Aspergillus flavus is one of the major moulds that colonize peanut in the field and during storage. The impact to human and animal health, and to the economy in agriculture and commerce, is significant since this mold produces the most potent known natural toxins, aflatoxins, which are carcinogenic, mutagenic, immunosuppressive, and teratogenic. A strain of marine Bacillus megaterium isolated from the Yellow Sea of East China was evaluated for its effect in inhibiting aflatoxin formation in A. flavus through down-regulating aflatoxin pathway gene expression as demonstrated by gene chip analysis. Aflatoxin accumulation in potato dextrose broth liquid medium and liquid minimal medium was almost totally (more than 98 %) inhibited by co-cultivation with B. megaterium. Growth was also reduced. Using expression studies, we identified the fungal genes down-regulated by co-cultivation with B. megaterium across the entire fungal genome and specifically within the aflatoxin pathway gene cluster (aflF, aflT, aflS, aflJ, aflL, aflX). Modulating the expression of these genes could be used for controlling aflatoxin contamination in crops such as corn, cotton, and peanut. Importantly, the expression of the regulatory gene aflS was significantly down-regulated during co-cultivation. We present a model showing a hypothesis of the regulatory mechanism of aflatoxin production suppression by AflS and AflR through B. megaterium co-cultivation.

Multivariate time series are often accompanied with missing values, especially in clinical time series, which usually contain more than 80% of missing data, and the missing rates between different variables vary widely. However, few studies address these missing rate differences and extract univariate missing patterns simultaneously before mixing them in the model training procedure. In this paper, we propose a novel recurrent neural network called variable sensitive GRU (VS-GRU), which utilizes the different missing rate of each variable as another input and learns the feature of different variables separately, reducing the harmful impact of variables with high missing rates. Experiments show that VS-GRU outperforms the state-of-the-art method in two real-world clinical datasets (MIMIC-III, PhysioNet).

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Background Both hypertension and obesity are strongly associated with disability, but these associations are in debate among older people. In this context, our study aimed to examine the interactive effect of hypertension and obesity with disability, especially including the control of blood pressure. Methods A cross-sectional study was conducted from August to October 2018 in Shanghai, 8648 community-dwelling individuals with a mean age of 70.39 years. Obesity was measured using the body mass index (BMI) in World Health Organization (WHO) Asia criteria. Hypertension control was defined as treatment with antihypertensive medication and a measured blood pressure of less than 140/90 mm Hg. Disability was measured using the self-reported physical self-maintenance scale (PSMS) and the instrumental activities of daily living (IADL) scale developed by Lawton and Brody. Logistic regression with 95% confidence intervals (CI) was used to explore the interactive effect of hypertension and obesity on disability. Results A total of 33.60% of participants reported hypertension control, 6.54% for poor hypertension control, 9.27% for ADL disability, and 32.47% for IADL disability. After adjusting social demographics and chronic conditions, versus without hypertension: in independent analyses, poor hypertension control was a risk factor (OR for ADL disability = 1.47, 95% CI = 1.10–1.96; OR for IADL disability = 1.55, 95% CI = 1.27–1.91); in interactive analyses, poor hypertension control was a risk factor in obese subset (OR for ADL disability = 1.73, 95% CI = 1.09–2.74; OR for IADL disability = 1.80, 95% CI = 1.31–2.47), but a protective factor in underweight subset (OR for ADL disability = 0.33, 95% CI = 0.18–0.62; OR for IADL disability = 0.32, 95% CI = 0.20–0.51). Conclusions Poor hypertension control, independent of its consequences, is a risk factor for disability among older people. In addition, hypertension and BMI status have interactive effect on disability among older people. Poor hypertension control is a risk factor among obese individuals, but a protective factor among underweight individuals.

Due to a range of economic incentives and policy supports, distributed photovoltaic (PV) systems are accelerating their penetration into the distribution network at all voltage levels. However, the PV systems are connected to the grid via power electronic converters, which are nonlinear devices characterized by inherent harmonic emission, and their cumulative harmonic injection into the grid is detrimental to the grid power quality. Although the existing literature proves that harmonic admittance matrix (HAM)-based models can represent well the supply voltage dependence of harmonics, the conventional HAM derivation approach is based on the harmonic sensitivity tests conducted under laboratory conditions, making it infeasible for infield implementation. To address this issue, this paper starts with investigating the harmonic emission and grid interaction mechanisms of PV systems analytically, followed by analyzing the power dependency of HAMs experimentally. Based on the findings, a HAM derivation and self-tuning approach is proposed for fluctuating power PV systems, where only the infield measurements at the point of connection are needed. The model accuracy is compared against the widely used constant current source model and harmonic Norton model, while its integration approach for harmonic power flow analysis is demonstrated via the simulated European low voltage test feeder.