Final-state effects in the radio frequency spectrum of strongly interacting fermions

We model the impact of final state interactions on the radio frequency spectrum of a strongly interacting two-component superfluid Fermi gas. In addition to a broad asymmetric peak coming from the break-up of Cooper pairs we find that, for appropriate parameters, one can observe a sharp symmetric "bound-bound" spectral line coming from the conversion of Cooper pairs in one channels to pairs/molecules in another.Comment: 4 pages, 4 figure

Fixed-Energy Sandpiles Belong Generically to Directed Percolation

Fixed-energy sandpiles with stochastic update rules are known to exhibit a nonequilibrium phase transition from an active phase into infinitely many absorbing states. Examples include the conserved Manna model, the conserved lattice gas, and the conserved threshold transfer process. It is believed that the transitions in these models belong to an autonomous universality class of nonequilibrium phase transitions, the so-called Manna class. Contrarily, the present numerical study of selected (1+1)-dimensional models in this class suggests that their critical behavior converges to directed percolation after very long time, questioning the existence of an independent Manna class.Comment: article (4 pages, 9 eps figures) + Supplement (8 pages, 9 eps figures); Phys. Rev. Lett. 201

Investigating the cause for the increase in the atmospheric methane burden from 2007 to present

The global atmospheric methane burden approached equilibrium from 1983 until 2006, after which there was an increase in the rate of change that’s been sustained since then. It’s uncertain if this change was due to an increase in emissions, or a decrease in the rate of sink, or a combination of both. Our method attempted to provide evidence to show whether or not this change was due to a decrease in the sink, corresponding to an increase in the lifetime of methane. A one-box model of methane was employed to determine what the lifetime of methane would need to be each year if emissions were held constant. The primary loss mechanism for methane is the same for carbon monoxide: reactions with hydroxyl radicals. If there was a change in the sink for methane, it’s very likely that this would be due to a change in the concentration of hydroxyl. It was shown that the corresponding change in carbon monoxide concentrations would be insignificant compared to the variability of the observations. This insignificant change renders our method inconclusive

Microglial activation induces neuronal death in Chandipura virus infection

Neurotropic viruses induce neurodegeneration either directly by activating host death domains or indirectly through host immune response pathways. Chandipura Virus (CHPV) belonging to family Rhabdoviridae is ranked among the emerging pathogens of the Indian subcontinent. Previously we have reported that CHPV induces neurodegeneration albeit the root cause of this degeneration is still an open question. In this study we explored the role of microglia following CHPV infection. Phenotypic analysis of microglia through lectin and Iba-1 staining indicated cells were in an activated state post CHPV infection in cortical region of the infected mouse brain. Cytokine Bead Array (CBA) analysis revealed comparatively higher cytokine and chemokine levels in the same region. Increased level of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), Nitric Oxide (NO) and Reactive Oxygen species (ROS) in CHPV infected mouse brain indicated a strong inflammatory response to CHPV infection. Hence it was hypothesized through our analyses that this inflammatory response may stimulate the neuronal death following CHPV infection. In order to validate our hypothesis supernatant from CHPV infected microglial culture was used to infect neuronal cell line and primary neurons. This study confirmed the bystander killing of neurons due to activation of microglia post CHPV infection

Graph theoretic network analysis reveals protein pathways underlying cell death following neurotropic viral infection

Complex protein networks underlie any cellular function. Certain proteins play a pivotal role in many network configurations, disruption of whose expression proves fatal to the cell. An efficient method to tease out such key proteins in a network is still unavailable. Here, we used graph-theoretic measures on protein-protein interaction data (interactome) to extract biophysically relevant information about individual protein regulation and network properties such as formation of function specific modules (sub-networks) of proteins. We took 5 major proteins that are involved in neuronal apoptosis post Chandipura Virus (CHPV) infection as seed proteins in a database to create a meta-network of immediately interacting proteins (1st order network). Graph theoretic measures were employed to rank the proteins in terms of their connectivity and the degree upto which they can be organized into smaller modules (hubs). We repeated the analysis on 2nd order interactome that includes proteins connected directly with proteins of 1st order. FADD and Casp-3 were connected maximally to other proteins in both analyses, thus indicating their importance in neuronal apoptosis. Thus, our analysis provides a blueprint for the detection and validation of protein networks disrupted by viral infections

Theory of the Normal/Superfluid interface in population imbalanced Fermi gases

We present a series of theoretical studies of the boundary between a superfluid and normal region in a partially polarized gas of strongly interacting fermions. We present mean-field estimates of the surface energy in this boundary as a function of temperature and scattering length. We discuss the structure of the domain wall, and use a previously introduced phenomonological model to study its influence on experimental observables. Our microscopic mean-field calculations are not consistent with the magnitude of the surface tension found from our phenomonological modelling of data from the Rice experiments. We conclude that one must search for novel mechanisms to explain the experiments.Comment: 15 pages, 9 figures (13 subfigures) -- v2: minor change

