搜索结果：Unprecedented

The decades-long absence of high-performance p-type amorphous oxide semiconductors (AOSs) remains a critical bottleneck in complementary circuit development and has severely limited exploration of charge trapping phenomena essential for neuromorphic computing. Here, we achieve a transformative breakthrough by demonstrating a p-channel amorphous oxide semiconductor through ultraviolet-ozone oxidation of crystalline tellurium to amorphous tellurium trioxide (a-TeO3). This revolutionary material integrates transistor, nonvolatile memory, photodetection, and synaptic functions in a single device, achieving an unprecedented level of functional integration. The a-TeO3 channel exhibits an unprecedented ultrawide memory window exceeding 58 V under ambient conditions, driven by oxygen vacancy-adsorbate interactions that enable robust multilevel switching. Ultraviolet illumination induces persistent photocurrent through carrier trapping/detrapping, enabling light-programmable synaptic plasticity and associative learning. Paired with monolayer molybdenum disulfide n-mode charge-trap memory (CTM), antagonistic charge-trap dynamics realize autonomous heating/cooling control and precise homeostasis. Hardware-constrained networks built on these complementary synaptic transistors achieve MNIST accuracy comparable to ideal digital systems. This light-reconfigurable p-n platform overcomes a critical materials barrier, unlocking scalable, energy-efficient neuromorphic architectures for edge artificial intelligence.

The past decade has witnessed an unprecedented convergence of exposomic technologies, population-scale genomics, and AI-enabled data science, creating the conditions for a new integrative discipline. Here we introduce ExposoGenomics, defined as the integrative study of how the genome and exposome, treated as jointly dynamic systems, interact across the life course to shape health and disease. ExposoGenomics moves beyond classical gene-environment interaction models by embedding high-dimensional, temporally resolved exposure data within a multi-omic and AI-enabled analytical architecture oriented toward causal discovery, mechanistic understanding, and translational application. We describe the conceptual foundations of this framework, its mechanistic architecture linking external exposures to genomic responses through physiologically based kinetic models and adverse outcome networks, and the analytical approaches, including causal machine learning, graph-based integration, and foundation models, required to realize its potential. We emphasize that computational prediction must be accompanied by rigorous empirical validation, and that findings must be grounded in biologically plausible, causally supported mechanisms. In conjunction with this Perspective, Human Genomics formally launches ExposoGenomics as a dedicated article category and invites submissions that advance this integrative agenda.

Infectious diseases continue to pose unprecedented challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce, and cause damage to host cells, while antibiotic resistance genes (ARGs) enable pathogens to withstand treatments that would otherwise be effective. The concurrent identification of VFs and ARGs is crucial for efficient pathogen surveillance. However, existing tools for predicting VFs or ARGs typically suffer from high false negative rates and limitations in identifying only high-identity genes against known reference VF or ARG databases. To address these challenges, we developed SEVA, an advanced model that integrates protein language models (pLMs) with structural and evolutionary protein features to predict VFs and ARGs from genome sequencing data. Integrating multiple homologous sequences can identify latent virulence or drug resistance caused by site mutations, reducing false negative rates. Meanwhile, the protein structure remains conserved despite the low sequence identity in some functional domains of VFs or ARGs. The aggregate of protein structure information further improves the identification abilities of VF and ARG. In addition, pLMs enable the model to capture high-dimensional feature representations more effectively. SEVA rigorously collected three datasets with over 20,000 genes and five reference databases. It outperforms state-of-the-art methods, including Diamond, VRprofile, FoldSeek, PreVFs-RG, PLM-ARG, ARG-BERT, and HyperVR, achieving an accuracy of 97.13% and confirming the efficacy of its key components, such as refined feature selection and multiple sequence alignment subsampling. SEVA takes protein sequences as input and derives evolutionary, structural, and statistical representations for prediction, making our model a reliable tool for VF and ARG prediction. This capability is particularly valuable in epidemic prevention and control, where accurate identification of VFs and ARGs is crucial. By providing concurrent and reliable predictions of VFs and ARGs, SEVA enhances our ability to respond to microbial threats effectively. This finding supports robust efforts to mitigate the spread of infectious diseases and safeguard public health, addressing a critical gap in contemporary epidemic response strategies. The SEVA model and data are available at https://github.com/kaiqili2/SEVA. Video Abstract.

