Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Review of physicochemical properties and analytical characterization of lignocellulosic biomass

Abstract: Lignocellulosic biomass is the most abundant and renewable material in the world for the production of biofuels. Using lignocellulosic biomass derived biofuels could reduce reliance on fossil fuels and contribute to climate change mitigation. A profound understanding of the physicochemical properties of lignocellulosic biomass and the analytical characterization methods for those properties is essential for the design and operation of associated biomass conversion processing facilities. The present article aims to present a comprehensive review of physicochemical properties of lignocellulosic biomass, including particle size, grindability, density, flowability, moisture sorption, thermal properties, proximate analysis properties, elemental composition, energy content and chemical composition. The corresponding characterization techniques for these properties and their recent development are also presented. This review is intended to provide the readers systematic knowledge in the physicochemical properties of lignocellulosic biomass and characterization techniques for the conversion of biomass and the application of biofuels.

Auxin controls seed dormancy through stimulation of abscisic acid signaling by inducing ARF-mediated ABI3 activation in Arabidopsis

Abstract: The transition from dormancy to germination in seeds is a key physiological process during the lifecycle of plants. Abscisic acid (ABA) is the sole plant hormone known to maintain seed dormancy; it acts through a gene expression network involving the transcription factor ABSCISIC ACID INSENSITIVE 3 (ABI3). However, whether other phytohormone pathways function in the maintenance of seed dormancy in response to environmental and internal signals remains an important question. Here, we show that the plant growth hormone auxin, which acts as a versatile trigger in many developmental processes, also plays a critical role in seed dormancy in Arabidopsis. We show that disruptions in auxin signaling in MIR160-overexpressing plants, auxin receptor mutants, or auxin biosynthesis mutants dramatically release seed dormancy, whereas increases in auxin signaling or biosynthesis greatly enhance seed dormancy. Auxin action in seed dormancy requires the ABA signaling pathway (and vice versa), indicating that the roles of auxin and ABA in seed dormancy are interdependent. Furthermore, we show that auxin acts upstream of the major regulator of seed dormancy, ABI3, by recruiting the auxin response factors AUXIN RESPONSE FACTOR 10 and AUXIN RESPONSE FACTOR 16 to control the expression of ABI3 during seed germination. Our study, thus, uncovers a previously unrecognized regulatory factor of seed dormancy and a coordinating network of auxin and ABA signaling in this important process.

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Abstract: We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students' ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at this http URL and the code is available at this https URL.