Prediction of white cabbage (Brassica oleracea var. capitata) self-incompatibility based on neural network and discriminant analysis of complex electrophoretic patterns

2010 ◽  
Vol 34 (2) ◽  
pp. 115-121 ◽  
Author(s):  
Piotr Waligórski ◽  
Maciej Szaleniec
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Brassica Oleracea ◽  
Self Incompatibility ◽  
Electrophoretic Patterns ◽  
White Cabbage

scholarly journals Coevolution of theS-Locus GenesSRK,SLGandSP11/SCRinBrassica oleraceaandB. rapa

Genetics ◽  
2002 ◽  
Vol 162 (2) ◽  
pp. 931-940 ◽  
Author(s):  
Keiichi Sato ◽  
Takeshi Nishio ◽  
Ryo Kimura ◽  
Makoto Kusaba ◽  
Tohru Suzuki ◽  
...  
Keyword(s):  
Brassica Oleracea ◽  
Gene Conversion ◽  
Phylogenetic Trees ◽  
Hypervariable Region ◽  
Recognition System ◽  
Self Incompatibility ◽  
Male And Female ◽  
Diversifying Selection ◽  
Tight Linkage ◽  
Nonsynonymous Site

AbstractBrassica self-incompatibility (SI) is controlled by SLG and SRK expressed in the stigma and by SP11/SCR expressed in the anther. We determined the sequences of the S domains of 36 SRK alleles, 13 SLG alleles, and 14 SP11 alleles from Brassica oleracea and B. rapa. We found three S haplotypes lacking SLG genes in B. rapa, confirming that SLG is not essential for the SI recognition system. Together with reported sequences, the nucleotide diversities per synonymous and nonsynonymous site (πS and πN) at the SRK, SLG, and SP11 loci within B. oleracea were computed. The ratios of πN:πS for SP11 and the hypervariable region of SRK were significantly >1, suggesting operation of diversifying selection to maintain the diversity of these regions. In the phylogenetic trees of 12 SP11 sequences and their linked SRK alleles, the tree topology was not significantly different between SP11 and SRK, suggesting a tight linkage of male and female SI determinants during the evolutionary course of these haplotypes. Genetic exchanges between SLG and SRK seem to be frequent; three such recent exchanges were detected. The evolution of S haplotypes and the effect of gene conversion on self-incompatibility are discussed.

Genotypic differences in nitrogen efficiency of white cabbage (Brassica oleracea L.)

2009 ◽  
Vol 328 (1-2) ◽  
pp. 313-325 ◽  
Author(s):  
Gunda Schulte auf’m Erley ◽  
Elsa Rakhmi Dewi ◽  
Olani Nikus ◽  
Walter J. Horst
Keyword(s):  
Brassica Oleracea ◽  
Nitrogen Efficiency ◽  
Genotypic Differences ◽  
White Cabbage ◽  
Brassica Oleracea L

Identification and characterization of BoPUB3: a novel interaction protein with S-locus receptor kinase in Brassica oleracea L.

2019 ◽  
Vol 51 (7) ◽  
pp. 723-733 ◽  
Author(s):  
Songmei Shi ◽  
Qiguo Gao ◽  
Tonghong Zuo ◽  
Zhenze Lei ◽  
Quanming Pu ◽  
...  
Keyword(s):  
Brassica Oleracea ◽  
Signaling Pathway ◽  
Chromosomal Region ◽  
The Self ◽  
Mature Stage ◽  
Self Incompatibility ◽  
Receptor Kinase ◽  
S Locus ◽  
Phylogenic Tree ◽  
Brassica Oleracea L

