Finger Knuckle Based Biometric Identifier Using Principal Component Analysis, Feature Extraction and K-NN Classifier

Amidst several biometric measures, the figure knuckle surface is becoming a preferred choice of researchers due to its natural ease of reproducibility and verification. For any purpose of personal identification or crime analysis, figure knuckles surface do not need to be a voluntarily presented, they get exposed naturally. Specific line pattern on the figure knuckle surfaces can be used as effective biometric measure on their own or in combination with other biometrics. Present paper demonstrates the development of a figure knuckle based biometric identification system. The system incorporates principal component analysis (PCA) for feature extraction out of pre-processed and enhanced input image as extracted from knuckle surface video capture. Secondly the system employs k-nn classifier as personal identification algorithm. The system has been tested, verified and validated with many sample test experiments. The paper illustrates the working of the system with detailed intermittent snapshots

Detection, Isolation and Confirmation of Crimean-Congo Hemorrhagic Fever Virus in Human, Ticks and Animals in Ahmadabad, India, 2010–2011

A nosocomial outbreak of CCHFV occurred in January 2011, in a tertiary care hospital in Ahmadabad, Gujarat State in western India. Out of a total five cases reported, contact transmission occurred to three treating medical professionals, all of whom succumbed to the disease. The only survivor was the husband of the index case. These results highlight the importance of considering CCHFV as a potential aetiology for Hemorrhagic fever (HF) cases in India. This also underlines the need for strict barrier nursing and patient isolation while managing these patients. During the investigation presence of CCHFV RNA in Hyalomma anatolicum ticks and livestock were detected in the village from where the primary case (case A) was reported. Further retrospective investigation confirmed two CCHF human cases in Rajkot village 20 kilometres to the west of Ahmadabad in 2010, and CCHFV presence in the livestock 200 kilometres to the north in the neighbouring State Rajasthan. This report shows the presence of CCHFV in human, ticks and animals in Gujarat, India. The fact of concern is the spread of this disease from one state to another due to trading of livestock

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Summary Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types

Adaption of Seasonal H1N1 Influenza Virus in Mice

The experimental infection of a mouse lung with influenza A virus has proven to be an invaluable model for studying the mechanisms of viral adaptation and virulence. The mouse adaption of human influenza A virus can result in mutations in the HA and other proteins, which is associated with increased virulence in mouse lungs. In this study, a mouse-adapted seasonal H1N1 virus was obtained through serial lung-to-lung passages and had significantly increased virulence and pathogenicity in mice. Genetic analysis indicated that the increased virulence of the mouse-adapted virus was attributed to incremental acquisition of three mutations in the HA protein (T89I, N125T, and D221G). However, the mouse adaption of influenza A virus did not change the specificity and affinity of receptor binding and the pH-dependent membrane fusion of HA, as well as the in vitro replication in MDCK cells. Notably, infection with the mouse adapted virus induced severe lymphopenia and modulated cytokine and chemokine responses in mice. Apparently, mouse adaption of human influenza A virus may change the ability to replicate in mouse lungs, which induces strong immune responses and inflammation in mice. Therefore, our findings may provide new insights into understanding the mechanisms underlying the mouse adaption and pathogenicity of highly virulent influenza viruses

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Viable Tumor Tissue Adherent to Needle Applicators after Local Ablation: A Risk Factor for Local Tumor Progression

Background. Local tumor progression (LTP) is a serious complication after local ablation of malignant liver tumors, negatively influencing patient survival. LTP may be the result of incomplete ablation of the treated tumor. In this study, we determined whether viable tumor cells attached to the needle applicator after ablation was associated with LTP and disease-free survival. Methods. In this prospective study, tissue was collected of 96 consecutive patients who underwent local liver ablations for 130 liver malignancies. Cells and tissue attached to the needle applicators were analyzed for viability using glucose-6-phosphate-dehydrogenase staining and autofluorescence intensity levels of H&E stained sections. Patients were followed-up until disease progression. Results. Viable tumor cells were found on the needle applicators after local ablation in 26.7% of patients. The type of needle applicator used, an open approach, and the omission of track ablation were significantly correlated with viable tumor tissue adherent to the needle applicator. The presence of viable cells was an independent predictor of LTP. The attachment of viable cells to the needle applicators was associated with a shorter time to LTP. Conclusions. Viable tumor cells adherent to the needle applicators were found after ablation of 26.7% of patients. An independent risk factor for viable cells adherent to the needle applicators is the omission of track ablation. We recommend using only RFA devices that have track ablation functionality. Adherence of viable tumor cells to the needle applicator after local ablation was an independent risk factor for LT

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation