scholarly journals The role of plants in the formation of species-specific features in grass flies (Diptera, Chloropidae, Meromyza)

2021 ◽  
Vol 9 ◽  
Author(s):  
Tatiana Triseleva ◽  
Varos Petrosyan ◽  
Aleksandra Yatsuk ◽  
Andrey Safonkin
Keyword(s):  
Discriminant Analysis ◽  
Host Plant ◽  
Plant Diversity ◽  
Classification Accuracy ◽  
Host Plants ◽  
Primary Role ◽  
Linear Discriminant ◽  
Anterior Process ◽  
Wide Range

In the current manuscript, we present the results of comparative analysis of seven species of Meromyza flies in the “variegata” cluster and of the evolutionary close species M. inornata, based the following criteria: 1) 14 external key features; 2) shape and area of the anterior processes of postgonites; 3) mtDNA CO1 region and 4) host plant diversity data. We could demonstrate the primary role of host plants in species formation inside genus Meromyza and calculated the timing of the divergence of M. inornata and the species of “variegata” cluster. Based on our estimates of evolution rate for mtDNA CO1 gene, we could conclude that that divergence of herbs happened before the speciation of grass flies Meromyza. Meromyza species, close to the ancestral species of the cluster, are adapted to the wide range of host plants. We revealed the most informative variables h1, S and Plant analysing data with the following statistical methods: linear discriminant analysis - LDA, regularised discriminant analysis - RDA, flexible discriminant analysis – FDA and probabilistic neural network - PNN. The highest classification accuracy was achieved using PNN (99%) and the lowest when using LDA (95.8%). When the Plant trait was excluded, the classification accuracy decreased by 14%. We revealed the significant trends in size change of the anterior process of the postgonite amongst studies species. This morphological structure is an element of male reproductive apparatus critical for the restriction of interspecies mating. We determined three branches of speciation in the “variegata” cluster and five trends in the evolution of this cluster, based on the external morphological features. We showed that M. variegata and especially M. mosquensis, the species closest to the ancestral haplotype, have the largest number of features typical of those of M. inornata. Based on the external features and the area of the anterior process of the postgonite, we reconstructed the phylogenetic position of M. elbergi in the cluster. In accordance with the obtained outcomes, we could conclude that the distribution, species diversity and the adaptation of the grass flies to narrow oligophagy were directly connected to host plant diversity. The adaptation to different host plants could be the main factor in divergence of grass flies and their evolution started later than the diversification in the Pooideae subfamily of grasses.

Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters

2017 ◽  
pp. 897-914 ◽  
Author(s):  
Ahmed.T. Sahlol ◽  
Aboul Ella Hassanien
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Random Forests ◽  
Classification Accuracy ◽  
Processing Time ◽  
Optimization Algorithms ◽  
Nearest Neighbors ◽  
Benchmark Dataset ◽  
K Nearest Neighbors ◽  
Linear Discriminant

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

Equivalence between LDA/QR and Direct LDA

2012 ◽  
pp. 338-353
Author(s):  
Rong-Hua Li ◽  
Shuang Liang ◽  
George Baciu ◽  
Eddie Chan
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Qr Decomposition ◽  
Linear Discriminant ◽  
Qr Algorithm ◽  
Special Cases ◽  
Benchmark Datasets ◽  
Pseudo Inverse

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They can be regarded as special cases of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA can also be considered as a two-stage LDA method. Interestingly, the first stage of DLDA can act as a dimension reduction algorithm. The experiment compares LDA/QR and DLDA algorithms in terms of classification accuracy, computational complexity on several benchmark datasets and compares their first stages. The results confirm the established equivalent relationship and verify their capabilities in dimension reduction.

scholarly journals Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6763
Author(s):  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Muhammad Zia ur Rehman ◽  
Imran Amjad ◽  
Muhammad Shafique ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Intraclass Correlation ◽  
Motor Impairment ◽  
Wrist Flexion ◽  
Stroke Patients ◽  
Linear Discriminant

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

The Role of Host Selection in the Ecology of Phytophagous Insects

1953 ◽  
Vol 85 (8) ◽  
pp. 276-282 ◽  
Author(s):  
A. J. Thorsteinson
Keyword(s):  
Host Plant ◽  
Host Selection ◽  
Host Plants ◽  
Host Plant Selection ◽  
Phytophagous Insects ◽  
Phytophagous Insect ◽  
Ecological Significance ◽  
Plant Selection ◽  
Suitable Host

It is a commonplace entomological observation that each phytophagous insect is restricted in its feeding to a small fraction only of the plant species that grow in any area. This phenomenon is generally referred to as “host selection”. The ecological significance of host plant selection among insects is manifest principally in an effect on the geographical distribution of insect species since phytophagous insects can occur only where suitable host plants are available.

