scholarly journals Genetic diversity of Norway spruce ecotypes assessed by GBS-derived SNPs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiří Korecký ◽  
Jaroslav Čepl ◽  
Jan Stejskal ◽  
Zuzana Faltinová ◽  
Jakub Dvořák ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Genotyping By Sequencing ◽  
High Elevation ◽  
Climate Conditions ◽  
Forest Reproductive Material ◽  
Reproductive Material ◽  
Harsh Climate ◽  
Distinct Genetic Cluster

AbstractWe investigated the genetic structure of three phenotypically distinct ecotypic groups of Norway spruce (Picea abies) belonging to three elevational classes; namely, low- (acuminata), medium- (europaea), and high-elevation (obovata) form, each represented by 150 trees. After rigorous filtering, we used 1916 Genotyping-by-Sequencing generated SNPs for analysis. Outputs from three multivariate analysis methods (Bayesian clustering algorithm implemented in STRUCTURE, Principal Component Analysis, and the Discriminant Analysis of Principal Components) indicated the presence of a distinct genetic cluster representing the high-elevation ecotypic group. Our findings bring a vital message to forestry practice affirming that artificial transfer of forest reproductive material, especially for stands under harsh climate conditions, should be considered with caution.

Lichens of the Oasis Molodyozhnyi and adjacent areas (Enderby Land, Antarctic)

2013 ◽  
Vol 47 ◽  
pp. 167-178 ◽  
Author(s):  
M. P. Andreev
Keyword(s):  
Liquid Water ◽  
Climate Conditions ◽  
Water Ice ◽  
Buellia Frigida ◽  
Antarctic Continent ◽  
Harsh Climate ◽  
The Antarctic ◽  
First Time ◽  
Lichen Flora

Lichen flora and vegetation in the vicinity of the Russian base «Molodyozhnaya» (Enderby Land, Antarctica) were investigated in 2010–2011 in details for the first time. About 500 specimens were collected in 100 localities in all available ecotopes. The lichen flora is the richest in the region and numbers 39 species (21 genera, 11 families). The studied vegetation is very poor and sparse, but typical for coastal oases of the Antarctic continent. The poorness is caused by the extremely harsh climate conditions, insufficient availability of liquid water, ice-free land, and high insolation levels. The dominant and most common lichens are Rinodina olivaceobrunnea, Amandinea punctata, Candelariella flava, Physcia caesia, Caloplaca tominii, Lecanora expectans, Caloplaca ammiospila, Lecidea cancriformis, Pseudephebe minuscula, Lecidella siplei, Umbilicaria decussata, Buellia frigida, Lecanora fuscobrunnea, Usnea sphacelata, Lepraria and Buellia spp.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Hybridisation of Malus sylvestris (L.) Mill. with Malus × domestica Borkh. and Implications for the Production of Forest Reproductive Material

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 367
Author(s):  
Mateja Kišek ◽  
Kristjan Jarni ◽  
Robert Brus
Keyword(s):  
Malus Domestica ◽  
Natural Populations ◽  
Morphological Characteristics ◽  
Apple Trees ◽  
Genetic Characteristics ◽  
Malus Domestica Borkh ◽  
Malus Sylvestris ◽  
Forest Reproductive Material ◽  
Reproductive Material ◽  
Crab Apple

This study focuses on the morphological and genetic characteristics of European crab apple (Malus sylvestris (L.) Mill.) and the occurrence of hybrids in its populations. We analyzed a total of 107 putative European crab apple trees in Slovenia: 92 from nine natural populations, five from a seed stand and 10 from a stand of unnatural origin. We also included 18 domesticated apple trees (Malus × domestica Borkh.) and two Japanese flowering crab apple trees (Malusfloribunda van Houtte) as outliers. The trees were classified into groups of European crab apples, hybrids and domesticated apples according to their morphological and genetic characteristics. Classification based on morphological traits produced different results (58.75% European crab apple, 37.11% hybrids and 4.14% domesticated apple) compared to those based on genetic analysis (70.10% European crab apple, 21.64% hybrids and 8.26% domesticated apple). When genetic and morphological characteristics were combined, only 40.20% of the trees were classified as European crab apple, and an additional group of feral cultivars of domesticated apples (6.18%) was identified. The analysis revealed that hybridization with domesticated apple is taking place in all studied natural European crab apple populations; however, hybrids and feral cultivars only occur to a limited extent. When introducing European crab apple into forests in the future, only genetically verified forest reproductive material obtained exclusively from suitable seed stands should be used.

Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

2021 ◽  
pp. 1-12
Author(s):  
Li Qian
Keyword(s):  
Big Data ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Data Classification ◽  
Fisher Discriminant Analysis ◽  
Fuzzy Clustering Algorithm ◽  
Local Fisher Discriminant Analysis ◽  
Big Data Classification

In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

An Accurate, Robust and Low Dimensionality Deep Learning Localization Approach in DM-MIMO Systems Based on RSS

2021 ◽  
Author(s):  
Seyedeh Samira Moosavi ◽  
Paul Fortier
Keyword(s):  
Massive Mimo ◽  
Clustering Algorithm ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Principal Component ◽  
Base Station ◽  
Location Estimation ◽  
Similar Data ◽  
High Coverage ◽  
Single Antenna

Abstract Currently, localization in distributed massive MIMO (DM-MIMO) systems based on the fingerprinting (FP) approach has attracted great interest. However, this method suffers from severe multipath and signal degradation such that its accuracy is deteriorated in complex propagation environments, which results in variable received signal strength (RSS). Therefore, providing robust and accurate localization is the goal of this work. In this paper, we propose an FP-based approach to improve the accuracy of localization by reducing the noise and the dimensions of the RSS data. In the proposed approach, the fingerprints rely solely on the RSS from the single-antenna MT collected at each of the receive antenna elements of the massive MIMO base station. After creating a radio map, principal component analysis (PCA) is performed to reduce the noise and redundancy. PCA reduces the data dimension which leads to the selection of the appropriate antennas and reduces complexity. A clustering algorithm based on K-means and affinity propagation clustering (APC) is employed to divide the whole area into several regions which improves positioning precision and reduces complexity and latency. Finally, in order to have high precise localization estimation, all similar data in each cluster are modeled using a well-designed deep neural network (DNN) regression. Simulation results show that the proposed scheme improves positioning accuracy significantly. This approach has high coverage and improves average root-mean-squared error (RMSE) performance to a few meters, which is expected in 5G and beyond networks. Consequently, it also proves the superiority of the proposed method over the previous location estimation schemes.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

scholarly journals UAV AND GIS BASED TOOL FOR COLLECTION AND PROPAGATION OF SEEDS MATERIAL – FIRST RESULTS

2016 ◽  
Vol XLI-B8 ◽  
pp. 663-667 ◽  
Author(s):  
K. Stereńczak ◽  
P. Mroczek ◽  
S. Jastrzębowski ◽  
G. Krok ◽  
M. Lisańczuk ◽  
...  
Keyword(s):  
Seed Quality ◽  
The State ◽  
Annual Reports ◽  
Estimation Methods ◽  
Selection Strategy ◽  
Seed Crop ◽  
Forest Reproductive Material ◽  
First Results ◽  
Reproductive Material ◽  
Seed Management

Seed management carried out by The State Forests National Forest Holding is an integral part of rational forest management. Seed collection takes place mainly from stands belonging to first category of forest reproductive material, which is the largest seed base in Poland. In smaller amount, seeds are collected in selective objects of highest forest reproductive material category (selected seed stands, seed orchards). The previous estimation methods of seed crop were based on visual assessment of cones in the stands for their harvest. Following the rules of FRM transfer is additional difficulty of rational seed management which limits the possibility of the use of planting material in Poland. <br><br> Statements concerning forecast of seed crop and monitoring of seed quality is based on annual reports from the State Forest Service. Forest Research Institute is responsible for preparing and publishing above-mentioned statements. A small extent of its automatization and optimization is a large disadvantage of this procedure. In order to make this process more effective web-based GIS application was designed. Its main performance will give a possibility to upload present-day information on seed efficiency, their spatial pattern and availability. Currently this system is under preparation. <br><br> As a result, the project team will get a possibility to increase participation of seed material collected from selected seed base and to share good practices on this issue in more efficient way. In the future this will make it possible to obtain greater genetic gain of selection strategy. <br><br> Additionally, first results presented in literature showed possible use of unmanned aerial system/vehicle (UAS/V) for supporting of seed crop forecast procedure.

scholarly journals Stand density biases the estimation of the site index especially on dry sites

2020 ◽  
Author(s):  
Astor Toraño Caicoya ◽  
Hans Pretzsch
Keyword(s):  
Norway Spruce ◽  
Stand Structure ◽  
Local Density ◽  
Generalized Additive Models ◽  
Stand Density ◽  
Moisture Stress ◽  
Site Index ◽  
Additive Models ◽  
Climate Conditions ◽  
Density Control

The Site Index (SI) has been widely used in forest management and silviculture. It relies on the assumption that the height of dominant trees in a stand is independent from the local density. However, research on climate change suggests that under certain moisture stress conditions, this may not hold. Here, based on 29 plots from 5 long-term research experiments, we have tested the effect of local stand density on the SI of Norway spruce (Picea abies (L.) H. Karst). With generalized additive models (GAMM), we analyzed the effect of stand structure and climate predictors on SI. The two evaluated models revealed that local stand density and age had a significant effect on SI (p≤0.001 ), showing a clear negative trend especially significant on sites with poor and dry soils, which may reduce the site index by a maximum of approximately 4 m for an increase in density between 400 and 600 trees/ha. We stress that the physiological characteristics of Norway spruce, flat-rooting system and xeromorphism, especially when growing in pure stands, may explain these effects. Thus, density control and growth in mixtures may help to reduce the water stress and losses in height growth under future climate conditions.

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