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We previously identified a dark blue appearance through the skin of abdomen, especially the colored chicken breeds, called hyperpigmentation of the visceral peritoneum (HVP) which characterized by intense pigmentation of connective tissue in the visceral peritoneum. The HVP has recently garnered increasing attention due to its negative impact on carcass appearance, and been an important concern in the poultry industry, especially for the Chinese yellow-feathered broilers. In this study, we measured the in vivo HVP at different time points, and analyzed the correlation between the HVP in vivo and postmortem. Then, established an accurate and reliable HVP phenotypic measuring method in vivo for early selection in chickens and analyzed the association of phenotypic variations with the in vivo HVP traits with growth traits. The results showed that the in vivo HVP at 21 d of age in chickens have a high heritability (h2 = 0.452) through estimating genetic parameters, and in vivo HVP levels at 21 and 42 d were both significantly associated with those postmortem in chickens, suggesting that directional selection on reducing HVP can be implemented as early as at 21 d in the breeding and production of chickens. Although, we found HVP had no effect on the body weight at 1 d, it could significantly reduce the body weight at 21, 42, 70 d and 91 d in chickens. This suggests HVP not only has a negative effect on carcass traits, but also significantly reduces the production in the poultry industry. [ABSTRACT FROM AUTHOR]

In recent years, deep learning achieves remarkable results in the field of artificial intelligence. However, the training process of deep neural networks may cause the leakage of individual privacy. Given the model and some background information of the target individual, the adversary can maliciously infer the sensitive feature of the target individual. Therefore, it is imperative to preserve the sensitive information in the training data. Differential privacy is a state-of-the-art paradigm for providing the privacy guarantee of datasets, which protects the private and sensitive information from the attack of adversaries significantly. However, the existing privacy-preserving models based on differential privacy are less than satisfactory since traditional approaches always inject the same amount of noise into parameters to preserve the sensitive information, which may impact the trade-off between the model utility and the privacy guarantee of training data. In this paper, we present a general differentially private deep neural networks learning framework based on relevance analysis, which aims to bridge the gap between private and non-private models while providing an effective privacy guarantee of sensitive information. The proposed model perturbs gradients according to the relevance between neurons in different layers and the model output. Specifically, during the process of backward propagation, more noise is added to gradients of neurons that have less relevance to the model output, and vice-versa. Experiments on five real datasets demonstrate that our mechanism not only bridges the gap between private and non-private models, but also prevents the disclosure of sensitive information effectively. [ABSTRACT FROM AUTHOR]

The PM 2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM 2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM 2.5 . In this paper, we attempted to realize the relevance analysis and short-term prediction of PM 2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM 2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO 2 , SO 2 , PM 10 ) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO 2 , PM 10 , and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM 2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM 2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM 2.5 and it also helps to broaden the application of big data and the multi-source data mining methods. [ABSTRACT FROM AUTHOR]

Artemisia argyi leaves (AAL) has been widely used as herbal medicine and food supplement and in China and other Asian countries. The aim of this work is to qualitative and quantitative characterization of phenolic compounds in AAL and screening of natural product inhibitors of α-glucosidase from AAL. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was employed to rapid and comprehensive identification of phenolic compounds in AAL, and a total of thirty-three phenolic compounds were identified. High performance liquid chromatography with diode array detection (HPLC-DAD) was established and validated to simultaneously determinate ten main bioactive phenolics compounds in different batches of AAL samples. Meanwhile, the inhibitory capacities of different batches of AAL samples on α-glucosidase were evaluated. Then, relevance analysis, including grey relational analysis and Pearson correlation analysis were employed to investigate the correlations between the contents of phenolic compounds and α-glucosidase inhibitory activities, and discover the α-glucosidase inhibitors in AAL. The relevance analysis results indicated that three phenolic compounds, 3-caffeoylquinic acid, 3,4-dicaffeoylquinic acid and 3,5-dicaffeoylquinic acid could be potential α-glucosidase inhibitors in AAL. Moreover, the α-glucosidase inhibitory activities of the three phenolic compounds were validated by in vitro and in vivo experiments. The possible inhibiting effect of the three phenolic compounds on α-glucosidase was also explored by molecular docking analysis, and the results indicated that the binding of the three α-glucosidase inhibitors to α-glucosidase mainly by hydrogen bonds, hydrophobic forces and ionic bonds. The present research provided a deep insight into phenolic compounds and α-glucosidase inhibitory activities of AAL, and discovered the α-glucosidase inhibitors in AAL. [Display omitted] • HPLC-Q/TOF-MS and HPLC-DAD were employed for qualitative and quantitative analysis of phenolic compounds in AAL. • The α-glucosidase inhibitory activities of AAL samples were evaluated. • Grey relational analysis and Pearson correlation analysis were employed to discover the α-glucosidase inhibitors in AAL • 3-Caffeoylquinic acid, 3,4-dicaffeoylquinic acid and 3,5-dicaffeoylquinic acid could be α-glucosidase inhibitors in AAL. • The inhibitory mechanisms of three selected α-glucosidase inhibitors were studied by molecular docking. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance Shenqi Fuzheng Injection (SFI) is an injectable traditional Chinese herbal formula comprised of two Chinese herbs, Radix codonopsis and Radix astragali, which were commonly used to improve immune functions against chronic diseases in an integrative and holistic way in China and other East Asian countries for thousands of years. Materials and methods This present study was designed to explore the bioactive components on immuno-enhancement effects in SFI using the relevance analysis between chemical fingerprints and biological effects in vivo. According to a four-factor, nine-level uniform design, SFI samples were prepared with different proportions of the four portions separated from SFI via high speed counter current chromatography (HSCCC). SFI samples were assessed with high performance liquid chromatography (HPLC) for 23 identified components. For the immunosuppressed murine experiments, biological effects in vivo were evaluated on spleen index (E1), peripheral white blood cell counts (E2), bone marrow cell counts (E3), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7). Based on the hypothesis that biological effects in vivo varied with differences in components, multivariate relevance analysis, including gray relational analysis (GRA), multi-linear regression analysis (MLRA) and principal component analysis (PCA), were performed to evaluate the contribution of each identified component. Results The results indicated that the bioactive components of SFI on immuno-enhancement activities were calycosin-7-O-β-d-glucopyranoside (P9), isomucronulatol-7,2'-di-O-glucoside (P11), biochanin-7-glucoside (P12), 9,10-dimethoxypterocarpan-3-O-xylosylglucoside (P15) and astragaloside IV (P20), which might have positive effects on spleen index (E1), splenic lymphocyte proliferation (E4), splenic natural killer cell activity (E5), peritoneal macrophage phagocytosis (E6) and the amount of interleukin-2 (E7), while 5-hydroxymethyl-furaldehyde (P5) and lobetyolin (P13) might have negative effects on E1, E4, E5, E6 and E7. Finally, the bioactive HPLC fingerprint of SFI based on its bioactive components on immuno-enhancement effects was established for quality control of SFI. Conclusions In summary, this study provided a perspective to explore the bioactive components in a traditional Chinese herbal formula with a series of HPLC and animal experiments, which would be helpful to improve quality control and inspire further clinical studies of traditional Chinese medicines. [ABSTRACT FROM AUTHOR]

