Maintaining phase of the crustacean tri-phasic pyloric rhythm

We construct and analyze a model network of the pyloric rhythm of the crustacean stomatogastric ganglion consisting of an oscillator neuron that inhibits two reciprocally inhibitory follower neurons. We derive analytic expressions that determine the phase of firing of the follower neurons with respect to the oscillator. An important aspect of the model is the inclusion of synapses that exhibit short-term synaptic depression. We show that these type of synapses allow there to be a complicated relationship between the intrinsic properties of the neurons and the synapses between them in determining phase relationships. Our analysis reveals the circumstances and ranges of cycle periods under which these properties work in concert with or independently from one another. In particular, we show that phase maintenance over a range of oscillator periods can be enhanced through the interplay of the two follower neurons if the synapses between these neurons are depressing. Since our model represents the core of the oscillatory pyloric network, the results of our analysis can be compared to experimental data and used to make predictions about the biological network.     

A high-density admixture scan in 1,670 African Americans with hypertension

Hypertension (HTN) is a devastating disease with a higher incidence in African Americans than European Americans, inspiring searches for genetic variants that contribute to this difference. We report the results of a large-scale admixture scan for genes contributing HTN risk, in which we screened 1,670 African Americans with HTN and 387 control individuals for regions of the genome with elevated proportion of African or European ancestry. No loci were identified that were significantly associated with HTN. We also searched for evidence of an admixture signal at 40 candidate genes and eight previously reported linkage peaks, but none appears to contribute substantially to the differential HTN risk between African and European Americans. Finally, we observed nominal association at one of the loci detected in the admixture scan of Zhu et al. 2005 (p = 0.016 at 6q24.3 correcting for four hypotheses tested), although we caution that the significance is marginal and the estimated odds ratio of 1.19 per African allele is less than what would be expected from the original report; thus, further work is needed to follow up this locus.     

Microbial diversity and function in crystalline basement beneath the Deccan Traps explored in a 3 km borehole at Koyna,western India

Deep terrestrial subsurface represents a huge repository of global prokaryotic biomass. Given its vastness and importance, microbial life within the deep subsurface continental crust remains under-represented in global studies. We characterize the microbial communities of deep, extreme and oligotrophic realm hosted by crystalline Archaean granitic rocks underneath the Deccan Traps, through sampling via 3000 m deep scientific borehole at Koyna, India through metagenomics, amplicon sequencing and cultivation-based analyses. Gene sequences 16S rRNA (7.37 × 10⁶) show considerable bacterial diversity and the existence of a core microbiome (5724 operational taxonomic units conserved out of a total 118,064 OTUs) across the depths. Relative abundance of different taxa of core microbiome varies with depth in response to prevailing lithology and geochemistry. Co-occurrence network analysis and cultivation attempt to elucidate close interactions among autotrophic and organotrophic bacteria. Shotgun metagenomics reveals a major role of autotrophic carbon fixation via the Wood–Ljungdahl pathway and genes responsible for energy and carbon metabolism. Deeper analysis suggests the existence of an ‘acetate switch’, coordinating biosynthesis and cellular homeostasis. We conclude that the microbial life in the nutrient- and energy-limited deep granitic crust is constrained by the depth and managed by a few core members via a close interplay between autotrophy and organotrophy.     

Variable selection in nonlinear function-on-scalar regression

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model. The proposed method provides a unified and flexible framework for variable selection in FOSR, allowing nonlinear effects of the covariates. Numerical analysis using simulation study illustrates the advantages of the proposed method over existing variable selection methods in FOSR even when the underlying covariate effects are all linear. The proposed procedure is demonstrated on accelerometer data from the 2003–2004 cohorts of the National Health and Nutrition Examination Survey (NHANES) in understanding the association between diurnal patterns of physical activity and demographic, lifestyle, and health characteristics of the participants.     

Metabolism of 3H-1α,25-dihydroxyvitamin D3 in cultured human keratinocytes

In the present investigation we studied the metabolism of 1α,25-dihydroxy-[1β-³H] vitamin D₃ (³H-1,25(OH)₂D₃) in culture-grown human keratinocytes (CHK). Our results showed that the cellular uptake of ³H-1,25(OH)₂D₃, upon incubation with CHK, occurred very rapidly; and it paralleled a decrease in the concentration of ³H-1,25(OH)₂D₃ in the medium. The amount of ³H-calcitroic acid, on the other hand, increased slowly in the medium, while the concentration of ³H-calcitroic acid in the cell remained undetectable during the whole period of incubation. When the cells were preincubated with 1,25(OH)₂D₃ (10^?8M), conversion of ³H-1,25(OH)₂D₃ to ³H-calcitroic acid increased almost twofold, indicating that 1,25(OH)₂D₃ catalyzed its own catabolism. © 1995 Wiley-Liss, Inc.     

