Identification of herb Acanthopanax senticosus (Rupr. Et Maxim.) harms by capillary electrophoresis with electrochemical detection

In this paper, a rapid, high efficient, sensitive and inexpensive approach based on a combination of simple ultrasonic extract and capillary electrophoresis (CE) separation with electrochemical detection (ED), is described to identify herbs by comparing their CE-ED profiles (namely, CE-ED electropherograms). The proposed method takes advantage of ultrasmall sample volume, low consumption of organic solvent, simple sample pretreatment and easy cleanup procedure. It was applied to analyze the CE-ED profiles of stems of herb Acanthopanax senticosus (Rupr. Et Maxim.) Harms from different sources and different parts (roots, rhizomes, stems and leaves) of this herb. By comparing peak number, peak height and peak height ratio, we found that the CE-ED profiles showed big differences for the herbs from the different sources and the different parts of this herb. In addition, the distribution of bioactive compounds (isofraxidin, rutin and chlorogenic acid) in the different parts of this herb and their content variations affected by the source were studied with the CE-ED method. Based on their own unique CE-ED profiles, these herbs from the different sources and the different parts of this herb could be easily distinguished. Therefore, the proposed approach could be used as a rapid, high efficient and sensitive method for the identification of herbal medicines.

Rapid identification of pathogenic bacteria by capillary electrophoretic analysis of rRNA genes

Molecular diagnosis is playing an increasingly important role in the rapid detection and identification of pathogenic organisms in clinical samples. The genetic variation of ribosomal genes in bacteria offers an alternative to culturing for the detection and identification of these organisms. Here 16S rRNA and 16S-23S rRNA spacer region genes were chosen as the amplified targets for single-strand conformation polymorphism (SSCP) and restriction fragment length polymorphism (RFLP) capillary electrophoresis analysis and bacterial identification. The multiple fluorescence based SSCP method for the 16S rRNA gene and the RFLP method for the 16S-23S rRNA spacer region gene were developed and applied to the identification of pathogenic bacteria in clinical samples, in which home-made short-chained linear polyacrylamide (LPA) was used as a sieving matrix; a higher sieving capability and shorter analysis time were achieved than with a commercial sieving matrix because of the simplified template preparation procedure. A set of 270 pathogenic bacteria representing 34 species in 14 genera were analyzed, and a total of 34 unique SSCP patterns representing 34 different pathogenic bacterial species were determined. Based on the use of machine code to represent peak patterns developed in this paper, the identification of bacterial species becomes much easier.

Identification, Selection, and Enrichment of Cardiomyocyte Precursors

The large-scale production of cardiomyocytes is a key step in the development of cell therapy and tissue engineering to treat cardiovascular diseases, particularly those caused by ischemia. the main objective of this study was to establish a procedure for the efficient production of cardiomyocytes by reprogramming mesenchymal stem cells from adipose tissue. First, lentiviral vectors expressing neoR and GFP under the control of promoters expressed specifically during cardiomyogenesis were constructed to monitor cell reprogramming into precardiomyocytes and to select cells for amplification and characterization. Cellular reprogramming was performed using 5'-azacytidine followed by electroporation with plasmid pOKS2a, which expressed Oct4, Sox2, and Klf4. Under these conditions, GFP expression began only after transfection with pOKS2a, and less than 0.015% of cells were GFP(+). These GFP(+) cells were selected for G418 resistance to find molecular markers of cardiomyocytes by RT-PCR and immunocytochemistry. Both genetic and protein markers of cardiomyocytes were present in the selected cells, with some variations among them. Cell doubling time did not change after selection. Together, these results indicate that enrichment with vectors expressing GFP and neoR under cardiomyocyte-specific promoters can produce large numbers of cardiomyocyte precursors (CMPs), which can then be differentiated terminally for cell therapy and tissue engineering.

A two-stage algorithm for identification of nonlinear dynamic systems

This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms. The main objective is to improve the compactness of the model that is obtained by the forward stepwise methods, while retaining the computational efficiency. The proposed algorithm first generates an initial model using a forward stepwise procedure. The significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced, resulting in an optimised compact model with significantly improved performance. The main contribution of this paper is that these two stages are performed within a well-defined regression context, leading to significantly reduced computational complexity. The efficiency of the algorithm is confirmed by the computational complexity analysis, and its effectiveness is demonstrated by the simulation results.

