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Solvated ions of the same valency and charge exhibit minor differences in bulk transport but may display strong ion-specific effects in nanoscale environments. Investigating such effects is challenged by the heterogeneous nature of conventional nanostructured membranes, which can smear out underlying structure-property correlations. We use a combination of electrochemical impedance spectroscopy, two-dimensional infrared spectroscopy, NMR relaxometry, and molecular dynamics simulations to systematically investigate anion transport in nanoporous polymers with uniform charged 1 nm scale pores. The pores are water-containing channels formed by lyotropic self-assembly of positively charged amphiphilic monomers that are then cross-linked to produce a highly ordered nanoporous polymer. Across a series of monovalent anions, we observe strong correlations of activation energy and conductivity with hydration enthalpy; more strongly hydrated species have higher conductivity and lower activation energies. These effects originate from differences in pore-wall interactions and solvation shell behavior, with more weakly hydrated species showing larger departures from their bulk behavior in their water coordination and activation energy in the membrane. Our results indicate that pore confinement amplifies the impact of water contributions to ion motion. Specifically, the ability to maintain hydration shell waters and concomitantly to avoid interactions with hydrophobic pore wall patches leads to significant differences in transport and to ion-specific trends that are unexpected in nanoporous materials. These results provide insight into ion transport in highly confined and hydration-limited geometries and suggest a mechanism by which ion selectivity can be explicitly manipulated.

Hospital-at-home (HaH) may help address rising healthcare demands in ageing populations by providing hospital-level care outside the hospital. In the Netherlands, an integrated care pathway (ICP) 'The Hague Respiratory Tract Infection Care Bridge', was developed for adults aged ≥65 years with moderate-to-severe lower respiratory tract infections, offering care through HaH, hospital-at-nursing-home (HaNH) or hospital admission. This study explored experiences with this ICP among the different care settings from the perspectives of patients, informal caregivers and general practitioners (GPs). Semi-structured interviews were conducted with participants across the 3 pathways, including 34 patients, 33 informal caregivers and 7 HaH GPs. Data were analysed using deductive thematic analysis guided by the Consolidated Framework for Implementation Research. Three themes emerged from patient and informal caregiver interviews: matching care to needs, shared decision-making and communication, and organisational collaboration. HaH and HaNH were generally seen as positive alternatives to hospitalisation, although feasibility depended on patients' and caregivers' physical and emotional circumstances. Overall satisfaction with care was high. Two themes were identified from GP interviews: practical feasibility and engagement in the HaH-pathway. Overall, GPs supported out-of-hospital treatments, but faced barriers regarding unclear responsibilities, minimal involvement in triage and fragmented communication across providers. Delivering hospital-level care outside the hospital for older adults requires context-sensitive, individualised approaches that align with patients' and informal caregivers' circumstances. Perceived safety and acceptability depended on this alignment and on effective organisational collaboration. Sustainable integration of HaH in GP practice requires clear communication pathways and greater involvement in decision-making. These findings provide practical guidance for implementing HaH and HaNH in acute care for older adults.

The growing crisis of antimicrobial resistance urges the immediate development of new therapeutic pharmaceuticals that can overcome current resistance mechanisms, including biofilm formation. This study included the synthesis and strategic design of a novel set of quinoline-based scaffolds (3-11) to enhance interactions with the bacterial Peptide Deformylase (PDF) enzyme. The compounds underwent extensive in vitro biological assessment, comprising initial inhibition zone (IZ) screening, followed by quantitative minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) tests against a range of Gram-positive, Gram-negative, and fungal strains. The synthesized derivatives exhibited potent, primarily bactericidal activity, with compounds 4, 6, and 10 emerging as notably outstanding candidates. These three compounds exhibited significant activity against S. aureus (MIC = 7.8 μg/mL), demonstrating equivalent potency to the standard reference antibiotic. Additionally, the anti-biofilm efficacy of these leading candidates was assessed against S. aureus and S. typhi at sub-inhibitory doses. Compound 6 emerged as a prominent dual-action drug, demonstrating strong bactericidal activity and significant dose-dependent biofilm destruction. It exhibited considerable biofilm inhibition against both S. aureus (56.76%) and S. typhi (65.93%) even at substantially diluted sub-lethal concentrations (25% MBC). The findings strongly confirm the substituted benzo[h]quinoline core as a highly promising pharmacophore for the development of next-generation antimicrobial medicines effective against both free-floating planktonic cells and tough structured biofilms.

