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بخشی از مقاله انگلیسی:

Introduction

Metal pollution of soil dust and agricultural soils arising from industrial activities, vehicular emissions, and waste disposal sites are well documented [1-3]. The cement industry forms part of the industries that are well known to be problematic as regards the introduction of heavy metals from the dust emanating from their operations [1-3]. The deposition of these trace metals occurred at various distances around the cement factories and are influenced by wind velocity, particle size, and stack fumes [4]. Typical raw cement is made up of 25 mg/kg Cr, 21 mg/kg Cu, 20 mg/kg Pb, and 53 mg/kg Zn [5]. Further to this elemental composition, it was also reported that about 0.07 kg of dust is generated into the atmosphere when 1 kg of cement is manufactured [2]. Soil contamination by heavy metals can cause longterm problems on the biogeochemical cycle, which may affect soil functioning systems, leading to changes in soil fauna [6]. From previous studies in other countries, it has been established that dust containing elevated amounts of trace metals emanating from the vicinity of cement factories may adversely affect humans, plants, and soil composition within the vicinity [7]. Most cement factories have been noted as potential sources of metals such as Hg, Zn, Pb, Cr, and Cd [8-11]. The effects and concentrations of the dust containing trace metals as pollutants vary and depend largely on technology employed from the cement industries to ameliorate environmental degradation. In humans, trace metals such as Pb may affect the brain and cause retarded growth, especially in children [12]. In plants, excessive [Pb] alters normal metabolic pathways by disrupting specific cellular enzymes and may also inhibit the photosynthetic ability of plants [13]. On a general note, excessive levels of heavy metals may result in the induction of oxidation stress, damage to DNA, and disturbances in the biosynthetic pathways [14]. Quality of the environment is vital for sustainable development, especially in the face of rapid developmental programs from developing countries. The rapid economic developments in South Africa over the past few years have resulted in an increased demand for cement production [15], which stood at 14.9 million tons in 2012 and is expected to reach 18.1 million tons in 2018 owing to the emergence of new cement manufacturing plants in South Africa and neighbouring countries such as Lesotho, Botswana, and Swaziland [15]. Although several studies have noted the impact of the cement industry on the environment from developed countries, few studies have been conducted in South Africa [1, 5, 6, 10]. The present study was carried out to investigate the concentrations of heavy metals from soils and plants collected around the Hercules cement factory in Pretoria. The study also assessed the level of heavy metal contamination in the topsoil based on pollution index (PI).

Methodology

The study was carried out at about 50 m from a cement factory in Pretoria. The cement factory is situated just next to a very busy road (GPS: 25º۴۳’۲۱ S, 28º۱۰’۱۵ E). The area falls on the western part of Pretoria. There are two major seasons in the area (winter and summer), although the city usually witnesses a short period of spring and autumn. Sampling was done during the two major seasons. Sampling was carried out in the northeastern (NE), northwestern (NW), and southwestern (SW) areas of the cement company. Soil and plant samples were collected from these directions around the area: 30 soil samples from the topsoil (0-15 cm) and 30 soil samples from the sub soil (15-30 cm). Plants samples were collected from each of the directions where soil samples were collected and were identified up to the species. The soil samples were ground in the laboratory and airdried. From the ground soil samples, 0.5 g of the soil were added with 2.0 ml of HCl, 2.0 ml of HClO4, 2.0 ml of HF, and 8 ml of HNO3. The resulting solutions were then analyzed for trace metals contents using ICP-MS in order to determine the concentrations of trace metals from the soil samples. The plant samples were partitioned into three parts, namely for analyses: root, stem, and leaves. From these parts, 0.2 g of each of the different parts were acid-digested using 2 ml HCl, 1 ml HClO4, 2 ml of HF, and 5 ml of HNO3, and the resulting solutions were then analysed for trace metal contents using ICP-MS. Quality assurance was done using Certified Reference Materials for both soil and plant samples and the analysis was also carried out in triplicate. The ability of plants to uptake trace metals from the soil was determined using the transfer factor model [16]. The transfer factor is calculated as the concentration of heavy metals in plant parts to the concentration present in the soil. This is an index of soil-plant transfer. Values >1 indicate that plants are enriched in elements from soil (accumulator), ratios around 1 indicate that plants are not influenced by elements (indicator), and values <1 show that plants exclude the element from soil (excluder). Pollution Assessment Pollution assessment of the soil was calculated using the pollution index (Pi) method and the geo-accumulation index (Igeo). The pollution index was calculated using the formula:

