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- About Implementation of the International Standards and Limits of.Reception of Foreign Experience to the Sphere of Execution of the Punishment in the Republic of Kazakhstan. 1
- Abstract Background: Pancreatic Ductal Adenocarcinoma (PDAC) is a cancer of the exocrine pancreas and 5-year survival rates remain constant at 7%. Along with PDAC, Periampullary Adenocarcinoma (PAC) accounts for 0.5–2% of all gastrointestinal malignancies. Genomic observations were well concluded for PDAC and PACs in western countries but no reports are available from India till now. Methods: Targeted Next Generation Sequencing were performed in 8 (5 PDAC and 3 PAC) tumour normal pairs, using a panel of 412 cancer related genes. Primary findings were replicated in 85 tumour samples (31 PDAC and 54 PAC) using the Sanger sequencing. Mutations were also validated by ASPCR, RFLP, and Ion Torrent sequencing. IHC along with molecular dynamics and docking studies were performed for the p.A138V mutant of TP53. Key polymorphisms at TP53 and its associated genes were genotyped by PCR-RFLP method and association with somatic mutations were evaluated. All survival analysis was done using the Kaplan-Meier survival method which revealed that the survival rates varied significantly depending on the somatic mutations the patients harboured. Results: Among the total 114 detected somatic mutations, TP53 was the most frequently mutated (41%) gene, followed by KRAS, SMAD4, CTNNB1, and ERBB3. We identified a novel hotspot TP53 mutation (p.A138V, in 17% of all patients). Low frequency of KRAS mutation (33%) was detected in these samples compared to patients from Western counties. Molecular Dynamics (MD) simulation and DNA-protein docking analysis predicted p.A138V to have oncogenic characteristics. Patients with p.A138V mutation showed poorer overall survival (p = 0.01). So, our finding highlights elevated prevalence of the p53p.A138V somatic mutation in PDAC and pancreatobiliary PAC patients. Conclusion: Detection of p.A138V somatic variant in TP53 might serve as a prognostic marker to classify patients. It might also have a role in determining treatment regimes. In addition, low frequency of KRAS hotspot mutation mostly in Indian PDAC patient cohort indicates presence of other early drivers in malignant transformation. Keywords: Pancreatic ductal adenocarcinoma, Periampullary adenocarcinoma, Novel somatic hotspot mutation, Frequently mutated genes, Next generation sequencing 1
- Abstract Oralism 1
- Abstract-Diabetic Maculopathy (DME) is the serious impediments of diabetes, which may cause permanent blindness unless timely detected. Vision impairment because of diabetes is substantially avoidable with well-timed screening and intervention at primary stages. Presence of most primitive and distinctive signs on the retinal surface is micro-aneurysm and haemorrhage, signify as dark spots and hard and soft exudates signifies as bright lesions. Hence, recognition of all these bright lesions is the first step of automated recognition of DME. In this paper, we present a multi class, multi-layer stacked ensemble classifier-based model with four base learners and one meta-learner for improved exudates (EXs) classification accuracy and maculopathy gradation system. The proposed system involves pre-processing, Scale-Space Extrema Detection(SSED) based extraction of clinically significant bright lesions, shape, colour, intensity, and statistical functions-based feature set creation, Minimum Redundancy-Maximum Relevance (mRMR) feature selection, stacking classifier with Bayesian optimization (BO) for hyper-parameter tuning and severity gradation. Information of location of all types of exudates is accounted for to provide the level of severity of DME. At both the image and lesions levels, the proposed system’s quan- titative assessment is carried out utilising publicly available databases. When compared to other state-of-the-art methodologies, our system’s results have achieved competitive performance in three and two class exudates classification and DME gradation. 1
- Abstract— On-device computing in biomedical sensors have become attractive towards developing wearable health monitoring systems. The challenge is to make a compromise between the latency and complexity in a resource constrained implementation. This paper describes an on-device implementation of multi-level signal quality aware and quality controlled compression (MSQQCC) that enhances the compression factor while preserving the clinical features in a wearable photoplethysmography (PPG) sensing application. The multi-level quality assessment (QA) provides three eligible PPG qualities, viz., ‘excellent’, ‘good’ and ‘average’, based on which corresponding upper limits are set for further compression using a discrete wavelet transform, while the ‘corrupted’ segments are discarded. A pretrained multilayer perceptron neural network provides the optimal quantization level of coefficients. The residual data is separately compressed using an autoencoder. MSQQCC was evaluated with 300 mins of PPG data from 3 public data sets and 110 mins of data collected at laboratory. The end-to-end pipeline was implemented in a standalone system with ARM Cortex A53 controller, requiring 35.51 KB of memory and 1.8s latency to process 4s PPG data. The on-device QA achieved 98.45 % overall accuracy (Ac), which outperforms published works on PPG QA. The mean deviation of PPG clinical features by 5 %, with overall CR and PRD were 40.85 and 2.52, which are superior than many published works. A real-time transmission over Bluetooth shows improvement of energy efficiency by a significant factor and 34% extended battery life for wearable PPG sensor. The results are encouraging for adoption of MSQQCC in wearable biomedical health monitoring. 1
- Abstract—Arrhythmia is the most fatal for human being among all cardiovascular diseases. Early detection of arrhythmia beats, from long term ECG record, is helpful to start treatment and saving life of patients. In this work, we presented a patient- adaptive scheme to discriminate normal and three classes of arrhythmia beats from ECG signal. Instead of conventional features, the proposed method uses a kernel based modeling technique of the ECG beats and the model coefficients are used as the features to characterize different types of beats. In this semi automatic scheme, a global training set is combined with a local learning set to form a patient adaptive training set to develop a patient specific classifier model. The results are validated on MIT-BIH arrhythmia database and the performance of the proposed technique is validated by three classifiers namely, support vector machine (SVM), vector valued regularized kernel function approximation (VVRKFA) technique and k-nearest neighbour (KNN) classifiers. Experimental results indicate that the proposed patient adaptive classification scheme increases the global accuracy by 12 to 16% than that of the accuracy obtained without using patient specific beats to global training set. The highest average accuracy obtained using this method is 96.63%, which is comparable and even better than most of the works available in the literature. 1
- ACADEMIC ACHIEVERS 1
- Academic Departments: HITK 1
- Academic Insights and Future Prospects of Soft Robotics: Architecture, Material, Control and Application 1
- Academic Programmes 1
- Academic Value-added Programmes (AVAP) 1
- ACADS: An Automatic Computer Aided Diagnostic System for Oil Spill Detection and Classification in SAR Images 1
- Accelerating 5G success in India / By Praveen Cherian 1
- Accelerating crop domestication through genome editing for sustainable agriculture 1
- Accelerating digital-led recovery to the next normal - Sanjay Kaul 1
- Accelerating Indigenous R&D and Intellectual Property (IP) – CDOT is taking Digital India into the Future 1
- Accelerating Learning Performance of Facial Expression Recognition using Convolution Neural Network Umesh Chauan, Dineen Kulkarni and Shioanand Hiremath 1
- Accelerating Sustainable Hydropower Development Around the World 1
- Accelerating Time-Current Curve Computation of Induction Motor from Manufacturer Data 1
- Accenture invests in hologram company Looking Glass Factory 1