Exercise is widely recommended in oncology; however, its role in orthopedic oncology remains insufficiently defined within a clinical context where tumor biology, skeletal fragility, muscle loss, systemic therapy, and reconstructive biomechanics converge. Existing rehabilitation frameworks primarily emphasize functional recovery, while the biological integration between mechanical loading and tumor-host systems remains incompletely characterized in clinical decision-making contexts. This narrative review synthesizes mechanistic evidence across tumor mechanobiology and its integration with bone-muscle endocrine crosstalk, marrow niche regulation, immunometabolism, and vascular-metabolic adaptation. Key pathways, including integrin-FAK/Src signaling, RhoA/ROCK dynamics, YAP/TAZ and β-catenin transcriptional regulation, Piezo-mediated mechanosensing, and load-sensitive RANKL/OPG balance, are examined within hybrid movement strategies that integrate aerobic, resistance, neuromotor, isometric, eccentric, and blood flow-restricted modalities. Emerging evidence indicates that mechanical stimuli may be associated with coordinated changes in extracellular matrix organization, perfusion dynamics, and immune cell trafficking, with secondary effects on mitochondrial function and marrow adiposity. These effects appear to remain context-dependent, with implications for fracture risk, cachexia, neuropathy, and treatment tolerance. However, the boundary between adaptive remodeling and pro-invasive signaling remains incompletely defined, supporting a threshold-dependent rather than linear dose-response relationship. Accordingly, this review proposes a safety-calibrated, phase-sensitive framework for translational movement dosing in orthopedic oncology. Future research should prioritize biomarker-driven clinical trials and temporally resolved dosing strategies to clarify how structured movement may support musculoskeletal integrity while remaining aligned with oncologic treatment constraints.

The phased framework of oncology trials is designed to ensure patient safety and conserve resources by advancing only promising therapies from early- to late-phase testing. Despite decades of refinement, overall trial success rates-defined by the proportion of studies ultimately supporting regulatory approval-remain low, with failures increasingly occurring in late-phase studies. These failures are often contributed to by methodological shortcomings, including suboptimal end point selection, restrictive eligibility criteria, and inefficient trial designs. Although traditional approaches to biomarker discovery, outcome validation, and eligibility refinement have yielded transformational advances, increasing molecular subclassification of tumors into rare subgroups results in the conventional drug development framework being no longer fit for purpose. Artificial intelligence (AI) offers opportunities to enhance the efficiency, precision, and patient-centeredness of oncology trials. Deep learning systems integrate and analyze large data sets to uncover complex patterns often inaccessible to conventional methods. AI has potential applications in patient-trial matching, optimization of eligibility criteria, statistical modeling of survival outcomes, and the identification of novel surrogate end points although these applications remain largely investigational and are not yet established for routine use. This scoping review provides a structured overview of AI applications in oncology trials, with emphasis on outcome selection and surrogate end point evaluation. We also highlight emerging areas with potential for immediate implementation, such as patient selection, biomarker identification and synthetic control arms, to accelerate development and enhance clinical care. However, broader harmonization is needed to ensure reproducibility, transparency, and regulatory confidence before implementation. Ultimately, early and sustained collaboration between trialists, AI developers, and regulators will be essential to ensure that AI delivers meaningful advances in the design, evaluation, and delivery of new medicines.

Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care. This study aims to systematically summarize NLP methodologies for psychiatric clinical notes, compare major modeling paradigms and application areas, and highlight emerging large language model (LLM) trends, key challenges, and future research directions. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a literature search was conducted for articles on NLP methods based on psychiatric clinical notes published from January 2021 to December 2025 in Ovid MEDLINE, Ovid EMBASE, PubMed, Scopus, Web of Science, the ACM Digital Library, and ScienceDirect. This scoping review analyzed NLP methods applied to psychiatric clinical notes, focusing on major trends, identifying suitable features for traditional machine learning (ML)-based models, applications of pretrained language models (PLMs), and key challenges. Approaches were categorized as rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based methods across information extraction and text classification tasks. In total, 101 studies were eligible for inclusion. Rule-based methods (n=36) and hybrid approaches (n=34) remained the most widely used techniques, largely favored for their interpretability in handling nuanced, subjective clinical notes. These were followed by DL (n=15), traditional ML (n=10), and LLM-based approaches (n=6). Traditional ML studies relied heavily on engineered features, which could be grouped into 5 broad categories: domain knowledge features, lexical and statistical features, vector-based semantic features, emotion-related features, and temporal features. PLMs improved performance mainly through domain adaptation and task-specific fine-tuning, enhancing the handling of psychiatric language, medical terminology, and clinical note structure. LLM-based studies, although still limited in number, indicated a growing shift toward generative and reasoning-based applications. Hybrid NLP approaches remain dominant, combining domain rules with ML for extraction and classification. DL approaches continue to advance, with domain adaptation supporting medical terminology and clinical semantics. LLMs may further automate complex workflows via zero-shot capabilities and reasoning, alongside growing interest in temporal modeling and multimodal integration. Key future needs include improved generalizability across institutions, privacy protection, and careful attention to ethical implications in clinical deployment.

