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Background and objective: Prior studies have shown higher green cover levels are associated with beneficial health outcomes. We sought to determine if residential green cover was also associated with direct healthcare costs. Methods: We linked residential Normalized Difference Vegetation Index (NDVI) satellite data for 5,189,303 members of Kaiser Permanente Northern California (KPNC) to direct individual healthcare costs for 2003–2015. Using generalized linear regression to adjust for confounding, we examined the association between direct healthcare costs and green cover within 250, 500, and 1000 meters (m) of an individual’s residence. Costs were determined from an internal cost accounting system that captures administrative and patient care costs for each clinical encounter. Sensitivity analyses included adjustments for comorbidity and an alternative measure of green cover, tree canopy. Results: We observed a significant inverse association between higher levels of residential green cover and lower direct healthcare costs. The relative rate of total cost for the highest compared to the lowest decile of NDVI was 0.92 (95% CI 0.90–0.93) for the 500 m buffer. The association was robust to adjustment from a broad array of confounders, found at each buffer size, and largely driven by hospitalization, and emergency department visits. Individuals in the top decile of residential green cover had adjusted healthcare costs of $374.04 (95% CI $307.31-$439.41) per person per year less than individuals living in the bottom or least green decile. Sensitivity analyses including tree canopy cover as the green space measure yielded similar findings. Analyses that included adjustment for comorbidity were consistent with the hypothesis that green cover reduces healthcare costs by improving health status. Conclusion: Green cover was associated with lower direct healthcare costs, raising the possibility that residential greening can have a significant healthcare cost impact across the population.

Do experiences with nature – from wilderness backpacking to plants in a preschool, to a wetland lesson on frogs—promote learning? Until recently, claims outstripped evidence on this question. But the field has matured, not only substantiating previously unwarranted claims but deepening our understanding of the cause-and-effect relationship between nature and learning. Hundreds of studies now bear on this question, and converging evidence strongly suggests that experiences of nature boost academic learning, personal development, and environmental stewardship. This brief integrative review summarizes recent advances and the current state of our understanding. The research on personal development and environmental stewardship is compelling although not quantitative. Report after report – from independent observers as well as participants themselves – indicate shifts in perseverance, problem solving, critical thinking, leadership, teamwork, and resilience. Similarly, over fifty studies point to nature playing a key role in the development of pro-environmental behavior, particularly by fostering an emotional connection to nature. In academic contexts, nature-based instruction outperforms traditional instruction. The evidence here is particularly strong, including experimental evidence; evidence across a wide range of samples and instructional approaches; outcomes such as standardized test scores and graduation rates; and evidence for specific explanatory mechanisms and active ingredients. Nature may promote learning by improving learners’ attention, levels of stress, self-discipline, interest and enjoyment in learning, and physical activity and fitness. Nature also appears to provide a calmer, quieter, safer context for learning; a warmer, more cooperative context for learning; and a combination of “loose parts” and autonomy that fosters developmentally beneficial forms of play. It is time to take nature seriously as a resource for learning – particularly for students not effectively reached by traditional instruction.

In the United States, schools serving urban, low-income students are among the lowest-performing academically. Previous research in relatively well-off populations has linked vegetation in schoolyards and surrounding neighborhoods to better school performance even after controlling for important confounding factors, raising the tantalizing possibility that greening might boost academic achievement. This study extended previous cross-sectional research on the “greenness”-academic achievement link to a public school district in which nine out of ten children were eligible for free lunch. In generalized linear mixed models, Light Detection and Ranging (LiDAR)-based measurements of green cover for 318 Chicago public schools predicted statistically significantly better school performance on standardized tests of math, with marginally statistically significant results for reading—even after controlling for disadvantage, an index combining poverty and minority status. Pupil/teacher ratio %bilingual, school size, and %female could not account for the greenness-performance link. Interactions between greenness and Disadvantage suggest that the greenness-academic achievement link is different for student bodies with different levels of disadvantage. To determine what forms of green cover were most strongly tied to academic achievement, tree cover was examined separately from grass and shrub cover; only tree cover predicted school performance. Further analyses examined the unique contributions of “school tree cover” (tree cover for the schoolyard and a 25 m buffer) and “neighborhood tree cover” (tree cover for the remainder of a school’s attendance catchment area). School greenness predicted math achievement when neighborhood greenness was controlled for, but neighborhood greenness did not significantly predict either reading or math achievement when school greenness was taken into account. Future research should assess whether greening schoolyards boost school performance.

