5-Aza-2′-deoxycytidine stress response and apoptosis in prostate cancer

While studying on epigenetic regulatory mechanisms (DNA methylation at C-5 of –CpG– cytosine and demethylation of methylated DNA) of certain genes (FAS, CLU, E-cadh, CD44, and Cav-1) associated with prostate cancer development and its better management, we noticed that the used in vivo dose of 5-aza-2′-deoxycytidine (5.0 to 10.0 nM, sufficient to inhibit DNA methyltransferase activity in vitro) helped in the transcription of various genes with known (steroid receptors, AR and ER; ER variants, CD44, CDH1, BRCA1, TGFβR1, MMP3, MMP9, and UPA) and unknown (DAZ and Y-chromosome specific) proteins and the respective cells remained healthy in culture. At a moderate dose (20 to 200 nM) of the inhibitor, cells remain growth arrested. Upon subsequent challenge with increased dose (0.5 to 5.0 μM) of the inhibitor, we observed that the cellular morphology was changing and led to death of the cells with progress of time. Analyses of DNA and anti-, pro-, and apoptotic factors of the affected cells revealed that the molecular events that went on are characteristics of programmed cell death (apoptosis)

A restricted L(2, 1)-labelling problem on interval graphs

In a graph G = (V, E), L(2, 1)-labelling is considered by a function ` whose domain is V and codomain is set of non-negative integers with a condition that the vertices which are adjacent assign labels whose difference is at least two and the vertices whose distance is two, assign distinct labels. The difference between maximum and minimum labels among all possible labels is denoted by λ2,1(G). This paper contains a variant of L(2, 1)-labelling problem. In L(2, 1)-labelling problem, all the vertices are L(2, 1)-labeled by least number of labels. In this paper, maximum allowable label K is given. The problem is: L(2, 1)-label the vertices of G by using the labels {0, 1, 2, . . . , K} such that maximum number of vertices get label. If K labels are adequate for labelling all the vertices of the graph then all vertices get label, otherwise some vertices remains unlabeled. An algorithm is designed to solve this problem. The algorithm is also illustrated by examples. Also, an algorithm is designed to test whether an interval graph is no hole label or not for the purpose of L(2, 1)-labelling.Publisher's Versio

Prophylactic Role of Boerhaavia diffusa in Ethylene Glycol Induced Calcium Oxalate Urolithiasis

Introduction: Boerhaavia diffusa Linn. (Family: Nyctaginaceae) is a widely used plant in India and Brazil as a traditional medicine for treatment of urolithiasis and other urinary disorders.Objectives: The aim of this study was to evaluate the antiurolithic activity of Boerhaavia diffusa root aqueous extract (BDE) as prophylaxis for renal stones.Methods: In vitro calcium oxalate (CaOx) crystallization inhibitory effect of BDE was determined by measuring change in turbidity at 620nm on addition of sodium oxalate in the synthetic urine. In a rat model of urolithiasis, induced by adding 0.75% ethylene glycol (EG) in drinking water and effect of simultaneous treatment of BDE (100-200 mg/kg) was observed for 28 days.Results: BDE inhibited CaOx nucleation, aggregation and crystal formation in the synthetic urine in vitro on addition of NaOx. The lithogenic treatment caused polyuria, weight loss, hyperoxaluria and impairment of renal function which was prevented by BDE. Hyperoxaluria and CaOx crystaldeposition in the renal tubules caused by EG intake was prevented by BDE treatment.Conclusion: This study indicates that the antiurolithic activity of Boerhaavia diffusa extract possibly mediated through inhibition of CaOx crystallization, diuresis and hypo-oxaluria may justify its prophylactic use in urolithiasis

Glassy magnetic phase driven by short range charge and magnetic ordering in nanocrystalline La1/3_{1/3}Sr2/3_{2/3}FeO3−δ_{3-\delta}: Magnetization, Mossbauer, and polarised neutron studies

The charge ordered La$_{1/3}$Sr$_{2/3}$FeO$_{3-\delta}$ (LSFO) in bulk and nanocrystalline forms are investigated using ac and dc magnetization, M\"{o}ssbauer, and polarised neutron studies. A complex scenario of short range charge and magnetic ordering is realized from the polarised neutron studies in nanocrystalline specimen. This short range ordering does not involve any change in spin state and modification in the charge disproportion between Fe$^{3+}$ and Fe$^{5+}$ compared to bulk counterpart as evident in the M\"{o}ssbauer results. The refinement of magnetic diffraction peaks provides magnetic moments of Fe$^{3+}$ and Fe$^{5+}$ are about 3.15$\mu_B$ and 1.57$\mu_B$ for bulk, and 2.7$\mu_B$ and 0.53$\mu_B$ for nanocrystalline specimen, respectively. The destabilization of charge ordering leads to magnetic phase separation, giving rise to the robust exchange bias (EB) effect. Strikingly, EB field at 5 K attains a value as high as 4.4 kOe for average size $\sim$ 70 nm, which is zero for the bulk counterpart. A strong frequency dependence of ac susceptibility reveals cluster-glass like transition around $\sim$ 65 K, below which EB appears. Overall results propose that finite size effect directs the complex glassy magnetic behavior driven by unconventional short range charge and magnetic ordering, and magnetic phase separation appears in nanocrystalline LSFO.Comment: 10 pages, 9 figures. Fig. 1 available upon request or in http://www.ffn.ub.es/oscar/Articles.html. Accepted in Phys. Rev.

