Dissipative particle dynamics simulation of flow in periodically grooved three-dimensional nano- and micro-channels

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.Nonequillibrium flow in three-dimensional grooved nano- and micro-channels is investigated using the Dissipative Particle Dynamics simulation method. Roughness is introduced by periodically placing rectangular protruding elements on the upper channel wall. The protrusion length and height are varied and their effect on the flow is examined. The computed macroscopic quantities of practical interest include density, velocity, pressure, and temperature profiles as well as relations between the friction factor and the Reynolds number. When compared to the smooth channel case, lower flow velocities are observed in the central part of the channel for all cases studied. This reduction of velocities becomes more pronounced as the protrusion height increases. For the micro-channel, density, pressure and temperature remain almost constant in the central part of the channel and their pattern near and inside the cavities depend on the protrusion shape. In the nanochannel case, lower temperatures and pressures are observed for all grooved channels relative to the smooth channel case. For all channel cases studied the calculated friction factor decreases as Reynolds number increases, following a power law relation

Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method

BACKGROUND: Prediction of the transmembrane strands and topology of β-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of β-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 β-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane β-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at

Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins

BACKGROUND: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. RESULTS: We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM's classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. CONCLUSION: The algorithms presented here, are easily implemented in any kind of a Hidden Markov Model, whereas the prediction method (HMM-TM) is freely available for academic users at , offering the most advanced decoding options currently available

A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

BACKGROUND: Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology

The incidence and risk factors for new onset atrial fibrillation in the PROSPER study

Aims Atrial fibrillation/flutter (AF) is the most common arrhythmia in older people. It associates with reduced exercise capacity, increased risk of stroke, and mortality. We aimed to determine retrospectively whether pravastatin reduces the incidence of AF and whether any electrocardiographic measures or clinical conditions might be risk factors for its development. Methods and results The PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) was a randomized, double-blind controlled trial that recruited 5804 individuals aged 70-82 years with a history of, or risk factors for, vascular disease. A total of 2891 were allocated to pravastatin and 2913 to placebo; mean follow-up was 3.2 years. Electrocardiograms (ECGs), which were recorded at baseline, annually thereafter, and at run-out, were processed by computer and reviewed manually. In all, 264 of 2912 (9.1%) of the placebo group and 283 of 2888 (9.8%) of the pravastatin-treated group developed AF [hazard ratio 1.08 (0.92,1.28), P = 0.35)]. Multivariate analysis showed that PR and QTc intervals, age, left ventricular hypertrophy, and ST-T abnormalities were related to development of AF after adjustment for many variables including alcohol consumption, which itself was univariately predictive of developing AF. Previous myocardial infarction on the ECG was not a risk factor. A history of vascular disease was strongly linked with developing AF but not diabetes and hypertension. Conclusion Pravastatin does not reduce the incidence of AF in older people at risk of vascular disease, at least in the short-medium term. Risk factors for AF include older age, prolongation of PR or QTc intervals, left ventricular hypertrophy, and ST-T abnormalities on the EC

Myocardial contractile function in survived neonatal piglets after cardiopulmonary bypass

<p>Abstract</p> <p>Background</p> <p>Hemodynamic function may be depressed in the early postoperative stages after cardiac surgery. The aim of this study was the analysis of the myocardial contractility in neonates after cardiopulmonary bypass (CPB) and mild hypothermia.</p> <p>Methods</p> <p>Three indices of left ventricular myocardial contractile function (dP/dt, (dP/dt)/P, and wall thickening) were studied up to 6 hours after CPB in neonatal piglets (CPB group; n = 4). The contractility data were analysed and then compared to the data of newborn piglets who also underwent median thoracotomy and instrumentation for the same time intervals but without CPB (non-CPB group; n = 3).</p> <p>Results</p> <p>Left ventricular dP/dt_maxand (dP/dt_max)/P remained stable in CPB group, while dP/dt_maxdecreased in non-CPB group 5 hours postoperatively (1761 ± 205 mmHg/s at baseline vs. 1170 ± 205 mmHg/s after 5 h; p < 0.05). However, with regard to dP/dt_maxand (dP/dt_max)/P there were no statistically significant differences between the two groups. Comparably, although myocardial thickening decreased in the non-CPB group the differences between the two groups were not statistically significant.</p> <p>Conclusions</p> <p>The myocardial contractile function in survived neonatal piglets remained stable 6 hours after cardiopulmonary bypass and mild hypothermia probably due to regional hypercontractility.</p

