Exercise training is an effective non-pharmacological strategy for reducing blood pressure; however, current guideline recommendations are largely derived from studies reporting resting clinic blood pressure rather than ambulatory blood pressure (ABP), which provides greater prognostic value. Comparative evidence on the effects of different exercise modalities on ABP remains limited. To compare the effects of major exercise modalities on 24-h systolic ABP using a Bayesian network meta-analysis, and to outline practical implications for antihypertensive exercise prescription. PubMed (MEDLINE) and the Cochrane Library were searched for randomized controlled trials published before January 2025 reporting changes in 24-h systolic ABP following an exercise intervention of at least 2 weeks' duration. Eligible studies included adults and compared aerobic exercise training (AET), resistance training (RT), high-intensity interval training (HIIT), combined training (CT), or isometric exercise training (IET), with a non-exercise control or another exercise modality. Bayesian network meta-analyses were conducted, with interventions ranked using surface under the cumulative ranking curve (SUCRA) values. Twenty-five studies (1096 participants) met inclusion criteria; 16 were included in the network meta-analysis. AET and HIIT reduced 24-h systolic ABP (-4.77 mmHg and -6.86 mmHg), with a smaller reduction following RT (-2.25 mmHg). Data were insufficient to analyse comparative effects for CT or IET. In the Bayesian network, rank order of effectiveness based on SUCRA values was HIIT (91.4%), AET (68.2%), and RT (35.4%). No statistically significant comparative differences were observed between exercise modalities. This Bayesian NMA demonstrates HIIT and AET as effective for reducing 24-h systolic ABP, supporting their role in hypertension management. However, the limited scale and connectivity of available trials restrict definitive comparisons between wider exercise modes. Larger, well-designed head-to-head trials incorporating ABP outcomes are needed to clarify the relative effectiveness of different exercise strategies and to better inform evidence-based exercise prescription for blood pressure management. Registration:PROSPERO: CRD420251177743.

Scaling limit of dependent random walk

Taiwan produces high-value specialty coffee on a limited scale, whereas Brazil is the largest global supplier and a major source of imports to Taiwan. For food authenticity, differentiation of roasted coffee from these origins is important. However, few metabolomics-based traceability studies have yet been conducted on Taiwan-roasted coffee. In this study, metabolic profiling was applied to evaluate origin-related chemical signatures in Arabica roasted coffee beans from Taiwan and Brazil. An untargeted metabolomic workflow of integrating full scan and data-dependent acquisition (IFSDDA) was implemented using UHPLC-QTOF-MS. Roasted coffee beans were extracted using optimized 50% ethanol, profiled by LC-MS/MS, and analyzed using multivariate statistics, ROC analysis, and spectral-library-based metabolite annotation. Multivariate analyses revealed a clear separation of Taiwanese and Brazilian roasted coffee samples. Among 26 annotated metabolites, four compounds showed the highest discriminatory power (VIP > 1.0), including 4-(1H-indol-3-yl)butan-2-one, citrulline, 7-methanesulfinylheptanenitrile, and phytosphingosine. The four-potential markers achieved excellent classification performance (AUC > 0.98), reflecting consistent origin-associated patterns. This study demonstrated that untargeted metabolic profiling can effectively distinguish roasted coffee from Taiwan and Brazil. Such profiling provided an approach for food safety surveillance by capturing chemical signatures linked to regional production environments and agricultural inputs. The annotated markers offer a practical framework for monitoring both imported and domestic roasted coffee.

