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Changes in the dimensions of psychological health in relation to workplace greenspace exposure and its relationship with job stress was not yet fully revealed. A neighboring park of the workplace and a forest were chosen to represent experimental stimuli and the control, respectively. The experiment was conducted in March 2021 in Taiwan, using a virtual reality. Results show that negative emotions decreased in the post-test in both environment settings. However, positive emotion increased only after exposure to the forest (t=-3.88, p <.001). After virtually visiting the workplace greenspace, depression in the high stress group decreased significantly (t = 3.48 , p <.01) while the counterpart did not exhibit any significant change. Based on the interviews, participants only indicated stressful elements of the workplace greenspace video, despite the video also contained natural elements. Topics related to work decreased feelings of being away. Greenspace in a work context should be carefully designed to avoid the stressful elements. [ABSTRACT FROM AUTHOR]

Drawing from environmental psychology and consumer well-being perspectives, this study leverages the theoretical mechanisms underlying the concept of restorative environments to develop and test a model based on Kaplan and Kaplan's (1989) Attention Restoration Theory framework to examine the restorative potentials of wellness tourism settings. A total of 357 valid questionnaires were collected. The results show that the restorative environment is an important aspect of the wellness experience, contributing to positive emotions and life satisfaction as well as determining an individual's loyalty and intention to revisit. Multi-group analysis was applied to further examine the moderating effect. This research provided a theoretically and empirically consistent explanation for the theoretical mechanisms of ART underlying the concepts of restorative experience, tourist well-being, and loyalty, the manifestation of which in the wellness tourism context extrapolates the importance of restorative environments for well-being. [ABSTRACT FROM AUTHOR]

For indoor air modelling, difficulties in collecting indoor parameters including life activity patterns and building characteristics are dilemmas when conducting a large-area study. Land-use/land cover information which is easier to obtain could represent as surrogates of emission sources for assessing indoor air quality. Moreover, low-cost sensors and machine learning provide a better way to enhance model accuracy. This study proposed an alternative estimation approach to assess daily PM 2.5 concentration for indoor environments of schools in a large area by integrating low-cost sensors, land-use/land cover predictors, and machine learning-based modelling approaches. Indoor PM 2.5 data was collected from 145 indoor AirBox sensors in Kaohsiung and Pingtung Counties of Taiwan. Geospatial predictors were extracted from the circular buffers surrounding each AirBox sensor. Spearman correlation analysis and stepwise variable selection procedures were performed to select variables for land-use regression (LUR) and integrated with XGBoost, Random Forest (RF), and LGBM machine learning models. The results revealed that outdoor PM 2.5 and distance to the nearest thermal power plant were the main determinants of indoor estimation variations, when there were no indoor sources. When incorporating machine learning, the R2 increased from 0.59 for LUR to 0.85 for LUR-XGBoost while the RMSE decreased from 8.63 to 5.27 μg/m3, which performed better than both LUR-RF and LUR-LGBM. This study demonstrates the value of the proposed alternative approach by incorporating data from a low-cost sensor with LUR model and machine learning algorithm in estimating the spatiotemporal variability of indoor PM 2.5 for a large area. [Display omitted] • An alternative approach was proposed to estimate indoor PM 2.5 for school campuses. • Low-cost sensor and land-use/land cover were incorporated using machine learning. • Important land-use/land cover variables affecting indoor PM 2.5 were identified. • The R2 of indoor LUR-XGBoost model was 0.85 with RMSE 5.27 μg/m3. [ABSTRACT FROM AUTHOR]

