In Vitro Safety Assessment of the Effect of Five Medicinal Plants on Human Peripheral Lymphocytes

Purpose: To evaluate, using ethnomedical data approach, five Indian plants used in traditional medicine for cancer and other diseases for their safety and cytotoxic activity on human lymphocytes.Methods: The antiproliferative effect of the 50, 100 and 200 ìg/ml aqueous extracts of five plants (leaves of Phyllanthus niruri, Coleus aromaticus, Azadirachta indica and Camellia sinensis, and driedfruit rind of Garcinia indica) were evaluated in vitro using trypan blue dye exclusion and clonogenic assay methods on two cell lines - Balb/c 3T3 mouse fibroblasts and human peripheral lymphocytes.Results: Among the five plants used traditionally to treat cancer and other infections, two of them (A. indica and C. aromaticus) exhibited cytotoxic effects on lymphocytes. Two other plants (G. indica and P.niruri) exhibited pronounced cytotoxic effects. Another plant (Camellia sinensis) exhibitedcytostimulatory effect (> 50 % cell proliferation). For the plants that are traditionally used in anticancer therapy, there was a correlation between the reported use of these plants and their cytotoxic activity onlymphocytes.Conclusion: The plant extracts of the leaves of P. niruri, C, aromaticus and A. indica, and the dried fruit rind of G. indica are cytotoxic to lymphocytes and this lends some credence to their traditional use for cancer treatment. However, green tea (C. sinensis) is cytostimulatory and safe for consumption

Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants

The nucleotide sequence representation of DNA can be inadequate for resolving protein-DNA binding sites and regulatory substrates, such as those involved in gene expression and horizontal gene transfer. Considering that sequence-like representations are algorithmically very useful, here we fused over 60 currently available DNA physicochemical and conformational variables into compact structural representations that can encode single DNA binding sites to whole regulatory regions. We find that the main structural components reflect key properties of protein-DNA interactions and can be condensed to the amount of information found in a single nucleotide position. The most accurate structural representations compress functional DNA sequence variants by 30% to 50%, as each instance encodes from tens to thousands of sequences. We show that a structural distance function discriminates among groups of DNA substrates more accurately than nucleotide sequence-based metrics. As this opens up a variety of implementation possibilities, we develop and test a distance-based alignment algorithm, demonstrating the potential of using the structural representations to enhance sequence-based algorithms. Due to the bias of most current bioinformatic methods to nucleotide sequence representations, it is possible that considerable performance increases might still be achievable with such solutions.Comment: 20 pages, 8 figures, 3 tables, conferenc

Increased Iron Uptake by Splenic Hematopoietic Stem Cells Promotes TET2-Dependent Erythroid Regeneration

Hematopoietic stem cells (HSCs) are capable of regenerating the blood system, but the instructive cues that direct HSCs to regenerate particular lineages lost to the injury remain elusive. Here, we show that iron is increasingly taken up by HSCs during anemia and induces erythroid gene expression and regeneration in a Tet2-dependent manner. Lineage tracing of HSCs reveals that HSCs respond to hemolytic anemia by increasing erythroid output. The number of HSCs in the spleen, but not bone marrow, increases upon anemia and these HSCs exhibit enhanced proliferation, erythroid differentiation, iron uptake, and TET2 protein expression. Increased iron in HSCs promotes DNA demethylation and expression of erythroid genes. Suppressing iron uptake or TET2 expression impairs erythroid genes expression and erythroid differentiation of HSCs; iron supplementation, however, augments these processes. These results establish that the physiological level of iron taken up by HSCs has an instructive role in promoting erythroid-biased differentiation of HSCs

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences

Xiangqi and combinatorial game theory

We explore whether combinatorial game theory (CGT) is suitable for analyzing endgame positions in Xiangqi (Chinese Chess). We discover some of the game values that can also be found in the analysis of International Chess, but we also experience the limitations of CGT when applied to a loopy and non-separable game like Xiangqi

Study the impact of different preparation methods on the structural and dielectric properties of nickel-zinc ferrite

In the current study, nickel-zinc ferrite nanoparticles Ni (1-x) ZnxFe2O4 (X= 0, 0.25, 0.50, 0.75, 1) have been arranged by sol-gel auto combustion and common chemical precipitation methods, The samples were described by x-ray (XRD) deflection, Fourier converts Infrared Spectroscopy (FTIR), dielectric perpetual and dielectric loss element. the XRD analysis confirms the cubic lone phase spinel configuration for all the synthesized materials. Average crystalline size is estimated of the (311) peaks of the x-ray diffractogram using Scherrer’s formulation institute in the range 38.90 to 37.71 nm for sol-gel auto burning method and from 18.61 to 23.41 nm for co-precipitation method.                                                                        The Fourier transform infrared spectroscopy was studied so as to assert the construction of the spinel phase and to recognize the kind of carbon remaining in the samples. The dielectric fixed and the dielectric loss factor were measured in the range between 50 Hz – 3 MHz at room temperature were located to be reduced with a rise in regularity. 

A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN–SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN–SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique