Studies on Fluorescent Analysis of Two Ethnomedicinal Plants Duranta erecta L. and Phyla nodiflora (L.) Greene

The fluorescent studies on two ethnomedicinal plants belongs to family Verbenaceae , Duranta erecta L. and Phyla nodiflora (L.) Greene. The present study will assist in standardization for quality, purity and sample identification. The etnomedicinal plants were analysed using standard methods.. The fluorescence analysis for two ethno-medicinal plants were conducted by using the visible light and ultra violet at 254nm and 354 nm, reveals the various colouration ranges from sea weed green to berry blue coloured highlighted compared with the source , Lularoe chart. The present study concludes that the data obtained can be used to authenticate, classify and standardize the above four ethno-medicinal plants. Keywords: Ethnomedicinal plants, fluorescent study, UV wavelength.

K-Ar Geochronology Results for the Mount Pisgah Quadrangle, Utah

This Open-File Report makes available raw analytical data from laboratory procedures completed to determine the age of a rock sample collected during geologic investigations funded or partially supported by the Utah Geological Survey (UGS). Table 1 provides the sample identification and location for the age data. The references listed in table 1 generally provide additional information such as sample location, geologic setting, and significance or interpretation of the sample in the context of the area where it was collected. This report was prepared by Krueger Enterprises, Inc., Geochron Laboratories Division in 1995 under contract to the UGS. These data are highly technical in nature and proper interpretation requires considerable training in the applicable geochronologic techniques.

Comparative Analysis of Standardized NATO Requirements for Laboratory Identification of Chemical Agents and Military Technical Means for Its Implementation

Deployable chemical laboratories are considered a highly specific part of the Armed Forces of the Czech Republic, intended for Chemical, Biological, Radiological and Nuclear Defence in operations. Their professional activity is determined by a number of scientific and technical requirements, which are formulated by standards for sample identification. To achieve the required degree of credibility, it is particularly important to have specific technical capacities. This instrumentation is crucial for the implementation of laboratory analyzes. The article describes the state of chemical laboratories of the Chemical Corps in the context of standardized requirements and discusses some points of selected Alliance agreements which the Armed Forces of the Czech Republic have signed.

Accurate, Efficient and User-Friendly Mutation Calling and Sample Identification for TILLING Experiments

TILLING (Targeting Induced Local Lesions IN Genomes) is a powerful reverse genetics method in plant functional genomics and breeding to identify mutagenized individuals with improved behavior for a trait of interest. Pooled high throughput sequencing (HTS) of the targeted genes allows efficient identification and sample assignment of variants within genes of interest in hundreds of individuals. Although TILLING has been used successfully in different crops and even applied to natural populations, one of the main issues for a successful TILLING experiment is that most currently available bioinformatics tools for variant detection are not designed to identify mutations with low frequencies in pooled samples or to perform sample identification from variants identified in overlapping pools. Our research group maintains the Next Generation Sequencing Experience Platform (NGSEP), an open source solution for analysis of HTS data. In this manuscript, we present three novel components within NGSEP to facilitate the design and analysis of TILLING experiments: a pooled variants detector, a sample identifier from variants detected in overlapping pools and a simulator of TILLING experiments. A new implementation of the NGSEP calling model for variant detection allows accurate detection of low frequency mutations within pools. The samples identifier implements the process to triangulate the mutations called within overlapping pools in order to assign mutations to single individuals whenever possible. Finally, we developed a complete simulator of TILLING experiments to enable benchmarking of different tools and to facilitate the design of experimental alternatives varying the number of pools and individuals per pool. Simulation experiments based on genes from the common bean genome indicate that NGSEP provides similar accuracy and better efficiency than other tools to perform pooled variants detection. To the best of our knowledge, NGSEP is currently the only tool that generates individual assignments of the mutations discovered from the pooled data. We expect that this development will be of great use for different groups implementing TILLING as an alternative for plant breeding and even to research groups performing pooled sequencing for other applications.

DEPP: Deep Learning Enables Extending Species Trees using Single Genes

Identifying samples in an evolutionary context is a fundamental step in the study of microbiome, and more broadly, biodiversity. Extending a reference phylogeny by placing new query sequences onto it has been increasingly used for sample identification and other applications. Existing phylogenetic placement methods have assumed that the query sequence is homologous to the data used to infer the reference phylogeny. Thus, they are designed to place data from a single gene onto a gene tree (e.g., they can place 16S sequences onto a 16S gene tree). While this assumption is reasonable, ultimately, sample identification is a question of identifying the species not individual genes. The placement of single gene data on a gene tree is therefore used as a proxy for a more ambitious goal: extending a species tree given sequence data from one or more gene. This goal poses difficult algorithmic questions. Nevertheless, a sufficiently accurate solution would not only improve sample identification using marker genes, it would also help achieving the long-standing goal of combining 16S and metagenomic data. We approach this problem using deep neural networks (DNN) and introduce a method called DEPP. Given a reference species tree and sequence data from one (or a handful of) genes, DEPP learns how to extend the species tree to include new species. DEPP does not rely on pre-specified models of sequence evolution or gene tree discordance; instead, it uses highly parameterized DNNs to learn both aspects from the data. We test DEPP both in simulations and on real microbial data and show high accuracy.

Happy Photocatalysts and Unhappy Photocatalysts: Electron Trap-distribution Analysis for Metal Oxide-sample Identification

A strange story, including a new concept of identification of inorganic solid materials and of photocatalyst design, is told here. Why is it that solid materials have not been identified,...