Location and Intensity Discrimination in the Leech Local Bend Response Quantified Using Optic Flow and Principal Components Analysis

2005 ◽  
Vol 93 (6) ◽  
pp. 3560-3572 ◽  
Author(s):  
Serapio M. Baca ◽  
Eric E. Thomson ◽  
William B. Kristan
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Optic Flow ◽  
Motion Tracking ◽  
Body Wall ◽  
Principal Component ◽  
The Body ◽  
Video Frames ◽  
Components Analysis ◽  
The Individual

In response to touches to their skin, medicinal leeches shorten their body on the side of the touch. We elicited local bends by delivering precisely controlled pressure stimuli at different locations, intensities, and durations to body-wall preparations. We video-taped the individual responses, quantifying the body-wall displacements over time using a motion-tracking algorithm based on making optic flow estimates between video frames. Using principal components analysis (PCA), we found that one to three principal components fit the behavioral data much better than did previous (cosine) measures. The amplitudes of the principal components (i.e., the principal component scores) nicely discriminated the responses to stimuli both at different locations and of different intensities. Leeches discriminated (i.e., produced distinguishable responses) between touch locations that are approximately a millimeter apart. Their ability to discriminate stimulus intensity depended on stimulus magnitude: discrimination was very acute for weak stimuli and less sensitive for stronger stimuli. In addition, increasing the stimulus duration improved the leech's ability to discriminate between stimulus intensities. Overall, the use of optic flow fields and PCA provide a powerful framework for characterizing the discrimination abilities of the leech local bend response.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

A Multivariate Analysis of Human Promoter Sequences.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4249-4249
Author(s):  
Mario-Antoine Dicato ◽  
Garry Mahon
Keyword(s):  
Principal Components Analysis ◽  
Covariance Matrix ◽  
Transcription Start Site ◽  
Principal Components ◽  
Consensus Sequence ◽  
Principal Component ◽  
Start Site ◽  
Transcription Start ◽  
Promoter Sequences ◽  
Components Analysis

Abstract The human genome has been estimated to contain tens of thousands of genes. Of these, the promoters have been experimentally verified for almost two thousand. We have examined the DNA sequences just up-stream of the transcription start site, a region which includes the TATA box. Genetic control sites, such as promoters, often have a characteristic consensus sequence, but the variation about a given consensus sequence has received little attention. Sequence variations may be related to functional differences amongst the control sites. Principal components analysis has been chosen because of its generality and the variety of phenomena which it reveals. Promoter sequences were considered because of the large number available and their importance in gene expression. The sequences of the 1977 promoters recognised by human RNA polymerase II were obtained from the Eukaryotic Promoter Database. Many of these promoters are of interest in oncology and the database includes sequences for growth factors (e.g. GM-CSF, interleukins), oncogenes and tumour viruses among others. Sub-sequences of 25 bases centred on position −13 relative to the transcription start site were extracted. Two bits were used to encode each base (a=11, c=00, g=10 and t=01) and the covariance matrix of the resulting 50 variables was determined. The eigenvalues and eigenvectors of the covariance matrix were calculated. All calculations were carried out by computer using MS-Excel and SYSTAT 11. The eigenvalues of the covariance matrix ranged from 0.571 down to 0.133. The eigenvectors were used to calculate principal components. Thus 50 more or less correlated variables were transformed into 50 uncorrelated variables with the same total variance. The sequences were sorted according to the principal components to reveal which features were associated with the most variation amongst the sequences. When the covariances among the coded sequences were calculated many associations were found, for example, a purine at position 15 was associated with a purine at position 16, and a purine at position 19 with a G or C at position 20. Although these correlations individually were not especially strong, together they were a notable feature of the set of sequences. The consensus sequence was observed to be agggg ggggg ggc(g/c)c ggggg gcgcc. A principal components analysis enabled the promoters to be identified which differed most (in opposite directions) from the consensus sequence, taking account of the correlations. Nearly all the elements of the first eigenvector were of alternating sign; thus the first principal component separated promoters which were rich in G from those rich in T. Almost all elements of the second eigenvector were positive, so the second principal component distinguished promoters rich in A from those rich in C. There was a remarkable concentration of promoters from genes for interleukins or IL repressors with large values for the second principal component:- IL1A, IL2, IL4, IL6-2, IL2RA1, IL2RA2 and IL8RB were in positions 160, 43, 14, 158, 131, 101 and 158 (out of 1977) respectively. The variation in the sequence of promoters about their consensus sequence is seen not to be random but to display detectable patterns. Correlations were found to be frequent within the promoter sequences considered here; in the absence of correlations all the eigenvalues would have been equal. The major principal components separated promoters with markedly different sequences. It is to be expected that the other principal components would yield further separations.