Convergence of the iterative algorithm for a general Hammerstein system identification

The convergence of the iterative identification algorithm for a general Hammerstein system has been an open problem for a long time. In this paper, it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data

The advent of next generation sequencing technologies (NGS) has expanded the area of genomic research, offering high coverage and increased sensitivity over older microarray platforms. Although the current cost of next generation sequencing is still exceeding that of microarray approaches, the rapid advances in NGS will likely make it the platform of choice for future research in differential gene expression. Connectivity mapping is a procedure for examining the connections among diseases, genes and drugs by differential gene expression initially based on microarray technology, with which a large collection of compound-induced reference gene expression profiles have been accumulated. In this work, we aim to test the feasibility of incorporating NGS RNA-Seq data into the current connectivity mapping framework by utilizing the microarray based reference profiles and the construction of a differentially expressed gene signature from a NGS dataset. This would allow for the establishment of connections between the NGS gene signature and those microarray reference profiles, alleviating the associated incurring cost of re-creating drug profiles with NGS technology. We examined the connectivity mapping approach on a publicly available NGS dataset with androgen stimulation of LNCaP cells in order to extract candidate compounds that could inhibit the proliferative phenotype of LNCaP cells and to elucidate their potential in a laboratory setting. In addition, we also analyzed an independent microarray dataset of similar experimental settings. We found a high level of concordance between the top compounds identified using the gene signatures from the two datasets. The nicotine derivative cotinine was returned as the top candidate among the overlapping compounds with potential to suppress this proliferative phenotype. Subsequent lab experiments validated this connectivity mapping hit, showing that cotinine inhibits cell proliferation in an androgen dependent manner. Thus the results in this study suggest a promising prospect of integrating NGS data with connectivity mapping. © 2013 McArt et al.

Impact of Testing Procedure on Permeability of Compacted Soils

The commonly used British Standard constant head triaxial permeability (BS) test, for permeability testing of fine grained soils, is known to have a relatively long test duration. Consequently, a reduction in the required time for permeability test provides potential cost savings, to the construction industry (specifically, for use during Construction Quality Control (CQA) of landfill mineral liners). The purpose of this article is to investigate and evaluate alternative short duration testing methods for the measurement of the permeability of fine grained soils.

As part of the investigation the feasibility of an existing method of short duration permeability test, known as the Accelerated Permeability (AP) test was assessed and compared with permeability measured using British Standard method (BS) and Ramp Accelerated Permeability (RAP). Four different fine grained materials, of a variety of physical properties were compacted at various moisture contents to produced analogous samples for testing using three the three different methodologies. Fabric analysis was carried out on specimens derived from post-test samples using Mercury Intrusion Porosimetry (MIP) and Scanning Electron Microscope (SEM) to assess the effects of testing methodology on soil structure. Results showed that AP testing in general under predicts permeability values derived from the BS test due to large changes in structure of the soil caused by AP test methodology, which is also validated using MIP and SEM observations. RAP testing, in general provides an improvement to the AP test but still under-predicts permeability values. The potential savings in test duration are shown to be relatively minimal for both the AP and RAP tests.

Identification of a high-risk haplotype for the dystrobrevin binding protein 1 (DTNBP1) gene in the Irish study of high-density schizophrenia families

A recent report showed significant associations between several SNPs in a previously unknown EST cluster with schizophrenia. (1). The cluster was identified as the human dystrobrevin binding protein 1 gene (DTNBP1) by sequence database comparisons and homology with mouse DTNBP1. (2). However, the linkage disequilibrium (LD) among the SNPs in DTNBP1 as well as the pattern of significant SNP-schizophrenia association was complex. This raised several questions such as the number of susceptibility alleles that may be involved and the size of the region where the actual disease mutation(s) could be located. To address these questions, we performed different single-marker tests on the 12 previously studied and 2 new SNPs in DTNBP1 that were re-scored using an improved procedure, and performed a variety of haplotype analyses. The sample consisted of 268 Irish multiplex families selected for high density of schizophrenia. Results suggested a simple structure where the LD in the target region could be explained by 6 haplotypes that together accounted for 96% of haplotype diversity in the whole sample. From these six, a single high-risk haplotype was identified that showed a significant association with schizophrenia and explained the pattern of significant findings in the analyses with individual markers. This haplotype was 30 kb long, had a large effect, could be measured with two tag SNPs only, had a frequency of 6% in our sample, seemed to be of relatively recent origin in evolutionary terms, and was equally distributed over Ireland. Implications of these findings for follow-up and replication studies are discussed.