scholarly journals A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

2020 ◽  
Vol 10 (10) ◽  
pp. 734
Author(s):  
Md Rakibul Mowla ◽  
Jesus D. Gonzalez-Morales ◽  
Jacob Rico-Martinez ◽  
Daniel A. Ulichnie ◽  
David E. Thompson
Keyword(s):  
Discriminant Analysis ◽  
System Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Classification Technique ◽  
Latency Jitter ◽  
Bci System

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

scholarly journals No fitness costs are induced by Spiroplasma infections of Aphis citricidus reared on two different host plants

2020 ◽  
Vol 80 (2) ◽  
pp. 311-318 ◽  
Author(s):  
A. S. Guidolin ◽  
F. L. Cônsoli
Keyword(s):  
Host Plant ◽  
Host Plants ◽  
Aphid Species ◽  
Immune Defense ◽  
Host Immune System ◽  
Fitness Costs ◽  
Ecological Traits ◽  
Plant Utilization ◽  
High Infection

Abstract Aphids can harbor several secondary symbionts that alter important aphid-related ecological traits, such as defense against natural enemies, heat tolerance and host plant utilization. One of these secondary symbionts, Spiroplasma, is well known in Drosophila as a sex modulator and by interacting with the host immune system. However, little is known on the effects of Spiroplasma on aphids, such as its influence on the host immune defense against fungi and on host plant utilization. Aphid infections by Spiroplasma are known to be low and few aphid species were reported to be infected with this secondary symbiont, however aphids belonging to the genus Aphis in neotropical regions show high infection rates by Spiroplasma. Thus, we investigated the association of Spiroplasma with the tropical aphid Aphis citricidus through comparative biology experiments on two host plants with different nutritional value to the aphid. We demonstrate Spiroplasma induced no significant fitness costs to A. citricidus on either host plant as no changes in the fitness traits we assessed were observed. Spiroplasma infection only induced sutle changes on host longevity and fecundity. Therefore, we concluded Spiroplasma established a neutral interaction with A. citricidus under the selection pressure we tested, and argue on stress conditions that could better demonstrate the role of Spiroplasma in A. citricidus bioecology and associated costs involved.

The Role of Multivariant Analysis on the Interpretation of FTIR and Raman Spectra

2020 ◽  
pp. 104-129 ◽  
Author(s):  
Sefa Celik ◽  
Ali Tugrul Albayrak ◽  
Sevim Akyuz ◽  
Aysen E. Ozel
Keyword(s):  
Biological Systems ◽  
Principal Component ◽  
Biological Macromolecules ◽  
Spectroscopic Techniques ◽  
Linear Discriminant ◽  
Wide Range ◽  
Vibrational Bands ◽  
Multivariant Analysis

FTIR and Raman spectroscopy are complementary spectroscopic techniques that play an important role in the analysis of molecular structure and the determination of characteristic vibrational bands. Vibrational spectroscopy has a wide range of applications including mainly in physics and biology. Its applications have gained tremendous speed in the field of biological macromolecules and biological systems, such as tissue, blood, and cells. However, the vibrational spectra obtained from the biological systems contain a large number of data and information that make the interpretation difficult. To facilitate the analysis, multivariant analysis comprising the reduction of the dimension of spectrum data and classification of them by eliminating redundancy data, which are obtained from the spectra and does not have any role, becomes critical. In this chapter, the applications of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their combination PCA-LDA, which are widely used among multivariant techniques on biological systems will be disclosed.

IMPROVED PSEUDOINVERSE LINEAR DISCRIMINANT ANALYSIS METHOD FOR DIMENSIONALITY REDUCTION

2012 ◽  
Vol 26 (01) ◽  
pp. 1250002 ◽  
Author(s):  
KULDIP K. PALIWAL ◽  
ALOK SHARMA
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Classical Method ◽  
Small Sample Size ◽  
Small Sample ◽  
Analysis Method ◽  
Small Sample Size Problem ◽  
Linear Discriminant ◽  
Discriminant Analysis Method

Pseudoinverse linear discriminant analysis (PLDA) is a classical method for solving small sample size problem. However, its performance is limited. In this paper, we propose an improved PLDA method which is faster and produces better classification accuracy when experimented on several datasets.

scholarly journals Detection of primary RGB colors projected on a screen using fNIRS

2017 ◽  
Vol 10 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Xiaolong Liu ◽  
Keum-Shik Hong
Keyword(s):  
Infrared Spectroscopy ◽  
Visual Cortex ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Linear Discriminant ◽  
Stimulation Period ◽  
Skewness And Kurtosis

In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the [Formula: see text]-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7[Formula: see text]s window for a given 10[Formula: see text]s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

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