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Al-Sehemi, Abdullah G., Tarek M. El-Gogary, Karl Peter Wolschann i Gottfried Koehler. "Structure and Stability of Chemically Modified DNA Bases: Quantum Chemical Calculations on 16 Isomers of Diphosphocytosine". ISRN Physical Chemistry 2013 (25.02.2013): 1–10. http://dx.doi.org/10.1155/2013/146401.
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We studied for the first time 16 tautomers/rotamers of diphosphocytosine by four computational methods. Some of these tautomers/rotamers are isoenergetic although they have different structures. High-level electron correlation MP2 and MP4(SDQ) ab initio methods and density functional methods employing a B3LYP and the new M06-2X functional were used to study the structure and relative stability of 16 tautomers/rotamers of diphosphocytosine. The dienol tautomers of diphosphocytosine are shown to be much more stable than the keto-enol and diketo forms. The tautomers/rotamers stability could be ranked as PC3 = PC12 < PC2 = PC11 < PC1 < PC10 < PC8 < PC9 < PC15 < PC16 < PC6 ~ PC7 < PC13 < PC4 ~ PC14 < PC5. This stability order was discussed in the light of stereo and electronic factors. Solvation effect has been modeled in a high dielectric solvent, water using the polarized continuum model (PCM). Consideration of the solvent causes some reordering of the relative stability of diphosphocytosine tautomers: PC3 ~ PC12 ~ PC2 ~ PC11 < PC1 < PC10 < PC8 < PC9 < PC15 ~ PC16 < PC13 < PC6 ~ PC7 ~ PC14 < PC4 ~ PC5.
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Jensen, Matt, Trent Stellingwerff, Courtney Pollock, James Wakeling i Marc Klimstra. "Can Principal Component Analysis Be Used to Explore the Relationship of Rowing Kinematics and Force Production in Elite Rowers during a Step Test? A Pilot Study". Machine Learning and Knowledge Extraction 5, nr 1 (17.02.2023): 237–51. http://dx.doi.org/10.3390/make5010015.
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Investigating the relationship between the movement patterns of multiple limb segments during the rowing stroke on the resulting force production in elite rowers can provide foundational insight into optimal technique. It can also highlight potential mechanisms of injury and performance improvement. The purpose of this study was to conduct a kinematic analysis of the rowing stroke together with force production during a step test in elite national-team heavyweight men to evaluate the fundamental patterns that contribute to expert performance. Twelve elite heavyweight male rowers performed a step test on a row-perfect sliding ergometer [5 × 1 min with 1 min rest at set stroke rates (20, 24, 28, 32, 36)]. Joint angle displacement and velocity of the hip, knee and elbow were measured with electrogoniometers, and force was measured with a tension/compression force transducer in line with the handle. To explore interactions between kinematic patterns and stroke performance variables, joint angular velocities of the hip, knee and elbow were entered into principal component analysis (PCA) and separate ANCOVAs were run for each performance variable (peak force, impulse, split time) with dependent variables, and the kinematic loading scores (Kpc,ls) as covariates with athlete/stroke rate as fixed factors. The results suggested that rowers’ kinematic patterns respond differently across varying stroke rates. The first seven PCs accounted for 79.5% (PC1 [26.4%], PC2 [14.6%], PC3 [11.3%], PC4 [8.4%], PC5 [7.5%], PC6 [6.5%], PC7 [4.8%]) of the variances in the signal. The PCs contributing significantly (p ≤ 0.05) to performance metrics based on PC loading scores from an ANCOVA were (PC1, PC2, PC6) for split time, (PC3, PC4, PC5, PC6) for impulse, and (PC1, PC6, PC7) for peak force. The significant PCs for each performance measure were used to reconstruct the kinematic patterns for split time, impulse and peak force separately. Overall, PCA was able to differentiate between rowers and stroke rates, and revealed features of the rowing-stroke technique correlated with measures of performance that may highlight meaningful technique-optimization strategies. PCA could be used to provide insight into differences in kinematic strategies that could result in suboptimal performance, potential asymmetries or to determine how well a desired technique change has been accomplished by group and/or individual athletes.
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Bijarania, Subhash, Anil Pandey, Mainak Barman, Monika Shahani i Gharsi Ram. "Assesment of divergence among soybean [Glycine max (L.) Merrill] genotypes based on phenological and physiological traits". Environment Conservation Journal 23, nr 1&2 (11.02.2022): 72–82. http://dx.doi.org/10.36953/ecj.021808-2117.
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A study was conducted to understand genetic divergence in Randomized complete block design accommodating 30 soybean [Glycine max (L.) Merrill] genotypes randomly in three replications. These genotypes were evaluated for twenty-seven traits: five phenological, nine agro-morphological, eight physiological traits (from field-trial) and five physiological traits (from laboratory experiment) recorded and subjected to PCA (Principal Component Analysis) and cluster analysis. Among all the studied cultivars, significant diversity, as well as analysis of dispersion, was recorded for different agro-morphological characters. D2-statistic (Tocher method) framed (generalized distance-based) nine clusters: largest with eight and five were oligo-genotypic. Harvest index>seed yield per plant>germination relative index>seedling dry weight contributed maximum towards total divergence. From the most divergent clusters, 21 crosses involving cluster v genotypes (PS-1347, RKS-18, PS-1092, NRC-142, VLS-94, NRC-136, and Shalimar Soybean-1) with monogenotypic cluster VII (AMS-2014), VIII (RSC-11-15) and III (RSC-10-71) suggested for future hybridization. Out of eighteen, only eight principal components revealed more than 1.00 eigen value and exhibited about 85.03% variability among the traits studied. The highest variability (25.41%) by PC1 followed by PC2 (15.60%), PC3 (12.35%), PC4 (10.13%), PC5 (7.20%), PC6 (5.43%), PC7 (4.80%) and PC8 (4.11%) for characters under study.
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Abarza, Liliann, Pablo Acuña-Mardones, Cristina Sanzana-Luengo i Víctor Beltrán. "Determination of Morphogeometric Patterns in Individuals with Total Mandibular Edentulism in the Interforaminal Region from Cone Beam Computed Tomography (CBCT) Scans: A Pilot Study". Applied Sciences 12, nr 8 (10.04.2022): 3813. http://dx.doi.org/10.3390/app12083813.
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The aim of this study was to determine the morphogeometric patterns of the interforaminal region from cone beam computed tomography (CBCT) scans of individuals with total mandibular edentulism. CBCT images were obtained from 40 patients with total edentulism who are older (12 men and 28 women; average age of 69.5 ± 9.4 years) and who wore a non-implant-supported, lower, removable, total prosthesis. We conducted a two-dimensional (2D) morphogeometric analysis of the Digital Imaging and Communication in Medicine (DICOM) files from the CBCT scans, and five equidistant cross sections were planned. For the three-dimensional (3D) morphogeometric analysis, standard triangular language (STL) files were obtained after segmentation of the interforaminal mandibular region, and four anatomical landmarks and their respective curves were digitized. The patterns among the shapes were determined using principal component analysis (PCA) on MorphoJ software (version 1.07a). The results of the 2D morphogeometric analyses for PCA of the interforaminal mandibular paramedian region were PC1 or elongated drop shape, 54.78%; PC2 or wineskin shape, 17.65%; PC3 or pear shape, 11.77%; and PC4 or eggplant shape, 5.71%, and those for PCA of the symphyseal region were PC1 or elongated drop shape, 62.13%; PC2 or ovoid shape, 11.64%; PC3 or triangular shape, 9.71%; and PC4 or tuber shape, 4.96%. The results of the 3D morphogeometric analyses for the interforaminal hemimandibular region were PC1, 59.83%; PC2, 10.39%; PC3, 7.67%; and PC4, 5.09%. This study provides relevant information for future clinical guidelines on prosthetics and implants, in addition to proposing the use of new technologies that support diagnosis and treatment in patients with edentulism.
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Sinha, Surabhi, Niraj Kumar, Bhavana P., H. C. Lal, Binay Kumar i C. S. Mahto. "Genetic Diversity Analysis in Medium Duration Pigeonpea [Cajanus cajan (L) Millsp.] Germplasm for Different Agronomic Traits and Biotic Factors". Journal of Advances in Biology & Biotechnology 27, nr 5 (24.04.2024): 712–20. http://dx.doi.org/10.9734/jabb/2024/v27i5833.
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For the people living in tropical and sub-tropical regions, Pigeon pea [Cajanus cajan (L) Millsp.] is a very essential pulse crop because of its high nutrition along with several important features such as feed and fodder for animal consumption, fuel for household work etc. Despite being a multipurpose and attractive crop, its productivity has remained up to 700-800kg ha-1. Along-with low productivity, Pigeon pea is also affected by a number of biotic stresses such as fusarium wilt, pod borer, pod fly. It is therefore, need of the hour to search for the genetic diversity present in the existing cultivars along with wild relatives and landraces. The present investigation was conducted with forty Pigeon pea germplasm to assess the genetic diversity by principal component analysis (PCA). PCA is an important statistical technique which reduces the dimension of the much large data set into a more concise data set while retaining a significant amount of information from the original data. PCA analysis revealed a significant amount of variability present in the germplasm. PC1 contributed maximum variance towards diversity (22.05%) followed by PC2 (15.87%), PC3 (11.39%), PC4 (10.18%), PC5 (9.10%) and PC6 (8.18%). Scatter plot diagram showed that genotypes number 1 (CRG 82), 5 (GJP 1721), 19 (ICPL 15062), and 31 (BAUPP-18-8) exhibited the highest diversity.
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Wulandari, Sari Ayu, Rudy Tjahyono i Dian Retno Sawitri. "PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE)". Majalah Ilmiah Teknologi Elektro 15, nr 1 (25.06.2016): 95. http://dx.doi.org/10.24843/mite.2016.v15i01p17.
