The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, ...fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively.
The present study was designed to find diversity analysis of Antilope cervicapra family in Pakistan. Fecal samples of Antilope cervicapra were collected from their different habitats of Pakistan. ...Fecal DNA was extracted and Polymerase Chain Reaction (PCR) was performed. Sequencing was performed by Big DyeTM Terminator method. Diversity and phylogenetic analysis was performed by different Bioinformatics tools. Less genetic variability was observed within Antilope cervicapra population through Multi-Dimensional Scaling (MDS). However, significant genetic variation was observed among other species and Antilope cervicapra. Phylogenetic analysis revealed distinct clade of this specie with respect to other species of deer. This is the first report from Pakistan that could help for designing effective strategy in future conservation practices of deer species.
A comparison of 10 most popular Multiple Sequence Alignment (MSA) tools, namely, MUSCLE, MAFFT(L-INS-i), MAFFT (FFT-NS-2), T-Coffee, ProbCons, SATe, Clustal Omega, Kalign, Multalin, and Dialign-TX is ...presented. We also focused on the significance of some implementations embedded in algorithm of each tool. Based on 10 simulated trees of different number of taxa generated by R, 400 known alignments and sequence files were constructed using indel-Seq-Gen. A total of 4000 test alignments were generated to study the effect of sequence length, indel size, deletion rate, and insertion rate. Results showed that alignment quality was highly dependent on the number of deletions and insertions in the sequences and that the sequence length and indel size had a weaker effect. Overall, ProbCons was consistently on the top of list of the evaluated MSA tools. SATe, being little less accurate, was 529.10% faster than ProbCons and 236.72% faster than MAFFT(L-INS-i). Among other tools, Kalign and MUSCLE achieved the highest sum of pairs. We also considered BALiBASE benchmark datasets and the results relative to BAliBASE- and indel-Seq-Gen-generated alignments were consistent in the most cases.
Protein structural alignment is one of the most fundamental and crucial areas of research in the domain of computational structural biology. Comparison of a protein structure with known structures ...helps to classify it as a new or belonging to a known group of proteins. This, in turn, is useful to determine the function of protein, its evolutionary relationship with other protein molecules and grasping principles underlying protein architecture and folding.
A large number of protein structure alignment methods are available. Each protein structure alignment tool has its own strengths and weaknesses that need to be highlighted. We compared and presented results of six most popular and publically available servers for protein structure comparison. These web-based servers were compared with the respect to functionality (features provided by these servers) and accuracy (how well the structural comparison is performed). The CATH was used as a reference. The results showed that overall CE was top performer. DALI and PhyreStorm showed similar results whereas PDBeFold showed the lowest performance. In case of few secondary structural elements, CE, DALI and PhyreStorm gave 100% success rate.
Overall none of the structural alignment servers showed 100% success rate. Studies of overall performance, effect of mainly alpha and effect of mainly beta showed consistent performance. CE, DALI, FatCat and PhyreStorm showed more than 90% success rate.
Indian Aseel chicken (Gallus gallus) is traditionally used as a favorite game bird all over the world. Bird fighting communities of Pakistan are the major source of its conservation and there are at ...least four distinctively recognized varieties of Aseel chicken based upon selective breeding, geographical location and color patterns. A pioneering study on genetic diversity of these varieties namely Lakha (n=17), Mushki (n=19), Mianwali (n=19) and Peshawari (n=13) was undertaken using FAO recommended 10 microsatellite loci. A total of 91 alleles were observed in 4 varieties of Aseel chicken with an average of 9.1 alleles per locus. Number of alleles varied between 4 to 8 in Lakha, 4 to 9 in Mushki, 3 to 10 in Mianwali and 3 to 7 in Pashawari. Mean polymorphic information content values were 0.67, 0.69, 0.71 and 0.65 in individual varieties, respectively. Mean observed and expected heterozygosity index values of 0.3941 and 0.7376 were recorded in Lakha, 0.4105 and 0.7468 for Mushki, 0.4105 and 0.7718 Mianwali and 0.3692 and 0.7191 for Peshawari. Mean Fixation index (Fst) value was calculated as 0.1264. Highest Nei’s standard genetic distance (Ds) value of 1.0735 was observed between Mushki and Peshawari, whereas its value was minimum (0.3533) between Lakha and Mushki. This report describes genetic diversity of Aseel chicken in Pakistan and provides foundation data to initiate extensive and more comprehensive studies on indigenous chicken genetic resource conservation and its future utilization in commercial breeding programs.
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