Data Inventory for Cancer Patients Receiving Radiotherapy for Outcome Analysis and Modeling

Data collection for cancer patients is recognized as an important task in the USA, where the National Program of Cancer Registries (NPCR) administered by the Centers for Disease Control and Prevention collects data on the occurrence, type, extent, and location of the cancer, and the type of initial treatment. The International Consortium for Health Outcomes Measurements (ICHOM) aims at providing a global resource of in-use outcome measures and risk adjustment factors by medical condition and creating a global standard for measuring results. 

These initiatives will enable public health professionals to understand and address the cancer burden more effectively. We have recently proposed to use the pre-treatment, planning, and treatment outcomes data for cancer patients undergoing radiation therapy to provide guidelines for optimal choice of both radiation modality and planning for new patients. It is important to determine the most influential patient features (or their combinations) that has the strongest correlation with the outcomes. We propose an Overlap Volume Histogram as a valuable representation of size and shape for tumor and organs at risk important for planning.

A Similarity Retrieval Tool for Functional Magnetic Resonance Imaging Statistical Maps

A fundamental goal in functional neuroimaging is to identify areas of activation in the brain relative to a given task. Functional magnetic resonance imaging (fMRI) is one technique used to identify such changes because changes in neuronal activity along a given region of the brain can be captured by a corresponding change in voxel value intensity on the acquired fMRI image. Statistical parametric mapping (SPM) [13] is the current popular technique used to analyze fMRI images. An SPM image contains test statistics determined at each pixel by the ratio between the intensity of the signal and its variance across experimental conditions.

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

In this work we introduce the use of penalized logistic regression (PLR) to the problem of classification of MRI images and automatic detection of Alzheimer’s disease. Classification of sMRI is approached as a large scale regularization problem which uses voxels as input features. 

We evaluate how differences in sMRI pre-processing steps such as smoothing, normalization, and template selection affect the performance of high dimensional classification methods. In addition, we compared the relative performance of PLR to a different approach based on support vector machines. To study these questions we used data from the Alzheimer Disease Neuroimaging Initiative (ADNI). 

Enhancing Visual Evoked Potentials Detection with Use of Computational Intelligence Tools

The analysis of evoked potentials (EPs) in the electroencephalogram (EEG) is usually inspected visually and demands subjective interpretation of the results. This paper aims at combining an statistical criterion based on the magnitude square multiple coherence (MSMC) estimate with computational intelligence methods in order to estimate the EPs detection rate (DR) using only portions of the frequency spectrum. 

Thus, networks were used to predict the DR in EEG signals of 15 normal subjects during stroboscopic stimulation. The algorithms were designed to receive the spectral information of two, four or six EEG derivations as the input and DR as the output. Our best result shows that the artificial neural networks can estimate DR with correlation coefficient of 0.97 compared with MSMC, even when a reduced amount of spectral information from the data is available.

Quantitative Structure Activity Relationship and Molecular Docking of Pim-1 Kinase Inhibitors

QSAR has established itself as an important means of virtual screening. Computational analysis of numerous drug molecules aided the facility to meaningfully screen specific ones for further use and experimentation. 

In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) associated simulations were done and stochastic models were developed considering 55 molecules of hydroxyflavanone, thaizolidene2, 4-dione, pyridone derivatives against proviral insertion site of moloney murine leukemia virus. As part of the study, molecular field analysis (MFA) was done along with receptor surface analysis (RSA). Moreover, the generalization ability of the developed model was rigorously validated using 12 test set molecules.

Bioinformatics offer new avenues for the clinical and Medical data analysis

Genomics and molecular biology has served as a source of inspiration for biology an d biotechnology researchers and these two fields together developed a massive data known as bioinformatics. Bioinformatics data cut the elaborative laboratory costs as the analysis of this offers valuable inputs related to cancer genomics and viral genomics apart from offering next generation sequencing that is capable of analyzing the massive data generated from the medical, biotechnological and clinical research globally. 

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DNA/RNA Fragmentation and Cytolysis in Human Cancer Cells Treated with Diphthamide Nano Particles Derivatives

Molecular structure activity studies for some Diphthamide Nano particles derivatives indicate that the conformational characteristics along with the nature and position of the substituents on the Diphthamide Nano particles derivatives ring play an important role in their biological and biochemical activities (Figure 1). Therefore, we have calculated the optimized molecular geometries of some Diphthamide Nano particles derivatives. 

Calculations are carried out on the structures of these medical, medicinal and pharmaceutical Nano drugs using Hartree–Fock calculations and also Density Functional Theory (DFT) by performing HF, PM3, MM2, MM3, AM1, MP2, MP3, MP4, CCSD, CCSD(T), LDA, BVWN, BLYP and B3LYP levels of theory using the standard 31G, 6–31G*, 6–31+G*, 6–31G(3df, 3pd), 6–311G, 6–311G* and 6–311+G* basis sets of the Gaussian 09. The comparative heats of formation and Natural Bond Orbital (NBO) charges are calculated for these Diphthamide Nano particles derivatives. We have finally obtained some conformational rules in terms of the natures and positions of the substituents on the Diphthamide Nano particles derivatives ring.

Selenoergothionein as a Potential Inhibitor against Amyloid β-Protein (Aβ): Docking and Molecular Dynamics Studies

Alzheimer‘s disease (AD) is a progressive neurodegenerative disorder, encircling the deterioration of cognitive functions and behavioral changes, characterized by the aggregation of amyloid β-protein (Aβ) into fibrillar amyloid plaques in elected areas of the brain with the lipid-carrier protein Apo lipoprotein E (apoE), the microtubule associated protein tau, and the presynaptic protein α-synuclein. 

High levels of fibrillary Aβ, the main constituent of senile plaques, are deposited in the AD brain that outcome in the thrashing of synapses, neurons and destruction of neuronal role. Aβ is derived from the amyloid precursor protein through sequential protein cleavage by aspartyl protease, β-secretase and presenilin-dependentβ-secretase triggering a spill of events such as oxidative damage,neurotoxicity, and inflammation that contributes to the progression of AD. 

Enhancing Visual Evoked Potentials Detection with Use of Computational Intelligence Tools

The analysis of evoked potentials (EPs) in the electroencephalogram (EEG) is usually inspected visually and demands subjective interpretation of the results. This paper aims at combining an statistical criterion based on the magnitude square multiple coherence (MSMC) estimate with computational intelligence methods in order to estimate the EPs detection rate (DR) using only portions of the frequency spectrum. 

Thus, networks were used to predict the DR in EEG signals of 15 normal subjects during stroboscopic stimulation. The algorithms were designed to receive the spectral information of two, four or six EEG derivations as the input and DR as the output. Our best result shows that the artificial neural networks can estimate DR with correlation coefficient of 0.97 compared with MSMC, even when a reduced amount of spectral information from the data is available.

DNA/RNA Fragmentation and Cytolysis in Human Cancer Cells Treated with Diphthamide Nano Particles Derivatives

Molecular structure activity studies for some Diphthamide Nano particles derivatives indicate that the conformational characteristics along with the nature and position of the substituents on the Diphthamide Nano particles derivatives ring play an important role in their biological and biochemical activities (Figure 1). Therefore, we have calculated the optimized molecular geometries of some Diphthamide Nano particles derivatives. 

Calculations are carried out on the structures of these medical, medicinal and pharmaceutical Nano drugs using Hartree–Fock calculations and also Density Functional Theory (DFT) by performing HF, PM3, MM2, MM3, AM1, MP2, MP3, MP4, CCSD, CCSD(T), LDA, BVWN, BLYP and B3LYP levels of theory using the standard 31G, 6–31G*, 6–31+G*, 6–31G(3df, 3pd), 6–311G, 6–311G* and 6–311+G* basis sets of the Gaussian 09. 

Understanding Dive Computers

Modern digital dive computers date to the early 80s, though analog devices simulating tissue gas uptake and elimination through porous membranes date back to the 70s. Analog devices were limited to nonstop diving and had a short shelf life. 

Digital dive computers proved highly successful and very useful right from the start, progressing from just table emulators to full up algorithmic staging devices across mixed gas, open circuit (OC), re breather (RB), nonstop, decompression, deep, and shallow diving. Dive computers are moderately expensive items these days, and high end units range beyond $1500. Basically, a decompression computer is a microprocessor consisting of a power source, pressure transducer, analog to digital signal converter, internal clock, chip with RAM (random access memory) and ROM (read only memory), and pixel display screen.

Likelihood Ratio Test of Hardy-Weinberg Equilibrium Using Uncertain Genotypes for Sibship Data

Testing for Hardy-Weinberg equilibrium of genotype frequencies is a crucial first step in the study of population genetics. In this paper, we develop an Expectation-Maximization algorithm to estimate the genotype frequencies for sibship data with genotype uncertainty. 

We also develop a likelihood ratio test of Hardy-Weinberg equilibrium for sibships with no parental genotypes available and with possible genotyping errors. Simulations show that our likelihood ratio test maintains valid control of the type I error rate and good statistical power. Finally, the likelihood ratio test is extended across strata when a sample is stratified by multiple ethnic populations with different genotype frequencies.