Reveal E. coli 2.0 method for detection of Escherichia coli O157:H7 in raw beef

Reveal E. coli 2.0 is a new lateral-flow immunodiagnostic test for detection of E. coli O157:H7 and O157:NM in raw beef trim and ground beef. Compared with the original Reveal E. coli O157:H7 assay, the new test utilizes a unique antibody combination resulting in improved test specificity. The device architecture and test procedure have also been modified, and a single enrichment protocol was developed which allows the test to be performed at any point during an enrichment period of 12 to 20 h. Results of inclusivity and exclusivity testing showed that the test is specific for E. coli serotypes O157:H7 and O157:NM, with the exception of two strains of O157:H38 and one strain of O157:H43 which produced positive reactions. In internal and independent laboratory trials comparing the Reveal 2.0 method to the U.S. Department of Agriculture-Food Safety and Inspection Service reference culture procedure for detection of E. coli O157:H7 in 65 and 375 g raw beef trim and ground beef samples, there were no statistically significant differences in method performance with the exception of a single internal trial with 375 g ground beef samples in which the Reveal method produced significantly more positive results. There were no unconfirmed positive results by the Reveal assay, for specificity of 100%. Results of ruggedness testing showed that the Reveal test produces accurate results even with substantial deviation in sample volume or device incubation time or temperature. However, addition of the promoter reagent to the test sample prior to introducing the test device is essential to proper test performance.     

Quick prediction of the retention of solutes in 13 thin layer chromatographic screening systems on silica gel by classification and regression trees

The use of classification and regression trees (CART) was studied in a quantitative structure-retention relationship (QSRR) context to predict the retention in 13 thin layer chromatographic screening systems on a silica gel, where large datasets of interlaboratory determined retention are available. The response (dependent variable) was the rate mobility (RM) factor, while a set of atomic contributions and functional substituent counts was used as an explanatory dataset. The trees were investigated against optimal complexity (number of the leaves) by external validation and internal crossvalidation. Their predictive performance is slightly lower than full atomic contribution model, but the main advantage is the simplicity. The retention prediction with the proposed trees can be done without computer or even pocket calculator.     

Massenspektroskopie und ihre Anwendung auf strukturelle und stereochemische Probleme, 44. Mitt.: Das Fragmentierungsverhalten monocyclischer Ketone

Zusammenfassung Die Massenspektren einer Reihe monocyclischer Ketone sind gemessen worden. Durch Vergleich der Spektren mit denen deuterierter Derivate ist es möglich, plausible Mechanismen für die Bildung charakteristischer Fragmente abzuleiten.Mit 6 Abbildungen43. Mitt. sieheD. H. Williams, C. Beard, H. Budzikiewicz undC. Djerassi, J. Amer. chem. Soc., in Druck.Die Arbeit wurde mit finanzieller Unterstützung der National Institutes of Health, U.S. Public Health Service (Grant No. 5T4 CA5061 und AM-04257) durchgeführt.     

MetaTREE,a Novel Database Focused on Metabolic Trees,Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.     

Periodic calculations of excited state properties for solids using a semiempirical approach

The semiempirical SCF MO method MSINDO (modified symmetrically orthogonalized intermediate neglect of differential overlap) [T. Bredow and K. Jug, Electronic Encyclopedia of Computational Chemistry, 2004] is extended to the calculation of excited state properties through implementation of the configuration interaction singles (CIS) approach. MSINDO allows the calculation of periodic systems via the cyclic cluster model (CCM) [T. Bredow et al., J. Comput. Chem., 2001, 22, 89] which is a direct-space approach and therefore can be in principle combined with all molecular quantum-chemical techniques. The CIS equations are solved for a cluster with periodic boundary conditions using the Davidson-Liu iterative block diagonalization approach. As a proof-of-principle, MSINDO-CCM-CIS is applied for the calculation of optical spectra of ZnO and TiO(2), oxygen-defective rutile, and F-centers in NaCl. The calculated spectra are compared to available experimental and theoretical literature data. After re-adjustment of the empirical parameters the quantitative agreement with experiment is satisfactory. The present approximate approach is one of the first examples of a quantum-chemical methodology for solids where excited states are correctly described as n-electron state functions. After careful benchmark testing it will allow calculation of photophysical and photochemical processes relevant to materials science and catalysis.     