Predicting Pair Correlation Functions of Glasses using Machine Learning

Glasses offer a broad range of tunable thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of glasses due to their enormous composition and chemical space. Here, we address this problem and develop a metamodel of composition-atomistic structure relation of a class of glassy material via a machine learning (ML) approach. Within this ML framework, an unsupervised deep learning technique, viz. convolutional neural network (CNN) autoencoder, and a regression algorithm, viz. random forest (RF), are integrated into a fully automated pipeline to predict the spatial distribution of atoms in a glass. The RF regression model predicts the pair correlation function of a glass in a latent space. Subsequently, the decoder of the CNN converts the latent space representation to the actual pair correlation function of the given glass. The atomistic structures of silicate (SiO2) and sodium borosilicate (NBS) based glasses with varying compositions and dopants are collected from molecular dynamics (MD) simulations to establish and validate this ML pipeline. The model is found to predict the atom pair correlation function for many unknown glasses very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-atomistic structure relations of glasses and other materials

Trace gases and CO2 isotope records from cabo de rama, India

Concentrations of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrous oxide (N2O) and hydrogen (H2), and the stable carbon (δ 13C-CO2) and oxygen (δ 18O-CO2) isotopic composition of CO2 have been measured in air samples collected from Cabo de Rama (CRI), India, for the period 1993-2002. The observations show clear signatures of Northern and Southern Hemispheric (NH and SH) air masses, mixed with their regional fluxes and chemical loss mechanisms, resulting in complex seasonal variation of these gases. The CRI measurements are compared with remote marine sites at Seychelles and Mauna Loa. Simulations of two major anthropogenic greenhouse gases (CO2 and CH4) concentrations using a chemistry-transport model for the CRI site suggest that globally optimized fluxes can produce results comparable to the observations. We discuss that CRI observations have provided critical guidance in optimizing the fluxes to constrain the regional source/sinks balance

Exchange bias effect in alloys and compounds

The phenomenology of exchange bias effects observed in structurally single-phase alloys and compounds but composed of a variety of coexisting magnetic phases such as ferromagnetic, antiferromagnetic, ferrimagnetic, spin-glass, cluster-glass and disordered magnetic states are reviewed. The investigations on exchange bias effects are discussed in diverse types of alloys and compounds where qualitative and quantitative aspects of magnetism are focused based on macroscopic experimental tools such as magnetization and magnetoresistance measurements. Here, we focus on improvement of fundamental issues of the exchange bias effects rather than on their technological importance

Nitride Single Photon Sources

Single photon sources are a key enabling technology for quantum communications, and in the future more advanced quantum light sources may underpin other quantum information processing paradigms such as linear optical quantum computation. In considering possible practical implementations of future quantum technologies, the nitride materials system is attractive since nitride quantum dots (QDs) achieve single photon emission at easily accessible temperatures [1], potentially enabling the implementation of quantum key distribution paradigms in contexts where cryogenic cooling is impracticable

Insight into the impact of atomic- and nano-scale indium distributions on the optical properties of InGaN/GaN quantum well structures grown on m -plane freestanding GaN substrates

We investigate the atomic scale structure of m-plane InGaN quantum wells grown on bulk m-plane GaN templates and reveal that as the indium content increases there is an increased tendency for non-random clustering of indium atoms to occur. Based on the atom probe tomography data used to reveal this clustering, we develop a k.p model that takes these features into account, and links the observed nanostructure to the optical properties of the quantum wells. The calculations show that electrons and holes tend to co-localise at indium clusters. The transition energies between the electron and hole states are strongly affected by the shape and size of the clusters. Hence, clustering contributes to the very large line widths observed in the experimental low temperature photoluminescence spectra. Also, the emission from m-plane InGaN quantum wells is strongly linearly polarised. Clustering does not alter the theoretically predicted polarisation properties, even when the shape of the cluster is strongly asymmetric. Overall, however, we show that the presence of clustering does impact the optical properties, illustrating the importance of careful characterisation of the nanoscale structure of m-plane InGaN quantum wells and that atom probe tomography is a useful and important tool to address this problem

Genotypes and haplotypes of the methyl-CpG-binding domain 2 modify breast cancer risk dependent upon menopausal status

INTRODUCTION: MBD2, the gene encoding methyl-CpG-binding domain (MBD)2, is a major methylation related gene and functions as a transcriptional repressor that can specifically bind to the methylated regions of other genes. MBD2 may also mediate gene activation because of its potential DNA demethylase activity. The present case-control study investigated associations between two single nucleotide polymorphisms (SNPs) in the MBD2 gene and breast cancer risk. METHODS: DNA samples from 393 Caucasian patients with breast cancer (cases) and 436 matched control individuals, collected in a recently completed breast cancer case–control study conducted in Connecticut, were included in the study. Because no coding SNPs were found in the MBD2 gene, one SNP in the noncoding exon (rs1259938) and another in the intron 3 (rs609791) were genotyped. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate cancer risk associated with the variant genotypes and the reconstructed haplotypes. RESULTS: The variant genotypes at both SNP loci were significantly associated with reduced risk among premenopausal women (OR = 0.41 for rs1259938; OR = 0.54 for rs609791). Further haplotype analyses showed that the two rare haplotypes (A-C and A-G) were significantly associated with reduced breast cancer risk (OR = 0.40, 95% CI = 0.20–0.83 for A-C; OR = 0.47, 95% CI = 0.26–0.84 for A-G) in premenopausal women. No significant associations were detected in the postmenopausal women and the whole population. CONCLUSION: Our results demonstrate a role for the MBD2 gene in breast carcinogenesis in premenopausal women. These findings suggest that genetic variations in methylation related genes may potentially serve as a biomarker in risk estimates for breast cancer