Surface-supported single rare-earth atom magnets represent an ultimate limit of magnetic miniaturisation, where information storage is reduced to the scale of an individual atom. At this limit, magnetism is intrinsically quantum mechanical and governed by the interplay of strong electron correlations, crystal-field effects, and spin-orbit coupling within the localized 4f shell. In this review, we summarise and analyse recent theoretical advances in the description of rare-earth adatoms, with particular emphasis on approaches that go beyond conventional static mean-field DFT+U , which may exhibit multiple metastable solutions and treat magnetic anisotropy in a semiclassical manner. We discuss a predictive framework combining relativistic density functional theory with an Anderson impurity model treatment of the multiconfigurational 4f shell (DFT+U (HIA)), enabling a consistent description of strong correlations, multiplet structure, and quantum tunnelling effects induced by transverse crystal-field terms.As a representative case, we review the electronic structure and magnetic anisotropy of Dy adatoms on insulating MgO and spin-polarised graphene/Ni substrates. For Dy@MgO, an apparent perpendicular anisotropy is strongly reduced by quantum tunnelling driven by transverse crystal-field terms, effectively shifting the easy axis towards the surface plane. In contrast, Dy@Gr/Ni(111) realises a robust perpendicular magnetic configuration at the single-atom level. Here, the large positive magnetic anisotropy energy arises from the interplay of crystal-field splitting and strong spin-orbit coupling within the Dy 4f shell, further reinforced by the exchange field generated by the ferromagnetic Ni substrate.We argue that Dy@Gr/Ni(111) may be viewed as a limiting atomic-scale analogue of perpendicular synthetic heterostructures used in magnetic memory technologies, where exchange coupling and strong anisotropy are engineered to stabilise nanoscale bits. The insights gained from these studies establish a microscopic design principle for achieving thermally robust magnetic anisotropy at the ultimate scaling limit and highlight the broader potential of strongly correlated rare-earth adatoms for atomic-scale spintronic applications.

Dermatomyositis creates a clinical demand for non-invasive, quantitative muscle weakness assessment, which can be achieved by decomposing neuromotor information from surface electromyography (sEMG). However, the absence of a stable electrode interface compatible with pathologically xerotic skin has hindered this approach. Here, we develop a conformable neuromotor electrode interface that overcomes this via molecular engineering, exhibiting an ultrasoft modulus (∼ 2.13 kPa), low impedance (∼ 0.9 kΩ at 1 kHz), and stable electrical retention over 7 days. This interface enables the acquisition of high-fidelity sEMG from dermatomyositis patients with low baseline noise (< 2 µV root mean square) and high inter-channel signal correlation, allowing robust decomposition of neuromotor biomarkers including motor unit count, action potential amplitude, and firing rate. Clinically, our multi-biomarker quantification strategy shows strong agreement with gold standard assessments. Moreover, our approach can detect subtle interpatient variations like early recruitment patterns, enabling personalized treatment beyond the clinical gold standard. This work establishes a transformative platform for precise muscle weakness evaluation, overcoming the limitations of needle electromyography and subjective clinical scales.

Identification of protein-bound DNA sites is key to understanding genome function and regulation, but studying protein-DNA interactions in living, unperturbed cells remains challenging. UV footprinting has been used to study such interactions in vivo by detecting changes in DNA photoproduct formation at protein-bound sites, but only on a limited scale. Here, we describe whole-genome deamination sequencing (Deam-seq), wherein photoproducts (pyrimidine dimers) induced by UV irradiation are revealed as mutations, enabling generation of quantitative photofootprints of the yeast Saccharomyces cerevisiae at ultradeep coverage. By comparing cellular and naked DNA, we find that this approach can resolve protein occupancy at high resolution without preference toward accessible regions. Cell/naked differential signals commonly aligned with predicted regulatory sites, ChIP peaks and DNase I protection footprints, and supported that yeast DNA binding proteins typically exhibit protective effects on UV damage. Our results provide proof of concept for using light and sequencing to study protein-DNA interactions in their native cellular context at genome scale.