Predictive Modeling of Critical Temperatures in Superconducting Materials

In this study, we have investigated quantitative relationships between critical temperatures of superconductive inorganic materials and the basic physicochemical attributes of these materials (also called quantitative structure-property relationships). We demonstrated that one of the most recent studies (titled "A data-driven statistical model for predicting the critical temperature of a superconductor” and published in Computational Materials Science by K. Hamidieh in 2018) reports on models that were based on the dataset that contains 27% of duplicate entries. We aimed to deliver stable models for a properly cleaned dataset using the same modeling techniques (multiple linear regression, MLR, and gradient boosting decision trees, XGBoost). The predictive ability of our best XGBoost model (R2 = 0.924, RMSE = 9.336 using 10-fold cross-validation) is comparable to the XGBoost model by the author of the initial dataset (R2 = 0.920 and RMSE = 9.5 K in ten-fold cross-validation). At the same time, our best model is based on less sophisticated parameters, which allows one to make more accurate interpretations while maintaining a generalizable model. In particular, we found that the highest relative influence is attributed to variables that represent the thermal conductivity of materials. In addition to MLR and XGBoost, we explored the potential of other machine learning techniques (NN, neural networks and RF, random forests).     

Borrowing information from relevant microarray studies for sample classification using weighted partial least squares

With an increasing number of publicly available microarray datasets, it becomes attractive to borrow information from other relevant studies to have more reliable and powerful analysis of a given dataset. We do not assume that subjects in the current study and other relevant studies are drawn from the same population as assumed by meta-analysis. In particular, the set of parameters in the current study may be different from that of the other studies. We consider sample classification based on gene expression profiles in this context. We propose two new methods, a weighted partial least squares (WPLS) method and a weighted penalized partial least squares (WPPLS) method, to build a classifier by a combined use of multiple datasets. The methods can weight the individual datasets depending on their relevance to the current study. A more standard approach is first to build a classifier using each of the individual datasets, then to combine the outputs of the multiple classifiers using a weighted voting. Using two quite different datasets on human heart failure, we show first that WPLS/WPPLS, by borrowing information from the other dataset, can improve the performance of PLS/PPLS built on only a single dataset. Second, WPLS/WPPLS performs better than the standard approach of combining multiple classifiers. Third, WPPLS can improve over WPLS, just as PPLS does over PLS for a single dataset.     

Parallel processing of large datasets from NanoLC-FTICR-MS measurements

A new approach for automatic parallel processing of large mass spectral datasets in a distributed computing environment is demonstrated to significantly decrease the total processing time. The implementation of this novel approach is described and evaluated for large nanoLC-FTICR-MS datasets. The speed benefits are determined by the network speed and file transfer protocols only and allow almost real-time analysis of complex data (e.g., a 3-gigabyte raw dataset is fully processed within 5 min). Key advantages of this approach are not limited to the improved analysis speed, but also include the improved flexibility, reproducibility, and the possibility to share and reuse the pre- and postprocessing strategies. The storage of all raw data combined with the massively parallel processing approach described here allows the scientist to reprocess data with a different set of parameters (e.g., apodization, calibration, noise reduction), as is recommended by the proteomics community. This approach of parallel processing was developed in the Virtual Laboratory for e-Science (VL-e), a science portal that aims at allowing access to users outside the computer research community. As such, this strategy can be applied to all types of serially acquired large mass spectral datasets such as LC-MS, LC-MS/MS, and high-resolution imaging MS results.     

Survival analysis of microarray expression data by transformation models

Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816-824] and show that the transformation model provides higher prediction precision than the proportional hazards model.     

A classical dataset from Williams,and its role in the study of supersaturated designs

A Plackett‐Burman type dataset from a paper by Williams [1], with 28 observations and 24 two‐level factors, has become a standard dataset for illustrating construction (by halving) of supersaturated designs (SSDs) and for a corresponding data analysis. The aim here is to point out that for several reasons this is an unfortunate situation. The original paper by Williams contains several errors and misprints. Some are in the design matrix, which will here be reconstructed, but worse is an outlier in the response values, which can be observed when data are plotted against the dominating factor. In addition, the data should better be analysed on log‐scale than on original scale. The implications of the outlier for SSD analysis are drastic, and it will be concluded that the data should be used for this purpose only if the outlier is properly treated (omitted or modified). Copyright © 2008 John Wiley & Sons, Ltd.     