Osteogenesis imperfecta (OI) is a rare genetic disorder characterized by bone fragility and recurrent fractures. Emerging biologics demonstrate promise by targeting bone-remodeling pathways, yet evidence for their efficacy and safety remains fragmented and heterogeneous, and no prior systematic review in OI has incorporated artificial intelligence (AI) to synthesize it. This study aims to systematically evaluate the efficacy and safety of novel biologics in patients with OI using an AI-assisted workflow for evidence synthesis. We conducted a systematic review and meta-analysis of interventional trials of denosumab, setrusumab, teriparatide, romosozumab, and fresolimumab. Data were retrieved from PubMed, Web of Science, Embase, ScienceDirect, the Cochrane Library, and ClinicalTrials.gov up to December 1, 2025. Eligible studies enrolled individuals with OI, reported areal bone mineral density (aBMD) and/or fractures, and were randomized, nonrandomized, or single-arm studies; case series were excluded. As a methodological feature, GPT-4o was integrated into the workflow to perform a parallel 2-stage screening (title/abstract and full text) and to assist with risk of bias assessment using an adapted Cochrane RoB 2 tool. The primary outcome, percentage change in aBMD, was synthesized using a random-effects meta-analysis. GPT-4o was benchmarked against human reviewers using sensitivity, specificity, and weighted Cohen κ. Thirteen trials (n=684) were systematically reviewed, of which 10 (n=333) contributed to meta-analyses. In children, denosumab produced the greatest 12-month increase in lumbar spine aBMD (25.49%, 95% CI 17.14%-33.84%). In adults, setrusumab at 12 months yielded the highest improvement (9.38%, 95% CI 6.5%-12.26%). Across trials, no biologic significantly reduced fracture incidence compared to bisphosphonates. Safety profiles varied: denosumab was associated with a high risk of hypercalcemia in children (30.95%), whereas setrusumab had no treatment-related serious adverse events. AI achieved high sensitivity in abstract (97.4%) and full-text (88.9%) screening, and reduced total screening time by over 95%. Although there was substantial agreement with humans in the quality assessment (Cohen κ=0.778, 95% CI 0.710-0.846), the model exhibited optimism and positional biases due to reliance on probabilistic language patterns rather than structured clinical reasoning. This review is the first to synthesize and quantitatively compare skeletal outcomes across multiple biologics in OI with an AI-assisted review workflow. Denosumab and setrusumab demonstrate promising efficacy in improving lumbar spine aBMD across ages, although current evidence does not support superior fracture reduction over bisphosphonates. GPT-4o can substantially accelerate evidence synthesis but should be deployed with explicit human oversight in tasks requiring contextual understanding and clinical reasoning. These findings should be interpreted cautiously given the small and heterogeneous trial base. Taken together, our workflow presented how evidence synthesis may be scaled and operationalized in real-world rare disease research.

This manuscript investigates the limitations of enzyme-electrocatalyst coupling in one-step designed amperometric cholesterol biosensors. One-step electrodeposition from Pd-ChOx-Nafion electrolyte produces a bioinorganic hybrid sensing layer, while preserving the enzyme's native conformation and biocatalytic activity. Despite structural coupling between Pd and ChOx, the amperometric response remains dominated by enzymatic H2O2 production, indicating that direct electronic communication between ChOx and the electrode is not established. More specifically, we show that direct electron transfer (DET) cannot occur even in ultrathin electrodeposited nanostructured films, likely due to oxygen-dominated electrochemistry. Furthermore, the sensitivity of cholesterol determination using Pd-NPs/ChOx/Nafion-modified electrodes was independent of layer thickness and architecture, highlighting the limitations of the Pd-NPs-enzyme interface. These findings provide mechanistic insight into ultrathin enzyme-electrocatalyst interfaces and are expected to impact the development of next-generation cholesterol biosensors.

