Isolation and characterization of α ‐amylase encoding gene in Bacillus amyloliquefaciens PAS

Amylolytic bacteria are a source of amylase, which is an essential enzyme to support microalgae growth in the bioreactor for microalgae culture. In a previous study, the highest bacterial isolate to hydrolyze amylum (namely PAS) was successfully isolated from Ranu Pani, Indonesia, and it was identified as Bacillus amyloliquefaciens. That bacterial isolate (B. amyloliquefaciens PAS) also has been proven to accelerate Chlorella vulgaris growth in the mini bioreactor. This study aims to detect, isolate, and characterize the PAS’s α‐amylase encoding gene. This study was conducted with DNA extraction, amplification of α‐amylase gene with polymerase chain reaction (PCR) method with the specific primers, DNA sequencing, phylogenetic tree construction, and protein modeling. The result showed that α‐amylase was successfully detected in PAS bacterial isolate. The α‐amylase DNA fragment was obtained 1,468 bp and that translated sequence has an identity of about 98.3% compared to the B. amylolyquefaciens α‐amylase 3BH4 in the Protein Data Bank (PDB). The predicted 3D protein model of the PAS’s α‐amylase encoding gene has amino acid variations that predicted affect the protein’s structure in the small region. This research will be useful for further research to produce recombinant α‐amylase.

Minimum Query Set for Decision Tree Construction

A new two-stage method for the construction of a decision tree is developed. The first stage is based on the definition of a minimum query set, which is the smallest set of attribute-value pairs for which any two objects can be distinguished. To obtain this set, an appropriate linear programming model is proposed. The queries from this set are building blocks of the second stage in which we try to find an optimal decision tree using a genetic algorithm. In a series of experiments, we show that for some databases, our approach should be considered as an alternative method to classical ones (CART, C4.5) and other heuristic approaches in terms of classification quality.

Systematic reinstatement of highly sacred Ficus krishnae based on differences in morphology and DNA barcoding from Ficus benghalensis (Moraceae)

Ficus krishnae is considered as native to India and is well-known for the peculiarity in nature of its cup-shaped leaves where both the vernacular name (Krishna Fig) and specific epithet were derived. The taxonomic status of Ficus krishnae is still unclear and currently treated as a subspecies or variety under Ficus benghalensis. In the present study, morphological characters and molecular analysis were employed to address their species delimitation. The spacer markers ITS2 and trnH-psbA were used for constructing phylogenetic trees along with morphometric analysis. Ficus krishnae distinctly differs from Ficus benghalensis by having cup-forming leaves and the nature of the aerial roots, stipules, petioles, ostiolar bracts of the receptacle, DNA content, chromosome differences and nodal anatomy. The results showed that the highest divergence is observed in trnH-psbA (20.8 ± 12.2), followed by ITS2 (5.7 ± 3.2). The phylogenetic tree construction using Bayesian analysis showed a divergent boundary between the two species suggesting that F. krishnae could be an independent species, not a variety of F. benghalensis. The present study’s findings support the view that these two floras can be treated as different species.

Multicast tree construction algorithm for dynamic traffic on software defined networks

Dynamic traffic of multicast communication in the Software Defined Network environment focused less though it is more natural and practical. In multicast communication, the traffic is dynamic due to the dynamic group memberships (i.e., participants join and leave the group anytime), which are not explored much in the previous research works. The multicast in dynamic traffic requires a method to handle dynamic group membership and minimum tree alteration for every join and leave of participants from the multicast group. This paper proposes a multicast tree construction algorithm, which considers receiving devices and network capability as base parameters to construct the multicast path. The proposed routing method uses Dijkstra’s Shortest Path algorithm for initial tree formation, identifies a multicast path, and processes the Shortest Path Tree to reduce the overall hop count and path cost. The multicast tree generated by the proposed enables the dynamic join and leaves of participating devices with reduced tree alteration using more common paths to reach the devices. The implementation and results show that the proposed method works efficiently in resource utilization with a reduced hop count and quality for multicast communication in static and dynamic scenarios. Also, the results demonstrate that the proposed method generates a stable common path for multicast communication.

