Significant Principal Components Research Articles

Assessment and management of pesticide pollution at a river basin level part II: Optimization of pesticide monitoring networks on surface aquatic ecosystems by data analysis methods

The high cost of extensive pesticide monitoring studies, required for the protection of water resources, and the necessity of early identification of environmental threats, highlighted the need for prioritization of pesticides and sampling sites to be monitored. The aim of this study was to develop an optimum surface water monitoring network at a catchment scale including only the sites of a catchment vulnerable to pesticide pollution. The identification of sampling sites vulnerable to pesticide pollution (VPS) was based on the data of an intensive monitoring survey of 302 pesticides in 102 stationary sampling sites located on the surface water network of a river basin. In the proposed methodology the left-censored data of the analytical results derived from the above mentioned monitoring campaign were included in the statistical analyses by transforming all the raw data into categorical variables and arranging them in ordinal scales based on ecotoxicological thresholds derived from pesticide toxicity tests on aquatic non-target organisms. The categorized data were subjected to Categorical Principal Component Analysis with Optimal Scaling. For the identification of the VPS, the Squared Mahalanobis Distance criterion was applied on the extracted values (scores) of the significant principal components. With this methodology a 46% reduction in the number of the monitoring stations was achieved. This approach will be valuable in establishing more cost effective monitoring schemes in the future in other basins and in developing targeted measures to eliminate or limit the effect of critical pollution sources in surface aquatic systems. Moreover, by applying the proposed methodology, historical monitoring data can be used to initiate more efficient pesticide monitoring campaigns in the future.

English

Rice is an important staple food crop that feeds over half of the global population and has become the cereal that provides a major source of calories for the urban and rural poor in Africa. This work aimed to evaluate the morphological of rice (Oryza sativa and Oryza glaberrima) germplasm. In the present study, 14 quantitative traits were used across 48 accessions or genotypes obtained from Central Agricultural Research Institute (CARI), Liberia and Plant Genetic Resources Research Institute (PGRRI), Ghana. In this research, Completely Randomized Design was conducted to study the genetic variability among the 48 genotypes or accessions obtained from CARI, Liberia and PGRRI, Ghana. Field data taken included 14 quantitative traits scored using the IRRI descriptor list. Analysis of variance revealed highly significant difference (P≤ 0.01) among the accessions for all quantitative traits studied. Four significant principal components analysis were identified and accounted for 55.3%. PC1 had Eigen-value of 18.5%, whereas PC2 accounted for Eigen-value of 14.5%. PC3 contributed 12.2% whereas PC4 had 10.1%. Correlation analysis indicated the length of ligule was highly significant and positive leaf width blade. Similar observation was made with grain length and length of ligule. Some accessions in the biplot showed longer vector distances, while shorter vector distances were observed referencing PC1; Gh1578 recorded the longest vector distances while Gh1526 and LAC 23-1, recorded the moderate distances from the vector origin at the similarity coefficients at 90%. The highly distant genetic diversity was found between ACSS37 and ACSS1 from Ghana and Liberia. Cluster X was the largest of all the clusters while Clusters VII and VIII were the second largest clusters with seven accessions each. The outcome of this study should be useful for the management of the germplasm conservation and future rice genetic improvement. However, all the accessions may be cultivated over time at different locations on the field to ascertain their stability and purity. Key words: Correlation, morphological, quantitative, qualitative, accessions.

Describing health care waste generation rates using regression modeling and principal component analysis

This work examined the dependence of the health care waste generation rates (HCWGR) from economic factors (gross domestic product per capita, health expenditure per capita), social and health-related factors (human development index, life expectancy at birth, mean years of schooling, deaths due to tuberculosis, under-five mortality rate, hospital beds, improved sanitation facilities, physicians, nurses and midwives, diabetes prevalence, deaths due to cancer, deaths due to asthma, deaths due to influenza and pneumonia), and one environmental sustainability factor (carbon dioxide emissions) from 41 countries using multiple linear regression modeling and principal component analysis (PCA). In addition, the Pearson correlation coefficients were calculated for all pairwise comparisons and a geographical grouping of the HCWGR was performed. The examined HCWGR included both the hazardous and the municipal fraction of health care waste (HCW). Results showed that the CO2 emissions and the life expectancy at birth positively correlated to the HCWGR (kg/bed/d) and can be used as adequate statistical predictors. The resulting best reduced model explained 84.7% of the variability. The hospital beds and the deaths due to cancer were not correlated to any principal component due to their low loadings. Only the diabetes prevalence was correlated to the F2 principal component. The other fourteen variables were correlated to the F1, which was the most significant principal component. Thus, the HCWGR and the other thirteen variables that were grouped to the F1 component have strong autocorrelation and can be treated as one variable.

Abstract 2077: Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients

Abstract Survival rates for osteosarcoma, the most common primary bone cancer, have changed little over the past three decades and are particularly low for patients with metastatic disease. To date, few prognostic factors have been identified to be associated with survival in patients with osteosarcoma. We conducted an international, multi-institutional genome-wide association study (GWAS) to identify germline genetic variants associated with overall survival in 632 patients with osteosarcoma. Subjects were genotyped as part of our previously published osteosarcoma susceptibility GWAS, including 523 patients of >80% European ancestry and 109 patients from Brazil. We performed a time-to-event analysis and estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox proportional hazards models, with and without adjustment for metastatic disease. Cox models were adjusted for age at diagnosis, gender, significant principal components, and study/center. SNPs that reached P<10-4 in patients of European ancestry were followed up in patients from Brazil. The results were combined across case sets using a random-effects meta-analysis, and between-sets heterogeneity was evaluated using Cochran's Q chi-squared statistic and the I2 metric. Age (P<0.001) and presence of metastases at diagnosis (P<0.001) were associated with patient survival. In the combined analysis of 632 patients, the strongest association was SNP rs55933544, which was inversely associated with overall survival at genome-wide significance (HR=1.89 per copy of the T allele; 95% CI 1.5-2.4; P=1.3×10-8; I2=0%). Rs5593354 is located in intron 23 of the GLDC gene on chromosome 9p24.1, adjacent to the IL33 gene. This association was consistent across both the European (HR=1.9; P=1.6×10-6) and Brazilian (HR=2.1; P=8.4×10-3) case sets, and after adjustment for metastatic disease at diagnosis (combined HR=1.92; P=1.3×10-8), suggesting that this locus affects overall survival independent of metastatic disease. Kaplan-Meier curve analyses indicated a statistically significant difference between survival rates over time (log-rank test P<0.001) for both the dominant and multiplicative models for rs55933544. Using publicly available data, the risk allele was associated with lower expression of IL33, and lower expression of IL33 in osteosarcoma tumors was associated with worse survival in an independent set of 112 osteosarcomas (P<0.05). In conclusion, we have identified the GLDC/IL33 locus on chromosome 9p24.1 as associated with overall survival in patients with osteosarcoma. Further studies are needed to confirm this association and shed light on the biologic underpinnings of this susceptibility locus. Citation Format: Roelof Koster, Orestis A. Panagiotou, William A. Wheeler, Eric Karlins, Julie M. Gastier-Foster, Silvia Regina Caminada de Toledo, Antonio S. Petrilli, Adrienne M. Flanagan, Roberto Tirabosco, Irene L. Andrulis, Jay S. Wunder, Nalan Gokgoz, Ana Patiño-Garcia, Fernando Lecanda, Massimo Serra, Claudia Hattinger, Piero Picci, Katia Scotlandi, David M. Thomas, Mandy L. Ballinger, Richard Gorlick, Donald A. Barkauskas, Logan G. Spector, Margaret Tucker, Belynda Hicks, Meredith Yeager, Robert Hoover, Stephen J. Chanock, Sharon A. Savage, Lisa J. Mirabello. Genome-wide association study identifies the GLDC/IL33 locus associated with survival of osteosarcoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2077.

