R-mode hierarchical cluster and principal component analysis (PCA) were simultaneously applied to surface water hydrochemical data from three different locations, Ankwaso, Dominase and Prestea, along ...the Ankobra Basin, Ghana, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the basin scale. Using the Kaiser criterion, principal components (PC) were extracted from the data and rotated using varimax normalization, for each location. The varimax rotation ensured that variation in the data was maximized for easy interpretation of the results. The analysis reduced 30, 33 and 33 data points, respectively, for Ankwaso, Dominase and Prestea to four, three and four PC representing the sources of variation in the hydrochemistry at the three different locations. Though the PC analysis proved to be more robust at unveiling the sources of variation in the hydrochemistry than the
R-mode hierarchical cluster analysis (HCA), the combined use of both techniques resulted in more reliable interpretations of the hydrochemistry. On the basis of these analyses, the hydrochemistry of the basin is controlled largely by the weathering of minerals (silicates, carbonates, gypsum and apatite) from the underlying meta-sediments of the Birimian and Tarkwaian Systems, and the decay of organic matter from the heavily forested regions. Concentrations of the major chemical parameters are within naturally acceptable limits and do not pose threats to the local ecology and humans. There is no strong evidence of high anthropogenic impacts on the major anions and cations used for this research, though there are variations at the different locations studied. The hydrochemistry at Ankwaso is principally controlled by the weathering of silicate minerals, whereas those of Dominase and Prestea are, respectively, influenced by precipitation and domestic wastewaters, and the decay of organic matter.
: Rice bean, a less known and underutilized legume, has emerged as a potential legume because of its nutritional potential. The nutritional quality of rice bean is higher as compared to many other ...legumes of Vigna family. In the present study, 16 diverse rice bean genotypes were evaluated for major nutritional constituents viz; protein content, total lipids, dietary fiber, total carbohydrates, vitamins, minerals, protein fractions, amino acid, and fatty acid profile. The protein content to the extent of 25.57% was observed in the genotype BRS‐2 with in vitro digestibility of 54.23%. The fatty acid profile revealed the higher percentage of unsaturated fatty viz., linoleic and linolenic acid, which are nutritionally desirable in the diet. Albumins (6.13% to 7.47%) and globulins (13.11% to 15.56%) constituted the major portion of proteins. Anti‐nutritional factors were in the range of: total phenolics (1.63% to 1.82%), total tannins (1.37% to 1.55%), condensed tannins (0.75% to 0.80%), hydrolysable tannins (0.56% to 0.79%), trypsin inhibitor (24.55 to 37.23 mg/g), phytic acid (7.32 to 8.17 mg/g), lipoxygenase activity (703 to 950 units/mg), and saponin content (1.2 to 3.1 mg/100 g). The oligosaccharides associated with the production of flatulence viz., raffinose, stachyose, and verbascose were in the limits of 1.66% to 2.58%, 0.94% to 1.88%, and 0.85% to 1.23%, respectively. In vitro protein digestibility up to 55.57% was observed in rice bean genotypes. The present study has revealed that rice bean is a nutritionally rich legume as compared to many other legumes of the category. Among different genotypes BRS‐2 was observed superior and could be advocated for consumption as well as for inclusion in crop improvement programs.
Practical Application: Rice bean is nutritionally rich legume, but despite its nutritional excellence, it has been put in underutilized category. Because of this and several other reasons the people are not aware of its nutritional benefits. Moreover, the complete nutritional details are also not available on this pulse. The present study gives the vivid description of nutritional attributes of this legume for making people aware of its nutritional excellence and provoking improved work in rice bean.
We have developed a new mass spectrometry (MS) technology, the Single-probe MS, capable of real-time, in situ metabolomic analysis of individual living cells. The Single-probe is a miniaturized ...multifunctional sampling and ionization device that is directly coupled to the mass spectrometer. With a sampling tip smaller than individual eukaryotic cells (<10 μm), the Single-probe can be inserted into single cells to sample the intracellular compounds for real-time MS analysis. We have used the Single-probe to detect several cellular metabolites and the anticancer small molecules paclitaxel, doxorubicin, and OSW-1 in individual cervical cancer cells (HeLa). Single cell mass spectrometry (SCMS) is an emerging scientific technology that could reshape the analytical science of many research disciplines, and the Single-probe MS technology is a novel method for SCMS that, through its accessible fabrication protocols, can be broadly applied to different research areas.
