Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

Functional magnetic resonance imaging study of group independent components underpinning item responses to paranoid-depressive scale

BACKGROUND Our study expand upon a large body of evidence in the field of neuropsychiatric imaging with cognitive,affective and behavioral tasks,adapted for the functional magnetic resonance imaging(MRI)(fMRI)experimental environment.There is sufficient evidence that common networks underpin activations in task-based fMRI across different mental disorders.AIM To investigate whether there exist specific neural circuits which underpin differ-ential item responses to depressive,paranoid and neutral items(DN)in patients respectively with schizophrenia(SCZ)and major depressive disorder(MDD).METHODS 60 patients were recruited with SCZ and MDD.All patients have been scanned on 3T magnetic resonance tomography platform with functional MRI paradigm,comprised of block design,including blocks with items from diagnostic paranoid(DP),depression specific(DS)and DN from general interest scale.We performed a two-sample t-test between the two groups-SCZ patients and depressive patients.Our purpose was to observe different brain networks which were activated during a specific condition of the task,respectively DS,DP,DN.RESULTS Several significant results are demonstrated in the comparison between SCZ and depressive groups while performing this task.We identified one component that is task-related and independent of condition(shared between all three conditions),composed by regions within the temporal(right superior and middle temporal gyri),frontal(left middle and inferior frontal gyri)and limbic/salience system(right anterior insula).Another com-ponent is related to both diagnostic specific conditions(DS and DP)e.g.It is shared between DEP and SCZ,and includes frontal motor/language and parietal areas.One specific component is modulated preferentially by to the DP condition,and is related mainly to prefrontal regions,whereas other two components are significantly modulated with the DS condition and include clusters within the default mode network such as posterior cingulate and precuneus,several occipital areas,including lingual and fusiform gyrus,as well as parahippocampal gyrus.Finally,component 12 appeared to be unique for the neutral condition.In addition,there have been determined circuits across components,which are either common,or distinct in the preferential processing of the sub-scales of the task.CONCLUSION This study has delivers further evidence in support of the model of trans-disciplinary cross-validation in psychiatry.

Research Progress on Functional Components and Pharmacological Effects of Red Jujube ( Ziziphus jujuba Mill.)

Red jujube(Ziziphus jujuba Mill.),as a traditional Chinese medicine and edible plant,has a long history and extensive uses.Red jujubes are not only widely used in the food industry and traditional Chinese medicine preparations,but also have attracted widespread research interest due to their rich functional components and various pharmacological effects.This paper reviewed the main functional components of jujubes,including polysaccharides,triterpenoid saponins,organic acids and alkaloids,and discussed in detail the pharmacological effects of these components such as blood sugar-regulating,antioxidant,anti-inflammatory and anti-tumor effects.Furthermore,the paper also summarized the safety issues such as drug interactions and toxic side effects of jujubes,and prospected the future development direction of jujube research.

DIRECT AND ADAPTIVE SLICING ON CAD MODEL OF IDEAL FUNCTIONAL MATERIAL COMPONENTS (IFMC)

A brand new direct and adaptive slicing approach is proposed, which canapparently improve the part accuracy and reduce the building time. At least two stages are includedin this operation: getting the crossing contour of the cutting plane with the solid part anddetermining the layer thickness. Apart from usual SPI algorithm, slicing of the solid model has itsspecial requirements. Enabling the contour line segments of the cross-section as long as possible isone of them, which is for improving manufacturing efficiency and is reached by adaptively adjustingthe step direction and the step size at every crossing point to obtain optimized secant height. Thelayer thickness determination can be divided into two phases: the geometry-based thicknessestimation and the material-based thickness verifying. During the former phase, the geometrytolerance is divided into two parts: a variety of curves are approximated by a circular arc, whichintroduces the first part, and the deviation error between the contour line in LM process and thecircular arc generates the second part. The latter phase is mainly verifying the layer thicknessestimated in the former stage and determining a new one if necessary. In addition, an example usingthis slicing algorithm is also illustrated.

