycoproteomic datasets. GlycoDiveR is open-source and freely available at https://github.com/riley-research/GlycoDiveR.