A close-packed sphere model for characterising porous networks in atomistic simulations and its application in energy storage and conversion

Hierarchical (micro, meso & macro) porosity in materials plays a crucial role in influencing the movement of ions which governs the energy and power density during energy storage and conversion. The extant available methods to characterise porosity across scales (nano to meso to macro) lacks rigour and accuracy. Having accurate assessment of the porosity in materials can unlock new designs of electrodes for energy efficient energy storage and conversion devices such as batteries, supercapacitors and fuel cells. Through this work, we report the systematic development of a method to fully characterise the carbon porous networks using a molecular dynamics simulation testbed. Our work entails modelling and simulation of porous carbon structures using quenched molecular dynamics (QMD) simulations using Gaussian Approximation potential (GAP) and benchmarking the results with prior literature. This modelling technique can reliably be used for quantitative characterisation of the interconnectivity in porous structures to study ionic movements and charge transfer mechanisms. A new parameter, namely nearest neighbour search (NNS) coefficient was introduced to quantify homogeneity and networking in the porous structures. NNS coefficient increased from 1.62 to 1.92 with decrease of the annealing temperature from 8000 K to 4000 K in carbon. The procedure outlined was although tested on porous carbon networks, but adaptable to study any other material system at multi-length scales

Role of KCNMA1 gene in breast cancer invasion and metastasis to brain

International audienceBACKGROUND: The prognosis for patients with breast tumor metastases to brain is extremely poor. Identification of prognostic molecular markers of the metastatic process is critical for designing therapeutic modalities for reducing the occurrence of metastasis. Although ubiquitously present in most human organs, large-conductance calcium- and voltage-activated potassium channel (BKCa) channels are significantly upregulated in breast cancer cells. In this study we investigated the role of KCNMA1 gene that encodes for the pore-forming alpha-subunit of BKCa channels in breast cancer metastasis and invasion. METHODS: We performed Global exon array to study the expression of KCNMA1 in metastatic breast cancer to brain, compared its expression in primary breast cancer and breast cancers metastatic to other organs, and validated the findings by RT-PCR. Immunohistochemistry was performed to study the expression and localization of BKCa channel protein in primary and metastatic breast cancer tissues and breast cancer cell lines. We performed matrigel invasion, transendothelial migration and membrane potential assays in established lines of normal breast cells (MCF-10A), non-metastatic breast cancer (MCF-7), non-brain metastatic breast cancer cells (MDA-MB-231), and brain-specific metastatic breast cancer cells (MDA-MB-361) to study whether BKCa channel inhibition attenuates breast tumor invasion and metastasis using KCNMA1 knockdown with siRNA and biochemical inhibition with Iberiotoxin (IBTX). RESULTS: The Global exon array and RT-PCR showed higher KCNMA1 expression in metastatic breast cancer in brain compared to metastatic breast cancers in other organs. Our results clearly show that metastatic breast cancer cells exhibit increased BKCa channel activity, leading to greater invasiveness and transendothelial migration, both of which could be attenuated by blocking KCNMA1. CONCLUSION: Determining the relative abundance of BKCa channel expression in breast cancer metastatic to brain and the mechanism of its action in brain metastasis will provide a unique opportunity to identify and differentiate between low grade breast tumors that are at high risk for metastasis from those at low risk for metastasis. This distinction would in turn allow for the appropriate and efficient application of effective treatments while sparing patients with low risk for metastasis from the toxic side effects of chemotherapy

Towards learning and verifying invariants of cyber-physical systems by code mutation

Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.Comment: Short paper accepted by the 21st International Symposium on Formal Methods (FM 2016

Emergent Properties of Tumor Microenvironment in a Real-life Model of Multicell Tumor Spheroids

Multicellular tumor spheroids are an important {\it in vitro} model of the pre-vascular phase of solid tumors, for sizes well below the diagnostic limit: therefore a biophysical model of spheroids has the ability to shed light on the internal workings and organization of tumors at a critical phase of their development. To this end, we have developed a computer program that integrates the behavior of individual cells and their interactions with other cells and the surrounding environment. It is based on a quantitative description of metabolism, growth, proliferation and death of single tumor cells, and on equations that model biochemical and mechanical cell-cell and cell-environment interactions. The program reproduces existing experimental data on spheroids, and yields unique views of their microenvironment. Simulations show complex internal flows and motions of nutrients, metabolites and cells, that are otherwise unobservable with current experimental techniques, and give novel clues on tumor development and strong hints for future therapies.Comment: 20 pages, 10 figures. Accepted for publication in PLOS One. The published version contains links to a supplementary text and three video file

Signal yields, energy resolution, and recombination fluctuations in liquid xenon

This work presents an analysis of monoenergetic electronic recoil peaks in the dark-matter-search and calibration data from the first underground science run of the Large Underground Xenon (LUX) detector. Liquid xenon charge and light yields for electronic recoil energies between 5.2 and 661.7 keV are measured, as well as the energy resolution for the LUX detector at those same energies. Additionally, there is an interpretation of existing measurements and descriptions of electron-ion recombination fluctuations in liquid xenon as limiting cases of a more general liquid xenon re- combination fluctuation model. Measurements of the standard deviation of these fluctuations at monoenergetic electronic recoil peaks exhibit a linear dependence on the number of ions for energy deposits up to 661.7 keV, consistent with previous LUX measurements between 2-16 keV with $^3$H. We highlight similarities in liquid xenon recombination for electronic and nuclear recoils with a comparison of recombination fluctuations measured with low-energy calibration data.Comment: 11 pages, 12 figures, 3 table