The optimal dietary arginine level of laying hens fed with low-protein diets

Background:Arginine(Arg)is an essential amino acid(EAA)in poultry,an important substrate for protein synthesis and a precursor of several molecules.Supplementation of EAAs with low protein(LP)diet increases the utilization efficiency of dietary crude protein(CP).However,if the EAA requirement is changed in hens fed a LP diet remains to be elucidated.The aim of the present study was to evaluate the optimal level of dietary Arg in the LP diet of hens.A total of 1350 Hy-Line Brown laying hens were randomly allocated to six dietary treatments:a basal diet(16%CP,positive control),or an isoenergetic LP diet(14%CP,0.80%Arg)supplemented 0,0.05%,0.10%,0.15%,and 0.20%L-Arg,corresponding to 0.80%,0.85%,0.90%,0.95%and 1.00%dietary Arg,respectively.Results:The feed efficiency was decreased(P<0.05)by 0.80%and 1.00%Arg-LP diets,compared to control.Within LP diets,dietary Arg level had significant quadratic effects(P<0.05)on laying rate,egg mass,and feed efficiency.Compared to control,the plasma CAT activity or T-AOC content were decreased by 0.80%(P<0.001).However,the hens offered 0.85%and 0.90%Arg-LP diets had higher CAT activity(P<0.001)than 0.80%Arg-LP diet.In contrast,1.00%Arg-LP group had the highest MDA and the lowest T-AOC content in plasma,liver,duodenal and jejunal mucosa(P<0.05).Compared to control,the villus height was decreased by 0.80%,0.95%and 1.00%Arg-LP diets,while the villus height to crypt depth(V/C)ratio was reduced by 0.95%and 1.00%Arg-LP diets in duodenum.Conclusion:The result demonstrates that LP diet(14%CP)deficient in Arg(0.80%Arg)result in augmented oxidative damage and impaired development of intestinal mucosa.According to the quadratic broken-line regression model,the optimal dietary arginine levels for Hy-Line Brown laying hens fed with low protein diet(14%CP)aged 33 to 40 weeks are 0.85%,0.86%,and 0.86%to obtained the maximum laying rate,egg mass,and feed efficiency,respectively.

A Simple yet Effective Framework for Active Learning to Rank

While China has become the largest online market in the world with approximately 1 billion internet users,Baidu runs the world's largest Chinese search engine serving more than hundreds of millions of daily active users and responding to billions of queries per day.To handle the diverse query requests from users at the web-scale,Baidu has made tremendous efforts in understanding users'queries,retrieving relevant content from a pool of trillions of webpages,and ranking the most relevant webpages on the top of the res-ults.Among the components used in Baidu search,learning to rank(LTR)plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models.To reduce the costs and time con-sumption of query/webpage labelling,we study the problem of active learning to rank(active LTR)that selects unlabeled queries for an-notation and training in this work.Specifically,we first investigate the criterion-Ranking entropy(RE)characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints,using a query-by-com-mittee(QBC)method.Then,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a query.Our empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries more.Finally,we combine these two complementary criteria as the sample selection strategies for active learning.Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models to achieve higher discounted cumulative gain(i.e.,the relative improvement DCG4=1.38%)with the same budgeted labellingefforts.

Link Prediction in Brain Networks Based on a Hierarchical Random Graph Model

Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks （Treponemapallidum metabolic network, terrorist networks, and grassland species food webs） using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.

An on-chip intestine-liver model for multiple drugs absorption and metabolism behavior simulation

A double-layer microfluidic chip integrated with a hollow fiber(HF)was developed to reconstitute the intestine-liver functionality for studying the absorption and metabolism of combination drugs.Caco-2 cells were inoculated in the HF cavity at the top of the serpentine channel to simulate the intestinal tissue for drug absorption and transport studied,and Hep G2 cells,seeded in the bottom chamber,were used to mimic the liver for metabolism-related studies.Genistein and dacarbazine were selected for combination drug therapy and its effects on cell viability,hepatotoxicity,and cell cycle arrest under drug-conditioned culture were investigated.The results suggested that the combined concentration below-100μg/m L had no significant inhibitory effect on Hep G2 cell viability,and therefore Hep G2 cells maintained their drug metabolism ability.When the drug concentration was increased above 250μg/m L,Hep G2 cells underwent apoptosis.Detection of metabolites by mass spectrometry proved the effective metabolism in the microchip model.This dynamic,co-culture microchip successfully provided a podium for long-term observation of absorption,transport,and metabolism of combination drugs,and could be an effective in vitro simulation model for further clinical research.

