Masonry buildings lacking box-like behaviour are highly susceptible to out-of-plane (OOP) failure during earthquakes, threatening both occupants and surrounding structures. To address this issue, this paper presents FaiMec (Failure Mechanisms), a rapid-assessment tool developed for the large-scale seismic evaluation of multi-storey masonry buildings. It considers both isolated and aggregate configurations, accounting for their direct influence on the activation of potential failure mechanisms. FaiMec integrates an advanced macro-block model with the established FaMIVE procedure, introducing three key innovations: (i) refined analytical formulations for activating load factors; (ii) explicit evaluation of seismic demand via floor response spectra; and (iii) a minimization procedure based on the capacity-to-demand ratio, enabling robust identification of governing OOP mechanisms. Using data from the CARTIS Building Form, developed by the ReLUIS Consortium and Italian Civil Protection Department since 2014, FaiMec computes load factors for multiple OOP mechanisms and classifies each building’s seismic risk under the Italian "Sismabonus" system, issued in Italy by a Ministerial Decree. The tool was first applied, tested, and validated on buildings affected by bradyseismic activity in Campi Flegrei (Italy), within a framework agreement with the ReLUIS Consortium coordinated by the Civil Protection Department. The paper presents a qualitative description of the procedure and the purpose-built IT application, supported by six case studies highlighting typological features that influence the activation of specific failure mechanisms. Overall, the tool offers an innovative framework for rapid, large-scale assessment of OOP vulnerability in masonry aggregates, overcoming limitations of existing approaches and supporting prevention and civil protection strategies.

The histone acetyltransferases E1A binding protein of 300 kDa (p300) and its homologue cyclic AMP response element binding protein (CREB) binding protein (CBP) are potential targets for cancer treatment. However, no drugs targeting p300/CBP have yet been approved. Various bioassay methods have been developed to evaluate the inhibitory potency of the corresponding inhibitors. However, suitable assays for high-throughput screening (HTS) of novel inhibitors targeting the p300/CBP bromodomain are lacking. To address this shortcoming, we developed a fluorescence polarization (FP) assay. This employs a rationally designed strategy that exhibits excellent characteristics, making it suitable for the large-scale evaluation of compound bioactivity. To enable direct-to-biology application of this FP assay, we constructed an 840-compound library on microplates via the CuAAC reaction and performed in situ HTS. Using this platform, we rapidly identified a series of small molecules that target the p300 bromodomain and exhibit potent activity. Computational chemistry studies have provided insight into the binding mode of the inhibitors, while cellular studies and H3K27 acetylation levels demonstrate the favorable properties of these compounds against acute myeloid leukemia (AML). Overall, this platform demonstrates a multifunctional approach, integrating rational FP probe design with combinatorial chemistry. This closes a methodological gap in the rapid discovery of p300/CBP bromodomain inhibitors, providing a new paradigm for accelerating drug discovery.

The fig tree (Ficus carica L.) is one of the oldest cultivated fruit trees in the Mediterranean region and represents an important genetic resource for both traditional and emerging production systems. Despite its agronomic and economic relevance, modern fig breeding remains limited, and large-scale phenotypic evaluations across Mediterranean germplasms are still scarce. The objective of this study was to assess phenotypic diversity and identify key agronomic traits relevant for fig breeding. A total of 257 female fig genotypes conserved in germplasm banks located in Spain, Turkey, and Tunisia were used. Over two consecutive seasons (2021 and 2022), a total of 27 morphological, phenological, and pomological traits were assessed according to the International Union for the Protection of New Varieties of Plants (UPOV) descriptors for fig (TG265/1), with 23 phenotypic traits retained for statistical analyses. Linear mixed models were used to estimate marginal means and to partition genetic and environmental variance, while multivariate analyses and trait correlations were employed to explore the structure of phenotypic diversity. The germplasm exhibits remarkable variation in productive type, reproductive behaviour, harvesting date, and fruit quality traits. Harvesting date spans nearly three months. Fruit weight ranges from 11.7 to 134.5 g, total soluble solids from 9 to 39 °Brix, and maturation index values reached high levels, indicating pronounced sweetness during fruit ripening. Most genotypes showed high skin scratch resistance, absence of cracking at maturity, and medium or small ostiole size, highlighting the presence of ideotypes specifically suited for fresh market production. Heritability estimates indicate strong genetic control of key traits, such as fruit weight, fruit size, and total soluble solids, highlighting their suitability for selection in breeding programs. Stakeholder prioritisation further confirmed the relevance of fruit size, sweetness, firmness, and ostiole characteristics, helping to identify best genotypes for breeding and agronomic purposes. Overall, this study demonstrates the value of Mediterranean fig germplasm as a reservoir of valuable agronomic and commercial traits and provides a robust phenotypic framework to support future breeding, conservation, and cultivar selection strategies.

