Landscape Mapping of Functional Proteins in Insulin Signal Transduction and Insulin Resistance: A Network-Based Protein-Protein Interaction Analysis

The type 2 diabetes has increased rapidly in recent years throughout the world. The insulin signal transduction mechanism gets disrupted sometimes and it's known as insulin-resistance. It is one of the primary causes associated with type-2 diabetes. The signaling mechanisms involved several proteins that include 7 major functional proteins such as INS, INSR, IRS1, IRS2, PIK3CA, Akt2, and GLUT4. Using these 7 principal proteins, multiple sequences alignment has been created. The scores between sequences also have been developed. We have constructed a phylogenetic tree and modified it with node and distance. Besides, we have generated sequence logos and ultimately developed the protein-protein interaction network. The small insulin signal transduction protein arrangement shows complex network between the functional proteins

Prevalence and dynamics of ribosomal DNA micro-heterogeneity are linked to population history in two contrasting yeast species

Despite the considerable number and taxonomic breadth of past and current genome sequencing projects, many of which necessarily encompass the ribosomal DNA, detailed information on the prevalence and evolutionary significance of sequence variation in this ubiquitous genomic region are severely lacking. Here, we attempt to address this issue in two closely related yet contrasting yeast species, the baker's yeast Saccharomyces cerevisiae and the wild yeast Saccharomyces paradoxus. By drawing on existing datasets from the Saccharomyces Genome Resequencing Project, we identify a rich seam of ribosomal DNA sequence variation, characterising 1,068 and 970 polymorphisms in 34 S. cerevisiae and 26 S. paradoxus strains respectively. We discover the two species sets exhibit distinct mutational profiles. Furthermore, we show for the first time that unresolved rDNA sequence variation resulting from imperfect concerted evolution of the ribosomal DNA region follows a U-shaped allele frequency distribution in each species, similar to loci that evolve under non-concerted mechanisms but arising through rather different evolutionary processes. Finally, we link differences between the shapes of these allele frequency distributions to the two species' contrasting population histories

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Signaling Cascades Modulate the Speed of Signal Propagation through Space

Cells are not mixed bags of signaling molecules. As a consequence, signals must travel from their origin to distal locations. Much is understood about the purely diffusive propagation of signals through space. Many signals, however, propagate via signaling cascades. Here, we show that, depending on their kinetics, cascades speed up or slow down the propagation of signals through space, relative to pure diffusion.We modeled simple cascades operating under different limits of Michaelis-Menten kinetics using deterministic reaction-diffusion equations. Cascades operating far from enzyme saturation speed up signal propagation; the second mobile species moves more quickly than the first through space, on average. The enhanced speed is due to more efficient serial activation of a downstream signaling module (by the signaling molecule immediately upstream in the cascade) at points distal from the signaling origin, compared to locations closer to the source. Conversely, cascades operating under saturated kinetics, which exhibit zero-order ultrasensitivity, can slow down signals, ultimately localizing them to regions around the origin.Signal speed modulation may be a fundamental function of cascades, affecting the ability of signals to penetrate within a cell, to cross-react with other signals, and to activate distant targets. In particular, enhanced speeds provide a way to increase signal penetration into a cell without needing to flood the cell with large numbers of active signaling molecules; conversely, diminished speeds in zero-order ultrasensitive cascades facilitate strong, but localized, signaling

Interpretability of deep learning models: A survey of results

Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as black-box function approximators, mapping a given input to a classification output. The next step in this human-machine evolutionary process-incorporating these networks into mission critical processes such as medical diagnosis, planning and control-requires a level of trust association with the machine output. Typically, statistical metrics are used to quantify the uncertainty of an output. However, the notion of trust also depends on the visibility that a human has into the working of the machine. In other words, the neural network should provide human-understandable justifications for its output leading to insights about the inner workings. We call such models as interpretable deep networks. Interpretability is not a monolithic notion. In fact, the subjectivity of an interpretation, due to different levels of human understanding, implies that there must be a multitude of dimensions that together constitute interpretability. In addition, the interpretation itself can be provided either in terms of the low-level network parameters, or in terms of input features used by the model. In this paper, we outline some of the dimensions that are useful for model interpretability, and categorize prior work along those dimensions. In the process, we perform a gap analysis of what needs to be done to improve model interpretability

