Wilson loops in the abelian lattice Higgs model

We consider the lattice Higgs model on $\mathbb{Z}^4$, with structure group given by $\mathbb{Z}_n$ for $n \geq 2$. We compute the expected value of the Wilson loop observable to leading order when the gauge coupling constant and hopping parameter are both sufficiently large. The leading order term is expressed in terms of a quantity arising from the related but much simpler $\mathbb{Z}_n$ model, which reduces to the Ising model when $n=2$. As part of the proof, we construct a coupling between the lattice Higgs model and the $\mathbb{Z}_n$ model.Comment: 56 pages, 8 figure

Wilson lines in the lattice Higgs model at strong coupling

We consider the 4D fixed length lattice Higgs model with Wilson action for the gauge field and structure group $\mathbb{Z}_n$. We study Wilson line observables in the strong coupling regime and compute their asymptotic behavior with error estimates. Our analysis is based on a high-temperature representation of the lattice Higgs measure combined with Poisson approximation. We also give a short proof of the folklore result that Wilson line (and loop) expectations exhibit perimeter law decay whenever the Higgs field coupling constant is positive.Comment: 48 pages, 6 figure

Hastings-Levitov aggregation in the small-particle limit

We establish some scaling limits for a model of planar aggregation. The model is described by the composition of a sequence of independent and identically distributed random conformal maps, each corresponding to the addition of one particle. We study the limit of small particle size and rapid aggregation. The process of growing clusters converges, in the sense of Caratheodory, to an inflating disc. A more refined analysis reveals, within the cluster, a tree structure of branching fingers, whose radial component increases deterministically with time. The arguments of any finite sample of fingers, tracked inwards, perform coalescing Brownian motions. The arguments of any finite sample of gaps between the fingers, tracked outwards, also perform coalescing Brownian motions. These properties are closely related to the evolution of harmonic measure on the boundary of the cluster, which is shown to converge to the Brownian web

Implantation of a colorectal stent as a therapeutic approach in the treatment of esophageal leakage

BACKGROUND: While the mortality of esophageal surgery has decreased due to technological advancements, there is still a complication rate of about 30%. One of the main complications is the anastomotic leakage associated with a significant rate of morbidity and mortality. To close the leakage the efficacy of self-expanding stents (SES) has been shown in different studies. However, the high rate of stent migration limits the use of commercial available stents. In our case we were faced with the problem that the diameter of all available stents was too small to attach tightly to the mucosal wall of the esophagogastric anastomosis. CASE PRESENTATION: We used, for the first time to our knowledge, a metal stent designed for colorectal application in an extensive anastomotic leak after esophageal resection in a patient with an esophageal cancer. After primary surgery with subtotal esohagectomy the anastomotic leak was stented endoscopically with a Polyflex self-expanding covered plastic stent after no response to intensive conventional management. Even though the stent was placed correctly, the diameter of the Polyflex stent was too small to attach onto the wall of the esophagogastric anastomosis. Again surgery was performed with a thoracal resection of the esophageal remnant and a hand made anastomosis. Unfortunately, again an anastomotic leak was detected soon after. To close the leak we decided to use a covered colorectal stent (Hanarostent) with an inner diameter of 30 mm. Sixteen weeks later the stent was extracted and complete mucosal healing of the esophageal leak was observed. CONCLUSION: The stent implantation with a large wide diameter offers a good chance to close more extensive leaks and prevent stent migration

Transmembrane protein topology prediction using support vector machines

Background: Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated.Results: We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/.Conclusion: The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins

IgTM: An algorithm to predict transmembrane domains and topology in proteins

<p>Abstract</p> <p>Background</p> <p>Due to their role of receptors or transporters, membrane proteins play a key role in many important biological functions. In our work we used Grammatical Inference (GI) to localize transmembrane segments. Our GI process is based specifically on the inference of Even Linear Languages.</p> <p>Results</p> <p>We obtained values close to 80% in both specificity and sensitivity. Six datasets have been used for the experiments, considering different encodings for the input sequences. An encoding that includes the topology changes in the sequence (from inside and outside the membrane to it and vice versa) allowed us to obtain the best results. This software is publicly available at: <url>http://www.dsic.upv.es/users/tlcc/bio/bio.html</url></p> <p>Conclusion</p> <p>We compared our results with other well-known methods, that obtain a slightly better precision. However, this work shows that it is possible to apply Grammatical Inference techniques in an effective way to bioinformatics problems.</p

A comprehensive assessment of N-terminal signal peptides prediction methods

Background: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools. Results: The available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap. Conclusion: The majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.12 page(s