Naringin supplementation affects performance, carcass traits, meat quality and oxidative stability of finishing pigs

Naringin is a major flavanone derivate that has many important biological functions in animals. However, its effect on pigs is unknown. The aim of this study was to investigate the effect of naringin supplementation on performance, carcass traits, meat quality and oxidative stability in finishing pigs. Ninety-six pigs, with an average initial body weight of 66.2 ± 0.63 kg, were randomly divided into four groups. One group was fed a basal diet without supplementation (control), and the three others were fed diets supplemented with 0.5, 1.0 or 1.5 g naringin /kg DM of feed for 50 days. Each treatment was replicated six times with four pigs per replicate. Feed and water were available ad libitum. The 0.5 g/kg naringin treatment group had an improved loin eye muscle area, reduced serum triglycerides and were leaner compared with the other groups. Pigs in the 1.5 g/kg naringin treatment had higher pH45min values and inosine monophosphate concentrations, and lower MyHC IIb mRNA expression in muscle than the other groups. MyHC IIa mRNA expression was significantly up-regulated in all naringin-supplemented diet groups. Naringin significantly increased superoxide dismutase (SOD) activity and total anti-oxidative capacity in meat, as well as SOD and glutathione peroxidase activity in the liver. These results indicate that the dietary addition of naringin at 0.5 g/kg improved carcass characteristics, while 1.5 g/kg improved the oxidative stability and pork quality in finishing pigs. Keywords: antioxidant capacity; carcass characteristics; naringin-supplemented diets; pork qualit

Onsite data processing and monitoring for the Daya Bay Experiment

The Daya Bay Reactor Neutrino Experiment started running on September 23, 2011. The offline computing environment, consisting of 11 servers at Daya Bay, was built to process onsite data. With current computing ability, onsite data processing is running smoothly. The Performance Quality Monitoring system (PQM) has been developed to monitor the detector performance and data quality. Its main feature is the ability to efficiently process multi-data-stream from three experimental halls. The PQM processes raw data files from the Daya Bay data acquisition system, generates and publishes histograms via a graphical web interface by executing the user-defined algorithm modules, and saves the histograms for permanent storage. The fact that the whole process takes only around 40 minutes makes it valuable for the shift crew to monitor the running status of all the sub-detectors and the data quality

Coevolution of synchronous activity and connectivity in coupled chaotic oscillators

We investigate the coevolution dynamics of node activities and coupling strengths in coupled chaotic oscillators via a simple threshold adaptive scheme. The coupling strength is synchronous activity regulated, which in turn is able to boost the synchronization remarkably. In the case of weak coupling, the globally coupled oscillators present a highly clustered functional connectivity with a power-law distribution in the tail with γ≃3.1, while for strong coupling, they self-organize into a network with a heterogeneously rich connectivity at the onset of synchronization but exhibit rather sparse structure to maintain the synchronization in noisy environment. The relevance of the results is briefly discussed

Fast and Accurate Recognition of Chinese Clinical Named Entities with Residual Dilated Convolutions

Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translation research. In recent years, deep learning methods have achieved significant success in CNER tasks. However, these methods depend greatly on Recurrent Neural Networks (RNNs), which maintain a vector of hidden activations that are propagated through time, thus causing too much time to train models. In this paper, we propose a Residual Dilated Convolutional Neural Network with Conditional Random Field (RD-CNN-CRF) to solve it. Specifically, Chinese characters and dictionary features are first projected into dense vector representations, then they are fed into the residual dilated convolutional neural network to capture contextual features. Finally, a conditional random field is employed to capture dependencies between neighboring tags. Computational results on the CCKS-2017 Task 2 benchmark dataset show that our proposed RD-CNN-CRF method competes favorably with state-of-the-art RNN-based methods both in terms of computational performance and training time.Comment: 8 pages, 3 figures. Accepted as regular paper by 2018 IEEE International Conference on Bioinformatics and Biomedicine. arXiv admin note: text overlap with arXiv:1804.0501

An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies

Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.Comment: 9 pages,1 figur