Modulation of neuronal proteome profile in response to Japanese Encephalitis Virus infection

In this study we have reported the in vivo proteomic changes during Japanese Encephalitis Virus (JEV) infection in combination with in vitro studies which will help in the comprehensive characterization of the modifications in the host metabolism in response to JEV infection. We performed a 2-DE based quantitative proteomic study of JEV-infected mouse brain as well as mouse neuroblastoma (Neuro2a) cells to analyze the host response to this lethal virus. 56 host proteins were found to be differentially expressed post JEV infection (defined as exhibiting ≥1.5-fold change in protein abundance upon JEV infection). Bioinformatics analyses were used to generate JEV-regulated host response networks which reported that the identified proteins were found to be associated with various cellular processes ranging from intracellular protein transport, cellular metabolism and ER stress associated unfolded protein response. JEV was found to invade the host protein folding machinery to sustain its survival and replication inside the host thereby generating a vigorous unfolded protein response, subsequently triggering a number of pathways responsible for the JEV associated pathologies. The results were also validated using a human cell line to correlate them to the human response to JEV. The present investigation is the first report on JEV-host interactome in in vivo model and will be of potential interest for future antiviral research in this field

Is There an Optimal CO2 Partial Column for Flux Inversions?

The fidelity of flux estimates from an atmospheric inversion depends on the ability of atmospheric transport models to simulate the measured quantity. For species such as CO2, with surface fluxes and a large network of surface measurements, this means correctly simulating the dynamics of the planetary boundary layer (PBL), which is one of the most uncertain aspects of atmospheric transport modeling. In contrast, the simulated total column average mole fraction of CO2 (XCO2) is largely insensitive to simulated PBL dynamics. Therefore, measurements of XCO2 provided by current and future short wave infrared (SWIR) greenhouse gas (GHG) satellites such as GOSAT and the OCO family would seem to be more appropriate to flux inversions, as far as minimizing transport model errors (the "noise") is concerned. Unfortunately, the flux-induced variation of CO2 (the "signal") is the largest within the PBL and smallest in XCO2. Therefore, assimilating XCO2 as opposed to PBL CO2 need not give us the strongest "signal to noise" in flux inversions. Recent work on GOSAT and OCO2 retrievals suggest that SWIR satellite spectra may be used to estimate a lower partial column CO2, which could be assimilated in a flux inversion, instead of XCO2.Here we report on a study to assess whether there is an optimal partial column average CO2, intermediate between PBL CO2 and XCO2, whose assimilation might yield the best signal to noise in flux inversions, where (as before) "signal" is the flux-induced variation and "noise" is the error in transport modeling. We simulate atmospheric CO2 with five different global transport models and a common surface CO2 flux over ten years. We consider the spread across the five models to be a proxy for transport model error (the "noise"), and the common variation of CO2 in all five models to be a proxy for the "signal". We compare these signals and noises at different spatiotemporal scales for different partial column specifications to investigate whether there exists an optimal partial column that has large surface flux-driven variations and yet is relatively insensitive to errors in transport models. Finally, we comment on the feasibility of estimating such a partial column from current and future SWIR GHG satellites in the light of recent work on vertically resolved CO2 from current SWIR GHG satellites

Network analysis reveals common host protein/s modulating pathogenesis of neurotropic viruses

Network analysis through graph theory provides a quantitative approach to characterize specific proteins and their constituent assemblies that underlie host-pathogen interactions. In the present study, graph theory was used to analyze the interactome designed out of 50 differentially expressing proteins from proteomic analysis of Chandipura Virus (CHPV, Family: Rhabdoviridae) infected mouse brain tissue to identify the primary candidates for intervention. Using the measure of degree centrality, that quantifies the connectedness of a single protein within a milieu of several other interacting proteins, DJ-1 was selected for further molecular validation. To elucidate the generality of DJ-1’s role in propagating infection its role was also monitored in another RNA virus, Japanese Encephalitis Virus (JEV, Family: Flaviviridae) infection. Concurrently, DJ-1 got over-expressed in response to reactive oxygen species (ROS) generation following viral infection which in the early phase of infection migrated to mitochondria to remove dysfunctional mitochondria through the process of mitophagy. DJ-1 was also observed to modulate the viral replication and interferon responses along with low-density lipoprotein (LDL) receptor expression in neurons. Collectively these evidences reveal a comprehensive role for DJ-1 in neurotropic virus infection in the brain