Construction of on-line supercritical fluid extraction with reverse phase liquid chromatography–tandem mass spectrometry for the determination of capsaicin

A novel on-line system composed of supercritical fluid extraction(SFE), dilution line and reverse phase liquid chromatography/mass spectrometry(RPLC/MS) was constructed for on-line extraction and reverse phase separation of some fat-soluble components in foods. Three columns including a trap column,concentration column and analytical column were used for trapping the fat-soluble components, on-line enrichment and reverse phase separation, respectively. Capsaicinoids were on-line extracted by a CO_2 supercritical fluid, then concentrated and separated by using the C_(18) columns, finally detected by mass spectrometry(MS). Capsaicin eluted at 10.1 min and limit of detection(LOD, S/N=3) for the standard solution is 0.55pg. The linearity was calculated with a value of coefficient of determination(R^2)≥0.998 in the range of 1.1–8.5 ng. Concentrations of capsaicin in the green, yellow, and red bell peppers were determined to be 60.33 ng/g, 31.79 ng/g, 35.38ng/g, respectively.

Nephrocyte-neurocyte interaction and cellular metabolic analysis on membrane-integrated microfluidic device

Cell-cell interaction and cell metabolic analysis provide new opportunities for better understanding of critical biochemical processes. Advanced microfluidic technologies enable to create more realistic in vitro microenvironment by spatial and temporal control of cell growth and co-culture. In this work, we design a microfluidic device to achieve the co-culture of PC12 cells and 293 cells, and study in vitro cell-cell interaction via cell metabolic analysis by mass spectrometry. The membraneintegrated microfluidic device was firstly used for cell co-culture, and the cellular metabolite was further investigated by mass spectrometer(MS). Our results showed that the differentiation of PC12 cells could be successfully induced by m NGF and also greatly influenced by the microchannel treatment of fetal bovine serum(FBS) solution. The identification of cell morphology, microtubule-associated protein 2(MAP-2) expression and viability of differentiated PC12 cells were conducted before 293 cells being introduced into the top microfluidic channels and stimulated to secrete cell metabolism products. The developed microfluidic device is a potentially useful tool for high throughput of cell-cell interaction study.

Quantification of selected monohydroxy metabolites of polycyclic aromatic hydrocarbons in human urine

An analytical method was developed to quantitatively determine selected monohydroxy metabolites of polycyclic aromatic hydrocarbons(PAHs) in human urine. The procedure included enzymatic hydrolysis to cleave the conjugated metabolites, solid-phase microextraction enrichment, and gas chromatography-mass spectrometry analysis. The method proved to be sensitive enough to detect the selected PAH metabolites in human urine.

Necessary exponential stability conditions for linear discrete time-delay systems and application

Necessary conditions for the exponential stability of the linear discrete time-delay systems are presented by employing the so-called Lyapunov–Krasovskii functional approach.These conditions not only provide a new tool for stability analysis of the linear discrete timedelay system by characterising instability domains,but also extend the existing results of the linear discrete time-delay system.Simultaneously,we investigate several crucial properties that connect the Lyapunov matrix and the fundamental matrix of the system.Finally,the robust stability analysis of the linear discrete time-delay systems with norm-bounded uncertainties is presented.Numerical examples illustrate the validity of the obtained results.

Research on Non Intrusive Intelligent Monitoring System for Elevator State

The current elevator status monitoring systems basically realize the monitoring of elevator status by collecting signals from the main board of the elevator.However it is costly and lacks universality,which also requires invasive installation.To address the above problems,a non-invasive intelligent monitoring method is proposed in this paper for elevator operation status.Themethod decomposes the acceleration signal into vertical and horizontal components,estimates the dynamics of the elevator using Kalman filter,performs vibration analysis on the horizontal components,establishes a baseline for normal operation,and automatically calibrates the sensors by combining the operating characteristics of the elevator.The traceless Kalman filter based on fused SLAM was performed to couple the sensor information and track the real-time position of the elevator.The effectiveness and robustness of the method are verified in actual operation,and the problem of elevator position error accumulation is basically solved without installing fiducials.The designed non-intrusive elevator status intelligent monitoring method is low-cost and universal,which is of practical significance for promoting on-demand elevator maintenance.

Modeling and Analysis of EEG Brain Network in High Altitude Task State

Long-term exposure to high altitude,low pressure and low oxygen will seriously threaten people’s cognitive function.To explore the changes in wholebrain network dynamics during brain activity in long-term high-altitude migrants,EEG signals from three subjects of 75 different altitudes were analyzed using the Stroop experimental paradigm and the network recombination prediction model.The sliding window method was used to explore the dynamic change process of the brain network.At the same time,the time period with significant difference between the brain networks of the altitude group was selected as the real response network to measure the model prediction accuracy.Then,according to different network prediction model rules,the weights of brain network 200 ms before stimulation were updated for each subject.Finally,the prediction model with the least difference between the prediction network and the real response network was selected for each subject.The experimental results showed that the prediction accuracy of the model reach 98.95%,and there is a significant difference in model selection between the elevation groups.It helps to understand the brain dynamics of healthy people,and reveals the abnormal changes in the brain networks of those who have stayed at high altitude for a long time,providing an important reference for the cognitive rehabilitation training of victims ex-posed at high altitude.

Forest soil CO_2 fluxes as a function of understory removal and N-fixing species addition

We report on the effects of forest management practices of understory removal and N-fixing species（Cassia alata） addition on soil CO2 fluxes in an Eucalyptus urophylla plantation（EUp）,Acacia crassicarpa plantation（ACp）,10-species-mixed plantation（Tp）,and 30-species-mixed plantation（THp） using the static chamber method in southern China.Four forest management treatments,including（1） understory removal（UR）;（2） C.alata addition（CA）;（3） understory removal and replacement with C.alata（UR＋CA）;and（4） control without any disturbances（CK）,were applied in the above four forest plantations with three replications for each treatment.The results showed that soil CO2 fluxes rates remained at a high level during the rainy season（from April to September）,followed by a rapid decrease after October reaching a minimum in February.Soil CO2 fluxes were significantly higher（P 〈 0.01） in EUp（132.6 mg/（m2.hr）） and ACp（139.8 mg/（m2.hr）） than in Tp（94.0 mg/（m2.hr）） and THp（102.9 mg/（m2.hr））.Soil CO2 fluxes in UR and CA were significantly higher（P 〈 0.01） among the four treatments,with values of 105.7,120.4,133.6 and 112.2 mg/（m2.hr） for UR＋CA,UR,CA and CK,respectively.Soil CO2 fluxes were positively correlated with soil temperature（P 〈 0.01）,soil moisture（P 〈 0.01）,NO3？-N（P 〈 0.05）,and litterfall（P 〈 0.01）,indicating that all these factors might be important controlling variables for soil CO2 fluxes.This study sheds some light on our understanding of soil CO2 flux dynamics in forest plantations under various management practices.

Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation

This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus.Different convolutional configurations were proposed to capture information obtained by a segmentation network.In addition,a generative adversarial network based on Pixel2Pixel was proposed.The generator was a codec structure combining a residual network and an attention mechanism to capture detailed information.The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the expert.Through the continuously transmitted losses of the generator and discriminator,the generator reached the optimal state of hippocampus segmentation.T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer’s disease Neuroimaging Initiative dataset were used as training and test data;similarity coefficient,sensitivity,and positive predictive value were used as evaluation indicators.Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases,such as Alzheimer’s disease.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

Multi-channel microfluidic chip-mass spectrometry platform for cell analysis

In this review, we highlight the latest development of multi-channel microfluidic chip-mass spectrometry（chip-MS） in cell analysis and metabolite detection. Following a brief introduction about history and development of multi-channel microchip and MS combination, we will elaborate the key issues of constructing chip-MS platform interface. Then exciting progresses made in this field should be reviewed with well exemplified works, including chip-MS technology for cell introduction, pretreatment of cell secretions and cell metabolite analysis. We will also describe the development of integrated total analysis systems proposed by our group. We hope this brief review will inspire interested readers and provide knowledge about chip-MS platform in the bioanalysis field, particularly in cell analysis and metabolite identifying applications.