If a patient with cancer is identified as having a pathogenic variant, at-risk relatives are eligible for genetic testing, known as cascade testing. However, in the United States, the patient is responsible for informing their family members, and only about 30% of these family members are ultimately informed and complete testing. There is a need to train patients with cancer to communicate risk information and motivate their family members to obtain genetic testing. This study evaluates "GRACE," an online relational agent that trains patients with cancer to talk to their family about cancer risk, including role-play simulations that enable patients to practice communication skills. A quasi-experimental study was conducted with 30 crowd workers with cancer. Primary measures included 5-point pre-post self-reported intent, importance, comfort, and confidence to share genetic test information with family members, as well as knowledge of cancer genetics (KnowGene), satisfaction with (10-item satisfaction measure), and usability of (SUS) the relational agent system. Likelihood of sharing genetic test information increased significantly pre-post from 4.43 (SD 1.04) to 4.67 (SD .66), Wilcoxon (Z=2.07, P=.04). Importance of sharing genetic test information increased significantly pre-post from 4.47 (SD .82) to 4.77 (SD .50), Wilcoxon (Z=2.46, P=.01). Comfort sharing genetic test information increased pre-post from 4.33 (SD 0.99) to 4.57 (SD 0.90), Wilcoxon (Z=1.811, P=.07). Confidence to share genetic test information increased significantly pre-post from 4.33 (SD 0.994) to 4.63 (SD 0.765), Wilcoxon (Z=2.23, P=.03). Knowledge of cancer genetics did not increase significantly (mean 13.27, range 1.911 to 13.7, SD 1.932, paired t29=1.245, P=.22). Participants gave high scores for usability (SUS score=71%) and satisfaction (6.09 SD 0.96 out of 7.0), significantly greater than neutral, t29=13.445, P<.001) with the relational agent system. GRACE provides communication skills training and information better enabling patients with cancer to reach out to their families, and our preliminary study indicates a potential for future impact. While results were generally positive, these findings should be interpreted with caution due to limitations in the population included in the pilot, the quasi-experimental design and small sample size. Future development should focus on larger-scale evaluation and in-depth follow-up of family communication dynamics following the use of GRACE.

