Noise-induced Regime Shifts: A Quantitative Characterization

Diverse complex dynamical systems are known to exhibit abrupt regime shifts at bifurcation points of the saddle-node type. The dynamics of most of these systems, however, have a stochastic component resulting in noise driven regime shifts even if the system is away from the bifurcation points. In this paper, we propose a new quantitative measure, namely, the propensity transition point as an indicator of stochastic regime shifts. The concepts and the methodology are illustrated for the one-variable May model, a well-known model in ecology and the genetic toggle, a two-variable model of a simple genetic circuit. The general applicability and usefulness of the method for the analysis of regime shifts is further demonstrated in the case of the mycobacterial switch to persistence for which experimental data are available.Comment: 10 Pages, 9 figures, revtex4-1, published versio

Probing the NMSSM via Higgs boson signatures from stop cascade decays at the LHC

Higgs signatures from the cascade decays of light stops are an interesting possibility in the next to minimal supersymmetric standard model (NMSSM). We investigate the potential reach of the light stop mass at the 13 TeV run of the LHC by means of five NMSSM benchmark points where this signature is dominant. These benchmark points are compatible with current Higgs coupling measurements, LHC constraints, dark matter relic density and direct detection constraints. We consider single and di-lepton search strategies, as well as the jet-substructure technique to reconstruct the Higgs bosons. We find that one can probe stop masses up to 1.2 TeV with 300 $\rm fb^{-1}$ luminosity via the di-lepton channel, while with the jet-substructure method, stop masses up to 1 TeV can be probed with 300 $\rm fb^{-1}$ luminosity. We also investigate the possibility of the appearance of multiple Higgs peaks over the background in the fat-jet mass distribution, and conclude that such a possibility is viable only at the high luminosity run of 13 TeV LHC.Comment: 20 pages, 5 figures; Two figures updated, typos corrected. Matched with the published versio

Drug utilization study of co-administration of nonsteroidal anti-inflammatory drugs and gastroprotective agents in an orthopaedics outpatients department of a tertiary care hospital in West Bengal

Background: Non steroidal anti-inflammatory drugs (NSAIDs) are the commonly prescribed analgesic in the orthopaedics department. NSAIDs are prescribed for a long period in both acute condition (like fracture of bones, muscle injury, postoperative procedures etc) or chronic conditions (osteoarthritis etc). However, they have many adverse effects, especially gastrointestinal toxicity when use regularly. For this reason NSAIDs are frequently co-prescribed with gastro protective agents. Common gastroprotective agents are proton pump inhibitors (PPI), H2 blockers, sucralfate, antacids and misoprostol (prostaglandin analogue).Methods: A cross-sectional, unicentric drug utilization study was conducted. Prescriptions were collected from patients attending the orthopaedic outpatients department. The prescription pattern of NSAIDs, gastroprotective agents or co-administration of NSAIDS and gastroprotective agents were analyzed.Results: A total of 977 prescriptions were studied; in which 928 prescriptions contained NSAIDs with gastroprotective agents (97.92%). The most common gastroprotective agents combined with NSAIDs was H2 receptor blockers (60.56%), followed by proton pump inhibitors (PPIs) (21.65%), while antacids are prescribed least (17.78%). Misoprostol or sucralfate were not used at all.Conclusions: NSAIDs are commonly co-prescribed with gastroprotective agents in high percentage

Implications of 98 GeV and 125 GeV Higgs scenario in non-decoupling SUSY with updated ATLAS, CMS and PLANCK data

