Growing small-world networks based on a modified BA model

We propose a simple growing model for the evolution of small-world networks. It is introduced as a modified BA model in which all the edges connected to the new nodes are made locally to the creator and its nearest neighbors. It is found that this model can produce small-world networks with power-law degree distributions. Properties of our model, including the degree distribution, clustering, and the average path length are compared with that of the BA model. Since most real networks are both scale-free and small-world networks, our model may provide a satisfactory description for empirical characteristics of real networks.Comment: 4 pages, 4 figure

Substitution of manure for chemical fertilizer affects soil microbial community diversity, structure and function in greenhouse vegetable production systems

Soil microbial communities and enzyme activities together affect various ecosystem functions of soils. Fertilization, an important agricultural management practice, is known to modify soil microbial characteristics; however, inconsistent results have been reported. The aim of this research was to make a comparative study of the effects of different nitrogen (N) fertilizer rates and types (organic and inorganic) on soil physicochemical properties, enzyme activities and microbial attributes in a greenhouse vegetable production (GVP) system of Tianjin, China. Results showed that manure substitution of chemical fertilizer, especially at a higher substitution rate, improved soil physicochemical properties (higher soil organic C (SOC) and nutrient (available N and P) contents; lower bulk densities), promoted microbial growth (higher total phospholipid fatty acids and microbial biomass C contents) and activity (higher soil hydrolase activities). Manure application induced a higher fungi/bacteria ratio due to a lower response in bacterial than fungal growth. Also, manure application greatly increased bacterial stress indices, as well as microbial communities and functional diversity. The principal component analysis showed that the impact of manure on microbial communities and enzyme activities were more significant than those of chemical fertilizer. Furthermore, redundancy analysis indicated that SOC and total N strongly influenced the microbial composition, while SOC and ammonium-N strongly influenced the microbial activity. In conclusion, manure substitution of inorganic fertilizer, especially at a higher substitution rate, was more efficient for improving soil quality and biological functions.</p

Manifold Learning Towards Masking Implementations: A First Study

Linear dimensionality reduction plays a very important role in side channel attacks, but it is helpless when meeting the non-linear leakage of masking implementations. Increasing the order of masking makes the attack complexity grow exponentially, which makes the research of nonlinear dimensionality reduction very meaningful. However, the related work is seldom studied. A kernel function was firstly introduced into Kernel Discriminant Analysis (KDA) in CARDIS 2016 to realize nonlinear dimensionality reduction. This is a milestone for attacking masked implementations. However, KDA is supervised and noise-sensitive. Moreover, several parameters and a specialized kernel function are needed to be set and customized. Different kernel functions, parameters and the training results, have great influence on the attack efficiency. In this paper, the high dimensional non-linear leakage of masking implementation is considered as high dimensional manifold, and manifold learning is firstly introduced into side channel attacks to realize nonlinear dimensionality reduction. Several classical and practical manifold learning solutions such as ISOMAP, Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE) are given. The experiments are performed on the simulated unprotected, first-order and second-order masking implementations. Compared with supervised KDA, manifold learning schemes introduced here are unsupervised and fewer parameters need to be set. This makes manifold learning based nonlinear dimensionality reduction very simple and efficient for attacking masked implementations

Impacts of Antibiotic Residues in the Environment on Bacterial Resistance and Human Health in Eastern China: An Interdisciplinary Mixed-Methods Study Protocol

Antibiotic resistance is a global health challenge that threatens human and animal lives, especially among low-income and vulnerable populations in less-developed countries. Its multi-factorial nature requires integrated studies on antibiotics and resistant bacteria in humans, animals, and the environment. To achieve a comprehensive understanding of the situation and management of antibiotic use and environmental transmission, this paper describes a study protocol to document human exposure to antibiotics from major direct and indirect sources, and its potential health outcomes. Our mixed-methods approach addresses both microbiological and pathogen genomics, and epidemiological, geospatial, anthropological, and sociological aspects. Implemented in two rural residential areas in two provinces in Eastern China, linked sub-studies assess antibiotic exposure in population cohorts through household surveys, medicine diaries, and biological sampling; identify the types and frequencies of antibiotic resistance genes in humans and food-stock animals; quantify the presence of antibiotic residues and antibiotic resistance genes in the aquatic environment, including wastewater; investigate the drivers and behaviours associated with human and livestock antibiotic use; and analyse the national and local policy context, to propose strategies and systematic measurements for optimising and monitoring antibiotic use. As a multidisciplinary collaboration between institutions in the UK and China, this study will provide an in-depth understanding of the influencing factors and allow comprehensive awareness of the complexity of AMR and antibiotic use in rural Eastern China

Time of Emergence of Surface Ocean Carbon Dioxide Trends in the North American Coastal Margins in Support of Ocean Acidification Observing System Design

Time of Emergence (ToE) is the time when a signal emerges from the noise of natural variability. Commonly used in climate science for the detection of anthropogenic forcing, this concept has recently been applied to geochemical variables, to assess the emerging times of anthropogenic ocean acidification (OA), mostly in the open ocean using global climate and Earth System Models. Yet studies of OA variables are scarce within costal margins, due to limited multidecadal time-series observations of carbon parameters. ToE provides important information for decision making regarding the strategic configuration of observing assets, to ensure they are optimally positioned either for signal detection and/or process elicitation and to identify the most suitable variables in discerning OA-related changes. Herein, we present a short overview of ToE estimates on an OA variable, CO2 fugacity f(CO2,sw), in the North American ocean margins, using coastal data from the Surface Ocean CO2 Atlas (SOCAT) V5. ToE suggests an average theoretical timeframe for an OA signal to emerge, of 23(±13) years, but with considerable spatial variability. Most coastal areas are experiencing additional secular and/or multi-decadal forcing(s) that modifies the OA signal, and such forcing may not be sufficiently resolved by current observations. We provide recommendations, which will help scientists and decision makers design and implement OA monitoring systems in the next decade, to address the objectives of OceanObs19 (http://www.oceanobs19.net) in support of the United Nations Decade of Ocean Science for Sustainable Development (2021–2030) (https://en.unesco.org/ocean-decade) and the Sustainable Development Goal (SDG) 14.3 (https://sustainabledevelopment.un.org/sdg14) target to “Minimize and address the impacts of OA.