Peach and apricot are valued for their sensory attributes and nutritional value; however large-scale evaluations of fruit quality and antioxidant-related traits remain limited. This study aimed to quantify phenotypic diversity and examine relationships between fruit quality and antioxidant traits in 100 peach and 32 apricot cultivars grown under the same experimental conditions. Substantial phenotypic diversity was observed for fruit quality and antioxidant traits. Total phenolic contents showed the greatest variation, differing by up to approximately sevenfold among peach and apricot cultivars, while high-antioxidant genotypes were relatively rare. Commercially important cultivars with high antioxidant contents were identified, highlighting potential value for health-oriented markets; of the most widely used cultivars it was 'Andross' and 'Big Top' that may be considered for having high nutritional importance to consumers. Ripening date was only correlated with antioxidant content in nectarine and apricot, indicating that it should not be regarded as a universal predictor of antioxidant accumulation. Juiciness, an important quality trait, varied nearly twofold in both species. Associations among peel and flesh color traits differed among fruit types. Multivariate analyses distinguished cultivar groups, with French-origin apricot cultivars associated with higher antioxidant content, later ripening, and higher ripening index, whereas Greek-origin cultivars clustered separately with generally lower antioxidant levels and reduced peel coloration. The observed diversity provides valuable opportunities for selecting cultivars with desirable sensory attributes and high nutritional value, thereby supporting the development of targeted fruit ideotypes and providing guidance for nutritionally aware consumers. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Survivors of critical illness frequently experience persistent cognitive impairment. However, structured cognitive rehabilitation interventions remain limited within intensive care pathways. While physical rehabilitation has received increasing attention, evidence guiding cognitive rehabilitation in intensive care unit (ICU) populations is sparse, and the feasibility of study procedures must be established before large-scale evaluation. To assess the feasibility of conducting a multi-arm randomised controlled trial evaluating a cognitive rehabilitation intervention (ICU CogHab) for ICU survivors. A pragmatic, five-arm randomised feasibility study was conducted across four ICUs at two Danish university hospitals. Feasibility outcomes included screening and recruitment, randomisation procedures, retention through 6-month follow-up and completion of patient-reported and performance-based outcome measures. Data were analysed descriptively to summarise feasibility parameters and outcome variability. Of 1679 patients screened, 115 met eligibility criteria and 83 were randomised. Recruitment was lower than anticipated, and retention at 6 months was 40%. Clinical data obtained from medical records were largely complete, whereas cognitive and patient-reported outcomes were more difficult to complete at ICU discharge. Outcome measures demonstrated substantial variability. This study shows that screening and randomisation procedures for a cognitive rehabilitation intervention are feasible within routine ICU practice. Retention and data completeness, particularly after hospital discharge, represent key challenges requiring refinement before further evaluation. Cognitive rehabilitation can be introduced within routine ICU workflows. However, the timing of assessments, follow-up procedures and integration into clinical practice require optimisation. These findings inform pragmatic adaptations to support future evaluation and implementation of cognitive rehabilitation for ICU survivors. The study was prospectively registered on the Open Science Framework (https://osf.io/b57fv/overview).

Playfulness-being and acting playful-is often associated with childhood, yet evidence suggests that it remains a meaningful resource throughout life. In later life, playfulness may support social connectedness, emotional well-being, and a sense of agency, even in contexts of illness or institutional living. Playfulness encompasses not only observable playful activities but also an inner disposition, such as curiosity, humor, or spontaneity, which may be constrained by environmental barriers, aging, or functional limitations. Despite its potential relevance for health and person-centered care, playfulness remains underexplored in gerontological and caregiving research. No validated instrument currently exists to assess playfulness among older adults in Swedish municipal care. This research program addresses this gap by clarifying the concepts of play and playfulness and by developing and psychometrically evaluating a new instrument, Play and Supportive Environments (PLAY-SE). The overall aim of the program is to clarify and operationalize the interrelated concepts of playfulness and playful activities among older adults receiving municipal care and to develop an instrument suitable for psychometric testing. The program applies a hybrid model of concept development combined with an exploratory sequential mixed methods design. Phase 1 involves literature reviews and qualitative studies with older adults and staff to explore and define the lived meanings of playfulness. These findings inform item generation and refinement of the PLAY-SE instrument. Phase 2 includes content validation, cognitive interviews, pilot testing, and large-scale psychometric evaluation using both classical test theory and Rasch measurement theory. Two PhD students were recruited to the program in September 2024 and September 2025, and an expert group was established in autumn 2025. The PhD students are funded, for four years each, by Kristianstad University (from 2024) and Red Cross University College (from 2025). Ethical approval for the qualitative studies in phase 1 was granted by the Swedish Ethical Review Authority (2025-00211-01; decision date: February 3, 2025). Data collection for qualitative interviews with older adults in municipal care was conducted between February and April 2025. Fifteen older adults (aged 68-100 y) were interviewed in nursing home settings. The phenomenological findings from phase 1.1 have been published in March 2026. Additional qualitative interviews and focus groups with staff are scheduled for 2026 to 2027. Pilot testing of the first version of the PLAY-SE instrument is planned for autumn 2026, followed by large-scale psychometric validation between 2027 and 2029. This program establishes a structured and theoretically grounded process for developing and validating an instrument to assess playfulness in later life. By integrating qualitative exploration with modern psychometric approaches, the PLAY-SE instrument is intended to support future research and contribute to the development of person-centered practices in municipal elder care.

