scholarly journals Genetic Variability, Population Structure, and Relatedness Analysis of Meagre Stocks as an Informative Basis for New Breeding Schemes

Fishes ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 78
Author(s):  
Orestis Nousias ◽  
Konstantinos Tzokas ◽  
Leonidas Papaharisis ◽  
Katerina Ekonomaki ◽  
Dimitrios Chatziplis ◽  
...  
Keyword(s):  
Genetic Data ◽  
Population Parameters ◽  
Genetic Population ◽  
Data Set ◽  
Overall Evaluation ◽  
Relatedness Analysis ◽  
Breeding Schemes ◽  
Established Population ◽  
Selection Of ◽  
Microsatellite Analyses

This study evaluates the genetic diversity of different meagre broodstocks sampled in Greece. A multiplex of twelve microsatellite markers was used to genotype 946 fish from eleven stocks and batches used for broodstock selection, and the genetic data was used to calculate genetic population parameters as well as to investigate the genetic differentiation between stocks. The results from a relatedness analysis were used as the guiding lines for a fine-tuned and overall evaluation of the genetic distance between stocks, and the choice of candidate breeders from some of them. The approach implemented in this study uses well-established population genetics methods to evaluate the selection of breeder candidates in aquaculture commercial conditions utilizing a descriptive genetic data set based on microsatellite analyses, and to outline an efficient methodology for establishing the basis of new breeding schemes.

scholarly journals Habitat conditions drive phylogenetic structure of dominant bacterial phyla of microbialite communities from different locations in Mexico

2016 ◽  
Vol 64 (3) ◽  
Author(s):  
Carla M. Centeno ◽  
Omar Mejía ◽  
Luisa I. Falcón
Keyword(s):  
Species Abundance ◽  
Genetic Data ◽  
Main Process ◽  
Phylogenetic Structure ◽  
Data Set ◽  
Bacterial Phyla ◽  
Hypervariable Regions ◽  
Complete Dataset ◽  
Random Events ◽  
Relatedness Analysis

Community structure and composition are dictated by evolutionary and ecological assembly processes which are manifested in signals of, species diversity, species abundance and species relatedness. Analysis of species coexisting relatedness, has received attention as a tool to identify the processes that influence the composition of a community within a particular habitat. In this study, we tested if microbialite genetic composition is dependent on random events versus biological/abiotical factors. This study was based on a large genetic data set of two hypervariable regions (V5 and V6) from previously generated barcoded 16S rRNA amplicons from nine microbialite communities distributed in Northeastern, Central and Southeastern Mexico collected in May and June of 2009. Genetic data of the most abundant phyla (Proteobacteria, Planctomycetes, Verrucomicrobia, Bacteroidetes, and Cyanobacteria) were investigated in order to state the phylogenetic structure of the complete communities as well as each phylum. For the complete dataset, Webb NTI index showed positive and significant values in the nine communities analysed, where values ranged from 31.5 in Pozas Azules I to 57.2 in Bacalar Pirate Channel; meanwhile, NRI index were positive and significant in six of the nine communities analysed with values ranging from 18.1 in Pozas Azules I to 45.1 in Río Mesquites. On the other hand, when comparing each individual phylum, NTI index were positive and significant in all groups, except in Cyanobacteria for which positive and significant values were only found in three localities; finally, NRI index was significant in only a few of the comparisons performed. The results suggest that habitat filtering is the main process that drives phylogenetic structure in bacterial communities associated to microbialites with the exception of Cyanobacteria where different lineages can contribute to microbialite formation and growth.

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

scholarly journals Data Set for the Reporting of Malignant Odontogenic Tumors: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting

2018 ◽  
Vol 143 (5) ◽  
pp. 587-592 ◽  
Author(s):  
Pieter J. Slootweg ◽  
Edward W. Odell ◽  
Daniel Baumhoer ◽  
Roman Carlos ◽  
Keith D. Hunter ◽  
...  
Keyword(s):  
International Collaboration ◽  
Case Series ◽  
Initial Step ◽  
Odontogenic Tumors ◽  
Data Set ◽  
Expert Opinions ◽  
Organ Systems ◽  
Standardized Reporting ◽  
Selection Of ◽  
Cancer Reporting

A data set has been developed for the reporting of excisional biopsies and resection specimens for malignant odontogenic tumors by members of an expert panel working on behalf of the International Collaboration on Cancer Reporting, an international organization established to unify and standardize reporting of cancers. Odontogenic tumors are rare, which limits evidence-based support for designing a scientifically sound data set for reporting them. Thus, the selection of reportable elements within the data set and considering them as either core or noncore is principally based on evidence from malignancies affecting other organ systems, limited case series, expert opinions, and/or anecdotal reports. Nevertheless, this data set serves as the initial step toward standardized reporting on malignant odontogenic tumors that should evolve over time as more evidence becomes available and functions as a prompt for further research to provide such evidence.

scholarly journals Population parameters and selection of kale genotypes using Bayesian inference in a multi-trait linear model

2017 ◽  
Vol 39 (1) ◽  
pp. 25
Author(s):  
Alcinei Mistico Azevedo ◽  
Valter Carvalho de Andrade júnior ◽  
Albertir Aparecido dos Santos ◽  
Aderbal Soares de Sousa Júnior ◽  
Altino Júnior Mendes Oliveira ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Linear Model ◽  
Population Parameters ◽  
Selection Of

