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Exploring the impact of landscape environments on tourists’ emotional fluctuations in Fujian’s Coastal National Parks using machine learning

In recent years, the impact of landscape environments on tourists’ emotions has increasingly become a significant topic in sustainable tourism and urban planning research. However, studies on the relationship between multidimensional environmental features of Coastal National Parks and tourists’ emotions remain relatively limited. This study integrates machine learning and multi-source data to systematically explore how the landscape environments of Fujian’s Coastal National Parks influence tourists’ emotional fluctuations. Using natural language processing (NLP) techniques, sentiment indices were calculated from social media textual data, while semantic segmentation models and image analysis were employed to extract environmental feature data. The Light Gradient Boosting Machine (LightGBM) model and SHapley Additive exPlanations (SHAP) method were used to evaluate the relative importance of different environmental variables on tourists’ emotions, with the findings visualized using ArcMap. The results indicate: (1) Over the past five years, 87.06% of emotions were positive, with the highest sentiment indices observed in the Fuyao Islands, Changle, and Xiamen. (2) Greenness (0.0–0.2) and aquatic rate (0.1–0.15) had the most significant positive impacts on emotions, whereas transportation proportion and paving degree had relatively minor effects. This study provides a theoretical basis for the sustainable development of Coastal National Parks and offers practical insights for optimizing landscape planning to enhance tourists’ emotional experiences.

A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy

Background Degenerative meniscus tears are often accompanied by varying degrees of osteoarthritis, making the prognostic outcome of arthroscopic partial meniscectomy (APM) difficult to predict. Our research objective is to develop and validate a multimodal deep learning radiology (MDLR) model based on the integration of multimodal data using deep learning radiology (DLR) scores from preoperative magnetic resonance imaging (MRI) images and clinical variables. Materials and methods From February 2020 to February 2022, 452 eligible patients with degenerative meniscus tear who underwent APM were retrospectively enrolled in cohorts. DLR features were extracted from MRI of the patient’s knee. Then, an MDLR model was used for the patient prognosis after arthroscopy. The MDLR model for prognostic risk stratification incorporated DLR signatures and clinical variable. Results The standalone DLR model performed poorly, with a micro average receiver operating characteristic (ROC) curve and macro average ROC line of 0.780 and 0.765 in the training set, 0.747 and 0.747 in the validation set, and 0.720 and 0.732 in the test set, respectively, for predicting postoperative outcomes in degenerative meniscus tears. Multivariate analysis identified gender, height, weight, duration of pain, ESR, and VAS as indicators of poor prognosis. After combining the above clinical features, the performance of the MDLR model has been significantly improved, with the best performance achieved under the Light Gradient Boosting Machine (GBM) algorithm. The micro average ROC curve and macro average ROC line of this model for predicting the postoperative effect of degenerative meniscus tear were 0.917 and 0.919 in the training set, 0.874 and 0.882 in the validation set, and 0.921 and 0.951 in the test set, respectively. With these variables, the MDLR model provides four levels of prognosis for arthroscopic partial meniscectomy: Poor, pain relief 0–25%, Average, pain relief 25–50%, Good, pain relief 50–75%, Excellent, pain relief 75–100%. Conclusion A tool based on MDLR was developed to consider that the pain exacerbation time is an important prognosis factor for arthroscopic partial meniscectomy in degenerative meniscus tear patients. MDLR showed outstanding performance for the prognostic efficiency stratification of degenerative meniscus tear patients who underwent arthroscopic partial meniscectomy and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.

Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive <i>N</i>-Arylations of Cyclic α-Substituted Amines in Aqueous Media

Racial bias in clinician assessment of patient credibility: Evidence from electronic health records

Objective Black patients disproportionately report feeling disbelieved or having concerns dismissed in medical encounters, suggesting potential racial bias in clinicians’ assessment of patient credibility. Because this bias may be evident in the language used by clinicians when writing notes about patients, we sought to assess racial differences in use of language either undermining or supporting patient credibility within the electronic health record (EHR). Methods We analyzed 13,065,081 notes written between 2016–2023 about 1,537,587 patients by 12,027 clinicians at a large health system with 5 hospitals and an extensive network of ambulatory practices in the mid-Atlantic region of the United States. We developed and applied natural language processing models to identify whether or not a note contained terms undermining or supporting patient credibility, and used logistic regression with generalized estimating equations to estimate the association of credibility language with patient race/ethnicity. Results The mean patient age was 43.3 years and 55.9% were female; 57.6% were non-Hispanic White, 28.0% non-Hispanic Black, 8.3% Hispanic/Latino, and 6.1% Asian. Clinician-authors were attending physicians (44.9%), physicians-in-training (40.1%) and advanced practice providers (15.0%). Terms specifically related to patient credibility were relatively uncommon, with 106,523 (0.82%) notes containing terms undermining patient credibility, and 33,706 (0.26%) supporting credibility. In adjusted analyses, notes written about non-Hispanic Black vs. White patients had higher odds of containing terms undermining credibility (aOR 1.29, 95% CI 1.27–1.32), and lower odds of supporting credibility (aOR 0.82; 95% CI 0.79–0.85). Notes written about Hispanic/Latino vs. White patients had similar odds of language undermining (aOR 0.99, 95% CI 0.95–1.03) and supporting credibility (aOR 0.95, 95% CI 0.89–1.02). Notes written about Asian vs. White patients had lower odds of language undermining credibility (aOR 0.85, 95% CI 0.81–0.89), and higher odds of supporting credibility (aOR 1.30, 95% CI 1.23–1.38). Conclusions Clinician documentation undermining patient credibility may disproportionately stigmatize Black individuals and favor Asian individuals. As stigmatizing language in medical records has been shown to negatively influence clinician attitudes and decision making, these racial differences in documentation may influence patient care and outcomes and exacerbate health inequities.

Reversible C–H Bond Activation of Unactivated Arenes by a Nickel-Silylene Complex

Assessment of photobiomodulation combined with new restorative material for teeth with molar incisor hypomineralization on control of hypersensitivity and longevity of restorations: Protocol for a randomized controlled blind clinical trial

Molar incisor hypomineralization (MIH) is a qualitative defect of enamel development that occurs in the mineralization phase. MIH affects one or more permanent molars and, occasionally, permanent incisors. The aim of the proposed study is to determine whether photobiomodulation combined with a new self-cure resin improves hypersensitivity in molars with MIH (primary outcome). Secondary outcomes include assessing the clinical performance of the self-cure composite resin in terms of restoration longevity and comparing the effectiveness of three interventions—photobiomodulation combined with self-cure resin, self-cure resin alone, and photobiomodulation combined with bulkfill photopolymerizable resin—in controlling hypersensitivity over time. Permanent molars with MIH in patients 6–10 years of age will be allocated to three groups. Group 1: photobiomodulation + self-cure composite resin restoration; Group 2: self-cure composite resin restoration; Group 3: photobiomodulation + restoration in bulk-fill photopolymerizable composite resin. Photobiomodulation will be performed in a single session involving low-level laser administered to four different points. The laser will be used at a wavelength of 808 nm, power of 100 mW and energy of 1 J per points; irradiance will be 3571 mW/cm2, with a total radiant exposure of 35.7 J/cm2. Data normality will be checked using the Shapiro-Wilk test, and variance homogeneity will be assessed with the Levene test. Descriptive statistics will be used to present the data, with continuous variables expressed as mean and standard deviation, and categorical variables by relative frequency. To compare the Wong-Baker Faces Pain Rating Scale and SCASS scales, repeated measures ANOVA will be employed, considering the 3 groups and 5 time points. Bonferroni adjustment will be applied for post-hoc comparisons. Sphericity will be tested with Mauchly’s test, and if violated, Greenhouse-Geisser correction will be applied. A significance level of 0.05 will be adopted. Trial registration ClinicalTrials.gov NCT06538142

SCORE: Serologic evidence of COVID-19 and social and occupational contacts in healthcare workers in long-term care and acute care facilities in Southeastern Ontario (SCORE)

