The relationship between membrane damage, release of protein and loss of viability in Escherichia coli exposed to high hydrostatic pressure

The aim of this work was to examine a possible association between resistance of two Escherichia coli strains to high hydrostatic pressure and the susceptibility of their cell membranes to pressure-induced damage. Cells were exposed to pressures between 100 and 700 MPa at room temperature (~20C) in phosphate-buffered-saline. In the more pressure-sensitive strain E. coli 8164, loss of viability occurred at pressures between 100 MPa and 300 MPa and coincided with irreversible loss of membrane integrity as indicated by uptake of propidium iodide (PI) and leakage of protein of molecular mass between 9 and 78 kDa from the cells. Protein release increased to a maximum at 400 MPa then decreased, possibly due to intracellular aggregation at the higher pressures. In the pressure-resistant strain E. coli J1, PI was taken up during pressure treatment but not after decompression indicating that cells were able to reseal their membranes. Loss of viability in strain J1 coincided with the transient loss of membrane integrity between approximately 200 MPa and 600 MPa. In E. coli J1 leakage of protein occurred before loss of viability and the released protein was of low molecular mass, between 8 and 11 kDa and may have been of periplasmic origin. In these two strains differences in pressure resistance appeared to be related to differences in the ability of their membranes to withstand disruption by pressure. However it appears that transient loss of membrane integrity during pressure can lead to cell death irrespective of whether cells can reseal their membranes afterwards

Looking for the Top-squark at the Tevatron with four jets

The scalar partner of the top quark is relatively light in many models of supersymmetry breaking. We study the production of top squarks (stops) at the Tevatron collider and their subsequent decay through baryon-number violating couplings such that the final state contains no leptons. Performing a detector-level analysis, we demonstrate that, even in the absence of leptons or missing energy, stop masses upto 210 \gev/c^2 can be accessible at the Tevatron.Comment: 4 pages, 4 embedded figures, RevTe

Permanent spin currents in cavity-qubit systems

In a recent remarkable experiment [P. Roushan et al., Nature Physics 13, 146 (2017)], a spin current in an architecture of three superconducting qubits was produced during a few microseconds by creating synthetic magnetic fields. The life-time of the current was set by the typical dissipative mechanisms that occur in those systems. We propose a scheme for the generation of permanent currents, even in the presence of such imperfections, and scalable to larger system sizes. It relies on striking a subtle balance between multiple nonequilibrium drives and the dissipation mechanisms, in order to engineer and stimulate chiral excited states which can carry current.Comment: 4 pages, 3 figure

Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation

Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on wired infrastructure especially with the advent of the multi-radio multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer viz., augmented network capacity, uninterrupted connectivity and reduced latency, are depreciated by the detrimental effect of prevalent interference. Interference mitigation is thus a prime objective in WMN deployments. It is often accomplished through prudent channel allocation (CA) schemes which minimize the adverse impact of interference and enhance the network performance. However, a multitude of CA schemes have been proposed in research literature and absence of a CA performance prediction metric, which could aid in the selection of an efficient CA scheme for a given WMN, is often felt. In this work, we offer a fresh characterization of the interference endemic in wireless networks. We then propose a reliable CA performance prediction metric, which employs a statistical interference estimation approach. We carry out a rigorous quantitative assessment of the proposed metric by validating its CA performance predictions with experimental results, recorded from extensive simulations run on an ns-3 802.11g environment

A Short Review on Machine Learning in Space Science and Exploration

Machine learning is revolutionizing space exploration by tackling massive datasets, empowering astronauts, and driving scientific breakthroughs. From Deep Space 1's autonomous navigation to the James Webb Space Telescope's AI-assisted exoplanet discovery, Machine learning is transforming the present and shaping the future. With missions like NASA's Parker Solar Probe and the development of AI-powered monitoring systems and astro robots, the possibilities for unravelling the cosmos and democratizing space exploration are limitless. The future of space exploration lies in harnessing the power of ML to unlock the universe's secrets and make them accessible to all