Abstract: Seismic event recognition is an important task for hazard assessment, eruption prediction and risk mitigation, since it can be used to determine the state of a volcano. Usually, expert technicians read features extracted from the seismogram, such as, cepstral derived coefficients, energy centroids, instant frequency, instant envelop, among others. However, there are few studies about the selection of important features for classifying several types of seismic events, i.e., taking into account the temporal contribution of each considered feature. This paper presents a feature selection strategy based on a relevance measure of time-variant features for seismic event classification. In this research, features are selected as those with the maximal information preserved within the time analysis. Since features selection stage is performed by incremental training, a simple k-nearest neighbor classification rule is used to properly determine the dimension of the final feature set. The employed feature extraction and feature selection methodologies are tested on an isolated event recognition task. The database used to test the methodology is composed of the following classes: volcano-tectonic, long period earthquakes, tremors and hybrid events. Data was recorded at the seismic monitoring stations located at the Nevado-del-Ruiz volcano, Colombia. Using a classifier based on hidden Markov models, accomplished results exhibit a performance improvement from 78% to 88% using the proposed methodology in comparison to the state-of-the-art feature sets. [Copyright &y& Elsevier]

Abstract: The computer-assisted analysis of biomedical records has become an essential tool in clinical settings. However, current devices provide a growing amount of data that often exceeds the processing capacity of normal computers. As this amount of information rises, new demands for more efficient data extracting methods appear. This paper addresses the task of data mining in physiological records using a feature selection scheme. An unsupervised method based on relevance analysis is described. This scheme uses a least-squares optimization of the input feature matrix in a single iteration. The output of the algorithm is a feature weighting vector. The performance of the method was assessed using a heartbeat clustering test on real ECG records. The quantitative cluster validity measures yielded a correctly classified heartbeat rate of 98.69% (specificity), 85.88% (sensitivity) and 95.04% (general clustering performance), which is even higher than the performance achieved by other similar ECG clustering studies. The number of features was reduced on average from 100 to 18, and the temporal cost was a 43% lower than in previous ECG clustering schemes. [Copyright &y& Elsevier]

• A rapid and sensitive method for simultaneous determination of 5 phenolic acids and 17 flavonoids in Artemisiae Argyi Folium. • The method was applied for the QC of mugwort samples and found Qi'ai possesses superiority in constituent abundance. • Relevance analysis was design to accurately define the key active ingredients in mugwort. A rapid and reliable LC–MS/MS method was developed for the quantitation of major components in Folium Artemisiae Argyi (mugwort), a widely used traditional Chinese herbal medicine. A total of 5 phenolic acids and 17 flavonoids were separated and simultaneously determined by using a Shiseido C 18 column (150 × 3.0 mm, 3 μm) and gradient elution of acetonitrile-aqueous formic acid (100:0.1, v/v) at a 0.5 mL min−1 flow rate, via multiple reaction monitor (MRM) in polarity switching mode. The quantitative method was validated in terms of sensitivity, linearity, precision, accuracy and stability, which proved to be sensitive, accurate and reproducible. Then 65 samples collected from different areas were selected for component analysis by LC–MS/MS and assessment of antioxidant activity using DPPH, ABTS, FRAP, O 2 − and OH scavenging assays. Grey relational analysis and partial least square regression were used to evaluate the relevance between chemicals and bioactivities, and the results indicated chlorogenic acid, isochlorogenic acid B, A, C, eriodictyol, jaceosidin and eupatilin made the key contribution to antioxidant activity. The present study combines chemical analysis and bioassay to identify bioactive markers, which possesses potential value for the activity-oriented quality control of mugwort. [ABSTRACT FROM AUTHOR]