We discuss both MSSM and NMSSM scenarios in which the lightest Higgs boson with $m_h=98$~GeV is consistent with the small excess ($\sim 2.3 \sigma$) observed at the LEP in $e^+ e^-\rightarrow Zh$, with $h \rightarrow b {\bar b}$ process and the heavier Higgs boson of mass close to 125~GeV as the observed candidate of the SM Higgs like particle at the LHC. We show the allowed regions in the non-decoupling Higgs zone of MSSM parameter space which are consistent with several low energy constraints coming from heavy flavour physics, latest experimental data on Higgs signals and lower limit on superparticle masses from 7~TeV and 8~TeV LHC run. We also implement the constraints from the relic density of the cold dark matter as obtained from the recent PLANCK data. Additionally, we discuss the possibility of observing the light Higgs boson of mass 98~GeV at the 14~TeV LHC run via $pp \rightarrow V h$, with $h \rightarrow b \bar b$ using the technique of jet substructure. Our analysis shows that at 14~TeV LHC run with 300 ${\rm fb}^{-1}$ luminosity the signal efficiency of such a light Higgs boson is at most 2.5$\sigma$. Finally, we make a comment on the prospect of proposed $e^+ e^-$ ILC to discover/exclude this light Higgs boson.Comment: 34 pages, 9 Figures, pdfLaTe

Dynamics of Protofibril Elongation and Association Involved in Aβ42 peptide Aggregation in Alzheimer\u27s Disease

Background: The aggregates of a protein called, ‘Aβ’ found in brains of Alzheimer’s patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called ‘soluble oligomers’ are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependant process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the ‘on-pathway’ products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways. Results: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer’s disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics. Conclusions: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids

Dynamics of Protofibril Elongation and Association Involved in Aβ42 peptide Aggregation in Alzheimer\u27s Disease

Background: The aggregates of a protein called, ‘Aβ’ found in brains of Alzheimer’s patients are strongly believed to be the cause for neuronal death and cognitive decline. Among the different forms of Aβ aggregates, smaller aggregates called ‘soluble oligomers’ are increasingly believed to be the primary neurotoxic species responsible for early synaptic dysfunction. Since it is well known that the Aβ aggregation is a nucleation dependant process, it is widely believed that the toxic oligomers are intermediates to fibril formation, or what we call the ‘on-pathway’ products. Modeling of Aβ aggregation has been of intense investigation during the last decade. However, precise understanding of the process, pre-nucleation events in particular, are not yet known. Most of these models are based on curve-fitting and overlook the molecular-level biophysics involved in the aggregation pathway. Hence, such models are not reusable, and fail to predict the system dynamics in the presence of other competing pathways. Results: In this paper, we present a molecular-level simulation model for understanding the dynamics of the amyloid-β (Aβ) peptide aggregation process involved in Alzheimer’s disease (AD). The proposed chemical kinetic theory based approach is generic and can model most nucleation-dependent protein aggregation systems that cause a variety of neurodegenerative diseases. We discuss the challenges in estimating all the rate constants involved in the aggregation process towards fibril formation and propose a divide and conquer strategy by dissecting the pathway into three biophysically distinct stages: 1) pre-nucleation stage 2) post-nucleation stage and 3) protofibril elongation stage. We next focus on estimating the rate constants involved in the protofibril elongation stages for Aβ42 supported by in vitro experimental data. This elongation stage is further characterized by elongation due to oligomer additions and lateral association of protofibrils (13) and to properly validate the rate constants involved in these phases we have presented three distinct reaction models. We also present a novel scheme for mapping the fluorescence sensitivity and dynamic light scattering based in vitro experimental plots to estimates of concentration variation with time. Finally, we discuss how these rate constants will be incorporated into the overall simulation of the aggregation process to identify the parameters involved in the complete Aβ pathway in a bid to understand its dynamics. Conclusions: We have presented an instance of the top-down modeling paradigm where the biophysical system is approximated by a set of reactions for each of the stages that have been modeled. In this paper, we have only reported the kinetic rate constants of the fibril elongation stage that were validated by in vitro biophysical analyses. The kinetic parameters reported in the paper should be at least accurate upto the first two decimal places of the estimate. We sincerely believe that our top-down models and kinetic parameters will be able to accurately model the biophysical phenomenon of Aβ protein aggregation and identify the nucleation mass and rate constants of all the stages involved in the pathway. Our model is also reusable and will serve as the basis for making computational predictions on the system dynamics with the incorporation of other competing pathways introduced by lipids and fatty acids