Deepak Kumar

Detection of pork adulteration by highly-specific PCR assay of mitochondrial D-loop

We describe a highly specific PCR assay for the authentic identification of pork. Accurate detection of tissues derived from pig (Sus scrofa) was accomplished by using newly designed primers targeting porcine mitochondrial displacement (D-loop) region that yielded an unique amplicon of 712 base pairs (bp). Possibility of cross-amplification was precluded by testing as many as 24 animal species (mammals, birds, rodent and fish). Suitability of PCR assay was confirmed in raw (n = 20), cooked (60, 80 and 100 °C), autoclaved (121 °C) and micro-oven processed pork. Sensitivity of detection of pork in other species meat using unique pig-specific PCR was established to be at 0.1%; limit of detection (LOD) of pig DNA was 10 pg (pico grams). The technique can be used for the authentication of raw, processed and adulterated pork and products under the circumstances of food adulteration related disputes or forensic detection of origin of pig species.

Multifunctional magnetic reduced graphene oxide dendrites: Synthesis, characterization and their applications

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal).nThis article has been retracted at the request of the Editor-in-Chief following concerns raised by a reader.nThe article uses an electron micrograph identical to another publication despite being labelled as different samples. Fig. 3F is the same as Fig. 1D published in Biosensors and Bioelectronics Volume 89 Part1, 15 March 2017, Pages 620-626, 10.1016/j.bios.2015.12.085.nIn addition, the extraordinary similarities observed between the data presented in Fig. 3C and in Fig. 3C in ACS Biomater. Sci. Eng., 2017, 3 (9), pp 2120–2135, 10.1021/acsbiomaterials.7b00089, Fig. 4A in Colloids and Surfaces B: Biointerfaces, Volume 142, 1 June 2016, Pages 248-258 10.1016/j.colsurfb.2016.02.053 and Fig. 4C in Biosensors and Bioelectronics, Volume 97, 15 November 2017, Pages 208-217, 10.1016/j.bios.2017.06.003 are highly unlikely.nThis problem with the data casts doubt on all the data, and accordingly also the conclusions based on that data, in this publication.nAs such this article represents a severe abuse of the scientific publishing system.nThe scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

Tuberculosis in Birds: Insights into the Mycobacterium avium Infections

Tuberculosis, a List B disease of World Organization for Animal Health, caused by M. avium or M. genavense predominantly affects poultry and pet or captive birds. Clinical manifestations in birds include emaciation, depression and diarrhea along with marked atrophy of breast muscle. Unlike tuberculosis in animals and man, lesions in lungs are rare. Tubercular nodules can be seen in liver, spleen, intestine and bone marrow. Granulomatous lesion without calcification is a prominent feature. The disease is a rarity in organized poultry sector due to improved farm practices, but occurs in zoo aviaries. Molecular techniques like polymerase chain reaction combined with restriction fragment length polymorphism and gene probes aid in rapid identification and characterization of mycobacteria subspecies, and overcome disadvantages of conventional methods which are slow, labour intensive and may at times fail to produce precise results. M. avium subsp. avium with genotype IS901+ and IS1245+ causes infections in animals and human beings too. The bacterium causes sensitivity in cattle to the tuberculin test. The paper discusses in brief the M. avium infection in birds, its importance in a zoonotic perspective, and outlines conventional and novel strategies for its diagnosis, prevention and eradication in domestic/pet birds and humans alike.

A fluorescent molecularly-imprinted polymer gate with temperature and pH as inputs for detection of alpha-fetoprotein

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal).nThis article has been retracted at the request of the Editor following concerns raised by a reader.nThe article reuses an electron micrograph from a previous publication while claiming that these are different particles.nFig. 1C/D were reused from Fig. 2B published in Biosensors and Bioelectronics, Volume 73, 15 November 2015, Pages 234–244, https://doi.org/10.1016/j.bios.2015.06.005.nThis problem with the data presented casts doubt on all the data, and accordingly also the conclusions based on that data, in this publication.nAs such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.