This paper describes the benchmark dataset for the electric vehicle routing problem with time windows. It is designed to facilitate the large-scale and reproducible evaluation of routing approaches under diverse charging scenarios. It is an extension of the Homberger 1000-customer vehicle-routing benchmark dataset through the incorporation of computationally derived charging-station data. For the 60 base instances included in the dataset, charging-station locations are randomly generated within the customer-coordinate bounds, and two variants are provided, resulting in 120 benchmark problems used in the validation and baseline analyses. A normalized local customer-density score is derived for each station. It is used to determine charging rates and log-normal parameters for prices and waiting times. Two variants are included in the dataset. Variant A maintains the original customer time-window constraints, while Variant B relaxes customer due dates based on the distance from the depot, subject to the depot closing time. The dataset is complemented by instance files, station attributes, parameters, and scripts. It also includes the results of feasibility tests, baseline solver tests, difficulty analyses, and sensitivity tests. These results show that the benchmark includes both easier and harder instance classes under different charging settings. Overall, the dataset is intended to support its use as a reproducible benchmark.

ObjectiveDespite advancements in technologies, the quality of intrapartum care has consistently not improved. This study evaluates the potential efficacy of a novel technology for newborn resuscitation, which provides a visual display of time since birth, video–audio recording, and ergonomic resuscitation equipment, on healthcare providers’ performance during ventilation in Nepal.MethodThis study utilized a prospective observational design conducted over 3 years at a referral hospital in Nepal. All infants who did not cry within 30 seconds of birth were included, and their ventilation performance was assessed across two phases: SUSTAIN (baseline phase) and Pre-MALA (pilot implementation phase). Ventilation performance was measured through direct observation and video annotation, with the median time to first ventilation compared between the two phases using the Mann–Whitney U test and generalized linear mixed model regression.ResultsA total of 164 newborn ventilation events were observed, with 78 during the SUSTAIN phase and 86 during Pre-MALA phase. Direct observation was done in both phases, while video-recording annotation was also conducted during Pre-MALA phase. The median time to first ventilation significantly decreased from 84.3 seconds (interquartile range (IQR): 55.4–114.0) during SUSTAIN to 48.2 seconds (IQR: 33.5–85.0) during Pre-MALA (p < 0.001). The duration of suctioning before ventilation was reduced by 17.8 seconds (adjusted coefficient = −17.8; 95% CI; −23.1, −11.8) and time to first ventilation was reduced by 33 seconds (adjusted coefficient = −33.2; 95% CI; −51.1, −15.4) during Pre-MALA.ConclusionThe result suggests that novel technology during resuscitation can reduce time to first ventilation and unnecessary suctioning in a clinical setting. Further large-scale evaluations are needed to fully assess the potential impact on neonatal care.

Adversarial rain attack and defensive deraining for DNN perception.

While tele-emergency care pilots have demonstrated potential utility as a model for assessing patients with acute concerns without requiring in-person emergency department evaluation, large-scale evaluations are lacking, and important questions regarding implementation remain. To examine associations between tele-emergency care receipt, modality, and clinician type, and subsequent outcomes. This cohort study included veterans who called the nurse advice line from January 2018 through April 2024 during business hours and were triaged as requiring urgent or emergent evaluation in a national, multicenter setting within the Veterans Health Administration. Tele-emergency care vs usual care, tele-emergency care modality (phone vs video), and tele-emergency care clinician type (physician vs advanced practice clinician). The primary outcome was emergency department visit within 7 days, and the secondary outcomes were hospitalization within 7 days and 30-day mortality. Propensity score weighting was applied, and heterogeneous difference-in-differences models were estimated. The study included 2 511 932 nurse advice line calls from 719 028 veterans (mean [SD] age, 62.3 [16.0] years; 627 515 [87.3%] male). After tele-emergency care implementation, 99 994 of eligible nurse calls (17.4%) resulted in a tele-emergency care visit. Tele-emergency care receipt was associated with a lower probability of emergency department visits within 7 days compared with propensity-weighted calls that did not (28.5% vs 45.0%; average treatment effect, -16.5%; 95% CI, -18.6% to -14.4%), with similar associations for hospitalization and no difference in mortality. The average treatment effect was greater for calls triaged as emergent (-22.0%; 95% CI, -24.1% to -19.9%) vs urgent (-5.6%; 95% CI, -8.4% to -2.9%). Minimal differences were observed by tele-emergency care modality or clinician type. At the facility level, tele-emergency care implementation was associated with a small reduction in emergency department visits for emergent calls (average treatment effect, -2.6%; 95% CI, -5.0% to -0.2%). In this cohort study, tele-emergency care receipt was associated with lower emergency department use, particularly for higher-acuity calls, regardless of modality or clinician type, though facility-level differences were limited. Broader implementation of tele-emergency care could be a solution for health systems seeking to mitigate emergency department crowding while addressing patients' acute care needs.

