Studying native fishes in Hamadan province

Studying native fishes of Hamadan province have been done in 159 stations from 51 important water resources (wetland, reservoir, spring, river and qanat) using with electric tool, cast-net, seine and gill-net gears from July 2010 to Oct. 2011 and the main aims were species identifying and determining their distribution and abundance in the studied area. In the study, 33411 fish specimens are caught in 257 times of sampling and selected randomly about 8500 individual and laboratory works showed the fish belong to 37 species from 7 families. Cyprinidae with 25, Nemacheilidae with 6 and Sisoridae with 2 species had the most diversity and Cobitidae, Poeciliidae, Salmonidae and Mastacembelidae had only a representative. 31 fish species were native or endemic and 6 species were alien. Fish species existed in all rivers of Ghezelozan and Sirvan sub-basins but there were not any fish in 10 rivers of Ghara-Chai sub-basin and in 6 rivers of Gamasiab sub-basin, too. Also, it was observe 1-3 fish species in 15 rivers, 4-6 fish species in 10 rivers, 7-10 fish species in 5 rivers and more than 10 fish species in 4 main water resources and Gamasiab sub-basin with 32 fish species was the most diversified and Ghezelozan sub-basin with 2 fish species was the least diversified. There were any species to 4 (mostly 1 or 2) fish species in studied qanats in Ghara-Chai and Gamasiab rivers sub-basins. 23 fish species existed in a subbasin, 12 species in 2 sub-basin, Capoeta capoeta in 3 sub-basin and Squalius cephalus in all sub-basins of studied area. Alburnoides nicolausi,Capoeta aculeata and Alburnus mossulensis have had the most frequency. Studying fish abundance showed Oxynoemacheilus argyrogramma with 17.8%, Garra rufa with 12.3%, A. mossulensis with 12.1% and C. aculeata with 10.2 % of total number of caught fish specimens are dominant. S. cephalus, Capoeta damascina, C. aculeata, C. trutta, Chondrostoma regium and A. mossulensis have had sport fishing value but Acanthobrama marmid, Oxynoemacheilus kiabii, Oxynoemacheilus kermanshahensis, Turcinoemacheilus kosswigi, Alburnus caeruleus and Mastacembelus mastacembelus have biodiversity value for being endemic or having limited habitats in Iran

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering

Background: Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings: To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions: The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

Ancient hydrothermal seafloor deposits in Eridania basin on Mars

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Functional Relationship between Protein Disulfide Isomerase Family Members during the Oxidative Folding of Human Secretory Proteins

We systematically depleted PDI family members and show that whereas ERp72 and P5 contributed minimally to oxidative protein folding, PDI and ERp57 were the predominant catalysts. Depletion of PDI or ERp57 alone modestly delayed folding, but depletion of both led to generalized protein misfolding and degradation