scholarly journals A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1499-1506 ◽  
Author(s):  
Yangwu Zhang ◽  
Guohe Li ◽  
Heng Zong
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Text Classification ◽  
Principal Components ◽  
Principal Component ◽  
Method Of Dimensionality Reduction ◽  
Components Analysis ◽  
Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

scholarly journals An Analysis of the Perceptions and Resources of Large University Classes

2017 ◽  
Vol 16 (2) ◽  
pp. ar33 ◽  
Author(s):  
Ceilidh Barlow Cash ◽  
Jessa Letargo ◽  
Steffen P. Graether ◽  
Shoshanah R. Jacobs
Keyword(s):  
Large Class ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
First Year Experience ◽  
Research Approach ◽  
Course Structure ◽  
Large Classes ◽  
Components Analysis ◽  
Categorical Principal Components Analysis

Large class learning is a reality that is not exclusive to the first-year experience at midsized, comprehensive universities; upper-year courses have similarly high enrollment, with many class sizes greater than 200 students. Research into the efficacy and deficiencies of large undergraduate classes has been ongoing for more than 100 years, with most research associating large classes with weak student engagement, decreased depth of learning, and ineffective interactions. This study used a multidimensional research approach to survey student and instructor perceptions of large biology classes and to characterize the courses offered by a department according to resources and course structure using a categorical principal components analysis. Both student and instructor survey results indicated that a large class begins around 240 students. Large classes were identified as impersonal and classified using extrinsic qualifiers; however, students did identify techniques that made the classes feel smaller. In addition to the qualitative survey, we also attempted to quantify courses by collecting data from course outlines and analyzed the data using categorical principal component analysis. The analysis maps institutional change in resource allocation and teaching structure from 2010 through 2014 and validates the use of categorical principal components analysis in educational research. We examine what perceptions and factors are involved in a large class that is perceived to feel small. Our analysis suggests that it is not the addition of resources or difference in the lecturing method, but it is the instructor that determines whether a large class can feel small.

scholarly journals Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

2012 ◽  
Vol 2 (3) ◽  
pp. 221-225 ◽  
Author(s):  
A. Ahmad ◽  
S. Quegan
Keyword(s):  
Spectral Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Geographical Information ◽  
Remotely Sensed Data ◽  
Tropical Conditions ◽  
Components Analysis ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference

Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA) of Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4), three thermal bands (29, 31 and 32), the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

scholarly journals Estimating strength of polygenic selection with principal components analysis of spatial genetic variation

2014 ◽  
Author(s):  
Davide Piffer
Keyword(s):  
Human Development ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Human Development Index ◽  
Selection Pressure ◽  
Genetic Relatedness ◽  
Principal Component ◽  
Development Index ◽  
Continental Population ◽  
Components Analysis

AbstractPrincipal components analysis on allele frequencies for 14 and 50 populations (from 1K Genomes and ALFRED databases) produced a factor accounting for over half of the variance, which indicates selection pressure on intelligence or genotypic IQ. Very high correlations between this factor and phenotypic IQ, educational achievement were observed (r>0.9 and r>0.8), also after partialling out GDP and the Human Development Index. Regression analysis was used to estimate a genotypic (predicted) IQ also for populations with missing data for phenotypic IQ. Socio-economic indicators (GDP and Human Development Index) failed to predict residuals, not providing evidence for the effects of environmental factors on intelligence. Another analysis revealed that the relationship between IQ and the genotypic factor was not mediated by race, implying that it exists at a finer resolution, a finding which in turn suggests selective pressures postdating sub-continental population splits.Genotypic height and IQ were inversely correlated but this correlation was mostly mediated by race. In at least two cases (Native Americans vs East Asians and Africans vs Papuans) genetic distance inferred from evolutionarily neutral genetic markers contrasts markedly with the resemblance observed for IQ and height increasing alleles.A principal component analysis on a random sample of 20 SNPs revealed two factors representing genetic relatedness due to migrations. However, the correlation between IQ and the intelligence PC was not mediated by them. In fact, the intelligence PC emerged as an even stronger predictor of IQ after entering the “migratory” PCs in a regression, indicating that it represents selection pressure instead of migrational effects.Finally, some observations on the high IQ of Mongoloid people are made which lend support to the “cold winters theory” on the evolution of intelligence.

scholarly journals ANÁLISE MULTIVARIADA NA DETERMINAÇÃO DO RISCO DE EROSÃO EM SOLOS SOB IRRIGAÇÃO.

Irriga ◽  
2010 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Celia Regina Paes Bueno ◽  
Christiano Luna Arraes ◽  
Gener Tadeu Pereira ◽  
Jose Eduardo Cora ◽  
Sergio Campos
Keyword(s):  
Cluster Analysis ◽  
Soil Erosion ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Clustering Analysis ◽  
Principal Component ◽  
Large Field ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Components Analysis

O objetivo deste trabalho foi abordar a utilização de técnicas de análise multivariada na discriminação do risco de erosão dos solos, sob pivô central, em diferentes classes de solos, relevo, uso e manejo. A área de estudo de 33 ha localizada na região de Carmo de Rio Claro, MG, sob pivô central, vem sendo cultivada com feijão, milho e café por um período de 7 anos. As amostragens foram feitas a intervalos regulares de 10 m na profundidade de 0,00-0,20m em uma transeção de 1050 m, perfazendo 59 amostras. Os parâmetros risco de erosão (A), potencial natural de erosão (PN) e expectativa de erosão (EE) foram avaliados por análise multivariada. A aplicação da análise multivariada mostrou uma boa associação entre os agrupamentos formados e os diferentes tipos de solos e, juntamente com os componentes principais, permitiram identificar dois grupos de maiores e menores perdas de solo, evidenciando que as áreas de maiores expectativas de perdas de solo estão correlacionadas com a classe de solo, o relevo e manejo do solo. O potencial natural da erosão do solo foi um fator importante para determinar os diferentes grupos. A análise multivariada mostrou que 98 % das variáveis foram classificadas dentro dos grupos e que estes pelo potencial erosivo requerem programas de manejo e conservação do solo.   UNITERMOS: Análise multivariada, componentes principais, solos, Equação Universal de Perdas de Solo.     BUENO, C. R. P.; ARRAES, C. L.; PEREIRA. G.T.; CORÁ. J.E.; CAMPOS, S.  MULTIVARIANCE ANALYSIS ON EROSION RISK DETERMINATION IN SOIL UNDER IRRIGATION.     2 ABSTRACT    The objective of this work was to verify the application of cluster analysis to evaluate soil erosion risk for different soil classes, soil slopes and soil managements. The study was conducted in a 33 ha section of a large field located in Carmo do Rio Claro County, MG, Brazil. The field had been managed in a corn/bean rotation under conventional tillage and under coffee plantation for seven years, both under sprinkle irrigation. Soil samples were obtained at every 10 m at 0.20 m depth along a transect of 1050 m. Soil erosion risk (A), natural potential erosion (PN), and erosion expectation (EE) were determined and submitted to a cluster and principal component analysis. The application of clustering analysis showed high correlation between the clusters and soil types. With clustering analysis plus principal components analysis, it was possible to identify groups of  high and low soil erosion expectation, showing that the areas with higher soil erosion expectation are correlated to the soil class, soil slope and soil management. Among the studied variables, the natural potential erosion (PN) showed to be the most important factor to identify different soil erosion groups. The cluster analysis showed that 98 % of the variables were classified within each group, and that they should be managed differently due to the soil erosive potential of each group,.   KEYWORDS: Cluster analysis, principal components analysis, soils, Universal Soil Loss Equation (USLE)    

scholarly journals Pengenalan Pola Daun untuk Membedakan Tanaman Padi dan Gulma Menggunakan Metode Principal Components Analysis (PCA) dan Extreme Learning Machine (ELM)

2020 ◽  
Vol 1 (1) ◽  
pp. 44-51
Author(s):  
Ahmad Izzuddin ◽  
M. Rizal Wahyudi
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Principal Components Analysis ◽  
Extreme Learning Machine ◽  
Principal Components ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Alternanthera Sessilis ◽  
Components Analysis ◽  
Learning Machine

Perkembangan ilmu pengetahuan serta pesatnya teknologi memberikan banyak manfaat bagi manusia dalam menjalankan aktifitasnya. Pemanfaatan ilmu pengetahuan dan teknologi tersebut di berbagai bidang termasuk di bidang pertanian. Pengembangan potensi pertanian suatu daerah dapat dioptimalkan melalui perkembangan ilmu pengetahuan dan teknologi itu sendiri. Salah satunya dengan pengenalan pola citra digital. Pengenalan pola bertujuan menentukan kelompok atau kategori pola berdasarkan ciri-ciri yang dimiliki oleh pola tersebut. Dengan kata lain, pengenalan pola membedakan suatu objek dengan objek lain. Dengan menggunakan metode ektraksi ciri Principal Component Analysis dan metode klasifikasi Extreme Learning Machine penulis melakukan penelitian untuk membedakan tanaman padi dan tanaman gulma. Implementasi PCA dan ELM mampu membedakan tanaman gulma dengan padi (Oryza sativa L) dalam hal ini gulma yang digunakan adalah jawan (Echinochloa cruss-galli) dan kremah (Alternanthera sessilis). Berdasarkan hasil pengujian yang dilakukan 8 kali running dengan merubah jumlah hidden neuron diperoleh nilai akurasi paling tinggi sebesar 91,67 % dengan menggunakan 10, 15, 30, 35, 40 hidden neuron, sedangkan untuk nilai akurasi paling rendah sebesar 58% dengan jumlah hidden neuron 5. Waktu yang dibutuhkan ELM untuk melakukan pelatihan dan pengujian sangat singkat 0.374 detik dan 0.500 detik pengukuran dilakukan dimulai dari running program sampai proses running program selesai.

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