Effects of osteopontin inhibition on radiosensitivity of MDA-MB-231 breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Osteopontin (OPN) is a secreted glycophosphoprotein that is overexpressed in various tumors, and high levels of OPN have been associated with poor prognosis of cancer patients. In patients with head and neck cancer, high OPN plasma levels have been associated with poor prognosis following radiotherapy. Since little is known about the relationship between OPN expression and radiosensitivity, we investigated the cellular and radiation induced effects of OPN siRNA in human MDA-MB-231 breast cancer cells.</p> <p>Methods</p> <p>MDA-MB-231 cells were transfected with OPN-specific siRNAs and irradiated after 24 h. To verify the OPN knockdown, we measured the OPN mRNA and protein levels using qRT-PCR and Western blot analysis. Furthermore, the functional effects of OPN siRNAs were studied by assays to assess clonogenic survival, migration and induction of apoptosis.</p> <p>Results</p> <p>Treatment of MDA-MB-231 cells with OPN siRNAs resulted in an 80% decrease in the OPN mRNA level and in a decrease in extracellular OPN protein level. Transfection reduced clonogenic survival to 42% (p = 0.008), decreased the migration rate to 60% (p = 0.15) and increased apoptosis from 0.3% to 1.7% (p = 0.04). Combination of OPN siRNA and irradiation at 2 Gy resulted in a further reduction of clonogenic survival to 27% (p < 0.001), decreased the migration rate to 40% (p = 0.03) and increased apoptosis to 4% (p < 0.005). Furthermore, OPN knockdown caused a weak radiosensitization with an enhancement factor of 1.5 at 6 Gy (p = 0.09) and a dose modifying factor (DMF₁₀) of 1.1.</p> <p>Conclusion</p> <p>Our results suggest that an OPN knockdown improves radiobiological effects in MDA-MB-231 cells. Therefore, OPN seems to be an attractive target to improve the effectiveness of radiotherapy.</p

Responses of grape berry anthocyanin and tritratable acidity to the projected climate change across the Western Australian wine regions

More than a century of observations has established that climate influences grape berry composition. Accordingly, the projected global climate change is expected to impact on grape berry composition although the magnitude and direction of impact at regional and subregional scales are not fully known. The aim of this study was to assess potential impacts of climate change on levels of berry anthocyanin and titratable acidity (TA) of the major grapevine varieties grown across all of the Western Australian (WA) wine regions. Grape berry anthocyanin and TA responses across all WA wine regions were projected for 2030, 2050 and 2070 by utilising empirical models that link these berry attributes and climate data downscaled (to ∼5 km resolution) from the csiro_mk3_5 and miroc3_2_medres global climate model outputs under IPCC SRES A2 emissions scenario. Due to the dependence of berry composition on maturity, climate impacts on anthocyanin and TA levels were assessed at a common maturity of 22 °Brix total soluble solids (TSS), which necessitated the determination of when this maturity will be reached for each variety, region and warming scenario, and future period.The results indicate that both anthocyanin and TA levels will be affected negatively by a warming climate, but the magnitude of the impacts will differ between varieties and wine regions. Compared to 1990 levels, median anthocyanins concentrations are projected to decrease, depending on global climate model, by up to 3–12 % and 9–33 % for the northern wine regions by 2030 and 2070, respectively while 2–18 % reductions are projected in the southern wine regions for the same time periods. Patterns of reductions in the median Shiraz berry anthocyanin concentrations are similar to that of Cabernet Sauvignon; however, the magnitude is lower (up to 9–18 % in southern and northern wine regions respectively by 2070). Similarly, uneven declines in TA levels are projected across the study regions. The largest reductions in median TA are likely to occur in the present day warmer wine regions, up to 40 % for Chardonnay followed by 15 % and 12 % for Shiraz and Cabernet Sauvignon, respectively, by 2070 under the high warming projection (csiro_mk3_5). It is concluded that, under existing management practices, some of the key grape attributes that are integral to premium wine production will be affected negatively by a warming climate, but the magnitudes of the impacts vary across the established wine regions, varieties, the magnitude of warming and future periods considered

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal