Genomic Signal Processing Techniques for Taxonomy Prediction

To analyze complex biodiversity in microbial communities, 16S rRNA marker gene sequences are often assigned to operational taxonomic units (OTUs). The abundance of methods that have been used to assign 16S rRNA marker gene sequences into OTUs brings discussions in which one is better. Suggestions on having clustering methods should be stable in which generated OTU assignments do not change as additional sequences are added to the dataset is contradicting some other researches contend that the methods should properly present the distances of sequences is more important. We add one more de novo clustering algorithm, Rolling Snowball to existing ones including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. We use GreenGenes, RDP, and SILVA 16S rRNA gene databases to show the success of the method. The highest accuracy is obtained with SILVA library

Hypervariable Regions in 16S rRNA Genes for the Taxonomic Classification

16S ribosomal RNA (rRNA) gene sequences are reliable markers for the taxonomic classification of microbes and widely used in environmental microbiology. Production of 16S rRNA gene amplicons in large amounts, encompassing the full length of genes is not yet feasible, because of the limitations of the current sequencing techniques. They are mostly in short reads of length less than 300 base pairs. Hence, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is a current research area. It is found that nine hypervariable regions (V1–V9), resides in bacterial 16S ribosomal RNA (rRNA) genes. Family, genus, and species-specific sequences within a given hypervariable region constitute useful targets for diagnostic assays and other scientific investigations. In this study systematic studies that compare the relative advantage of hypervariable regions grouped as V1–V2–V3, V4–V5–V6, and V7–V8–V9 for specific diagnostic goals are done. In the present research, the built in function Longest–Common–Subsequence in computer algebra package MATHEMATICA is used to create an in silico pipeline to evaluate the taxonomic classification sensitivity of the hypervariable regions compared with the corresponding full-length sequences. Conclusions: Our results suggest that V4–V5–V6 region might be an optimal sub-region for the design of universal primers with superior phylogenetic resolution for bacterial phyla

A De Novo Clustering Method: Snowball for Assigning 16S Operational Taxonomic Units

To analyze complex biodiversity in microbial communities, 16S rRNA marker gene sequences are often assigned to operational taxonomic units (OTUs). The abundance of methods that have been used to assign 16S rRNA marker gene sequences into OTUs brings discussions in which one is better. Suggestions on having clustering methods should be stable in which generated OTU assignments do not change as additional sequences are added to the dataset is contradicting some other researches contend that the methods should properly present the distances of sequences is more important. We add one more de novo clustering algorithm, Rolling Snowball to existing ones including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. We use GreenGenes, RDP, and SILVA 16S rRNA gene databases to show the success of the method. The highest accuracy is obtained with SILVA library

Parallel computing for artificial neural network training

The big-data is an oil of this century. A high amount of computational power is required to get knowledge from data. Parallel and distributed computing is essential to processing a large amount of data. Artificial Neural Networks (ANNs) need as much as possible data to have high accuracy, whereas parallel processing can help us to save time in ANNs training. In this paper, we have implemented exemplary parallelization of neural network training by dint of Java and its native socket libraries. During the experiments, we have noticed that Java implementation tends to have memory issues when a large amount of training data sets are involved in training. We have remarked that exemplary parallelization of a neural network training will not outperform drastically when additional nodes are introduced into the system after a certain point. This is widely due to network communication complexity in the system

Leaf Area Assessment By Image Analysis

A measurement of leaf area is apparently simple and fundamental.  Various methods are available to measure leaf area. Available methods are time consuming, humdrum and laborious. Less expensive methods involving image processing based on video camera images and computer programs for analysis of these images have been alternative for all other techniques for leaf area assessment.  In this paper we introduce a computer program that can calculate leaf area meter based on the pixel count in very short time and highly accurate by image processing. This program provides very fast, inexpensive and highly accurate measurement

6-Bromo-3-{2-[2-(diphenyl­methyl­ene)hydrazin­yl]-1,3-thia­zol-5-yl}-2H-chromen-2-one chloro­form monosolvate