Abstract Armadillo repeat containing 1 (ARC1) is phosphorylated by S-locus receptor kinase (SRK) and functions as a positive regulator in self-incompatibility response of Brassica. However, ARC1 only causes partial breakdown of the self-incompatibility response, and other SRK downstream factors may also participate in the self-incompatibility signaling pathway. In the present study, to search for SRK downstream targets, a plant U-box protein 3 (BoPUB3) was identified from the stigma of Brassica oleracea L. BoPUB3 was highly expressed in the stigma, and its expression was increased with the stigma development and reached to the highest level in the mature-stage stigma. BoPUB3, a 76.8-kDa protein with 697 amino acids, is a member of the PUB-ARM family and contains three domain characteristics of BoARC1, including a U-box N-terminal domain, a U-box motif, and a C-terminal arm repeat domain. The phylogenic tree showed that BoPUB3 was close to BoARC1. The synteny analysis revealed that B. oleracea chromosomal region containing BoPUB3 had high synteny with the Arabidopsis thaliana chromosomal region containing AtPUB3 (At3G54790). In addition, the subcellular localization analysis showed that BoPUB3 primarily localized in the plasma membrane and also in the cytoplasm. The combination of the yeast two-hybrid and in vitro binding assay showed that both BoPUB3 and BoARC1 could interact with SRK kinase domain, and SRK showed much higher level of β-galactosidase activity in its interaction with BoPUB3 than with BoARC1. These results implied that BoPUB3 is a novel interactor with SRK, which lays a basis for further research on whether PUB3 participates in the self-incompatibility signaling pathway.

scholarly journals Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 102
Author(s):  
Michele Lo Giudice ◽  
Giuseppe Varone ◽  
Cosimo Ieracitano ◽  
Nadia Mammone ◽  
Giovanbattista Gaspare Tripodi ◽  
...  
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Epileptic Seizures ◽  
Permutation Entropy ◽  
Diagnostic Tools ◽  
Support Vector ◽  
Feature Maps ◽  
Time Frequency ◽  
Interictal Eeg

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.

scholarly journals IDENTIFIKASI SPESIES IKAN BERDASARKAN KONTUR OTOLITH MENGGUNAKAN METODE OTSU DAN BACK PROPAGATION NEURAL NETWORK

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 389
Author(s):  
Heri Susanto ◽  
Jamal Jamal
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Backpropagation Neural Network ◽  
Image Thresholding

Otolith merupakan organ yang sangat penting, karena melalui otolith dapat diketahui jenis ikan, pertumbuhan, lingkungan, serta sejarah kehidupannya, misalnya, umur, reproduksi, dan migrasi. Dengan semakin canggihnya komputer dan pengolahan di bidang citra, diharapkan kemampuan mengidenifikasi jenis ikan yang dimiliki oleh manusia bisa diadopsi dan diterapkan pada perangkat komputer. Threshold adalah sebuah teknik penting dalam aplikasi segmentasi citra. Hal mendasar dari threshold adalah menentukan nilai batas optimal dari citra keabuan, untuk memisahkan antara obyek dengan latar belakang. Metode Backpropagation Neural Network, merupakan metode klasifikasi yang handal untuk perhitungan yang rumit dengan waktu komputasi lebih sedikit, dan mampu memberikan nilai akurasi yang tinggi. Untuk keperluan segmentasi citra menggunakan metode Otsu karena metode ini merupakan metode paling berhasil untuk image thresholding. Proses klasifikasi untuk pengenalan spesies ikan berdasar otolith menggunakan metode Backpropagation Neural Network. Hasil eksperimen diperoleh akurasi sebesar 95% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

scholarly journals Comparing the Accuracy of Multiple Discriminant Analyisis, Logistic Regression, and Neural Network to estimate pay and not to pay Dividend

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Triasesiarta Nur
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Stock Price ◽  
Dividend Policy ◽  
Multiple Discriminant Analysis ◽  
Book Value ◽  
Accuracy Of Prediction ◽  
Research Period ◽  
Accuracy Performance

This study compares the accuracy of prediction to estimate the companies dividend policy; in this case, the company will pay or not pay dividends. The models used in this research are Multiple Discriminant Analysis, Logistic Regression, and Neural Network. The samples are divided into two groups, namely companies that always pay and not pay dividends during the 2015-2018 research period, resulting in 256 samples not paying dividends and 128 samples paying dividends. The results showed that the average Neural Network accuracy performance exceeded the other two models. The best predictor of the company's Dividend Policy in this study is Price to Book Value, Stock Price, Firm Cycle, current ratio, ROA and Exchange Rate. Keywords: Multiple Discriminant Analysis, Logistic Regression, Neural Network, Dividend Policy

Human body activity recognition using wearable inertial sensors integrated with a feature extraction–based machine-learning classification algorithm

2020 ◽  
pp. 095440542093789
Author(s):  
Chih-Ta Yen ◽  
Jia-De Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Activity Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
Daily Activities ◽  
Linear Discriminant ◽  
Wearable Inertial Sensors

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

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