An E2-ubiquitin thioester-driven approach to identify substrates modified with ubiquitin and ubiquitin-like molecules.

Covalent modifications of proteins with ubiquitin and ubiquitin-like molecules are instrumental to many biological processes. However, identifying the E3 ligase responsible for these modifications remains a major bottleneck in ubiquitin research. Here, we present an E2-thioester-driven identification (E2~dID) method for the targeted identification of substrates of specific E2 and E3 enzyme pairs. E2~dID exploits the central position of E2-conjugating enzymes in the ubiquitination cascade and provides in vitro generated biotinylated E2~ubiquitin thioester conjugates as the sole source for ubiquitination in extracts. This enables purification and mass spectrometry-based identification of modified proteins under stringent conditions independently of the biological source of the extract. We demonstrate the sensitivity and specificity of E2-dID by identifying and validating substrates of APC/C in human cells. Finally, we perform E2~dID with SUMO in S. cerevisiae, showing that this approach can be easily adapted to other ubiquitin-like modifiers and experimental models

Phosphorus nutritional knowledge among dialysis health care providers and patients: a multicenter observational study

Background-aims Phosphorus nutritional knowledge level of hemodialysis patients and renal nurses has been found to be low, while respective knowledge of nephrologists has not been studied yet. There are equivocal results regarding the association of phosphorus nutritional knowledge level and serum phosphorus values. The aim of this study was to assess phosphorus nutritional knowledge of hemodialysis patients, nephrologists and renal nurses and seek potential interventions to improve patients’ adherence to phosphorus and overall nutritional guidelines. Methods This cross-sectional observational study was conducted on sixty eight hemodialysis patients, 19 renal nurses and 11 nephrologists who were recruited from 3 hemodialysis units in Greece. Phosphorus nutritional knowledge of the participants was assessed by a 25-item item questionnaire (CKDKAT–N) which included 15 questions on phosphorus and 10 questions on protein, sodium, and potassium knowledge. Results Nephrologists had higher CKDKAT–N total (19.1 ± 3.6 vs 14.1 ± 2.8 and 13.2 ± 2.8, P < 0.01) and phosphorus knowledge scores (10.6 ± 2.7 vs 7.6 ± 2.2 and 7.3 ± 2.0, P < 0.01) compared to renal nurses and patients respectively. There were no differences in total and phosphorus knowledge scores between nurses and patients. Patients and nurses answered correctly significantly less questions regarding phosphorus compared with the rest of the questions (P < 0.01) while no such difference was found in nephrologists. Serum phosphorus was positively correlated with phosphorus knowledge score (r = 0.31, P = 0.02), and negatively correlated with patient age (r = −0.34, P < 0.05). None of the patients, 11% of the nurses and 27% of the nephrologists answered correctly all three questions regarding P, K and Na dietary recommendations (P < 0.01). Conclusions The study confirms that hemodialysis patients have low renal nutrition knowledge while higher nutritional phosphorus knowledge does not lead to lower serum phosphorus values. Alarmingly, renal nurses have been found to have a similar level of knowledge with hemodialysis patients, something that needs to be taken into account when training the new dialysis staff. Nephrologists have superior knowledge; however they are still lacking essential nutritional knowledge that could affect patients' and nurses’ overall understanding. Continuing education on nutrition of nephrologists and renal nurses could improve nutrition care of hemodialysis patients