BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named `BOolean Operation based Screening and Testing'(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool.Comment: Submitte

Learning Cross-modal Context Graph for Visual Grounding

Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the resulting semantic ambiguities. Prior works typically focus on learning representations of individual phrases with limited context information. To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task. In particular, we introduce a modular graph neural network to compute context-aware representations of phrases and object proposals respectively via message propagation, followed by a graph-based matching module to generate globally consistent localization of grounding phrases. We train the entire graph neural network jointly in a two-stage strategy and evaluate it on the Flickr30K Entities benchmark. Extensive experiments show that our method outperforms the prior state of the arts by a sizable margin, evidencing the efficacy of our grounding framework. Code is available at "https://github.com/youngfly11/LCMCG-PyTorch".Comment: AAAI-202

Effect of Radix Platycodonis and Radix Cyathulae in Xuefu Zhuyu Tang on tissuedistribution of paeoniflorin in blood-stasis mice by HPLC: Experimentalevidence on Shi ingredients in traditional formula compatibility

Xuefu Zhuyu Tang (XFZY), a famous formula in traditional Chinese medicine, has been demonstrated to show goodtherapeutic effects on diseases caused by blood stasis syndrome. Two of its eleven herbs, Radix Platycodonis and RadixCyathulae, have been considered as Shi ingredients in the hierarchy of traditional formula compatibility and provenpossessing synergistic properties that strengthen the formula's potency of activating blood circulation and resolving bloodstagnation. However, its mechanism is still not clearly elucidated. In our previous study, we observed their effects onpaeoniflorin pharmacokinetics of XFZY in rats. In this study, we continued by detecting and comparing their effect on thetissue distribution of paeoniflorin after oral administration of XFZY and its three variants (XFZY without RadixPlatycodonis or/and Radix Cyathulae) in blood-stasis mice via HPLC assay. The results indicated that combining usage ofRadix Platycodonis and Radix Cyathula increased the distribution of paeoniflorin in the lung and kidney and introduced thepaeoniflorin into the liver, spleen and heart. It might explain their synergistic properties that strengthen the formula's effectof invigorating blood and dissolving stasis and provide experimental evidence to understand the pharmacological effects ofShi herbs in the hierarchy of traditional formula compatibility

Assessing the in vivo ameliorative effects of Lactobacillus acidophilus KLDS1.0901 for induced non-alcoholic fatty liver disease treatment

Reputed as a significant metabolic disorder, non-alcoholic fatty liver disease (NAFLD) is characterized by high-fat deposits in the liver and causes substantial economic challenges to any country's workforce. Previous studies have indicated that some lactic acid bacteria may effectively prevent or treat NAFLD. Overall, L. acidophilus KLDS1.0901 protected against HFD-induced NAFLD by improving liver characteristics and modulating microbiota composition, and thus could be a candidate for improving NAFLD. This study aimed to assess the protective effects of L. acidophilus KLDS1.0901 on a high-fat diet(HFD)-induced NAFLD. First, hepatic lipid profile and histological alterations were determined to study whether L. acidophilus KLDS1.0901 could ameliorate NAFLD. Then, the intestinal permeability and gut barrier were explored. Finally, gut microbiota was analyzed to elucidate the mechanism from the insights of the gut–liver axis. The results showed that Lactobacillus KLDS1.0901 administration significantly decreased body weight, Lee's index body, fat rate, and liver index. L. acidophilus KLDS1.0901 administration significantly improved lipid profiles by decreasing the hepatic levels of total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) and by increasing the high-density lipoprotein cholesterol (HDL-C) levels. A conspicuous decrease of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum was observed after L. acidophilus KLDS1.0901 administration. Meanwhile, the H&E and Oil Red O-stained staining showed that L. acidophilus KLDS1.0901 significantly reduced liver lipid accumulation of HFD-fed mice by decreasing the NAS score and lipid area per total area. Our results showed that L. acidophilus KLDS1.0901 administration decreased the interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-alpha (TNF-α) concentrations accompanied by the increase of interleukin-10 (IL-10). L. acidophilus KLDS1.0901 administration could improve the intestinal barrier function by upregulating the mRNA levels of occludin, claudin-1, ZO-1, and Muc-2, which were coupled to the decreases of the concentration of LPS and D-lactic acid. Notably, L. acidophilus KLDS1.0901 administration modulated the gut microbiota to a near-normal pattern. Hence, our results suggested that L. acidophilus KLDS1.0901 can be used as a candidate to ameliorate NAFLD

Multishelled NiO Hollow Spheres Decorated by Graphene Nanosheets as Anodes for Lithium-Ion Batteries with Improved Reversible Capacity and Cycling Stability

Graphene-based nanocomposites attract many attentions because of holding promise for many applications. In this work, multishelled NiO hollow spheres decorated by graphene nanosheets nanocomposite are successfully fabricated. The multishelled NiO microspheres are uniformly distributed on the surface of graphene, which is helpful for preventing aggregation of as-reduced graphene sheets. Furthermore, the NiO/graphene nanocomposite shows much higher electrochemical performance with a reversible capacity of 261.5 mAh g−1 at a current density of 200 mA g−1 after 100 cycles tripled compared with that of pristine multishelled NiO hollow spheres, implying the potential application in modern science and technology

Spatial Coherency Model Considering Focal Mechanism Based on Simulated Ground Motions

The spatial coherencies of ground motions are the key to establishing multi-support excitation for large-dimension structures. Most of the existing models were established based on ground motions recorded at dense observation arrays which barely show any detailed information on the focal mechanism. However, in the near field, ground motions are dominated by the source, and so are the spatial coherencies of ground motions. In this paper, a deterministic physics-based method was used to simulate ground motions in the near field for various focal mechanism scenarios. The coherencies of the simulated ground motions were calculated. The Loh coherency model was used to fit the variation in the calculated coherencies for each scenario. The results show that the focal mechanism has a significant effect on the spatial coherencies of simulated ground motions. Finally, the probability density distributions of the parameters, a and b, of the Loh coherency model were obtained, and a coherency model was proposed, based on the Loh coherency model, in which the parameters are taken to be dependent on the focal mechanism

PINC: A Tool for Non-Coding RNA Identification in Plants Based on an Automated Machine Learning Framework

There is evidence that non-coding RNAs play significant roles in the regulation of nutrient homeostasis, development, and stress responses in plants. Accurate identification of ncRNAs is the first step in determining their function. While a number of machine learning tools have been developed for ncRNA identification, no dedicated tool has been developed for ncRNA identification in plants. Here, an automated machine learning tool, PINC is presented to identify ncRNAs in plants using RNA sequences. First, we extracted 91 features from the sequence. Second, we combined the F-test and variance threshold for feature selection to find 10 features. The AutoGluon framework was used to train models for robust identification of non-coding RNAs from datasets constructed for four plant species. Last, these processes were combined into a tool, called PINC, for the identification of plant ncRNAs, which was validated on nine independent test sets, and the accuracy of PINC ranged from 92.74% to 96.42%. As compared with CPC2, CPAT, CPPred, and CNIT, PINC outperformed the other tools in at least five of the eight evaluation indicators. PINC is expected to contribute to identifying and annotating novel ncRNAs in plants