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Perbedaan pigmentasi mempengaruhi metode pengenalan pola citra retinopati diabetik beserta setting poinnya. Dibutuhkan sebuah perangkat lunak, yang mampu menjadi alat bantu pengenalan citra retinopati diabetik. Telah dilakukan penelitian tentang pengenalan pola citra retinopati diabetik, dengan menggunakan citra kanal kuning (Yellow), dengan menggunakan filter gabor dan ciri yang diambil dari tiap citra adalah ciri rerata (Means), variasi Varians), skewness dan entropy, yang dilanjutkan dengan ekstraksi ciri PCA (Principle Component Analysis). Pada ekstraksi ciri PCA, Matriks hasil PCA merupakan matriks bujur sangkar, yang jumlah kolomnya, sama dengan jumlah ciri. Penelitian menggunakan 4 ciri, dengan demikian, terdapat 4 buah PC (Principle Component), PC1, PC2, PC3 dan PC4. Pada artikel ini akan dibahas mengenai tingkat akurasi tertinggi dari penggunaan pasangan PC. Tingkat akurasi, dihitung dengan menggunakan model linear dari SVM. Model dengan akurasi tertinggi dan tercepat adalah model pasangan PC1 dan PC2, yang mempunyai akurasi citra pembelajaran tertinggi yaitu 100% dan waktu tercepat, yang secara eksplisit diperlihatkan pada jumlah support vektor terkecil, yaitu 2. Pasangan yang mempunyai tingkat akurasi terburuk adalah PC3 dan PC4. Pengenalan turun pada citra pengujian, yaitu hanya 93,75%, hal ini disebabkan oleh pelebaran daerah cakupan. Pelebaran daerah cakupan kemungkinan disebabkan oleh pemilihan nilai rerata pada PCA, sebelum matriks reduksi. Pada penelitian berikutnya, bisa dilakukan dengan menggunakan pencarian nilai standart deviasi atau varians, dengan begitu, akan diketahui matriks reduksi yang mewakili sebaran angka pada matriks. DOI: 10.24843/MITE.1501.17
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Kondi, Ravi, Sonali Kar i Soumya Surakanti. "Agro-morphological and biochemical characterization and principal component analysis for yield and quality characters in fine-scented rice genotypes". Genetika 54, nr 3 (2022): 1005–21. http://dx.doi.org/10.2298/gensr2203005k.
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Forty-one fine-scented rice genotypes were evaluated for 18 agro-morphological and quality characters for characterization, and 21 quantitative characters were evaluated for principal component analysis in R-studio software. Characterization of agro-morphological traits viz., plant height, days to 50% flowering, panicle length, number of effective tillers per plant, test weight, grain length, grain breadth, grain L: B ratio, kernel length, kernel breadth, kernel dimensions, awns, colour of awns, distribution of awns, and quality traits viz., alkali spreading value, gel consistency, grain aroma, and amylose content showed huge diversity among the genotypes. PCA revealed that PC1 showed the highest amount of variance (32.0%) followed by PC2 (15.7%), PC3 (9.0%), PC4 (8.1%), PC5 (7.8%), PC6 (5.4%) for quantitative characters. Out of 21 principal components, only 6 showed an eigenvalue greater than 1 and contributes about 78.1% total variance Genotypes in PC1 showed higher values for grain L: B ratio and kernel L: B ratio. Similarly, PC2 showed higher variable values for characters like test weight, kernel length, grain length, grain breadth, alkali spreading value, grain yield per plot and amylose content. PC3 for harvest index, panicle length, gel consistency, no. of effective tillers per plant and head rice recovery. PC4 for characters like plant height, kernel breadth and days to 50% flowering. PC5 for characters like kernel elongation ratio, and filled grains per panicle. PC6 for characters like no. of tillers in a square meter and no. of panicles in a square meter. This pre-breeding characterization study may be useful in finding potential genotypes which are having both yield and quality characters which may be useful in breeding for high-yielding varieties with good-quality characters.
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Gibson, Sigrid, i Margaret Ashwell. "Dietary patterns among British adults: compatibility with dietary guidelines for salt/sodium, fat, saturated fat and sugars". Public Health Nutrition 14, nr 8 (6.05.2011): 1323–36. http://dx.doi.org/10.1017/s1368980011000875.
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AbstractObjectiveTo examine dietary patterns among British adults, associations with Na and macronutrient intakes, and implications for dietary advice.DesignPrincipal component analysis of 7 d weighed dietary records.SubjectsAdults aged 19–64 years (n 1724).SettingNational Diet and Nutrition Survey (2000/2001).ResultsHigh Na intake was associated with more energy-dense diets, higher in fat and SFA (percentage of energy) but lower in non-milk extrinsic sugars (NMES). Eight patterns (PC1 to PC8) explained 40 % of the total variance in food intakes. Three patterns – PC3 (high loadings on bread, fats and cheese), PC2 (meat products, eggs and chips) and PC7 (red meat, sauces and alcohol) – were associated with high Na intake. Of these, PC3 correlated with high Na density and Na:K ratio, while PC2 correlated with fat. By contrast, three patterns – ‘health-conscious’ (PC1; vegetables, fruit, fruit juice, fish), ‘breakfast cereals and milk’ (PC6) and ‘chicken and rice’ (PC8) – were associated with modest Na intake, lower Na density and lower fat and SFA. PC2 was positively correlated, and PC1 was negatively correlated, with adding salt to food. Other patterns were ‘tea/coffee and cakes’ (PC4; associated with high SFA and NMES) and ‘soft drinks and snacks’ (PC5; associated with high NMES but not fat or SFA). The dietary patterns of males and females differed slightly.ConclusionsDietary patterns PC1, PC6, PC8 (vegetables, fruit, fish, milk, breakfast cereals, poultry) were broadly compatible with guidelines for salt, fat, SFA and NMES. However, other patterns tended to be high in either salt or NMES.
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Sekandari, Milad, i Amin Beiranvand Pour. "Fuzzy Logic Modeling for Integrating the Thematic Layers Derived from Remote Sensing Imagery: A Mineral Exploration Technique". Environmental Sciences Proceedings 6, nr 1 (25.02.2021): 8. http://dx.doi.org/10.3390/iecms2021-09349.
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In this study, fuzzy logic modeling was implemented to fuse the thematic layers derived from principal components analysis (PCA) in order to generate mineral prospectivity maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing data were used. A spatial subset zone of the Central Iranian Terrane (CIT), Iran was selected in this study. The PCA technique was implemented for the processing of the datasets and for the production of alteration thematic layers. PCA4, PCA5, and PCA8 were selected as the most rational alteration thematic layers of ASTER for the generation of a prospectivity map. The fuzzy gamma operator was used to fuse the selected alteration thematic layers. The PCA3, PCA4, and PCA6 thematic layers (most rational alteration thematic layers) of WV-3 were fused using the fuzzy AND operator. Field reconnaissance, X-ray diffraction (XRD) analysis, and Analytical Spectral Devices (ASD) spectroscopy were carried out to verify the image processing results. Subsequently, mineral prospectivity maps were produced showing high-potential zones of Pb-Zn mineralization in the study area.
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Lee, Myeounggon, Changhong Youm, Byungjoo Noh i Hwayoung Park. "Gait Characteristics Based on Shoe-Type Inertial Measurement Units in Healthy Young Adults during Treadmill Walking". Sensors 20, nr 7 (8.04.2020): 2095. http://dx.doi.org/10.3390/s20072095.
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This study investigated the gait characteristics of healthy young adults using shoe-type inertial measurement units (IMU) during treadmill walking. A total of 1478 participants were tested. Principal component analyses (PCA) were conducted to determine which principal components (PCs) best defined the characteristics of healthy young adults. A non-hierarchical cluster analysis was conducted to evaluate the essential gait ability, according to the results of the PC1 score. One-way repeated analysis of variance with the Bonferroni correction was used to compare gait performances in the cluster groups. PCA outcomes indicated 76.9% variance for PC1–PC6, where PC1 (gait variability (GV): 18.5%), PC2 (pace: 17.8%), PC3 (rhythm and phase: 13.9%), and PC4 (bilateral coordination: 11.2%) were the gait-related factors. All of the pace, rhythm, GV, and variables for bilateral coordination classified the gait ability in the cluster groups. We suggest that the treadmill walking task may be reliable to evaluate the gait performances, which may provide insight into understanding the decline of gait ability. The presented results are considered meaningful for understanding the gait patterns of healthy adults and may prove useful as reference outcomes for future gait analyses.
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Gadelha, Antonio Jose Ferreira, Clarice Oliveira da Rocha, Jose Germano Veras Neto i Mirelly Alexandre Gomes. "Multivariate statistical analysis of physicochemical parameters of groundwater quality using PCA and HCA techniques". Ecletica Quimica 48, nr 4 (3.10.2023): 37–47. http://dx.doi.org/10.26850/1678-4618eqj.v48.4.2023.p37-47.
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Multivariate analysis techniques are powerful tools in the study of groundwater quality, providing an expanded view of quality parameters. This work presents a multivariate analysis of groundwater quality in the city of Sousa, Paraíba state, through the techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA). Samples from 13 tubular wells were collected in different districts of the city of Sousa, during the rainy and dry seasons. For these samples, 11 parameters were analyzed: hydrogenic potential (pH), total dissolved solids, total alkalinity, carbonates, bicarbonates, total hardness, magnesium, calcium, sodium, potassium, and chlorides. PC1, PC2, PC3 and PC4 explain 87.48% of the total variance of the data. The PCA shows that there was a change in patterns between the analyzed periods. The correlation matrix corroborates the PCA data, showing the relationships between the physical-chemical variables evaluated. The HCA confirmed the correlations between the samples, making it possible to assess the degree of similarity between the composition of the wells and between the parameters evaluated.
