ON THE PERFORMANCE OF DATA-DEPENDENT SUPERIMPOSED TRAINING WITHOUT CYCLIC PREFIX FOR SISO/MIMO SYSTEMS

Compared with channel estimation method based on explicit training sequences,bandwidth is saved for those methods using superimposed training sequences,while it is wasted when Cyclic Prefix(CP) is added.In previous work of McLernon,the Mean Square Error(MSE) performance of Data-Dependent Superimposed Training(DDST) without CP for Single-Input Single-Output(SISO) system was analyzed under the assumption that the data-dependent sequence matrix was a circulant matrix and not interfered by others.In fact,for the system without CP,the data-dependent sequence matrix is not circulant any more and will be interfered.This paper derives the exact expression of MSE for the system without CP and also gives its extension to Multiple-Input Multiple-Output(MIMO) system without CP.

Detecting and Identifying in vivo Metabolites of Brodimoprim via LC/ESI-MS with Data-dependent Scanning

The present article covers a simple approach to detect and subsequently identify in vivo metabolites of brodimoprim, using high performance liquid chromatography coupled to ion trap mass spectrometer（LC/ESI-MS）, which is based on a data-dependent acquisition of isotope ions and result verified by full scan mass spectrum. The distinguished advantage of data-dependent scan is rapidness because it requires minimum sample preparation, and all the necessary data can be obtained in one chromatographic run. In addition, it is highly sensitive and selective, allowing detection of trace metabolites even in the presence of complex biomatrix. As a result, four phase-Ⅰ（M1--M4） and four Phase-Ⅱ（M5--M8） metabolites of brodimoprim were identified in urine after the oral administration of hrodimoprim to Wistar rats. Their chemical structures were proposed based on the interpretation of their CID fragmentation characterizations and the metabolic pathway was exhibited in this article.

Changes in milk fat globule membrane proteins along lactation stage of Laoshan dairy goat

The milk fat globule membrane(MFGM)is a complex structure with numerous functions,and its composition is affected by many factors.There have been few systematic investigations on goat MFGM proteome profiling during lactation.Individual milk samples from 15 healthy dairy goats were obtained at six lactation time points for investigation of the MFGM proteome using both data-independent acquisition(DIA)and data-dependent acquisition(DDA)proteomics techniques combined with multivariate statistical analysis.Using the DIA method,890 variably abundant MFGM proteins were discovered throughout the lactation cycle.From 1 to 240 d,butyrophilin subfamily 1 member A1,lipoprotein lipase,perilipin-2,and adipose triglyceride lipase were upregulated,while APOE,complement C3,clusterin,and IgG were downregulated.Furthermore,from 1 to 90 d,annexin A1,annexin A2,and antithrombin-ll were downregulated,then upregulated by d 240.Albumin had a high degree of connectedness,indicating that it was a key protein,according to protein-protein interaction research.Overall,our findings gave new insights into the biological features of MFGM protein in goat milk throughout lactation,which may aid in the creation of specialized MFGM products and infant formula.

Deeper Chemical Perceptions for Better Traditional Chinese Medicine Standards

Traditional Chinese medicines (TCMs), a complex system of natural resources with many diverse components, are widely used as approved medicinal agents in China. Quality control of TCMs is a huge challenge for the government and for testing institutes and is associated with numerous scientific issues. Among these considerations include the following questions: How many components are in TCMs? How can the multiple components in TCMs be comprehensively delineated and subsequently characterized? What is the level and range of these (active) metabolites within these multiple-component TCMs, in order to recommend standards? and What are the qualities required for a marker constituent to be selected, and from a practical perspective, how can these components be assessed with low cost and in a short time? All of these factors require significant and deep thinking in order to understand the individualistic chemistry of TCM in order to develop enhanced TCM quality standards for improved and consistent patient care. In this review, the latest exploratory research in TCM chemistry analytical techniques and methods is summarized in order to begin to develop responses to these scientific issues. Advances in these methods have included multidimensional separation for liquid chromatography–high-resolution mass spectrometry (LC–HRMS), smart triggering data-dependent acquisition of LC–HRMS, target analysis with liquid chromatography–mass spectrometry (LC–MS), supercritical fluid chromatography, and data mining of large mass spectrometry (MS) datasets. In addition, two quality strategies have been introduced in order to save reference standards and the analysis time for a TCM quality standard, including the application of the single standard to determine multi-components (SSDMC) and monomethodheterotrait matrix methods. Finally, a series of future improvements for analytical methods for TCMs are proposed.

An iterative modified kernel based on training data

To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.

Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks

Lipidomics coverage improvement is essential for functional lipid and pathway construction.A powerful approach to discovering organism lipidome is to combine various data acquisitions,such as full scan mass spectrometry(full MS),data-dependent acquisition(DDA),and data-independent acquisition(DIA).Caenorhabditis elegans(C.elegans)is a useful model for discovering toxic-induced metabolism,highthroughput drug screening,and a variety of human disease pathways.To determine the lipidome of C.elegans and investigate lipid disruption from the molecular level to the system biology level,we used integrative data acquisition.The methyl-tert-butyl ether method was used to extract L4 stage C.elegans after exposure to triclosan(TCS),perfluorooctanoic acid,and nanopolystyrene(nPS).Full MS,DDA,and DIA integrations were performed to comprehensively profile the C.elegans lipidome by Q-Exactive Plus MS.All annotated lipids were then analyzed using lipid ontology and pathway analysis.We annotated up to 940 lipids from 20 lipid classes involved in various functions and pathways.The biological investigations revealed that when C.elegans were exposed to nPS,lipid droplets were disrupted,whereas plasma membrane-functionalized lipids were likely to be changed in the TCS treatment group.The nPS treatment caused a significant disruption in lipid storage.Triacylglycerol,glycerophospholipid,and ether class lipids were those primarily hindered by toxicants.Finally,toxicant exposure frequently involved numerous lipid-related pathways,including the phosphoinositide 3-kinase/protein kinase B pathway.In conclusion,an integrative data acquisition strategy was used to characterize the C.elegans lipidome,providing valuable biological insights into hypothesis generation and validation.

The seam offset identification based on support vector regression machines

The principle of the support vector regression machine（SVR） is first analysed. Then the new data-dependent kernel function is constructed from information geometry perspective. The current waveforms change regularly in accordance with the different horizontal offset when the rotational frequency of the high speed rotational arc sensor is in the range from 15 Hz to 30 Hz. The welding current data is pretreated by wavelet filtering, mean filtering and normalization treatment. The SVR model is constructed by making use of the evolvement laws, the decision function can be achieved by training the SVR and the seam offset can be identified. The experimental results show that the precision of the offset identification can be greatly improved by modifying the SVR and applying mean filteringfrom the longitudinal direction.

Parallel Execution of Prolog on Shared-Memory Multiprocessors

Logic programs offer many opportunities for the exploitation of parallelism.But the parallel execution of a task incurs various overheads This paper focuses on the issues relevant to parallelizing Prolog on shared-memory multiprocessors efficiently.