Serum procalcitonin and C-reactive protein as markers of sepsis and outcome in patients with neurotrauma and subarachnoid haemorrhage

This prospective study evaluated serum procalcitonin (PCT) and C-reactive protein (CRP) as markers for systemic inflammatory response syndrome (SIRS)/sepsis and mortality in patients with traumatic brain injury and subarachnoid haemorrhage. Sixty-two patients were followed for 7 days. Serum PCT and CRP were measured on days 0, 1, 4, 5, 6 and 7. Seventy-seven per cent of patients with traumatic brain injury and 83% with subarachnoid haemorrhage developed SIRS or sepsis (P= 0.75). Baseline PCT and CRP were elevated in 35% and 55% ofpatients respectively (P=0.03). There was a statistically non-significant step-wise increase in serum PCT levels from no SIRS (0.4 +/- 0.6 ng/ml) to SIRS (3.05 +/- 9.3 ng/ml) to sepsis (5.5 +/- 12.5 ng/ml). A similar trend was noted in baseline PCT in patients with mild (0.06 +/- 0.9 ng/ml), moderate (0.8 +/- 0.7 ng/ml) and severe head injury (1.2 +/- 1.9 ng/ml). Such a gradation was not observed with serum CRP There was a non-significant trend towards baseline PCT being a better marker of hospital mortality compared with baseline CRP (ROC-AUC 0.56 vs 0.31 respectively). This is the first prospective study to document the high incidence of SIRS in neurosurgical patients. In our study, serum PCT appeared to correlate with severity of traumatic brain injury and mortality. However, it could not reliably distinguish between SIRS and sepsis in this cohort. This is in pan because baseline PCT elevation seemed to correlate with severity of injury. Only a small proportion ofpatients developed sepsis, thus necessitating a larger sample size to demonstrate the diagnostic usefulness of serum PCT as a marker of sepsis. Further clinical trials with larger sample sizes are required to confirm any potential role of PCT as a sepsis and outcome indicator in patients with head injuries or subarachnoid haemorrhage.

Accelerating precursory activity within a class of earthquake analogue automata

A statistical fractal automaton model is described which displays two modes of dynamical behaviour. The first mode, termed recurrent criticality, is characterised by quasi-periodic, characteristic events that are preceded by accelerating precursory activity. The second mode is more reminiscent of SOC automata in which large events are not preceded by an acceleration in activity. Extending upon previous studies of statistical fractal automata, a redistribution law is introduced which incorporates two model parameters: a dissipation factor and a stress transfer ratio. Results from a parameter space investigation indicate that a straight line through parameter space marks a transition from recurrent criticality to unpredictable dynamics. Recurrent criticality only occurs for models within one corner of the parameter space. The location of the transition displays a simple dependence upon the fractal correlation dimension of the cell strength distribution. Analysis of stress field evolution indicates that recurrent criticality occurs in models with significant long-range stress correlations. A constant rate of activity is associated with a decorrelated stress field.

Thapsigargin modulates osteoclastogenesis through the regulation of RANKL-induced signaling pathways and reactive oxygen species production

Introduction: Apoptosis and differentiation are among the consequences of changes in intracellular Ca2+ levels. In this study, we investigated the effects of the endoplasmic reticular Ca2+-ATPase inhibitor, thapsigargin (TG), on osteoclast apoptosis and differentiation. Materials and Methods: Both RAW264.7 cells and primary spleen cells were used to examine the effect of TG on RANKL-induced osteoclastogenesis. To determine the action of TG on signaling pathways, we used reporter gene assays for NF-kappa B and activator protein-1 (AP-1) activity, Western blotting for phosphoextracellular signal-related kinase (ERK), and fluorescent probes to measure changes in levels of intracellular calcium and reactive oxygen species (ROS). To assess rates of apoptosis, we measured changes in annexin staining, caspase-3 activity, and chromatin and F-actin microfilament structure. Results: At concentrations that caused a rapid rise in intracellular Ca2+, TG increased caspase-3 activity and promoted apoptosis in osteoclast-like cells (OLCs). Low concentrations of TG, which were insufficient to measurably alter intracellular Ca2+, unexpectedly suppressed caspase-3 activity and enhanced RANKL-induced osteoclastogenesis. At these lower concentrations, TG potentiated ROS production and RANKL-induced NF-kappa B activity, but suppressed RANKL-induced AP-1 activity and had little effect on ERK phosphorylation. Conclusion: Our novel findings of a biphasic effect of TG are incompletely explained by our current understanding of TG action, but raise the possibility that low intensity or local changes in subcellular Ca2+ levels may regulate intracellular differentiation signaling. The extent of cross-talk between Ca2+ and RANKL-mediated intracellular signaling pathways might be important in determining whether cells undergo apoptosis or differentiate into OLCs.

Conjunctival benign reactive lymphoid hyperplasia associated with myopic scleral thinning

Known causes of conjunctival salmon patches include lymphoma, amyloidosis, sarcoidosis, leukaemia and benign reactive lymphoid hyperplasia. The aetiology of benign reactive lymphoid hyperplasia is thought to be a localized reactive change induced by an irritative or antigenic stimulus. The case of benign reactive lymphoid hyperplasia reported herein occurred in a myopic patient with extremely thin sclera. The authors' hypothesis is that choroidal antigens are able to perfuse through thin sclera and act as chronic irritants to the overlying conjunctiva resulting in a lymphoid response and subsequent salmon patch formation.

A probabilistic switch model of information flow in social networks: Application for virtual circuits to organizational design

Network building and exchange of information by people within networks is crucial to the innovation process. Contrary to older models, in social networks the flow of information is noncontinuous and nonlinear. There are critical barriers to information flow that operate in a problematic manner. New models and new analytic tools are needed for these systems. This paper introduces the concept of virtual circuits and draws on recent concepts of network modelling and design to introduce a probabilistic switch theory that can be described using matrices. It can be used to model multistep information flow between people within organisational networks, to provide formal definitions of efficient and balanced networks and to describe distortion of information as it passes along human communication channels. The concept of multi-dimensional information space arises naturally from the use of matrices. The theory and the use of serial diagonal matrices have applications to organisational design and to the modelling of other systems. It is hypothesised that opinion leaders or creative individuals are more likely to emerge at information-rich nodes in networks. A mathematical definition of such nodes is developed and it does not invariably correspond with centrality as defined by early work on networks.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

Power system sensitivity analysis for probabilistic small signal stability assessment in a deregulated environment

Deregulations and market practices in power industry have brought great challenges to the system planning area. In particular, they introduce a variety of uncertainties to system planning. New techniques are required to cope with such uncertainties. As a promising approach, probabilistic methods are attracting more and more attentions by system planners. In small signal stability analysis, generation control parameters play an important role in determining the stability margin. The objective of this paper is to investigate power system state matrix sensitivity characteristics with respect to system parameter uncertainties with analytical and numerical approaches and to identify those parameters have great impact on system eigenvalues, therefore, the system stability properties. Those identified parameter variations need to be investigated with priority. The results can be used to help Regional Transmission Organizations (RTOs) and Independent System Operators (ISOs) perform planning studies under the open access environment.

Deceleration affects anticipatory and reactive components of triggered postural responses

Understanding the physiological and psychological factors that contribute to healthy and pathological balance control in man has been made difficult by the confounding effects of the perturbations used to test balance reactions. The present study examined how postural responses were influenced by the acceleration-deceleration interval of an unexpected horizontal translation. Twelve adult males maintained balance during unexpected forward and backward surface translations with two different acceleration-deceleration intervals and presentation orders (serial or random). SHORT perturbations consisted of an initial acceleration (peak acceleration 1.3 m s(-2); duration 300 ms) followed 100 ms later by a deceleration. LONG perturbations had the same acceleration as SHORT perturbations, followed by a 2-s interval of constant velocity before deceleration. Surface and intra-muscular electromyography (EMG) from the leg, trunk, and shoulder muscles were recorded along with motion and force plate data. LONG perturbations induced larger trunk displacements compared to SHORT perturbations when presented randomly and larger EMG responses in proximal and distal muscles during later (500-800 ms) response intervals. During SHORT perturbations, activity in some antagonist muscles was found to be associated with deceleration and not the initial acceleration of the support surface. When predictable, SHORT perturbations facilitated the use of anticipatory mechanisms to attenuate early (100-400 ms) EMG response amplitudes, ankle torque change and trunk displacement. In contrast, LONG perturbations, without an early deceleration effect, did not facilitate anticipatory changes when presented in a predictable order. Therefore, perturbations with a short acceleration-deceleration interval can influence triggered postural responses through reactive effects and, when predictable with repeated exposure, through anticipatory mechanisms.

Clinically active Crohn's disease in the presence of a low C-reactive protein

Objective. Clinical interest in C-reactive protein (CRP) - a component of the innate immune system - has focused mainly on its worth as an indicator of disease activity. There has been a resurgence of interest in CRP in the Crohn's disease ( CD) literature because several trials of new treatments for active CD have been characterized by both a large proportion of patients with low CRP ( < 10 mg/l) at entry to the trials and by a negative therapeutic outcome. It is therefore of interest to study the clinical characteristics of patients who are thought to have at the same time both active CD and a low CRP. Material and methods. Patients were prospectively recruited as part of the Brisbane IBD clinical and research programme. Subjects were included in the low CRP group only if there were complete datasets for CRP on all occasions of active CD, and CRP was < 10 mg/l. Active disease was defined as CD activity index (CDAI) > 200. The low CRP group was compared with patients in the raised CRP group for a range of clinical variables as well as the major NOD2 variants. Results. There were data sets for 223 CD patients, with a mean disease duration of 12 years. Of these, 22 patients fulfilled the criteria for low CRP. The low CRP group ( group 1) showed significant differences for disease site (p < 0.01) and for BMI (p = 0.006) compared to the raised CRP group ( group 2). Specifically, group 1 had a predominance of pure ileal disease (95% versus 53%) and lack of pure colonic disease (0% versus 24%) compared to group 2, and their BMI was significantly lower (20.3 kg/m(2) versus 25.0 kg/m(2)). Groups 1 and 2 did not differ with respect to Vienna behaviour at diagnosis, smoking, appendicectomy, extra-intestinal manifestations of CD, or NOD2 SNP variants. There was a trend for low CRP patients with previous ileal resection to evolve to a stricturing phenotype. Fat wrapping was noted in 11/13 (85%) of low CRP patients undergoing ileal resections. Conclusions. Patients with CD and a persistently low CRP in the face of active disease were characterized by an almost exclusive ileal disease distribution and a low BMI, compared to those with a raised CRP. These patients had a similar frequency and distribution of NOD2/CARD15 variants. Stricturing ( v inflammatory or penetrating) behaviour may explain some low CRP. Despite the abnormally low BMI, fat wrapping was noted in the majority of low CRP patients undergoing ileal resection.

Compatibilization of starch-polyester blends using reactive extrusion

Maleic anhydride (MA) and dicumyl peroxide (DCP) were used as crosslinking agent and initiator respectively for blending starch and a biodegradable synthetic aliphatic polyester using reactive extrusion. Blends were characterized using dynamic mechanical and thermal analysis (DMTA). Optical micrographs of the blends revealed that in the optimized blend, starch was evenly dispersed in the polymer matrix. Optimized blends exhibited better tensile properties than the uncompatibilized blends. Xray photoelectron spectroscopy supported the proposed structure for the starch-polyester complex. Variation in the compositions of crosslinking agent and initiator had an impact on the properties and color of the blends.

Matching of catalogues by probabilistic pattern classification

We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

Optimization of integrated chemical-biological degradation of a reactive azo dye using response surface methodology

The integrated chemical-biological degradation combining advanced oxidation by UV/H2O2 followed by aerobic biodegradation was used to degrade C.I. Reactive Azo Red 195A, commonly used in the textile industry in Australia. An experimental design based on the response surface method was applied to evaluate the interactive effects of influencing factors (UV irradiation time, initial hydrogen peroxide dosage and recirculation ratio of the system) on decolourisation efficiency and optimizing the operating conditions of the treatment process. The effects were determined by the measurement of dye concentration and soluble chemical oxygen demand (S-COD). The results showed that the dye and S-COD removal were affected by all factors individually and interactively. Maximal colour degradation performance was predicted, and experimentally validated, with no recirculation, 30 min UV irradiation and 500 mg H2O2/L. The model predictions for colour removal, based on a three-factor/five-level Box-Wilson central composite design and the response surface method analysis, were found to be very close to additional experimental results obtained under near optimal conditions. This demonstrates the benefits of this approach in achieving good predictions while minimising the number of experiments required. (c) 2006 Elsevier B.V. All rights reserved.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.