3D Medical Image Lossless Compressor Using Deep Learning Approaches

The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling

DELINEATING THE BINDING SITES OF MASON-PFIZER MONKEY VIRUS (MPMV) GAG PRECURSOR POLYPROTEIN (Pr78GAG) ON GENOMIC RNA FOR ITS SELECTIVE PACKAGING

A key step in retroviral life cycle is the selective packaging of its dimeric RNA genome (gRNA) from a pool of cellular and spliced viral RNAs into nascent virions. This involves binding of the retroviral Gag polyprotein to sequences at the 5’ end of the viral genome, the packaging signal. The aim of this study was to identify full-length Gag polyprotein (Pr78Gag) binding sites on Mason-Pfizer monkey virus (MPMV) gRNA, a promising candidate for the development of safe human gene therapy vectors. Towards this end, recombinant MPMV Pr78Gag-His6-tagged protein was cloned and expressed in bacterial cells, and purified from the soluble fraction using immobilized metal affinity chromatography (IMAC) followed by size exclusion chromatography (SEC). The biological activity of the purified protein was determined by its ability to assemble virus like particles (VLPs), while its ability to package MPMV specific subgenomic RNAs was confirmed in eukaryotic cells. Competitive band shift assays demonstrated preferential Pr78Gag binding to unspliced over spliced viral RNA. Further competitive band shift assays were performed using mutants in two purinerich motifs consisting of a 16-nucleotide stretch of single-stranded purines (ssPurines; U191UAAAAGUGAAAGUAA206) and a partially base-paired purine-rich region (bpPurines; G246AAAGUAA253), previously found to be important for MPMV gRNA packaging. To map the precise Pr78Gag binding sites on the MPMV gRNA, in vitro Gag-RNA foot-printing experiments followed by high-throughput selective 2\u27 hydroxyl acylation analyzed by primer extension (hSHAPE) were performed. These revealed that Pr78Gag binds to ssPurines, and the A252AGUGUU258 loop, corresponding to two unpaired adenosine residues of the bpPurines and the adjacent region called the “GU-rich region” (G254UGUU258), both of which flank the major splice donor. Hence, ssPurines are present on both the genomic and spliced viral RNAs, while the A252AGUGUU258 loop is found only on the gRNA, revealing how MPMV discriminates between genomic and spliced RNAs. Collectively, this study reveals how MPMV Pr78Gag binds in a redundant fashion to the two single-stranded loops (ssPurines and the A252AGUGUU258 loop) to bring about selective gRNA packaging over spliced viral RNAs. These results should help in understanding virion assembly and facilitate development of safe and efficient retroviral vectors for human gene therapy

Solving fuzzy linear programming problems by using the fuzzy exponential barrier method

In order to resolve the fuzzy linear programming problem, the fuzzy exponential barrier approach is the major strategy employed in this article. To overcome the problems with fuzzy linear programming, this method uses an algorithm. In this concept, a fuzzy inequality constraint is produced since the objective functions are convex.numerical examples are provided

DELINEATING THE BINDING SITES OF MASON-PFIZER MONKEY VIRUS (MPMV) GAG PRECURSOR POLYPROTEIN (Pr78GAG) ON GENOMIC RNA FOR ITS SELECTIVE PACKAGING

A key step in retroviral life cycle is the selective packaging of its dimeric RNA genome (gRNA) from a pool of cellular and spliced viral RNAs into nascent virions. This involves binding of the retroviral Gag polyprotein to sequences at the 5’ end of the viral genome, the packaging signal. The aim of this study was to identify full-length Gag polyprotein (Pr78Gag) binding sites on Mason-Pfizer monkey virus (MPMV) gRNA, a promising candidate for the development of safe human gene therapy vectors. Towards this end, recombinant MPMV Pr78Gag-His6-tagged protein was cloned and expressed in bacterial cells, and purified from the soluble fraction using immobilized metal affinity chromatography (IMAC) followed by size exclusion chromatography (SEC). The biological activity of the purified protein was determined by its ability to assemble virus like particles (VLPs), while its ability to package MPMV specific subgenomic RNAs was confirmed in eukaryotic cells. Competitive band shift assays demonstrated preferential Pr78Gag binding to unspliced over spliced viral RNA. Further competitive band shift assays were performed using mutants in two purinerich motifs consisting of a 16-nucleotide stretch of single-stranded purines (ssPurines; U191UAAAAGUGAAAGUAA206) and a partially base-paired purine-rich region (bpPurines; G246AAAGUAA253), previously found to be important for MPMV gRNA packaging. To map the precise Pr78Gag binding sites on the MPMV gRNA, in vitro Gag-RNA foot-printing experiments followed by high-throughput selective 2\u27 hydroxyl acylation analyzed by primer extension (hSHAPE) were performed. These revealed that Pr78Gag binds to ssPurines, and the A252AGUGUU258 loop, corresponding to two unpaired adenosine residues of the bpPurines and the adjacent region called the “GU-rich region” (G254UGUU258), both of which flank the major splice donor. Hence, ssPurines are present on both the genomic and spliced viral RNAs, while the A252AGUGUU258 loop is found only on the gRNA, revealing how MPMV discriminates between genomic and spliced RNAs. Collectively, this study reveals how MPMV Pr78Gag binds in a redundant fashion to the two single-stranded loops (ssPurines and the A252AGUGUU258 loop) to bring about selective gRNA packaging over spliced viral RNAs. These results should help in understanding virion assembly and facilitate development of safe and efficient retroviral vectors for human gene therapy