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OWEN, H., i L. E. MATHER. "PCA OR PAA ?" British Journal of Anaesthesia 64, nr 5 (maj 1990): 650–51. http://dx.doi.org/10.1093/bja/64.5.650-b.
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ARMITAGE, E. N. "PCA OR PAA ?" British Journal of Anaesthesia 64, nr 5 (maj 1990): 651. http://dx.doi.org/10.1093/bja/64.5.651.
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Okoda, Yuki, Yoko Oya, Shotaro Abe, Ayano Komaki, Yoshimasa Watanabe i Satoshi Yamamoto. "Molecular Distributions of the Disk/Envelope System of L483: Principal Component Analysis for the Image Cube Data". Astrophysical Journal 923, nr 2 (1.12.2021): 168. http://dx.doi.org/10.3847/1538-4357/ac2c6c.
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Abstract Unbiased understanding of molecular distributions in a disk/envelope system of a low-mass protostellar source is crucial for investigating physical and chemical evolution processes. We have observed 23 molecular lines toward the Class 0 protostellar source L483 with ALMA and have performed principal component analysis (PCA) for their cube data (PCA-3D) to characterize their distributions and velocity structures in the vicinity of the protostar. The sum of the contributions of the first three components is 63.1%. Most oxygen-bearing complex organic molecule lines have a large correlation with the first principal component (PC1), representing the overall structure of the disk/envelope system around the protostar. Contrary, the C18O and SiO emissions show small and negative correlations with PC1. The NH2CHO lines stand out conspicuously at the second principal component (PC2), revealing more compact distribution. The HNCO lines and the high-excitation line of CH3OH have a similar trend for PC2 to NH2CHO. On the other hand, C18O is well correlated with the third principal component (PC3). Thus, PCA-3D enables us to elucidate the similarities and the differences of the distributions and the velocity structures among molecular lines simultaneously, so that the chemical differentiation between the oxygen-bearing complex organic molecules and the nitrogen-bearing ones is revealed in this source. We have also conducted PCA for the moment 0 maps (PCA-2D) and that for the spectral line profiles (PCA-1D). While they can extract part of characteristics of the molecular line data, PCA-3D is essential for comprehensive understandings. Characteristic features of the molecular line distributions are discussed on NH2CHO.
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Chandraker, Pratibha, Bhawana Sharma, Mangla Parikh i Ritu R. Saxena. "Assessment of Genetic Diversity in Aromatic Short Grain Rice (Oryza sativa L.) Genotypes using PCA and Cluster Analysis". International Journal of Plant & Soil Science 36, nr 5 (18.03.2024): 82–94. http://dx.doi.org/10.9734/ijpss/2024/v36i54504.
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A population panel of 90 aromatic short grain rice accessions were evaluated for 26 agro-morphological and quality traits using principal component analysis (PCA) and cluster analysis for the determination of genetic variation pattern, and identification of the major traits contributing to the diversity. First six principal components (PCs) exhibited Eigenvalue more than one with 74.4 per cent of total variability among the 26 characters. The PC1 showed 24.55% while, PC2, PC3, PC4, PC5 and PC6 exhibited 15.48 %, 11.48 %, 9.96 %, 7.89 % and 5.12 % variability, respectively among the accessions for the traits under study. The results of PCA suggested that characters such as effective tillers per plant, number of spikelets per panicle, number of filled spikelets per panicle, spikelet fertility %, milling %, head rice recovery %, kernel length and kernel length after cooking were the principal discriminatory characteristics of aromatic short grain accessions of rice. Seven divergent clusters were formed by UPGMA clustering method. The pattern of group constellation proved the existence of significant amount of variability. The intra cluster distance ranged from 0.00 (cluster VI) to 6.33 (cluster V). The inter cluster distance was maximum between cluster VI and VII (18.854) and minimum between cluster II and cluster IV (7.673). To realize much variability and high heterotic effect, parents should be selected from two clusters having wider inter-cluster distance. Considering the importance of genetic distance and relative contribution of characters towards total divergence, the present study indicated that parental lines selected from cluster VI (IGSR -3-1-5) for number of spikelets per panicle, number of filled spikelets per panicle, grain length, kernel length and length breadth ratio, and from cluster VII (Khasakani, Kolijoha) for effective tillers per plant, 1000 grain weight, grain yield per plant, harvest index, grain breadth, length breadth ratio after cooking and elongation index could be used in crossing programmes to achieve desired segregants.
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Uikey, Shivani, Stuti Sharma, M. K. Shrivastava i Pawan K. Amrate. "Study of principal component analyses for pod traits in soybean". INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, nr 2 (15.06.2021): 341–49. http://dx.doi.org/10.15740/has/ijas/17.2/341-349.
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Yield being a complex entity influenced by several components and environments. PCA is a well-known method of dimension reduction that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set (Massay, 1965 and Jolliffie, 1986). In present study, PCA preformed for pod and yield traits revealed that out of fourteen, only five principal components (PCs) exhibited more than 1.0 eigen value and showed about 70.44% total variability among the traits. PC1 showed 23.83% variability with eigen value 3.33 indicating the maximum variation in comparison to other four PCs. The PC1 was more related to traits viz., days to 50% flowering, total number of pods per plant, number of seeds per plant, biological yield per plant and seed yield per plant. Thus, PC1 allowed for simultaneous selection of yield related traits and it can be regarded as yield factor. PC2 exhibited positive effect for days to maturity, number of primary branches per plant and number of nodes per plant, The PC3 was more related to number of two seeded pods per plant, 100 seed weight and harvest index traits, whereas PC4 was more loaded with three seeded pods per plant. PC5 was more related to plant height and number of one seeded pods per plant. A high value of PC score of a particular advanced line in a particular PC denotes high value for those variables. Genotypes namely KS 103, JS 20-15, PS 1423, Cat 1957, Cat 1958, JS 20-06 and JS 20-66 can be considered an ideotype breeding material for selection and for further utilization in precise breeding programme.
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Williams, Greetty, i Y. Anbuselvam. "Assessment of Genetic Divergence through Principal Component Analysis and Clustering in Tomato Germplasm Accessions". Environment and Ecology 41, nr 4D (grudzień 2023): 3060–65. http://dx.doi.org/10.60151/envec/ylss4838.
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The base material of this study comprises of 104 tomato accessions including local landraces, varieties and germplasm collections. The collected tomato accessions were evaluated using 13 quantitative traits by Principal Component Analysis (PCA) and Hierarchial clustering. PCA was done to quantify diversity among the germplasm accessions and also the contribution of individual traits towards diversity. In our study, only the first four (PC1, PC2, PC3 and PC4) of the thirteen principal components yielded eigen value more than one indicating the greater influence of identified traits under study. The first six PCs accounts for 84% of variability whereas, PC1 exhibited 41% of total variability. Cluster analysis aids to classify the genotypes based on the grouping pattern of the accessions under evaluation. According to the dendrogram obtained, cluster analysis grouped 104 tomato accessions into two significant clusters. The first cluster consists of 16 genotypes whereas, the second cluster consists of 88 genotypes. Among the genotypes used in this study EC617055, EC617061, EC638302, Periakulam local and EC631390 were found to be best performing in terms of yield and quality. These accessions can be used as a base material in future breeding programs.
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Munkhsuren, Badrakh, Batkhuyag Enkhdalai, Tserendash Narantsetseg, Khurelchuluun Udaanjargal, Demberel Orolmaa i Dolgorjav Munkhjin. "Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia". Mongolian Geoscientist 26, nr 53 (30.12.2021): 37–54. http://dx.doi.org/10.5564/mgs.v26i53.1790.
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This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.
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Alkhasawneh, Mutasem Shabeb. "Software Defect Prediction through Neural Network and Feature Selections". Applied Computational Intelligence and Soft Computing 2022 (26.09.2022): 1–16. http://dx.doi.org/10.1155/2022/2581832.
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Software failure such as software defect causes billion of dollar loss every year. Software failure also affects billion of people worldwide. Inadequate software testing can cause software failure. To predict the software defect, this study proposed a model consisting of feature selection and classifications. The correlation base method was used for feature selection, and radial base function neural network (RBF) was used for classification. Also, for testing the proposed system, fourteen NASA data sets were used including CM1, JM1, KC1, KC2, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. The data set was divided using the well-known K-cross-validation methods which were performed to divide the data set for training and testing the RBF. The RBF were trained and tested before and after feature selections. Precision, recall, F-measure, and accuracy are four methods used to evaluate the performance of the proposed methods. The precision obtained for the fourteen data sets was CM1, 94.01%; JM1, 85.18%; KC1, 83.24%; KC2, 81.27%; KC3, 79.30%; KC4, 85.29%; MC1, 99.89%; MC2, 73.27%; MW1, 90.90%; PC1, 98.79%; PC2, 100%; PC3, 95.67%; PC4, 95.12%; and PC5, 80.89%. Recall was as follows: CM1, 95.78%; JM1, 87.89%; KC1, 86.24%; KC2, 83.82%; KC3, 82.10%; KC4, 86.28%; MC1, 100%; MC2, 76.67%; MW1, 92.09%; PC1, 99.98%; PC2, 100%; PC3, 96.23%; PC4, 95.17%; and PC5, 81.80%. F-measure was as follows: CM1, 0.95; JM1, 0.87; KC1, 0.83; KC2, 0.82; KC3, 0.85; KC4, 0.86; MC1, 0.99; MC2, 0.76; MW1, 0.95; PC1, 0.99; PC2, 0.99; PC3, 0.97; PC4, 0.95; and PC5, 0.80. The accuracy obtained was as follows: CM1, 93.99%; JM1, 84.87%; KC1, 83.25%; KC2, 79.11%; KC3, 78.25%; KC4, 83.18%; MC1, 99.01%; MC2, 70.18%; MW1, 88.90%; PC1, 98.99%; PC2, 99.80%; PC3, 94.11%; PC4, 94.4%; and PC5, 79.02%. The proposed method results were compared with the result obtained from different methods. The proposed model obtained better results than other methods for data set CM1, KC4, MC1, PC1, PC2, PC3, PC4, and PC5.
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Freyer, C., L. M. Kilpatrick, L. A. Salamonsen i G. Nie. "Pro-protein convertases (PCs) other than PC6 are not tightly regulated for implantation in the human endometrium". Reproduction 133, nr 6 (czerwiec 2007): 1189–97. http://dx.doi.org/10.1530/rep-06-0285.
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Pro-protein convertases (PCs) are a family of serine proteases (furin, PC1/3, PC2, PACE4, PC4, PC5/6, PC7/8) responsible for post-translational processing and activation of inactive precursors of many regulatory proteins. Endometrial PC6 is critical for implantation in mice and for decidualization of human endometrial stromal cells (ESCs). This study investigated the endometrial expression of other PCs during the menstrual cycle and early pregnancy to elucidate potential redundancies. Furin, PC4, PACE4, and PC7 along with PC6 transcripts were detected in total endometrial RNA, whereas PC1 and PC2 transcription levels were negligible. Quantitative RT-PCR demonstrated highest levels of furin mRNA during menstruation and lowest levels during the proliferative phase. Furin protein was immunolocalized in endometrial luminal and glandular epithelia, stromal fibroblasts, endothelia, and leukocytes. PACE4 and PC7 proteins were also immunodetected in endometrial stroma and glands. Total furin, PC7, and PACE4 proteins were constitutive in both stromal and glandular compartments throughout the cycle and during first trimester pregnancy. Furthermore, Furin and PC7 transcription was unaltered during decidualization of ESCsin vitroin contrast to PC6 which is significantly up-regulated during decidualization. Thus, whereas PC6 is tightly regulated during endometrial preparation for implantation, furin, PACE4, and PC7 are constitutively expressed in human endometrium, but must be considered if PC6 is to be targeted for manipulation of fertility.
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Sonaniya, Rahul, Rajani Bisen i Pallavi Sonaniya. "Assessment of Exotic Sesame (Sesamum indicum) Accessions through Principal Component Analysis". International Journal of Environment and Climate Change 13, nr 11 (7.10.2023): 282–90. http://dx.doi.org/10.9734/ijecc/2023/v13i113170.
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The present research conducted over 96 exotic sesame accessions with five checks, to identify the minimum number of components, which could explain maximum variability out of the total variability using Principal Component Analysis (PCA); The investigation was performed under Project Co-ordinating Unit (Sesame and Niger) Research Farm, JNKVV, Jabalpur (M.P.) during kharif 2018 using Augmented block design. Among the studied traits, Component 1 had the contribution from the traits viz., number of primary branches per plant, number of capsules per plant, number of seeds per capsule, oil content and seed yield/plant, which accounted 30.71% to the total variability. Days to flower initiation and days to 50% flowering had contributed 17.11% to the total variability in component 2. The remaining variabilities of 11.26%, 9.94%, 7.48% and 6.73% were consolidated in PC3, PC4, PC5 and PC6 respectievely by various traits like number of secondary branches/plant, capsule length, days to maturity, thousand seed weight and plant height . The cumulative variance of 83.23% of total variation among 12 characters was explained by the first six axes. On the basis of PC scores PC1, PC3 and PC5 accounting mainly to yield and quality traits containing accesions viz., EC-334998, ES-38, EC-346426, EC-334958, EC-340538, RT-351 and GT-10 might be further utilized in breeding programme.
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Zilhadia, Zilhadia, Bayyinah Bayyinah i Ofa Suzanti Betha. "Metode Analisis Gelatin Sapi dan Babi pada Kapsul Lunak menggunakan Kromatografi Cair Kinerja Tinggi dan Analisis Komponen Utama". Jurnal Sains Farmasi & Klinis 10, nr 1 (30.04.2023): 129. http://dx.doi.org/10.25077/jsfk.10.1.129-136.2023.
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Penggunaan gelatin babi pada kapsul lunak menimbulkan kekhawatiran konsumen terkait status kehalalannya. Penelitian ini bertujuan untuk mengetahui perbedaan gelatin sapi dan babi yang digunakan pada kapsul lunak menggunakan High Performance Liquid Chromatography (HPLC) kombinasi Principal Component Analysis (PCA) melalui data komposisi asam amino. Metode HPLC dilakukan dengan derivatisasi prakolom dan fase gerak sistem gradien menggunakan pelarut dapar asetat-fosfat dan acetonitrile. Persen tinggi puncak yang menunjukkan konsentrasi asam amino digunakan sebagai data yang diinput ke dalam software kemometrik dan menu yang dipilih adalah PCA. Hasil komposisi asam amino yang diolah dengan PCA menunjukkan bahwa gelatin babi memiliki nilai PC1 positif dan PC2 negatif sedangkan gelatin sapi memiliki nilai PC1 dan PC2 positif. Kapsul lunak gelatin babi memiliki nilai PC1 negatif dan PC2 positif sedangkan kapsul lunak gelatin sapi memiliki nilai PC1 dan PC2 negatif. Dapat disimpulkan bahwa gelatin babi dan sapi serta kapsul lunak yang terbuat dari gelatin babi dan sapi dapat dibedakan berdasarkan perbedaan profil asam amino. Namun penelitian ini belum dapat mengklasifikasikan gelatin yang ada pada kapsul lunak yang ada di pasaran.
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Parker, James, i Lina Lundgren. "Surfing the Waves of the CMJ; Are There between-Sport Differences in the Waveform Data?" Sports 6, nr 4 (8.12.2018): 168. http://dx.doi.org/10.3390/sports6040168.
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The ability to analyse countermovement jump (CMJ) waveform data using statistical methods, like principal component analysis, can provide additional information regarding the different phases of the CMJ, compared to jump height or peak power alone. The aim of this study was to investigate the between-sport force-time curve differences in the CMJ. Eighteen high level golfers (male = 10, female = 8) and eighteen high level surfers (male = 10, female = 8) performed three separate countermovement jumps on a force platform. Time series of data from the force platform was normalized to body weight and each repetition was then normalized to 0–100 percent. Principal component analyses (PCA) were performed on force waveforms and the first six PCs explained 35% of the variance in force parameters. The main features of the movement cycles were characterized by magnitude (PC1 and PC5), waveform (PC2 and PC4), and phase shift features (PC3). Surf athletes differ in their CMJ technique and show a greater negative centre of mass displacement when compared to golfers (PC1), although these differences are not necessarily associated with greater jump height. Principal component 5 demonstrated the largest correlation with jump height (R2 = 0.52). Further studies are recommended in this area, to reveal which features of the CMJ that relate to jumping performance, and sport specific adaptations.
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Melo, Thiago de Sousa, Wandrick Hauss De Sousa, João Paulo de Farias Ramos, Felipe Queiroga Cartaxo, Larissa Kellen da Cunha Morais, Thais Thatiane dos Santos Souza, Francinilda Alves De Sousa i in. "Multivariate techniques for analyzing morphological characteristics of cactus pear genotypes in semiarid Brazil". OBSERVATÓRIO DE LA ECONOMÍA LATINOAMERICANA 22, nr 2 (19.02.2024): e3299. http://dx.doi.org/10.55905/oelv22n2-135.
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The objective of this study was to identify variables with better discriminatory power between treatments and reduce the dimensions of the original data set. Multivariate principal component analysis (PCA) and dendrogram clustering techniques were used on a data set of morphological characteristics of cactus pear genotypes. The palm genotypes of the genera Nopalea and Opuntia were arranged in a completely randomized design, with 30 treatments and six replications. After 180 days of cultivation, morphological characteristics were evaluated, including plant height (PH) and width (PW), total number of plant cladodes (NCP), length (CL), width (CW), perimeter (CP), cladode thickness (CT), cladode area (CA), active photosynthetic area (APA) and cladode area index (CAI). Varimax rotation with Kaiser normalization was used to prevent variables with greater explanatory capacity from affecting the rotation. Measurements were obtained as part of cluster model generation, using a full set of morphological variables for subsequent analysis of principal component and cluster analyses. The main components obtained were efficient in reducing the total variation accumulated in 30 original variables correlated to two linear combinations, which explained 81.04% of the total variation present in the original variables. Grouping using the Ward hierarchical method formed 3 groups, namely, group I consisted of genotypes F27, F48, G1, G14, G15, G20, G3, PC2, PCD, PM, T72, V07 and V20; group II was composed of the genotypes BA, F16, F34, G4, G5, OEA, OEM, POEA, T35, T80 and V19 and; group III was composed of genotypes F24, PC1, PC3, PC4, PC5 and V17. PCA demonstrated that APA, CAI, CA, NCP, PH, PW and CT are morphological variables that determine the best palm characteristics. The association of cactus morphological variables varied according to the genotypes, within each genus Nopalea and Opuntia. The genotypes with the best morphological characteristics were F27, F48, G1, G14, G15, G20, G3, PC2, PCD, PM, T72, V07 and V20.
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Nguyen, Thanh Giao, i Quang Minh Vo. "The Categorized of Surface Water Quality Variation using Multivariate Statistical Approaches: A Case Study of Ben Tre Province, Vietnam". Trends in Sciences 19, nr 8 (29.03.2022): 3468. http://dx.doi.org/10.48048/tis.2022.3468.