Altered intra- and inter-network brain functional connectivity in upper-limb amputees revealed through independent component analysis

Although cerebral neuroplasticity following amputation has been observed, little is understood about how network-level functional reorganization occurs in the brain following upper-limb amputation. The objective of this study was to analyze alterations in brain network functional connectivity(FC) in upper-limb amputees(ULAs). This observational study included 40 ULAs and 40 healthy control subjects;all participants underwent resting-state functional magnetic resonance imaging. Changes in intra-and inter-network FC in ULAs were quantified using independent component analysis and brain network FC analysis. We also analyzed the correlation between FC and clinical manifestations, such as pain. We identified 11 independent components using independent component analysis from all subjects. In ULAs, intra-network FC was decreased in the left precuneus(precuneus gyrus) within the dorsal attention network and left precentral(precentral gyrus) within the auditory network;but increased in the left Parietal_Inf(inferior parietal, but supramarginal and angular gyri) within the ventral sensorimotor network, right Cerebelum_Crus2(crus Ⅱ of cerebellum) and left Temporal_Mid(middle temporal gyrus) within the ventral attention network, and left Rolandic_Oper(rolandic operculum) within the auditory network. ULAs also showed decreased inter-network FCs between the dorsal sensorimotor network and ventral sensorimotor network, the dorsal sensorimotor network and right frontoparietal network, and the dorsal sensorimotor network and dorsal attention network. Correlation analyses revealed negative correlations between inter-network FC changes and residual limb pain and phantom limb pain scores, but positive correlations between inter-network FC changes and daily activity hours of stump limb. These results show that post-amputation plasticity in ULAs is not restricted to local remapping;rather, it also occurs at a network level across several cortical regions. This observation provides additional insights into the plasticity of brain networks after upper-limb amputation, and could contribute to identification of the mechanisms underlying post-amputation pain.

Design of Drilling and Riveting Multi-functional End Effector for CFRP and Aluminum Components in Robotic Aircraft Assembly

To fulfill the demands for higher quality,efficiency and flexibility in aviation industry,a multi-functional end effector is designed to automate the drilling and riveting processes in assembling carbon fiber reinforced polymer(CFRP)and aluminum components for a robotic aircraft assembly system.To meet the specific functional requirements for blind rivet installation on CFRP and aluminum materials,additional modules are incorporated on the end effector aside of the basic processing modules for drilling.And all of these processing modules allow for a onestep-drilling-countersinking process,hole inspection,automatic rivet feed,rivet geometry check,sealant application,rivet insertion and installation.Besides,to guarantee the better quality of the hole drilled and joints riveted,several online detection and adjustment measures are applied to this end effector,including the reference detection and perpendicular calibration,which could effectively ensure the positioning precision and perpendicular accuracy as demanded.Finally,the test result shows that this end effector is capable of producing each hole to a positioning precision within ±0.5 mm,aperpendicular accuracy within 0.3°,a diameter tolerance of H8,and a countersink depth tolerance of±0.01 mm.Moreover,it could drill and rivet up to three joints per minute,with acceptable shearing and tensile strength.