Identifying Nanoscale Structure-Function Relationships Using Multimodal Atomic Force Microscopy, Dimensionality Reduction, and Regression Techniques.

Correlating nanoscale chemical specificity with operational physics is a long-standing goal of functional scanning probe microscopy (SPM). We employ a data analytic approach combining multiple microscopy modes using compositional information in infrared vibrational excitation maps acquired via photoinduced force microscopy (PiFM) with electrical information from conductive atomic force microscopy. We study a model polymer blend comprising insulating poly(methyl methacrylate) (PMMA) and semiconducting poly(3-hexylthiophene) (P3HT). We show that PiFM spectra are different from FTIR spectra but can still be used to identify local composition. We use principal component analysis to extract statistically significant principal components and principal component regression to predict local current and identify local polymer composition. In doing so, we observe evidence of semiconducting P3HT within PMMA aggregates. These methods are generalizable to correlated SPM data and provide a meaningful technique for extracting complex compositional information that is impossible to measure from any one technique.

Patient phenotypes associated with outcome following surgery for mild degenerative cervical myelopathy: a principal component regression analysis

Background ContextPredictors of outcome after surgery for degenerative cervical myelopathy (DCM) have been determined previously through hypothesis-driven multivariate statistical models that rely on a priori knowledge of potential confounders, exclude potentially important variables because of restrictions in model building, cannot include highly collinear variables in the same model, and ignore intrinsic correlations among variables. PurposeThe present study aimed to apply a data-driven approach to identify patient phenotypes that may predict outcomes after surgery for mild DCM. Study DesignThis is a principal component analysis of data from two related prospective, multicenter cohort studies. Patient SampleThe study included patients with mild DCM, defined by a modified Japanese Orthopaedic Association score of 15–17, undergoing surgical decompression as part of the AOSpine CSM-NA or CSM-I trials. Outcome MeasuresPatient outcomes were evaluated preoperatively at baseline and at 6 months, 1 year, and 2 years after surgery. Quality of life (QOL) was evaluated by the Neck Disability Index (NDI) and Short Form-36 version 2 (SF-36v2). These are both patient self-reported measures that evaluate health-related QOL, with NDI being specific to neck conditions and SF-36v2 being a generic instrument. Materials and MethodsThe analysis included 154 patients. A heterogeneous correlation matrix was created using a combination of Pearson, polyserial, and polychoric regressions among 67 variables, which then underwent eigen decomposition. Scores of significant principal components (PCs) (with eigenvalues>1) were included in multivariate logistic regression analyses for three dichotomous outcomes of interest: achievement of the minimum clinically important difference [MCID] in (1) NDI (≤−7.5), (2) SF-36v2 Physical Component Summary (PCS) score (≥5), and (3) SF-36v2 Mental Component Summary (MCS) score (≥5). ResultsTwenty-four significant PCs accounting for 75% of the variance in the data were identified. Two PCs were associated with achievement of the MCID in NDI. The first (PC 1) was dominated by variables related to surgical approach and number of operated levels; the second (PC 21) consisted of variables related to patient demographics, severity and etiology of DCM, comorbid status, and surgical approach. Both PC 1 and PC 21 also correlated with SF-36v2 PCS score, in addition to PC 4, which described patients' physical profile, including gender, height, and weight, as well as comorbid renal disease; PC 6, which received large loadings from variables related to cardiac disease, impaired mobility, and length of surgery and recovery; and PC 9, which harbored large contributions from features of upper limb dysfunction, cardiorespiratory disease, surgical approach, and region. In addition to PC 21, a component profiling patients' socioeconomic status and support systems and degree of physical disability (PC 24) was associated with achievement of the MCID in SF-36 MCS score. ConclusionsThrough a data-driven approach, we identified several phenotypes associated with disability and physical and mental health-related QOL. Such data reduction methods may separate patient-, disease-, and treatment-related variables more accurately into clinically meaningful phenotypes that may inform patient care and recruitment into clinical trials.

Non-targeted glycosidic profiling of international wines using neutral loss-high resolution mass spectrometry

Many metabolites naturally occur as glycosides, since sugar moieties can be crucial for their biological activity and increase their water solubility. In the plant kingdom they may occur as glycosides or sugar esters, depending on precursor chemical structure, and in wine they have traditionally attracted attention due to their organoleptic properties, such as astringency and bitterness, and because they affect the colour and aroma of wines.A new approach directed at detailed description of glycosides in a large selection of monovarietal wines (8 samples each of Pinot Blanc, Muller Thurgau, Riesling, Traminer, Merlot, Pinot Noir and Cabernet Sauvignon) was developed by combining high performance liquid chromatography with high resolution tandem mass spectrometry. Analytical separation was performed on an Accucore™ Polar Premium LC column, while mass analysis was performed in negative ion mode with an non-targeted screening approach, using a Full MS/AIF/NL dd-MS2 experiment at a resolving power of 140,000 FWHM.Over 280 glycoside-like compounds were detected, of which 133 (including low-molecular weight phenols, flavonoids and monoterpenols) were tentatively identified in the form of pentose (6), deoxyhexose (17), hexose (73), hexose-pentose (16), hexose-deoxyhexose (7), dihexose (5) and hexose ester (9) derivatives. It was not possible to univocally define the corresponding chemical structure for the remaining 149 glycosides. Non-parametric statistical analysis showed it was possible to well characterise the glycosylated profile of all red and Traminer wines, while the identified glycosides were almost entirely lacking in Pinot Blanc, Riesling and Muller Thurgau wines. Also Tukey’s Honestly Significant Difference test (p < 0.05) and Principal Component Analysis confirmed that it was possible to almost entirely distinguish the selected red wines from each other according to their glycosylated profile.

Factors behind the variability of phosphorus accumulation in Finnish lakes

Phosphorus retention (TPacc) is one of the major water quality regulators in lakes. The current study aimed at ascertaining the specific lake characteristics regulating TPacc. Moreover, we were interested whether NAO (North Atlantic Oscillation), a proxy of climatic forcing, can explain variability in TPacc, additionally to that ascribed to lake characteristics. Sediment cores were obtained from 21 Finnish lakes, subject to radiometric dating and measurements of TP concentrations. Principal components (PCs) were generated using lake characteristics that are usually included into the modelling of TPacc (e.g. lake area, lake depth, catchment area, P inflow) but also the parameters that the classical models usually missed (e.g. anoxic factor). We used significant principal components (PCs), specific combinations of lake characteristics and monthly NAO values as predictors of TPacc. Lake characteristics explained the bulk of TPacc variability. The most influential factors (positive drivers) behind TPacc included PC1 (representing mainly deep lakes), PC2 (small lakes with high levels of anoxia and water column stability), PC3 (productive lakes with large catchment area and short water residence time), PC4 (lakes with high water column stability, low anoxic factor and relatively high sediment focusing) and PC5 (lakes with high levels of P inflow, anoxia and long water residence time). Additionally, we found a potential negative effect of NAO in October on the annual TPacc. This NAO was significantly positively related to temperatures in surface and near-bottom water layer (also their difference) in autumn, suggesting the possible implications for the internal P dynamics. Increased mineralization of organic matter is the most likely explanation for the reduced TPacc associated with NAO-driven water temperature increase. The analysis presented here contributes to the knowledge of the factors controlling P retention. Moreover, this spatially and temporally comprehensive sediment data can potentially be a valuable source for modelling climate change implications.