Sport-specific mechanical demands and progression from youth to elite competition may influence musculotendinous morphology and function, that potentially contribute to knee pain and injury in athletes. The objective of this study was to assess sport- and age-related differences in the knee extensor musculotendinous morphology and function, and self-reported knee pain and injury incidence in competitive athletes. A total of ninety male athletes volunteered for the present study: cyclists (CY), basketball players (BP), and ice hockey-players (IH), with 15 youth (U19 teams - 17.3&#xa0;&#xb1;&#xa0;1.1&#xa0;&#xa0;years) and 15 elite men (27.2&#xa0;&#xb1;&#xa0;3.8&#xa0;&#xa0;years) per discipline. Muscle thickness (vastus lateralis, rectus femoris, vastus medialis) and patellar tendon cross-sectional area (CSA) were assessed with ultrasonography. Maximal isometric and isokinetic torque, and countermovement jump (CMJ) height were measured. Anterior knee pain (AKP) and knee injury history (KIJ) were collected via structured interviews. Data were analyzed using a two&#x2011;way ANOVA with discipline and age category as between&#x2011;subject factors. A discipline&#xa0;&#xd7;&#xa0;age interaction was observed for quadriceps muscle size (QMS) (p&#xa0;=&#xa0;0.01). Elite athletes showed larger muscles than youth and CY had smaller absolute QMS but higher body-mass-normalized values than BP and IH (p&#xa0;<&#xa0;0.05). Muscle and tendon size and maximal torque were greater in elite athletes (p&#xa0;<&#xa0;0.03), but differences disappeared when normalized for body mass. CY displayed lower patellar tendon stress and CMJ height than BP and IH (p&#xa0;<&#xa0;0.05). Elite athletes reported higher AKP and KIJ than youth (p&#xa0;&#x2264;&#xa0;0.05). Cycling was associated with a greater muscle size-to-body mass ratio, yet lower tendon stress, whereas basketball and ice hockey were associated with superior explosive performance. Despite proportional scaling from youth to elite suggesting a coordinated muscle-tendon development, elite athletes suffer more from knee pain and injury rates.

Hepato-pancreato-biliary (HPB) cancers frequently develop peritoneal metastases, signalling a poor prognosis and severely limiting the efficacy of systemic treatments. This review evaluates the feasibility, efficacy, and safety of Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) for these malignancies. We conducted a systematic review in accordance with the PRISMA guidelines. A comprehensive, structured search strategy was applied to PubMed, Scopus, and Web of Science to identify eligible non-interventional studies involving adult patients with HPB cancers and peritoneal metastases undergoing PIPAC. Ten studies encompassing 332 patients were included. PIPAC was feasible, with non-access rates ranging from 0% to 11.8%. Patients completed a mean of 1.3-1.8 cycles for hepatobiliary and 2.1-3 cycles for pancreatic cancers. Median overall survival ranged from 85 days to 15.1 months for hepatobiliary malignancies, and 8.5 to 12.7 months for pancreatic cancers. The procedure demonstrated a high safety profile, with no severe postoperative systemic toxicities (Common Terminology Criteria for Adverse Events grade &#x2265;3) reported. PIPAC appears to be a safe, feasible and potentially efficacious intervention for HPB peritoneal metastases. However, current evidence remains heterogeneous, necessitating standardised, prospective trials to clearly define its clinical role.

Chiari malformation type I (CM-I) is traditionally conceptualized as a structural disorder of the posterior cranial fossa characterized by cerebellar tonsillar herniation. However, increasing clinical and experimental evidence suggests that CM-I may be associated with distributed neurocognitive and affective sequelae consistent with cerebellar network dysfunction. The authors conducted a PRISMA-compliant systematic review of the MEDLINE and Embase databases from database inception through November 2025 to identify studies reporting standardized neuropsychological outcomes in pediatric and adult patients with CM-I. Given substantial heterogeneity in study design, cognitive instruments, and outcome reporting, findings were synthesized using a structured narrative approach. Methodological quality was assessed using an adapted Newcastle-Ottawa Scale and mapped to Agency for Healthcare Research and Quality criteria. Thirty-four studies comprising 2113 individuals with CM-I met inclusion criteria. Across pediatric and adult cohorts, selective cognitive vulnerabilities were most consistently observed in complex attention, executive function, visuospatial processing, learning and memory, language, and higher-order social cognition. These profiles closely resembled features of cerebellar cognitive affective syndrome. Cognitive deficits varied by developmental stage and were modulated by chronic pain, psychiatric comorbidity, and neurodevelopmental factors. Advanced neuroimaging studies demonstrated widespread disruption of cerebello-thalamo-cortical and cortico-ponto-cerebellar networks. Reported effects of posterior fossa decompression on neurocognitive outcomes were heterogeneous and inconsistent across domains and study designs. The available evidence supports reconceptualizing CM-I as a heterogeneous, large-scale brain network disorder with clinically meaningful cognitive and affective consequences in a subset of patients. Integration of domain-specific neuropsychological assessment into routine clinical evaluation may improve prognostication and guide future mechanism-based interventions.

