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Identification and Application of Informative Genetic Markers in Studies of Genetic Diversity of Experimental and Wild Populations of Lemna minor The aim of the study was to measure the genetic diversity of wild population L. minor clones using different genetic markers and the genetic variability in experimental clones exposed to increased electromagnetic radiation by GPx, Cat and APx gene sequencing. Materials and methods - The wild type L. minor samples from Nemunas River near Kaunas (4 samples) and Neris River near Vilnius (8 samples) were collected. The genetic diversity of the wild type L. minor was studied using the genetic markers of the chloroplast genome including microsatellite sequences surrounded by non-coding regions and the antioxidant genes (GPx, Cat, APx). The growth parameters and DNA sequences of genetic markers were measured of L. minor grown in Petri dishes placed at different distances from the source of electromagnetic radiation. Results - Seven genetic markers (GPx6, GPx7, Cat4b, Cat4b, Cat7, APx1, APx2) were identified as suitable for wild type and experimental L. minor clones genetic diversity studies. Results indicate that the interpopulation diversity identified using microsatellite markers is higher (30% (L4), 29% (L14), 33% (L16)) compared to genetic markers encompassing fragments of antioxidant genes (5% (GPx6), 10% (GPx7), 15 % (Cat4-Cat4b), 10% (Cat7), 19% (APx1-APx2)). Growth of plants were suppressed after 14th week from the beginning of the exposure to low-frequency (50 Hz) electromagnetic radiation and new nucleotide varations in DNA sequences of antioxidant genes were inducted.

Pagrindinis darbo tikslas – atlikti kelių tiesioginio sklidimo neuroninių tinklų sistemų lyginamąją analizę siekiant įvertinti jų funkcionalumą. Šiame darbe apžvelgiama: biologinio ir dirbtinio neuronų modeliai, neuroninių tinklų klasifikacija pagal jungimo konstrukciją (tiesioginio sklidimo ir rekurentiniai neuroniniai tinklai), dirbtinių neuroninių tinklų mokymo strategijos (mokymas su mokytoju, mokymas be mokytojo, hibridinis mokymas). Analizuojami pagrindiniai tiesioginio sklidimo neuroninių tinklų metodai: vienasluoksnis perceptronas, daugiasluoksnis perceptronas realizuotas „klaidos skleidimo atgal” algoritmu, radialinių bazinių funkcijų neuroninis tinklas. Buvo nagrinėjama 14 skirtingų tiesioginio sklidimo neuroninių tinklų sistemos. Programos buvo suklasifikuotos pagal kainą, tiesioginio sklidimo neuroninių tinklo mokymo metodų taikymą, galimybę vartotojui keisti parametrus prieš apmokant tinklą ir techninį programos įvertinimą. Programos buvo įvertintos dešimtbalėje vertinimo sistemoje pagal mokymo metodų įvairumą, parametrų keitimo galimybes, programos stabilumą, kokybę, bei kainos ir kokybės santykį. Aukščiausiu balu įvertinta „Matlab” programa (10 balų), o prasčiausiai – „Sharky NN” (2 balai). Detalesnei analizei pasirinktos keturios programos („Matlab“, „DTREG“, „PathFinder“, „Cortex“), kurios buvo įvertintos aukščiausiais balais, galėjo apmokyti tiesioginio sklidimo neuroninį tinklą daugiasluoksnio perceptrono metodu ir bent dvi radialinių bazinių funkcijų... [toliau žr. visą tekstą] The main aim – to perform a comparative analysis of several feedforward neural system networks in order to identify its functionality. The work presents both: biological and artificial neural models, also classification of neural networks, according to connections’ construction (of feedforward and recurrent neural networks), studying strategies of artificial neural networks (with a trainer, without a trainer, hybrid). The main methods of feedforward neural networks: one-layer perceptron, multilayer perceptron, implemented upon “error feedback” algorithm, also a neural network of radial base functions have been considered. The work has included 14 different feedforward neural system networks, classified according its price, application of study methods of feedforward neural networks, also a customer’s possibility to change parameters before paying for the network and a technical evaluation of a program. The programs have been evaluated from 1 point to 10 points according to the following: variety of training systems, possibility to change parameters, stability, quality and ratio of price and quality. The highest evaluation has been awarded to “Matlab” (10 points), the lowest – to “Sharky NN” (2 points). Four programs (”Matlab“, “DTREG“, “PathFinder“,”Cortex“) have been selected for a detail analysis. The best evaluated programs have been able to train feedforward neural networks using multilayer perceptron method, also at least two radial base function networks. “Matlab“ and... [to full text]