A new and less destructive laboratory procedure for the physical separation of distal glass tephra shards from sediments

Tephrochronology, a key tool in the correlation of Quaternary sequences, relies on the extraction of tephra shards from sediments for visual identification and high-precision geochemical comparison. A prerequisite for the reliable correlation of tephra layers is that the geochemical composition of glass shards remains unaltered by natural processes (e.g. chemical exchange in the sedimentary environment) and/or by laboratory analytical procedures. However, natural glasses, particularly when in the form of small shards with a high surface to volume ratio, are prone to chemical alteration in both acidic and basic environments. Current techniques for the extraction of distal tephra from sediments involve the ‘cleaning’ of samples in precisely such environments and at elevated temperatures. The acid phase of the ‘cleaning’ process risks alteration of the geochemical signature of the shards, while the basic phase leads to considerable sample loss through dissolution of the silica network. Here, we illustrate the degree of alteration and loss to which distal tephras may be prone, and introduce a less destructive procedure for their extraction. This method is based on stepped heavy liquid flotation and which results in samples of sufficient quality for analysis while preserving their geochemical integrity. In trials, this method out-performed chemical extraction procedures in terms of the number of shards recovered and has resulted in the detection of new tephra layers with low shard concentrations. The implications of this study are highly significant because (i) the current database of distal tephra records and their corresponding geochemical signatures may require refinement and (ii) the record of distal tephras may be incomplete due to sample loss induced by corrosive laboratory procedures. It is therefore vital that less corrosive laboratory procedures are developed to make the detection and classification of distal glass tephra more secure.

Identification of vegetable oil botanical speciation in refined vegetable oil blends using an innovative combination of chromatographic and spectroscopic techniques

European Regulation 1169/2011 requires producers of foods that contain refined vegetable oils to label the oil types. A novel rapid and staged methodology has been developed for the first time to identify common oil species in oil blends. The qualitative method consists of a combination of a Fourier Transform Infrared (FTIR) spectroscopy to profile the oils and fatty acid chromatographic analysis to confirm the composition of the oils when required. Calibration models and specific classification criteria were developed and all data were fused into a simple decision-making system. The single lab validation of the method demonstrated the very good performance (96% correct classification, 100% specificity, 4% false positive rate). Only a small fraction of the samples needed to be confirmed with the majority of oils identified rapidly using only the spectroscopic procedure. The results demonstrate the huge potential of the methodology for a wide range of oil authenticity work.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Molecular detection and identification of zygomycetes species from paraffin-embedded tissues in a murine model of disseminated zygomycosis: a collaborative European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Fungal Infection Study Group (EFISG) evaluation.

The present study was performed to assess the interlaboratory reproducibility of the molecular detection and identification of species of Zygomycetes from formalin-fixed paraffin-embedded kidney and brain tissues obtained from experimentally infected mice. Animals were infected with one of five species (Rhizopus oryzae, Rhizopus microsporus, Lichtheimia corymbifera, Rhizomucor pusillus, and Mucor circinelloides). Samples with 1, 10, or 30 slide cuts of the tissues were prepared from each paraffin block, the sample identities were blinded for analysis, and the samples were mailed to each of seven laboratories for the assessment of sensitivity. A protocol describing the extraction method and the PCR amplification procedure was provided. The internal transcribed spacer 1 (ITS1) region was amplified by PCR with the fungal universal primers ITS1 and ITS2 and sequenced. As negative results were obtained for 93% of the tissue specimens infected by M. circinelloides, the data for this species were excluded from the analysis. Positive PCR results were obtained for 93% (52/56), 89% (50/56), and 27% (15/56) of the samples with 30, 10, and 1 slide cuts, respectively. There were minor differences, depending on the organ tissue, fungal species, and laboratory. Correct species identification was possible for 100% (30 cuts), 98% (10 cuts), and 93% (1 cut) of the cases. With the protocol used in the present study, the interlaboratory reproducibility of ITS sequencing for the identification of major Zygomycetes species from formalin-fixed paraffin-embedded tissues can reach 100%, when enough material is available.

Exogeneity, weak identification and instrument selection in econometrics

La dernière décennie a connu un intérêt croissant pour les problèmes posés par les variables instrumentales faibles dans la littérature économétrique, c’est-à-dire les situations où les variables instrumentales sont faiblement corrélées avec la variable à instrumenter. En effet, il est bien connu que lorsque les instruments sont faibles, les distributions des statistiques de Student, de Wald, du ratio de vraisemblance et du multiplicateur de Lagrange ne sont plus standard et dépendent souvent de paramètres de nuisance. Plusieurs études empiriques portant notamment sur les modèles de rendements à l’éducation [Angrist et Krueger (1991, 1995), Angrist et al. (1999), Bound et al. (1995), Dufour et Taamouti (2007)] et d’évaluation des actifs financiers (C-CAPM) [Hansen et Singleton (1982,1983), Stock et Wright (2000)], où les variables instrumentales sont faiblement corrélées avec la variable à instrumenter, ont montré que l’utilisation de ces statistiques conduit souvent à des résultats peu fiables. Un remède à ce problème est l’utilisation de tests robustes à l’identification [Anderson et Rubin (1949), Moreira (2002), Kleibergen (2003), Dufour et Taamouti (2007)]. Cependant, il n’existe aucune littérature économétrique sur la qualité des procédures robustes à l’identification lorsque les instruments disponibles sont endogènes ou à la fois endogènes et faibles. Cela soulève la question de savoir ce qui arrive aux procédures d’inférence robustes à l’identification lorsque certaines variables instrumentales supposées exogènes ne le sont pas effectivement. Plus précisément, qu’arrive-t-il si une variable instrumentale invalide est ajoutée à un ensemble d’instruments valides? Ces procédures se comportent-elles différemment? Et si l’endogénéité des variables instrumentales pose des difficultés majeures à l’inférence statistique, peut-on proposer des procédures de tests qui sélectionnent les instruments lorsqu’ils sont à la fois forts et valides? Est-il possible de proposer les proédures de sélection d’instruments qui demeurent valides même en présence d’identification faible? Cette thèse se focalise sur les modèles structurels (modèles à équations simultanées) et apporte des réponses à ces questions à travers quatre essais. Le premier essai est publié dans Journal of Statistical Planning and Inference 138 (2008) 2649 – 2661. Dans cet essai, nous analysons les effets de l’endogénéité des instruments sur deux statistiques de test robustes à l’identification: la statistique d’Anderson et Rubin (AR, 1949) et la statistique de Kleibergen (K, 2003), avec ou sans instruments faibles. D’abord, lorsque le paramètre qui contrôle l’endogénéité des instruments est fixe (ne dépend pas de la taille de l’échantillon), nous montrons que toutes ces procédures sont en général convergentes contre la présence d’instruments invalides (c’est-à-dire détectent la présence d’instruments invalides) indépendamment de leur qualité (forts ou faibles). Nous décrivons aussi des cas où cette convergence peut ne pas tenir, mais la distribution asymptotique est modifiée d’une manière qui pourrait conduire à des distorsions de niveau même pour de grands échantillons. Ceci inclut, en particulier, les cas où l’estimateur des double moindres carrés demeure convergent, mais les tests sont asymptotiquement invalides. Ensuite, lorsque les instruments sont localement exogènes (c’est-à-dire le paramètre d’endogénéité converge vers zéro lorsque la taille de l’échantillon augmente), nous montrons que ces tests convergent vers des distributions chi-carré non centrées, que les instruments soient forts ou faibles. Nous caractérisons aussi les situations où le paramètre de non centralité est nul et la distribution asymptotique des statistiques demeure la même que dans le cas des instruments valides (malgré la présence des instruments invalides). Le deuxième essai étudie l’impact des instruments faibles sur les tests de spécification du type Durbin-Wu-Hausman (DWH) ainsi que le test de Revankar et Hartley (1973). Nous proposons une analyse en petit et grand échantillon de la distribution de ces tests sous l’hypothèse nulle (niveau) et l’alternative (puissance), incluant les cas où l’identification est déficiente ou faible (instruments faibles). Notre analyse en petit échantillon founit plusieurs perspectives ainsi que des extensions des précédentes procédures. En effet, la caractérisation de la distribution de ces statistiques en petit échantillon permet la construction des tests de Monte Carlo exacts pour l’exogénéité même avec les erreurs non Gaussiens. Nous montrons que ces tests sont typiquement robustes aux intruments faibles (le niveau est contrôlé). De plus, nous fournissons une caractérisation de la puissance des tests, qui exhibe clairement les facteurs qui déterminent la puissance. Nous montrons que les tests n’ont pas de puissance lorsque tous les instruments sont faibles [similaire à