The search for actionable genetic targets in cancer has evolved substantially over the past decade. Earlier approaches were focused on single genes or individual molecular alterations, but this is insufficient to capture tumor complexity in vivo. Cancer is influenced not only by genomic changes but also by transcriptional plasticity, epigenetic regulation, protein activity, metabolic adaptation, and dynamic interactions with the tumor microenvironment. Consequently, bioinformatic target discovery has shifted toward integrative, systems-level models of tumor biology. This article discusses the evolution of bioinformatic approaches for cancer target identification, underscoring key achievements and persistent challenges. Advances from 2018-2025 are analyzed, including multi-omics integration, single-cell sequencing, and functional genomics, which enhance the identification of context-dependent molecular vulnerabilities. Also, the role of machine learning in analyzing large-scale datasets to uncover potential therapeutic targets is discussed. Precision medicine now recognizes that genetic background alone is insufficient to define actionable targets. Factors such as cell type, tumor spatial context, environmental influences, clonal lineage and epigenetic state are vital. Current bioinformatic frameworks increasingly incorporate artificial intelligence, offer unprecedented opportunities to integrate these dimensions and refine target discovery.

Robotic proctoring is often considered a safety and training mechanism for surgeons adopting robotic platforms. While the benefits of proctoring for adopting surgeons have been described across specialties, the educational impact of proctoring on the proctor remains largely unexplored. This Perspective examines robotic proctoring from the proctor's vantage point and argues that proctoring represents a unique professional privilege and a powerful form of advanced surgical learning, offering rare access to the collective experience of surgeons and the accumulated influence of their mentors, institutions, and training traditions. Proctoring enables deliberate observation of diverse operative styles, intraoperative judgment, and system-level factors across institutions, facilitating comparative learning that is difficult to reproduce through personal operative experience alone. Educational theory supports this observation: the protégé effect demonstrates that teaching enhances the teacher's own learning, and the surgical coaching literature increasingly recognizes the bidirectional nature of structured peer-level educational exchange. This Perspective contrasts the largely informal evolution of laparoscopy with the structured implementation of robotics and discusses how centralized training ecosystems and mandated proctoring created an unprecedented opportunity for bidirectional educational exchange. Finally, implications for surgical education, credentialing, and faculty development are outlined, including the practical importance of institutional support, professional flexibility, and fair compensation to sustain proctoring as an enduring source of professional growth.

Systematical screening of the metabolic profile of marine fungus Aspergillus sp. EGF 15-0-3 using OSMAC-GNPS molecular networking cascade followed by target isolation of the environmental-induced products resulted the identification of eight unprecedented indole diketopiperazine-based hybrids (1-8) featuring three distinct chemical backbones. The structures of the obtained compounds were established by combination of extensive spectroscopic analyses, X-ray crystallography, and ECD calculations. All these environmental-induced metabolites were demonstrated to be unusual tyrosyl-DNA phosphodiesterase 2 inhibitors. Compound 3, as the most outstanding example, showed selective synergistic antitumor effect when combined with the chemotherapeutic drugs.

TLD1433 (Ruvidar®) is a Ru(II) polypyridyl complex currently in phase II clinical trials for the treatment of non-muscle invasive bladder cancer using photodynamic therapy (PDT). Previous studies have demonstrated that TLD1433 exhibits dual Type I/II photosensitization, arising from a combination of a prolonged intrinsic excited state lifetime and the redox-active α-terthienyl group, which together enable both singlet oxygen sensitization and light-driven redox processes. Despite this, molecular determinants contributing to its pronounced phototoxicity are still being elucidated. Herein, we investigated whether light-induced lipid membrane perturbation could represent a contributing photochemical property relevant to cytotoxic activity. To this end, all-atom molecular dynamics simulations and NMR experiments were employed to examine the insertion, partitioning, and residence of TLD1433 within a lipid bilayer in both dark and photoactivated states in normoxia and hypoxia, as well as the resulting effects on membrane structure integrity. Under normoxic conditions, oxidation events from the triplet excited state of TLD1433 promote localized accumulation of oxidized lipids and induce membrane deformations characterized by increased water penetration and transient pore-like features. In contrast, under low ROS-mediated lipid oxidation, membrane perturbations are more limited, although increased permeability and structural disorder are still observed. Taken together, these results suggest that light-induced lipid membrane perturbation, including poration, may represent a contributing factor to the phototoxic effects of TLD1433 under normoxic conditions. This combined computational and experimental study details how the photochemical properties of TLD1433 influence membrane response in a light- and oxygen-dependent manner, offering a framework for understanding how such effects may contribute to PDT efficacy at an unprecedented molecular level.

