Healing built-environment effects on health outcomes: environment–occupant–health framework

An investigation examined the structured scientific evidence on healthcare facilities (the healing built environment – HBE) and its impact on patients’ health outcomes under a holistic conceptual evaluative framework. The integrative review considered 127 papers (of which 59 were review papers). It found there was no adequate framework that could integrate existing research findings holistically. Such a holistic framework needs to demonstrate the cumulative and interactive effects of various HBE characteristics on patients’ health outcomes and wellbeing. An environment–occupant–health (E-O-H) framework is proposed, taking a holistic perspective to identify and evaluate different HBE characteristics. The E-O-H framework should support future research by (1) identifying the HBE characteristics that affect health outcomes; (2) defining appropriate future research designs; and (3) understanding the need for holistic analysis of the integrated effects of diverse HBE characteristics on health outcomes

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

Caracterização da qualidade acústica de salas de aula para prática e ensino musical

Resumo O músico necessita perceber adequadamente o som nos recintos destinados ao estudo e prática musical, o que é possível quando estes locais estão acusticamente preparados e permitem o desenvolvimento e aprimoramento da percepção sonora musical. Neste trabalho três salas de estudo e três salas de aula coletiva, destinadas ao ensino e prática de Música de uma universidade, foram caracterizadas acusticamente através da opinião dos músicos usuários e de medições da sua resposta impulsiva. As salas descritas pelos músicos como secas tiveram, nas bandas de frequência de oitava de 500 a 1000 Hz, um Tempo de Reverberação em torno de 0,3 segundos, entre 14 e 22 dB de Clareza e entre 88% a 96% de Definição. As salas caracterizadas como reverberantes tiveram um tempo ao redor de 1,5 segundos, Clareza de 1 dB e Definição de 40%. A opinião dos músicos permitiu compreender as preferências da qualidade acústica das salas e as informações fornecidas pelos músicos se mostraram coerentes com os dados das medições

Relationships Between Residence Characteristics and \u3ci\u3eNursing Home Compare\u3c/i\u3e Database Quality Measures

Objective: This study evaluated relationships between physical characteristics of nursing home residences and quality-of-care measures. Design: This was a cross-sectional ecologic study. The dependent variables were 5 Centers for Medicare & Medicaid Services (CMS) Nursing Home Compare database long-stay quality measures (QMs) during 2019: percentage of residents who displayed depressive symptoms, percentage of residents who were physically restrained, percentage of residents who experienced 1 or more falls resulting in injury, percentage of residents who received antipsychotic medication, and percentage of residents who received anti-anxiety medication. The independent variables were 4 residence characteristics: ownership type, size, occupancy, and region within the United States. We explored how different types of each residence characteristic compare for each QM. Setting, participants, and measurements: Quality measure values from 15,420 CMS-supported nursing homes across the United States averaged over the 4 quarters of 2019 reporting were used. Welch’s analysis of variance was performed to examine whether the mean QM values for groups within each residential characteristic were statistically different. Results: Publicly owned and low-occupancy residences had the highest mean QM values, indicating the poorest performance. Nonprofit and high-occupancy residences generally had the lowest (ie, best) mean QM values. There were significant differences in mean QM values among nursing home sizes and regions. Conclusion: This study suggests that residence characteristics are related to 5 nursing home QMs. Results suggest that physical characteristics may be related to overall quality of life in nursing homes

Common variation within the SETBP1 gene is associated with reading-related skills and patterns of functional neural activation

Epidemiological population studies highlight the presence of substantial individual variability in reading skill, with approximately 5–10% of individuals characterized as having specific reading disability (SRD). Despite reported substantial heritability, typical for a complex trait, the specifics of the connections between reading and the genome are not understood. Recently, the SETBP1 gene has been implicated in several complex neurodevelopmental syndromes and disorders that impact language. Here, we examined the relationship between common polymorphisms in this gene, reading, and reading associated behaviors using data from an ongoing project on the genetic basis of SRD (n = 135). In addition, an exploratory analysis was conducted to examine the relationship between SETBP1 and brain activation using functional magnetic resonance imaging (fMRI; n = 73). Gene-based analyses revealed a significant association between SETBP1 and phonological working memory, with rs7230525 as the strongest associated single nucleotide polymorphism (SNP). fMRI analysis revealed that the rs7230525-T allele is associated with functional neural activation during reading and listening to words and pseudowords in the right inferior parietal lobule (IPL). These findings suggest that common genetic variation within SETBP1 is associated with reading behavior and reading-related brain activation patterns in the general population