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This study evaluated surface water quality changes in Ben Tre province using multivariate statistical analyses. The water monitoring data were collected from the Department of Natural Resources and Environment of Ben Tre province in 2020, which 13 water parameters have been measured, including pH, water temperature (T), salinity (Sal), turbidity (Turb), total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonium (NH4+_N), nitrate (NO3-_N), orthophosphate (PO43-_P), iron (Fe) and coliform bacteria. Water quality was assessed using national technical regulations on surface water quality of QCVN 08-MT: 2015/BTNMT. Spatiotemporal variation of water quality was evaluated using cluster analysis (CA) while potential pollution sources and key water variables influencing water quality were evaluated using principal component analysis (PCA). The findings showed that the water parameters of turbidity, salinity, TSS, DO, BOD, COD, NH4+_N, PO43-_P, Fe and coliform exceeded the allowable limits of QCVN 08-MT:2015/BTNMT. The water parameters of BOD, COD, NO3-_N, PO43-_P in the rainy season tended to be higher than those in the dry season. Cluster analysis divided surface water quality into 7 clusters, thus reducing 8 sampling sites, and 2 monitoring times of frequency. Principal component analysis identified 13 potential pollution sources affecting surface water quality in the study area, in which, 76.10 % of the variation in surface water quality were contributed by PC1, PC2, PC3, PC4 and PC5. PCA results also showed that 13 observed water parameters significantly contribute to the variation in water quality. The current study results could be very useful in reducing sites and frequency of surface water quality monitoring in Ben Tre province. HIGHLIGHTS The water quality in the study area was polluted by suspended particulate matters, organics, nutrients, heavy metals and salinity. Thirteen potential water pollution sources have been identified by principal component ananlysis (PCA), in which, 76.1 % of surface water quality variations were explained by five key sources sources of PC1-PC5 The water quality parameters of BOD, COD, NO3-_N, PO43-_P in the rainy season tended to be higher than those in the dry season Cluster analysis (CA) suggested the current surface water monitoring sites and frequency of the sampling could be reduced by 8 and 2, respectively, thus, saving the monitoring cost GRAPHICAL ABSTRACT
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Tiwari, Priya, i Stuti Sharma. "Principal component analyses in mungbean genotypes under summer season". INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, nr 2 (15.06.2021): 287–92. http://dx.doi.org/10.15740/has/ijas/17.2/287-292.
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Yield is a complex trait subjective to several components and environmental factors. Therefore, it becomes necessary to apply such technique which can identify and prioritize the key traits to lessen the number of traits for valuable selection and genetic gain. Principal component analysis is primarily a renowned data reduction technique which identifies the least number of components and explain maximum variability, it also rank genotypes on the basis of PC scores. PCA was calculated using Ingebriston and Lyon (1985) method. In present study, PCA performed for phenological and yield component traits presented that out of thirteen, only five principal components (PCs) exhibited more than 1.00 eigen value, and showed about 80.28 per cent of total variability among the traits. Scree plot explained the percentage of variance associated with each principal component obtained by illustrating a graph between eigen values and principal component numbers. PC1 showed 26.12 per cent variability with eigen value 3.40. Graph depicted that the maximum variation was observed in PC1 in contrast to other four PCs. The PC1 was further associated with the phenological and yield attributing traits viz., number of nodes per plant, number of pod cluster per plant, number of pod per plant. PC2 exhibited positive effect for harvest index. The PC3 was more related to yield related traits i.e., number of seeds per pod, number of seeds per plant and biological yield per plant, whereas PC4 was more loaded with phenological traits. PC5 was further related to yield and yield contributing traits i.e. number of primary branches per plant, seed yield per plant and 100 seed weight. A high value of PC score of a particular genotype in a particular PC denotes high value for those variables falling under that specific principal component. Pusa Vishal found in PC 2, in PC 3, PC 4 and PC 5, can be considered as an ideal breeding material for selection and for further deployment in defined breeding programme.
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Wang, Zhenyi, Yalei Wang i Xiaoliang Jin. "Prediction of Grade Classification of Rock Burst Based on PCA-SSA-PNN Architecture". Geofluids 2023 (13.06.2023): 1–11. http://dx.doi.org/10.1155/2023/5299919.
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The uncertainty and complexity of rock burst brings great difficulties to the prediction of rock burst grades. In order to estimate the risk grades of rock burst, an integrated method combining principal component analysis (PCA) and sparrow search algorithm (SSA) with probabilistic neural network (PNN) was proposed. Considering that the in situ stress of rock mass, the strength of rock, and the strength of rock mass are the key influencing factors of rock bursts, the maximum in situ stress σ max , maximum tangential stress σ θ , rock strength σ ci , rock mass strength σ cm , and three rock burst evaluation indexes ( σ θ / σ ci , σ ci / σ max , and σ cm / σ max ) were selected to constitute the rock burst grade evaluation index system. Forty-three groups of rock burst engineering data were gathered. After preprocessing the rock burst data using PCA, four of the new linearly independent indexes PCA1, PCA2, PCA3, and PCA4 were obtained for estimating rock burst grades. The SSA was utilized to optimize the smoothing factor in the PNN. Using PCA-SSA-PNN-based architecture, a new multi-index rock burst grade prediction method was proposed. The results from the new multi-index rock burst grade prediction method were compared with those from single- and multi-index prediction methods. It shows that the predictions from the multi-index rock burst prediction methods are closer to the actual rock burst grades than that from the single-index rock burst prediction methods; compared with other multi-index rock burst prediction methods, the prediction accuracy of PCA-SSA-PNN is greater (up to 90%) and more available in the prediction of rock burst grades. The results presented herein may provide reference for the rock burst warning.
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Benson, Lauren C., i Kristian M. O’Connor. "The Effect of Exertion on Joint Kinematics and Kinetics During Running Using a Waveform Analysis Approach". Journal of Applied Biomechanics 31, nr 4 (sierpień 2015): 250–57. http://dx.doi.org/10.1123/jab.2014-0138.
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About half of all runners sustain a running-related injury every year. Exertion may contribute to risk of injury by altering joint mechanics. The purpose of this study was to examine the effects of exertion on runners’ joint mechanics using principal component analysis (PCA). Three-dimensional motion analysis of the lower extremity was performed on 16 healthy female runners before and after their typical training run. PCA was used to determine exertion-related changes in joint mechanics at the ankle, knee, and hip. Statistical significance for repeated-measures MANOVA of the retained principal components at each joint and plane of motion was at P < .05. Exercise effects were identified at the ankle (greater rate of eversion [PC2: P = .027], and decreased plantar flexion moment [overall: P = .044] and external rotation moment [PC3: P = .003]), knee (increased adduction [overall: P = .044] and internal rotation [PC3: P = .034], and decreased abduction moment [overall: P = .045]), and hip (increased internal rotation [PC1: P = .013] and range of mid- to late-stance rotation [PC2: P = .009], and decreased internal rotation moment [PC1: P = .001]). The observed changes in running mechanics reflect a gait profile that is often linked to running injury. The effects of more strenuous activity may result in mechanics that present an even greater risk for injury.
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Yuliantini, Anne. "DETEKSI TESPONG (Oenanthe javanica) PADA BAHAN BAKU DAUN ASHITABA (Angelica keiskei) MENGGUNAKAN METODE FTIR YANG DIKOMBINASIKAN DENGAN PCA". INDONESIA NATURAL RESEARCH PHARMACEUTICAL JOURNAL 5, nr 2 (30.10.2020): 114–23. http://dx.doi.org/10.52447/inspj.v5i2.4230.
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Ashitaba (Angelica keiskei) merupakan salah satu tanaman yang digunakan sebagai bahan baku obat tradisional dan tespong (Oenanthe javanica) diketahui sebagai tanaman yang satu famili dengan ashitaba. Karena ketersediaan ashitaba yang sedikit ditambah dengan harganya yang relative mahal, dapat menjadi alasan ditambahkannya bahan lain dalam bahan baku ashitaba. Tujuan dari penelitian ini adalah untuk mengidentifikasi adanya tespong pada serbuk daun ashitaba menggunakan metode FTIR yang dikombinasikan dengan PCA. Penelitian ini terdiri dari 4 tahapan utama, yaitu determinasi tanaman, maserasi dengan etanol, pengukuran spectrum IR, dan analisis PCA. Hasil analisis PCA menunjukkan nilai PC1 dan PC2 berturut-turut sebesar 71 dan 22% dengan nilai eigen value lebih dari 1 dan plot PCA menggambarkan keterpisahan daerah antara ashitaba dan tespong. Dari ketiga sampel yang diproyeksikan terhadap plot PCA, terdapat sampel yang diduga mengandung tespong dan bahan campuran lain. Dari hasil penelitian ini disimpulkan bahwa metode FTIR yang dikombinasikan dengan PCA dapat menjadi metode alternative dalam mendeteksi tespong dalam bahan baku ashitaba.Kata kunci: Ashitaba; FTIR; PCA; tespong
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Shah, Vrutangkumar, Daniel Muzyka, Carolyn Guidarelli, Kristen Sowalsky, Kerri M. Winters-Stone i Fay B. Horak. "Abstract 4397: Chemotherapy-induced peripheral neuropathy and falls in cancer survivors relate to digital balance and gait impairments". Cancer Research 83, nr 7_Supplement (4.04.2023): 4397. http://dx.doi.org/10.1158/1538-7445.am2023-4397.