Under the combined pressures of frequent extreme climate events and the spread of unilateral trade policies, global agricultural industry and supply chains are facing an unprecedented crisis. Digital technology (DT) is a key driver of the new technological revolution and is profoundly reshaping food production, distribution, and consumption. However, its comprehensive impact on food security and the underlying mechanisms remain unclear. This study systematically elaborates the theoretical logic through which digital technology enhances food security. Using panel data from 285 prefecture-level cities in China from 2011 to 2024, we construct an innovative urban DT composite index based on the number of artificial intelligence patents and the coverage rate of digital infrastructure. A dynamic spatial Durbin model and a mediation effects model are employed for empirical testing. The findings reveal three main results. First, DT has a significant direct positive effect on urban food security and a significant positive spatial spillover effect, meaning that local DT development improves food security in neighboring regions. These results remain robust after using instrumental variable methods, substituting core variables, and performing multiple robustness checks. Second, the mechanism analysis shows that DT indirectly enhances food security by improving agricultural socialized services, accelerating land transfer efficiency, and reducing climate-induced volatility in agricultural production. Among these channels, agricultural socialized services have the strongest mediating effect. Third, heterogeneity analysis indicates that the enabling effect of DT on food security is more pronounced in non-major grain-producing areas, regions with flat terrain, and cities with higher levels of financial development, suggesting that DT helps bridge the digital divide in agricultural development across regions. This study deepens the understanding of food security mechanisms in the digital economy era and provides a solid theoretical and empirical foundation for formulating precise, differentiated policies for digital villages and smart agriculture, thereby supporting the development of new-quality productivity in agriculture.

Diabetes mellitus affects approximately 589 million adults worldwide, underscoring the need for accurate and noninvasive glucose monitoring to reduce complications. Although Raman spectroscopy shows promise for noninvasive blood glucose detection due to its molecular specificity and minimal interference, its application has been limited by the oversimplification of glucose as an open-chain structure in solution and unresolved assignments of characteristic bands-particularly the widely used 1125 cm⁻¹ peak. In this study, we combine density functional theory simulations with experimental Raman spectroscopy to elucidate the vibrational origins of glucose bands across all physiologically relevant tautomers and derivatives, including α-D-Glucopyranose and β-D-Glucopyranose, α-D-Glucofuranose and β-D-Glucofuranose, the open-chain form, D-Glucose-monohydrate, and 1,5-Anhydroglucitol. We present the first comprehensive, assignment-validated Raman spectral atlas and vibrational assignment of accurate aqueous glucose structures, achieving unprecedented agreement between theory and experiment. Highly specific spectral markers are identified to distinguish pyranose and furanose rings, anomeric configurations, hydration states, and the bicyclic features of 1,5-Anhydroglucitol. These findings resolve decades-long controversies in glucose vibrational spectroscopy and provide essential reference standards to advance Raman-based noninvasive monitoring of short-term glycemic control and early glycation risk in diabetic patients.

In recent years, German long-term care (LTC) insurance has experienced an unprecedented increase in the number of beneficiaries. This raises the question of the role of health care in preventing or delaying the need for LTC. At present, related findings are limited. Addressing this research gap could promote longevity and improve quality of life while reducing the financial strain on the social security system. This study aimed to investigate the associations between the utilization of health care services and first-time LTC need. This retrospective cohort study examined nationwide linked claims data from the German statutory health and LTC insurance fund, AOK. The dataset included all individuals aged &#x2265;60 years. Using multiple logistic regression, we investigated the association between health care utilization during the 5-year exposure period from 2016 to 2020 and the occurrence of first-time LTC need during the first quarter of 2021. Physician care, pharmaceutical care, physiotherapy, and medical aids were analyzed while adjusting for age, sex, regional variables, and comorbidities. Metrically scaled variables were categorized using the Fisher-Jenks algorithm to explore possible nonlinearities. Individuals who needed LTC prior to 2021 were excluded. The study population comprised 5.3 million individuals. A total of 54.3% (n=2.9 million) were women, and the mean age was 71.3 (SD 8.16) years. Receiving more than 2 of the 5 recommended screenings and vaccinations examined, compared with receiving none, was associated with a strong reduction in the odds of first-time LTC need (odds ratio [OR] 0.62, 95% CI 0.60-0.64; P<.001). Odds of first-time LTC need were also significantly lower with high numbers of specialist groups and low numbers of specialist days (more than 7 specialist groups consulted and fewer than 48 billing days) compared with no specialist utilization (any specialist utilization: OR 0.82, 95% CI 0.79-0.85; P<.001; more than 7 instead of fewer than 5 specialist groups: OR 0.94, 95% CI 0.92-0.96; P<.001). Likewise, a physiotherapy prescription in between 5 and 10 quarters of the 5-year period instead of none was related to lower odds of first-time LTC need (OR 0.81, 95% CI 0.79-0.83; P<.001). Generalist care, hospitalizations, polypharmacy, and potentially inadequate medication were concomitant with first-time LTC need. Among the disease management programs and medical aids examined, there were both positive and negative relationships with first-time LTC need. Utilization of recommended screenings, vaccinations, specialist physician care, and physiotherapy is substantially and significantly associated with the nonoccurrence of first-time LTC need in the older German population. Our study provides a foundation for future research on orienting health care toward preventing functional decline.

Aqueous zinc metal batteries (AZMBs) are promising candidates for large-scale energy storage owing to their intrinsic safety. However, their lifespan is severely limited by side reactions such as dendrite growth and hydrogen evolution at the Zn-electrolyte interface. Conventional single-electrolyte-additive approaches are thermodynamically constrained, yielding only insufficient coverage of the inner-Helmholtz plane (IHP) and poor control of interfacial reactions. Here, we report an interfacial fluorinated-ion crowding strategy by simultaneously introducing multiple low-concentration fluorinated additives. Computational and spectroscopic analyses reveal that various-sized F-groups densely occupy the IHP, displacing water molecules and homogenizing Zn2+ flux. This emergent crowding effect, inaccessible to single-additive strategies, enables unprecedented interfacial regulation. Electrochemical tests demonstrate ultrastable Zn plating/stripping over 1200 h at 5&#xa0;mA cm-2 and 1800 h at 10&#xa0;mA cm-2, more than tenfold longer than the baseline electrolyte. This work establishes interfacial ion crowding as a powerful design principle, rooted in fundamental electrochemistry, offering a pathway toward high-performance and durable AZMBs.

Pentachlorophenol (PCP) is a legacy persistent organic pollutant posing continuous threats to aquatic ecosystems. In this study, the systemic toxic mechanisms of PCP in zebrafish were investigated using a comprehensive toxico-multi-omics approach, integrating targeted metabolomics (GC-MS/MS) and proteomics (LC-Orbitrap-MS/MS). Zebrafish were exposed to PCP for 48&#x202f;h in three groups: control (no exposure), low exposure (1/10 LC50), and high exposure (LC50). Integrative analysis identified 75 metabolomic and 376 proteomic biomarkers, revealing severe perturbations in interconnected pathways, including the pentose phosphate pathway and multiple amino acid metabolisms. Notably, the downregulation of crucial proteins (echs1 and hibch) and metabolites (N-acetyl aspartate) reflected a coordinated disruption of valine/branched-chain amino acid catabolism and mitochondrial energy metabolism, supported by concordant protein and metabolite changes. Furthermore, significantly elevated malondialdehyde levels confirmed structural cellular damage, while robust compensatory activations in innate defense mechanisms, specifically taurine and glucuronate metabolisms, were elucidated. Collectively, this integrated multi-omics investigation provides unprecedented mechanistic insights into the intricate interplay between PCP-induced metabolic and redox disruptions and the compensatory cellular defense systems in aquatic organisms.

The COVID-19 pandemic has imposed unprecedented psychological demands on nurses working in care homes, profoundly affecting their well-being. Thus, this cross-sectional study examined the psychological burden among German care home nurses during the fourth COVID-19 wave and the early phase of the fifth wave in Germany, focusing on posttraumatic stress disorder (PTSD) and burnout, and assessed the availability of workplace psychosocial resources. A total of 719 nurses completed an online cross-sectional convenience survey between October 2021 and January 2022. Given the voluntary online recruitment, the findings should be interpreted with caution due to the potential for self-selection bias. PTSD symptoms were assessed using the International Trauma Questionnaire (ITQ), and burnout dimensions were assessed using the Copenhagen Burnout Inventory (CBI). Multiple linear regression analyses were conducted to identify factors associated with PTSD and different dimensions of burnout, including personal burnout, work-related burnout and resident-related burnout. Among nurses, 91.4% fulfilled PTSD screening criteria and/or exceeded the indicative CBI cut-off for at least one burnout dimension, indicating a high psychological burden within the present study sample. Specifically, 29.5% met screening criteria for PTSD, 90.0% exceeded the indicative CBI cut-off for personal burnout, 78.9% for work-related burnout, and 33.4% for resident-related burnout. Only 21.7% reported availability of psychosocial support at work. PTSD was significantly associated with poorer general health, lower job satisfaction, COVID-19-related deaths among residents in care home, dementia-focused care and female gender. Personal burnout was significantly associated with poorer general health, lower job satisfaction, female gender, absence of participatory leadership at care home, younger age, no COVID-19 infection history, and lower posttraumatic growth. Work-related burnout was significantly associated with lower job satisfaction, poorer general health, lower posttraumatic growth, female gender, no COVID-19 infection history, absence of participatory leadership at care home, and full-time employment. Resident-related burnout was significantly associated with lower job satisfaction, poorer general health, lower posttraumatic growth, and no COVID-19 infection history. The high levels of PTSD and burnout found in the present study likely reflect strain placed by the pandemic as well as constant work-related strain on care home nurses. These findings underscore the urgent need for improved psychosocial support and organizational strategies to strengthen mental health in long-term care settings. Not applicable.

Seeking universal rules that govern leaf size variation is a long-standing aspiration in ecology. Early studies propose that the inverse of the product of leaf tissue density and thickness, termed the Hughes constant, is approximately conserved within species, which renders the ratio of leaf area (An) to fresh mass (m) invariant. We tested this proposition with an unprecedented dataset encompassing c. 157&#x2009;000 leaves from 335 woody species across China. Using the allometric model A n = c &#xb7; m &#x3b1; , we assessed for each species whether &#x3b1; &#x2009;=&#x2009;1, as required for a constant An : m. We further examined whether and how &#x3b1; and An : m varied across plant habits and climate regimes. The grand mean (0.923) of &#x3b1; across species was significantly lower than unity, and 69% of species exhibited &#x3b1; &#x2009;<&#x2009;1, indicating diminishing returns of leaf area on increasing fresh-mass investments. Notably, evergreen species exhibited lower An : m ratios (higher construction costs per unit area) but higher &#x3b1; values (greater returns to scale) than deciduous species. Climatic factors explained little variation of &#x3b1;, but higher temperatures were associated with lower An : m ratios. Altogether, the Hughes constant represents an approximate tendency rather than a universal rule. Yet, An : m ratios are highly species-specific, bearing a functional significance in discriminating plant habits and thermal niches.

Electrochemical urea synthesis offers a sustainable strategy for the purification and resource utilization of greenhouse gases and water pollutants. However, kinetic disparities between the competing reduction pathways of CO2 and NO3- lead to spatiotemporal mismatches between C- and N-intermediates, severely hindering efficient C-N coupling. In this context, we demonstrated a strategic design of enzyme-inspired "spatial decoupling-temporal coupling" on a ferroelectric SnS-Cu0.15-TiO2 heterojunction. A high urea yield rate of 2392.5 &#x3bc;g h-1 mgcat-1 with a Faradaic efficiency (FE) of 71.2% outperforms the state-of-the-art electrocatalysts. Atomically dispersed Cu1 sites induce an unprecedented ferroelectricity enhancement, whereby a strengthened built-in electric field generates spatially resolved charge domains for coactivation of both CO2 and NO3-. Correlated in situ Raman and infrared spectroscopic studies disclose that the S-Cu1-O interfacial channels mediate remote *CO spillover and its dynamic, temporal coupling with *NH2. Moreover, a coupled electrolyzer integrating the electrocatalytic upcycling of wastewater-derived NO3- and Cl- achieves a remarkable 79.4% urea FE, while enabling simultaneous environmental remediation. This work highlights the unique advantages of ferroelectric heterojunctions-mediated dynamic catalysis in regulating complex reaction networks for urea electrosynthesis, opening sustainable electrochemical routes toward CO2 mitigation and the green transformation of coastal wastewater treatment.

