Genetics of intellectual disability in consanguineous families

Autosomal recessive (AR) gene defects are the leading genetic cause of intellectual disability (ID) in countries with frequent parental consanguinity, which account for about 1/7th of the world population. Yet, compared to autosomal dominant de novo mutations, which are the predominant cause of ID in Western countries, the identification of AR-ID genes has lagged behind. Here, we report on whole exome and whole genome sequencing in 404 consanguineous predominantly Iranian families with two or more affected offspring. In 219 of these, we found likely causative variants, involving 77 known and 77 novel AR-ID (candidate) genes, 21 X-linked genes, as well as 9 genes previously implicated in diseases other than ID. This study, the largest of its kind published to date, illustrates that high-throughput DNA sequencing in consanguineous families is a superior strategy for elucidating the thousands of hitherto unknown gene defects underlying AR-ID, and it sheds light on their prevalence

The XMM Cluster Survey analysis of the SDSS DR8 redMaPPer Catalogue:mplications for scatter, selection bias, and isotropy in cluster scaling relations

In this paper, we present the X-ray analysis of SDSS DR8 redMaPPer (SDSSRM) clusters using data products from the XMM Cluster Survey (XCS). In total, 1189 SDSSRM clusters fall within the XMM-Newton footprint. This has yielded 456 confirmed detections accompanied by X-ray luminosity (LX) measurements. Of these clusters, 381 have an associated X-ray temperature measurement (TX). This represents one of the largest samples of coherently derived cluster TX values to date. Our analysis of the X-ray observable to richness scaling relations has demonstrated that scatter in the TX − λ relation is roughly a third of that in the LX − λ relation, and that the LX − λ scatter is intrinsic, i.e. will not be significantly reduced with larger sample sizes. Analysis of the scaling relation between LX and TX has shown that the fits are sensitive to the selection method of the sample, i.e. whether the sample is made up of clusters detected “serendipitously” compared to those deliberately targeted by XMM. These differences are also seen in the LX − λ relation and, to a lesser extent, in the TX − λ relation. Exclusion of the emission from the cluster core does not make a significant impact on the findings. A combination of selection biases is a likely, but yet unproven, reason for these differences. Finally, we have also used our data to probe recent claims of anisotropy in the LX − TX relation across the sky. We find no evidence of anistropy, but stress this may be masked in our analysis by the incomplete declination coverage of the SDSS

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Risk factors for moderate and severe persistent pain in patients undergoing total knee and hip arthroplasty : a prospective predictive study

Persistent post-surgical pain (PPSP) is a major clinical problem with significant individual, social and health care costs. The aim of this study was to examine the joint role of demographic, clinical and psychological risk factors in the development of moderate and severe PPSP after Total Knee and Hip Arthroplasty (TKA and THA, respectively). This was a prospective study wherein a consecutive sample of 92 patients were assessed 24 hours before (T1), 48 hours after (T2) and 4-6 months (T3) after surgery. Hierarchical logistic regression analyses were performed to identify predictors of moderate and severe levels of PPSP. Four to six months after TKA and THA, 54 patients (58.7%) reported none or mild pain (Numerical Rating Scale: NRS 3). In the final multivariate hierarchical logistic regression analyses, illness representations concerning the condition leading to surgery (osteoarthritis), such as a chronic timeline perception of the disease, emerged as a significant predictor of PPSP. Additionally, post-surgical anxiety also showed a predictive role in the development of PPSP. Pre-surgical pain was the most significant clinical predictive factor and, as expected, undergoing TKA was associated with greater odds of PPSP development than THA. The findings on PPSP predictors after major joint arthroplasties can guide clinical practice in terms of considering cognitive and emotional factors, together with clinical factors, in planning acute pain management before and after surgery.This work was supported by a Project grant (PTDC/SAU-NEU/108557/2008) and by a PhD grant (SFRH/BD/36368/2007) from the Portuguese Foundation of Science and Technology, COMPETE and FEDER. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript