Exploring the reasons for the large density of triplex-forming oligonucleotide target sequences in the human regulatory regions

BACKGROUND: DNA duplex sequences that can be targets for triplex formation are highly over-represented in the human genome, especially in regulatory regions. RESULTS: Here we studied using bioinformatics tools several properties of triplex target sequences in an attempt to determine those that make these sequences so special in the genome. CONCLUSION: Our results strongly suggest that the unique physical properties of these sequences make them particularly suitable as "separators" between protein-recognition sites in the promoter region

Association of structural brain imaging markers with alcoholism incorporating structural connectivity information: a regularized statistical approach

poster abstractAbstract: Brain imaging studies collect multiple imaging data types, but most analyses are done for each modality separately. Statistical methods that simultaneously utilize and combine multiple data types can instead provide a more holistic view of brain function. Here we model associations between alcohol abuse phenotypes and imaging data while incorporating prior scientific knowledge. Specifically, we utilize cortical thickness and integrated rectified mean curvature measures obtained by FreeSurfer software [1] to predict the alcoholism-related phenotypes while incorporating prior information from the structural connectivity between cortical regions. The sample consisted of 148 young (21-35 years) social-to-heavy drinking male subjects from several alcoholism risk studies [2,3,4]. Structural connectivity model [5] was used to estimate the density of connections between 66 cortical regions based on Desikan-Killiany atlas [6]. We employed a functional linear model with a penalty operator to quantify the relative contributions of imaging markers obtained from high resolution structural MRI (cortical thickness and curvature) as predictors of drinking frequency and risk-relevant personality traits, while co-varying for age. Model parameters were estimated by a unified approach directly incorporating structural connectivity information into the estimation by exploiting the joint eigenproperties of the predictors and the penalty operator [7]. We found that the best predictive imaging markers of the Alcohol Use Disorders Identification Test (AUDIT) score were the average thickness of left frontal pole (-), right transverse temporal gyrus (+), left inferior parietal lobule (+), right supramarginal gyrus (-), right rostral middle frontal gyrus (+), right precentral gyrus (+), left superior parietal lobule (-), left lateral orbitofrontal cortex (+), left rostral middle frontal gyrus (+), left postcentral gyrus (+) and left supramarginal gyrus (-), where (+) denotes positive and (-) negative association. In summary, the use of structural connectivity information allowed the incorporation of different modalities in associating cortical measures and alcoholism risk

Positive Connectivity Predicts the Dynamic Intrinsic Topology of the Human Brain Network

Functional connectivity MRI (fcMRI) has become instrumental in facilitating research of human brain network organization in terms of coincident interactions between positive and negative synchronizations of large-scale neuronal systems. Although there is a common agreement concerning the interpretation of positive couplings between brain areas, a major debate has been made in disentangling the nature of negative connectivity patterns in terms of its emergence in several methodological approaches and its significance/meaning in specific neuropsychiatric diseases. It is still not clear what information the functional negative correlations or connectivity provides or how they relate to the positive connectivity. Through implementing stepwise functional connectivity (SFC) analysis and studying the causality of functional topological patterns, this study aims to shed light on the relationship between positive and negative connectivity in the human brain functional connectome. We found that the strength of negative correlations between voxel-pairs relates to their positive connectivity path-length. More importantly, our study describes how the spatio-temporal patterns of positive connectivity explain the evolving changes of negative connectivity over time, but not the other way around. This finding suggests that positive and negative connectivity do not display equivalent forces but shows that the positive connectivity has a dominant role in the overall human brain functional connectome. This phenomenon provides novel insights about the nature of positive and negative correlations in fcMRI and will potentially help new developments for neuroimaging biomarkers.This research was supported by grants from the National Institutes of Health K23EB019023 to JS, T32EB013180-06 to LO-T, Postdoctoral Fellowship Program from the Basque Country Government to ID and R01EB022574, R01MH108467 to JG, and Indiana Clinical and Translational Sciences Institute (UL1TR001108) to JG

Aeromonas spp. and Traveler’s Diarrhea: Clinical Features and Antimicrobial Resistance

Traveler’s diarrhea is the most common health problem of international travelers. We determined the prevalence of Aeromonas spp. associated with traveler’s diarrhea and analyzed the geographic distribution, clinical features, and antimicrobial susceptibility. Aeromonas spp. were isolated as a cause of traveler’s diarrhea in 18 (2%) of 863 patients. A. veronii biotype sobria was isolated in nine patients, A. caviae in seven patients, and A. jandai and A. hydrophila in one patient each. Aeromonas spp. were isolated with a similar prevalence in Africa, Latin America, and Asia. Watery and persistent diarrhea, fever, and abdominal cramps were common complaints. All strains were resistant to ampicillin; showed variable resistance to chloramphenicol, tetracycline, and cotrimoxazole; and were susceptible to cefotaxime, ciprofloxacin, and nalidixic acid. The persistence of symptoms made antimicrobial treatment necessary

Proteome analysis of human serum proteins adsorbed onto different titanium surfaces used in dental implants

Titanium dental implants are commonly used due to their biocompatibility and biochemical properties; blasted acid-etched Ti is used more frequently than smooth Ti surfaces. In this study, physico-chemical characterisation revealed important differences in roughness, chemical composition and hydrophilicity, but no differences were found in cellular in vitro studies (proliferation and mineralization). However, the deposition of proteins onto the implant surface might affect in vivo osseointegration. To test that hypothesis, protein layers formed on discs of both surface type after incubation with human serum were analysed. Using mass spectrometry (LC/MS/MS), 218 proteins were identified, 30 of which were associated with bone metabolism. Interestingly, Apo E, antithrombin and protein C adsorbed mostly onto blasted and acid-etched Ti, whereas the proteins of the complement system (C3) were found predominantly on smooth Ti surfaces. These results suggest that physico-chemical characteristics could be responsible for the differences observed in the adsorbed protein layer.This work was supported by Ministerio de Economía y Competitividad (MINECO) [MAT 2014-51918-C2-2-R], Universidad de Castellón [P11B2014-19], Plan de Promoción de la Investigación de la Universidad Jaume I under grant [Predoc/2014/25] and Generalitat Valenciana under grant [Grisolia/2014/016]. The authors would like to thank Antonio Coso and Jaime Franco (GMI-Ilerimplant) for their inestimable contribution to this study, and Iraida Escobes (CIC bioGUNE) for her valuable technical assistance

Resting state network profiles of Alzheimer disease and frontotemporal dementia: A preliminary examination

OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases

BECA: A Software Tool for Integrated Visualization of Human Brain Data

Visualization plays an important role in helping neuroscientist understanding human brain data. Most publicly available software focuses on visualizing a specific brain imaging modality. Here we present an extensible visualization platform, BECA, which employ a plugin architecture to facilitate rapid development and deployment of visualization for human brain data. This paper will introduce the architecture and discuss some important design decisions in implementing the BECA platform and its visualization plugins

Computational classifiers for predicting the short-term course of Multiple sclerosis

Abstract Background The aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS). Methods We obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center. Results We found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later. Conclusions The usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.</p