scholarly journals Does the micro land snail, Kaliella barrakporensis (Mollusca: Gastropoda), exhibit plant preference and aggregation? A spatial scale analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Himangshu Barman ◽  
Gautam Aditya
Keyword(s):  
Spatial Scale ◽  
Host Plants ◽  
Negative Binomial ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ground Level ◽  
Land Snail ◽  
Citrus Limon ◽  
Dispersion Pattern ◽  
Hibiscus Rosa Sinensis

The spatial scale occurrence of the micro land snail Kaliella barrakporensis (L. Pfeiffer, 1852) in the host plants was assessed in selected sites of West Bengal, India. In course of a survey, the collection of K. barrakporensis from randomly selected plants was accomplished for the purpose of highlighting – (a) distribution in host plants, (b) variation in abundance in different height and (c) the dispersion pattern. Although the snails were observed in seven different plants, the presence was more prominent in the lemon plant (Citrus limon), with an average of about 24 individuals / 100 leaves. The logit based principal component regression indicated significant differences in the choice of the host plants with abundance in C. limon followed  by Hibiscus rosa sinensis  and  Nyctanthes arbor-tristis, which was further substantiated through ANOVA (F(1),6, 69 = 10.918; P < 0.001). The heterogeneity in the distribution of K. barrakporensis at different heights of the plant C. limon was also observed with maximum abundance at about 90cm height with least number of snails at the ground level (F(1)6,139 = 3.797;P < 0.0001). On the basis of the variance to mean ratio (s2/m = 1.847±0.161SE), negative binomial aggregation parameter k (1.034± 0.33 SE) and Lloyd mean crowding (ṁ) (1.083 ± 0.16SE) the dispersion of the snail appeared to comply with the clumped distribution in host plants. Apparently, the micro land snail K. barrakporensis exhibited clumped distribution in selected plant species that serve as the preferred resource and complies with the arboreal adaptation. However, further studies should be initiated on the resource preferences of the micro snail K. barrakporensis, to support conservation initiative and spread beyond native habitats.

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Application of Derivative, Derivative Ratio, and Multivariate Spectral Analysis and Thin-Layer Chomatography-Densitometry for Determination of a Ternary Mixture Containing Drotaverine Hydrochloride, Caffeine, and Paracetamol

2007 ◽  
Vol 90 (2) ◽  
pp. 391-404 ◽  
Author(s):  
Fadia H Metwally ◽  
Yasser S El-Saharty ◽  
Mohamed Refaat ◽  
Sonia Z El-Khateeb
Keyword(s):  
Thin Layer ◽  
Least Squares ◽  
Ternary Mixture ◽  
Principal Component Regression ◽  
Pharmaceutical Preparations ◽  
Principal Component ◽  
Standard Addition ◽  
Drotaverine Hydrochloride ◽  
Assay Precision

Abstract New selective, precise, and accurate methods are described for the determination of a ternary mixture containing drotaverine hydrochloride (I), caffeine (II), and paracetamol (III). The first method uses the first (D1) and third (D3) derivative spectrophotometry at 331 and 315 nm for the determination of (I) and (III), respectively, without interference from (II). The second method depends on the simultaneous use of the first derivative of the ratio spectra (DD1) with measurement at 312.4 nm for determination of (I) using the spectrum of 40 μg/mL (III) as a divisor or measurement at 286.4 and 304 nm after using the spectrum of 4 μg/mL (I) as a divisor for the determination of (II) and (III), respectively. In the third method, the predictive abilities of the classical least-squares, principal component regression, and partial least-squares were examined for the simultaneous determination of the ternary mixture. The last method depends on thin-layer chromatography-densitometry after separation of the mixture on silica gel plates using ethyl acetatechloroformmethanol (16 + 3 + 1, v/v/v) as the mobile phase. The spots were scanned at 281, 272, and 248 nm for the determination of (I), (II), and (III), respectively. Regression analysis showed good correlation in the selected ranges with excellent percentage recoveries. The chemical variables affecting the analytical performance of the methodology were studied and optimized. The methods showed no significant interferences from excipients. Intraday and interday assay precision and accuracy values were within regulatory limits. The suggested procedures were checked using laboratory-prepared mixtures and were successfully applied for the analysis of their pharmaceutical preparations. The validity of the proposed methods was further assessed by applying a standard addition technique. The results obtained by applying the proposed methods were statistically analyzed and compared with those obtained by the manufacturer's method.

