A method for coupling the phase-field model based on a grand-potential formalism to thermodynamic databases

In this paper we derive an approach for the effective utilization of thermodynamic data in phase-field simulations. While the most widely used methodology for multi-component alloys is following the work by Eiken et al. (2006), wherein, an extrapolative scheme is utilized in conjunction with the TQ interface for deriving the driving force for phase transformation, a corresponding simplistic method based on the formulation of a parabolic free-energy model incorporating all the thermodynamics has been laid out for binary alloys in the work by Folch and Plapp (2005). In the following, we extend this latter approach for multi-component alloys in the framework of the grand-potential formalism. The coupling is applied for the case of the binary eutectic solidification in the Cr-Ni alloy and two-phase solidification in the ternary eutectic alloy (Al-Cr-Ni). A thermodynamic justification entails the basis of the formulation and places it in context of the bigger picture of Integrated Computational Materials Engineering. (C) 2015 Elsevier Ltd. All rights reserved.

Differential proteomic and genomic profiling of mouse striatal cell model of Huntington's disease and control; probable implications to the disease biology

Huntington's disease (HD) is an autosomal dominant disorder of central nervous system caused by expansion of CAG repeats in exon1 of the huntingtin gene (Htt). Among various dysfunctions originated from the mutation in Htt gene, transcriptional deregulation has been considered to be one of the most important abnormalities. Large numbers of investigations identified altered expressions of genes in brains of HD patients and many models of HD. In this study we employed 2D SDS-PAGE/MALDI-MS coupled with 2D-DIGE and real-time PCR experiments of an array of genes focused to HD pathway to determine altered protein and gene expressions in STHdh(Q111)/Hdh(Q111) cells, a cell model of HD and compared with STHdh(Q7)/Hdh(Q7) cells, its wild type counterpart. We annotated 76 proteins from these cells and observed differential expressions of 31 proteins (by 2D-DIGE) involved in processes like unfolded protein binding, negative regulation of neuron apoptosis, response to superoxides etc. Our PCR array experiments identified altered expressions of 47 genes. Altogether significant alteration of 77 genes/proteins could be identified in this HD cell line with potential relevance to HD biology. Biological significance: In this study we intended to find out differential proteomic and genomic profiles in HD condition. We used the STHdh cells, a cellular model for HD and control. These are mouse striatal neuronal cell lines harboring 7 and 111 knock -in CAG repeats in their two alleles. The 111Q containing cell line (STHdh(Q111)/Hdh(Q111)) mimics diseased condition, whereas the 7Q containing ones (STHdh(Q7)/Hdh(Q7)), serves as the proper control cell line. Proteomic experiments were performed earlier to obtain differential expressions of proteins in R6/2 mice models, Hdh(Q) knock -in mice and in plasma and CSF from HD patients. However, no earlier report on proteomic alterations in these two HD cell lines and control was available in literature. It was, therefore, an important objective to find out differential expressions of proteins in these two cell lines. In this study, we annotated 76 proteins from STHdh(Q7)/Hdh(Q7) and STHdh(Q111)/Hdh(Q111) cells using 2D-gel/mass spectrometry. Next, by performing 2D-DIGE, we observed differential expressions of 31 proteins (16 upregulated and 15 downregulated) between these two cell lines. We also performed customized qRT-PCR array focused to HD pathway and found differential expressions of 47 genes (8 gene exptessions increased and 39 genes were decreased significantly). A total of 77 genes/proteins (Htt downregulated in both the studies) were found to be significantly altered from both the experimental paradigms. We validated the differential expressions of Vim, Hypk, Ran, Dstn, Hspa5 and Sod2 either by qRT-PCR or Western blot analysis or both. Out of these 77, similar trends in alteration of 19 out of 31 and 38 out of 47 proteins/genes were reported in earlier studies. Thus our study confirmed earlier observations on differential gene/protein expressions in HD and are really useful. Additionally, we observed differential expression of some novel genes/proteins. One of this was Hypk, a Htt-interacting chaperone protein with the ability to solubilize mHtt aggregated structures in cell lines. We propose that downregulation of Hypk in STHdh-Qm (Q111)/Hdh(Q111) has a causal effect towards HD pathogenesis. Thus the novel findings from our study need further research and might be helpful to understand the molecular mechanism behind HD pathogenesis. (C) 2015 Elsevier B.V. All rights reserved.

Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.