Black holes in the varying speed of light theory

We consider the effect of the \emph{Varying Speed of Light} theory on non-rotating black holes. We show that in any varying-$c$ theory, the Schwarzschild solution is neither static nor stationary. For a no-charged black hole, the singularity in the Schwarzschild horizon cannot be removed by coordinate transformation. Hence, no matter can enter the horizon, and the interior part of the black hole is separated from the rest of the Universe. If $\dot{c}<0$, then the size of the Schwarzschild radius increases with time. The higher value of the speed of light in the very early Universe may have caused a large reduction in the probability of the creation of the primordial black holes and their population.The same analogy is also considered for the charged black holes.Comment: 5 page

Chameleonic Generalized Brans--Dicke model and late-time acceleration

In this paper we consider Chameleonic Generalized Brans--Dicke Cosmology in the framework of FRW universes. The bouncing solution and phantom crossing is investigated for the model. Two independent cosmological tests: Cosmological Redshift Drift (CRD) and distance modulus are applied to test the model with the observation.Comment: 20 pages, 15 figures, to be published in Astrophys. Space Sci. (2011

Breast cancer risk factors in Iran: A systematic review & Meta-analysis

Objectives: Breast cancer is known as one of the deadliest forms of cancer, and it is increasing globally. There are a variety of proven and controversial risk factors for this malignancy. Herein, we aimed to undertake a systematic review and meta-analysis focus on the epidemiology of breast cancer risk factors in Iran. Methods: We performed a systematic search via PubMed, Scopus, Web of Science, and Persian databases for identifying studies published on breast cancer risk factors up to March 2019. Meta-analyses were done for risk factors reported in more than one study. We calculated odds ratios (ORs) with corresponding 95 confidence intervals (CIs) using a fixed/random-effects models. Results: Thirty-nine studies entered into the meta-analysis. Pooling of ORs showed a significant harmful effect for risk factors including family history (OR: 1.80, 95CI 1.47-2.12), hormonal replacement therapy (HRT) (OR: 5.48, 95CI 0.84-1.74), passive smokers (OR: 1.68, 95CI 1.34-2.03), full-term pregnancy at age 30 (OR: 3.41, 95CI 1.19-5.63), abortion (OR: 1.84, 95CI 1.35-2.33), sweets consumption (OR: 1.71, 95CI 1.32-2.11) and genotype Arg/Arg (crude OR: 1.59, 95CI 1.07-2.10), whereas a significant protective effect for late menarche (OR: 0.58, 95CI 0.32-0.83), nulliparity (OR: 0.68, 95CI 0.39-0.96), 13-24 months of breastfeeding (OR: 0.68, 95CI 0.46-0.90), daily exercise (OR: 0.59, 95CI 0.44-0.73) and vegetable consumption (crude OR: 0.28, 95CI 0.10-0.46). Conclusions: This study suggests that factors such as family history, HRT, passive smokers, late full-term pregnancy, abortion, sweets consumption and genotype Arg/Arg might increase risk of breast cancer development, whereas late menarche, nulliparity, 13-24 months breastfeeding, daily exercise and vegetable consumption had an inverse association with breast cancer development. © 2020 Amir Shamshirian et al., published by De Gruyter

Determinants of Mosaic Chromosomal alteration Fitness

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI toPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (

Metabolomic Profiling Reveals a Role for Androgen in Activating Amino Acid Metabolism and Methylation in Prostate Cancer Cells

Prostate cancer is the second leading cause of cancer related death in American men. Development and progression of clinically localized prostate cancer is highly dependent on androgen signaling. Metastatic tumors are initially responsive to anti-androgen therapy, however become resistant to this regimen upon progression. Genomic and proteomic studies have implicated a role for androgen in regulating metabolic processes in prostate cancer. However, there have been no metabolomic profiling studies conducted thus far that have examined androgen-regulated biochemical processes in prostate cancer. Here, we have used unbiased metabolomic profiling coupled with enrichment-based bioprocess mapping to obtain insights into the biochemical alterations mediated by androgen in prostate cancer cell lines. Our findings indicate that androgen exposure results in elevation of amino acid metabolism and alteration of methylation potential in prostate cancer cells. Further, metabolic phenotyping studies confirm higher flux through pathways associated with amino acid metabolism in prostate cancer cells treated with androgen. These findings provide insight into the potential biochemical processes regulated by androgen signaling in prostate cancer. Clinically, if validated, these pathways could be exploited to develop therapeutic strategies that supplement current androgen ablative treatments while the observed androgen-regulated metabolic signatures could be employed as biomarkers that presage the development of castrate-resistant prostate cancer

Measuring Granger Causality between Cortical Regions from Voxelwise fMRI BOLD Signals with LASSO

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis

Casual Compressive Sensing for Gene Network Inference

We propose a novel framework for studying causal inference of gene interactions using a combination of compressive sensing and Granger causality techniques. The gist of the approach is to discover sparse linear dependencies between time series of gene expressions via a Granger-type elimination method. The method is tested on the Gardner dataset for the SOS network in E. coli, for which both known and unknown causal relationships are discovered

Identification of Circulating Proteins associated With General Cognitive Function among Middle-Aged and Older adults

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p \u3c 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer\u27s disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p \u3c 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets

Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges

Pathway analysis has become the first choice for gaining insight into the underlying biology of differentially expressed genes and proteins, as it reduces complexity and has increased explanatory power. We discuss the evolution of knowledge base–driven pathway analysis over its first decade, distinctly divided into three generations. We also discuss the limitations that are specific to each generation, and how they are addressed by successive generations of methods. We identify a number of annotation challenges that must be addressed to enable development of the next generation of pathway analysis methods. Furthermore, we identify a number of methodological challenges that the next generation of methods must tackle to take advantage of the technological advances in genomics and proteomics in order to improve specificity, sensitivity, and relevance of pathway analysis