Ground-state electronic structure calculations using Kohn-Sham density functional theory (KS-DFT) offer an unprecedented balance between efficiency and accuracy, now paradigmatic to the fields of quantum chemistry and condensed matter physics. KS-DFT can be extended to model electronic excitations through density mapping onto a non-interacting ensemble state in which, unlike in thermal theories, the weights assigned to the excited states vary independently. Ensemble DFT (eDFT) has lately become a vibrant area of research thanks to its numerous appeals, such as the adequate treatment of multiple excitations for which the widely used time-dependent extension of DFT struggles. Recently, an enlarged type of ensemble, referred to as a N-centered (Nc) ensemble, has been introduced to describe within the same unified formalism both neutral and charged electronic excitations. This perspective paper provides a detailed exposition of exact Nc-eDFT, with a comprehensive review of its formal developments. To cut practical computational tools out of the exact theory, three original strategies are presented, complementing existing approaches. The first one, related to the design of ensemble density-functional approximations, consists in recycling regular ground-state functionals by dressing them with a weight-dependent scaling function deduced from exact properties of eDFT. We then explore quasi-degenerate formulations of ensemble density-functional perturbation theory, suggesting alternative definitions for the ensemble Hartree, exchange, and correlation energies, individually, and paving the way toward robust orbital-dependent eDFAs. Finally, we revisit and generalize the concept of quantum bath for an ensemble of non-interacting states, laying the foundations of an in-principle exact (in the sense of lattice eDFT) quantum embedding theory of excited states.

Single-cell multimodal omics offer unprecedented resolution of cellular networks, yet translating continuous computational attributions into structured, testable biological mechanisms remains a persistent bottleneck. To address this limitation, we introduce an analytical pipeline employing decision trees to discretize continuous neural network attributions into explicit regulatory thresholds. These boundaries then structurally constrain large language models, enabling them to integrate established literature with empirical data to synthesize context-specific hypotheses. Applying this continuous-to-discrete framework across sparse datasets yielded novel biological mechanisms. Specifically, the framework articulated a cytoskeletal gating hierarchy governing EGF-stimulated pathways, identified transcriptomic drivers of input resistance in cortical interneurons, and delineated translational logic predicting Ki-67 abundance within spatial transcriptomics. Retrospective benchmarking validated the capacity of the framework to autonomously reconstruct published regulatory logic. Supported by a locally deployable open-weight language model and a code-free interface, this approach establishes an auditable methodology to extract robust experimental hypotheses from high-dimensional single-cell data.

The accelerating convergence of global crises, including climate change, pandemics, geopolitical conflict, forced migration, digital transformation, and rising social inequality, has fundamentally reshaped the developmental environment for emerging adults (ages 18-29). Positioned at the critical juncture of identity formation and societal integration, this cohort faces unprecedented structural and psychological challenges. While scientific discourse, exemplified by the recent Lancet Psychiatry Commission, increasingly links macro-level megatrends to deteriorating mental health in this group, the specific transmission mechanisms remain analytically challenging. Building on this discourse, the article proposes a conceptual systematization of these pathways. The crisis concept is expanded beyond objective events to encompass crisis as a permanent narrative and a mechanism of ideological disruption. A critical reflection addresses the Western-centric bias in precarity discourse and conceptualizes the "polycrisis" as a syndemic that exacerbates vulnerabilities differently across the Global North and South. By integrating structural precarity, erosion of the symbolic order, and mechanisms underlying compromised transitions, a multi-layered transactional framework is outlined. This model links systemic disruptions to the dual developmental tasks of identity formation and social integration, providing an analytical lens to understand how structural incoherence contributes to individual psychopathology.

This review systematically summarizes the principles, technological advancements, and diverse applications of Stokes-vector and Mueller-matrix polarization imaging. The polarization of light is a fundamental property that provides unique and valuable information about the interaction between light and matter. While traditional polarimetry is limited to point-by-point measurements, polarization imaging captures the polarization state in a twodimensional scene, generating a spatial map of parameters for complex and heterogeneous systems. The Stokes-Mueller formalism is the only complete mathematical tool for polarization analysis, using the Stokes vector to describe the light's state and the Mueller matrix as the transfer function to fully characterize the polarization-altering properties of any medium, including complex depolarizing tissues. The evolution of polarization imaging from time-sequential to snapshot paradigms represents a pivotal shift, driven by the pressing need for real-time, motion-artifact-free characterization of dynamic systems. While time-sequential systems laid the foundational framework, their inherent trade-off between acquisition speed and polarization completeness has spurred intense innovation in snapshot methodologies. This transition is not merely a technical improvement but a fundamental enabler for applying polarization analysis to in vivo biological processes and real-time industrial inspection. Applications of polarization imaging are wide-ranging, including label-free diagnostics for cancer detection, non-destructive analysis of anisotropic materials, polarization-enhanced target detection, and so on. A future direction is the convergence of polarization imaging with hyperspectral detection to form "hyper-Stokes/Mueller imaging," which promises unprecedented specificity and real-time capability.