Algebraic computing in general relativity and supergravity : space-time embeddings and higher dimensional theories

Imperial Users onl

Experimental and Numerical Study of CO2 Corrosion in Carbon Steel

This research involves study corrosion of low carbon steel under static and flow conditions at 200L/h in the absence and presence of CO2 at two rates 9 and 30 ml/min at four temperatures by electrochemical method using potentiostat. Numerical model was achieved to compare between the experimental and theoretical results to estimate corrosion rate. The results show that the presence of CO2 under static conditions shifts the Ecorr toward noble direction, while under flow condition the presence of CO2 shift Ecorr toward active or noble direction at two rates of gas. The data of corrosion rate in mm/y indicate that the presence of CO2 with two flow rate increased the rate compared with the case of absence of CO2 under static conditions except one case, while under flow conditions, the presence of 9 ml/min. CO2 increases the corrosion rate, while the presence of 30 ml/min. CO2 decreases the rate because of formation FeCO3 scale except at 298K. A Mathematical model was done which show the volumetric flow rate of CO2 and finally the corrosion rate of CO2 correlated with dimensionless groups and independent parameters

Informal healthcare provision in Lebanon: an adaptive mechanism among displaced Syrian health professionals in a protracted crisis

Abstract: Background: Syrian healthcare workers (HCWs) are among those who fled the Syrian conflict only to face further social and economic challenges in host countries. In Lebanon, this population group cannot formally practice, yet many are believed to be operating informally. These activities remain poorly documented and misunderstood by the academic, policy and humanitarian communities. This study aims to understand mechanisms of informal provision of services, the facilitators and barriers for such practices and to present policy recommendations for building on this adaptive mechanism. Method: A qualitative descriptive study based on an in-depth interview approach with a sample of Syrian informal healthcare workers (IHCWs) residing in Lebanon was adopted. Known sponsor networks followed by snowball sampling approaches were used to recruit participants. Data collection occurred between September and December 2017. All interviews were audio-recorded, transcribed and translated into English. An inductive thematic analysis was used. Results: Twenty-two participants were recruited. Motivational factors that led HCWs to practice informally were personal (e.g. source of income/livelihood), societal (cultural competency), and need to fulfill a gap in the formal health service sector. Being connected to a network of IHCWs facilitated initiation of the informal practice until eventually becoming part of a community of informal practice. The central challenge was the informal nature of their practice and its negative consequences. Most IHCWs were afraid of arrest by the government upon identification. Most interviewees indicated being discriminated against by host communities in the form of differential wages and tense interpersonal relationships. Almost all recommended a change in policy allowing them to practice formally under a temporary registration until their return to Syria. Conclusion: Our study confirmed the presence of IHCWs operating in Lebanon. Despite its informal nature, participants perceived that this practice was filling a gap in the formal health system and was helping to alleviate the burden of IHCWs and refugee health needs. In line with interviewees’ views, we recommend that policy decision makers within humanitarian agencies and the Government of Lebanon explore the possibilities for allowing temporary registration of displaced Syrian IHCW to benefit local host communities and refugee populations

Human neutrophil clearance of bacterial pathogens triggers anti-microbial gamma delta T cell responses in early infection

Human blood Vc9/Vd2 T cells, monocytes and neutrophils share a responsiveness toward inflammatory chemokines and are rapidly recruited to sites of infection. Studying their interaction in vitro and relating these findings to in vivo observations in patients may therefore provide crucial insight into inflammatory events. Our present data demonstrate that Vc9/Vd2 T cells provide potent survival signals resulting in neutrophil activation and the release of the neutrophil chemoattractant CXCL8 (IL-8). In turn, Vc9/Vd2 T cells readily respond to neutrophils harboring phagocytosed bacteria, as evidenced by expression of CD69, interferon (IFN)-c and tumor necrosis factor (TNF)-a. This response is dependent on the ability of these bacteria to produce the microbial metabolite (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMB-PP), requires cell-cell contact of Vc9/Vd2 T cells with accessory monocytes through lymphocyte function-associated antigen-1 (LFA-1), and results in a TNF-a dependent proliferation of Vc9/Vd2 T cells. The antibiotic fosmidomycin, which targets the HMB-PP biosynthesis pathway, not only has a direct antibacterial effect on most HMB-PP producing bacteria but also possesses rapid anti-inflammatory properties by inhibiting cd T cell responses in vitro. Patients with acute peritoneal-dialysis (PD)-associated bacterial peritonitis – characterized by an excessive influx of neutrophils and monocytes into the peritoneal cavity – show a selective activation of local Vc9/Vd2 T cells by HMB-PP producing but not by HMB-PP deficient bacterial pathogens. The cd T celldriven perpetuation of inflammatory responses during acute peritonitis is associated with elevated peritoneal levels of cd T cells and TNF-a and detrimental clinical outcomes in infections caused by HMB-PP positive microorganisms. Taken together, our findings indicate a direct link between invading pathogens, neutrophils, monocytes and microbe-responsive cd T cells in early infection and suggest novel diagnostic and therapeutic approaches.Martin S. Davey, Chan-Yu Lin, Gareth W. Roberts, Sinéad Heuston, Amanda C. Brown, James A. Chess, Mark A. Toleman, Cormac G.M. Gahan, Colin Hill, Tanya Parish, John D. Williams, Simon J. Davies, David W. Johnson, Nicholas Topley, Bernhard Moser and Matthias Eber