Teachers wishing to offer lessons in nature may hold back for fear of leaving students keyed up and unable to concentrate in subsequent, indoor lessons. This study tested the hypothesis that lessons in nature have positive—not negative—aftereffects on subsequent classroom engagement. Using carefully matched pairs of lessons (one in a relatively natural outdoor setting and one indoors), we observed subsequent classroom engagement during an indoor instructional period, replicating these comparisons over 10 different topics and weeks in the school year, in each of two third grade classrooms. Pairs were roughly balanced in how often the outdoor lesson preceded or followed the classroom lesson. Classroom engagement was significantly better after lessons in nature than after their matched counterparts for four of the five measures developed for this study: teacher ratings; third-party tallies of “redirects” (the number of times the teacher stopped instruction to direct student attention back onto the task at hand); independent, photo-based ratings made blind to condition; and a composite index each showed a nature advantage; student ratings did not. This nature advantage held across different teachers and held equally over the initial and final 5 weeks of lessons. And the magnitude of the advantage was large. In 48 out of 100 paired comparisons, the nature lesson was a full standard deviation better than its classroom counterpart; in 20 of the 48, the nature lesson was over two standard deviations better. The rate of “redirects” was cut almost in half after a lesson in nature, allowing teachers to teach for longer periods uninterrupted. Because the pairs of lessons were matched on teacher, class (students and classroom), topic, teaching style, week of the semester, and time of day, the advantage of the nature-based lessons could not be attributed to any of these factors. It appears that, far from leaving students too keyed up to concentrate afterward, lessons in nature may actually leave students more able to engage in the next lesson, even as students are also learning the material at hand. Such “refueling in flight” argues for including more lessons in nature in formal education.

Abstract Objective The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning’s potential to predict ED dispositions, thus leading to promising substantial benefits. This study’s objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems. Methods In this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018–2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner. Results The ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis. Conclusions Findings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions.

Abstract Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Abstract An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q, I 2 , and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.

Abstract A graduate course on vaccine development challenged students to propose concepts to improve upon current vaccine development paradigms in the context of a “Shark Tank‐style” format where students were asked to develop an abbreviated business plan and make a pitch to the “Sharks” (experienced academic and industrial vaccine researchers and developers) where they could request funding, research collaborations or regulatory guidance. Students were graded based on the components of their plan and on their ability to convince the “Sharks” of the feasibility and innovation potential of their project proposals. This approach to teaching vaccine development explored areas where novel approaches would be helpful and assessed current gaps in vaccine innovation. The class also utilized artificial intelligence‐based contributions using ChatGPT which has also been summarized. This summary of the collective view of the class provides recommendation for future campaigns to develop new vaccines and therapeutics.

Repetitive motion or exercise is associated with oxidative stress and muscle inflammation, which can lead to declining grip strength and muscle damage. Oleanolic acid and ursolic acid have anti-inflammatory and antioxidant properties and can be extracted from Chaenomeles speciosa through ultrasonic sonication. We investigated the association between grip strength declines and muscle damage induced by lambda carrageenan (LC) injection and exercise exposure in rats. We also assessed the reparative effects of transdermal pretreatment and post-treatment with C. speciosa extracts (CSEs) by using a supersonic atomizer. The half-maximal inhibitory concentration (IC50) of CSEs for cells was 10.5 mg/mL. CSEs significantly reduced the generation of reactive oxygen species and inflammatory factors (interleukin [IL]-6 and IL-1β) in in vitro cell tests. Rats subjected to LC injection and 6 weeks of exercise exhibited significantly increased inflammatory cytokine levels (IL-1β, TNF-α, and IL-6). Hematoxylin and eosin staining revealed inflammatory cell infiltration and evident muscle damage in the gastrocnemius muscle, which exhibited splitting and the appearance of the endomysium and perimysium. The treated rats’ grip strength significantly declined. Following treatment with CSEs, the damaged muscles exhibited decreased IL-1β, TNF-α, and IL-6 levels and normal morphologies. Moreover, grip strength significantly recovered. Pretreatment with CSEs yielded an immediate and significant increase in grip strength, with an increase of 180% and 165% occurring in the rats exposed to LC injection and exercise within the initial 12 h period, respectively, compared with the control group. Pretreatment with CSEs delivered transdermally using a supersonic atomizer may have applications in sports medicine and training or competitions.

In times of widespread epidemics, numerous individuals are at risk of contracting viruses, such as COVID-19, monkeypox, and pneumonia, leading to a ripple effect of impacts on others. Consequently, the Centers for Disease Control (CDC) typically devises strategies to manage the situation by monitoring and tracing the infected individuals and their areas. For convenience, “targets” and “areas” represent the following individuals and areas. A global navigation satellite system (GNSS) can assist in evaluating the located areas of the targets with pointing-in-polygon (PIP) related technology. When there are many targets and areas, relying solely on PIP technology for classification from targets to areas could be more efficient. The classification technique of k-nearest neighbors (KNN) classification is widely utilized across various domains, offering reliable classification accuracy. However, KNN classification requires a certain quantity of targets with areas (training dataset) for execution, and the size of the training dataset and classification time often exhibit an exponential relationship. This study presents a strategy for applying KNN technology to classify targets into areas. Additionally, within the strategy, we propose an adaptive KNN algorithm to enhance the efficiency of the classification procedure.

There are differences between Western mainstream culture and traditional Indigenous culture in the way they address sustainable development. The spirit of sustainability has been emphasized and practiced by Indigenous cultures for hundreds or even thousands of years, but it is increasingly disappearing over time due to the threat of natural disasters. It is necessary to recover this practice of sustainable development from its roots in traditional Indigenous knowledge. This study considers the possibility and utility of incorporating science, technology, engineering, and mathematics (STEM) into Indigenous education for sustainability, a topic that has not been addressed by other studies. Based on a literature review, the proposed framework and content for this study focus on Indigenous disaster prevention. The specific topic was chosen to be most relevant to young Indigenous children. STEM indicators from the US next-generation science standards (NGSS) were referenced to create the proposed STEM teaching objectives, which were designed to be specifically appropriate for Indigenous curricula and teaching activities. Additionally, the cultural curriculum model was adopted to reform the Indigenous curriculum and teaching model by utilizing the transformation and social action approaches. Finally, the five-stage learning cycle was used as the framework to implement the curriculum, intertwined with the principles of the spiral curriculum, to co-construct an instructional example of Indigenous education for sustainability for future reference. [ABSTRACT FROM AUTHOR]

Skeletal muscle atrophy is a disorder characterized by reductions in muscle size and strength. Cumin extract (CE) possesses anti-inflammatory, antioxidant, and hypoglycemic properties. Its pharmaceutical applications are hindered by its low water solubility and by its cytotoxicity when administered at high doses. In this study, we have developed a simplified water distillation method using a rotary evaporator to isolate the active components in cumin seeds. The anti-inflammatory effects of CE and its potential to ameliorate skeletal muscle atrophy in rats with streptozotocin (STZ)-induced diabetes were evaluated. The half-maximal inhibitory concentration (IC50) of CE for cells was 80 μM. By encapsulating CE in chitosan nanoparticles (CECNs), an encapsulation efficacy of 87.1% was achieved with a slow release of 90% of CE after 24 h of culturing, resulting in CECNs with significantly reduced cytotoxicity (IC50, 1.2 mM). Both CE and CECNs significantly reduced the inflammatory response in interleukin (IL)-6 and IL-1β assays. STZ-induced diabetic rats exhibited sustained high blood glucose levels (>16.5 mmol/L), small and damaged pancreatic β islets, and skeletal muscle atrophy. CE and CECN treatments ameliorated skeletal muscle atrophy, recovered muscle fiber striated appearance, increased grip strength, and decreased IL-6 level. Furthermore, CE and CECNs led to a reduction of damage to the pancreas, restoring its morphological phenotype, increasing serum insulin levels, and lowering blood glucose levels in STZ-induced diabetic rats. Taken together, treatment with CECNs over a 6-week period yielded positive ameliorative effects in STZ-induced rats of muscle atrophy.

Recanalization therapy is the most effective treatment for eligible patients with acute ischemic stroke (AIS). Gut microbiota are involved in the pathological mechanisms and outcomes of AIS. However, the association of gut microbiota features with adverse recanalization therapy outcomes remains unclear. Herein, we investigated gut microbiota features associated with neurological deficits in patients with AIS after recanalization therapy and whether they predict the patients’ functional outcomes. We collected fecal samples from 51 patients with AIS who received recanalization therapy and performed 16S rRNA gene sequencing (V3–V4). We compared the gut microbiota diversity and community composition between mild to moderate and severe disability groups. Next, the characteristic gut microbiota was compared between groups, and we noted that the characteristic gut microbiota in patients with mild to moderate disability included Bilophila, Butyricimonas, Oscillospiraceae_UCG-003, and Megamonas. Moreover, the relative abundance of Bacteroides fragilis, Fusobacterium sp., and Parabacteroides gordonii was high in patients with severe disability. The characteristic gut microbiota was correlated with neurological deficits, and areas under the receiver operating characteristic curves confirmed that the characteristic microbiota predicted adverse recanalization therapy outcomes. In conclusion, gut microbiota characteristics are correlated with recanalization therapy outcomes in patients with AIS. Gut microbiota may thus be a promising biomarker associated with early neurological deficits and predict recanalization therapy outcomes.

We investigated the relationship between the personal backgrounds, learning experiences, and learning outcomes of college students serving as student association cadres by applying the indicators of the Council for the Advancement of Standards in Higher Education (CAS) in the United States. The participants of this study were students at the university student associations in Taiwan, and the sampling was carried out through purposeful sampling. We distributed 1850 questionnaires, and 1761 valid questionnaires were received with a recovery rate of 95%. The self-assessed learning outcomes of the student association cadres were good. Among them, “integration and speculation of cognition” was the highest, followed by “integration and application of knowledge”, “inner self-development”, “interpersonal interaction ability”, “practice ability”, and “social care and civic literacy”. The self-assessment of the student association cadres in their learning experience was also positive. Among them, “experience in engagement” was the highest, followed by “experience in management”, “experience in handling activities”, and “experience in training courses”; while “experience in participating in activities” was the lowest. Through a hierarchical linear regression analysis of the American CAS learning outcome indicator, the student association cadres’ personal background and learning experience had significant explanatory power. The R-squared value of each aspect ranged from 39.6% to 61.1%. It was obvious that the American CAS indicator could be used to examine the learning outcomes of higher education student associations in Taiwan. The results confirm that studying student associations could become an essential approach for cultivating students’ learning outcomes and become an essential reference for Taiwan’s universities and colleges to promote community education.

This research aims to discover the existing state of public junior high school teachers’ work stress, leisure constraint, and leisure participation. A questionnaire investigation approach was adopted to collect data from public junior high school teachers in Nantou County. A total of 300 questionnaires were distributed, and 295 valid questionnaires were returned. Descriptive statistics and multiple regression analysis were carried out with the data, and the following results were obtained. Nantou County public junior high school teachers’ work stress was at an upper-medium level. Student behavior had the most significant impact on work stress. Teachers’ leisure constraint was at a lower-medium level. The highest leisure constraint was from the structural constraint. Teachers’ leisure participation was at a lower-medium level, and the most frequent participation was entertainment activities. Work stress and leisure constraints had a significant effect on leisure participation. Conclusions and suggestions are provided for the reference of the junior high school teachers, junior high school, and educational institutions for future studies.

Many highly contagious infectious diseases, such as COVID-19, monkeypox, chickenpox, influenza, etc., have seriously affected or currently are seriously affecting human health, economic activities, education, sports, and leisure. Many people will be infected or quarantined when an epidemic spreads in specific areas. These people whose activities must be restricted due to the epidemic are represented by targets in the article. Managing targets by using targeted areas is an effective option for slowing the spread. The Centers for Disease Control (CDC) usually determine management strategies by tracking targets in specific areas. A global navigation satellite system (GNSS) that can provide autonomous geospatial positioning of targets by using tiny electronic receivers can assist in recognition. The recognition of targets within a targeted area is a point-in-polygon (PtInPy) problem in computational geometry. Most previous methods used the method of identifying one target at a time, which made them unable to effectively deal with many targets. An earlier method was able to simultaneously recognize several targets but had the problem of the repeated recognition of the same targets. Therefore, we propose a GNSS coordinate recognition algorithm. This algorithm can efficiently recognize a large number of targets within a targeted area, which can provide assistance in epidemic management.

Background and Objectives: The relationship between three-dimensional (3D) scanning-derived body surface measurements and biomarkers in patients with coronary artery disease (CAD) were assessed. Methods and Methods: The recruitment of 98 patients with CAD confirmed by cardiac catheterization and 98 non-CAD patients were performed between March 2016 and December 2017. A health questionnaire on basic information, life style variables, and past medical and family history was completed. 3D body surface measurements and biomarkers were obtained. Differences between the two groups were assessed and multivariable analysis performed. Results: It was found that chest width (odds ratio [OR] 0.761, 95% confidence interval [CI] = 0.586–0.987, p = 0.0399), right arm length (OR 0.743, 95% CI = 0.632–0.875, p = 0.0004), waist circumference (OR 1.119, 95% CI = 1.035–1.21, p = 0.0048), leptin (OR 1.443, 95% CI = 1.184–1.76, p = 0.0003), adiponectin (OR 0.978, 95% CI = 0.963–0.994, p = 0.006), and interleukin 6 (OR 1.181, 95% CI = 1.021–1.366, p = 0.0254) were significantly associated with CAD. The combination of biomarker scores and body measurement scores had the greatest area under the curve and best association with CAD (area under the curve of 0.8049 and 95% CI = 0.7440–0.8657). Conclusions: Our study suggests that 3D derived body surface measurements in combination with leptin, adiponectin, and interleukin 6 levels may direct us to those at risk of CAD, allowing a non-invasive approach to identifying high-risk patients.

This study explored college students’ learning experiences and outcomes as club committee members. Using a linear regression model, it investigated the relevance of personal background variables and club learning experiences to club learning outcomes. This study selected 15 universities and colleges’ student club committee members in TaiwanA total of 1850 questionnaires were distributed, and 1761 valid questionnaires were recovered, with a recovery rate of over 95%. The study findings are as follows: Regarding learning experiences and learning outcomes, the student club committee members was good. According to this study’s linear regression analysis: The personal background of student club committee members and their club learning experience had significant explanatory power on the learning outcomes, with R2 values ranging from 39.6% to 61.1% for each dimension. This indicates that learning from club activities can be an essential pathway to cultivating students’ learning outcomes and a valuable reference for promoting club education in colleges and universities in Taiwan. Higher education practitioners should plan activities or programs for student club leaders with learning outcomes in mind, and design learning programs to meet the needs of student club leaders in each school so that students can achieve higher quality learning outcomes. In addition, this study also found that the assessment indicators of learning outcomes of the CAS of the U.S. can be applied to check the learning outcomes of student clubs in higher education in Taiwan.