TriRNASP: A knowledge-based potential with three-body effects for accurate RNA structure evaluation.

Code search is essential for code reuse, allowing developers to efficiently locate relevant code snippets. The advent of powerful decoder-only Large Language Models (LLMs) has revolutionized many code intelligence tasks. However, their effectiveness for the retrieval-based task of code search, particularly compared to established encoder-based models, remains underexplored. This paper addresses this gap by presenting a large-scale systematic evaluation of eleven decoder-only LLMs, analyzing their performance across zero-shot and fine-tuned settings. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Our results show that fine-tuned decoder-only models, particularly CodeGemma, significantly outperform encoder-only models like UniXcoder, achieving a 40.4% higher Mean Average Precision (MAP) on the CoSQA⁺ benchmark. Our analysis further reveals two crucial nuances for practitioners: first, the relationship between model size and performance is non-monotonic, with mid-sized models often outperforming larger variants; second, the composition of the training data is critical, as a multilingual dataset enhances generalization while a small amount of data from a specific language can act as noise and interfere with model effectiveness. These findings offer a comprehensive guide to selecting and optimizing modern LLMs for code search.

This study presents one of the first large-scale exploratory evaluations of SEE Learning® in urban Indian elementary schools, offering novel insights into the contextual adaptation and developmental considerations of compassion-based social emotional learning (SEL) in low- and middle-income countries (LMICs). Conducted across nine schools in Maharashtra, India, the research assessed changes in students’ interpersonal and intrapersonal competencies, with a particular focus on gender related patterns in emotional and behavioral responses. Findings revealed increases in perspective-taking, empathic concern, self-compassion, and academic goal setting, alongside a decrease in emotional reactivity. However, increases in self-judgment and decreases in classroom connection were also observed, suggesting complex emotional shifts that may accompany the growing awareness that may be fostered by social and emotional development in the classroom. Challenges such as linguistic diversity, large class sizes, and limited instructional time affected program delivery and adaptation, emphasizing the need for targeted approaches by education policymakers to address structural challenges using culturally responsive approaches. The exploratory findings point to possible directions such as integrating SEL into national education frameworks, strengthening teacher capacity-building, and refining implementation strategies to ensure equitable access and effectiveness in diverse educational settings. This study contributes to broader discussions on SEL scalability and contextual adaptation in low- and middle-income countries, offering insights for education policymakers, practitioners, and researchers. As a policy response to ongoing SEL debates in India, the study highlights the need for culturally responsive, system-level integration. • This exploratory study presents the first large‑scale preliminary evaluation of SEE Learning implementation in Indian classrooms. • Students and teachers reported perceived improvements in students’ emotional regulation, empathy, and self‑compassion over the course of the program. • Successful implementation requires multilingual delivery and thoughtful cultural adaptations. • Teachers’ shifts toward facilitative and supportive pedagogy were central to program‑associated improvements. • Sustained teacher development and inclusive instructional practices may be critical for equitable SEL delivery.

