A growing number of adults are aging with spinal cord injury (SCI) acquired earlier in life. Social integration is important for health and participation after SCI. However, little is known about the role of the community built environment for supporting social integration among adults aging with SCI. Using a structured telephone survey with 182 adults aging with SCI in the Midwestern United States, we found that more community built environment facilitators (e.g., curb cuts, automatic doors, paved surfaces) and fewer barriers (e.g., gravel surfaces, crowds) significantly increased the odds of regularly engaging in both formal and informal social activities.

UNASSIGNED: Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities\' multidimensional nature.
UNASSIGNED: In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status.
UNASSIGNED: We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: \"sparse rural,\" \"developed rural,\" \"inner suburb,\" and \"deprived urban core.\" Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with \"developed rural,\" T2D onset odds were higher in \"deprived urban core\" (1.24, CI = 1.16-1.33) and \"inner suburb\" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status.
UNASSIGNED: Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

The study explored subjective mental health change in adults aged 60 to 100 by experiencing immersive virtual reality (IVR)-based on the natural environment. It investigated changes in thinking, behaviour, and emotions from a socioecological perspective. The study conducted quantitative surveys of 540 older adults via IVR who lived in 20 longevity villages in South Korea from Dec. 2022 to Nov. 2023. It also paralleled a qualitative study with 38 of the 540. Study results predicting subjective mental health changes after experiencing IVR in those over 70 showed the highest gladness and happiness. In over 70 compared with 60 to 69 ages, the study found that relieving stress and depression, gladness and happiness, and relaxing the mind have about two-fold subjective mental health changes after experiencing IVR. The study suggests that it can be utilized to promote subjective mental health through the experience of an IVR-based natural environment for older adults.

This dataset comprises oil palm fresh fruit bunch (FFB) images that may potentially be used in the study related to fruit ripeness detection via image processing. The FFB dataset was collected from palm oil plantations in Johor, Negeri Sembilan, and Perak, Malaysia. The data collection involved acquiring pictures of FFB from various angles and classifying them based on their ripeness level, categorised into five classes: damaged bunch, empty bunch, unripe, ripe, and overripe. An experienced grader carefully labelled each FFB image with the corresponding ground truth information. The dataset provides valuable insights into the colour variations of FFBs throughout their ripening process, which is essential for assessing oil quality. It includes observations on the external fruit colours as well as characteristics related to the presence of empty sockets in the FFB as a key indicator of ripeness. The reusability potential of this dataset is significant for researchers in the field of oil palm fruit classification and grading, which requires an extensive outdoor dataset that comprise FFB\'s both on the tree and on the ground. Our work enables the development and validation of machine learning pipelines for outdoor automated FFB grading. Furthermore, the dataset may also support studies to improve oil palm cultivation practices, enhance yield, and optimise oil quality.

Accurate identification of the fungal community spontaneously colonizing food products, aged in natural and not controlled environments, provides information about potential mycotoxin risk associated with its consumption. Autochthonous mycobiota colonizing cheese aging in Dossena mines, was investigated and characterized by two approaches: microbial isolations and metabarcoding. Microbial isolations and metabarcoding analysis were conducted on cheese samples, obtained by four batches, produced in four different seasons of the year, aged for 90 and 180 days, by five dairy farms. The two approaches, with different taxonomical resolution power, highlighted Penicillium biforme among filamentous fungi, collected from 58 out of 68 cheeses, and Debaryomyces hansenii among yeasts, as the most abundant species (31 ÷ 65%), none representing a health risk for human cheese consumption. Shannon index showed that the richness of mycobiota increases after 180 days of maturation. Beta diversity analysis highlighted significant differences in composition of mycobiota of cheese produced by different dairy farms and aged for different durations. Weak negative growth interaction between P. biforme and Aspergillus westerdijkiae by in vitro analysis was observed leading to hypothesize that a reciprocal control is possible, also affected by natural environmental conditions, possibly disadvantageous for the last species.