English

Sesame (Sesamum indicum L.) known as queen of oil seed crops is mainly grown for its oil of local consumption, sources of income and great contribution for the national economy of Ethiopia. However, there is a low productivity and production due to environments, genotypes, interaction and management variation. Four sesame genotypes were evaluated for their interactions with environments and seed yield stability analysis at three environments during the 2015 main cropping season. The objectives of the study were to estimate the magnitude and nature of GEI and to identify stable and/or high yielding white seeded sesame genotypes in Abergelle Agricultural Research Center mandate areas, Northern Ethiopia. The study was conducted using a randomized complete block design with three replications at each environment. The combined analysis of variance revealed highly significant (P≤0.01) environment (E), genotype (G) and genotype × environment interaction (GEI). Environment explained 79.84% of the total (G + E +GE) variation, whereas G and GE explained 17.21 and 2.95% of the total variation, respectively. The magnitude of the environment was 4.6 times greater than the genotype, implying that most of the variation in seed yield was due to the environment. The significant genotype by environment interaction effects were further partitioned into two significant interaction principal components using the genotype main effect plus genotype x environment interaction (GGE) biplots model. The first two principal components for seed yield stability of the GGE biplot analysis explained 96.81% of the total variation caused by G+GE of PC1 and PC2 accounted for 90.88 and 5.93% sum of squares, respectively, while 3.19% was attributed to noise. Thus, model diagnosis (fitting) showed that the first two PCs were significant and can be taken to interpret this data. The which-won-where biplot identified one winning genotype in one mega environment. The winning genotype across locations was Humera-1. Thus, the GGE (genotype and genotype by environment interaction) biplot analysis indicated that Humera-1 was considered as the most desirable and stable one’s, therefore, can be recommended for wider cultivation due to better seed yield and stability performance across the test environments in the dry lowland areas of Southeast and Central zones of Tigray region, Northern Ethiopia. Key words: Dry lowland, GEI, GGE biplot, mega environment, S. Indicum L.

Ứng dụng phương pháp phân tích thành phần chính và hồi quy logistic trong đánh giá cảm quan nước ép gấc - chanh dây

In this study, panellists were trained to evaluate various attributes of the combined Momordica cochinchinensis Spreng-passion (MCSP) fruit juice. The weight of passion fruit juice was ranged from 50 to 200 g (MCS weight of 150 g) and dilution ratio of MCSP juice with water (1:8 to 1:15). Principal component analysis (PCA) identified two significant principal components that accounted for 80.43% of the variance in the sensory attribute data. PCA indicated that the important sensory attribute of the MCSP juice primarily corresponded to taste, color and consistency. Overall acceptibility of product was modelled by logistic regression analysis (LRA) as a function of passion fruit juice concentration and dilution ratio of water. There was a statistically significant relationship between the variables (P

Towards Mapping Suitable Areas for Weather Modification in East Africa Community

In order to map suitable areas for weather modification in East Africa Community (EAC), investigations were performed to determine spatio-temporal variability and relationship of aerosol, clouds and precipitation during March- April-May (MAM) and October-November-December (OND). Principal Component Analysis (PCA), Hybrid Single- Particle Lagrangian Integrated Trajectory (HYSPLIT) and Multivariate Regression Analysis (MRA) were used. Identification of near homogeneous zones of Aerosol Optical Depth (AOD), Fine Mode Fraction (FMF), Cloud Top and 3B42 Tropical Rainfall Measuring Mission (TRMM) yielded 13 (14), 20 (18), 11 (10) and 16 (17) significant Principal Components (PCs) for MAM (OND) with explained variance greater than 57%. Aerosols and clouds had positive relationship with precipitation in areas with strong factor loadings. MRA indicated independence of variables used and normality in the model residuals. Backward trajectory analysis indicated differences in origins of transported particles in the atmosphere with strong vertical mixing inlands with mixed aerosols resulting due to mountain blocking systems accounted for enhanced rainfall. Enhanced rainfall was attributed to highly varied AOD and unaffected FMF in the atmosphere. Locations east and west EAC with mean temperatures greater than -10°C were unsuitable for cloud seeding while central EAC region along the great rift-valley and coastal Tanzania exhibited optimal temperatures suitable for cloud seeding. Successful precipitation enhancement will increase available fresh water sources and thus alleviate existing and projected water stress.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components.

Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method. Using simulations, we compared the methods. Our newly automated procedure is competitive with the best methods when considering both accuracy and speed and is the most accurate when the number of objects is small compared with the number of attributes. We applied the method to a proteomics data set from patients with acute myeloid leukemia. Proteins in the apoptosis pathway could be explained using 6 PCs. By clustering the proteins in PC space, we were able to replace the PCs by 6 “biological components,” 3 of which could be immediately interpreted from the current literature. We expect this approach combining PCA with clustering to be widely applicable.

Principal component analysis of coagulation assays in severely injured children

BackgroundTrauma-induced coagulopathy is common and associated with poor outcome in injured children. Our aim is to identify patterns of coagulation dysregulation after injury and associate these phenotypes with relevant clinical outcomes. MethodsWe performed principal components analysis on prospectively collected data from children with the highest-level trauma activation June 2015–June 2016. Parameters included admission international normalized ratio, platelet count and thromboelastograms. Variables were reduced to principal components; principal component scores were generated for each subject and used in logistic regression with outcomes including mortality, disability, venous thromboembolism, and blood transfusion in the first 24 hours. ResultsWe included 133 subjects with median interquartile range age =10 (5–13 years), median interquartile range Injury Severity Score =17 (9–25), 73.5% boys, 70.8% blunt trauma. principal component analysis identified 3 significant principal components accounting for 75.0% of overall variance. Principal component 1 reflected clot strength; principal component 2 reflected abnormal fibrinolysis, both hyperfibrinolysis and fibrinolysis shutdown; principal component 3 reflected global clotting factor depletion. High principal component 1 score was associated with increased mortality (odds ratio =1.63) and blood transfusion (odds ratio 1.36). Principal component 2 score was correlated with Injury Severity Score (rho 0.4) and associated with venous thromboembolism (odds ratio 1.84), functional disability (odds ratio 1.66), mortality (odds ratio 2.07) and blood transfusion (odds ratio 2.79). PC3 score was associated with increased mortality (odds ratio 1.92) and blood transfusion (odds ratio 1.25). ConclusionPrincipal component analysis detects 3 patterns of coagulation dysregulation using widely available laboratory parameters: (1) abnormalities in clot strength; (2) abnormalities in fibrinolysis, and (3) clotting factor depletion. While all were associated with mortality and transfusion, fibrinolytic dysregulation was associated with injury severity and portends particularly poor outcome including venous thromboembolism and disability.

Effect of whey protein‐iron based edible coating on the quality of Paneer and process optimisation

Whey protein concentrate (WPC)‐based edible coating containing one of the four different iron salts was used to enhance the nutritional quality of Paneer in the study. Ferric ammonium citrate containing WPC coating solutions decreased the L* but increased a* and b*. Principal component analysis identified three significant principal components that accounted for 88.85% of the variation in the sensory and instrumental colour data. Response surface methodology predicted that maximum iron content in Paneer (93.5 ppm) could be achieved with 100 mL of dipping volume, 1.5 cm of cube size and 10‐min dipping time as processing parameters.

Extraction of reflected events from sonic-log waveforms using the Karhunen-Loève transform

Sonic-reflection logging, a recently developed borehole geophysical scheme, is in principle capable of providing a clear view of outside the well bore. In this type of acoustic well logging, a key technical obstacle is that the reflected wave signal is almost entirely obscured by the directly arriving P-, S-, and Stoneley wave modes. Effective extraction of these reflection signals from the full acoustic waveforms is therefore a critical data-processing step. We have examined the use of the Karhunen-Loève (KL) transform, combined with a band-limiting filter, as a technique for the extraction of reflections of interest from a mixture with directly arriving wave modes of much higher amplitude. Under the assumption that large energy (squared-amplitude) differences exist between each wave component, the direct Stoneley wave, S-wave, and the P-wave are eliminated sequentially by subtracting the most significant principal components, after which the remaining signal is seen to be dominated by reflected events. Thereafter, the extracted reflections can be used in migration to provide interpretable images of the structures outside the borehole. Synthetic data are used to develop and justify our procedure for subtraction of appropriate KL principal components. Laboratory data are used to demonstrate in detail the suppression of unwanted modes. For comparison, the multiscale slowness-time-coherence method is applied to extract reflections from the same data set. The procedure is exemplified on a field data case with attention paid in particular to the consequences to imaging of near-borehole structures.