Introduction We established a longitudinal cohort of healthcare workers (HCWs) in an acute care hospital (ACH) and four long-term care homes (LTCHs) in Ontario, Canada, to follow the incidence of SARS-CoV-2 infection, humoral immune response to infection and/or vaccination, and determinants of infection risk. Here, we 1) describe the cohort regarding the distribution of main exposures, outcomes and serologic assays, 2) describe the unadjusted incidence of SARS-CoV-2 infection risk in the overall population, and 3) summarize the analysis and its pertinence. Methods and participants HCWs were recruited between November 24, 2020, and July 24, 2021. They completed a baseline survey, monthly surveillance for 9–12 months, a post-Omicron-wave survey, and provided blood samples for anti-SARS-CoV-2 antibody measurements. We collected data on host-related (humoral response to vaccines and SARS-CoV-2 infection) and environmental factors (social contact history and occupational, household and community conditions). Descriptive analysis by setting, comparison of distributions, and unadjusted survival analysis were performed. Results In total, 143 HCWs from the ACH and 57 from LTCHs had complete data, and 72% were followed until September 2022. Nearly 60% of the sample consisted of nurses, nurse assistants and personal support workers. Survival analysis showed that the risk of infection was bimodal, with low risk throughout the study period until the first Omicron wave. ACH HCWs had a higher risk of infection during the Omicron waves than during the preceding waves (Odds Ratio = 7.64; CI95%: 4.24–13.7), while LTCH HCWs at high-risk facilities experienced a similar risk of infection before and during the Omicron waves (OR = 1.76; CI95%: 0.63–4.9). During the Omicron waves, the use of protective equipment by HCWs working with institutional COVID-19 cases increased, but the use of community protective measures diminished. Household infections reported by participating HCWs also increased during the Omicron waves compared to previous waves. Immunoglobulin G (IgG) antibody levels increased over two time periods, (Pre vs Post- Omicron) likely due to the immune response to high levels of both vaccination and SARS-CoV-2 infections. Discussion We observed a low incidence of COVID-19 until the onset of the Omicron waves, which highlights the drastic impact of this Variants of Concern (VOC) on transmission and the importance of infectious agent characteristics. Our analysis indicated a ninefold increased risk of infection compared to that in earlier pandemic periods. Further analysis will allow the estimation of 1) the risk factors for SARS-CoV-2 infection at the community, household and healthcare facility levels, 2) the relationship between humoral responses and SARS-CoV-2 infection/vaccination, and 3) the role of social contact in work, household and community settings in the risk of infection.

Postsynthetic Construction of Single-Crystal Formamide-Linked Covalent Organic Frameworks

The time-varying bidirectional causal relationship between household education expenditure and resident credit behavior: Dynamic quantile evidence and heterogeneous mechanisms

This study aims to investigate the time-varying bidirectional causal relationship between household education expenditure and resident credit behavior, as well as the heterogeneous mechanisms under different economic conditions and household characteristics. By constructing a TVP-SV-VAR model and a QVAR-DY model, we analyze urban household data in China from January 2015 to December 2024, unveiling the dynamic relationship between education expenditure and credit behavior, along with their asymmetry and heterogeneity. The findings reveal a significant bidirectional causal relationship between household education expenditure and resident credit behavior, which exhibits heterogeneity across different quantile levels and is influenced by household income, education level, and credit interest rates. Additionally, this study employs static and dynamic window methods to analyze the short-term, medium-term, and long-term spillover effects. Based on these findings, we propose policy recommendations for optimizing household education investment and credit market management under low, medium, and high-risk levels.