Previous literature highlights the effects of forests on reviving psychological and physiological health. Given that middle-aged women are vulnerable to stress due to the transition in their lives, the purpose of this study was to investigate the effects of forest bathing on their stress recovery using a field experiment design. This study examined both psychological and physiological responses after a forest therapy program among a middle-aged female group. Sixteen middle-aged (46.88 ± 7.83 years) women were recruited for a two-day (one-night) forest therapy program in the Aowanda National Forest Recreation Area (Nantou, Taiwan). Psychological indices were measured by Profile of Mood States (POMS) and State-Trait Anxiety Inventory (STAI). Physiological indicators included pulse rate, systolic or diastolic blood pressure, and salivary α-amylase. Both psychological and physiological measurements were collected before and after the program. The results showed that negative mood states (i.e., confusion, fatigue, anger-hostility, and tension) and anxiety levels were significantly reduced after visiting forests. In contrast, positive mood state (vigor) was improved after the program. Regarding the physiological responses, a significant decrease in systolic blood pressure response was found after the program. In sum, the two-day forest therapy program benefited to mental health and systolic blood pressure among the middle-aged female group. [ABSTRACT FROM AUTHOR]

Objective: This study applied an ecological-based analysis aimed to evaluate on a global scale the association between greenness exposure and suicide mortality., Methods: Suicide mortality data provided by the Institute for Health Metrics and Evaluation and the Normalized Difference Vegetation Index (NDVI) were employed. The generalized additive mixed model was applied to evaluate with an adjustment of covariates the association between greenness and suicide mortality. Sensitivity tests and positive-negative controls also were used to examine less overt insights. Subgroup analyses were then conducted to investigate the effects of greenness on suicide mortality among various conditions., Results: The main finding of this study indicates a negative association between greenness exposure and suicide mortality, as greenness significantly decreases the risk of suicide mortality per interquartile unit increment of NDVI (relative risk = 0.69, 95%CI: 0.59-0.81). Further, sensitivity analyses confirmed the robustness of the findings. Subgroup analyses also showed a significant negative association between greenness and suicide mortality for various stratified factors, such as sex, various income levels, urbanization levels, etc., Conclusions: Greenness exposure may contribute to a reduction in suicide mortality. It is recommended that policymakers and communities increase environmental greenness in order to mitigate the global health burden of suicide., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Asri, Tsai, Wong, Lee, Pan, Guo, Wu, Su, Wu and Spengler.)

Drosophila melanogaster has been used as a genetic model organism to understand the fundamental molecular mechanisms in human biology including memory formation that has been reported involving protein synthesis and/or post-translational modification. In this study, we employed a proteomic platform based on fluorescent 2DE and MALDI-TOF MS to build a standard D. melanogaster head proteome map for proteome-proteome comparison. In order to facilitate the comparison, an interactive database has been constructed for systematically integrating and analyzing the proteomes from different conditions and further implicated to study human diseases related to D. melanogaster model. In summary, the fundamental head proteomic database and bioinformatic analysis will be useful for further elucidating the biological mechanisms such as memory formation and neurodegenerative diseases., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

IBMPFD, Inclusion body myopathy associated with Paget's disease of bone and frontotemporal dementia, is a hereditary degenerative disorder due to single missense mutations in VCP (Valosin-Containing Protein). The mechanisms of how mutations of VCP lead to IBMPFD remain mysterious. Here we utilize two-dimensional difference gel electrophoresis (2D-DIGE) combined with mass spectrometry to study the IBMPFD disorder at the protein level. With this set-up, we are able to employ comparative proteomics to analyze IBMPFD disease using Drosophila melanogaster as our disease model organism. Head proteome of transgenic D. melanogaster expressing wild type VCP is compared, respectively, with the head proteome of transgenic mutant type VCPs that correspond to human IBMPFD disease alleles (TER94(A229E), TER94(R188Q), and TER94(R152H)). Of all the proteins identified, a significant fraction of proteins altered in TER94(A229E) and TER94(R188Q) mutants belong to the same functional categories, i.e. apoptosis and metabolism. Among these, Drosophila transferrin is observed to be significantly up-regulated in mutant flies expressing TER94(A229E). A knock-down experiment suggests that fly transferrin might be a potential modifier in IBMPFD disease. The molecular analysis of IBMPFD disease may benefit from the proteomics approach which combines the advantages of high throughput analysis and the focus on protein levels.