The Potential of Machine Learning for Future Mars Exploration

The pursuit of understanding Mars, our neighboring planet, is rife with challenges that range from treacherous conditions for potential human astronauts to the vast distances that complicate communication. However, a beacon of hope emerges in the form of machine learning, a technological frontier that promises to transform the landscape of Martian exploration. As we embark on this interplanetary journey, the recognition of machine learning's potential is growing. It offers innovative solutions to some of the most pressing challenges, ushering in a new era of autonomous exploration. Imagine rovers and orbiter spacecraft equipped with the ability to analyze Martian data on-site, reducing the need for slow communications with Earth. This revolutionary approach is already in action with rovers like Curiosity, where machine learning enables self-directed exploration and continuous data analysis on the Martian surface. The applications of machine learning extend beyond mere autonomy. They hold the promise of addressing communication limitations, providing greater operational autonomy, and unlocking the mysteries that shroud the Red Planet. From identifying sources of atmospheric gases, such as oxygen and methane, to interpreting geological features like cloud distributions and weather patterns, machine learning is proving itself to be a versatile and indispensable tool in unraveling the complexities of Mars. Venturing deeper into the Martian climate, machine learning becomes a powerful ally. By leveraging this technology to analyze climate data, we have the potential to generate predictive models crucial for planning future surface missions and assessing the habitability of Mars. Additionally, the application of machine learning on Earth offers a unique opportunity to decode uncertainties related to Martian atmospheric interactions, the dynamics of dust storms, and conditions beneath the surface. Anticipating the wealth of data that future Mars missions will yield, the integration of machine learning emerges as a game-changer. Its efficiency in discerning intricate patterns within extensive datasets has the potential to revolutionize our scientific understanding of Mars. As we delve deeper into the mysteries of the Red Planet, machine learning stands as a pivotal catalyst, promising not just incremental but transformative discoveries. It becomes the linchpin in our ongoing quest to answer the age-old question: Did life ever exist on Mars? In the realm of Martian exploration, machine learning is proving to be the technological cornerstone that propels us towards unprecedented scientific revelations

Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks

Designing high performance channel assignment schemes to harness the potential of multi-radio multi-channel deployments in wireless mesh networks (WMNs) is an active research domain. A pragmatic channel assignment approach strives to maximize network capacity by restraining the endemic interference and mitigating its adverse impact on network performance. Interference prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being a crucial aspect that is seldom addressed in research endeavors. In this effort, we propose a set of intelligent channel assignment algorithms, which focus primarily on alleviating the RCI. These graph theoretic schemes are structurally inspired by the spatio-statistical characteristics of interference. We present the theoretical design foundations for each of the proposed algorithms, and demonstrate their potential to significantly enhance network capacity in comparison to some well-known existing schemes. We also demonstrate the adverse impact of radio co- location interference on the network, and the efficacy of the proposed schemes in successfully mitigating it. The experimental results to validate the proposed theoretical notions were obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n environments.Comment: Accepted @ ICACCI-201

Reliable Prediction of Channel Assignment Performance in Wireless Mesh Networks

The advancements in wireless mesh networks (WMN), and the surge in multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of network performance issues. These issues are intricately linked to the adverse impact of endemic interference. Thus, interference mitigation is a primary design objective in WMNs. Interference alleviation is often effected through efficient channel allocation (CA) schemes which fully utilize the potential of MRMC environment and also restrain the detrimental impact of interference. However, numerous CA schemes have been proposed in research literature and there is a lack of CA performance prediction techniques which could assist in choosing a suitable CA for a given WMN. In this work, we propose a reliable interference estimation and CA performance prediction approach. We demonstrate its efficacy by substantiating the CA performance predictions for a given WMN with experimental data obtained through rigorous simulations on an ns-3 802.11g environment.Comment: Accepted in ICACCI-201