Estimation of Postmortem Interval by Evaluation of Cells in Cerebro Spinal Fluid

Postmortem interval is of interest to the forensic pathologist from time immemorial. Due to the number of variables affecting the range of physical, biochemical and molecular changes in the postmortem, the postmortem interval could be determined only as a range. The aim of this study is to analyse the number of cells in CSF in postmortem and their morphological changes, to determine the postmortem interval. 39 cases were studied in which 27 cases were male and 12 cases were female of age ranging from 6 – 65 years. The cerebrospinal fluid sample was collected by cisternal puncture. The cases are with known postmortem interval and are grouped into 5 intervals – 0-6 hours, 6 -12 hours, 12 – 18 hours, 18 – 24 hours and more than 24 hours. There is a raise in cell count in CSF up to 12 hours while there are no degenerative changes detected in the first 6 hours. In the first 6 hours, lymphocyte count is higher than the neutrophil count. In the next six hours, the cell count increases while the lymphocytes remain higher than the neutrophil count. After 20 hours, the cell morphology is unreliable. The changes in the CSF cell count and cytology can be used along with other parameters to estimate the range of postmortem interval

Parallel MR Image Reconstruction Using Augmented Lagrangian Methods

Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data-SENSE-reconstruction-using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., -norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85846/1/Fessler4.pd

An Accelerated Iterative Reweighted Least Squares Algorithm for Compressed Sensing MRI

Compressed sensing for MRI (CS-MRI) attempts to recover an object from undersampled k-space data by minimizing sparsity-promoting regularization criteria. The iterative reweighted least squares (IRLS) algorithm can perform the minimization task by solving iteration-dependent linear systems, recursively. However, this process can be slow as the associated linear system is often poorly conditioned for ill-posed problems. We propose a new scheme based on the matrix inversion lemma (MIL) to accelerate the solving process. We demonstrate numerically for CS-MRI that our method provides significant speed-up compared to linear and nonlinear conjugate gradient algorithms, thus making it a promising alternative for such applications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85957/1/Fessler250.pd

Road Safety Assessment using iRAP – Case Study of National Highway

Black spots with high crash frequency were identified through the recording of accidents from FIR reports or which is simply through secondary data collection before this number of accidents and number of persons (killed, grievous, minor) injured are identified for the selected study area NH44 (Devanahalli toll plaza to Bagepalli toll plaza) using FIR report data of the particular police stations under study area. Then the remedial measures are suggested based on factors contributing to road crashes that were identified on the selected concerned black spots. Remedial measures are suggested for the particularly selected black spots from identified black spots by road safety audit, audit performed for this study is the safety performance examination of an existing road at intersections, to provide remedial measures so as to mitigate the impact of accidents through the use of IRC Codal provisions, the factors contributing accidents. Risk Maps use detailed crash data to illustrate the actual number of deaths and injuries on a road network. Star Ratings provide a simple and objective measure of the level of safety provided by a road’s design. Safer Roads Investment Plans draw on approximately 90 proven road improvement options to generate affordable and economically sound infrastructure options for saving lives

Regression Data Analysis Approach On COVID-19 Prediction

The Proposed method is to develop the regression models for the observed frequency distribution process and generate expected frequency distribution. This study analyzed the daily COVID 19 cases site, Regression models they are used to estimate daily confirmed, Death and New cases data of per day. The error estimates RMSE, MAE of forecasts from the above models is compared to identify the most suitable approaches for forecasting trend analysis

An Inverse Approximation and Saturation Order for Kantorovich Exponential Sampling Series

In the present article, an inverse approximation result and saturation order for the Kantorovich exponential sampling series $I_{w}^{\chi}$ are established. First we obtain a relation between the generalized exponential sampling series $S_{w}^{\chi}$ and $I_{w}^{\chi}$ for the space of all uniformly continuous and bounded functions on $\mathbb{R}^{+}.$ Next, a Voronovskaya type theorem for the sampling series $S_{w}^{\chi}$ is proved. The saturation order for the series $I_{w}^{\chi}$ is obtained using the Voronovskaya type theorem. Further, an inverse result for $I_{w}^{\chi}$ is established for the class of log-H\"{o}lderian functions. Moreover, some examples of kernels satisfying the conditions, which are assumed in the hypotheses of the theorems, are discussed