Urbanisation and health in China.

China has seen the largest human migration in history, and the country's rapid urbanisation has important consequences for public health. A provincial analysis of its urbanisation trends shows shifting and accelerating rural-to-urban migration across the country and accompanying rapid increases in city size and population. The growing disease burden in urban areas attributable to nutrition and lifestyle choices is a major public health challenge, as are troubling disparities in health-care access, vaccination coverage, and accidents and injuries in China's rural-to-urban migrant population. Urban environmental quality, including air and water pollution, contributes to disease both in urban and in rural areas, and traffic-related accidents pose a major public health threat as the country becomes increasingly motorised. To address the health challenges and maximise the benefits that accompany this rapid urbanisation, innovative health policies focused on the needs of migrants and research that could close knowledge gaps on urban population exposures are needed

MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p

Pattern Recognition of the COVID-19 Pandemic in the United States: Implications for Disease Mitigation

The novel coronavirus (COVID-19) pandemic presents a severe threat to human health worldwide. The United States (US) has the highest number of reported COVID-19 cases, and over 16 million people were infected up to the 12 December 2020. To better understand and mitigate the spread of the disease, it is necessary to recognize the pattern of the outbreak. In this study, we explored the patterns of COVID-19 cases in the US from 1 March to 12 December 2020. The county-level cases and rates of the disease were mapped using a geographic information system (GIS). The overall trend of the disease in the US, as well as in each of its 50 individual states, were analyzed by the seasonal-trend decomposition. The disease curve in each state was further examined using K-means clustering and principal component analysis (PCA). The results showed that three clusters were observed in the early phase (1 March-31 May). New York has a unique pattern of the disease curve and was assigned one cluster alone. Two clusters were observed in the middle phase (1 June-30 September). California, Texas and Florida were assigned in the same cluster, which has the pattern different from the remaining states. In the late phase (1 October-12 December), California has a unique pattern of the disease curve and was assigned a cluster alone. In the whole period, three clusters were observed. California, Texas and Florida still have similar patterns and were assigned in the same cluster. The trend analysis consolidated the patterns identified from the cluster analysis. The results from this study provide insight in making disease control and mitigation strategies

Landscape Fragmentation as a Risk Factor for Buruli Ulcer Disease in Ghana

Land cover and its change have been linked to Buruli ulcer (BU), a rapidly emerging tropical disease. However, it is unknown whether landscape structure affects the disease prevalence. To examine the association between landscape pattern and BU presence, we obtained land cover information for 20 villages in southwestern Ghana from high resolution satellite images, and analyzed the landscape pattern surrounding each village. Eight landscape metrics indicated that landscape patterns between BU case and reference villages were different (P < 0.05) at the broad spatial extent examined (4 km). The logistic regression models showed that landscape fragmentation and diversity indices were positively associated with BU presence in a village. Specifically, for each increase in patch density and edge density by 100 units, the likelihood of BU presence in a village increased 2.51 (95% confidence interval [CI] = 1.36–4.61) and 4.18 (95% CI = 1.63–10.76) times, respectively. The results suggest that increased landscape fragmentation may pose a risk to the emergence of BU

Reflective imaging improves spatiotemporal resolution and collection efficiency in light sheet microscopy

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Nature Communications 8 (2017): 1452, doi:10.1038/s41467-017-01250-8.Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, and gentle imaging of live specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of four complementary views in 250 ms, doubling speed and improving information content relative to symmetric dual-view LSFM. We also report a modified deconvolution algorithm that removes associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to <300 nm in all three dimensions) by applying our method to single-view LSFM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture. We demonstrate the broad applicability of our method in a variety of samples, studying mitochondrial, membrane, Golgi, and microtubule dynamics in cells and calcium activity in nematode embryos.This work was supported by the Intramural Research Program of the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health. P.L. and H.S. acknowledge summer support from the Marine Biological Laboratory at Woods Hole, through the Whitman- and Fellows- program. P.L. acknowledges support from NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under grant number R01EB017293. C.S. acknowledges funding from the National Institute of General Medical Sciences of NIH under Award Number R25GM109439 (Project Title: University of Chicago Initiative for Maximizing Student Development [IMSD]) and NIBIB under grant number T32 EB002103. Partial funding for the computation in this work was provided by NIH grant numbers S10 RRO21039 and P30 CA14599. A.U. and I.R.-S. were supported by the NSF grant number 1607645

Diarrheal Diseases in Rural Bangladesh: Spatial-Temporal Patterns, Risk Factors and Pathogen Detection