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Abstract Background and Aim: Standing postural sway and gait tests with body-worn inertial sensors provide a multitude of objective, digital balance, and gait measures that represent several different domains controlling mobility. However, it is not clear which domains of balance or gait best reflect the impact of treatment-induced peripheral neuropathy on mobility or differentiate fall risk in cancer survivors. This study aimed to determine which domains of balance and gait differed between cancer survivors who reported 1) symptoms of peripheral neuropathy versus asymptomatic survivors and 2) falls in the previous six months versus non-fallers. Methods: Both postural sway during 30 seconds of quiet stance and gait characteristics from the Instrumented Time-Up-and-Go test (ITUG) were recorded with six synchronous inertial sensors (Opals by APDM Wearable Technology, a Clario company). The sensors were placed on both shanks and wrists, and one on the lumbar spine and sternum in 425 female cancer survivors (age: 62 ± 6 years). A principal component analysis (PCA) approach was used first to identify independent domains of mobility from 14 balance and gait measures for subsequent analysis. Results: PCA analysis revealed 5 independent domains (PC1: Sway amplitude, PC2: Gait pace; PC3: Sway frequency; PC4: Gait spatial-temporal and PC5: Turning) that accounted for 81% of the variance of performance across the participants. Cancer survivors who reported neuropathy showed a significantly higher sway frequency (PC3) than asymptomatic survivors. Cancer survivors who reported falls showed a significantly larger sway area (PC1) and slower gait pace (PC2) than non-fallers. Conclusions: Wearable sensor instrumentation from short balance and gait tests capture objective digital gait and balance outcomes reflective of treatment-related neuropathy and fall risk in women treated with chemotherapy for cancer. Wearable sensors could be used for oncology clinical trials to assess the impact of cancer treatment and symptom mitigation strategies to limit changes in mobility that might lead to excess falls in people with cancer. Citation Format: Vrutangkumar Shah, Daniel Muzyka, Carolyn Guidarelli, Kristen Sowalsky, Kerri M. Winters-Stone, Fay B. Horak. Chemotherapy-induced peripheral neuropathy and falls in cancer survivors relate to digital balance and gait impairments. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4397.
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Koochzakzaei, Alireza, Mohammadamin Sabbaghiyan Bidgoli i Siyamak Safapour. "Feasibility Assessment of the Identification of the Source of Condensed Tannins in Leathers by FTIR Spectroscopy and Chemometrics". Journal of the American Leather Chemists Association 117, nr 12 (1.12.2022): 515–19. http://dx.doi.org/10.34314/jalca.v117i12.6387.
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This study aimed to investigate the feasibility of identifying the type of plant used in the tanning of leathers by cost-effective Fourier transform infrared (FTIR) spectroscopy. The investigation was performed on European horse-chestnut (fruit peel), mimosa, and quebracho and three specimens of mimosa-tanned leathers. Tannin extraction from plants was performed in an ultrasonic bath using acetone-water solvent (70%). Tannin extraction from leathers was carried out from the corium fibers using acetone-water solvent (1:1). After extraction, filtration, centrifugation, and solvent removal, the samples were subjected to FTIR spectroscopy. Principal Component Analysis (PCA) and hierarchical clustering were used to identify the source of tannins based on FTIR results. In addition to FTIR spectra, their first and second derivatives were also used in statistical analyses. The obtained FTIR spectra and their derivatives and the results of PCA and hierarchical clustering showed that rich plant sources of condensed tannins can be well differentiated by spectroscopy in the fingerprint region (700-1800cm-1). The PC1-PC2 plot in the analysis of FTIR spectra and the PC2-PC3 plot in the analysis of derivatives showed the best ability to differentiate and identify the extracts. Multivariate PCA and cluster analyses performed well in identifying the type of plant used in the tanning of the studied leathers, especially when applied to the derivatives of FTIR spectra.
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Feibus, Allison H., Oliver Sartor, Raju Thomas, Michael M. Maddox, Benjamin Lee, Justin Levy, Carrie A. Stewart, Julie Wang, Krishnarao Moparty i Jonathan L. Silberstein. "Clinical performance of PCA3 and TMPRSS2:ERG urinary biomarkers for African American men undergoing prostate biopsy." Journal of Clinical Oncology 33, nr 7_suppl (1.03.2015): 91. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.91.
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91 Background: Urinary assaysfor PCA3 and TMPRSS2:ERG (T2:ERG) fusion are established biomarkers for the detection of prostate cancer (PCa). However few African Americans (AA) have been included in previous studies. We sought to determine the performance characteristics of these assays in a racially diverse group of men who elected to undergo prostate biopsy. Methods: Following IRB approval, from 12/2013-10/2014, post digital rectal exam urine was collected in 152 patients without a diagnosis of PCa, prior to biopsy. PCA3 and T2:ERG RNA copies were quantified using transcription-mediated amplification assays and normalized to PSA mRNA copies. Results: Of the 152 patients who met study inclusion, 93 (61%) were AA, 59 were non-AA (39%); 72(47%) were diagnosed with PCa (55% AA, 36% non-AA). Both PCA3 and T2:ERG scores were greater in men with biopsy-proven PCa, those with ≥3 PCa cores, ≥33.3% PCa cores, >50% PCa involvement of greatest biopsy core and Epstein significant PCa (all p-values ≤ 0.02). PCA3 but not T2:ERG scores were greater in men with Gleason grade ≥7 (p = 0.0003). ROC analyses for prediction of biopsy outcome resulted in AUCs of 0.7, 0.61 and 0.59 for PCA3, T2:ERG and serum PSA. For the subgroup of AA, PCA3 and T2:ERG scores were greater in men with biopsy-proven PCa, those with ≥3 PCa cores, ≥33.3% PCa cores, >50% PCa involvement of greatest biopsy core and Epstein significant PCa (all p-values ≤0.01). Both PCA3 and T2:ERG scores were greater in men with Gleason grade ≥7 (p ≤ 0.03). ROC analyses for prediction of biopsy outcome for AA only resulted in AUCs of 0.66, 0.66 and 0.58 for PCA3, T2:ERG and serum PSA. For the non-AA cohort, PCA3 scores were greater in men with biopsy-proven PCa, those with ≥3 PCa cores, ≥33.3% PCa cores, >50% PCa involvement of greatest biopsy core, Epstein significant PCa and Gleason grade ≥7 (all p-values ≤ 0.03). T2:ERG did not reach significance for any of these variables. In this subgroup, ROC analyses for prediction of biopsy outcome resulted in AUCs of 0.73, 0.54 and 0.56 for PCA3, T2:ERG and serum PSA. Conclusions: In AA men undergoing prostate biopsy, both PCA3 and T2:ERG urinary assays demonstrate clinical utility in predicting biopsy outcome and PCa disease characteristics.
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Dinanti, Aldila, i Joko Purwadi. "Analisis Performa Algoritma K-Nearest Neighbor dan Reduksi Dimensi Menggunakan Principal Component Analysis". Jambura Journal of Mathematics 5, nr 1 (1.02.2023): 155–65. http://dx.doi.org/10.34312/jjom.v5i1.17098.
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This paper discusses the performance of the K-Nearest Neighbor Algorithm with dimension reduction using Principal Component Analysis (PCA) in the case of diabetes disease classification. A large number of variables and data on the diabetes dataset requires a relatively long computation time, so dimensional reduction is needed to speed up the computational process. The dimension reduction method used in this study is PCA. After dimension reduction is done, it is continued with classification using the K-Nearest Neighbor Algorithm. The results on diabetes case studies show that dimension reduction using PCA produces 3 main components of the 8 variables in the original data, namely PC1, PC2, and PC3. Then classification result using K-Nearest Neighbor shows that by choosing 3 closest neighbor parameters (K), for K = 3, K = 5, and K = 7. The result for K = 3 has an accuracy of 67,53%, for K = 5 had an accuracy is 72,72%, and for K=7 had an accuracy of 77,92%. Thus, it was concluded that the best accuracy performance for the classification of diabetes was achieved at K=7 with an accuracy of 77.92%.
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Zhang, Yi, Yong Yang, Leixi Xue, Jian Wen, Lin Bo, Mei Tang, Ru Yang, Dong Yan i Zhichun Liu. "Clinical characteristics of patients under 40 years old with early-onset hyperuricaemia: a retrospective monocentric study in China". BMJ Open 9, nr 8 (sierpień 2019): e025528. http://dx.doi.org/10.1136/bmjopen-2018-025528.
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ObjectiveTo investigate the clinical characteristics of patients with early-onset hyperuricaemia (HUC).MethodsA retrospective study using data from the Second Affiliated Hospital of Soochow University was conducted. 623 patients with HUC were divided into early-onset group and late-onset group. Another 201 healthy subjects ≤40 years old were regarded as control group. The data of physical measurements and biochemistry test were collected. Clinical data of early-onset group were compared with late-onset group and control group by analysis of variance (ANOVA) and χ2test. Principal component analysis (PCA) was applied. Logistic regression was used to identify the clinical factors correlated with patients with early-onset HUC.ResultsThe patients of early-onset group had different body mass index (BMI), serum albumin, alanine transaminase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), gamma-glutamyltransferase (GGT), creatinine (Cr), triglyceride (TG), total cholesterol (TC), low density lipoprotein (LDL), TG/high density lipoprotein (HDL) ratio, HDL and percentage of males, hypertension (HBP) as well as fatty liver compared with healthy people in the control group. Early-onset group patients had different albumin, ALT, fasting blood glucose, Cr, percentage of males and HBP compared with late-onset group patients. PCA identified four significant patterns including PC1 (labelled ‘TG and HDL’), PC2 (labelled ‘fatty liver and liver enzymes’), PC3 (labelled ‘TC and LDL’) and PC4 (labelled ‘AKP’). The results of univariate and multivariate logistic regression analysis showed that BMI, HBP and albumin were correlative factors for early onset of HUC when the patients with early-onset and late-onset HUC were involved, while gender, BMI, PC1, PC2 and PC4 were correlative factors for early-onset HUC when the early-onset and control groups were involved.ConclusionThis study described a group of patients with early-onset HUC with distinct clinical features. Gender, BMI, ‘TG and HDL’, ‘fatty liver and liver enzymes’ and ‘AKP’ have higher values than HBP, type 2 diabetes mellitus and ‘TC and LDL’ in patients under 40 years old with early-onset HUC.