scholarly journals Principal component regression in GAMLSS applied to Greek–German government bond yield spreads

2021 ◽  
pp. 1471082X2110229
Author(s):  
D. Stasinopoulos Mikis ◽  
A. Rigby Robert ◽  
Georgikopoulos Nikolaos ◽  
De Bastiani Fernanda
Keyword(s):  
Additive Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Complex Nature ◽  
Government Bond ◽  
German Government ◽  
Yield Spreads ◽  
Explanatory Variables ◽  
Interaction Terms ◽  
Bond Yield

A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale and shape (GAMLSS) is given here using as an example the Greek–German government bond yield spreads from 25 April 2005 to 31 March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals 508. Biomarker elevation during COVID-19: Differences between ambulatory and hospitalized individuals

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S319-S320
Author(s):  
Paul W Blair ◽  
Charlotte Lanteri ◽  
Deborah Striegel ◽  
Brian Agan ◽  
Ryan C Maves ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Negative Binomial ◽  
Principal Component ◽  
Component Analysis ◽  
Ambulatory Setting ◽  
Level Of Care ◽  
Symptom Duration ◽  
Duration Of Symptoms ◽  
Course Of Illness ◽  
D Dimer

Abstract Background While the majority of illness due to COVID-19 does not require hospitalization, little has been described about the host inflammatory response in the ambulatory setting. Differences in the levels of inflammatory signaling proteins between outpatient and hospitalized populations could identify key maladaptive immune responses during COVID-19. Methods Samples were collected from 76 participants (41% female, mean 46.8 years of age) enrolled at five military treatment facilities between March 20, 2020 and June 17, 2020 in an ongoing prospective COVID-19 cohort. This analysis was restricted to those with positive SARS-CoV-2 (severe acute respiratory syndrome–coronavirus 2) RT-PCR testing and included hospitalized (N=29; 10 requiring an ICU stay) and non-hospitalized (N=43) participants. Severity markers (IL6, D-dimer, procalcitonin, ferritin, ICAM-1, IL5, lipocalin, RAGE, TNFR, VEGFA, IFNγ, IL1β) were measured in plasma (mg/dL) using the Ella immunoassay and natural log transformed. Univariate negative binomial regression was performed to determine relative risk of hospitalization. Using the full marker panel, we performed a Principal Component Analysis (PCA) to determine directions of maximal variance in the data. Pearson’s correlation coefficient was determined between analytes and each axis. Results Participants requiring ambulatory-, hospital-, and ICU-level care had samples collected at 44.0 (IQR: 35.0–51.0), 40.0 (13.0–51.0), and 47.5 (21.0–54.0) days, respectively. Higher unadjusted levels of IL6, D-dimer, procalcitonin, or ferritin were each associated with hospitalization (Table 1). The PCA showed a separation along axes between level of care and duration of symptoms (Fig 1). While significant correlations were noted with a number of biomarkers, PC1 most correlated with TNFR1 (r=0.88) and PC2 most correlated with IL6Ra (r=0.95). PC1 axis variation accounted for 36.5% of variance and the PC2 axis accounted for 20.0% of variance. Figure 1. Principal Component Analysis (PCA) of biomarkers by level of care and symptom duration. Conclusion TNFR1 and IL6Ra levels correlated with differences in the proinflammatory states between hospitalized and non-hospitalized individuals including time points late in the course of illness. Further analysis of these preliminary findings is needed to evaluate for differences by stages of illness. Disclosures All Authors: No reported disclosures

scholarly journals Biofoam of Spittlebug, Poophilus costalis (Walker): Preferential Sites, Temperature Regulation, Chemical Composition and Antimicrobial Activity

