Textile dye wastewater characteristics and constituents of synthetic effluents : a critical review

Textile industries are responsible for one of the major environmental pollution problems in the world, because they release undesirable dye effluents. Textile wastewater contains dyes mixed with various contaminants at a variety of ranges. Therefore, environmental legislation commonly obligates textile factories to treat these effluents before discharge into the receiving watercourses. The treatment efficiency of any pilot-scale study can be examined by feeding the system either with real textile effluents or with artificial wastewater having characteristics, which match typical textile factory discharges. This paper presents a critical review of the currently available literature regarding typical and real characteristics of the textile effluents, and also constituents including chemicals used for preparing simulated textile wastewater containing dye, as well as the treatments applied for treating the prepared effluents. This review collects the scattered information relating to artificial textile wastewater constituents and organises it to help researchers who are required to prepare synthetic wastewater. These ingredients are also evaluated based on the typical characteristics of textile wastewater, and special constituents to simulate these characteristics are recommended. The processes carried out during textile manufacturing and the chemicals corresponding to each process are also discussed

Spatial frequency based video stream analysis for object classification and recognition in clouds

The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure

Ecology and Genetic Identification of Freshwater Turtles in Pakistan

Background: The turtle population plays an important role in sustaining the water ecosystem by minimizing pollution from water. The identification and molecular investigation of freshwater fauna is essential for conservation of the species that are near to extinction. The quality of water, type of flora, fauna, and environmental condition are the major factors that directly affect the distribution of freshwater turtles. Studies on the species diversity and habitat of freshwater turtle have not been focused previously in the region. The present study was the first conducted to estimate the habitat and genetic diversity of freshwater turtles using 12S rRNA (ribosomal RNA) gene in Pakistan.Materials, Methods & Results: A total of 26 samples were collected from various localities using hand net, cast net, gills net, steel hooks, thick chemical wire, using chicken intestine and small fishes. The collected turtle specimens were morpho-taxonomically categorized into two genera, Lissemys punctata andersoni (n=13, 50%) and Nilssonia gangetica (n=13, 50%). The collected species showed an aggressive and active behavior in captivity during summer. Genomic DNA was extracted from collected specimens and used in PCR reaction by using specific primers for the amplification of short fragments of 12S rRNA gene. Analysis of generated sequences confirmed the existence of L. p. andersoni in the region. The generated sequences of L. p. andersoni correspond to Clad A and showed a close resemblance among different species of the genus Lissemys.Discussion: This study is the first investigation about the habitat and of the endemic turtle species L. p. andersoni and N. gangetica in Pakistan. The genetic identification followed by phylogenetic analysis based on 12S rRNA partial genes revealed a closest similarity with the sequences generated for the same species from the neighboring countries. This study provided information to conduct further molecular studies that are essential to provide significant genetic data about turtle species.Keywords: turtle, ecology, diversity, phylogeny, Pakistan