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Harp, Jordan P., Lisa M. Koehl, Kathryn L. Van Pelt, Christy L. Hom, Eric Doran, Elizabeth Head, Ira T. Lott i Frederick A. Schmitt. "Cognitive and Behavioral Domains That Reliably Differentiate Normal Aging and Dementia in Down Syndrome". Brain Sciences 11, nr 9 (25.08.2021): 1128. http://dx.doi.org/10.3390/brainsci11091128.
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Primary care integration of Down syndrome (DS)-specific dementia screening is strongly advised. The current study employed principal components analysis (PCA) and classification and regression tree (CART) analyses to identify an abbreviated battery for dementia classification. Scale- and subscale-level scores from 141 participants (no dementia n = 68; probable Alzheimer’s disease n = 73), for the Severe Impairment Battery (SIB), Dementia Scale for People with Learning Disabilities (DLD), and Vineland Adaptive Behavior Scales—Second Edition (Vineland-II) were analyzed. Two principle components (PC1, PC2) were identified with the odds of a probable dementia diagnosis increasing 2.54 times per PC1 unit increase and by 3.73 times per PC2 unit increase. CART analysis identified that the DLD sum of cognitive scores (SCS < 35 raw) and Vineland-II community subdomain (<36 raw) scores best classified dementia. No significant difference in the PCA versus CART area under the curve (AUC) was noted (D(65.196) = −0.57683; p = 0.57; PCA AUC = 0.87; CART AUC = 0.91). The PCA sensitivity was 80% and specificity was 70%; CART was 100% and specificity was 81%. These results support an abbreviated dementia screening battery to identify at-risk individuals with DS in primary care settings to guide specialized diagnostic referral.
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San Park, Tu, Cayla Baynes, Seong-In Cho i Jeong-Yeol Yoon. "Paper microfluidics for red wine tasting". RSC Adv. 4, nr 46 (2014): 24356–62. http://dx.doi.org/10.1039/c4ra01471e.
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A smartphone takes a digital image of the paper microfluidic chip, after a red wine sample filled the entire 8 channels (each pre-loaded with a different chemical dye). The PCA score plot shows good statistical difference among the 6 red wine samples, where PC1 corresponded to the sweetness and PC2 to the bodyness of a red wine. The PCA model was validated with an additional 4 red wine samples from a different manufacturer.
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Sholihin. "AMMI stability for starch yield of cassava in the acid area for determining clones’ stability". E3S Web of Conferences 306 (2021): 01005. http://dx.doi.org/10.1051/e3sconf/202130601005.
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The study aimed to evaluate the phenotypic stability of cassava promising clones’ cassava clones in acidic regions based on AMMI stability. The study was done during 2015-2018 in several environments in Lampung. The study was done using a randomized complete block design with three replications. Plants were planted in 5 m x 4.8 m plot size, with 1 m distance between rows and 0.8 m within row. The plants were fertilized with 93 kg N, 36 kg P2O5 and 60 kg K2O per hectare. Seven cassava promising clones and two check varieties were used in this study. Data were analyzed using Excel, MSTAT-C and PBTOOLs. Research showed that compared with clone PC2, PC3, PC5 and clone PC7, clone PC1, PC4, PC6, UJ3 and clone UJ5 are more stable. According to the AMMI analysis, based on the seven-month starch yield, the important environmental factors that determined the stability of cassava clones are the content of N and P2O5 in the upper soil layer and the cation exchange capacity ground. The starch yield in seven months of PC4 was the highest among the clones. Clone PC4 is potential to be developed in acid area.
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Ilia, Evan Butrus, Mahmood Fadhil Saleem i Hamed Hassanzadeh. "A comparative study of the physical and cooking characteristics of common types of rice collected from the market by quantitative statistical analysis". Korean Journal of Food Preservation 30, nr 4 (sierpień 2023): 602–16. http://dx.doi.org/10.11002/kjfp.2023.30.4.602.
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Fifteen types of rice collected from Kurdistan region-Iraq were investigated by principal component analysis (PCA) in terms of physical properties and cooking characteristics. The dimensions of evaluated grains correspond to 5.05-8.75 mm for length, 1.54-2.47 mm for width, and 1.37-1.95 for thickness. The equivalent diameter was in the range of 5.23-10.03 mm, and the area took 13.30-28.25 mm2. The sphericity analysis values varied from 0.32 to 0.56, the aspect ratio from 0.17 to 0.39, and the volume of the grain was measured in the range from 4.48 to 17.74 mm3, hectoliter weight values were 730-820 kg/m3, and true density from 0.6 to 0.96 g/cm3. The broken grain ratio was 1.5-18.3%, thousand kernel weight corresponded to 15.88 to 22.42 g. The water uptake ratios for 30 min of soaking were increased at 60°C compared to 30 and 45°C. The PCA was used to study the correlation of the most effective factors. Results of PCA showed that the first (PC1) and second (PC2) components retained 63.4% and 34.8% of the total variance, which PC1 was mostly related to hectoliter, broken ratio, and moisture content characteristics while PC2 was mostly concerned with hardness and true density. For cooking properties, the PC1 and PC2 retained 88.5% and 9.3% of the total variance, respectively. PC1 was mostly related to viscosity, spring value, and hardness after cooking, while PC2 was mostly concerned with spring value, hardness before cooking, and hardness after cooking.
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Tóthová, J., J. Sádecká i P. Májek. "Total luminescence spectroscopy for differentiating between brandies and wine distillates". Czech Journal of Food Sciences 27, No. 6 (23.12.2009): 425–32. http://dx.doi.org/10.17221/125/2008-cjfs.
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In this study, the differentiation was investigated between brandy and wine distillate samples by fluorescence spectroscopy in combination with multivariate analysis. The samples corresponding to eight brandies from three producers and sixteen wine distillates from five producers were acquired in the local supermarkets. Total luminescence spectra of diluted and undiluted samples were recorded. In order to extract reliable information from the data sets, two multivariate analysis methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), were applied separately on the excitation and emission spectra. The best differentiation was achieved using the emission spectra (400–470 nm) recorded at the excitation wavelength of 340 nm, or the excitation spectra (240–380 nm) recorded at the emission wavelength of 450 nm. The similarity map defined by the PC1 and PC2 of the PCA performed on the excitation spectra accounted for 94.9% of the total variance (PC1 90.3%, PC2 4.6%) and allowed a good discrimination between the beverages. Although the PCA similarity map defined by the PC1 (84.2%) and PC2 (13.0%) performed on the emission spectra did not lead to a clear discrimination between the beverages, a general trend pointing out the brandies and wine distillates was observed on the map. HCA performed on the excitation spectra provided a better differentiation between the two classes, without any classification error, while HCA performed on the emission spectra allowed 95.8% correct classification.
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Obeid, Emil, i Khaled Younes. "Uncovering Key Factors in Graphene Aerogel-Based Electrocatalysts for Sustainable Hydrogen Production: An Unsupervised Machine Learning Approach". Gels 10, nr 1 (12.01.2024): 57. http://dx.doi.org/10.3390/gels10010057.
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The application of principal component analysis (PCA) as an unsupervised learning method has been used in uncovering correlations among diverse features of aerogel-based electrocatalysts. This analytical approach facilitates a comprehensive exploration of catalytic activity, revealing intricate relationships with various physical and electrochemical properties. The first two principal components (PCs), collectively capturing nearly 70% of the total variance, attested the reliability and efficacy of PCA in unveiling meaningful patterns. This study challenges the conventional understanding that a material’s reactivity is solely dictated by the quantity of catalyst loaded. Instead, it unveils a complex perspective, highlighting that reactivity is intricately influenced by the material’s overall design and structure. The PCA bi-plot uncovers correlations between pH and Tafel slope, suggesting an interdependence between these variables and providing valuable insights into the complex interactions among physical and electrochemical properties. Tafel slope stands to be positively correlated with PC1 and PC2, showing an evident positive correlation with the pH. These findings showed that the pH can have a positive correlation with the Tafel slope, however, it does not necessarily reflect a direct positive correlation with the overpotential. The impact of pH on current density (j)and Tafel slope underscores the importance of adjusting pH to lower overpotential effectively, enhancing catalytic activity. Surface area (from 30 to 533 m2 g−1) emerges as a key physical property, inclusively inverse correlation with overpotential, indicating its direct role in lowering overpotential and increasing catalytic activity. The introduction of PC3, in conjunction with PC1, enriches the analysis by revealing consistent trends despite a slightly lower variance (60%). This reinforces the robustness of PCA in delineating distinct characteristics of graphene aerogels, affirming their potential implications in diverse electrocatalytic applications. In summary, PCA proves to be a valuable tool for unraveling complex relationships within aerogel-based electrocatalysts, extending insights beyond catalytic sites to emphasize the broader spectrum of material properties. This approach enhances comprehension of dataset intricacies and holds promise for guiding the development of more effective and versatile electrocatalytic materials.
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Kumar, Preeti, Nilanjaya i Pankaj Shah. "Study of genetic diversity in rice (Oryza sativa L.) genotypes under direct seeded condition by using principal component analysis". Ecology, Environment and Conservation 29 (2023): 211–19. http://dx.doi.org/10.53550/eec.2023.v29i03s.040.