Ferroelectric-capacitor-based memory (FeRAM) is a promising emerging non-volatile memory technology that offers fast access and high endurance. Recent studies have demonstrated Ferroelectric-capacitor-based memory (FeRAM) has the potential to support Non-Destructive Readout (NDRO) as well as 3D stacking, enabling read operations that do not disturb or minor disturb the stored polarization state and higher density. However, variations in ferroelectric materials and layer structures lead to diverse hysteresis behaviors, resulting in different sensing requirements. In addition, the reduced capacitor size in deeply stacked 3D architectures further decreases the available charge, making accurate readout increasingly challenging. To better understand these issues, this work presents a highly scalable direct-capacitance-conversion characterization circuit that is capable of extracting the equivalent ferroelectric capacitance from a 3D FeRAM array. Leveraging a continuous-time delta-sigma modulator (CTDSM), the proposed platform can accurately quantize small ferroelectric equivalent capacitances ranging from 0 to 72fF with low noise and high resolution, and achieves a capacitance resolution of 0.045fF<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> while consuming only 3μW power and occupying 0.001mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chip area under a 22nm FDSOI technology. These results demonstrate the capability to characterize 3D FeRAM arrays with up to 64 vertically stacked ferroelectric capacitors (FeCAPs) and highlight strong scalability for large-scale FeRAM evaluation and design exploration.

Woody plant encroachment into grassy ecosystems is a worldwide phenomenon that radically transforms ecosystem services. Billions of dollars have been spent to remove woody plants, but there is great uncertainty about the conditions in which such removals are beneficial. We conducted a collaborative, large-scale monitoring experiment in the Chihuahuan Desert of southern New Mexico, USA, to test whether woody plant removal restored historical states, created novel states, or exacerbated land degradation and to examine the environmental conditions affecting outcomes. We monitored vegetation cover in 43 pairs of plots representing herbicide-treated and herbicide-untreated conditions of the same plant community and environmental setting, including measurements at baseline, 5, 10, and in some cases 15 years posttreatment. We compared outcomes to measurements in reference sites. Woody plant removal led to increases in plant species richness and perennial grass cover, but increases were due to disturbance-adapted grasses rather than species characteristic of reference states. On average, grass cover in treatments did not attain levels observed in the reference state. Negative effects of woody plant removal on total canopy cover (related to soil erosion risk) and other plant functional groups of concern for wildlife were not observed. Elevation, slope, and baseline cover were important predictors of treatment-associated gains in plant cover, while higher grazing intensity was related to increases in richness and forb cover. Our results indicate that while woody plant removal cannot be considered to restore a reference state due to the continued absence of reference grass species, it does not lead to obvious land degradation. Furthermore, more positive outcomes are maximized where (and when) soil moisture limitations are lowest or the cover of responding plants is highest prior to treatments. We recommend that future restoration actions be conducted as experiments that pay special attention to co-production mechanisms, standardized monitoring methods, and salient and easily measured indicators.

• A simulation-based framework is proposed for evaluating pavement sensor arrangement strategies. • FEM-derived attenuation functions are used to compare different sensor layouts without repeated full-scale simulations. • The influence of vehicle paths and wheel-track constraints on sensor response distribution is examined. • The framework supports comparative assessment of sensor arrangements under defined modeling assumptions. An efficient sensor arrangement is critical for intelligent pavement monitoring, as dense sensor arrangements increase construction complexity and pavement disturbance. Finite element modeling (FEM) can accurately represent vehicle-pavement-sensor interactions, but repeated simulations limit its efficiency for large-scale layout evaluation. This study proposes a simulation-based method (ESAM) for evaluating pavement sensor arrangements using lead-free potassium sodium niobate (KNN) piezoelectric sensors with low piezoelectric response. Finite element outputs are used to describe sensor voltage responses under vehicle loading, and a voltage-distance relationship is used to estimate responses without repeated FEM simulations. Multiple sensor arrangements are examined under representative vehicle passage scenarios and lane configurations. Sensor arrangements are evaluated using a rule-based confusion matrix, from which a classification accuracy ( AC ) is derived as an indicator of signal separability. The results show that, under the modeled conditions, several layouts can reduce sensor numbers compared with dense deployments while maintaining high AC under the simulated conditions. Uniform Arrangement demonstrated high performance across the single-lane scenarios. Centrally Weighted Arrangement had more advantages in handling the classification of large vehicles. Minimum Coverage Arrangement significantly reduced the number of sensors required when high accuracy was not essential. The results clarify how sensor number and spatial arrangement influence classification performance in the simulated cases. The proposed method enables rapid and consistent comparison of sensor arrangement strategies in KNN-based pavement sensing systems.

Detection and intervention of psychosis-risk states are typically delivered through clinical high risk for psychosis (CHR) clinics grounded in CHR research frameworks. However, real-world implementation is limited by major challenges, including clinical efficiency, comorbidity, and transdiagnostic presentations. At McLean Hospital, we launched the Outreach and Enhanced Referral Support project to address these challenges and extend early detection and intervention beyond traditional CHR clinic settings. Here we describe the project and report interim findings. This pilot project comprises three phases: needs assessment, outreach training, and enhanced referral support. Surveys assessed feasibility, acceptability, and selected clinical outcomes. Ten referral support cases were completed between July 2024 and April 2025. Feasibility ratings ranged 80%-100%, and acceptability was 91.7%. The mean wait time for support services was 3 days (95% CI 2.11-3.89, SD = 1.25; range, 0-7). Preliminary findings indicate that the Outreach and Enhanced Referral Support model is feasible and acceptable in a large psychiatric hospital setting, warranting larger-scale evaluation of its clinical impact.

Peroxisome proliferator-activated receptor-gamma (PPARγ) is a pivotal nuclear receptor regulating glucose and lipid homeostasis, making it a primary therapeutic target for type 2 diabetes mellitus (T2DM). Despite the therapeutic potential of Traditional Chinese Medicine (TCM), a systematic, large-scale evaluation of its modulatory effects on PPARγ remains insufficient. We developed a high-throughput screening platform utilizing a pGL4.17-PPARγ promoter-luciferase reporter system in 293 T cells. A library of 639 TCM samples (345 single formulas, SF; 294 multiple formulas, MF) was evaluated. Leading candidates were validated through RT-qPCR, Western blotting, and ELISA to assess PPARγ mRNA expression and protein abundance. Screening identified 139 TCMs that enhanced PPARγ activity, with 47 SF and 2 MF achieving an effective rate (ER) exceeding 60%. Notably, 25 of these are novel candidates for T2D, while 24 align with previous reports. Specifically, Astragali Radix (S241) and Puerariae Thomsonii Radix (S258) were identified as potent inducers, significantly upregulating PPARγ at both transcriptional and translational levels. Conversely, 55 samples, including Gymnema sylvestre (S169) and Paris polyphylla (S3), exhibited robust inhibitory effects, reducing ER by -52% to -99%. This study identified 45 novel TCM candidates for T2D management. While several TCMs demonstrated PPARγ inhibition despite their traditional use for diabetic symptom alleviation, our findings suggest that a comprehensive evaluation is essential to balance their immediate clinical benefits with long-term metabolic management and potential side effects.

Abstract Unsupervised isolation tree-based anomaly detection methods are widely used for their simplicity, scalability, and strong empirical performance. However, the standard partition rule, whether drawing thresholds uniformly from feature ranges or sampling random normal vectors for oblique hyperplanes, ignores the underlying data distribution and often cuts through dense clusters rather than sparse regions. To address this limitation, we introduce density-aware partitioning (DAP), a unified framework that guides data partitioning according to local data density. DAP consists of two components: density-aware split, which replaces uniform threshold sampling with a density-aware rejection sampler guided by a counting map, and density-aware direction, which samples a normal vector for hyperplanes that tend to separate low-density gaps rather than dense clusters. DAP can be integrated into various isolation tree-based models, enhancing anomaly score discrimination with minimal runtime overhead, as the simplified tree structure compensates for the slight increase in construction time. Through synthetic visualizations and large-scale benchmark evaluations, we demonstrate that DAP consistently improves detection accuracy without incurring significant computational overhead. Moreover, we theoretically analyze the counting map and establish its key properties, providing a formal justification for the tolerance parameter and the efficiency of the rejection mechanism. Overall, DAP offers a simple yet effective enhancement that improves partitioning capability without altering the core design of isolation tree-based models.