The spatial distribution and transport characteristics of heavy metals in an antimony mine area (Xikuangshan, China) were systematically studied using a field survey and geostatistical analytical ...methods. In the study area, 52 soil and sediment samples were collected from bare land, grassland, woodland and river sediments covering a surface area of 20 km2. The soil properties and heavy metal concentrations were measured by wavelength dispersive X-ray fluorescence spectrometry and inductively coupled plasma-mass spectrometry, respectively. Correlation analysis and principal component analysis suggest that Cu, Zn, Cd, As, Pb and Sb can be attributed to anthropogenic inputs, whereas Cr, Mn and Ni are of natural origin. Distribution maps of heavy metals were generated using the Kriging interpolation method to identify their distribution trends. The results show the influence of wind, river, distance and vegetation on the spatial distribution. The results also revealed that windborne transport may play a significant role in the spreading of contaminants. In addition, the environmental risk of heavy metal pollution was evaluated using their geoaccumulation indexes in the whole region. All of the results indicate that the heavy metal distributions in the soils were consistent with the local prevailing wind direction. In addition, the environmental quality could be seriously threatened by heavy metal contaminants from the smelter and tailings.
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●As and Sb showed similar geochemical behavior on transport and deposition.●Lower concentrations for Cr, Mn and Ni may be controlled by natural factors.●Cu, Zn, Cd and Pb may be released from sulfide minerals.●Heavy metal distribution trends were associated with local wind direction.●The environment quality was obviously threatened by accumulation of Sb and Cd.
National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of ...covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979-2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979-2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional clustered CV: 0.66 0.47) compared to models with no (0.37 0) or all variables (0.64 0.27). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.
Karst terrain is the typical area covered with a high background of heavy metals under geochemical anomaly. This research explored the accumulation of geochemical elements and soil sources in karst ...terrain from rock and soil exposed in carbonate areas. The comprehensive ecological risk and enrichment of heavy metals from parent rock weathered to soil was investigated in 11 formations in the carbonate and clastic areas of the Weining and Hezhang counties in northwest Guizhou. The single factor pollution index, geoaccumulation index, and the potential risk coefficient were used to assess the environmental risk. The results revealed that the heavy metals in an overall geologically high background level of soil in northwest Guizhou is at a slight risk level. However, except for Cd, the heavy metals did not exceed the standard pollution reference. Moreover, the UNMIX model and Cd and Pb isotopes were used to analyze the source of heavy metals, comprising of cadmium (Cd), arsenic (As), lead (Pb), chromium (Cr), copper (Cu), nickel (Ni), and zinc (Zn), and the geochemical elements of silicon (Si), aluminum (Al), iron (Fe), magnesium (Mg), and calcium (Ca). The study showed that most elements in the soil carbonate area exceed the national standard, and the heavy metals in the soil showed a strong enrichment, while the major elements Si and Mg display strong loss. Heavy metal concentrations in soil in the carbonate area were higher than in the clastic area. Geological sources and atmospheric deposition were the main contributors to heavy metal concentrations in both carbonate and clastic areas, and their concentrations differ according to soils developing in different formations.
Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides ...(NO x ) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R 2) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R 2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R 2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.
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•Phenolic acids, stilbenes, flavanol, anthocyanin, flavonol and flavanone were detected in all flowers.•The orange flower cosmos presented the highest content of phenolic ...compounds.•Bioaccessibility of phenolics varied among the compounds and within the flower source.•Before in vitro digestion mini rose displayed the highest activity in FRAP and DPPH assays.•The dialyzed fraction of cravine showed the highest activity in DDPH assay.•Cosmos and mini rose are source of bioaccessible phenolics with great antioxidant activity.
This study assessed the phenolics and their bioaccessibility through an in vitro digestion system coupled to a simulated intestinal barrier in eight edible flowers of distinct colors, namely mini rose, torenia, mini daisy, clitoria, cosmos, cravine, begonia and tagete. The antioxidant activity of the flowers before in vitro digestion, in their derived dialyzed and non-dialyzed fractions was evaluated using distinct approaches. All flowers presented in their composition phenolic acids, stilbenes, flavanol, anthocyanin, flavonol and flavanone, however distinct compounds and contents were found in each flower. The bioaccessibility varied among the phenolics and within the flower source (p < 0.05). Cosmos presented the highest (p < 0.05) content of phenolics and activity in ORAC assay before in vitro digestion and in dialyzed and non-dialyzed fraction; the observed activity was correlated (r = 0.9) to its major compounds, hesperidin and rutin, as well as to caftaric acid and procyanidin B2. Mini rose displayed the highest antioxidant activity in FRAP and DPPH assays before in vitro digestion; its dialyzed and non-dialyzed fraction showed the highest activity in FRAP, correlated to pelargonidin 3,5-diglucoside, catechin, epicatechin galate, epicagocatechin galate, procyanidin A2, quercitin 3-glucoside and trans-resveratrol (r = 0.9). In DPPH assay, mini rose showed the highest activity in the non-dialyzed fraction, while cravine showed the highest activity in the dialyzed fraction, which was mainly correlated to syringic acid (r = 1.0), pelargonidin 3,5-diglucoside and epicatechin (r = 0.9). Results show great variability in the phenolic composition and their bioaccessibility among the edible flowers studied. Our findings indicate cosmos and mini rose as sources of bioaccessible phenolics with great antioxidant activity.
Key challenges of biopsy-based determination of prostate cancer aggressiveness include tumour heterogeneity, biopsy-sampling error, and variations in biopsy interpretation. The resulting uncertainty ...in risk assessment leads to significant overtreatment, with associated costs and morbidity. We developed a performance-based strategy to identify protein biomarkers predictive of prostate cancer aggressiveness and lethality regardless of biopsy-sampling variation.
Prostatectomy samples from a large patient cohort with long follow-up were blindly assessed by expert pathologists who identified the tissue regions with the highest and lowest Gleason grade from each patient. To simulate biopsy-sampling error, a core from a high- and a low-Gleason area from each patient sample was used to generate a 'high' and a 'low' tumour microarray, respectively.
Using a quantitative proteomics approach, we identified from 160 candidates 12 biomarkers that predicted prostate cancer aggressiveness (surgical Gleason and TNM stage) and lethal outcome robustly in both high- and low-Gleason areas. Conversely, a previously reported lethal outcome-predictive marker signature for prostatectomy tissue was unable to perform under circumstances of maximal sampling error.
Our results have important implications for cancer biomarker discovery in general and development of a sampling error-resistant clinical biopsy test for prediction of prostate cancer aggressiveness.
Groundwater is essential for the sustainable development of the Guanzhong Basin, China, and its quality is mainly controlled by hydrogeochemical processes and anthropogenic pollution. This study used ...statistical and multivariate statistical analysis approaches to recognize the hydrogeochemical processes and affecting factors of groundwater in the central part of the Guanzhong Basin. Correlations among 14 hydrochemical parameters were statistically examined. Principal component analysis (PCA), factor analysis (FA), and hierarchical cluster analysis (HCA) techniques were applied to analyze the physicochemical variables to understand the affecting factors of groundwater quality in the study area. The correlation analysis results indicate that cation exchange is the dominant process affecting the concentration of Na
+
and Ca
2+
in the groundwater. Both the PCA and FA indicate that minerals dissolution/precipitation and human activities are the key factors that affect groundwater quality. All parameters except CO
3
2−
and pH increase from C1 to C4 obtained through the Q mode HCA. C4 has a hydrochemical type of SO
4
–Na·K, indicating that the sample of this cluster is primarily influenced by anthropogenic processes.
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