Modeling the relationship between ground surface settlements induced by shield tunneling and the operational and geological parameters based on the hybrid PCA/ANFIS method

This paper proposes a methodology that combines the Principal Component Analysis (PCA) with Adaptive Neuro Fuzzy based Inference System (ANFIS) to model the nonlinear relationship between ground surface settlements induced by an earth pressure balanced TBM and the operational and geological parameters. The study is based on data recorded during the excavation of contract 2 of the subway line B tunnel in Toulouse (France).Prior to modeling, principal components analysis (PCA) and agglomerative hierarchical clustering (AHC) are used to describe the interrelation pattern between the TBM parameters and geology profiles.At first, a model ANFIS based on 10 selected TBM operation parameters (geology conditions considered as homogenous) is developed and validated by drawing the settlement profiles for different reference points.Secondly, to take into account the effect of geology on the settlements, 5 parameters representing the thicknesses of categories of soil were added as input variables. Then, a model ANFIS using the significant principal components as inputs is developed and validated. The results indicate a high correlation between predicted and measured settlements despite the low amount of data used in the analysis. In addition, the model is able to predict Gaussian troughs for the representative groups identified by the AHC. The results show that the shape of the predicted settlement troughs could be explained by the TBM parameters and soil profiles that characterize each group.

Short communication: Discrimination between retail bovine milks with different fat contents using chemometrics and fatty acid profiling

We used a multivariate chemometric approach to differentiate or associate retail bovine milks with different fat contents and non-dairy beverages, using fatty acid profiles and statistical analysis. We collected samples of bovine milk (whole, semi-skim, and skim; n = 62) and non-dairy beverages (n = 27), and we analyzed them using gas-liquid chromatography. Principal component analysis of the fatty acid data yielded 3 significant principal components, which accounted for 72% of the total variance in the data set. Principal component 1 was related to saturated fatty acids (C4:0, C6:0, C8:0, C12:0, C14:0, C17:0, and C18:0) and monounsaturated fatty acids (C14:1 cis-9, C16:1 cis-9, C17:1 cis-9, and C18:1 trans-11); whole milk samples were clearly differentiated from the rest using this principal component. Principal component 2 differentiated semi-skim milk samples by n-3 fatty acid content (C20:3n-3, C20:5n-3, and C22:6n-3). Principal component 3 was related to C18:2 trans-9,trans-12 and C20:4n-6, and its lower scores were observed in skim milk and non-dairy beverages. A cluster analysis yielded 3 groups: group 1 consisted of only whole milk samples, group 2 was represented mainly by semi-skim milks, and group 3 included skim milk and non-dairy beverages. Overall, the present study showed that a multivariate chemometric approach is a useful tool for differentiating or associating retail bovine milks and non-dairy beverages using their fatty acid profile.

Spatial and temporal variations of physicochemical and heavy metal pollution in Ramganga River—a tributary of River Ganges, India

The River Ganges being the most sacred river and lifeline to millions of Indians in serving their water requirements is facing excessive threat of pollution. Under various river management and conservation strategies for its protection, the assessment of water quality of its main tributary Ramganga River is lacking. This study focuses on assessment of physicochemical and heavy metal pollution of the Ramganga River by application of multivariate statistical techniques. Sampling of Ramganga River at sixteen sampling sites was carried out in three seasons (summer, monsoon and winter) of 2014. The collected water samples were analyzed for physicochemical parameters and heavy metals. Results from cluster analysis (CA) of the data divided the whole stretch of the river into three clusters as elevation from 1304 to 259 m as less polluted, from 207 to 154 m as moderately polluted and from elevation 154 to 139 m as high-polluted stretches with anthropogenic as main sources of pollution in high-polluted stretch. Principal component analysis of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72–8% of total variance with strong loadings (>0.75) of PC1 on fluoride (F−), chloride (Cl−), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3 −), total dissolved solids and electrical conductivity. Temporal variation by one-way ANOVA (Analysis of Variance) showed significant seasonal variation was in the pH, chemical oxygen demand, biochemical oxygen demand, turbidity, HCO3 −, F−, Zn, cadmium (Cd) and Mn (p < 0.05). Turbidity showed approximately a twofold increase in monsoon season due to rainfall in the catchment area and subsequent flow of runoff into the river. Concentration of HCO3 −, F− and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summers compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters.

