Glucose challenge increases circulating progenitor cells in Asian Indian male subjects with normal glucose tolerance which is compromised in subjects with pre-diabetes: A pilot study

<p>Abstract</p> <p>Background</p> <p>Haematopoietic stem cells undergo mobilization from bone marrow to blood in response to physiological stimuli such as ischemia and tissue injury. The aim of study was to determine the kinetics of circulating CD34⁺and CD133⁺CD34⁺progenitor cells in response to 75 g glucose load in subjects with normal and impaired glucose metabolism.</p> <p>Methods</p> <p>Asian Indian male subjects (n = 50) with no prior history of glucose imbalance were subjected to 2 hour oral glucose tolerance test (OGTT). 24 subjects had normal glucose tolerance (NGT), 17 subjects had impaired glucose tolerance (IGT) and 9 had impaired fasting glucose (IFG). The IGT and IFG subjects were grouped together as pre-diabetes group (n = 26). Progenitor cell counts in peripheral circulation at fasting and 2 hour post glucose challenge were measured using direct two-color flow cytometry.</p> <p>Results</p> <p>The pre-diabetes group was more insulin resistant (p < 0.0001) as measured by homeostasis assessment model (HOMA-IR) compared to NGT group. A 2.5-fold increase in CD34⁺cells (p = 0.003) and CD133⁺CD34⁺(p = 0.019) cells was seen 2 hours post glucose challenge in the NGT group. This increase for both the cell types was attenuated in subjects with IGT. CD34⁺cell counts in response to glucose challenge inversely correlated with neutrophil counts (ρ = -0.330, p = 0.019), while post load counts of CD133⁺CD34⁺cells inversely correlated with serum creatinine (ρ = -0.312, p = 0.023).</p> <p>Conclusion</p> <p>There is a 2.5-fold increase in the circulating levels of haematopoietic stem cells in response to glucose challenge in healthy Asian Indian male subjects which is attenuated in subjects with pre-diabetes.</p

An early warning method for agricultural products price spike based on artificial neural networks prediction

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Specialization Can Drive the Evolution of Modularity

Organismal development and many cell biological processes are organized in a modular fashion, where regulatory molecules form groups with many interactions within a group and few interactions between groups. Thus, the activity of elements within a module depends little on elements outside of it. Modularity facilitates the production of heritable variation and of evolutionary innovations. There is no consensus on how modularity might evolve, especially for modules in development. We show that modularity can increase in gene regulatory networks as a byproduct of specialization in gene activity. Such specialization occurs after gene regulatory networks are selected to produce new gene activity patterns that appear in a specific body structure or under a specific environmental condition. Modules that arise after specialization in gene activity comprise genes that show concerted changes in gene activities. This and other observations suggest that modularity evolves because it decreases interference between different groups of genes. Our work can explain the appearance and maintenance of modularity through a mechanism that is not contingent on environmental change. We also show how modularity can facilitate co-option, the utilization of existing gene activity to build new gene activity patterns, a frequent feature of evolutionary innovations

New Constraints (and Motivations) for Abelian Gauge Bosons in the MeV-TeV Mass Range

We survey the phenomenological constraints on abelian gauge bosons having masses in the MeV to multi-GeV mass range (using precision electroweak measurements, neutrino-electron and neutrino-nucleon scattering, electron and muon anomalous magnetic moments, upsilon decay, beam dump experiments, atomic parity violation, low-energy neutron scattering and primordial nucleosynthesis). We compute their implications for the three parameters that in general describe the low-energy properties of such bosons: their mass and their two possible types of dimensionless couplings (direct couplings to ordinary fermions and kinetic mixing with Standard Model hypercharge). We argue that gauge bosons with very small couplings to ordinary fermions in this mass range are natural in string compactifications and are likely to be generic in theories for which the gravity scale is systematically smaller than the Planck mass - such as in extra-dimensional models - because of the necessity to suppress proton decay. Furthermore, because its couplings are weak, in the low-energy theory relevant to experiments at and below TeV scales the charge gauged by the new boson can appear to be broken, both by classical effects and by anomalies. In particular, if the new gauge charge appears to be anomalous, anomaly cancellation does not also require the introduction of new light fermions in the low-energy theory. Furthermore, the charge can appear to be conserved in the low-energy theory, despite the corresponding gauge boson having a mass. Our results reduce to those of other authors in the special cases where there is no kinetic mixing or there is no direct coupling to ordinary fermions, such as for recently proposed dark-matter scenarios.Comment: 49 pages + appendix, 21 figures. This is the final version which appears in JHE

Modelling negative feedback networks for activating transcription factor 3 predicts a dominant role for miRNAs in immediate early gene regulation

Activating transcription factor 3 (Atf3) is rapidly and transiently upregulated in numerous systems, and is associated with various disease states. Atf3 is required for negative feedback regulation of other genes, but is itself subject to negative feedback regulation possibly by autorepression. In cardiomyocytes, Atf3 and Egr1 mRNAs are upregulated via ERK1/2 signalling and Atf3 suppresses Egr1 expression. We previously developed a mathematical model for the Atf3-Egr1 system. Here, we adjusted and extended the model to explore mechanisms of Atf3 feedback regulation. Introduction of an autorepressive loop for Atf3 tuned down its expression and inhibition of Egr1 was lost, demonstrating that negative feedback regulation of Atf3 by Atf3 itself is implausible in this context. Experimentally, signals downstream from ERK1/2 suppress Atf3 expression. Mathematical modelling indicated that this cannot occur by phosphorylation of pre-existing inhibitory transcriptional regulators because the time delay is too short. De novo synthesis of an inhibitory transcription factor (ITF) with a high affinity for the Atf3 promoter could suppress Atf3 expression, but (as with the Atf3 autorepression loop) inhibition of Egr1 was lost. Developing the model to include newly-synthesised miRNAs very efficiently terminated Atf3 protein expression and, with a 4-fold increase in the rate of degradation of mRNA from the mRNA/miRNA complex, profiles for Atf3 mRNA, Atf3 protein and Egr1 mRNA approximated to the experimental data. Combining the ITF model with that of the miRNA did not improve the profiles suggesting that miRNAs are likely to play a dominant role in switching off Atf3 expression post-induction

Adult Neurogenesis Transiently Generates Oxidative Stress

An increasing body of evidence suggests that alterations in neurogenesis and oxidative stress are associated with a wide variety of CNS diseases, including Alzheimer’s disease, schizophrenia and Parkinson’s disease, as well as routine loss of function accompanying aging. Interestingly, the association between neurogenesis and the production of reactive oxidative species (ROS) remains largely unexamined. The adult CNS harbors two regions of persistent lifelong neurogenesis: the subventricular zone and the dentate gyrus (DG). These regions contain populations of quiescent neural stem cells (NSCs) that generate mature progeny via rapidly-dividing progenitor cells. We hypothesized that the energetic demands of highly proliferative progenitors generates localized oxidative stress that contributes to ROS-mediated damage within the neuropoietic microenvironment. In vivo examination of germinal niches in adult rodents revealed increases in oxidized DNA and lipid markers, particularly in the subgranular zone (SGZ) of the dentate gyrus. To further pinpoint the cell types responsible for oxidative stress, we employed an in vitro cell culture model allowing for the synchronous terminal differentiation of primary hippocampal NSCs. Inducing differentiation in primary NSCs resulted in an immediate increase in total mitochondria number and overall ROS production, suggesting oxidative stress is generated during a transient window of elevated neurogenesis accompanying normal neurogenesis. To confirm these findings in vivo, we identified a set of oxidation-responsive genes, which respond to antioxidant administration and are significantly elevated in genetic- and exercise-induced model of hyperactive hippocampal neurogenesis. While no direct evidence exists coupling neurogenesis-associated stress to CNS disease, our data suggest that oxidative stress is produced as a result of routine adult neurogenesis