Artificial Light at Night and Social Vulnerability: an Environmental Justice analysis in the US 2012-2019

BACKGROUND: Artificial Light at Night (ALAN) is an emerging health risk factor that has been linked to a wide range of adverse health effects. Recent study suggested that disadvantaged neighborhoods may be exposed to higher levels of ALAN. Understanding how social disadvantage correlates with ALAN levels is essential for identifying the vulnerable populations and for informing lighting policy. METHODS: We used satellite data from the National Aeronautics and Space Administration\u27s (NASA) Black Marble data product to quantify annual ALAN levels (2012-2019), and the Center for Disease Control and Prevention\u27s (CDC) Social Vulnerability Index (SVI) to quantify social disadvantage, both at the US census tract level. We examined the relationship between the ALAN and SVI (overall and domain-specific) in over 70,000 tracts in the Contiguous U.S., and investigated the heterogeneities in this relationship by the rural-urban status and US regions (i.e., Northeast, Midwest, South, West). RESULTS: We found a significant positive relationship between SVI and ALAN levels. On average, the ALAN level in the top 20% most vulnerable communities was 2.46-fold higher than that in the 20% least vulnerable communities (beta coefficient (95% confidence interval) for log-transformed ALAN, 0.90 (0.88, 0.92)). Of the four SVI domains, minority and language status emerged as strong predictors of ALAN levels. Our stratified analysis showed considerable and complex heterogeneities across different rural-urban categories, with the association between greater vulnerability and higher ALAN primarily observed in urban cores and rural areas. We also found regional differences in the association between ALAN and both overall SVI and SVI domains. CONCLUSIONS: Our study suggested ALAN as an environmental justice issue that may carry important public health implications. Funding National Aeronautics and Space Administration

Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells

We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening

Trans-ethnic Mendelian-randomization study reveals causal relationships between cardiometabolic factors and chronic kidney disease.

Funder: Government Department of BusinessFunder: Energy and Industrial Strategy (BEIS)Funder: Vice-Chancellor Fellowship from the University of BristolFunder: Shanghai Thousand Talents ProgramFunder: Academy of Medical Sciences (AMS) Springboard AwardFunder: BBSRC Innovation fellowshipFunder: NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of BristolBACKGROUND: This study was to systematically test whether previously reported risk factors for chronic kidney disease (CKD) are causally related to CKD in European and East Asian ancestries using Mendelian randomization. METHODS: A total of 45 risk factors with genetic data in European ancestry and 17 risk factors in East Asian participants were identified as exposures from PubMed. We defined the CKD by clinical diagnosis or by estimated glomerular filtration rate of 25 kg/m2. CONCLUSIONS: Eight cardiometabolic risk factors showed causal effects on CKD in Europeans and three of them showed causality in East Asians, providing insights into the design of future interventions to reduce the burden of CKD.This research has been conducted using the UK Biobank resource under Application Numbers ‘40135’ and ‘15825’. J.Z. is funded by a Vice-Chancellor Fellowship from the University of Bristol. This research was also funded by the UK Medical Research Council Integrative Epidemiology Unit [MC_UU_00011/1, MC_UU_00011/4 and MC_UU_00011/7]. J.Z. is supported by the Academy of Medical Sciences (AMS) Springboard Award, the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy (BEIS), the British Heart Foundation and Diabetes UK [SBF006\1117]. This study was funded/supported by the NIHR Biomedical Research Centre at University Hospitals Bristol NHS Foundation Trust and the University of Bristol (G.D.S., T.R.G. and R.E.W.). This study received funding from the UK Medical Research Council [MR/R013942/1]. J.Z., Y.M.Z. and T.R.G are funded by a BBSRC Innovation fellowship. J.Z. is supported by the Shanghai Thousand Talents Program. Y.M.Z. is supported by the National Natural Science Foundation of China [81800636]. H.Z. is supported by the Training Program of the Major Research Plan of the National Natural Science Foundation of China [91642120], a grant from the Science and Technology Project of Beijing, China [D18110700010000] and the University of Michigan Health System–Peking University Health Science Center Joint Institute for Translational and Clinical Research [BMU2017JI007]. N.F. is supported by the National Institutes of Health awards R01-MD012765, R01-DK117445 and R21-HL140385. R.C. is funded by a Wellcome Trust GW4 Clinical Academic Training Fellowship [WT 212557/Z/18/Z]. The Trøndelag Health Study (the HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), Trøndelag County Council, Central Norway Regional Health Authority and the Norwegian Institute of Public Health. M.C.B. is supported by the UK Medical Research Council (MRC) Skills Development Fellowship [MR/P014054/1]. S.F. is supported by a Wellcome Trust PhD studentship [WT108902/Z/15/Z]. Q.Y. is funded by a China Scholarship Council PhD scholarship [CSC201808060273]. Y.C. was supported by the National Key R&D Program of China [2016YFC0900500, 2016YFC0900501 and 2016YFC0900504]. The China Kadoorie Biobank baseline survey and the first resurvey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust [202922/Z/16/Z, 088158/Z/09/Z and 104085/Z/14/Z]. Japan-Kidney-Biobank was supported by AMED under Grant Number 20km0405210. P.C.H. is supported by Cancer Research UK [grant number: C18281/A19169]. A.K. was supported by DFG KO 3598/5–1. N.F. is supported by NIH awards R01-DK117445, R01-MD012765 and R21-HL140385. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health

Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China

As an important cause of global warming, CO2 emissions have become a research hotspot in recent years. Industrial transfer impacts regional CO2 emissions and is related to the low-carbon development of regional industries. Taking the Beijing-Tianjin-Hebei region (BTH region) as an example, this study analysed industrial transfer’s direct and indirect impacts on CO2 emissions based on a mediating model and two-way fixed effect panel regression. The results obtained indicate that industrial transfer-in has promoted CO2 emissions to a small extent, and the positive impact of industrial transfer-in on CO2 emissions wanes over time. Industrial transfer affects CO2 emissions by acting on the economic level, on population size, and on urbanisation level, but the indirect effect is weaker than the direct effect. Industrial transfer does not lead to technological upgrading, but the latter is an effective means of carbon emission reduction. Industrial transfer-in has shown a positive effect on CO2 emissions for most cities, but there are exceptions, such as Cangzhou. In the future, the BTH region should maintain coordinated development among cities and improve the cooperative innovation mechanism for energy conservation and emission reduction

Fabrication of Flame Retarded Cellulose Aerogel with Hydrophobicity via MF/MTMS Double Cross-Linking

In order to overcome the disadvantage of highly flammable and poor mechanical performance of cellulose aerogel, herein, cellulose nanofibrils (CNF) were cross-linked by melamine formaldehyde (MF), and further cross-linked and surface decorated by methyltrimethoxysilane (MTMS) to obtain flame retarded compound aerogels. FT-IR spectra confirmed that cross-linking reaction occurred between MF/MTMS and CNF matrix. The morphological analysis demonstrated that the pore size shrinked, whereas the pore amount of compound aerogels increased after cross-linking, thereby resulting in the improvement of mechanical performance. Via double cross-linking, the compound aerogels also exhibited excellent hydrophobicity (contact angle up to 132.3°) and flame retardant properties. The limited oxygen index (LOI) value of double cross-linked specimen (Si-CNF/MF) increased from 19.5% to 37.1%, and the vertical combustion test (UL-94) reached V-0 grade. Microcalorimetry measurement showed that the peak heat release rate (pHRR) and total heat release (THR) of Si-CNF/MF decreased by 50.6% and 64.3% in comparison with pure CNF aerogel, respectively. The analysis results of char residue showed that both condensed phase and gas phase flame retardant mechanisms occurred during combustion, and synergistic effect existed between MF and MTMS. Moreover, the very low thermal conductivity of compound aerogels permitted their application as heat preservation materials

Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity

Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, and has been widely used. However, the current field of intelligent plant diseases identification still faces the problems of insufficient data and inaccurate classification. Aiming to resolve these problems, the present research proposes an image information contribution evaluation method based on the analysis of inter-class similarity. Combining this method with the active learning image selection strategy can provide guidance for the collection and annotation of intelligent identification datasets of plant diseases, so as to improve the recognition effect and reduce the cost. The method proposed includes two modules: the inter-classes similarity evaluation module and the image information contribution evaluation module. The images located on the decision boundary between high similarity classes will be analysis as high information contribution images, they will provide more information for plant diseases classification. In order to verify the effectiveness of this method, experiments were carried on the fine-grained classification dataset of tomato diseases. Experimental results confirm the superiority of this method compared with others. This research is in the field of plant disease classification. For the detection and segmentation, further research is advisable

Remote Sensing Image Information Quality Evaluation via Node Entropy for Efficient Classification

Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs. How to use the limited acquisition budget to obtaina better remote sensing image dataset is a problem worth studying. In response to this problem, this paper proposes a remote sensing image quality evaluation method based on node entropy, which can be combined with active learning to provide low-cost guidance for remote sensing image collection and labeling. The method includes a node selection module and a remote sensing image quality evaluation module. The function of the node selection module is to select representative images, and the remote sensing image quality evaluation module evaluates the remote sensing image information quality by calculating the node entropy of the images. The image at the decision boundary of the existing images has a higher information quality. To validate the method proposed in this paper, experiments are performed on two public datasets. The experimental results confirm the superiority of this method compared with other methods

Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis

Artificial light at night and social vulnerability: An environmental justice analysis in the U.S. 2012–2019

Background: Artificial Light at Night (ALAN) is an emerging health risk factor that has been linked to a wide range of adverse health effects. Recent study suggested that disadvantaged neighborhoods may be exposed to higher levels of ALAN. Understanding how social disadvantage correlates with ALAN levels is essential for identifying the vulnerable populations and for informing lighting policy. Methods: We used satellite data from the National Aeronautics and Space Administration’s (NASA) Black Marble data product to quantify annual ALAN levels (2012–2019), and the Center for Disease Control and Prevention’s (CDC) Social Vulnerability Index (SVI) to quantify social disadvantage, both at the US census tract level. We examined the relationship between the ALAN and SVI (overall and domain-specific) in over 70,000 tracts in the Contiguous U.S., and investigated the heterogeneities in this relationship by the rural-urban status and US regions (i.e., Northeast, Midwest, South, West). Results: We found a significant positive relationship between SVI and ALAN levels. On average, the ALAN level in the top 20% most vulnerable communities was 2.46-fold higher than that in the 20% least vulnerable communities (beta coefficient (95% confidence interval) for log-transformed ALAN, 0.90 (0.88, 0.92)). Of the four SVI domains, minority and language status emerged as strong predictors of ALAN levels. Our stratified analysis showed considerable and complex heterogeneities across different rural-urban categories, with the association between greater vulnerability and higher ALAN primarily observed in urban cores and rural areas. We also found regional differences in the association between ALAN and both overall SVI and SVI domains. Conclusions: Our study suggested ALAN as an environmental justice issue that may carry important public health implications.FundingNational Aeronautics and Space Administration