Northern China is one of the most densely populated regions in the world. Agricultural activities have intensified since the 1980s to provide food security to the country. However, this intensification has likely contributed to an increasing scarcity in water resources, which may in turn be endangering food security. Based on in-situ measurements of soil moisture collected in agricultural plots during 1983–2012, we find that topsoil (0–50 cm) volumetric water content during the growing season has declined significantly (p < 0.01), with a trend of −0.011 to −0.015 m3 m−3 per decade. Observed discharge declines for the three large river basins are consistent with the effects of agricultural intensification, although other factors (e.g. dam constructions) likely have contributed to these trends. Practices like fertilizer application have favoured biomass growth and increased transpiration rates, thus reducing available soil water. In addition, the rapid proliferation of water-expensive crops (e.g., maize) and the expansion of the area dedicated to food production have also contributed to soil drying. Adoption of alternative agricultural practices that can meet the immediate food demand without compromising future water resources seem critical for the sustainability of the food production system.

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The non-structural protein 1 (NS1) of different influenza A virus (IAV) strains can differentially regulate the activity of c-Jun terminal kinase (JNK) and PI-3 kinase (PI3K). Whether varying JNK and PI3K activation impacts autophagy and IAV replication differently remains uncertain. Here we report that H5N1 (A/mallard/Huadong/S/2005) influenza A virus induced functional autophagy, as evidenced by increased LC3 lipidation and decreased p62 levels, and the presence of autolysosomes in chicken fibroblast cells. H9N2 (A/chicken/Shanghai/F/98) virus weakly induced autophagy, whereas H1N1 virus (A/PR/8/34, PR8) blocked autophagic flux. H5N1 virus activated JNK but inhibited the PI-3 kinase pathway. In contrast, N9N2 virus infection led to modest JNK activation and strong PI-3 kinase activation; whereas H1N1 virus activated the PI-3 kinase pathway but did not activate JNK. SP600125, a JNK inhibitor, inhibited H5N1 virus-induced autophagy and virus replication in a DF-1 chicken fibroblast cell line. Our study uncovered a previously unrecognized role of JNK in IAV replication and autophagy.

Protein–protein interactions (PPIs) are essential for most living organisms’ process. Thus, detecting PPIs is extremely important to understand the molecular mechanisms of biological systems. Although many PPIs data have been generated by high-throughput technologies for a variety of organisms, the whole interatom is still far from complete. In addition, the high-throughput technologies for detecting PPIs has some unavoidable defects, including time consumption, high cost, and high error rate. In recent years, with the development of machine learning, computational methods have been broadly used to predict PPIs, and can achieve good prediction rate. In this paper, we present here PCVMZM, a computational method based on a Probabilistic Classification Vector Machines (PCVM) model and Zernike moments (ZM) descriptor for predicting the PPIs from protein amino acids sequences. Specifically, a Zernike moments (ZM) descriptor is used to extract protein evolutionary information from Position-Specific Scoring Matrix (PSSM) generated by Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then, PCVM classifier is used to infer the interactions among protein. When performed on PPIs datasets of Yeast and H. Pylori, the proposed method can achieve the average prediction accuracy of 94.48% and 91.25%, respectively. In order to further evaluate the performance of the proposed method, the state-of-the-art support vector machines (SVM) classifier is used and compares with the PCVM model. Experimental results on the Yeast dataset show that the performance of PCVM classifier is better than that of SVM classifier. The experimental results indicate that our proposed method is robust, powerful and feasible, which can be used as a helpful tool for proteomics research.

Balancing crop production and greenhouse gas (GHG) emissions from agriculture soil requires a better understanding and quantification of crop GHG emissions intensity, a measure of GHG emissions per unit crop production. Here we conduct a stateof-the-art estimate of the spatial-temporal variability of GHG emissions intensities for wheat, maize, and rice in China from 1949 to 2012 using an improved agricultural ecosystem model (Dynamic Land Ecosystem Model-Agriculture Version 2.0) and meta-analysis covering 172 field-GHG emissions experiments. The results show that the GHG emissions intensities of these croplands from 1949 to 2012, on average, were 0.10-1.31 kg CO 2-eq/kg, with a significant increase rate of 1.84-3.58 × 10-3 kg CO 2-eq kg −1 year −1. Nitrogen fertilizer was the dominant factor contributing to the increase in GHG emissions intensity in northern China and increased its impact in southern China in the 2000s. Increasing GHG emissions intensity implies that excessive fertilizer failed to markedly stimulate crop yield increase in China but still exacerbated soil GHG emissions. This study found that overfertilization of more than 60% was mainly located in the winter wheat-summer maize rotation systems in the North China Plain, the winter wheat-rice rotation systems in the middle and lower reaches of the Yangtze River and southwest China, and most of the double rice systems in the South. Our simulations suggest that roughly a one-third reduction in the current N fertilizer application level over these "overfertilization" regions would not significantly influence crop yield but decrease soil GHG emissions by 29.60%-32.50% and GHG emissions intensity by 0.13-0.25 kg CO 2-eq/ kg. This reduction is about 29% and 5% of total agricultural soil GHG emissions in China and the world, respectively. This study suggests that improving nitrogen use efficiency would be an effective strategy to mitigate GHG emissions and sustain China's food security.

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Grammatical aspect captures ways in which a language uses grammatical markers to describe the temporal structure of an event. An event-related potential experiment was conducted to investigate event-related potential correlates of agreement violations of Chinese grammatical aspect. Participants read sentences containing either aspect agreement violations, semantic violations, or no violations. Semantic violations elicited an N400, whereas aspectual violations elicited a 200-400 ms posterior and left central negativity, followed by a P600, instead of left anterior negativity or N400, suggesting that left anterior negativities may not reflect a general, rule-governed, syntactically compositional process, and that grammatical aspect processing is at least not completely semantically driven. The negativity mostly reflects a failure to bind aspect markers or the detection of aspectual errors.

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