Reduced order modeling of subsurface multiphase flow models using deep residual recurrent neural networks

We present a reduced order modeling (ROM) technique for subsurface multi-phase flow problems building on the recently introduced deep residual recurrent neural network (DR-RNN) [1]. DR-RNN is a physics aware recurrent neural network for modeling the evolution of dynamical systems. The DR-RNN architecture is inspired by iterative update techniques of line search methods where a fixed number of layers are stacked together to minimize the residual (or reduced residual) of the physical model under consideration. In this manuscript, we combine DR-RNN with proper orthogonal decomposition (POD) and discrete empirical interpolation method (DEIM) to reduce the computational complexity associated with high-fidelity numerical simulations. In the presented formulation, POD is used to construct an optimal set of reduced basis functions and DEIM is employed to evaluate the nonlinear terms independent of the full-order model size. We demonstrate the proposed reduced model on two uncertainty quantification test cases using Monte-Carlo simulation of subsurface flow with random permeability field. The obtained results demonstrate that DR-RNN combined with POD-DEIM provides an accurate and stable reduced model with a fixed computational budget that is much less than the computational cost of standard POD-Galerkin reduced model combined with DEIM for nonlinear dynamical systems

AN ALGORITHM FOR SOLVING INTUITIONISTIC FUZZY LINEAR BOTTLENECK ASSIGNMENT PROBLEMS

The linear bottleneck assignment problem (LBAP), which is a variation of the classical assignment problem, seeks to minimize the longest completion time rather than the sum of the completion times when a number of jobs are to be assigned to the same number of workers. If the completion times are not certain, then it is said to be a fuzzy LBAP. Here we propose a new algorithm to solve fuzzy LBAP with completion times as intuitionistic fuzzy numbers

Data Painter: A Tool for Colormap Interaction

The choice of a mapping from data to color should involve careful consideration in order to maximize the user understanding of the underlying data. It is desirable for features within the data to be visually separable and identifiable. Current practice involves selecting a mapping from predefined colormaps or coding specific colormaps using software such as MATLAB. The purposes of this paper are to introduce interactive operations for colormaps that enable users to create more visually distinguishable pixel based visualizations, and to describe our tool, Data Painter, that provides a fast, easy to use framework for defining these color mappings. We demonstrate the use of the tool to create colormaps for various application areas and compare to existing color mapping methods. We present a new objective measure to evaluate their efficacy

A Prospective Study of Kidney releated Diseases and their Clinical Management in Patients from South Tamilnadu

BACKGROUND: This prospective study was conducted at Nephrology department in “Meenakshi Mission Hospital and Research Centre” MMHRC) MMHRC: This hospital was located in lake area at Madurai. This is a 750 plus bedded hospital built in late 20th century by Dr. SETHURAMAN (FOUNDER). The hospital was especially built for lower realms of humanity in our country and it provides multispecialty medical and surgical treatment at very high levels. The quality control service for each department has been done and their presentation also has been done in every year. This hospital is a deemed autonomous teaching hospital which have academic programs for paramedical disciplines. This study was conducted between December 2012 – July 2013. AIM AND OBJECTIVES: Drug toxicity in hospitalized patients is a frequent adverse event with nephrotoxicity accounting for nearly 20% of all drug toxicity. Hospitalized patients are vulnerable to renal failure from a variety of causes, including diagnostic procedures (1V contrast), sudden decrease in blood pressure (gastrointestinal bleed, sepsis, variceal bleed) and the addition of nephrotoxic medications (aminoglycosides, amphotericin, chemotherapy). Upto 16% of patients with baseline normal renal function who experience renal injury within the hospital setting have medication induced renal failure. The present study has made an attempt to reveal the following details: 1. To study on various renal diseases reported in the nephrology department of the hospital. 2. Study on patients with drug induced renal diseases. 3. Etiology of drug induced renal diseases. 4. Study on corresponding elevations in laboratory parameters in drug induced renal diseased patients. 5. Clinical management of patients with drug induced renal disease. PLAN OF THE WORK: The present dissertation was undertaken to study the incidence of drug induced renal disease and its clinical management. • Collection of patients admitted with drug induced renal disease in the nephrology unit of the hospital. • Collection of case history to point out the etiology of drug induced renal disease. • Corresponding elevations in the clinical parameters are observed. • Clinical management of drug induced renal disease undertaken is studied. • Consultation with the Nephrologist. • Submission of reports obtained. CONCLUSION: In this prospective study 538 patients were having various kidney diseases. Prevalence of kidney disease is very high between age group 51to 60 and 41 to 50 were 160 and 146 respectively, and it is due to metabolic rate, food habits, environment etc. Sex distribution among the sample population. It was observed that male (68%) predominance exists in this study compared to females (32%). Drug induced renal disease: Drug induced renal disease has occurred in 5.57% of the hospitalized patients coming with various renal diseases during the study period. In this study, 30 patients with drug induced nephrotoxicity have been observed out of this noted 20% female (6) and 80% male (24) patients, it is mainly due to sedentary life style. Out of these 30 subjcts 22 subjects having intra renal disease,8 subject having pre renal disease due to insufficient in take of water, quality of water, calcium rich diet like cow milk, dry fish, variety of pickles etc. Population having metabolic disorder like Diabetes mellitus and hypertension were more prone to get drug induced kidney disease because of food habits and life style. Age distribution In this study, patients with an age group between 10 – 80 were exposed to drug induced renal disease