Guggenberger(2008)]. Cependant, la puissance existe tant qu’au moins un seul instruments est fort. La conclusion de Guggenberger (2008) concerne le cas où tous les instruments sont faibles (un cas d’intérêt mineur en pratique). Notre théorie asymptotique sous les hypothèses affaiblies confirme la théorie en échantillon fini. Par ailleurs, nous présentons une analyse de Monte Carlo indiquant que: (1) l’estimateur des moindres carrés ordinaires est plus efficace que celui des doubles moindres carrés lorsque les instruments sont faibles et l’endogenéité modérée [conclusion similaire à celle de Kiviet and Niemczyk (2007)]; (2) les estimateurs pré-test basés sur les tests d’exogenété ont une excellente performance par rapport aux doubles moindres carrés. Ceci suggère que la méthode des variables instrumentales ne devrait être appliquée que si l’on a la certitude d’avoir des instruments forts. Donc, les conclusions de Guggenberger (2008) sont mitigées et pourraient être trompeuses. Nous illustrons nos résultats théoriques à travers des expériences de simulation et deux applications empiriques: la relation entre le taux d’ouverture et la croissance économique et le problème bien connu du rendement à l’éducation. Le troisième essai étend le test d’exogénéité du type Wald proposé par Dufour (1987) aux cas où les erreurs de la régression ont une distribution non-normale. Nous proposons une nouvelle version du précédent test qui est valide même en présence d’erreurs non-Gaussiens. Contrairement aux procédures de test d’exogénéité usuelles (tests de Durbin-Wu-Hausman et de Rvankar- Hartley), le test de Wald permet de résoudre un problème courant dans les travaux empiriques qui consiste à tester l’exogénéité partielle d’un sous ensemble de variables. Nous proposons deux nouveaux estimateurs pré-test basés sur le test de Wald qui performent mieux (en terme d’erreur quadratique moyenne) que l’estimateur IV usuel lorsque les variables instrumentales sont faibles et l’endogénéité modérée. Nous montrons également que ce test peut servir de procédure de sélection de variables instrumentales. Nous illustrons les résultats théoriques par deux applications empiriques: le modèle bien connu d’équation du salaire [Angist et Krueger (1991, 1999)] et les rendements d’échelle [Nerlove (1963)]. Nos résultats suggèrent que l’éducation de la mère expliquerait le décrochage de son fils, que l’output est une variable endogène dans l’estimation du coût de la firme et que le prix du fuel en est un instrument valide pour l’output. Le quatrième essai résout deux problèmes très importants dans la littérature économétrique. D’abord, bien que le test de Wald initial ou étendu permette de construire les régions de confiance et de tester les restrictions linéaires sur les covariances, il suppose que les paramètres du modèle sont identifiés. Lorsque l’identification est faible (instruments faiblement corrélés avec la variable à instrumenter), ce test n’est en général plus valide. Cet essai développe une procédure d’inférence robuste à l’identification (instruments faibles) qui permet de construire des régions de confiance pour la matrices de covariances entre les erreurs de la régression et les variables explicatives (possiblement endogènes). Nous fournissons les expressions analytiques des régions de confiance et caractérisons les conditions nécessaires et suffisantes sous lesquelles ils sont bornés. La procédure proposée demeure valide même pour de petits échantillons et elle est aussi asymptotiquement robuste à l’hétéroscédasticité et l’autocorrélation des erreurs. Ensuite, les résultats sont utilisés pour développer les tests d’exogénéité partielle robustes à l’identification. Les simulations Monte Carlo indiquent que ces tests contrôlent le niveau et ont de la puissance même si les instruments sont faibles. Ceci nous permet de proposer une procédure valide de sélection de variables instrumentales même s’il y a un problème d’identification. La procédure de sélection des instruments est basée sur deux nouveaux estimateurs pré-test qui combinent l’estimateur IV usuel et les estimateurs IV partiels. Nos simulations montrent que: (1) tout comme l’estimateur des moindres carrés ordinaires, les estimateurs IV partiels sont plus efficaces que l’estimateur IV usuel lorsque les instruments sont faibles et l’endogénéité modérée; (2) les estimateurs pré-test ont globalement une excellente performance comparés à l’estimateur IV usuel. Nous illustrons nos résultats théoriques par deux applications empiriques: la relation entre le taux d’ouverture et la croissance économique et le modèle de rendements à l’éducation. Dans la première application, les études antérieures ont conclu que les instruments n’étaient pas trop faibles [Dufour et Taamouti (2007)] alors qu’ils le sont fortement dans la seconde [Bound (1995), Doko et Dufour (2009)]. Conformément à nos résultats théoriques, nous trouvons les régions de confiance non bornées pour la covariance dans le cas où les instruments sont assez faibles.