Structural and Electronic Tuning of Luminescent Zn^II Complexes Based on an <i>o</i>-Terphenyl Ligand Motif

Upper limb muscle reflexes in real and virtual environments: Insights into sensorimotor adaptations

Introduction The mechanisms influencing neuromuscular adaptations in the upper limb within dynamic environments remain understudied, especially when exposed to altered visual and emotional conditions such as those simulated in virtual reality (VR). Here we utilize VR to manipulate visual feedback, inducing motion sickness and modulating sympathetic arousal, while assessing adaptations in sensorimotor integration in the upper extremity using electrically evoked and muscle stretch reflexes. Methods Eighteen healthy young adults experienced four experimental conditions while sustaining submaximal activation of their flexor carpi radialis (FCR) muscle by isometrically supporting a weighted load: baseline real-world (Pre-VR), stationary VR (VR-BL), dynamic VR with motion perception via a virtual rollercoaster ride (VR-C), and post-VR following re-entry to the real environment (Post-VR). Muscle activity was monitored via electromyography (EMG), while reflex activity was assessed using electrical (H-reflexes) and mechanically induced (noisy tendon vibration; NTV) reflexes in the FCR. Additionally, electrodermal activity (EDA) and psychosocial indicators of motion sickness (subjective questionnaires) were measured throughout. Results H-reflex amplitude was suppressed during VR-C, which persisted into Post-VR; whereas NTV-reflexes were unaffected across conditions. Sympathetic arousal (e.g., EDA) and motion sickness symptoms increased significantly during VR-C compared to Pre-VR, but rapidly returned to baseline Post-VR. EMG within the target muscle (FCR) as well as in the brachioradialis was maintained across conditions, though increased activation was observed in the biceps brachii beginning at the onset of VR immersion (VR-BL). Discussion These findings suggest suppression of spinal excitability (H-reflex) when the perception of motion (VR-C) was added to a stationary VR experience. Meanwhile, muscle spindle sensitivity (NTV-reflex) remained consistent, highlighting potential fusimotor adaptations to maintain sensorimotor function under altered visual and emotional states. Persistent H-reflex suppression post-VR indicates lingering neuromuscular effects of immersive VR, underscoring the need for further exploration of VR’s implications for rehabilitation and virtual training environments.

Effect of Chirality on Ultrafast Triplet Exciton Formation in Electron Donor–Acceptor Cocrystals

Retraction: Knowledge, attitude, practice towards COVID-19 pandemic and its prevalence among hospital visitors at Ataye district hospital, Northeast Ethiopia

A humidity measure that accounts for redistribution of water across the landscape

Piezoelectrically Enhanced Charge Carriers Transfer in a Highly Conjugated Nickel(II)-Acetylide Framework for Photocatalytic CO₂ Reduction

Methods for improving the identification of acute stroke during ambulance calls: A scoping review

Background Accurately identifying strokes during ambulance calls remains challenging, leading to low diagnostic accuracy and delays in dispatching appropriate services. Limited evidence exists regarding methods for improving call handlers’ stroke recognition. This scoping review explores methods for enhancing stroke identification during emergency calls in ambulance control centres (ACCs). Methods We conducted a scoping review following the methodology of the Joanna Briggs Institute and adhered to PRISMA-ScR guidelines. A systematic search was performed across five databases: Embase, Medline, Scopus, Web of Science, and CINAHL, also grey literature sources, covering publications from January 1964 to July 2024. We included studies that examined methods to improve stroke identification during emergency calls in ACCs. To assess the effectiveness of these methods, eligible studies must evaluate at least one of the following outcomes: accuracy of stroke diagnosis, time to diagnosis, effectiveness of staff training, and acceptability of identification techniques. Two reviewers independently screened the studies, extracted the data, and conducted an inductive thematic analysis to identify common themes. Results Of the 3,619 studies identified, seven met the inclusion criteria. Included studies focused on technology and algorithms (n = 3), training and educational programs (n = 2), and improved triage tools (n = 2) to enhance stroke identification during emergency calls to ACCs. Studies on technology and algorithms have reported increased stroke identification sensitivity and positive predictive value (PPV) when using new algorithms compared to standard protocols. Training programs have led to improved dispatcher sensitivity in stroke recognition. Improved triage tools also reduce time-to-diagnosis and facilitate quicker emergency responses. Conclusion This review highlights several methods for improving stroke identification in ACCs. Despite improvements in PPV, sensitivity, and diagnosis time, the lack of generalised standards, single-centre studies, and various population characteristics hinder broader impact. Future research should prioritise well-designed studies with standardised benchmarks to determine effectiveness, enabling effective prehospital stroke identification strategies.

Modular Synthetic Platform for the Elaboration of Fragments in Three Dimensions for Fragment-Based Drug Discovery

An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using high-resolution mass spectrometry

A comprehensive understanding of drug metabolism is crucial for advancements in drug development. Automation has improved various stages of this process, from compound procurement to data analysis, but significant challenges persist in the metabolite identification (MetID) of macromolecules due to their size, structural complexity, and associated computational demands. This study introduces new algorithms for automated Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) data analysis applicable to macromolecules. A novel peak detection approach based on the most abundant mass (MaM) is presented and systematically compared with the monoisotopic mass (MiM) approach, commonly used in small molecules MetID. Additionally, three structure visualization strategies, expanded (atom-level), non-expanded (monomer-level), and a hybrid mode, are evaluated for their impact on computation data processing time and interpretability, based on their distinct fragmentation strategies. The workflow was validated using six diverse datasets, comprising linear and cyclic peptides and oligonucleotides with both natural and unnatural monomers, covering a molecular weight range of 700–7630 Da. A total of 970 metabolites were identified under various experimental and ionization conditions. The MaM algorithm demonstrated higher scores and a greater number of matches, instilling greater confidence in the accurate prediction of metabolite structures, while the non-expanded visualization significantly reduced processing times (ranging from minutes to under an hour for most peptides). Furthermore, the visualization algorithm, which integrates monomer-level and atom/bond notation, enables clear localization of metabolic biotransformations. Compared to previous studies, the proposed workflow demonstrated reduced processing time, consistent detection of degradation products, and enhanced visualization capabilities, advancing automated MetID for macromolecules.

Epoxide Stereochemistry Controls Regioselective Ketoreduction in Epoxyquinoid Biosynthesis

Investigating Gender-based violence against internally displaced women in Debre Berhan, Central Ethiopia: A mixed-methods study using the socio-ecological framework

Background Gender-based violence (GBV) is a major health problem affecting displaced populations disproportionately. However, limited research existed on the prevalence, barriers, and facilitators for survivors seeking care. Objective This study aims to estimate the prevalence of GBV and investigate the barriers and facilitators influencing survivors’ access to care. Methods A mixed-methods cross-sectional study was conducted in 2024 involving 1,863 women. Women were recruited through random sampling. The qualitative component included five NGO workers and eleven GBV survivors, who were selected purposively. Quantitative data were collected using the Assessment Screen to Identify Survivors Toolkit. The qualitative data were analysed thematically with Atlas Ti 8, guided by the socio-ecological framework. Results Nearly one-third (31%) of women experienced GBV, with 25.2% of them facing it in the past year. The most common types of violence were threats of violence (32.1%), physical violence (25.8%), forced marriage (19.1%), and sexual violence (10.0%). Nearly 80% of GBV incidents took place in IDP camps, mainly perpetrated by intimate partners and family members. Barriers to seeking GBV services at the individual level included self-isolation, reluctance to disclose survivor status, and lack of awareness. Community-level restrictions comprised social stigma, gossip, and inadequate social support, while institutional challenges involved budget constraints and a lack of confidentiality. Structural barriers included camp overcrowding, insecurity, and mistrust in the justice system. Self-efficacy acted as an individual-level enabler for survivors to seek care. Enablers at the institutional level included support from NGOs, access to secure housing, and availability of a one-stop centre. Access to community-based GBV workers was viewed as a crucial community-level facilitator for survivors seeking care. Conclusions GBV is widespread among internally displaced women, particularly in camps. Despite the presence of some facilitators, GBV survivors encounter numerous barriers at all levels of the socio-ecological framework. Overcoming these barriers requires comprehensive and coordinated efforts. Key strategies include increasing awareness of the available GBV services, reducing community stigma, building supportive networks, safeguarding survivors’ privacy, decreasing overcrowding in camps, enhancing security measures, and rebuilding trust in justice systems.