In the title compound, C25H16BrN3O2S·CHCl3, the thia­zole ring is approximately planar [maximum deviation = 0.002 (3) Å] and makes dihedral angles of 10.75 (14) and 87.75 (15)/2.80 (14)° with the pyran ring system and the two terminal phenyl rings, respectively. The solvent mol­ecule is disordered over two sets of sites, with refined occupancies of 0.639 (7) and 0.361 (7). In the crystal, mol­ecules are connected via pairs of weak C—H⋯O inter­actions, forming centrosymmetric dimers. An intra­molecular C—H⋯O hydrogen bond generates an S(6) ring motif

3-{2-[2-(2-Fluoro­benzyl­idene)hydrazin­yl]-1,3-thia­zol-4-yl}-2H-chromen-2-one

In the title compound, C19H12FN3O2S, the chromene ring system and the thia­zole ring are approximately planar [maximum deviations of 0.023 (3) Å and 0.004 (2) Å, respectively]. The chromene ring system is inclined at angles of 4.78 (10) and 26.51 (10)° with respect to the thia­zole and benzene rings, respectively, while the thia­zole ring makes a dihedral angle of 23.07 (12)° with the benzene ring. The mol­ecular structure is stabilized by an intra­molecular C—H⋯O hydrogen bond, which generates an S(6) ring motif. The crystal packing is consolidated by inter­molecular N—H⋯O hydrogen bonds, which link the mol­ecules into chains parallel to [100], and by C—H⋯π and π–π [centroid–centroid distance = 3.4954 (15) Å] stacking inter­actions

3-{2-[2-(3-Hy­droxy­benzyl­idene)hydrazin-1-yl]-1,3-thia­zol-4-yl}-2H-chromen-2-one hemihydrate

In the title compound, C19H13N3O3S·0.5H2O, both organic mol­ecules (A and B) exist in E configurations with respect to the acyclic C=N bond and have similar overall conformations. In mol­ecule A, the essentially planar thia­zole ring [maximum deviation = 0.010 (2) Å] is inclined at inter­planar angles of 11.44 (10) and 32.50 (12)°, with the 2H-chromene ring system and the benzene ring, respectively. The equivalent values for mol­ecule B are 0.002 (2) Å, 7.71 (9) and 12.51 (12)°. In the crystal structure, neighbouring mol­ecules are inter­connected into infinite layers lying parallel to (010) by O—H⋯O, O—H⋯N, N—H⋯O and C—H⋯O hydrogen bonds. Further stabilization of the crystal structure is provided by weak inter­molecular C—H⋯π and π–π [centroid–centroid distance = 3.6380 (19) Å] inter­actions

Analyzing the effectiveness of logistics networks during the immediate response phase of three different natural disasters

MBA Professional ReportEvery year, natural disasters affect millions of people around the world. Natural disasters are an unavoidable part of our lives, but effective disaster management increases the number of survivors and helps the victims. After disaster hits, the most important job is enabling an effective response operation. This operation involves many logistics activities and some special demand for relief goods. Today, supply chain management increases the effectiveness of logistics activities for many companies. The same thinking and modeling may help increase the effectiveness of response operations. An important milestone for this achievement is to be able to evaluate the performance of response operations in system thinking. In this project, we introduced three theoretical performance metrics: demand and supply equilibrium, transportation utilization, and information sharing, which help analyze the performance of overall response operations. We chose three different types of natural disasters: Hurricane Katrina, the 2004 Asian Tsunami, and the 2010 Haiti Earthquake to show the usefulness and applicability of these metrics. Unavailability of data associated with logistics operations made a thorough analysis impossible, but we assessed each disaster according to our metrics. The last part of this project focuses on the managerial implications of response operations considering these three metrics.http://archive.org/details/analyzingeffecti109451049

Artificial Neural Networks in Bacteria Taxonomic Classification

In 1980s, the face of the microbiology dramatically changed with the rRNA-based phylogenetic classifications, by Carl Woese. He delineated the three main branches of life. He used the technique not only to explore microbial diversity but also as a method for bacterial annotation. Today, rRNA-based analysis remains a central method in microbiology. Many researchers followed this track, using several new generations of Artificial Neural Networks they obtained high accuracies using available datasets of their time. Recently the number of known bacteria increased enormously. In this article we used ANN's to annotate bacterial 16S rRNA gene sequences from five selected phylums in Greengenes database taxonomy: Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, and Chloroflexi. 93% average accuracy is obtained in classif-ications. When we used the bundle testing technique, the average accuracy easily raised to 100%