About the ergodic regime in the analogical Hopfield neural networks. Moments of the partition function

In this paper we introduce and exploit the real replica approach for a minimal generalization of the Hopfield model, by assuming the learned patterns to be distributed accordingly to a standard unit Gaussian. We consider the high storage case, when the number of patterns is linearly diverging with the number of neurons. We study the infinite volume behavior of the normalized momenta of the partition function. We find a region in the parameter space where the free energy density in the infinite volume limit is self-averaging around its annealed approximation, as well as the entropy and the internal energy density. Moreover, we evaluate the corrections to their extensive counterparts with respect to their annealed expressions. The fluctuations of properly introduced overlaps, which act as order parameters, are also discussed.Comment: 15 page

Sentinel Node Total Tumour Load As a Predictive Factor for Non-Sentinel Node Status in Early Breast Cancer Patients – The porttle study

OSNA is a molecular assay for the detection of sentinel node metastasis. TTL emerged as a concept that seems to accurately predict the status of the NSN. Authors tried to confirm this motion. This is a retrospective and multicentric study that analyzed 2164 patients, 579 of whom had positive SN and completion AD. Logistic regression models were performed in order to identify a suitable cutoff to identify patients who benefit from AD. Univariate and multivariate regression analysis showed a relationship between TTL>30000 and the presence of NSN metastasis (OR 2.84, CI 1.99-4.08, p < 0.001). Logistic regression indicated that the cutoff of 30000 copies/μL better discriminates patients with NSN positivity and allows wide use of these criteria. This cutoff value may safely assist clinicians and patients to decide to proceed or not with an AD.info:eu-repo/semantics/publishedVersio

Dysembryoplastic neuroepithelial tumor and probable sudden unexplained death in epilepsy: a case report

<p>Abstract</p> <p>Introduction</p> <p>This is the first report of the case of a patient with a natural history of dysembryoplastic neuroepithelial tumor associated with probable sudden unexplained death in epilepsy. These tumors are benign, arising within the supratentorial cortex. Over 100 cases have been reported in the literature since the first description by Daumas-Duport in 1988.</p> <p>Case presentation</p> <p>A 24- year-old Caucasian woman had a long period of intractable complex partial seizures, sometimes with tonic-clonic generalization and neuropsychological abnormalities. Magnetic resonance imaging showed a cortico-subcortical parietal tumor with all the characteristics of these types of tumors. After 14 years of evolution, our patient died suddenly during sleep.</p> <p>Conclusion</p> <p>To the best of our knowledge, this is the first case of probable sudden unexplained death in symptomatic epilepsy due to dysembryoplastic neuroepithelial tumor with natural history. Early and complete excision, with functional studies before and during the surgery, leads to better control of seizures, avoiding neuropsychological changes and the risk of death. Patients with refractory epilepsy should be evaluated for any sleep disorders and should have complete cardiology assessments including electrocardiographic evaluation of cardiac rhythm disturbances.</p

PRIDB: a protein–RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB

Prevalence of ocular and oculodermal melanocytosis in Spanish population with uveal melanoma

Producción CientíficaThe aim of this study was to determine the prevalence of ocular and oculodermal melanocytosis (ODM) among patients with uveal melanoma (UM) in a Spanish population. METHODS: Retrospective review of the medical records of patients with ODM among patients with UM. RESULTS: Ten (11 eyes) of 400 patients (2.7%) with UM associated had ODM. The mean age at diagnosis of UM among patients with ODM was 62 years. One patient had bilateral tumours. UM was diagnosed during a routine-examination in two cases. All tumours were medium (7/11) or large (4/11) in size, with a mean maximum base of 13 mm and height of 7 mm. No patient had extraocular extension or metastatic disease at diagnosis. Enucleation was done in five cases and I-125-brachytherapy in six. The mean follow-up was 43 months. One patient died because of metastasis 2 years after enucleation; one patient is currently on treatment of systemic metastasis 11 years after. CONCLUSIONS: ODM is more frequent in spanish population with UM than in American population. Despite the risk of UM in ODM, it is often diagnosed late when a conservative treatment is not indicated

Prognostic value of MGMT promoter methylation in glioblastoma patients treated with temozolomide-based chemoradiation : a Portuguese multicentre study

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. The identification of novel molecular prognostic markers of GBM has recently been an area of great interest in neuro-oncology. The methylation status of the MGMT gene promoter is currently a promising molecular prognostic marker, but some controversial data have precluded its clinical use. We analyzed MGMT methylation by methylation-specific PCR in 90 GBM patients from four Portuguese hospitals, uniformly treated with radiotherapy combined with concomitant and adjuvant temozolomide (Stupp protocol). The Kaplan-Meier method was used to construct survival curves, and the log-rank test and a Cox-regression model were used to analyze patient survival. The methylation status of MGMT was successfully determined in 89% (80/90) of the tumors. The frequency of tumoral MGMT promoter methylation was 47.5%. The median overall survivals (OSs) were 16 months (95% CI 12.2-19.8) and 13 months (95% CI 13.3-18.7) for patients whose tumors had a methylated or unmethylated MGMT, respectively. Univariate and multivariate analyses did not show any statistically significant association between MGMT methylation status and patient OS (P=0.583 by the log-rank test; P=0.617 by the Cox-regression test) or progression-free survival (P=0.775 by the log-rank test; P=0.691 by the Cox-regression test). None of the patient clinical features were significantly correlated with survival. This is the first study to report the frequency of MGMT methylation among Portuguese GBM patients. Our data did not show statistically significant associations between MGMT promoter methylation and the outcome of GBM patients treated with temozolomide. Additional robust prospective studies are warranted to clarify whether the MGMT status should be used in clinical decisions.This project was sponsored, in part, by Schering-Ploug Farma (Portugal). B.M.C. and O.M. are recipients of fellowships from the Portuguese Science and Technology Foundation (SFRH/BPD/33612/2009 and SFRH/BD/36463/ 2007). The funding institutions had no role in the study design, data collection and analysis, interpretation of the results, the preparation of the manuscript, or the decision to submit the manuscript for publication

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Background: Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. Results: We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data. Conclusion: The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 10 (2009): S4, doi: 10.1186/1471-2105-10-S4-S4. Posted with permission.</p