Capsicum frutescens L. Landraces of North-East India: From Phenotypic Diversity Perspective of Unexplored Collection

Mizoram and Manipur are the two north-eastern states of India, where maximum diversity of Capsicum frutescens is found. Seventy two Capsicum landraces, consisting predominantly of C. frutescens accessions were collected from the major growing states of north-eastern India. Landraces were compared for 29 qualitative traits and 14 quantitative traits using multivariate methods, including clustering and principal component analysis to assess the patterns of morphological variation. Landraces collected revealed significant differences (P 4 cm was found in three landraces (MN-1, MN-2 and MZBEC-2), whereas smallest C. frutescens landraces (in length) were MZBEC-42 (1.15 cm), MZBEC-13 (1.17 cm) and MZBEC-55 (1.27 cm); a variation of 6.5 folds was found in fruit length. Among the 91 inter correlation coefficients studied in quantitative traits, 17 were positively significant (8 at P < 0.01 and 9 at P < 0.05) and three were negatively significant (2 at P < 0.01 and 1 at P < 0.05). Five significant principal components (PC) were identified and accounted for a cumulative variation of 69.88%. Hierarchical cluster analysis grouped 72 landraces into five clusters based on the qualitative traits and into six clusters based on quantitative traits. Altogether, the present study has generated baseline information about the vast intra-specific diversity of C. frutescens landraces of North-East India.

Individual clotting factor contributions to mortality following trauma.

Acute traumatic coagulopathy affects 20% to 30% of trauma patients, but the extensive collinearity of the coagulation cascade complicates attempts to clarify global clotting factor dysfunction. This study aimed to characterize phenotypes of clotting factor dysfunction and their contributions to mortality after major trauma. This prospective cohort study examines all adult trauma patients of the highest activation level presenting to San Francisco General Hospital between February 2005 and February 2015. Factors II, V, VII, VIII, IX, and X and protein C activity on admission and mortality status at 28 days were assessed. Predictors of 28-day mortality in univariate analysis were included in multiple logistic regression controlling for traumatic brain injury (TBI), acidosis, age, and mechanism of injury. Principal component analysis was utilized to identify phenotypic coagulation. Complete coagulation factor data were available for 876 (61%) of 1,429 patients. In multiple logistic regression, factors V (odds ratio [OR], 0.86; 95% confidence interval [CI], 0.76-0.97), VIII (OR, 0.97; 95% CI, 0.95-0.99), and X (OR, 0.79; 95% CI, 0.68-0.92) and protein C (OR, 1.17; 95% CI, 1.05-1.30) significantly predicted 28-day mortality after controlling for age, base deficit, mechanism of injury, and TBI. Principal component analysis identified two significant principal components (Phenotypes 1 and 2) that accounted for 66.3% of the total variance. Phenotype 1 (factors II, VII, IX, and X and protein C abnormalities) explained 49.3% and was associated with increased injury, coagulopathy, TBI, and mortality. Phenotype 2 (factors V and VIII abnormalities) explained 17.0% and was associated with increased coagulopathy, blunt injury, and mortality. Only Phenotype 2 remained significantly associated with 28-day mortality in multiple logistic regression. Principal component analysis identified two distinct phenotypes within the entirety of global clotting factor abnormalities, and these findings substantiate the crucial association of factors V and VIII on mortality following trauma. This may be the first step toward identifying unique phenotypes after injury and personalizing hemostatic resuscitation. Prognostic study, level III.