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بخشی از مقاله انگلیسی:

Introduction

High throughput sequencing of genomes (DNA-Seq) and transcriptomes (RNA-Seq) has opened the way to study the genetic and functional information stored within any organism at an unprecedented scale and speed. For example, RNA-Seq allows in principle for the simultaneous study of transcript structure (such as alternative splicing), allelic information (e.g., SNPs), and expression with high resolution and large dynamic range1 . These advances greatly facilitate functional genomics research in species for which genetic or financial resources are limited, including many ‘non-model’ organisms, which are nevertheless of substantial ecological or evolutionary importance. While many genomic applications have traditionally relied on the availability of a highquality genome sequence, such sequences have only been determined for a very small portion of known organisms. Furthermore, sequencing and assembling a genome is still a costly endeavor in many cases, due to genome size and repeat content. Conversely, since the transcriptome is only a fraction of the total genomic sequence, RNA-Seq data can provide a rapid and cheaper ‘fast track’, within reach of any lab, to delineating a reference transcriptome for downstream applications such as alignment, phylogenetics or marker construction. Indeed, even within a whole genome sequencing project, RNA-Seq has become an essential source of evidence for transcribed gene identification and exon structure annotation. Realizing the full potential of RNA-Seq requires computational methods that can assemble a transcriptome even when a genome sequence is not available. There are primarily two ways to convert raw RNA-Seq data to transcript sequences: with the guidance of assembled genomic sequences or via de novo assembly2, 3. The genome-guided approach to transcriptome studies has quickly become a standard approach to RNA-Seq analysis for model organisms, and several software packages exist for this purpose4, 5. It cannot, however, be applied to organisms without a well-assembled genome, and even if one is present, the results may vary across genome assembly versions. In such cases, a de novo transcriptome assembler is required. However, the process of assembling a transcriptome violates many of the assumptions of assemblers written for genomic DNA. For example, uniform coverage and the ‘one locus – one contig’ paradigm are not valid for RNA: an accurate transcriptome assembler will produce one contig per distinct transcript (isoform) rather than per locus, and different transcripts will have different coverage, reflecting their different expression levels. Several tools are now available for de novo assembly of RNA-Seq. Trans-ABySS 6 , VelvetOases7 , and SOAPdenovo-trans (http://soap.genomics.org.cn/SOAPdenovo-Trans.html) are all extensions of earlier developed genome assemblers. We previously described an alternative and novel method for transcriptome assembly called Trinity8 . Trinity partitions RNA-Seq data into many independent de Bruijn graphs, ideally one graph per expressed gene, and uses parallel computing to reconstruct transcripts from these graphs, including alternatively spliced isoforms. Trinity can leverage strand-specific Illumina Paired-End (PE) libraries, but can also accommodate non-strand-specific and single-end (SE) read data. Trinity reconstructs transcripts accurately with a simple and intuitive interface that requires little to no parameter tuning. Several independent studies have demonstrated that Trinity is highly effective compared to alternative methods (e.g.9-11, The DREAM Project’s Alternative Splicing Challenge (http://www.the-dream-project.org/result/alternativesplicing)). Indicating Trinity’s utility, since its publication in May 2011, it has acquired an avid user base with ~200 citations from May 2011 to March 2013 (http:// scholar.google.com/scholar?oi=bibs&hl=en&cites=14735674943942667509). Trinity users study a broad range of model and non-model organisms from all Kingdoms, and come from small labs and large genome projects alike (e.g., the pea aphid genome annotation v2; Fabrice Legeai, INRA and Terence Murphy, RefSeq NCBI, personal communications). Trinity also has an active developer community, which has greatly enhanced its performance and utility (see http://trinityrnaseq.sourceforge.net). For example, while the runtime performance of the first release was not computationally efficient11, the Trinity developer community has since improved its efficiency, halving memory requirements and increasing processing speed through increased parallelization and improved algorithms (12; M. Ott, personal communication). Furthermore, Trinity was converted into a modular platform that seamlessly uses third-party tools, such as Jellyfish13 for building the initial k-mer catalog. Other third party tools integrated into Trinity have enhanced the utility of its reconstructed transcriptomes. For example, as described below, Trinity now supports tools (e.g., RSEM14 , edgeR15 and DESeq 16) that take its output transcripts and test for differential expression, while accounting for both technical and biological sources of variation17-19 and correcting for multiple hypothesis testing. Given Trinity’s popularity and substantial enhancements since publication, it is important to provide detailed protocols that leverage its various features. The protocols we present below will maximize Trinity’s utility to users for studies in non-model organisms, and inform the broad developer community on areas for future enhancements.