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

scholarly journals Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 545-559 ◽  
Author(s):  
Mirjana Jankulovska ◽  
Sonja Ivanovska ◽  
Ana Marjanovic-Jeromela ◽  
Snjezana Bolaric ◽  
Ljupcho Jankuloski ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Vegetative Growth ◽  
Quantitative Traits ◽  
Principal Component ◽  
Breeding Program ◽  
Oil Yield ◽  
Growth Duration ◽  
Data Set ◽  
Combined Application ◽  
Selection Of

In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP). NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes? clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods can assist in deciding how, and based on which traits to select the genotypes, especially in early generations, at the beginning of a breeding program.

scholarly journals Automatic Benchmark Generation Framework for Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Guanghui Liang ◽  
Jianmin Pang ◽  
Zheng Shan ◽  
Runqing Yang ◽  
Yihang Chen
Keyword(s):  
Initial Data ◽  
Malware Detection ◽  
Detection Methods ◽  
Small Data ◽  
Security Threats ◽  
Improved Genetic Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Manual Selection ◽  
Selection Of

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.

Enhanced Early Warning Diagnostic Rules for Gas Turbines Leveraging on Bayesian Networks

2020 ◽  
Author(s):  
Ernesto Escobedo ◽  
Liliana Arguello ◽  
Marzia Sepe ◽  
Ilaria Parrella ◽  
Stefano Cioncolini ◽  
...  
Keyword(s):  
Gas Turbines ◽  
Probabilistic Approach ◽  
Training Dataset ◽  
Auxiliary Equipment ◽  
Data Set ◽  
Deep Dive ◽  
Industrial Systems ◽  
Business Application ◽  
Model Training ◽  
Selection Of

Abstract The monitoring and diagnostics of Industrial systems is increasing in complexity with larger volume of data collected and with many methods and analytics able to correlate data and events. The setup and training of these methods and analytics are one of the impacting factors in the selection of the most appropriate solution to provide an efficient and effective service, that requires the selection of the most suitable data set for training of models with consequent need of time and knowledge. The study and the related experiences proposed in this paper describe a methodology for tracking features, detecting outliers and derive, in a probabilistic way, diagnostic thresholds to be applied by means of hierarchical models that simplify or remove the selection of the proper training dataset by a subject matter expert at any deployment. This method applies to Industrial systems employing a large number of similar machines connected to a remote data center, with the purpose to alert one or more operators when a feature exceeds the healthy distribution. Some relevant use cases are presented for an aeroderivative gas turbine covering also its auxiliary equipment, with deep dive on the hydraulic starting system. The results, in terms of early anomaly detection and reduced model training effort, are compared with traditional monitoring approaches like fixed threshold. Moreover, this study explains the advantages of this probabilistic approach in a business application like the fleet monitoring and diagnostic advanced services.

An Effective AQI Estimation Using Sensor Data and Stacking Mechanism

2021 ◽  
Author(s):  
Dat Q. Duong ◽  
Quang M. Le ◽  
Tan-Loc Nguyen-Tai ◽  
Hien D. Nguyen ◽  
Minh-Son Dao ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Research Topic ◽  
Sensor Data ◽  
Weather Data ◽  
Quality Estimation ◽  
Data Set ◽  
Adverse Health Effects ◽  
Overall Performance ◽  
Selection Of

Accurately assessing the air quality index (AQI) values and levels has become an attractive research topic during the last decades. It is a crucial aspect when studying the possible adverse health effects associated with current air quality conditions. This paper aims to utilize machine learning and an appropriate selection of attributes for the air quality estimation problem using various features, including sensor data (humidity, temperature), timestamp features, location features, and public weather data. We evaluated the performance of different learning models and features to study the problem using the data set “MNR-HCM II”. The experimental results show that adopting TLPW features with Stacking generalization yields higher overall performance than other techniques and features in RMSE, accuracy, and F1-score.

Subverting the Organizational Cartel: Explaining Cross-Party Leadership Selection in U.S. State Houses

2019 ◽  
Vol 48 (4) ◽  
pp. 475-483
Author(s):  
Matthew N. Green
Keyword(s):  
House Of Representatives ◽  
State Legislatures ◽  
State Level ◽  
Original Data ◽  
Data Set ◽  
Leadership Selection ◽  
Majority Party ◽  
Party Ideology ◽  
Level I ◽  
Selection Of

In the U.S. House of Representatives, the majority party constitutes an organizational cartel that monopolizes the selection of chamber leaders. But in state legislatures, that cartel power is sometimes circumvented by a bipartisan bloc that outvotes the leadership preferences of a majority of the majority party. Drawing from an original data set of instances of cross-party organizational coalitions at the state level, I use statistical analysis to test various hypotheses for when these coalitions are more likely to form. The analysis reveals that party ideology does not adequately explain the violation of these cartels; rather, violations depend on the costs associated with keeping the party unified and the benefits that come from selecting the chamber’s top leadership post. This finding underscores the potential vulnerability of organizational cartels and suggests that governing parties are strategic when deciding how fiercely to defend their cartel power.

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