Diarrheal diseases are still a leading cause of child mortality in less developed countries. In the past three decades, in an effort to reduce the transmission of diarrheal diseases, millions of tubewells have been installed as a way to provide safe drinking water in Bangladesh. However, this effort may have been counterproductive since widespread arsenic contamination has been found in groundwater. Thus, there is a reason to rethink the use of tubewells and to assess risk factors related to diarrheal disease in Bangladesh. This study primarily focused on 142 villages of Matlab, a rural area in Bangladesh, using datasets collected through a local health surveillance system to explore the spatiotemporal patterns of diarrheal disease and its relevant risk factors. First, a geographic information system (GIS) and spatial statistics were used to illustrate the occurrence and spatial-temporal clusters of diarrhea (including community childhood diarrhea data and hospital data on diarrhea caused by rotavirus and Shigella). Second, the study determined the relationship between diarrheal disease among children under five and identified several important risk factors, such as tubewell access, depth and arsenic levels. Additionally, simple and rapid concentration methods were developed and evaluated to detect adenovirus, a common etiologic pathogen of diarrhea in water. The study attempted to answer the following questions: What are the trends and spatial patterns of diarrheal diseases? Are tubewells protective against diarrheal diseases? Does arsenic mitigation by well switching raise the risk of diarrheal disease among children? The results obtained from this study provide some useful information to help policy-makers implement relevant scientific measures for diarrhea reduction and arsenic mitigation. The concentration methods developed in this study are applicable to monitor pathogens in water in Bangladesh and worldwide

Tandem mass spectrometric analysis of protein and peptide adducts of lipid peroxidation-derived aldehydes

The adduction of proteins and other biomolecules by electrophilic lipid peroxidation products such as 4-hydroxy-2-nonenal (HNE), 4-oxo-2-nonenal (ONE), malondialdehyde (MDA) or acrolein (ACR) is thought to be an initiating and/or propagating factor in the pathophysiology of several diseases such as atherosclerosis, diabetes, Alzheimer's, Parkinson's and other age-related disorders. The identification of protein sites modified by oxylipids is of key relevance for advancing our understanding how oxidative damage affects structure and function of proteins. Here, the use of MALDI tandem mass spectrometry with high energy collision-induced dissociation (CID) on a TOF/TOF instrument for sequencing oxylipid-peptide conjugates was systematically studied. Three synthesized model peptides containing one nucleophilic residue (i.e. Cys, His or Lys) were reacted with MDA, HNE, ONE and ACR. MALDI-MS analysis and MS/MS analysis were performed to confirm the adduct type and the modification sites. Michael adducts and Schiff bases were the predominant products under pH 7.4 within 2 hours. All MS/MS spectra of Michael adducts show the neutral loss of the oxylipid moiety ions. MS/MS spectra of Cys-containing peptide oxylipid conjugates exhibit additional characteristic neutral loss of HS-oxylipid moiety ions. MS/MS spectra of His-containing peptide oxylipid conjugates show characteristic oxylipid-containing His immonium ions. Spectra of Lys-containing peptide oxylipid conjugates (Schiff base) also show oxylipid-containing Lys immonium ions. However, there is no neutral loss of the oxylipid moiety ion for these Schiff bases. Determining the extent or relative amounts of the oxidative damage in cells could provide valuable insights into the molecular mechanisms of the diseases caused by oxidative stress. Relative quantitation of oxylipid-modified proteins in biological samples is a challenging problem because of the complexity and extreme dynamic range that characterize these samples. In this study, the reagents, N'-aminooxymethylcarbonylhydrazino-D-biotin (ARP) and iodoacetyl-PEO2- biotin (IPB), were used to enrich acrolein-modified Cys-containing peptides and the corresponding unmodified ones from subsarcolemmal mitochondria (SSM). The ratios between them were determined by nanoLC-SRM analysis. Model Cys-containing peptides labeled with ARP-acrolein and IPB were employed to demonstrate this method. Seven acrolein-modifed Cys-containing peptides from five mitochondrial proteins were quantified. The ratios for those seven peptides from the CCl₄-treated rats are higher than the control ones indicating that the ratios of acrolein-modified peptides to unmodified ones are potential markers of oxidative stress in vivo. Age-dependent changes of protein carbonyls were investigated in subsarcolemmal mitochondria by using LC-SRM analysis of distinct ACR-modified Cys-containing peptides. Immunochemical analysis using an anti-ACR monoclonal antibody supported an increase of proteins modified by acrolein with age. However, total protein carbonyls measurement using ARP in Western blot analysis did not conform to this change suggesting that age-related changes in protein carbonyls are complex and would benefit from more specific measurement protocols

Tracking Major Sources of Water Contamination Using Machine Learning

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water