بخشی از مقاله انگلیسی:

I. INTRODUCTION

Within the last three decades, global concern has accelerated and focused on anthropogenic activities that alter the natural environment during natural resources exploitation and the attendant impact on the physical environment. These concerns have translated into several initiatives at the global level intended for adoption at national and local levels ultimately, with a view to engendering environmental sustainability (1). In 1992, the United Nations Conference on Environment and Development (UNCED), held in Rio de Janeiro, produced an action document tagged Agenda 21. The document acknowledged the perpetuation and worsening deterioration of ecosystems on which we depend for our well-being, amongst other social-economic disparities between nations. Similarly, the United Nations Framework Convention on Climate Change (UNFCCC) came into force in 1994 (now totaling 194 parties/countries). Further, the Millennium Declaration in 2000 by 189 countries produced the Millennium Development Goals (MDGs) document which broadly seeks to address environmental degradation issues and socio-economic disparities among nations before the year 2015. In spite of the broad dimensions of the various global instruments generally aimed at sustainability in environmental resources utilization, of specific interest to this study is the MDG No. 7 which states, inter alia: To ensure environmental sustainability, vide: Target 7A: Integrate the principles of sustainable development into country policies and programs; reverse loss of environmental resources;…. Target 7C: Halve, by 2015, the proportion of the population without sustainable access to safe drinking water…. This study therefore, intends to verify the level of adoption of (part of) the MDG No. 7 at the Dangote Cement Company, Yandev, a part of rural Nigeria where limestone mining and cement production activities have been on-going over the past 3 decades. Although the overall global proportion of people using an improved water source rose from 76% in 1990 to 89% in 2010, over 40% of all people without improved drinking water live in sub-Saharan Africa (2). This presents a significant challenge to countries within and peoples living in the Sub-Saharan African region. Therefore, there is every need to protect the available water resources in Sub-Saharan Africa, with specific emphasis on prevention. As rightly indicated by (3), the knowledge of extent of pollution and the status of water becomes essential in order to preserve the valuable source of water for present and future generations. According to (4; 5), the qualities of a water resource (surface and sub-surface) depend on the management of anthropogenic discharges within, as well as the natural physic-chemical characteristics of the catchment areas. The focus in this study is however, on the influence of anthropogenic activities (limestone mining and cement production) on the quality of surface water around the study area. The study is deemed necessary as there was no environmental impact assessment carried out prior to the establishment of the factory. Aside being a major environmental component, water is also invaluable to human, animal and plant populations. Hence, investigation into the status of water quality at the study area, and indeed elsewhere, is of scientific, economic and environmental significance.

II. OBJECTIVES OF THE STUDY

The central objectives of this study are:  Determine the level of concentration of pollutants in water within the study area;  Compare the water quality between host communities and a control community to ascertain the degree of variance in amount of water pollutants present between the two; and,  Compare WHO water quality guidelines with amount of pollutants found in water within the study area to assess water quality status

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