Factors Defining the Functional Oligomeric State of Escherichia coli DegP Protease

Escherichia coli DegP protein is a periplasmic protein that functions both as a protease and as a chaperone. In the absence of substrate, DegP oligomerizes as a hexameric cage but in its presence DegP reorganizes into 12 and 24-mer cages with large chambers that house the substrate for degradation or refolding. Here, we studied the factors that determine the oligomeric state adopted by DegP in the presence of substrate. Using size exclusion chromatography and electron microscopy, we found that the size of the substrate molecule is the main factor conditioning the oligomeric state adopted by the enzyme. Other factors such as temperature, a major regulatory factor of the activity of this enzyme, did not influence the oligomeric state adopted by DegP. In addition, we observed that substrate concentration exerted an effect only when large substrates (full-length proteins) were used. However, small substrate molecules (peptides) always triggered the same oligomeric state regardless of their concentration. These results clarify important aspects of the regulation of the oligomeric state of DegP

Time-varying time-frequency complexity measures for epileptic EEG data analysis

Objective: Our goal is to use existing and to propose new time-frequency entropy measures that objectively evaluate the improvement on epileptic patients after medication by studying their resting state EEG recordings. An increase in the complexity of the signals would confirm an improvement in the general state of the patient. Methods: We review the Rényi entropy based on time-frequency representations, along with its time-varying version. We also discuss the entropy based on singular value decomposition computed from a time-frequency representation, and introduce its corresponding time-dependant version. We test these quantities on synthetic data. Friedman tests are used to confirm the differences between signals (before and after proper medication). Principal component analysis is used for dimensional reduction prior to a simple threshold discrimination. Results: Experimental results show a consistent increase in complexity measures in the different regions of the brain. These findings suggest that extracted features can be used to monitor treatment. When combined, they are useful for classification purposes, with areas under ROC curves higher than 0.93 in some regions. Conclusion: Here we applied time-frequency complexity measures to resting state EEG signals from epileptic patients for the first time. We also introduced a new time-varying complexity measure. We showed that these features are able to evaluate the treatment of the patient, and to perform classification. Significance: The time-frequency complexities, and their time-varying versions, can be used to monitor the treatment of epileptic patients. They could be applied to a wider range of problems

A new approach to sample entropy of multi-channel signals: application to EEG signals

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the existing method, the one proposed here has the advantage of maintaining good results as the number of channels increases. The new and already-existing algorithms were applied on multivariate white Gaussian noise signals, pink noise signals, and mixtures of both. For high number of channels, the existing method failed to show that white noise is always the most irregular whereas the proposed method always had the entropy of white noise the highest. Application of both algorithms on MIX process signals also confirmed the ability of the proposed method to handle larger number of channels without risking erroneous results. We also applied the proposed algorithm on EEG data from epileptic patients before and after treatments. The results showed an increase in entropy values after treatment in the regions where the focus was localized. This goes in the same way as the medical point of view that indicated a better health state for these patients

Multivariate improved weighted multi-scale permutation entropy and its application on EEG data

This paper introduces an entropy based method that measures complexity in non-stationary multivariate signals. This method, called Mutivariate Improved Weighted Multiscale Permutation Entropy (mvIWMPE), has two main advantages: (i) it shows lower variance for the results when applied on a wide range of multivariate signals; (ii) it has good accuracy quantifying complexity of different recorded states in signals and hence discriminating them. mvIWMPE is based on two previously introduced permutation entropy algorithms, Improved Multiscale Permutation Entropy (IMPE) and Multivariate Weighted Mul-tiscale Permutation Entropy (mvWMPE). It combines the concept of coarse graining from IMPE and the introduction of the weight of amplitudes of the signals from mvWMPE. mvIWMPE was validated on both synthetic and human electroencephalographic (EEG) signals. Several synthetic signals were simulated: mixtures of white Gaussian noise (WGN) and pink noise, chaotic and convergent Lorenz system signals, stochastic and deterministic signals. As for real signals, resting-state EEG recorded in healthy and epileptic children during eyes closed and eyes open sessions were analyzed. Our method was compared to multivariate multiscale, multivariate weighted multiscale and multivariate improved multiscale permutation entropy methods. Performance on synthetic as well as on EEG signals showed more undeviating results and higher ability for mvIWMPE discriminating different states of signals (chaotic vs convergent, WGN vs pink noise, stochastic vs deterministic simulated signals, and eyes open vs eyes closed EEG signals). We herein proposed an efficient method to measure the complexity of multivariate non-stationary signals. Experimental results showed the accuracy and the robustness (in terms of variance) of the method