End water content determines the magnitude of N2_{2}O pulse from nitrifier denitrification after rewetting a fluvo-aquic soil

Large nitrous oxide (N$_{2}$O) emissions pulses have been observed after rewetting dry soil. However, few studies have uncoupled the effects of drought severity from the degree to which the soil is saturated. In this study, we conducted three aerobic incubation experiments to investigate the effects of soil rewetting on N$_{2}$O emissions from a dryland soil. The results showed that, at constant soil moisture, total N$_{2}$O emissions in soil with 90% water-holding capacity (WHC) were significantly higher than those in 30%, 45%, 60% and 75% WHC treatments. In the dry–wet group, the soil moisture content was adjusted from 30%, 45% and 60% WHC to the end content of 75% and 90% WHC, respectively; the cumulative N$_{2}$O emissions in the 30–90%, 45–90% and 60–90% WHC nitrogen (N) treatments were significantly higher than those in the 30–75%, 45–75% and 60–75% WHC N treatments. Regarding fertilizer N types, there was no significant difference in N$_{2}$O emissions from soil at 90% WHC when (NH$_{4}$)$_{2}$SO$_{4}$ or urea was applied. Nitrification inhibitor significantly reduced N$_{2}$O emissions in soil applied with NH$_{4}$$^{+}$-N fertilizer, indicating that nitrification played a major role in N$_{2}$O emissions from soils. The contribution of denitrification was negligible, according to the low emission rate of soils with only NO$_{3}$$^{-}$ additions. High N$_{2}$O emissions occurred in soil treated with NO$_{2}$$^{-}$, accounting for about 83.6% of those of the NH$_{4}$$^{+}$ treatment. Therefore, in this study we concluded that the end water content of soil was more important than the role of drought severity in the dry-wet process and that nitrifier denitrification was probably the main pathway of N$_{2}$O production under the condition of 90% WHC moisture after rewetting soil

FusionQ: a novel approach for gene fusion detection and quantification from paired-end RNA-Seq

Background: Gene fusions, which result from abnormal chromosome rearrangements, are a pathogenic factor in cancer development. The emerging RNA-Seq technology enables us to detect gene fusions and profile their features. Results: In this paper, we proposed a novel fusion detection tool, FusionQ, based on paired-end RNA-Seq data. This tool can detect gene fusions, construct the structures of chimerical transcripts, and estimate their abundances. To confirm the read alignment on both sides of a fusion point, we employed a new approach, residual sequence extension , which extended the short segments of the reads by aggregating their overlapping reads. We also proposed a list of filters to control the false-positive rate. In addition, we estimated fusion abundance using the Expectation-Maximization algorithm with sparse optimization, and further adopted it to improve the detection accuracy of the fusion transcripts. Simulation was performed by FusionQ and another two stated-of-art fusion detection tools. FusionQ exceeded the other two in both sensitivity and specificity, especially in low coverage fusion detection. Using paired-end RNA-Seq data from breast cancer cell lines, FusionQ detected both the previously reported and new fusions. FusionQ reported the structures of these fusions and provided their expressions. Some highly expressed fusion genes detected by FusionQ are important biomarkers in breast cancer. The performances of FusionQ on cancel line data still showed better specificity and sensitivity in the comparison with another two tools. Conclusions: FusionQ is a novel tool for fusion detection and quantification based on RNA-Seq data. It has both good specificity and sensitivity performance. FusionQ is free and available at http://www.wakehealth.edu/CTSB/Software/Software.htm

Climatic change controls productivity variation in global grasslands.

Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2-71.2% during 1982-2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms

Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Molecular Quantum Dot Cellular Automata Based on Diboryl Monoradical Anions

Field-effect transistor (FET)-based microelectronics is approaching its size limit due to unacceptable power dissipation and short-channel effects. Molecular quantum dot cellular automata (MQCA) is a promising transistorless paradigm that encodes binary information with bistable charge configurations instead of currents and voltages. However, it still remains a challenge to find appropriate candidate molecules for MQCA operation. Inspired by recent progress in boron radical chemistry, we theoretically predicted a series of new MQCA candidates built from diboryl monoradical anions. The unpaired electron resides mainly on one boron center and can be shifted to the other by an electrostatic stimulus, forming bistable charge configurations required by MQCA. By investigating various bridge units with different substitutions (ortho-, meta-, and para-), we suggested several candidate molecules that have potential MQCA applications