Peripheral neuropathies (PNs) associated with T-cell lymphoproliferative disorders (T-Ly) are exceptionally rare and poorly characterized. Whether their mechanisms, clinical features, and outcome differ from PN associated with B-cell lymphoproliferative disorders (B-Ly) remains unclear. We aimed to characterize the clinical, electrophysiologic, and pathologic spectrum of T-Ly-associated PN, evaluate treatment responses, and compare these findings with B-Ly-associated PN. We conducted a retrospective multicentric cohort study across 17 PN reference centers in France and Switzerland. Adult patients with confirmed PN and T-Ly were included after exclusion of alternative PN etiologies and isolated CNS involvement. Clinical, electrophysiologic, imaging, and histopathologic data were collected. PN were classified as neurolymphomatosis or dysimmune neuropathy based on multidisciplinary consensus. Functional outcomes were assessed using the modified Rankin Scale (mRS) and Overall Neuropathy Limitations Scale. The results were compared with a reference cohort of B-Ly-associated PN. Nineteen patients were included (mean age 67.0 ± 11.4 years, 52.6% female) with a median follow-up of 22 months (interquartile range 11.5-30.5). Neurolymphomatosis accounted for 11 cases (58%) and typically presented with multifocal, predominantly axonal neuropathy, including meningoradiculitis and multiple mononeuropathies. Eight patients (42%) had dysimmune neuropathy, most often demyelinating and strongly associated with angio-immunoblastic T-cell lymphoma. Neurologic improvement occurred in 72% of treated patients; however, most patients with neurolymphomatosis showed only partial recovery and persistent disability. IV immunoglobulins were uniformly ineffective, whereas corticosteroids yielded the greatest PN-specific benefit. Compared with B-Ly-associated PN (n = 33), T-Ly-associated PN were associated with more severe disability (median mRS 3 vs 2, p = 0.014), more frequent motor involvement, pain, cranial nerve involvement, and a higher prevalence of neurolymphomatosis. T-Ly-associated PN are predominantly driven by neurolymphomatosis and exhibit more severe clinical profiles and poorer neurologic outcomes than B-Ly-associated PN. Dysimmune neuropathies represent a substantial subset, particularly in angioimmunoblastic T-cell lymphoma. Limitations include the retrospective design and heterogeneity of diagnostic investigations. These findings underscore the need for early recognition and mechanism-specific therapeutic strategies.

ABSTRACT Hospitality micro and small enterprises (SMEs) operating in island and peripheral economies face persistent challenges related to limited scale, seasonal demand, resource constraints, and the liability of smallness and newness. In such contexts, entrepreneurial survival depends less on formal planning and more on the ability of owner-managers to learn from experience, build legitimacy, and adapt strategically under uncertainty. This study examines how social media engagement and online reviews function as entrepreneurial learning and adaptation mechanisms rather than as conventional marketing tools. Using Weizhou Island as a motivating case, this study conducts a qualitative systematic literature review (SLR) guided by the PRISMA framework, synthesizing recent qualitative and mixed-methods research on hospitality SMEs in island and peripheral tourism settings. The findings show that platform-based feedback enables hospitality entrepreneurs to engage in sensemaking, experiment with incremental changes, and develop dynamic capabilities under conditions of resource scarcity. Social media interactions and online review narratives support legitimacy building through authenticity, responsiveness, and relational trust, while also facilitating learning-by-doing and strategic renewal. These processes are strongly moderated by cultural norms, institutional arrangements, and local embeddedness characteristic of island economies. By integrating insights from entrepreneurial learning, dynamic capabilities, and legitimacy theory, the study advances a process-oriented understanding of how digital platforms support resilience in micro-enterprises. This paper offers theoretical contributions to entrepreneurship research and policy-relevant implications for strengthening SME resilience and self-employment in island and peripheral tourism destinations.

ABSTRACT Amid overwhelming evidence from the social sciences about the inherently extractive nature of predominant machine learning imaginaries, and visceral designerly and artistic explorations of how algorithmic governance of urban resources consumes our labour and bodies, calls to put people back into AI are growing. This paper describes a participatory experimental design project that adopts a critical making approach to rehearse collective machine teaching to distribute rescued food. It argues that collective training data generation is key to translating the affective and convivial aspects of the community’s deliberations into the algorithmic c of the distribution infrastructure. By creating a blueprint for community-based machine teaching while shedding light on the data-labour-related costs of optimisation and the limitations of scaling, it opens pathways to counter extractivist machine learning paradigms with just community- and commoning-based alternatives.