CAE-integrated Fabrication of Functionally Graded Components

A typical contemporary computerized product develop me nt workflow is outlined in Fig.1. Product geometry information is first prep ared with computer-aided design (CAD) software. The CAD format can then be com municated to other downstream-computerized applications like, computer-aided e ngineering analysis (CAE), computer-aided manufacturing (CAM) and/or rapid prot otyping. Since design may need to be modified to incorporate new requirements, a loop back path is also depicted in Fig.1. The design engineers will check ac cording to their experience, result of physical test and CAE simulation to decid e whether redesign is needed or not. If the design passes all tests, its pr ototype or product can be produced. Otherwise, the current practice is to chang e its geometry and/or select a more appropriate material. The iteration repeat s until the latest version satisfies the engineering specification and customer requirements. Note that the material is homogeneous in the part to be designed. With the advent of functionally graded material (FGM) research, a new workflow will become possible. Components incorporating FGM’s can be designed to achieve levels of performance superior to that of homogeneous materials by combining the desirable properties of each constituent phase. Theoretically, the material composition can be tailo red within a component to achieve local control of properties; for example, form ability, corrosion resistance, hardness, toughness, and so on. By such local co ntrol, monolithic components can be created that integrate the function of multi ple discrete components, saving part count, space, weight, and enabling concepts that would otherwise be impractical. Controlling the spatial distribution of p roperties via composition will allow for control of the state of the entire comp onent (the state of residual stress in a component). There are various methods p roposed to produce FGM components. In particular, solid freeform fabrication ( SFF) methods are commonly used to directly fabricate an FGM part in an additive fashion directly from a computer controlled, layer-by-layer, additive process in which a standard CAD is sliced into a series of horizontal planes. Common SF F techniques being investigated include three-dimensional printing (3DP), Lamin ate Object Manufacturing (LOM), Extrusion Freeform Fabrication (EFF), Selective Laser Sintering (SLS) and even Stereolithography (SL). Fig.1 Current CAE design workflow Fig.2 Proposed CAE design workflow for FGM Albeit the feasibility to fabricate FGM components, one gap still needs to be fi lled for real life FGM product design; namely, where and how to grade the compon ent. This paper will, thus, address issues on incorporating FGM for design impr ovement. Rather than changing the geometry or reselecting a new material, a FGM approach can be employed in design enhancement as shown in Fig.2. The same geo metry and material is retained except that functional property in needed regions is selectively reinforced. As in conventional workflow, CAE simulation is perf ormed after CAD modelling. CAE simulation is preferred since physical test is v ery expensive and most of them are destructive. Moreover, the experience of the engineers may not be accurate. More importantly, the result of CAE simulation is used in this research to produce a stress intensity map for selective reinfor cement. The map will be converted to tool path control signals for generating FG component via SFF machine. On the implementation side, SolidWorks is used fo r CAD modeling, COSMOS/Works is used for CAE simulation. The model is then selec tively reinforced according to the simulation result to produce a FGM enriched p ath plan to drive the Z-corp machine. Case studies are performed to verify the approach. The preliminary result is positive. Future extension to material oth er than starch and plaster powders and enhancement other than stress distributio n may be explored. In conclusion, a CAE-based methodology for FGM product des ign

Research Progress of Tea Functional Components in the Prevention and Treatment of Lung Cancer

The incidence and mortality of lung cancer rank first among all malignant tumors,so the prevention and treatment of lung cancer has become a worldwide concern.Tea is one of the most popular healthy drinks in the world.A large number of studies have shown that tea polyphenols,theophylline,tea pigments,theanine,tea polysaccharides and other functional components in tea can promote apoptosis of cancer cells,inhibit proliferation and division of cancer cells,and regulate the expression of tumor-related gene protein through strong antioxidant activity,inhibiting the activity of key enzymes and blocking signal transduction to prevent and treat the occurrence,development and metastasis of lung cancer.In this study,the application of tea in the prevention and treatment of lung cancer was elaborated through the traceability,basic components,basic theory of traditional Chinese medicine,anti-lung cancer mechanism,expert opinions and so on,in order to provide direction for the prevention and treatment of tea and lung cancer.

INVARIANT SUBSPACES AND GENERALIZED FUNCTIONAL SEPARABLE SOLUTIONS TO THE TWO-COMPONENT b-FAMILY SYSTEM

Invariant subspace method is exploited to obtain exact solutions of the two- component b-family system. It is shown that the two-component b-family system admits the generalized functional separable solutions. Furthermore, blow up and behavior of those exact solutions are also investigated.

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.

Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

Functional statistics is a new technique for dealing with data thatcan be viewed as curves or images. Parallel to this approach, the Near-InfraredReflectance (NIR) spectroscopymethodology has been used in modern chemistryas a rapid, low-cost, and exact means of assessing an object’s chemicalproperties. In this research, we investigate the quality of corn and cookiedough by analyzing the spectroscopic technique using certain cutting-edgestatistical models. By analyzing spectral data and applying functional modelsto it, we could predict the chemical components of corn and cookie dough.Kernel Functional Classical Estimation (KFCE), Kernel Functional QuantileEstimation (KFQE), Kernel Functional Expectile Estimation (KFEE),Semi-Partial Linear Functional Classical Estimation (SPLFCE), Semi-PartialLinear Functional Quantile Estimation (SPLFQE), and Semi-Partial LinearFunctional Expectile Estimation (SPLFEE) are models used to accuratelyestimate the different quantities present in Corn and Cookie dough. Theselection of these functional models is based on their ability to constructa forecast region with a high level of confidence. We demonstrate that theconsidered models outperform traditional models such as the partial leastsquaresregression and the principal component regression in terms of predictionaccuracy. Furthermore, we show that the proposed models are morerobust than competing models such as SPLFQE and SPLFEE in the sensethat data heterogeneity has no effect on their efficiency.

Unsupervised Functional Data Clustering Based on Adaptive Weights

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

Wigner function of coherent state of N components

In this paper, we study the Wigner function of coherent state of N components, especially two components and three components. This function consists of two terms： the Gaussian term and the interference term with the negativity. The first term comprises N Gaussian surfaces evenly centred on a circle of radius ｜β｜ = ｜α｜ with a separate angle of 2π/N, and the second term is composed of 1/2N（N - 1） Gaussian-cosine surfaces evenly centred in a circular region of radius ｜β｜ 〈 ｜α｜. Here, a is the eigenvalue of the annihilation operator α, and β is a variable in some complex space in which the Wigner function is defined. We have proved that the essential condition to eliminate the negativity of the Wigner function is that the mean photon count of the coherent state is equal to that of the Glouber coherent state.

The Principal Component Transform of Parametrized Functions

Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to Principal Component Analysis, we call the corresponding transformation Principal Component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.

Mapping crustal S-wave velocity structure with SV-component receiver function method

In this article, we analyze the characters of SV-component receiver function of teleseismic body waves and its advantages in mapping the S-wave velocity structure of crust in detail. Similar to radial receiver function, SV-component receiver function can be obtained by directly deconvolving the P-component from the SV-component of teleseismic recordings. Our analyses indicate that the change of amplitude of SV-component receiver function against the change of epicentral distance is less than that of radial receiver function. Moreover, the waveform of SV-component receiver function is simpler than the radial receiver function and gives prominence to the PS converted phases that are the most sensitive to the shear wave velocity structure in the inversion. The synthetic tests show that the convergence of SV-component receiver function inversion is faster than that of the radial receiver function inversion. As an example, we investigate the S-wave velocity structure beneath HIA sta-tion by using the SV-component receiver function inversion method.

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.

Numerical study of influence of anisotropy on flanging earing based on Barlat six-component yield function

The anisotropy of stamped sheet metal exerts evident effects on the formability. Barlat six component yield function was introduced into the elastoplastic finite element formulation based on the principle of virtual velocity and the discrete Kirchhoff triangular element model. The flanging earing in cylindrical cup drawing process of circular sheet metal was numerically simulated. The influence of anisotropy on the forming process was studied.

Effects of the Component and Fiber Gradient Distributions on the Strength of Cement-based Composite Materials

The effects of the component gradient distribution at interface and the fiber gradient distribution on the strength of cement-based materials were studied. The results show that the flexural strength and compressive strength of the mortar and concrete with interface component and fiber gradient distributions are obviously improved. The strengthes of the fiber gradient distributed mortar and concrete (FGDM/C) are higher than those of fiber homogeneously distributed mortar and concrete (FHDM/C). To obtain the same strength, therefore, a smaller fiber volume content in FGDM/C is needed than that in FHDM/C. The results also show that the component gradient distribution of the concrete can be obtained by means of multi-layer vibrating formation.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

ROBUST ESTIMATION OF VARIANCE COMPONENTS MODEL

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