Ecology and genetic identification of freshwater turtles in Pakistan

Background: The turtle population plays an important role in sustaining the water ecosystem by minimizing pollution from water. The identification and molecular investigation of freshwater fauna is essential for conservation of the species that are near to extinction. The quality of water, type of flora, fauna, and environmental condition are the major factors that directly affect the distribution of freshwater turtles. Two families including eight species of freshwater turtles are found in Pakistan. The Geoemydidae (Geoclemys hamiltonii, Hardella thurjii, Pangshura smithii, and Pangshura tecta) and Trionychidae (Chitra indica, Nilssonia gangetica, Nilssonia hurum, and Lissemys punctata andersoni). Studies on the species diversity and habitat of freshwater turtle have not been focused previously in the region. The present study was the first conducted to estimate the habitat and genetic diversity of freshwater turtles using 12S rRNA (ribosomal RNA) gene in Pakistan. Materials, Methods & Results: A total of 26 samples were collected from various localities using hand net, cast net, gills net, steel hooks, thick chemical wire, using chicken intestine and small fishes. The collected turtle specimens were morpho-taxonomically categorized into two genera, Lissemys punctata andersoni (n=13, 50%) and Nilssonia gangetica (n=13, 50%). The collected species showed an aggressive and active behavior in captivity during summer. Genomic DNA was extracted from collected specimens and used in PCR reaction by using specific primers for the amplification of short fragments of 12S rRNA gene. Analysis of generated sequences confirmed the existence of L. p. andersoni in the region. The generated sequences of L. p. andersoni correspond to Clad A and showed a close resemblance among different species of the genus Lissemys. Discussion: The climatic change such as temperature and rainfall have great effects on the occurrence of turtles. Habitat degradation occurred due to various factors such as draining wetlands, deforestation, converting clear water rivers to stagnant multi-purpose reservoirs and mortality on roads when turtles move around to feed. Current study concluded that the freshwater turtles L. p. andersoni and N. gangetica are interested in natural feeds. The analysis of 359 bp of 12S rRNA gene of the genus Lissemys turtles showed relationships of these turtles with cyclanorbines flap shell turtles, which agrees with previous reports. The African taxa are paraphyletic with respect to the Asian Lissemys. The ancestors of the extant genus cyclanorbines spread from North America to Asia [26]. It should be expected that each of the 3 taxa, L. p. andersoni, L. p. punctata and L. scutata represents a distinct genetic lineage. Present molecular investigation concluded that Clad A comprising L. p. punctata, L. scutata, L. cylonensis also include L. p. andersoni species. Clad B also contains one sequence from India, identified as L. p. andersoni. Their classification as conspecific evolutionary lineages are suggested by similar genetic divergences, the observation of mismatches between morphology (spotted vs. unspotted) and mitochondrial haplotypes in clades A and B. The clades A and B provides evidence for gene flow between the spotted subspecies L. p. andersoni and adjacent populations with unspotted flap shell turtles. This study is the first investigation about the habitat and of the endemic turtle species L. p. andersoni and N. gangetica in Pakistan. The genetic identification followed by phylogenetic analysis based on 12S rRNA partial genes revealed a closest similarity with the sequences generated for the same species from the neighboring countries. This study provided information to conduct further molecular studies that are essential to provide significant genetic data about turtle species

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques

Peer support for type 2 diabetes management in Low- and Middle-Income Countries (LMICs): A scoping review

Background: Although there is evidence of peer support in high-income countries, the use of peer support as an intervention for cardiometabolic disease management, including type 2 diabetes (T2DM), in low- and middle-income countries (LMICs), is unclear. Methods: A scoping review methodology was used to search the databases MEDLINE, Embase, Emcare, PsycINFO, LILACS, CDSR, and CENTRAL. Results: Twenty-eight studies were included in this scoping review. Of these, 67% were developed in Asia, 22% in Africa, and 11% in the Americas. The definition of peer support varied; however, peer support offered a social and emotional dimension to help individuals cope with negative emotions and barriers while promoting disease management. Conclusions: Findings from this scoping review highlight a lack of consistency in defining peer support as a component of CMD management in LMICs. A clear definition of peer support and ongoing program evaluation is recommended for future research

The bovine alveolar macrophage DNA methylome is resilient to infection with Mycobacterium bovis

DNA methylation is pivotal in orchestrating gene expression patterns in various mammalian biological processes. Perturbation of the bovine alveolar macrophage (bAM) transcriptome, due to Mycobacterium bovis (M. bovis) infection, has been well documented; however, the impact of this intracellular pathogen on the bAM epigenome has not been determined. Here, whole genome bisulfite sequencing (WGBS) was used to assess the effect of M. bovis infection on the bAM DNA methylome. The methylomes of bAM infected with M. bovis were compared to those of non-infected bAM 24 hours post-infection (hpi). No differences in DNA methylation (CpG or non-CpG) were observed. Analysis of DNA methylation at proximal promoter regions uncovered >250 genes harbouring intermediately methylated (IM) promoters (average methylation of 33-66%). Gene ontology analysis, focusing on genes with low, intermediate or highly methylated promoters, revealed that genes with IM promoters were enriched for immune-related GO categories; this enrichment was not observed for genes in the high or low methylation groups. Targeted analysis of genes in the IM category confirmed the WGBS observation. This study is the first in cattle examining genome-wide DNA methylation at single nucleotide resolution in an important bovine cellular host-pathogen interaction model, providing evidence for IM promoter methylation in bAM