OBJECTIVE: To examine the associaiton between environmental measures and brain volumes and its potential mediators.
METHODS: This was a prospective study.
METHODS: Our analysis included 34,454 participants (53.4% females) aged 40-73 years at baseline (between 2006 and 2010) from the UK Biobank. Brain volumes were measured using magnetic resonance imaging between 2014 and 2019.
RESULTS: Greater proximity to greenspace buffered at 1000 m at baseline was associated with larger volumes of total brain measured 8.8 years after baseline assessment (standardized β (95% CI) for each 10% increment in coverage: 0.013(0.005,0.020)), grey matter (0.013(0.006,0.020)), and white matter (0.011(0.004,0.017)) after adjustment for covariates and air pollution. The corresponding numbers for natural environment buffered at 1000 m were 0.010 (0.004,0.017), 0.009 (0.004,0.015), and 0.010 (0.004,0.016), respectively. Similar results were observed for greenspace and natural environment buffered at 300 m. The strongest mediator for the association between greenspace buffered at 1000 m and total brain volume was smoking (percentage (95% CI) of total variance explained: 7.9% (5.5-11.4%)) followed by mean sphered cell volume (3.3% (1.8-5.8%)), vitamin D (2.9% (1.6-5.1%)), and creatinine in blood (2.7% (1.6-4.7%)). Significant mediators combined explained 18.5% (13.2-25.3%) of the association with total brain volume and 32.9% (95% CI: 22.3-45.7%) of the association with grey matter volume. The percentage (95% CI) of the association between natural environment and total brain volume explained by significant mediators combined was 20.6% (14.7-28.1%)).
CONCLUSIONS: Higher coverage percentage of greenspace and environment may benefit brain health by promoting healthy lifestyle and improving biomarkers including vitamin D and red blood cell indices.

Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors.

Cotton, a vital textile raw material, is intricately linked to people\'s livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.

BACKGROUND: Environment and genes both contribute to schizophrenia. However, the impact of different natural environments surrounding residential addresses on schizophrenia in urban settings remains unknown. This study aimed to investigate the association of urbanisation, measured by residential environments, with late-onset schizophrenia and explore whether genetic risk for schizophrenia modified the associations.
METHODS: We examined the associations between residential environments and late-onset schizophrenia and its interaction with genetic risk factors in UK Biobank, followed from 2006 to 2010 (baseline) to Dec 2021. Residential environments, including greenspace, domestic garden, blue space, and total natural environment, were evaluated using land use coverage percentage. The polygenic risk score (PRS) of schizophrenia was derived using a Bayesian approach and adjusted it against ancestry. Cox proportional hazard regression model was used to assess the associations between per interquartile (IQR) increase of each type of residential environments and late-onset schizophrenia. Interactive effects of PRS and residential environments on late-onset schizophrenia were assessed on both additive and multiplicative scales.
RESULTS: A total of 393,680 participants were included in the analysis, with 844 cases of late-onset schizophrenia being observed after 12.8 years of follow-up. Within 300 m buffer surrounding the residential addresses, per interquartile increase in greenspace (31.5 %) and total natural environment (34.4 %) were both associated with an 11 % (HR = 0.89, 95 % CI 0.80, 0.99) lower risk of late-onset schizophrenia. Domestic garden and blue space did not show significant protective effects on late-onset schizophrenia. A strong dose-response relationship between schizophrenia PRS and schizophrenia was found, while no additive or multiplicative interaction effects were present between residential environments and PRS on late-onset schizophrenia.
CONCLUSIONS: Residential greenspace and total natural environment may protect against late-onset schizophrenia in older people regardless of genetic risk. These findings shed light on the prevention of schizophrenia and urban planning to optimise ecosystem benefits linked to schizophrenia.

Researchers, practitioners, and policy-makers are having to deal with the negative impact of body image concerns in populations globally. One cost-effective way of promoting healthier body image outcomes is through exposure to natural environments. A growing body of research has shown that spending time in, interacting with, and even just looking at natural environments can promote healthier body image outcomes. In this narrative review, I consider the different forms of evidence documenting an association between nature exposure and body image (i.e., cross-sectional and mediational, experimental and quasi-experimental, comparative, prospective, experience sampling, and qualitative research). Beyond this, I shine a critical light on the available evidence, highlighting concerns with methodological (i.e., who research has focused on and what types of natural environments have been considered), psychometric (i.e., how body image and nature exposure are measured), and conceptual issues (how the association is explained). I conclude that, although there are issues affecting the way the existing body of research is to be understood, there are reasons to be hopeful that nature exposure can be leveraged to promote healthier body image outcomes in diverse populations.