Mid-infrared spectroscopy for detection of Huanglongbing (greening) in citrus leaves

In recent years, Huanglongbing (HLB) also known as citrus greening has greatly affected citrus orchards in Florida. This disease has caused significant economic and production losses costing about $750/acre for HLB management. Early and accurate detection of HLB is a critical management step to control the spread of this disease. This work focuses on the application of mid-infrared spectroscopy for the detection of HLB in citrus leaves. Leaf samples of healthy, nutrient-deficient, and HLB-infected trees were processed in two ways (process-1 and process-2) and analyzed using a rugged, portable mid-infrared spectrometer. Spectral absorbance data from the range of 5.15-10.72 μm (1942-933 cm⁻¹) were preprocessed (baseline correction, negative offset correction, and removal of water absorbance band) and used for data analysis. The first and second derivatives were calculated using the Savitzky-Golay method. The preprocessed raw dataset, first derivatives dataset, and second derivatives dataset were first analyzed by principal component analysis. Then, the selected principal component scores were classified using two classification algorithms, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN). When the spectral data from leaf samples processed using process-1 were used for data analysis, the kNN-based algorithm yielded higher classification accuracies (especially nutrient-deficient leaf class) than that of the other spectral data (process-2). The performance of the kNN-based algorithm (higher than 95%) was better than the QDA-based algorithm. Moreover, among different types of datasets, preprocessed raw dataset resulted in higher classification accuracies than first and second derivatives datasets. The spectral peak in the region of 9.0-10.5 μm (952-1112 cm⁻¹) was found to be distinctly different between the healthy and HLB-infected leaf samples. This carbohydrate peak could be attributed to the starch accumulation in the HLB-infected citrus leaves. Thus, this study demonstrates the applicability of mid-infrared spectroscopy for HLB detection in citrus.     

Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics

Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.     

Books Section

Abstract

Colombo, A.G. (1992). Environmental Impact Assessment. Kluwer Academic Publishers, Dordrecht, p. 334 + xii pp., Hardcover, ISBN: 0-7923-1589-8, Dfl180.00; £61.00.

Martin, G. and Laffort, P. (1991). Odeurs et Désodorisation dans l'Environnement. (Odors and Desodorisation in the Environment). TEC & DOC – Lavoisier, Paris. p. 452 + xxiv pp., Hardcover, In French, ISBN: 2-85206-605-X, FF 565.00.

Yalkowsky, S. H. and Banerjee, S. (1992). Aqueous Solubility: Methods of Estimation for Organic Compounds. Marcel Dekker, Inc., New York. p. 264 + vi pp., Hardcover, ISBN: 0-8247-8615-7, US$99.75 (U.S. and Canada) US$114.50 (all other countries).     

来源于Alcaligenes A-6的D-氨基酰化酶基因的合成与融合表达

将来源于Alcaligenes A-6的D-氨基酰化酶基因用大肠杆菌中的丰沛密码子替换, 利用化学和基于聚合酶链反应(PCR)技术的酶促方法进行基因全合成, 利用pET-32a构建重组表达载体pET-dan, 转化进E.coil BL21(DE3)中进行融合表达. 经SDS-PAGE电泳、 Western-blot检测和活性测定发现, D-ANase可在大肠杆菌中高效表达, 目的蛋白可达到菌体总蛋白的69.2%, 密码子优化后基因构建的工程菌发酵活性为96 U/mL, 重组蛋白经超声细胞破碎及Ni²⁺柱亲和层析纯化, 比活可达1692.3 U/mg, 纯度可达95%以上.     

k‐Nearest neighbors optimization‐based outlier removal

Datasets of molecular compounds often contain outliers, that is, compounds which are different from the rest of the dataset. Outliers, while often interesting may affect data interpretation, model generation, and decisions making, and therefore, should be removed from the dataset prior to modeling efforts. Here, we describe a new method for the iterative identification and removal of outliers based on a k‐nearest neighbors optimization algorithm. We demonstrate for three different datasets that the removal of outliers using the new algorithm provides filtered datasets which are better than those provided by four alternative outlier removal procedures as well as by random compound removal in two important aspects: (1) they better maintain the diversity of the parent datasets; (2) they give rise to quantitative structure activity relationship (QSAR) models with much better prediction statistics. The new algorithm is, therefore, suitable for the pretreatment of datasets prior to QSAR modeling. © 2014 Wiley Periodicals, Inc.     

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.