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The present investigation was carried out to assess the genetic diversity by using principal component analysis for yield and yield contributing traits in thirty-two genotypes of rice under direct seeded condition (DSR). The experiment was conducted at Dr. Rajendra Prasad Central Agricultural University, Pusa, Bihar in randomized block design with three replications. The results revealed that first four component axes had eigen values 1.0, representing a cumulative variability of 76.86 %. Principal component analysis (PCA) indicate that four components (PC1 to PC4) accounted for about 76.86% of the total variation present among all the traits. Out of total principal components PC1, PC2, PC3 and PC4 with values 33.781%, 19.02%, 13.859% and 10.206% respectively, contributed more to the total variation. The first principal component had high positive loading for 15 traits out of 17. Similarly, second and third principal component had 7 traits each, fourth component with 6 traits had high positive loadings which contributed more to the diversity. Genotypes in cluster V showed higher mean performance for most of the yield attributing traits. Therefore, selection of parents for different traits would be effective from this cluster. Thus, result of the present study could be exploited in planning and execution of future breeding programme in rice under direct seeded condition.
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Sala, Florin, Dorin Camen, Mihai Valentin Herbei i Cosmin Blidariu. "Analysis of Vine Nutrition and Productivity Based on Statistical Indicators". Horticulturae 10, nr 4 (13.04.2024): 397. http://dx.doi.org/10.3390/horticulturae10040397.
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Different fertilization systems, methods and techniques result in differences in plant nutrition, with variations in the mineral element contents in the leaves and values of physiological, yield, and production quality indices. This results in large datasets that are often relatively difficult to interpret. Starting from these aspects, the present study analyzed data on the state of plant nutrition (N, P, K, Ca, and Mg content in leaves, in % of dry matter; Fe, Mn, Cu, and Zn content in leaves, in mg/kg of dry matter), physiological indices (chlorophyll leaf content—Chl; leaf area—LA), yield (kg/vine, t/ha), and grape berry quality (sugar and acidity content in berries) of the ‘Burgund mare’ grape cultivar in relation to organic, mineral and foliar fertilization. According to a PCA multivariate analysis, four groups of components were obtained: PC1 including five factors, with positive action in the case of K (r = 0.963), P (r = 0.770) and Cu (r = 0.752), and negative action in the case of Mg (r = −0.940) and Mn (r = −0.759); PC2 including three factors, with positive action for Chl (r = 0.938), LA (r = 0.938) and Ca (r = 0.722); PC3 including one factor (N), with negative action (r = −0.916); and PC4 including two factors, with positive action for Fe (r = 0.869) and Zn (r = 0.761). Based on the one-sample t test (parametric) and the Wilcoxon test (non-parametric) and a threshold value of α = 0.05, the differences between the index values for fertilization variants T2–T13 compared with those for the control variant (T1) were verified. Significant differences were recorded in PC1 for P, Mg and Cu; in PC2 for Chl, LA and Ca; and in PC3 for N. Insignificant differences were recorded in PC1 for K and Mn and in PC4 for Fe and Zn. In the case of the productivity elements and quality indices, significant differences were recorded.
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Arozi, Moh, Wahyu Caesarendra, Mochammad Ariyanto, M. Munadi, Joga D. Setiawan i Adam Glowacz. "Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements". Symmetry 12, nr 4 (3.04.2020): 541. http://dx.doi.org/10.3390/sym12040541.
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A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.
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Feibus, Allison H., A. Oliver Sartor, Krishnarao Moparty, Michael W. Kattan, Kevin M. Chagin, Elisa M. Ledet, Justin Levy, Benjamin Lee, Raju Thomas i Jonathan L. Silberstein. "Utility of PCA3 and TMPRSS2:ERG urinary biomarkers in African American men undergoing prostate biopsy." Journal of Clinical Oncology 34, nr 2_suppl (10.01.2016): 126. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.126.
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126 Background: To determine the performance characteristics of urinary PCA3 andTMPRSS2:ERG (T2:ERG) in a racially diverse group of men. Methods: Following IRB approval, from 2013-2015, post digital rectal exam (DRE) urine was prospectively collected in patients without known prostate cancer (PCa), prior to biopsy. PCA3 and T2:ERG RNA copies were quantified and normalized to PSA mRNA copies using Progensa assay (Hologic, San Diego, CA). Prediction models for PCa and high-grade PCa were created using standard of care (SOC) variables (age, race, family history of PCa, prior prostate biopsy and abnormal DRE) plus PSA. Decision Curve Analysis was performed to compare the net benefit of using SOC, plus PSA, with the addition of PCA3 and T2:ERG. Results: Of 304 patients, 182 (60%) were AA; 139(46%) were diagnosed with PCa (69% AA). PCA3 and T2:ERG scores were greater in men with PCa, ≥ 3 cores, ≥ 33.3% cores, > 50% involvement of greatest biopsy core and Epstein significant PCa (p-values < 0.04). PCA3 added to the SOC plus PSA model for the detection of any PCa in the overall cohort (0.747 vs 0.677; p < 0.0001), in AA only (0.711 vs 0.638; p = 0.0002) and non-AA (0.781 vs 0.732; p = 0.0016). PCA3 added to the model for the prediction of high-grade PCa for the overall cohort (0.804 vs 0.78; p = 0.0002) and AA only (0.759 vs 0.717; p = 0.0003) but not non-AA. Decision curve analysis demonstrated significant net benefit with the addition of PCA3 compared with SOC plus PSA. For AA, T2:ERG did not improve concordance statistics for the detection any or high-grade PCa. Conclusions: For AA, urinary PCA3 improves the ability to predict the presence of any and high-grade PCa. However for this population, T2:ERG urinary assay does not add significantly to standard detection and risk stratification tools.
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Mulsanti, I. W., A. Risliawati i N. Yunani. "Agro-morphological characterization based genetic diversity of Indonesian local rice germplasm". IOP Conference Series: Earth and Environmental Science 948, nr 1 (1.12.2021): 012004. http://dx.doi.org/10.1088/1755-1315/948/1/012004.
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Abstract The present study was carried out to characterize 103 Indonesian local rice germplasm on the basis of 20 agro-morphological traits. The local rice germplasm originated from Riau and Jambi province in Sumatera island. Principal Component Analysis (PCA) was used to analyzed 11 quantitative data. PCA explained the genetic diversity of the rice germplasm accession. Most of the morphological characters showed variation in different accession. PC1 and PC2 explained about 32.5% and 22.1% of the variability, respectively. PCI and PC2 mostly related with traits such as productive tiller number (PTN), vegetative tiller number (VTN), plant height (PH), and culm length (CL). PCA-Biplot showed accession from Jambi and Riau manage to be separated, even though the PCA percent is only 54.6%. Accession originated from Riau marked by number of tillers (PTN, VTN), and grain weight (GW) traits. Based on cluster analysis, rice germplasm grouped in to 4 main clusters. Most of the accession from Jambi and Riau grouped in the same cluster, which is cluster I. Whereas eight local accessions from Riau grouped in the same cluster separately (cluster II). Two accession which are Padi Jarum (Acc 2711) and Ketan Hitam (Acc 9300) are individually, separated from others.
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Lin, Chin Lun, Bluyé DeMessie, Kenny Ye, Shanshan Hu i Michael L. Lipton. "Neck strength alone does not mitigate adverse associations of soccer heading with cognitive performance in adult amateur players". PLOS ONE 19, nr 5 (16.05.2024): e0302463. http://dx.doi.org/10.1371/journal.pone.0302463.
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Objectives Soccer heading is adversely associated with neurocognitive performance, but whether greater neck strength or anthropometrics mitigates these outcomes is controversial. Here, we examine the effect of neck strength or anthropometrics on associations of soccer heading with neurocognitive outcomes in a large cohort of adult amateur players. Methods 380 adult amateur league soccer players underwent standardized measurement of neck strength (forward flexion, extension, left lateral flexion, right lateral flexion) and head/neck anthropometric measures (head circumference, neck length, neck circumference and neck volume). Participants were assessed for heading (HeadCount) and cognitive performance (Cogstate) on up to 7 visits over a period of two years. Principal components analysis (PCA) was performed on 8 neck strength and anthropometric measures. We used generalized estimating equations to test the moderation effect of each of the three PCs on 8 previously identified adverse associations of 2-week and 12-month heading estimates with cognitive performance (psychomotor speed, immediate verbal recall, verbal episodic memory, attention, working memory) and of unintentional head impacts on moderate to severe central nervous system symptoms. Results 3 principal components (PC’s) account for 80% of the variance in the PCA. In men, PC1 represents head/neck anthropometric measures, PC2 represents neck strength measures, and PC3 represents the flexor/extensor (F/E) ratio. In women, PC1 represents neck strength, PC2 represents anthropometrics, and PC3 represents the F/E ratio. Of the 48 moderation effects tested, only one showed statistical significance after Bonferroni correction, which was not robust to extensive sensitivity analyses. Conclusion Neither neck strength nor anthropometrics mitigate adverse associations of soccer heading with cognitive performance in adult amateur players.
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Pasero, Christine L. "PCA". American Journal of Nursing 96, nr 9 (wrzesień 1996): 22–23. http://dx.doi.org/10.1097/00000446-199609000-00035.
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Ross, E. L., i P. Perumbeti. "PCA". Anesthesiology 69, nr 3A (1.09.1988): A710. http://dx.doi.org/10.1097/00000542-198809010-00710.
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Ginsberg, B., K. M. Gil, M. Muir i D. Sykes. "PCA". Anesthesiology 71, Supplement (wrzesień 1989): A687. http://dx.doi.org/10.1097/00000542-198909001-00687.
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Heo, Gyeongyong, Paul Gader i Hichem Frigui. "RKF-PCA: Robust kernel fuzzy PCA". Neural Networks 22, nr 5-6 (lipiec 2009): 642–50. http://dx.doi.org/10.1016/j.neunet.2009.06.013.
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