Generation of Gross Chromosomal Rearrangements by a Single Engineered DNA Double Strand Break

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Quinoa whole grain diet compromises the changes of gut microbiota and colonic colitis induced by dextran Sulfate sodium in C57BL/6 mice

A plethora of evidence highlights that the dysbiosis of gut microbiota is a critical factor for inflammatory bowel disease (IBD). Both in vivo and in vitro studies have demonstrated that quinoa possesses potential prebiotic effects. The present study aims to examine the potential in using quinoa to ameliorate the dysbiosis and colitis induced by dextran sodium sulfate (DSS). A total of 40 C57BL/6 mice were fed either an AIN-93M diet or a quinoa-based diet, separately. Colitis was induced for 10 animals/dietary group with a 5-days exposure to 2.5% DSS. The clinical symptoms were monitored every other day, and the gut microbiota was characterized by 165 rRNA gene sequencing. The results indicated that consumption of quinoa lessened clinical symptoms as indicated by the reduced disease activity index and the degree of histological damage (P \u3c 0.05). As expected, the DSS treatment induced significant dysbiosis of gut microbiota in mice on an AIN-93M diet. However, compared to mice fed the AIN-93M diet, the consumption of quinoa alleviated the DSS-induced dysbiosis remarkably, as indicated by increased species richness and diversity, decreased abnormal expansion of phylum Proteobacteria, and decreased overgrowth of genera Escherichia/Shigella and Peptoclostridium (P \u3c 0.05). The relative abundances of Firmicutes and Bacteroidetes were less altered in mice fed with quinoa comparing to those mice fed the AIN-93M diet. In summary, the consumption of quinoa suppressed the dysbiosis of gut microbiota and alleviated clinical symptoms induced by DSS, indicating the potential to utilize quinoa as a dietary approach to improve intestinal health

Spatial distribution of cultural ecosystem services demand and supply in urban and suburban areas: a case study from Shanghai, China

In the urban ecosystem, the demand for cultural ecosystem services (CES) has greatly increased, and the imbalance of CES supply and demand has been prominent. This paper integrated multi-source data to analyze and visualize the spatial differences in CES demand and supply capacity between Shanghai urban center and suburbs. Based on the geo-tagged photo data, the spatial distribution differences of the four types of CES demand, Recreation & tourism services (RTS) demand, Aesthetic services (AS) demand, Heritage & cultural services (HCS) demand, and Spiritual & religious services (SRS) demand, were analyzed. Residents and tourists had a strong demand for recreation and tourism, and the spatial agglomeration effect was the most obvious. Overall, CES demand was more concentrated in urban center, while the spatial distribution of suburbs was relatively discrete. At the same time, there were under supply areas of CES near the Huangpu River in urban center and suburbs. Results from bivariate Moran's I method showed: 1) there was a significant positive spatial correlation between CES demand and CES supply capacity in urban center; 2) CES supply had a positive external impact on CES demand; and 3) the increase in CES supply capacity can promote the growth of CES demand

Ammonia and salinity tolerance of Penaeus monodon across eight breeding families

© 2016 Chen et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Ammonia nitrogen and salinity tolerance of Penaeus monodon from eight selected breeding families were evaluated at the concentration of 67.65 mg L−1 ammonia-N and reducing salinity from 15 to 0 ‰. The final survival of family A (88.67 ± 9.81 %) was highest, and the final survival of family B was lowest (24.33 ± 14.01 %) after the ammonia tolerance test. Upon completing the sudden drop salinity test from 15 to 0 ‰, the highest survival was observed in family B (98.00 ± 1.73 %), and the lowest survival was found in family H (18.00 ± 1.73 %). Family A showed the strongest ability to tolerate ammonia stress, and family B showed the strongest tolerance to low salinity. This study suggests that the tolerance of salinity and ammonia nitrogen varied between breeding families. Results from the present study provide useful information towards selective breeding in shrimp in aquaculture for environmental tolerance

Online Streaming Video Super-Resolution with Convolutional Look-Up Table

Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applying existing video super-resolution methods to online streaming is non-trivial. Existing video codecs and streaming protocols (\eg, WebRTC) dynamically change the video quality both spatially and temporally, which leads to diverse and dynamic degradations. Furthermore, online streaming has a strict requirement for latency that most existing methods are less applicable. As a result, this paper focuses on the rarely exploited problem setting of online streaming video super resolution. To facilitate the research on this problem, a new benchmark dataset named LDV-WebRTC is constructed based on a real-world online streaming system. Leveraging the new benchmark dataset, we proposed a novel method specifically for online video streaming, which contains a convolution and Look-Up Table (LUT) hybrid model to achieve better performance-latency trade-off. To tackle the changing degradations, we propose a mixture-of-expert-LUT module, where a set of LUT specialized in different degradations are built and adaptively combined to handle different degradations. Experiments show our method achieves 720P video SR around 100 FPS, while significantly outperforms existing LUT-based methods and offers competitive performance compared to efficient CNN-based methods

PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation

Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions are restricted to pre-defined, static sparsity patterns due to significant overheads associated with preprocessing. Efficient execution of dynamic sparse computation often faces the misalignment between the GPU-friendly tile configuration for efficient execution and the sparsity-aware tile shape that minimizes coverage wastes (non-zero values in tensor). In this paper, we propose PIT, a deep-learning compiler for dynamic sparsity. PIT proposes a novel tiling mechanism that leverages Permutation Invariant Transformation (PIT), a mathematically proven property, to transform multiple sparsely located micro-tiles into a GPU-efficient dense tile without changing the computation results, thus achieving both high GPU utilization and low coverage waste. Given a model, PIT first finds feasible PIT rules for all its operators and generates efficient GPU kernels accordingly. At runtime, with the novel SRead and SWrite primitives, PIT rules can be executed extremely fast to support dynamic sparsity in an online manner. Extensive evaluation on diverse models shows that PIT can accelerate dynamic sparsity computation by up to 5.9x (average 2.43x) over state-of-the-art compilers