Soil Contamination Interpretation by the Use of Monitoring Data Analysis

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0–20 cm) and subsurface (20–40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition—one cluster for coastal and mountain sites and another—for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm

Empirical comparison of cross-platform normalization methods for gene expression data

<p>Abstract</p> <p>Background</p> <p>Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.</p> <p>Results</p> <p>Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.</p> <p>Conclusions</p> <p>Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at <url>http://alborz.sdsu.edu/conor</url> and is available from CRAN.</p

Integrated Assessment of Heavy Metal Contamination in Sediments from a Coastal Industrial Basin, NE China

The purpose of this study is to investigate the current status of metal pollution of the sediments from urban-stream, estuary and Jinzhou Bay of the coastal industrial city, NE China. Forty surface sediment samples from river, estuary and bay and one sediment core from Jinzhou bay were collected and analyzed for heavy metal concentrations of Cu, Zn, Pb, Cd, Ni and Mn. The data reveals that there was a remarkable change in the contents of heavy metals among the sampling sediments, and all the mean values of heavy metal concentration were higher than the national guideline values of marine sediment quality of China (GB 18668-2002). This is one of the most polluted of the world’s impacted coastal systems. Both the correlation analyses and geostatistical analyses showed that Cu, Zn, Pb and Cd have a very similar spatial pattern and come from the industrial activities, and the concentration of Mn mainly caused by natural factors. The estuary is the most polluted area with extremely high potential ecological risk; however the contamination decreased with distance seaward of the river estuary. This study clearly highlights the urgent need to make great efforts to control the industrial emission and the exceptionally severe heavy metal pollution in the coastal area, and the immediate measures should be carried out to minimize the rate of contamination, and extent of future pollution problems

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Risk of potential pesticide use to honeybee and bumblebee survival and distribution: A country‐wide analysis for The Netherlands

Diversity and Distributions Published by John Wiley and Sons Ltd. Aim: Bees play an important role in natural ecosystems and the world's food supply. In the past decades, bee abundance and diversity have declined globally. This has resulted in decreased pollination services for natural ecosystems and the agricultural sector at the field scale. One of the causes of the decline in bee abundance and diversity is the use of pesticides. Linking pesticide use, land use and bee presence could provide crucial insights into areas, and pesticides that pose a significant threat to the abundance and diversity of bees. Obtaining actual figures of farmer pesticide use is rarely possible. Therefore, we designed a method to study the effects of potential pesticide use on the survival and distribution of honeybees and bumblebees. Location: The Netherlands. Methods: A pesticide risk model was implemented incorporating a hazard quotient as the risk assessment. The number of allowed pesticide active ingredients per crop that could pose a risk to honeybees and bumblebees was linked to the Dutch crop parcel locations for 2015 and 2016. The potential pesticide risk maps were analysed using honeybee colony survival and bumblebee presence data. Results: Non-significant negative effects of potential pesticide risk on honeybee colony survival and bumblebee presence were found. A significant negative effect of greenhouses was identified for both honeybees and bumblebees. The most important factors in the models predicting honeybee colony survival and bumblebee presence were urban land areas and natural grasslands, respectively, both showing a positive effect. Main conclusions: Here, the first attempt to estimate and map pesticide risk to bees in the Netherlands in a country-wide manner is presented. The results could provide crucial high-resolution insights for bee conservation action and facilitate the increase of pollination services in natural ecosystems and the agricultural sector on a local and country-wide scale

Zastosowanie metod modelowania numerycznego oraz modelowania fraktalnego do analizy jakości skał w celu określenia charakterystyki górotworu w obszarze złoża Cu-Mo w Kahang, środkowy Iran

Identification of rock mass properties in terms of Rock Quality Designation (RQD) plays a significant role in mine planning and design. This study aims to separate the rock mass characterisation based on RQD data analysed from 48 boreholes in Kahang Cu-Mo porphyry deposit situated in the central Iran utilising RQD-Volume (RQD-V) and RQD-Number (RQD-N) fractal models. The log-log plots for RQD-V and RQD-N models show four rock mass populations defined by RQD thresholds of 3.55, 25.12 and 89.12% and 10.47, 41.68 and 83.17% respectively which represent very poor, poor, good and excellent rocks based on Deere and Miller rock classification. The RQD-V and RQD-N models indicate that the excellent rocks are situated in the NW and central parts of this deposit however, the good rocks are located in the most parts of the deposit. The results of validation of the fractal models with the RQD block model show that the RQD-N fractal model of excellent rock quality is better than the RQD-V fractal model of the same rock quality. Correlation between results of the fractal and the geological models illustrates that the excellent rocks are associated with porphyric quartz diorite (PQD) units. The results reveal that there is a multifractal nature in rock characterisation with respect to RQD for the Kahang deposit. The proposed fractal model can be intended for the better understanding of the rock quality for purpose of determination of the final pit slope.Identyfikacja właściwości górotworu odgrywa zasadniczą rolę w planowaniu wydobycia i projektowaniu kopalni. Praca niniejsza ma na celu określenie charakterystyki górotworu w oparciu o dane o jakości skał zebrane na podstawie próbek uzyskanych z 48 odwiertów wykonanych w złożu porfiru Cu-Mo w Kahang, zalegającym w środkowym Iranie przy użyciu modeli fraktalnych RQD-V - Rock Quality Determination-Volume [Określenie jakości skał-objętość]) i RQD-N (Rock Quality Determination-Number [Określenie jakości skał-liczba]). Wykresy logarytmiczne wykonane dla modeli RQD-V i RQD-N wykazują istnienie czterech populacji warstw górotworu, określonych na podstawie parametrów progowych: 3.55; 25.12; 89.12% oraz 10.47; 41.68 i 83.17%, odpowiadającym kolejno stopniom jakości: bardzo słaby, słaby, dobry i bardzo dobry, zgodnie z klasyfikacją skał Deere i Millera. Wyniki uzyskane przy zastosowaniu modeli RQD-V i RQD-N wskazują, że najlepsze skały zalegają w północno- zachodniej i centralnej części złoża, z kolei dobrej jakości skały znaleźć można w obrębie całego złoża. Walidacja modeli fraktalnych w oparciu o model blokowy (RQD block model) wskazuje, że model RQD-N dla bardzo dobrej jakości skał jest skuteczniejszy niż model RQD-V dla tej samej jakości skał. Wysoki stopień korelacji pomiędzy wynikami uzyskanymi w oparciu o modele fraktalne i geologiczne pokazuje, że najwyższej jakości skały związane są z obecnością porfirowego diorytu kwarcowego. Badanie wykazuje fraktalną naturę charakterystyki jakości skał w złożu Kahang. Zaproponowany model fraktalny wykorzystać można do lepszego poznania zagadnienia jakości skał w celu obliczenia nachylenia wyrobiska