Effect of SST Variation on ITCZ in APE Simulations

The effect of meridional variation of sea surface temperature (SST) on tropical atmospheric circulation is analyzed using Aqua-planet Experiment (APE) simulations. The meridional SST gradient around the narrow SST peak in CONTROL simulation favours a strong and single equatorial Intertropical Convergence Zone (ITCZ, defined by the maximum of zonally averaged total precipitation) in all APE models. In contrast, flat equatorial SST peak (FLAT simulation) favours split/double ITCZs flanking the SST maximum, in the majority of the APE models. Although there is reasonable agreement for SST sensitivity of ITCZ among the APE models in CONTROL, there exists disparity among them in FLAT case. Similarly, while the total and convective precipitation responses are consistent among the models, the large-scale precipitation response shows considerable inter-model variations in FLAT case. The APE intercomparison indicates that the occurrence and positioning of the ITCZ are primarily related to boundary layer moisture convergence as a response to the meridional variation of SST. Furthermore, the meridional gradient of tropospheric temperature is found to be an important factor that can influence the positioning of ITCZ. FLAT SST distribution is found to be similar to the observed distribution over the Indian region during summer season. Models that yield double ITCZs in this case simulate an easterly jet over the equatorial region (similar to 15 degrees equatorward of the ITCZ). This is analogous to the Tropical Easterly Jet (TEJ), which is a unique feature observed over the Indian region during summer monsoon season, with its core at 12 degrees N, equatorward of the seasonal convergence zone centered along 25 degrees N. In these models, positive meridional temperature gradient and the associated easterly shear in the atmosphere strengthened by moisture convergence penetrate up to the upper troposphere, with which TEJ is in thermal wind balance.

STAT-1 expression is regulated by IGFBP-3 in malignant glioma cells and is a strong predictor of poor survival in patients with glioblastoma Laboratory investigation

Object. Insulin-like growth factor binding proteins (IGEBPs) have been implicated in the pathogenesis of glioma. In a previous study the authors demonstrated that IGFBP-3 is a novel glioblastoma biomarker associated with poor survival. Since signal transducer and activator of transcription 1 (STAT-1) has been shown to be regulated by IGFBP-3 during chondrogenesis and is a prosurvival and radioresistant molecule in different tumors, the aim in the present study was to explore the functional significance of IGFBP-3 in malignant glioma cells, to determine if STAT-1 is indeed regulated by IGFBP-3, and to study the potential of STAT-1 as a biomarker in glioblastoma. Methods. The functional significance of IGFBP-3 was investigated using the short hairpin (sh)RNA gene knockdown approach on U251MG cells. STAT-1 regulation by IGFBP-3 was tested on U251MG and U87MG cells by shRNA gene knockdown and exogenous treatment with recombinant IGFBP-3 protein. Subsequently, the expression of STAT-1 was analyzed with real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) in glioblastoma and control brain tissues. Survival analyses were done on a uniformly treated prospective cohort of adults with newly diagnosed glioblastoma (136 patients) using Kaplan-Meier and Cox regression models. Results. IGFBP-3 knockdown significantly impaired proliferation, motility, migration, and invasive capacity of U251MG cells in vitro (p < 0.005). Exogenous overexpression of IGFBP-3 in U251MG and U87MG cells demonstrated STAT-1 regulation. The mean transcript levels (by real-time RT-PCR) and the mean labeling index of STAT-1 (by IHC) were significantly higher in glioblastoma than in control brain tissues (p = 0.0239 and p < 0.001, respectively). Multivariate survival analysis revealed that STAT-1 protein expression (HR 1.015, p = 0.033, 95% CI 1.001-1.029) along with patient age (HR 1.025, p = 0.005, 95% CI 1.008-1.042) were significant predictors of shorter survival in patients with glioblastoma. Conclusions. IGFBP-3 influences tumor cell proliferation, migration, and invasion and regulates STAT-1 expression in malignant glioma cells. STAT-1 is overexpressed in human glioblastoma tissues and emerges as a novel prognostic biomarker.

Studies on the effects of varying secondary gas properties in a low entrainment ratio supersonic ejector

A dilution cum purge ejector for application in fuel cells represents a domain of ejector operation involving low entrainment ratio with differing secondary and primary gas; which is hardly investigated and a cohesive design framework is not readily available. We comprehensively study a constant area ejector using analytical, experimental and numerical tools at low entrainment ratio (0.004-0.065) with Air, Helium and Argon as secondary gas while the primary gas is Air. For the first time, limits of operating parameters used in control volume method to design the ejector are found to be highly dependent on the secondary molecular weight. The entrainment ratio in the ejector (low for Helium and high for Argon) is affected by the molecular weight and the static pressure within the ejector (low for Air and high for Argon & Helium) by the gamma of the secondary gas. Sufficient suction pressure (0.3-0.55 bar) is generated by the ejector thereby preventing any backflow of secondary gas at all primary stagnation pressures (1.5, 2.2 and 3.1 bar). Numerical results agree well with experimental results. The ejector is shown to completely dilute and purge the secondary flow, meeting all key design requirements. (C) 2014 Elsevier Ltd. All rights reserved.

Early postnatal exposure to lithium in vitro induces changes in AMPAR mEPSCs and vesicular recycling at hippocampal glutamatergic synapses

Lithium is an effective mood stabilizer but its use is associated with many side effects. Electrophysiological recordings of miniature excitatory postsynaptic currents (mEPSCs) mediated by glutamate receptor AMPA-subtype (AMPARs) in hippocampal pyramidal neurons revealed that CLi (therapeutic concentration of 1 mM lithium, from days in vitro 4-10) decreased the mean amplitude and mean rectification index (RI) of AMPAR mEPSCs. Lowered mean RI indicate that contribution of Ca2+-permeable AMPARs in synaptic events is higher in CLi neurons (supported by experiments sensitive to Ca2+-permeable AMPAR modulation). Co-inhibiting PKA, GSK-3 beta and glutamate reuptake was necessary to bring about changes in AMPAR mEPSCs similar to that seen in CLi neurons. FM1-43 experiments revealed that recycling pool size was affected in CLi cultures. Results from minimum loading, chlorpromazine treatment and hyperosmotic treatment experiments indicate that endocytosis in CLi is affected while not much difference is seen in modes of exocytosis. CLi cultures did not show the high KCl associated presynaptic potentiation observed in control cultures. This study, by calling attention to long-term lithium-exposure-induced synaptic changes, might have implications in understanding the side effects such as CNS complications occurring in perinatally exposed babies and cognitive dulling seen in patients on lithium treatment.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Design, specification, and synthesis of aircraft electric power systems control logic

Cyber-physical systems integrate computation, networking, and physical processes. Substantial research challenges exist in the design and verification of such large-scale, distributed sensing, ac- tuation, and control systems. Rapidly improving technology and recent advances in control theory, networked systems, and computer science give us the opportunity to drastically improve our approach to integrated flow of information and cooperative behavior. Current systems rely on text-based spec- ifications and manual design. Using new technology advances, we can create easier, more efficient, and cheaper ways of developing these control systems. This thesis will focus on design considera- tions for system topologies, ways to formally and automatically specify requirements, and methods to synthesize reactive control protocols, all within the context of an aircraft electric power system as a representative application area.

This thesis consists of three complementary parts: synthesis, specification, and design. The first section focuses on the synthesis of central and distributed reactive controllers for an aircraft elec- tric power system. This approach incorporates methodologies from computer science and control. The resulting controllers are correct by construction with respect to system requirements, which are formulated using the specification language of linear temporal logic (LTL). The second section addresses how to formally specify requirements and introduces a domain-specific language for electric power systems. A software tool automatically converts high-level requirements into LTL and synthesizes a controller.

The final sections focus on design space exploration. A design methodology is proposed that uses mixed-integer linear programming to obtain candidate topologies, which are then used to synthesize controllers. The discrete-time control logic is then verified in real-time by two methods: hardware and simulation. Finally, the problem of partial observability and dynamic state estimation is ex- plored. Given a set placement of sensors on an electric power system, measurements from these sensors can be used in conjunction with control logic to infer the state of the system.