Quantum Cloning, Bell's Inequality and Teleportation

We analyze a possibility of using the two qubit output state from Buzek-Hillery quantum copying machine (not necessarily universal quantum cloning machine) as a teleportation channel. We show that there is a range of values of the machine parameter $\xi$ for which the two qubit output state is entangled and violates Bell-CHSH inequality and for a different range it remains entangled but does not violate Bell-CHSH inequality. Further we observe that for certain values of the machine parameter the two-qubit mixed state can be used as a teleportation channel. The use of the output state from the Buzek-Hillery cloning machine as a teleportation channel provides an additional appeal to the cloning machine and motivation of our present work.Comment: 7 pages and no figures, Accepted in Journal of Physics

Proteinopathy, oxidative stress and mitochondrial dysfunction: cross talk in alzheimer’s disease and parkinson’s disease

Alzheimer's disease and Parkinson's disease are two common neurodegenerative diseases of the elderly people that have devastating effects in terms of morbidity and mortality. The predominant form of the disease in either case is sporadic with uncertain etiology. The clinical features of Parkinson's disease are primarily motor deficits, while the patients of Alzheimer's disease present with dementia and cognitive impairment. Though neuronal death is a common element in both the disorders, the postmortem histopathology of the brain is very characteristic in each case and different from each other. In terms of molecular pathogenesis, however, both the diseases have a significant commonality, and proteinopathy (abnormal accumulation of misfolded proteins), mitochondrial dysfunction and oxidative stress are the cardinal features in either case. These three damage mechanisms work in concert, reinforcing each other to drive the pathology in the aging brain for both the diseases; very interestingly, the nature of interactions among these three damage mechanisms is very similar in both the diseases, and this review attempts to highlight these aspects. In the case of Alzheimer's disease, the peptide amyloid beta (A beta) is responsible for the proteinopathy, while alpha-synuclein plays a similar role in Parkinson's disease. The expression levels of these two proteins and their aggregation processes are modulated by reactive oxygen radicals and transition metal ions in a similar manner. In turn, these proteins - as oligomers or in aggregated forms - cause mitochondrial impairment by apparently following similar mechanisms. Understanding the common nature of these interactions may, therefore, help us to identify putative neuroprotective strategies that would be beneficial in both the clinical conditions

Robust Regression Analysis for Non-Normal Situations under Symmetric Distributions Arising In Medical Research

In medical research, while carrying out regression analysis, it is usually assumed that the independent (covariates) and dependent (response) variables follow a multivariate normal distribution. In some situations, the covariates may not have normal distribution and instead may have some symmetric distribution. In such a situation, the estimation of the regression parameters using Tiku’s Modified Maximum Likelihood (MML) method may be more appropriate. The method of estimating the parameters is discussed and the applications of the method are illustrated using real sets of data from the field of public health

Deep Neural Network Cloud-Type Classification (DeepCTC) model and its application in evaluating PERSIANN-CCS

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation

Stereospecific synthesis of the aglycone of pseudopterosin E

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