Molecular Diversity of Bacillus thuringiensis and Bioinformatics Analysis of Local Isolate of Auky Island, Padaido District in Biak Numfor Papua as a Control of Anopheles Mosquito Larvae

This research aims to analyze the level of similarity and diversity among local isolates of B. thuringiensis Auky Island Padaido District in Biak Numfor Regency with NCBI gene bank base, the basis of which is to obtain B. thuringiensis isolates from jayapura local isolates that can act as controllers of Anopheles mosquito larvae. Several steps in the research are 16s gene amplification, PCR product purification, cloning using pTA2 vectors and transformation into competent E. coli Zymo 5α cells, confirmation with PCR colonies, recombinant plasmid isolation, sequencing analysis and phylogenetic tree construction. The isolates of ABNP8, ABNP9, ABNP11, ABNP12 and ABNP18 have been detected as local isolates from in Auky Island Padaido District in Biak Numfor Papua Regency that have great potential as bioinsecticides, and capable of controlling and killing Anopheles mosquito larvae. Of the five isolates, ABNP8 isolates had unique diversity and characteristics and were different from the four other isolates. Based on the similarity analysis in the MEGA7 program, the similarity rate reached 84%. Its diversity can be seen from the uniqueness of the sequence and its position in different branching dendrograms.

Molecular Barcoding of Melissa Officinalis L. (Badranjboye) in Iran and Identification of Adulteration in Its Medicinal Services

Medication plants are an important source of disease treatment in many countries. Today, quality control of the products of medicinal plants is a major task. Customer health may be at risk due to fraud and misconduct in the sales associates ' sales centres. Melissa officinalis (Badranjboye) is an important medicinal plant in Iran used for several diseases. In Iran, the species of Dracocephalum, Hymencrater, Nepeta and Stachys are mistakenly sold under the name of badranjboye in the selling centers of medicinal plants that have different pharmaceuticals properties. To avoid this mistake, we will follow the following goals in this research: 1 - Check the cheating and identification of badranjboye in the Iran market of medicinal plants and 2 - Provision of molecular barcode for the medicinal species of Melissa officinalis. We compared the plant samples sold (leaf) and reference species with morphological properties, odor, and molecular sequences and performed various molecular analyzes, such as sequencing, genetic distance determination, and phylogenetic tree construction. The reports indicated that internal transcribed spacer (ITS) and psbA–trnH intergenic spacer (psbA–trnH) sequences are an efficient molecular marker to produce barcode gap and differentiating Melissa officinalis from other species.

Re-assessing the phylogenetic status and evolutionary relationship of Forest Owlet [Athene blewitti (Hume 1873)] using genomic data

ABSTRACTPhylogenetic relationships are often challenging to resolve in recent/younger lineage when only a few loci are used. Ultra Conserved Elements (UCE) are highly conserved regions across taxa that help resolve shallow and deep divergences. We utilized UCEs harvested from whole genomes to assess the phylogenetic position and taxonomic affiliation of an endangered endemic owlet in the family Strigidae – the Forest Owlet Athene blewitti. The taxonomic placement of this species has been revised multiple times. A multigene study attempted to address the question but showed a discrepancy across datasets in its placement of the species within genus Athene. We assembled a dataset of 5018 nuclear UCE loci with increased taxon sampling. Forest Owlet was found to be an early split from the Athene clade but sister to other Athene; and consistent across three approaches - maximum likelihood, bayesian, and the multispecies coalescence. Divergence dating using fossil calibrations suggest that the Athene lineage split from its ancestor about 7.6Mya, and the Forest Owlet diverged about 5.2Mya, consistent with previous multigene approaches. Despite osteological differences from other Athene, we suggest the placement of the Forest Owlet as a member of the Athene to emphasize its evolutionary relationship.Graphical AbstractHIGHLIGHTSPhylogenomics using genome-wide nuclear markers yielded a well-supported topology for Athene and Glaucidium lineages.Three different methods of phylogenetic tree construction showed that Forest Owlet is an early split from all other Athene species.Divergence dating in the bayesian framework puts the Forest Owlet age between 5.0my to 5.5my.

