Prototypic sequences for human repetitive DNA

Summary We report a collection of 53 prototypic sequences representing known families of repetitive elements from the human genome. The prototypic sequences are either consensus sequences or selected examples of repetitive sequences. The collection includes: prototypes for high and medium reiteration frequency interspersed repeats, long terminal repeats of endogenous retroviruses, alphoid repeats, telomere-associated repeats, and some miscellaneous repeats. The collection is annotated and available electronically.[/ap ]Offprint requests to: J. Jurka     

Soil restoration under pasture after lignite mining: Management effects on soil biochemical properties and their relationships with herbage yields

The recovery of soil biochemical properties under grazed, grass-clover pasture, after simulated lignite mining, was studied over a 5-year period in a mesic Typic Dystrochrept soil at Waimumu, Southland, New Zealand. The restoration procedures involved four replacement treatments, after A, B, and C horizon materials had been separately removed, from all except the control, and stockpiled for 2–3 weeks. In each replacement treatment, the effects of ripping to 1.8 m depth, mole drainage, and the use of fertilizer nitrogen were also investigated.Replacement treatment markedly influenced the recovery of herbage production and soil organic C and total N contents, N mineralization, microbial biomass (as indicated by mineral-N flush) and invertase and sulphatase activities. The effectiveness of replacement treatments decreased in the order: 1. control (no stripping or replacement). 2 A, B, and C horizon materials replaced in the same order. 3. A, B, and C horizon materials each mixed with an equal amount of siltstone overburden and replaced in order, 4. A and B horizon materials mixed before replacing over C horizon material.Ripping increased herbage production, net N mineralization, and to some extent microbial biomass. Drainage had little, if any, effect.Fertilizer N also stimulated herbage production, but depressed clover growth. Over 2.5 years, it had little detectable effect on the soil properties.Increases in soil invertase and, to a lesser extent, sulphatase activity during the trial were closely related to changes in herbage production. Microbial biomass increased more rapidly than did soil organic C in the early stages of the trial.Rates of net N mineralization strongly suggest that N availability would have limited pasture growth, especially in the treatments with mixed soil materials.     

The impact of surface mine reclamation on headwater streams in Southwest Virginia

Recovery of headwater streams following the cessation of mining and the application of terrestrial (vegetative) reclamation techniques was assessed by comparison of water quality and aquatic biota in two such systems (reclaimed four to seven years) with that of an unimpacted stream and of streams draining mine areas which were abandoned without reclamation. Alkalinity, hardness, sulfate, and conductivity were elevated in the reclaimed mine streams as were fine-particle suspended solids and sediment. Overall water quality was comparable to streams draining unreclaimed lands. Benthic invertebrate and fish populations were significantly lower in abundance in the reclaimed mine streams than in the reference stream and showed less taxonomic richness and stability; they were similar in these respects to the biota of the unreclaimed mine streams. Continued sedimentation from mined areas and haul roads affected stream habitat and appeared to be the major factor limiting biotic recovery. These findings emphasize that terrestrial reclamation does not assure lotic restoration. Water quality criteria merit consideration in the refinement of reclamation procedures for mined lands.     

The Blue Water Footprint of Primary Copper Production in Northern Chile

Water consumption related to the life cycle of metals is seldom reported, even though mines are often situated in very dry regions. In this study we quantified the life cycle consumption of groundwater and fresh surface water (blue water footprint [WF_blue]) for the extraction and production of high‐grade copper refined from both a copper sulfide ore and a copper oxide ore in the Atacama Desert of northern Chile. Where possible, we used company‐specific data. The processes for extracting copper from the two types of ore are quite different from each other, and the WF_blue of the sulfide ore refining process is 2.4 times higher than that of the oxide ore refining process (i.e., 96 cubic meters per metric ton [tonne] of copper versus 40 cubic meters per tonne of copper). Most of the water consumption (59% of WF_blue) in the sulfide ore process occurred at the concentrator plant, via seepage, accumulation, and also by evaporation. In the oxide ore process, the main user of water is the heap‐leaching process, with 45% of WF_blue. The crushing and agglomeration operations, electrowinning cells, and solution pools are also significant contributors to the total consumption of water in the oxide ore process. Most of the water consumed in the oxide ore process was lost to evaporation. The WF_blue of the oxide ore process can be reduced by preventing water evaporation and using more sophisticated devices during irrigation of the leaching heaps. The WF_blue of the sulfide ore refining process can be reduced by improving water recovery (i.e., reducing seepage, accumulation, and evaporation) from the tailings dam at the concentrator plant. Using seawater in the production of copper is also a promising option to reduce the WF_blue by up to 62%.     

Spatial distribution and environmental risk assessment of heavy metals identified in soil of a decommissioned uranium mining area

Abstract

The spatial distribution of six heavy metals (Cd, As, Zn, Pb, Cr, and Cu) in the soil of a decommissioned uranium mining area was investigated and their potential environmental risk was assessed. Soil samples were collected along the main riversides enclosing the mining area. The heavy metal distribution was determined by geospatial interpolation. Pearson correlation coefficient and principal component analysis were used to locate the sources of pollution which are the mine ore as natural source, and dressing plants and tailing area from human activity. The results indicate that the average concentrations of As and Cd strongly exceed the recommended EQSS (Environmental Quality Standard for Soils of China) limits at all sampling sites, whereas Zn concentrations were found to be slightly over the limit only at sampling sites close to the mining area. The concentrations of Cr, Cu and Pb were all within the recommended limits. Environmental risk was assessed using the toxicity characteristic leaching procedure defining the degree to which extend the metal is released into the solution. High leaching rates were found only for Zn and Cd, suggesting that together with its high concentrations Cd is the most toxic metal around the mining area, followed by As.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Detection of Breast Cancer Based on Fuzzy Frequent Itemsets Mining

Background: Breast cancer, a type of malignant tumor, affects women more than men. About one third of women with breast cancer die of this disease. Hence, it is imperative to find a tool for the proper identification and early treatment of breast cancer. Unlike the conventional data mining algorithms, fuzzy logic based approaches help in the mining of association rules from quantitative transactions.Methods: In this study a novel fuzzy methodology IFFP (Improved Fuzzy Frequent Pattern Mining), based on a fuzzy association rule mining for biological knowledge extraction, is introduced to analyze the dataset in order to find the core factors that cause breast cancer. This method consists of two phases. During the first phase, fuzzy frequent itemsets are mined using the proposed algorithm IFFP. Fuzzy association rules are formed during the second phase, indicating whether a person belongs to benign or malignant. This algorithm is applied on WBCD (Wisconsin Breast Cancer Database) to detect the presence of breast cancer.Results: It is determined that the factor, Mitoses has low range of values on both malignant and benign and hence it does not contribute to the detection of breast cancer. On the other hand, the high range of Bare Nuclei shows more chances for the presence of breast cancer.Conclusion: Experimental evaluations on real datasets show that our proposed method outperforms recently proposed state-of-the-art algorithms in terms of runtime and memory usage.