To examine the dimension-level network linking patient safety climate and barriers to medication administration error reporting among nursing students in psychiatric clinical training. Patient safety climate and reporting barriers are important to nursing students' medication safety, but they are often examined separately or as total scores, limiting understanding of dimension-level connections. A cross-sectional, dimension-level network analysis. Data were collected from 1565 nursing students undertaking psychiatric clinical internships at a tertiary psychiatric hospital in Chengdu, China, between July 2020 and March 2025. Participants were recruited by convenience sampling and completed an anonymous online questionnaire distributed by teaching coordinators. Patient safety climate was measured using the Safety Climate Scale and reporting barriers were measured using the Barriers to Medication Administration Error Reporting Questionnaire. Dimension-level network analysis was used to identify central and bridge dimensions, with robustness examined using bootstrap and sensitivity analyses. The network showed two distinct but connected communities: patient safety climate and reporting barriers. The strongest connections were Fear-Face, Training-Worker Safety, Training-Team Positive Attitude and Disagreement over Medication Error-Reporting Effort. Blame, Worker Safety, Fear and Training were among the most central dimensions. Blame and Fear were the main bridge dimensions, followed by Worker Safety and Disagreement over Medication Error. Patient safety climate and reporting barriers were linked through psychosocial and psychiatric-context-specific dimensions. Findings suggest further development and evaluation of student-oriented reporting policy, just-culture education, psychiatry-specific guidance, worker-safety preparation and structured debriefing. Future studies should evaluate reporting readiness, psychological safety and reporting behaviour.

Spatiotemporal vortices are polychromatic modes that intertwine orbital angular momentum in space and time. Here, we introduce a new class of such vortices, "spatiotemporal plasmonic vortices," carrying nontrivial topological spin textures. They are generated by chronotopic interference of temporally delayed plasmonic eigenvortices, where a &#x3c0;-phase dislocation in the space-frequency domain maps into a 2&#x3c0; spiraling phase in space-time, with the resulting focus-defocus dynamics emulating U(1) gauge transitions. Using interferometric time-resolved photoemission electron microscopy, we directly image their nanometer-attosecond evolution and control vortex number and position. Quantum-path analysis of coherent two-photon photoemission processes reveals the nonlinear plasmonic polarization fields and angular-momentum conservation, establishing spatiotemporal plasmonic vortices as a platform for probing spatiotemporally structured quantum matter.

Executive functioning (EF) in early childhood plays a critical role in shaping later cognitive, socioemotional, and academic outcomes. Although the influence of caregiving on EF is well documented in preschool and older children, relatively little is known about how early relational factors contribute to the emergence of rudimentary EF abilities during the first year of life. The present study examined the contributions of maternal sensitivity and mind-mindedness to the emergence of EF in infancy. Fifty-one typically developing infants aged 9-13 months were invited to participate. Infants completed the A-not-B task to assess their EF abilities. Maternal sensitivity and appropriate mind-minded comments were assessed during a structured interaction task, which included both non-distress (free-play) and distress-eliciting episodes. Results showed that both maternal sensitivity and appropriate mind-minded comments were related to infant EF, yet only mind-mindedness in non-distress contexts emerged as a significant predictor of early EF abilities. These findings emphasize the importance of caregivers' attunement to their infants' mental states in fostering the emergence of EF.