L-Arginine/nitric oxide regulates skeletal muscle development via muscle fibre-specific nitric oxide/mTOR pathway in chickens

L-Arginine (L-Arg), the precursor of nitric oxide (NO), plays an important role in muscle function. Fasttwitchglycolytic fibres are more susceptible to age-related atrophy than slow-twitch oxidative fibres.The effect of L-Arg/NO on protein metabolism of fast- and slow-twitch muscle fibres was evaluated inchickens. In Exp. 1, 48 chicks at 1 day old were divided into 4 groups of 12 birds and subjected to 4treatments: basal diet without supplementation or supplemented with 1% L-Arg, and water supplementedwith or without L-nitro-arginine methyl ester (L-NAME, 18.5 mM). In Exp. 2, 48 chicks weredivided into 4 groups of 12 birds fed with the basal diet and subjected to the following treatments: tapwater (control), tap water supplemented with L-NAME (18.5 mM), or molsidomine (MS, 0.1 mM), or18.5 mM L-NAME t 0.1 mM MS (NAMS). The regulatory effect of L-Arg/NO was further investigatedin vitro with myoblasts obtained from chicken embryo pectoralis major (PM) and biceps femoris (BF).In vivo, dietary L-Arg supplementation increased breast (t14.94%, P < 0.05) and thigh muscle mass(t23.40%, P < 0.05);whereas, MS treatment had no detectable influence. However, L-NAME treatmentblocked the beneficial influence of L-Arg on muscle development. L-Arg decreased (P < 0.05) proteinsynthesis rate, phosphorylated mTOR and ribosomal protein S6 kinase beta-1 (p70S6K) levels in breastmuscle, which was recovered by L-NAME treatment. In vitro, L-Arg or sodium nitroprusside (SNP)reduced protein synthesis rate, suppressed phosphorylated mTOR/p70S6K and decreased atrogin-1 andmuscle RING finger 1 (MuRF1) in myoblasts from PM muscle (P < 0.05). L-NAME abolished the inhibitoryeffect of L-Arg on protein synthesis and the mTOR/p70S6K pathway. However, myoblasts from BF muscleshowed the weak influence. Moreover, blocking the mTOR/p70S6K pathway with rapamycin suppressedprotein synthesis of the 2 types of myoblasts;whereas, the protein expression of atrogin-1 and MuRF1levels were restricted only in myoblasts from PM muscle. In conclusion, L-Arg/NO/mTOR/p70S6Kpathway enhances protein accumulation and muscle development in fast-twitch glycolytic muscle inchickens. L-Arg/NO regulates protein turnover in a muscle fibre specific way, which highlights the potentialclinical application in fast-twitch glycolytic muscle fibres.

Imipramine Inhibits Adipogenic Differentiation in Both 3T3-L1 Preadioocvtes and Mouse Marrow Stromal Cells

Imipramine （IM） has been widely used clinically for the treatment of mental disorders. Its actions on tissues or organs other than the nervous system also need to be understood for its proper clinical use. In this study, the effects of IM on adipogenic differentiation in both 3T3-L1 preadipocytes and mouse marrow stromal cells （MSCs） were investigated. The results showed that fewer adipocytic cells were developed from 3T3-L1 preadipocytes in the presence of 0.001 to 1 μmol/L of IM as compared to control. Similar inhibitory effect was also observed in mouse MSCs. The decrease in the formation of adipocytes was accompanied with significant down-regulation at mRNA expression of the early adipogenic transcription factor, peroxisome proliferator-activated receptor γ2 （PPARγ2）. Western blot analysis further revealed that the protein expression of PPARγ2 was reduced markedly in ceils treated with IM at concentrations of 0.01, 0.1 and 1 μmol/L, suggesting that the suppression on PPAR72 was involved in IM＇s inhibition on MSCs adipogenesis. Moreover, IM at the above concentrations could stimulate the mRNA expression of β2-adrenergic receptor （AR） and β3-AR, which implicated that the effect of IM on adipogenic differentiation was partially mediated by β-ARs. Our results demonstrated for the first time that the conventional antidepressive imipramine exerts accompanied inhibitory effect on adipocyte formation, which may have possible clinical implications.