DNA barcoding: an effective tool to overcome morphological identification constraints in the assessment of the ecological quality

DNA barcoding has the potential to overcome taxonomic challenges in biological community assessments. However, fulfilling that potential requires successful amplification of a large and unbiased portion of the community. In this study, we attempted to identify mitochondrial gene cytochrome c oxidase I (COI) barcodes from 1024 benthic invertebrate specimens belonging to 54 taxa from low salinity environments of the Mira estuary and Torgal riverside (SW Portugal). Up to 17 primer pairs and several reaction conditions were attempted among specimens from all taxa, with amplification success defined as a single band of approximately 658 bp visualized on a pre-cast agarose gel, starting near the 5' end of the COI gene and suitable for sequencing. Amplification success was achieved for 99.6% of the 54 taxa, though no single primer was successful for more than 88.9% of the taxa. However, only 68.5% of the specimens within these taxa successfully amplified. Inhibition factors resulting from a non-purified DNA extracted and inexistence of species-specific primers for COI were pointed as the main reasons for an unsuccessful amplification. These results suggest that DNA barcoding can be an effective tool for application in low salinity environments where taxa such as chironomids and oligochaetes are challenging for morphological identification. Nevertheless, its implementation is not simple, as methods are still being standardized and multiple species

Vector Fitting based Automatic Circuit Identification

This paper describes a method to automatically obtain, from a set of impedance measurements at different frequencies, an equivalent circuit composed of lumped elements based on the vector fitting algorithm. The method starts from the impedance measurement of the circuit and then, through the recursive use of vector fitting, identifies the circuit topology and the component values of lumped elements. The method can be expanded to include other components usually used in impedance spectroscopy. The method is firstly described and then two examples highlight the robustness of the method and showcase its applicability.

Analysis of Pancreas Histological Images for Glucose Intolerance Identification using Wavelet Decomposition

Subtle structural differencescan be observed in the islets of Langer-hans region of microscopic image of pancreas cell of the rats having normal glucose tolerance and the rats having pre-diabetic(glucose intolerant)situa-tions. This paper proposes a way to automatically segment the islets of Langer-hans region fromthe histological image of rat's pancreas cell and on the basis of some morphological feature extracted from the segmented region the images are classified as normal and pre-diabetic.The experiment is done on a set of 134 images of which 56 are of normal type and the rests 78 are of pre-diabetictype. The work has two stages: primarily,segmentationof theregion of interest (roi)i.e. islets of Langerhansfrom the pancreatic cell and secondly, the extrac-tion of the morphological featuresfrom the region of interest for classification. Wavelet analysis and connected component analysis method have been used for automatic segmentationof the images. A few classifiers like OneRule, Naïve Bayes, MLP, J48 Tree, SVM etc.are used for evaluation among which MLP performed the best.