PROBLEM OF A DISCRETE DATA ARRAY APPROXIMATION BY A SET OF ELEMENTARY GEOMETRIC ALGORITHMS

Context. In this paper, a problem of a discrete data array approximation by a set of elementary geometric algorithms and a recognition model representation in a form of algorithmic classification tree has been solved. The object of the present study is a concept of a classification tree in a form of an algorithm trees. The subject of this study are the relevant models, methods, algorithms and schemes of different classification tree construction. Objective. The goal of this work is to create a simple and efficient method and algorithmic scheme of building the tree-like recognition and classification models on the basis of the algorithm trees for training selections of large-volume discrete information characterized by a modular structure of independent recognition algorithms assessed in accordance with the initial training selection data for a wide class of applied tasks. Method. A scheme of classification tree (algorithm tree) synthesis has been suggested being based on the data array approximation by a set of elementary geometric algorithms that constructs a tree-like structure (the ACT model) for a preset initial training selection of arbitrary size. The latter consists of a set of autonomous classification/recognition algorithms assessed at each step of the ACT construction according to the initial selection. A method of the algorithmic classification tree construction has been developed with the basic idea of step-by-step arbitrary-volume and structure initial selection approximation by a set of elementary geometric classification algorithms. When forming a current algorithm tree vertex, node and generalized attribute, this method provides alignment of the most effective and high-quality elementary classification algorithms from the initial set and complete construction of only those paths in the ACT structure, where the most of classification errors occur. The scheme of synthesizing the resulting classification tree and the ACT model developed allows one to reduce considerably the tree size and complexity. The ACT construction structural complexity is being assessed on the basis of a number of transitions, vertices and tiers of the ACT structure that allows the quality of its further analysis to be increased, the efficient decomposition mechanism to be provided and the ACT structure to be built in conditions of fixed limitation sets. The algorithm tree synthesis method allows one to construct different-type tree-like recognition models with various sets of elementary classifiers at the preset accuracy for a wide class of artificial intelligence theory problems. Results. The method of discrete training selection approximation by a set of elementary geometric algorithms developed and presented in this work has received program realization and was studied and compared with those of logical tree classification on the basis of elementary attribute selection for solving the real geological data recognition problem. Conclusions. Both general analysis and experiments carried out in this work confirmed capability of developed mechanism of constructing the algorithm tree structures and demonstrate possibility of its promising use for solving a wide spectrum of applied recognition and classification problems. The outlooks of the further studies and approbations might be related to creating the othertype algorithmic classification tree methods with other initial sets of elementary classifiers, optimizing its program realizations, as well experimental studying this method for a wider circle of applied problems.

Research on Online Scene Teaching Mode of Tobacco Picking Decision Tree Construction Process Integrating Deep Learning

Objectives: In view of the characteristics of online teaching during the coronavirus pandemic and the importance of practical teaching in training students’ skills in the process of graduate education, this paper proposes an online scene teaching mode that takes projects as the carrier and integrates with deep learning. In order to meet the demand for information and communication engineering professionals in the big data context, the whole teaching process is divided into four stages: Topic selection, Teaching project setting, online teaching interaction and teaching evaluation. In the teaching process of Python Data Analysis Foundations, the project “establishment process of tobacco picking decision tree based on information gain” is taken as the teaching case. Prior knowledge and references are pushed through the cloud platform before class, and The scene of tobacco picking affected by the weather is set in the online classroom to guide students to seek solutions to problems, and the results are presented with graphics to assist students to summarize, and then reset the scene to promote knowledge transfer, so as to integrate deep learning into the teaching process, and modify the corresponding stages according to the teaching evaluation results. The content of the scene is gradually increased from easy to difficult, from simple to complex, and from least to most, gradually increasing the difficulty, which enhances students’ learning interest and sense of achievement. Meanwhile, students’ initiative to participate in curriculum research further strengthens the effectiveness of the course in serving scientific research, which has a certain value of popularization and application.