Exposure to Bisphenol a Substitutes and Gestational Diabetes Mellitus: A Prospective Cohort Study in China

Background: The association of bisphenol A (BPA) and gestational diabetes mellitus (GDM) has been investigated in only a small number of studies, and research on the associations between BPA substitutes and GDM is scarce.Objective: We aimed to investigate the associations of four bisphenols [bisphenol A (BPA), bisphenol S (BPS), bisphenol F (BPF), and bisphenol AF (BPAF)] levels in urine sample with the risk of gestational diabetes mellitus (GDM) and plasma glucose levels.Methods: A total of 1,841 pregnant women from a cohort study were recruited at their first prenatal examination between 2013 and 2015 in Wuhan, China. Concentrations of four bisphenols (BPA, BPS, BPF, BPAF) were measured in first-trimester urine samples using Ultra-high performance liquid chromatography system coupled to a Triple Quadrupole mass spectrometer (UHPLC-TQMS). An oral glucose tolerance test (OGTT) was performed at 24–28 gestational weeks and GDM was diagnosed post hoc using International Association of Diabetes and Pregnancy Study Groups criteria. We used multivariable logistic regression models to examine the associations of urinary bisphenols with the risk of GDM, and multiple linear regression models to determine the associations between bisphenols exposure and plasma glucose levels.Results: Urinary BPAF was associated with increased odds of GDM among women with normal pre-pregnancy BMI [adjusted odds ratio (aOR) = 1.70 (95% CI: 1.08, 2.67) for the highest group compared to the lowest group], and the association remained significant after additional adjustment for other bisphenols [aOR = 1.68 (95% CI: 1.03, 2.72)]. No significant associations were observed for other bisphenols and GDM. Consistent with the result of GDM, women in the highest BPAF category had a mean of 0.05 mmol/L (95% CI: 0.01, 0.09) higher fasting plasma glucose (FPG) levels than women in the lowest category. For BPA and plasma glucose, non-linear associations were observed between urinary BPA and FPG and the sum of the PG z-score among women who were overweight (p for non-linear association &lt; 0.05). We also found that the per-unit increase in natural log transformed specific gravity adjusted BPS [ln (SG-adj BPS)] was associated with a 0.03 mmol/L (95% CI: 0.01, 0.04) increase in FPG levels and the associations might be modified by fetal sex (p for interaction &lt; 0.05). Among women with female fetus, a per-unit increase in ln (SG-adj BPS) was associated with a 0.04 mmol/L (95% CI: 0.02, 0.06) increase in FPG, a 0.11 mmol/L (95% CI: 0.04, 0.17) increase in 1 h-PG and a 0.19 mmol/L (95% CI: 0.08, 0.30) increase in the sum of PG z-score.Conclusions: Our results provide evidence that BPAF and BPS might be potential risk factors of GDM, which require to be studied further

Association of NT-proBNP and Multiple Biomarkers with Severity of Angiographic Coronary Artery Disease in Diabetic and Pre-Diabetic Chinese Patients

Background: Little is known about the plasma levels of N-terminal pro-brain natriuretic peptide (NT-proBNP), and the relationship between the severity of coronary heart disease (CHD) with NT-proBNP and multiple biomarkers in diabetic and pre-diabetic patients, compared to individuals with normal glucose levels. Methods: Four hundred and fifteen consecutive Chinese patients of both sexes were assigned to three groups on the basis of the new hemoglobin (Hb) A1c (HbA1c) cut-off points for diagnosis of diabetes and pre-diabetes. The three groups were divided into tertiles according to NT-proBNP, hs-CRP, cystatin C, and troponin T levels. Gensini scores were compared among the three groups and biomarker tertiles. Receiver operating characteristic (ROC) curves were used to obtain the angiographic CHD cut-off points for each biomarker. Stepwise multivariate linear correlation analysis was applied to examine the association between the severity of CHD and biomarker levels. Results: Gensini scores increased with increasing biomarker tertile levels and HbA1c. Gensini scores were significantly different in the middle and upper NT-proBNP tertiles of the diabetic, pre-diabetic and control groups. NT-proBNP had the highest positive and negative predictive values and area under the curve for CHD. Only NT-proBNP was identified as an independent variable for Gensini score. Conclusions: Plasma NT-proBNP may be an important biomarker to evaluate the severity of CHD and screen for CHD i

Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Global Biogeochemical Cycles 32 (2018): 389-416, doi:10.1002/2017GB005790.Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO2 uptake split evenly between tidal wetlands and shelf waters, and estuarine CO2 outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.NASA Interdisciplinary Science program Grant Number: NNX14AF93G; NASA Carbon Cycle Science Program Grant Number: NNX14AM37G; NASA Ocean Biology and Biogeochemistry Program Grant Number: NNX11AD47G; National Science Foundation's Chemical Oceanography Program Grant Number: OCE‐12605742018-10-0