Parton Distributions in Impact Parameter Space

Fourier transform of the generalized parton distributions (GPDs) at zero skewness with respect to the transverse momentum transfer gives the distribution of partons in the impact parameter space. We investigate the GPDs as well as the impact parameter dependent parton distributions (ipdpdfs) by expressing them in terms of overlaps of light front wave functions (LFWFs) and present a comparative study using three different model LFWFs.Comment: 13 pages, 6 figure

MicroRNA-466 inhibits tumor growth and bone metastasis in prostate cancer by direct regulation of osteogenic transcription factor RUNX2.

MicroRNAs (miRNAs) have emerged as key players in cancer progression and metastatic initiation yet their importance in regulating prostate cancer (PCa) metastasis to bone has begun to be appreciated. We employed multimodal strategy based on in-house PCa clinical samples, publicly available TCGA cohorts, a panel of cell lines, in silico analyses, and a series of in vitro and in vivo assays to investigate the role of miR-466 in PCa. Expression analyses revealed that miR-466 is under-expressed in PCa compared to normal tissues. Reconstitution of miR-466 in metastatic PCa cell lines impaired their oncogenic functions such as cell proliferation, migration/invasion and induced cell cycle arrest, and apoptosis compared to control miRNA. Conversely, attenuation of miR-466 in normal prostate cells induced tumorigenic characteristics. miR-466 suppressed PCa growth and metastasis through direct targeting of bone-related transcription factor RUNX2. Overexpression of miR-466 caused a marked downregulation of integrated network of RUNX2 target genes such as osteopontin, osteocalcin, ANGPTs, MMP11 including Fyn, pAkt, FAK and vimentin that are known to be involved in migration, invasion, angiogenesis, EMT and metastasis. Xenograft models indicate that miR-466 inhibits primary orthotopic tumor growth and spontaneous metastasis to bone. Receiver operating curve and Kaplan-Meier analyses show that miR-466 expression can discriminate between malignant and normal prostate tissues; and can predict biochemical relapse. In conclusion, our data strongly suggests miR-466-mediated attenuation of RUNX2 as a novel therapeutic approach to regulate PCa growth, particularly metastasis to bone. This study is the first report documenting the anti-bone metastatic role and clinical significance of miR-466 in prostate cancer

Magnetic moments of the low-lying JP= 1/2−J^P=\,1/2^-, 3/2−3/2^- Λ\Lambda resonances within the framework of the chiral quark model

The magnetic moments of the low-lying spin-parity $J^P=$ $1/2^-$, $3/2^-$ $\Lambda$ resonances, like, for example, $\Lambda(1405)$ $1/2^-$, $\Lambda(1520)$ $3/2^-$, as well as their transition magnetic moments, are calculated using the chiral quark model. The results found are compared with those obtained from the nonrelativistic quark model and those of unitary chiral theories, where some of these states are generated through the dynamics of two hadron coupled channels and their unitarization

Oscillation of a neutral difference equation

AbstractThis paper is concerned with the oscillation of the bounded solutions of neutral difference equation where Δ is the forward difference operator defined by Δn = n+1 - n

Faster learning by reduction of data access time

Nowadays, the major challenge in machine learning is the Big Data challenge. The big data problems due to large number of data points or large number of features in each data point, or both, the training of models have become very slow. The training time has two major components: Time to access the data and time to process (learn from) the data. So far, the research has focused only on the second part, i.e., learning from the data. In this paper, we have proposed one possible solution to handle the big data problems in machine learning. The idea is to reduce the training time through reducing data access time by proposing systematic sampling and cyclic/sequential sampling to select mini-batches from the dataset. To prove the effectiveness of proposed sampling techniques, we have used Empirical Risk Minimization, which is commonly used machine learning problem, for strongly convex and smooth case. The problem has been solved using SAG, SAGA, SVRG, SAAG-II and MBSGD (Mini-batched SGD), each using two step determination techniques, namely, constant step size and backtracking line search method. Theoretical results prove the same convergence for systematic sampling, cyclic sampling and the widely used random sampling technique, in expectation. Experimental results with bench marked datasets prove the efficacy of the proposed sampling techniques and show up to six times faster training