Adaptive immune receptors (AIRs), including antibodies and T-cell receptors (TCRs), mediate antigen recognition and represent a major class of therapeutic biomolecules. Their unique architecture, combining conserved framework with highly diverse complementarity-determining regions (CDRs), poses challenges for structure prediction and design. Recent advances in deep learning have transformed these fields, yet AIR-antigen interactions remain difficult targets due to limited structural data, weak co-evolutionary signals, and conformational heterogeneity. Herein, we review recent progress in structure-based deep learning approaches for AIRs, including AIR-specific language models, structure prediction, and epitope-conditioned design. We discuss commonly used datasets, evaluation metrics, and sources of bias that complicate cross-study comparisons and highlight the need for improved benchmarks. We also review emerging generative design strategies such as inverse folding, diffusion-based backbone generation, sequence-space diffusion, and sequence-structure co-design, and outline key challenges, including accurate modeling of flexible CDR loops, reliable ranking of AIR-antigen complexes, and scalable epitope-specific AIR design.

The production, use, and disposal of pharmaceuticals contribute to widespread pollution of the aquatic environment. Conventional wastewater treatment plants are not designed to completely remove bioactive pharmaceuticals such as diclofenac, posing risks to aquatic ecosystems and drinking water sources. To address this challenge, we report the production of biogenic adsorbent beads for enhanced removal of anionic contaminants from water. The beads consist of glutaraldehyde-cross-linked chitosan (or quaternary amine-functionalized chitosan) combined with lignin, and a macroporous structure was introduced via freeze-drying. The resulting cryogel beads were characterized and benchmarked against two commercially available adsorbents, an activated carbon and an ion-exchange resin. Compared to these reference materials, the presented cryogel beads exhibit significantly faster diclofenac removal kinetics and a higher adsorption capacity. Furthermore, upon treatment with a brine solution, the cryogel beads can desorb diclofenac and show good performance over five adsorption-desorption cycles. The present study demonstrates the potential of biopolymer cryogel beads as sustainable materials for water treatment.

One-dimensional (1D) structures provide a unique platform to study the correlated quantum interactions and phase transitions such as unconventional magnetism and superconducting states. Here, we report that iron chalcogenide K_{3}Fe_{2}Se_{4} exhibits an unusual block-type canted antiferromagnetic (AFM) order with a clear single chain quasi-1D structure, which is structurally different from the two-leg ladder BaFe_{2}Se_{3}, through both experimental measurements and density matrix renormalization group (DMRG) calculations. The narrow bandgap semiconductor K_{3}Fe_{2}Se_{4} has a quasi-1D edge-shared FeSe_{4} tetrahedra chain structure and orders antiferromagnetically below 110&#xa0;K. The magnetic moments couple antiferromagnetically along the quasi-1D chain direction of the b axis and form an up-down-down-up (&#x2191;-&#x2193;-&#x2193;-&#x2191;)-like spin structure with a commensurate propagation vector k=(0,0,0), where block-type spin &#x2191;-&#x2191; or &#x2193;-&#x2193; coupling are between the longer Fe-Fe bonds of the quasi-1D chain. DMRG results show that block antiferromagnetic state is stable in K_{3}Fe_{2}Se_{4} and reveal that the block-ordered arrangement of Fe^{2.5+} ions spins arise from the competition between ferromagnetic and AFM interaction in the presence of strong electronic correlation. Our research results not only report the discovery of a clear block-type canted antiferromagnetic structure in a real quasi-1D chain material but also provide a theoretical approach to understand the block-type antiferromagnetism in quasi-1D iron chalcogenides.

We provide a new formulation of time-dependent density functional theory (TDDFT) based on the geometric structure of the set of states constrained to have a fixed density. Orbital-free TDDFT is formulated using a hydrodynamics equation involving a new density-to-current functional map. In the corresponding Kohn-Sham equation, the density is reproduced using a nonlocal operator. Finally, we present numerical simulations for one-dimensional soft-Coulomb systems.

Altermagnetic order, characterized by the N&#xe9;el vector, breaks time-reversal symmetry (TRS) even in the nonrelativistic limit. Although spin-polarized and anomalous transport phenomena emerge with this order, they are mutually compensated by TRS-connected antiphase domains with opposite N&#xe9;el vectors. Here we employ polarized neutron diffraction to directly probe the altermagnetic order in MnTe. Pronounced nuclear-magnetic interference terms were observed, providing direct evidence of a net N&#xe9;el vector in the bulk crystal. Moreover, a weak ferromagnetic moment (WFM), originating from relativistic spin-orbit coupling, was found to be coupled with the altermagnetic order. Both the altermagnetic order and the WFM can be switched by milli-Tesla-scale magnetic field cooling.