Dynamic NF-κB and E2F interactions control the priority and timing of inflammatory signalling and cell proliferation

© Ankers et al. Dynamic cellular systems reprogram gene expression to ensure appropriate cellular fate responses to specific extracellular cues. Here we demonstrate that the dynamics of Nuclear Factor kappa B (NF-κB) signalling and the cell cycle are prioritised differently depending on the timing of an inflammatory signal. Using iterative experimental and computational analyses, we show physical and functional interactions between NF-κB and the E2 Factor 1 (E2F-1) and E2 Factor 4 (E2F-4) cell cycle regulators. These interactions modulate the NF-κB response. In S-phase, the NF-κB response was delayed or repressed, while cell cycle progression was unimpeded. By contrast, activation of NF-κB at the G1/S boundary resulted in a longer cell cycle and more synchronous initial NF-κB responses between cells. These data identify new mechanisms by which the cellular response to stress is differentially controlled at different stages of the cell cycle

Simulation of Abnormal/Normal Brain States Using the KIV Model

Recent studies have focused on the phenomena of abnormal electrical brain activity which may transition into a debilitating seizure state through the entrainment of large populations of neurons.Starting from the initial epileptogenisis of a small population of abnormally firing neurons, to the mobilization of mesoscopic neuron populations behaving in a synchronous manner, a model has been formulated that captures the initial epileptogenisis to the semi-periodic entrainment of distant neuron populations.The normal non-linear dynamic signal captured through EEG, moves into a semi-periodic state, which can be quantified as the seizure state.Capturing the asynchronous/synchronous behavior of the normal/pathological brain state will be discussed.This model will also demonstrate how electrical stimulation applied to the limbic system restores the seizure state of the brain back to its original normal condition.Human brain states are modeled using a biologically inspired neural network, the KIV model.The KIV model exhibits the noisy, chaotic attributes found in the limbic system of brains of higher forms of organisms, and in its normal basal state, represents the homogeneous activity of millions of neuron activations.The KIV can exhibit the ’unbalanced state’ of neural activity, whereas when a small cluster of abnormal firing neurons starts to exhibit periodic neural firings that eventually entrain all the neurons within the limbic system, the network has moved into the ‘seizure’ state.These attributes have been found in human EEG recordings and have been duplicated in this model of the brain.The discussion in this dissertation covers the attributes found in human EEG data and models these attributes.Additionally, this model proposes a methodology to restore the modeled ‘seizure’ state, and by doing so, proposes a manner for external electrical titration to restore the abnormal seizure state back to a normal chaotic EEG signal state.Quantification measurements of normal, abnormal, and restoration to normal brain states will be exhibited using the following approaches:Analysis of human EEG dataQuantification measurements of brain states.Development of models of the different brain states, i.e. fit parameters of the model on individual personal data/history.Implementation of quantitative measurements on “restored” simulated seizure state

Production Ergonomics Evaluation – Needs, Procedures and Digital Human Modeling Tools

In production systems, human operators may be at risk for developing work-relatedmusculoskeletal disorders (MSDs), resulting in pain, inability to work and high costs. Anincreasingly capable tool for identifying MSD risks early in the production designprocess are Digital Human Models (DHMs), although their built-in analysis tools are ingreat need of development regarding how they address time-related aspects of loadexposure. Some examples of time-related exposure phenomena provoking MSDs arerepetitive work patterns, lack of variation, fatigue effects, work rotation effects, anddistributions of activity/rest. The aim of this thesis is therefore two-fold; to explorepragmatic industrial needs regarding ergonomics evaluation and compare this to theState-of-the-art of scientific evaluation methods that address time-related aspects.The first approach, a case study in an automotive setting, revealed that switching fromone evaluation method to another in a factory may be for pragmatic contextual reasonsrather than based on educated selection. It was also shown that companies who do thismay unintentionally risk producing evaluation results that are not equal regardingcriteria levels or degree of analysis detail, rendering results unsatisfactory to use forsome actors in the process.The second approach, a literature review, categorizes several time-related ergonomicsterms and has proposed a ‘process-flow’ framework for the terms, based on an inputthroughput-output concept. This framework can give DHM tool developers an overviewof which time-related aspects interact and which combinations are suited to differentanalysis goals.Lastly, the thesis reflects on actor roles and time perspectives

Using a Coaching Model To Develop Lead School Counselors’ Leadership Self-Efficacy

The supervision role and responsibilities of lead school counselors vary widely across and within schools and school districts. One role is increasingly significant – the role of clinical supervisor. The researcher used a case study design to investigate the leadership self-efficacy of three lead school counselors working in a major suburban school district. The intent of this record of study was to examine whether and how the implementation of a leadership intervention impacted counselors' sense of self-efficacy related to their ability to lead and supervise other school counselors. The findings suggest that a lead school counselor professional development model, based on a conceptual framework that includes leadership and coaching principles, resulted in positive changes in lead school counselors’ leadership self-efficacy. Recommendations for further research include examining the model with a broader range of lead school counselors and involving school administrators in the conception and use of the clinical supervision model

Computer Simulation Studies of Trishomocubane Heptapeptide of the Type Ac-Ala3-Tris-Ala3-NHMe

As part of an extension on the cage peptide chemistry, the present work involves an assessment of the conformational profile of trishomocubane heptapeptide of the type Ac-Ala3-Tris-Ala3-NHMe using molecular dynamics (MD) simulations. All MD protocols were explored within the framework of a molecular mechanics approach using the PARM94 force field parameters modified in-house to mimic the implicit and explicit solvent conditions. The 50 ns MD trajectories revealed a tendency of the trishomocubane polypeptide to adopt bent conformations in vacuo, MEOH and TIP3P solvent models, consistent with previous studies undertaken in our laboratory. The aim of this paper is to exemplify the tendency of the highly constrained cage residues to promote reverse-turn characteristics in the polypeptide chains, which could play a pivotal role in the design of new cagepeptidomimetics.Keywords: Trishomocubane, molecular dynamics, Amber, CLASICO, β-turn, α-helical PDF and supplementary file attached

Modelling the generation of toxic combustion products and its transport in enclosure fires

Combustion products generated in enclosure fires can be transported throughout the enclosure causing death and injury to occupants and a great deal of damage to property and the environment. The ability to estimate the generation and transport of toxic combustion products in real fire scenarios involving common building materials is of great importance to fire protection engineers in producing detailed quantified risk assessment and in the design of fire-safe buildings. Most common building materials are polymer based. Thus toxic products evolving from burning polymers is the single most important factor in fire fatalities. Fire hazard calculations require modelling of heat generation, toxic combustion products generation and its transport in realistic building scenarios involving common building materials. However, the thermal decomposition, combustion behaviour and chemical kinetics for common polymers like wood, plastics, rubber and textiles are extremely complex. In the present study, a methodology (STEM-LER: the Scalar Transport Equation based Model using the Local Equivalence Ratio concept) based on solving separate transport equations for the species and using the yield correlations obtained from bench-scale experiments to model the source terms is proposed to predict the products generation and its transport during enclosure fires. Modelling of complex solid phase degradation and chemical kinetics of polymers is bypassed by measuring the product yields as a function of equivalence ratio by burning the samples in a bench-scale combustion apparatus called Purser furnace. Since the accuracy of prediction depends upon the quality of the yield data obtained from the Purser furnace, attempts were also made to numerically investigate this bench-scale toxicity test method in order to understand its modus operandi