Simulating star formation in molecular cloud cores IV. The role of turbulence and thermodynamics

We perform SPH simulations of the collapse and fragmentation of low-mass cores having different initial levels of turbulence (alpha_turb=0.05,0.10,0.25). We use a new treatment of the energy equation which captures the transport of cooling radiation against opacity due to both dust and gas (including the effects of dust sublimation, molecules, and H^- ions). We also perform comparison simulations using a standard barotropic equation of state. We find that -- when compared with the barotropic equation of state -- our more realistic treatment of the energy equation results in more protostellar objects being formed, and a higher proportion of brown dwarfs; the multiplicity frequency is essentially unchanged, but the multiple systems tend to have shorter periods (by a factor ~3), higher eccentricities, and higher mass ratios. The reason for this is that small fragments are able to cool more effectively with the new treatment, as compared with the barotropic equation of state. We find that the process of fragmentation is often bimodal. The first protostar to form is usually, at the end, the most massive, i.e. the primary. However, frequently a disc-like structure subsequently forms round this primary, and then, once it has accumulated sufficient mass, quickly fragments to produce several secondaries. We believe that this delayed fragmentation of a disc-like structure is likely to be an important source of very low-mass hydrogen-burning stars and brown dwarfs.Comment: 14 pages, 8 figures. Accepted for publication by A&

LIPPRED: A web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach

Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed α-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications

A predictor of membrane class: Discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications

TATPred:a Bayesian method for the identification of twin arginine translocation pathway signal sequences

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Nave-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942

BioCIDER: a Contextualisation InDEx for biological Resources discovery

Summary The vast, uncoordinated proliferation of bioinformatics resources (databases, software tools, training materials etc.) makes it difficult for users to find them. To facilitate their discovery, various services are being developed to collect such resources into registries. We have developed BioCIDER, which, rather like online shopping ‘recommendations’, provides a contextualization index to help identify biological resources relevant to the content of the sites in which it is embedded

Adaptive smoothing lengths in SPH

Context: There is a need to improve the fidelity of SPH simulations of self-gravitating gas dynamics. Aims: We remind users of SPH that, if smoothing lengths are adjusted so as to keep the number of neighbours, ${\cal N}$, in the range ${\cal N}_{_{\rm NEIB}}\pm\Delta{\cal N}_{_{\rm NEIB}}$, the tolerance, $\Delta{\cal N}_{_{\rm NEIB}}$, should be set to zero, as first noted by Nelson & Papaloizou. We point out that this is a very straightforward and computationally inexpensive constraint to implement. Methods: We demonstrate this by simulating acoustic oscillations of a self-gravitating isentropic monatomic gas-sphere (cf. Lucy), using ${\cal N}_{_{\rm TOT}}\sim6,000$ particles and ${\cal N}_{_{\rm NEIB}}=50$. Results: We show that there is a marked reduction in the rates of numerical dissipation and diffusion as $\Delta{\cal N}_{_{\rm NEIB}}$ is reduced from 10 to zero. Moreover this reduction incurs a very small computational overhead. Conclusions: We propose that this should become a standard test for codes used in simulating star formation. It is a highly relevant test, because pressure waves generated by the switch from approximate isothermality to approximate adiabaticity play a critical role in the fragmentation of collapsing prestellar cores. Since many SPH simulations in the literature use ${\cal N}_{_{\rm NEIB}}=50$ and $\Delta{\cal N}_{_{\rm NEIB}}\geq10$, their results must be viewed with caution.Comment: 5 pages, 2 figures, accepted for publication in A&

Utopia documents: linking scholarly literature with research data

Motivation: In recent years, the gulf between the mass of accumulating-research data and the massive literature describing and analyzing those data has widened. The need for intelligent tools to bridge this gap, to rescue the knowledge being systematically isolated in literature and data silos, is now widely acknowledged

Comparing laparoscopic antireflux surgery with esomeprazole in the management of patients with chronic gastro-oesophageal reflux disease: a 3-year interim analysis of the LOTUS trial

BACKGROUND: With the introduction of laparoscopic antireflux surgery (LARS) for gastro-oesophageal reflux disease (GORD) along with the increasing efficacy of modern medical treatment, a direct comparison is warranted. The 3-year interim results of a randomised study comparing both the efficacy and safety of LARS and esomeprazole (ESO) are reported. METHODS: LOTUS is an open, parallel-group multicentre, randomised and controlled trial conducted in dedicated centres in 11 European countries. LARS was completed according to a standardised protocol, comprising a total fundoplication and a crural repair. Medical treatment comprised ESO 20 mg once daily, which could be increased stepwise to 40 mg once daily and then 20 mg twice daily in the case of incomplete GORD control. The primary outcome variable was time to treatment failure (Kaplan-Meier analysis). Treatment failure was defined on the basis of symptomatic relapse requiring treatment beyond that stated in the protocol. RESULTS: 554 patients were randomised, of whom 288 were allocated to LARS and 266 to ESO. The two study arms were well matched. The proportions of patients who remained in remission after 3 years were similar for the two therapies: 90% of surgical patients compared with 93% medically treated for the intention to treat population, p = 0.25 (90% vs 95% per protocol). No major unexpected postoperative complications were experienced and ESO was well tolerated. However, postfundoplication complaints remain a problem after LARS. CONCLUSIONS: Over the first 3 years of this long-term study, both laparoscopic total fundoplication and continuous ESO treatment were similarly effective and well-tolerated therapeutic strategies for providing effective control of GORD

Change in chirality of semiconducting single-walled carbon nanotubes can overcome anionic surfactant stabilisation: a systematic study of aggregation kinetics

Single-walled carbon nanotubes’ (SWNT) effectiveness in applications is enhanced by debundling or stabilisation. Anionic surfactants are known to effectively stabilise SWNTs. However, the role of specific chirality on surfactant-stabilised SWNT aggregation has not been studied to date. The aggregation behaviour of chirally enriched (6,5) and (7,6) semiconducting SWNTs, functionalised with three anionic surfactants – sodium dodecyl sulfate, sodium dodecyl benzene sulfonate and sodium deoxycholate – was evaluated with time-resolved dynamic light scattering. A wide range of mono- (NaCl) and divalent (CaCl2) electrolytes as well as a 2.5 mg total organic carbon (TOC) L–1 Suwannee River humic acid were used as background chemistry. Overall, sodium dodecyl benzene sulfonate showed the most effectiveness in stabilising SWNTs, followed by sodium deoxycholate and sodium dodecyl sulfate. However, the larger diameter (7,6) chirality tubes (compared to (6,5) diameter), compromised the surfactant stability due to enhanced van der Waals interaction. The presence of divalent electrolytes overshadowed the chirality effects and resulted in similar aggregation behaviour for both the SWNT samples. Molecular modelling results elucidated key differences in surfactant conformation on SWNT surfaces and identified interaction energy changes between the two chiralities to delineate aggregation mechanisms. The stability of SWNTs increased in the presence of Suwannee River humic acid under 10 mM monovalent and mixed-electrolyte conditions. The results suggest that change in chirality can overcome surfactant stabilisation of semiconducting SWNTs. SWNT stability can also be strongly influenced by the anionic surfactant structure