Dairy heifer manure management, dietary phosphorus, and soil test P effects on runoff phosphorus.

Manure application to cropland can contribute to runoff losses of P and eutrophication of surface waters. We conducted a series of three rainfall simulation experiments to assess the effects of dairy heifer dietary P, manure application method, application rate, and soil test P on runoff P losses from two successive simulated rainfall events. Bedded manure (18-21% solids) from dairy heifers fed diets with or without supplemental P was applied on a silt loam soil packed into 1- by 0.2-m sheet metal pans. Manure was either surface-applied or incorporated (Experiment 1) or surface-applied at two rates (Experiment 2) to supply 26 to 63 kg P ha. Experiment 3 evaluated runoff P from four similar nonmanured soils with average Bray P1-extractable P levels of 11, 29, 51, and 75 mg kg. We measured runoff quantity, total P (TP), dissolved reactive P (DRP), and total and volatile solids in runoff collected for 30 min after runoff initiation from two simulated rain events (70 mm h) 3 or 4 d apart. Manure incorporation reduced TP and DRP concentrations and load by 85 to 90% compared with surface application. Doubling the manure rate increased runoff DRP and TP concentrations an average of 36%. In the same experiment, P diet supplementation increased water-extractable P in manure by 100% and increased runoff DRP concentration threefold. Concentrations of solids, TP, and DRP in runoff from Rain 2 were 25 to 75% lower than from Rain 1 in Experiments 1 and 2. Runoff DRP from nonmanured soils increased quadratically with increasing soil test P. These results show that large reductions in P runoff losses can be achieved by incorporation of manure, avoiding unnecessary diet P supplementation, limiting manure application rate, and managing soils to prevent excessive soil test P levels.

Hepatitis C virus genotypes distribution and transmission risk factors in Luxembourg from 1991 to 2006.

AIM: To analyze the Hepatitis C virus (HCV) genotype distribution and transmission risk factors in a population of unselected patients in Luxembourg. METHODS: Epidemiological information (gender, age and transmission risks) were collected from 802 patients newly diagnosed for hepatitis C and living in Luxembourg, among whom 228 patients referred from prison. Genotyping using 5'noncoding (5'NC) sequencing was performed. We compared categorical data using the Fisher's exact F-test and odds ratios (OR) were calculated for evaluating association of HCV genotype and risk factors. RESULTS: The sex ratio was predominantly male (2.2) and individuals aged less than 40 years represented 49.6% of the population. Genotype 1 was predominant (53.4%) followed by genotype 3 (33%). Among risk factors, intravenous drug usage (IVDU) was the most frequently reported (71.4%) followed by medical-related transmission (17.6%) including haemophilia, transfusion recipients and other nosocomial reasons. Genotype 3 was significantly associated to IVDU (OR = 4.84, P < 0.0001) whereas genotype 1 was significantly associated with a medical procedure (OR = 2.42, P < 0.001). The HCV genotype distribution from inmate patients differed significantly from the rest of the population (Chi-square test with four degrees of freedom, P < 0.0001) with a higher frequency of genotype 3 (46.5% vs 27.5%) and a lower frequency of genotype 1 and 4 (44.7% vs 56.8% and 5.3% vs 9.6%, respectively). IVDU was nearly exclusively reported as a risk factor in prison. CONCLUSION: We report the first description of the HCV genotype distribution in Luxembourg. The repartition is similar to other European countries, with one of the highest European prevalence rates of genotype 3 (33%). Since serology screening became available in 1991, IVDU remains the most common way of HCV transmission in Luxembourg.

Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management.

Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.

Data mining the NCI cancer cell line compound GI(50) values: identifying quinone subtypes effective against melanoma and leukemia cell classes.

Using data mining techniques, we have studied a subset (1400) of compounds from the large public National Cancer Institute (NCI) compounds data repository. We first carried out a functional class identity assignment for the 60 NCI cancer testing cell lines via hierarchical clustering of gene expression data. Comprised of nine clinical tissue types, the 60 cell lines were placed into six classes-melanoma, leukemia, renal, lung, and colorectal, and the sixth class was comprised of mixed tissue cell lines not found in any of the other five classes. We then carried out supervised machine learning, using the GI(50) values tested on a panel of 60 NCI cancer cell lines. For separate 3-class and 2-class problem clustering, we successfully carried out clear cell line class separation at high stringency, p < 0.01 (Bonferroni corrected t-statistic), using feature reduction clustering algorithms embedded in RadViz, an integrated high dimensional analytic and visualization tool. We started with the 1400 compound GI(50) values as input and selected only those compounds, or features, significant in carrying out the classification. With this approach, we identified two small sets of compounds that were most effective in carrying out complete class separation of the melanoma, non-melanoma classes and leukemia, non-leukemia classes. To validate these results, we showed that these two compound sets' GI(50) values were highly accurate classifiers using five standard analytical algorithms. One compound set was most effective against the melanoma class cell lines (14 compounds), and the other set was most effective against the leukemia class cell lines (30 compounds). The two compound classes were both significantly enriched in two different types of substituted p-quinones. The melanoma cell line class of 14 compounds was comprised of 11 compounds that were internal substituted p-quinones, and the leukemia cell line class of 30 compounds was comprised of 6 compounds that were external substituted p-quinones. Attempts to subclassify melanoma or leukemia cell lines based upon their clinical cancer subtype met with limited success. For example, using GI(50) values for the 30 compounds we identified as effective against all leukemia cell lines, we could subclassify acute lymphoblastic leukemia (ALL) origin cell lines from non-ALL leukemia origin cell lines without significant overlap from non-leukemia cell lines. Based upon clustering using GI(50) values for the 60 cancer cell lines laid out by the RadViz algorithm, these two compound subsets did not overlap with clusters containing any of the NCI's 92 compounds of known mechanism of action, a few of which are quinones. Given their structural patterns, the two p-quinone subtypes we identified would clearly be expected to possess different redox potentials/substrate specificities for enzymatic reduction in vivo. These two p-quinone subtypes represent valuable information that may be used in the elucidation of pharmacophores for the design of compounds to treat these two cancer tissue types in the clinic.