Insects ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 340
Author(s):  
Kitherian Sahayaraj ◽  
Balakrishnan Saranya ◽  
Samy Sayed ◽  
Loko Yêyinou Laura Estelle ◽  
Koilraj Madasamy
Keyword(s):  
Antimicrobial Activity ◽  
Chemical Composition ◽  
High Performance ◽  
Host Plants ◽  
Ground Level ◽  
College Campus ◽  
The Other ◽  
Mimosa Pudica ◽  
Ft Ir ◽  
Gas Chromatography Mass Spectroscopy

The foam produced by nymphs of Poophilus costalis on eleven different host plants belonging to eight families on St. Xavier’s College campus in India was studied over five months. The chemical composition and antimicrobial activity of these biofoams were investigated. The results revealed that P. costalis preferred Theporsia purpurea and Mimosa pudica for laying their eggs and producing foam, over the other tested plants. P. costalis produce their foam on either nodes or internodes on monocotyledons (30%) (p < 0.05), whereas on dicotyledons, they produce more foam on the stems (63.8%) than on the leaves (6.2%) (p < 0.01). The number of nymphs in each piece of foam from P. costalis varied from 1 to 3 (mean = 1.8 per plant). They produced their foam (5.7 to 45.2 cm) from the ground level on a plant. The length and breadth of a piece of foam ranged from 1.0 to 3.9 cm and 0.6 to 4.7 cm, respectively. The foam tended to be cooler than the environment. Qualitative profiling showed that the foam consists of carbohydrates, including maltose; trypsin; amino acids; protease. The foam was also analyzed using a spectrophotometer, Fourier transform infrared spectroscopy (FT-IR), gas chromatography–mass spectroscopy (GC-MS), and high-performance liquid chromatography (HPLC). The antimicrobial activity of the biofoam was the greatest against Staphylococcus aureus, the growth of which was reduced by 55.9 ± 3.9%, suggesting that the foam could be used as an antimicrobial product. However, no activities were observed against Fusarium oxysporum and Candida albicans.

scholarly journals Multivariate Analysis as a Tool for Quantification of Conformational Transitions in DNA Thin Films

2021 ◽  
Vol 11 (13) ◽  
pp. 5895
Author(s):  
Kristina Serec ◽  
Sanja Dolanski Babić
Keyword(s):  
Thin Films ◽  
Learning Algorithm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Conformational Transitions ◽  
Cancer Diagnostics ◽  
Dna Conformation ◽  
Support Vector ◽  
Multivariate Statistical ◽  
The Impact

The double-stranded B-form and A-form have long been considered the two most important native forms of DNA, each with its own distinct biological roles and hence the focus of many areas of study, from cellular functions to cancer diagnostics and drug treatment. Due to the heterogeneity and sensitivity of the secondary structure of DNA, there is a need for tools capable of a rapid and reliable quantification of DNA conformation in diverse environments. In this work, the second paper in the series that addresses conformational transitions in DNA thin films utilizing FTIR spectroscopy, we exploit popular chemometric methods: the principal component analysis (PCA), support vector machine (SVM) learning algorithm, and principal component regression (PCR), in order to quantify and categorize DNA conformation in thin films of different hydrated states. By complementing FTIR technique with multivariate statistical methods, we demonstrate the ability of our sample preparation and automated spectral analysis protocol to rapidly and efficiently determine conformation in DNA thin films based on the vibrational signatures in the 1800–935 cm−1 range. Furthermore, we assess the impact of small hydration-related changes in FTIR spectra on automated DNA conformation detection and how to avoid discrepancies by careful sampling.

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