Hyaluronic acid engrafted metformin loaded graphene oxide nanoparticle as CD44 targeted anti-cancer therapy for triple negative breast cancer

BackgroundTriple negative breast cancer (TNBC) is the most aggressive form of breast cancer with limited treatment modalities. It is associated with high propensity of cancer recurrence.MethodsUV Spectroscopy, FTIR, DLS, Zeta potential, TEM and SEM were employed to characterize nanoparticles. MTT assay, Wound healing assay, SEM, Immunocytochemistry analysis, Western blot, RT-PCR, mammosphere formation assay were employed to study apoptosis, cell migration and stemness. Tumor regression was studied in chick embryo xenograft and BALB/c mice model.ResultsHylaluronic acid engrafted metformin loaded graphene oxide (HA-GO-Met) nanoparticles exhibited an anti-cancer efficacy at much lower dosage as compared to metformin alone. HA-GO-Met nanoparticles induced apoptosis and inhibited cell migration of TNBC cells by targeting miR-10b/PTEN axis via NFkB-p65. Upregulation of PTEN affected pAKT(473) expression that induced apoptosis. Cell migration was inhibited by reduction of pFAK/integrinβ1 expressions. Treatment inhibited epithelial mesenchymal transition (EMT) and reduced stemness as evident from the increase in E-cadherin expression, inhibition of mammosphere formation and low expression levels of stemness markers including nanog, oct4 and sox2 as compared to control. Moreover, tumor regression was studied in chick embryo xenograft and BALB/c mice model. HA-GO-Met nanoparticle treatment reduced tumor load and nullified toxicity in peripheral organs imparted by tumor.ConclusionsHA-GO-Met nanoparticles exhibited an enormous anti-cancer efficacy in TNBC in vitro and in vivo.General significanceHA-GO-Met nanoparticles induced apoptosis and attenuated cell migration in TNBC. It nullified overall toxicity imparted by tumor load. It inhibited EMT and reduced stemness and thereby addressed the issue of cancer recurrence.     

Network Class Superposition Analyses

Networks are often used to understand a whole system by modeling the interactions among its pieces. Examples include biomolecules in a cell interacting to provide some primary function, or species in an environment forming a stable community. However, these interactions are often unknown; instead, the pieces'' dynamic states are known, and network structure must be inferred. Because observed function may be explained by many different networks (e.g., for the yeast cell cycle process [1]), considering dynamics beyond this primary function means picking a single network or suitable sample: measuring over all networks exhibiting the primary function is computationally infeasible. We circumvent that obstacle by calculating the network class ensemble. We represent the ensemble by a stochastic matrix , which is a transition-by-transition superposition of the system dynamics for each member of the class. We present concrete results for derived from Boolean time series dynamics on networks obeying the Strong Inhibition rule, by applying to several traditional questions about network dynamics. We show that the distribution of the number of point attractors can be accurately estimated with . We show how to generate Derrida plots based on . We show that -based Shannon entropy outperforms other methods at selecting experiments to further narrow the network structure. We also outline an experimental test of predictions based on . We motivate all of these results in terms of a popular molecular biology Boolean network model for the yeast cell cycle, but the methods and analyses we introduce are general. We conclude with open questions for , for example, application to other models, computational considerations when scaling up to larger systems, and other potential analyses.     

Molecular dynamics simulation studies of GSK-3β ATP competitive inhibitors: understanding the factors contributing to selectivity

Glycogen synthase kinase-3 is a constitutively acting, multifunctional serine threonine kinase, the role of which has been implicated in several physiological pathways and has emerged as a promising target for the treatment of type-II diabetes and Alzheimer’s disease. In order to provide a detailed understanding of the origin of selectivity determinants of ATP competitive inhibitors, molecular dynamics simulations in combination with MM-PBSA binding energy calculations were performed using crystal structures of GSK-3β and CDK-2 in complex with 12 ATP competitive inhibitors. Analysis of energy contributions indicate that electrostatic interaction energy dictates the selectivity of ATP competitive inhibitors against CDK-2. Key interactions as well as residues that potentially make a major contribution to the binding free energy were identified at the ATP binding site. This analysis stresses the need for the inhibitors to interact with Lys85, Thr138, and Arg141 in the binding site of GSK-3β to show selectivity. The residue-wise energy decomposition analysis further suggested the additional role of Gln185 in determining the selectivity of maleimides. The results obtained in this study can be utilized to design new selective GSK-3 ATP competitive inhibitors.