Atomic electron tomography (AET) is a powerful technique for determining the three-dimensional atomic structure of matter in real space. However, conventional AET requires numerous projections across a wide angular range. The high dose and prolonged acquisition severely limit its application. Here, we propose an interpretable and universal algorithm for low-dose, fast and tilt-constrained AET: null-space iterative reconstruction (NSIRE), which uses an unsupervised diffusion model to iteratively compute null-space solutions of projection equations, thereby generating tomograms consistent with both projection constraints and atomic potential prior. NSIRE can resolve a wide range of complex materials under 6-12° sparse projection or within ±29° small tilt-range without retraining. Using the NSIRE, we determine the three-dimensional atomic structures of a 4-nm Pt nanoparticle with grain boundaries and a 3-nm PtCo nanoalloy (the smallest one resolved so far), achieving a root-mean-square displacement of <20 pm and high projection consistency, overcoming the scale, dose and time limitations of AET.

Protein tyrosine kinases govern essential cellular processes, including proliferation, survival, and migration. Dysregulation of this enzyme family has been implicated in multiple hallmarks of cancer. These kinases catalyze tyrosine phosphorylation in a tightly controlled manner, with substrate recognition dictated by the local chemical environment of surrounding residues. Although several strategies have been developed to define protein tyrosine kinase substrate specificity, these approaches have notable limitations in scale and resolution. To address these challenges, we present a modernized phage display platform integrated with next-generation sequencing, enabling comprehensive and high-throughput profiling of tyrosine kinase substrates. This method enabled the simultaneous assessment of the relative phosphorylation of billions of potential substrates for Hck kinase, followed by analysis of positional and motif enrichment to guide iterative substrate design. The novel substrates developed through this process exhibited robust kinetic behavior (Km < 30 μM) when evaluated by HPLC/MS assays, yielding insights into Hck substrate preferences within multipositional sequence contexts.

Accurate arterial input functions (AIFs) are essential for quantitative dynamic contrast-enhanced (DCE) MRI, yet direct measurement is challenging and population-averaged AIFs neglect patient-specific variability. Blind deconvolution provides an alternative by estimating patient-specific AIFs directly from tissue data, up to a scale factor. This study comparedblindly estimated AIFs with carefully measured aortic AIFs in breast DCE-MRI. Data from 25 patients with breast cancer were analyzed, each with a carefully measured AIF. Blind AIF estimates were obtained using model-constrained deconvolution in the Perflab toolkit with both Tofts-Kety (TK) and two-compartment exchange (2CXM) tissue models. To help isolate AIF shape blind AIFs were scaled using cardiac output. These blind AIFs were compared with measured AIFs using scale-invariant and scale-dependent metrics which assess the similarity of the AIF shape. Blind estimates were obtained for all 25 patients with both tissue models. Compared with measured AIFs, blind estimates derived using the 2CXM showed stronger agreement across all metrics than those derived using the TK model. However, the weak correlation in scale-dependent metrics suggests limitations of cardiac output scaling. Blind AIF estimation using the 2CXM provides more reliable recovery of AIF shape and dispersion than the TK model in breast DCE-MRI. While blind deconvolution shows promise for estimating local patient-specific AIFs other scaling strategies may need to be employed in practice.

Biased sampling in molecular dynamics simulations overcomes time scale limitations and delivers free-energy landscapes, essential to understand complex atomistic phenomena. However, when applied across diverse systems and processes, biasing protocols often require time- and resource-consuming fine-tuning. In search of robustness, we automate a prominent biasing method, Umbrella Sampling, by leveraging the Umbrella Integration formalism for free-energy calculation. To estimate the value of an integral, i.e., the free energy, our Bayesian Umbrella Quadrature (BUQ) method iteratively selects gradient samples, i.e., bias locations, that most reduce the posterior integral variance based on a noise-tolerant Gaussian process model, which also effectively interpolates between samples. We validate the method for a conformational change in a small peptide, a water-to-ice phase transition, and a substitution chemical reaction; demonstrating that BUQ provides an automated free-energy calculation framework that is accurate and robust across fundamentally different systems and processes. To ease adoption, we interface BUQ with widespread simulation packages and share hyperparametrization guidelines.

Sorption-based atmospheric water harvesting (AWH) offers a decentralized, sustainable solution to global freshwater scarcity, enabling clean water in diverse environments. However, translating ideal sorption properties of small-scale materials into practical, large-scale systems faces critical kinetic challenges. Here, we conceptualize a hierarchical textile fiber for wearable AWH, addressing the scaling limitations of traditional sorbents. These fibers feature an open-pore surface topology and internal hierarchical pore structures, which accelerate surface vapor liquefaction and subsequent water transport, demonstrating exceptional water uptake and rapid sorption kinetics across varying relative humidity (RH). When woven into textiles, the fibers maintain efficient vapor diffusion through their macroporous, breathable architecture, achieving a 3- to 10-fold improvement over traditional sorbents at scale. We engineered a wearable prototype combining the AWH textile with a portable collector, achieving 3.76 to 7.45 literswater per kilogramsorbent per day and collecting 410 to 894 milliliters across 20 to 80% RH. By overcoming kinetic limitations, our study advances AWH toward scalability and wearability with implications for global water sustainability.

Coordination engineering of single-atom catalysts (SACs) is a powerful strategy to address durability and activity challenges in the acidic oxygen evolution reaction (OER). Here, we obtain two distinct Ir single-atom configurations on MoO3 support by regulating the second-shell coordination environment. Compared with the weakly interacting Ir─O─Mo structure, atomic pair sites formed through direct Ir─Mo coordination exhibit strong electronic coupling with the support, thereby enhancing atomic dispersion and structural stability. In situ experimental and theoretical studies reveal that the Ir─Mo pair sites trigger a new oxide-mediated pathway, in which dynamic hydroxyl spillover from Mo to Ir site effectively facilitates *OOH formation. This process breaks the linear scaling relationship between *OH and *OOH adsorption, lowering the energy barrier of the rate-limiting step and enabling superior OER kinetics. As a result, the IrO+Mo/MoO3 catalyst achieves outstanding stability for over 1500h at 10mA cm-2 in acidic electrolyte and sustains continuous operation for 300h at 1.0A cm-2 in the proton exchange membrane water electrolyzer. This work provides novel insights into the coordination engineering of SACs and opens a promising avenue for overcoming scaling limitations in acidic OER catalysis.

Generating equilibrium ensembles of structures is essential for modeling molecules and materials, yet traditional simulators like molecular dynamics suffer from limited sampling efficiency. Boltzmann Generators introduced the concept of one-shot deep learning for equilibrium sampling, but scalability to large systems has remained a major challenge. Here, we overcome this scaling limitation with a Boltzmann Generator architecture that can model large materials systems. Our approach combines augmented coupling flows with graph neural networks to exploit local environments, enabling energy-based training and rapid inference. Compared to previous designs, it trains faster, uses fewer resources, and achieves superior sampling efficiency. Crucially, it transfers to much larger system sizes, allowing efficient sampling of materials with simulation cells exceeding a thousand atoms. We demonstrate its capabilities on Lennard-Jones crystals, mW water ice phases, and the silicon phase diagram, producing accurate equilibrium ensembles and free energies across scales where finite-size effects vanish.

Electrocatalytic seawater splitting powered by renewable electricity is a promising route for large-scale green hydrogen production, yet remains challenging due to electrode corrosion and competing chlorine evolution at high current densities. Developing robust and efficient anodic catalysts is therefore essential. Here, we show an in-situ growth strategy to transform iron foam into boron-doped cobalt-iron oxyhydroxide (B-FeCoOOH) nanosheets. Boron incorporation and a disordered lattice generate abundant oxygen vacancies, lowering kinetic barriers for water oxidation. The catalyst requires only 325 mV to deliver 1.0 A cm-2 and exhibits less than 2% performance decay after 600 h in alkaline seawater. Mechanistic studies indicate that oxygen vacancies activate the lattice oxygen oxidation mechanism, bypassing the scaling limitations of the conventional adsorbate evolution mechanism. The disordered structure enhances structural flexibility to accommodate dynamic reconstruction, mitigating active site dissolution. This strategy is extendable to other transition metal-based oxyhydroxides, enabling the fabrication of large-area, self-supporting electrodes. This work establishes a general strategy for designing durable catalysts for practical seawater electrolysis.

Dual-atom catalysts (DACs) with heterogeneous active sites represent an emerging frontier in photoelectrochemical (PEC) water splitting. However, the corresponding reaction mechanism on DACs is still unclear. Herein, we present a photoanode architecture comprising NiFe dual atoms (DAs) anchored on a TiOx-coated BiVO4 photoanode (NiFe DAs/TiOx/BiVO4), which delivers an impressive photocurrent density of 6.13mA cm-2 at 1.23 VRHE, sustained stability exceeding 150h, and an applied bias photon-to-current efficiency of 2.2%. Our investigation reveals a dual functionality of the NiFe DAs which serve as an efficient oxygen evolution cocatalyst and enhance charge separation-an aspect largely overlooked in previous studies. Through in situ spectroscopic investigations combined with density functional theory calculations, we elucidate that the NiFe DAs/TiOx/BiVO4 enables a mechanistic shift from the conventional adsorbate evolution mechanism (AEM) observed in single atom-modified-TiOx/BiVO4 to an *O─O* coupling mechanism (OCM). Specifically, this OCM pathway bypasses the formation of *OOH and produces the *O─O* bridging species, broking the *OOH/*OH scaling limitation in the AEM pathway. This work uncovers PEC water oxidation mechanism at the atomic level, establishing a foundational framework for designing high-performance photoelectrodes through precise atomic-scale engineering.

ABSTRACTBackgroundMost clinical assessments of botulinum toxin rely on subjective observation, limiting reproducibility. The Aura 3D Imaging System combines high‐resolution photogrammetry imaging with AI analysis to provide standardized wrinkle measurements.ObjectiveValidate the Aura 3D Imaging System and the AURA‐W scale as an objective tool for assessing wrinkle reduction for botulinum toxin treatment.MethodsStandardized 3D captures were obtained before treatment and at follow‐up (about 27 days after) for 100 participants. Letibotulinum Toxin A was injected into the glabellar, forehead, and periorbital regions. The AURA‐W was used to quantify wrinkle severity across facial regions.ResultsWrinkle scores decreased consistently after treatment, with the greatest improvements in the upper face. Periocular changes were smaller but still evident.ConclusionThe AURA‐W enables objective, reproducible assessment of wrinkle reduction following botulinum toxin therapy. By integrating 3D imaging with AI analysis, it overcomes limitations of subjective scales and enhances precision in both clinical evaluation and research.

Introduction:Lumbar degenerative disease (LDD) is one of the significant causes of chronic low back pain and disability in postmenopausal women. Although age-related degeneration has been well studied, little has been done to establish how the onset of menopause and duration since menopause affect the rate of lumbar degeneration. Menopause-induced estrogen deficiency is thought to increase the rate of degenerative alteration in intervertebral disks and vertebral structures that could affect the severity of the disease and clinical outcome.Purposes:The purpose of the study was to determine the effect of age at menopause and years since menopause on the intensity of LDD and to determine the relationship between clinical manifestations and radiographical evidence in postmenopausal females.Materials and Methods:The study was a tertiary care center-based, observational cross-sectional study which lasted 12 months at this hospital. There was a total of 50 postmenopausal women aged 45 years or older who presented with persistent low back pain. The participants were divided into the early (<45 years), normal (45–50 years), and late (>50 years) menopause. Clinical evaluation involved the severity of pain based on the Visual Analog Scale and functional limitation. The lumbar spine assessment was conducted and assessed using the standard imaging on radiological assessment, and graded on the degenerative severity. Descriptive and comparative statistical data analysis was done to analyze the data.Findings:The lumbar degeneration was moderate to severe in 74% of the respondents. The highest level of severe degeneration was observed among the early menopause group (50%), normal (23.8%), and late menopause groups (20%). The highest pain scores were seen in women who were early menopausal (6.8 ± 1.4) and progressively with radiological severity. The trend of an increase in degenerative severity was noted with the length of time which passed after menopause. The clinical-radiological correlation was positive, and the higher scores on pain were associated with more advanced degenerative grades.Conclusion:Earlier menopause and longer postmenopausal years were both positively related to the severity of LDD, the level of pain, and functional impairment. The onset of menopause seems to be a significant biological factor in the development of lumbar degeneration, which is the reason why the history of menopause should be included in the clinical assessment and prevention instead of preventive measures.