SAAGs: Biased stochastic variance reduction methods for large-scale learning

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients. In this paper, we have proposed novel variants of SAAG-I and II (Stochastic Average Adjusted Gradient) (Chauhan et al. 2017), called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch in SAAG-IV, respectively. To determine the step size, we have used Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We have extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed two update rules for smooth and non-smooth problems. Moreover, our theoretical results have proved linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies have proved the efficacy of proposed methods against the state-of-art techniques

Chiral constituent quark model and the coupling strength of eta'

Using the latest data pertaining to \bar u-\bar d asymmetry and the spin polarization functions, detailed implications of the possible values of the coupling strength of the singlet Goldstone boson \eta' have been investigated in the \chiCQM with configuration mixing. Using \Delta u, \Delta_3, \bar u-\bar d and \bar u/\bar d, the possible ranges of the coupling parameters a, \alpha^ 2, \beta^ 2 and \zeta^ 2, representing respectively the probabilities of fluctuations to pions, K, \eta and \eta^{'}, are shown to be 0.10 \lesssim a \lesssim 0.14, 0.2\lesssim \alpha \lesssim 0.5, 0.2\lesssim \beta \lesssim 0.7 and 0.10 lesssim |\zeta| \lesssim 0.70. To constrain the coupling strength of \eta', detailed fits have been obtained for spin polarization functions, quark distribution functions and baryon octet magnetic moments corresponding to the following sets of parameters: a=0.1, \alpha=0.4, \beta=0.7, |\zeta|=0.65 (Case I); a=0.1, \alpha=0.4, \beta=0.6, |\zeta|=0.70 (Case II); a=0.14, \alpha=0.4, \beta=0.2, \zeta=0 (Case III) and a=0.13, \alpha=\beta=0.45, |\zeta|=0.10 (Case IV). Case I represents the calculations where a is fixed to be 0.1, in accordance with earlier calculations, whereas other parameters are treated free and the Case IV represents our best fit. The fits clearly establish that a small non-zero value of the coupling of \eta' is preferred over the higher values of \eta' as well as when \zeta=0, the latter implying the absence of \eta' from the dynamics of \chiCQM. Our best fit achieves an overall excellent fit to the data, in particular the fit for \Delta u, \Delta d, \Delta_8 as well as the magnetic moments \mu_{n}, \mu_{\Sigma^-}, \mu_{\Sigma^+} and \mu_{\Xi^-} is almost perfect, the \mu_{\Xi^-} being a difficult case for most of the similar calculations.Comment: 8 RevTeX pages, 2 Tables, Revised version to appear in Int.J.Mod.Phys

Genomic profiling of circulating tumor DNA from cerebrospinal fluid to guide clinical decision making for patients with primary and metastatic brain tumors

Despite advances in systemic therapies for solid tumors, the development of brain metastases remains a significant contributor to overall cancer mortality and requires improved methods for diagnosing and treating these lesions. Similarly, the prognosis for malignant primary brain tumors remains poor with little improvement in overall survival over the last several decades. In both primary and metastatic central nervous system (CNS) tumors, the challenge from a clinical perspective centers on detecting CNS dissemination early and understanding how CNS lesions differ from the primary tumor, in order to determine potential treatment strategies. Acquiring tissue from CNS tumors has historically been accomplished through invasive neurosurgical procedures, which restricts the number of patients to those who can safely undergo a surgical procedure, and for which such interventions will add meaningful value to the care of the patient. In this review we discuss the potential of analyzing cell free DNA shed from tumor cells that is contained within the cerebrospinal fluid (CSF) as a sensitive and minimally invasive method to detect and characterize primary and metastatic tumors in the CNS