Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3-30-300 green space rule.

BACKGROUND AND AIMS: Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health. METHODS: We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health. RESULT: Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (ß: 0.76, p < 0.01) and social (ß: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, ß: 0.46-0.51, P < 0.05). CONCLUSION: Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met.

An Integrated Platform for In Vivo Electrophysiology in Spatial Cognition Experiments.

Spatial cognition research requires behavioral paradigms that can distinguish between different navigational elements, such as allocentric (map-like) navigation and egocentric (e.g., body centered) navigation. To fill this need, we developed a flexible experimental platform that can be quickly modified without the need for significant changes to software and hardware. In this paper, we present this inexpensive and flexible behavioral platform paired with software which we are making freely available. Our behavioral platform serves as the foundation for a range of experiments, and although developed for assessing spatial cognition, it also has applications in the nonspatial domain of behavioral testing. There are two components of the software platform, "Maze" and "Stim Trigger." While intended as a general platform, presently both programs can work in conjunction with Neuralynx and Open Ephys electrophysiology acquisition systems, allowing for precise time stamping of neural events. The Maze program includes functionality for automatic reward delivery based on user defined zones. "Stim Trigger" permits control of brain stimulation via any equipment that can be paired with an Arduino board. We seek to share our software and leverage the potential by expanding functionality in the future to meet the needs of a larger community of researchers.

Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning.

Backgrounds: Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown. Methods: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap. Results: Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers. Conclusions: Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN.

Accurate and spectral efficient channel estimation using inter-block precoding and superimposed pilots in optical OFDM systems.

A new inter-block precoding-based channel estimation (CE) scheme is proposed and experimentally demonstrated in an optical OFDM system with a superimposed pilot (SP). The proposed inter-block precoding scheme targets on eliminating the statistical mean of the unknown data symbols, and thereby improves the performance of SP-aided CE. We investigate the impact that both the precoding matrix and SP have on the system performance, from which we obtain the optimum value of signal-to-pilot power ratio (SPR) as well as the block length. We show through simulations and experiments that the proposed CE scheme, in comparison with the conventional preamble based scheme, has the advantage of entailing a much smaller overhead size, while offering similar performance in terms of CE accuracy and bit-error ratio (BER) performances. Furthermore, the proposed precoding scheme has no limit to the design of SP, and thus is applicable for any periodic pilots.

Fluorescent immunosorbent assay for the detection of alpha lactalbumin in dairy products with monoclonal antibody bioconjugated with CdSe/ZnS quantum dots.

In this work, a new method termed competitive fluorescence-linked immunosorbent assay (FLISA) was developed for specifically quantification of bovine α-lactalbumin (α-La) in dairy products. The monoclonal antibodies (mAbs) against α-La were produced through hybridoma technology, and the mAbs were covalently conjugated with the CdSe/ZnS quantum dots (QDs) using the crossing-linking reagents. Moreover, a competitive FLISA based on QD-mAb conjugates was established to detect α-La in dairy products. It was shown that there was a good linear relationship between inhibition efficiency, and logarithm of α-La concentration after the detection parameters were optimised in which the concentration of α-La varied from 0.1 to 1000ng/mL. The value of IC50 was 0.03µg/mL, and the FLISA method exhibited high sensitivity with the LOD at 0.1ng/mL. The developed FLISA has been successfully applied to determine α-La in commercial dairy products, providing more sensitive analysis compared with the ELISA method.