Advances in Developing Therapies to Combat Zika Virus: Current Knowledge and Future Perspectives

Zika virus (ZIKV) remained largely quiescent for nearly six decades after its first appearance in 1947. ZIKV reappeared after 2007, resulting in a declaration of an international “public health emergency” in 2016 by the World Health Organization (WHO). Until this time, ZIKV was considered to induce only mild illness, but it has now been established as the cause of severe clinical manifestations, including fetal anomalies, neurological problems, and autoimmune disorders. Infection during pregnancy can cause congenital brain abnormalities, including microcephaly and neurological degeneration, and in other cases, Guillain-Barré syndrome, making infections with ZIKV a substantial public health concern. Genomic and molecular investigations are underway to investigate ZIKV pathology and its recent enhanced pathogenicity, as well as to design safe and potent vaccines, drugs, and therapeutics. This review describes progress in the design and development of various anti-ZIKV therapeutics, including drugs targeting virus entry into cells and the helicase protein, nucleosides, inhibitors of NS3 protein, small molecules, methyltransferase inhibitors, interferons, repurposed drugs, drugs designed with the aid of computers, neutralizing antibodies, convalescent serum, antibodies that limit antibody-dependent enhancement, and herbal medicines. Additionally, covalent inhibitors of viral protein expression and anti-Toll-like receptor molecules are discussed. To counter ZIKV-associated disease, we need to make rapid progress in developing novel therapies that work effectually to inhibit ZIKV.

Fluoranthene derivatives as blue fluorescent materials for non-doped organic light-emitting diodes

In this study, we report synthesis of symmetrically and non-symmetrically functionalized fluoranthene-based blue fluorescent molecular materials for non-doped electroluminescent devices. The solid state structure of these fluorophores has been established by single crystal X-ray diffraction analysis. Furthermore, a detailed experimental and theoretical study has been performed to understand the effect of substitution of symmetric and non-symmetric functional groups on optical, thermal and electrochemical properties of fluoranthene. These materials exhibit a deep blue emission and high PLQY in solution and solid state. The vacuum deposited, non-doped electroluminescent devices with the device structure ITO/NPD (15 nm)/CBP (15 nm)/EML (40 nm)/TPBI (30 nm)/LiF (1 nm)/Al were fabricated and characterized. A systematic shift in the peak position of EL emission was observed from sky blue to bluish-green with EL maxima from 477 nm to 490 nm due to different functional groups on the periphery of fluoranthene. In addition, a high luminance of >= 2000 cd m(-2) and encouraging external quantum efficiency (EQE) of 1.1-1.4% were achieved. A correlation of the molecular structure with device performance has been established.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

MAPS: midline analysis and propagation of segmentation

Scenic word images undergo degradations due to motion blur, uneven illumination, shadows and defocussing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scenic word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a sub-image and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation. This midline based approach limits the impact of degradations that happens to the image. The segmented text image is recognized using the trial version of Omnipage OCR. We have tested our method on ICDAR 2003 and ICDAR 2011 datasets. Our word recognition results of 64.5% and 71.6% are better than those of methods in the literature and also methods that competed in the Robust reading competition. Our method makes an implicit assumption that degradation is not present in the middle row.

OTCYMIST: Otsu-Canny Minimal Spanning Tree for Born-Digital Images

Text segmentation and localization algorithms are proposed for the born-digital image dataset. Binarization and edge detection are separately carried out on the three colour planes of the image. Connected components (CCs) obtained from the binarized image are thresholded based on their area and aspect ratio. CCs which contain sufficient edge pixels are retained. A novel approach is presented, where the text components are represented as nodes of a graph. Nodes correspond to the centroids of the individual CCs. Long edges are broken from the minimum spanning tree of the graph. Pair wise height ratio is also used to remove likely non-text components. A new minimum spanning tree is created from the remaining nodes. Horizontal grouping is performed on the CCs to generate bounding boxes of text strings. Overlapping bounding boxes are removed using an overlap area threshold. Non-overlapping and minimally overlapping bounding boxes are used for text segmentation. Vertical splitting is applied to generate bounding boxes at the word level. The proposed method is applied on all the images of the test dataset and values of precision, recall and H-mean are obtained using different approaches.

Power-law transformation for enhanced recognition of born-digital word images

In this paper, we discuss the issues related to word recognition in born-digital word images. We introduce a novel method of power-law transformation on the word image for binarization. We show the improvement in image binarization and the consequent increase in the recognition performance of OCR engine on the word image. The optimal value of gamma for a word image is automatically chosen by our algorithm with fixed stroke width threshold. We have exhaustively experimented our algorithm by varying the gamma and stroke width threshold value. By varying the gamma value, we found that our algorithm performed better than the results reported in the literature. On the ICDAR Robust Reading Systems Challenge-1: Word Recognition Task on born digital dataset, as compared to the recognition rate of 61.5% achieved by TH-OCR after suitable pre-processing by Yang et. al. and 63.4% by ABBYY Fine Reader (used as baseline by the competition organizers without any preprocessing), we achieved 82.9% using Omnipage OCR applied on the images after being processed by our algorithm.