Do Hypnosis and Mindfulness Practices Inhabit a Common Domain? Implications for Research, Clinical Practice, and Forensic Science

Hypnosis and mindfulness practices provide clinicians with two viable yet distinct methods, or more accurately families of methods, for increasing well-being and ameliorating problems in living. In this article, we compare and contrast hypnotic and mindfulness interventions, address the question of whether they inhabit a common domain, describe how they may be combined to advantage, and discuss clinical and research implications. We contend that hypnosis and mindfulness inhabit a common, albeit broad, domain of suggestive approaches. However, we also argue that meaningful differences exist that are particularly salient and consequential in the forensic arena

Insight into the Architecture of the NuRD Complex: Structure of the RbAp48-MTA1 Subcomplex

The nucleosome remodeling and deacetylase (NuRD) complex is a widely conserved transcriptional co-regulator that harbors both nucleosome remodeling and histone deacetylase activities. It plays a critical role in the early stages of ES cell differentiation and the reprogramming of somatic to induced pluripotent stem cells. Abnormalities in several NuRD proteins are associated with cancer and aging. We have investigated the architecture of NuRD by determining the structure of a subcomplex comprising RbAp48 and MTA1. Surprisingly, RbAp48 recognizes MTA1 using the same site that it uses to bind histone H4, showing that assembly into NuRD modulates RbAp46/48 interactions with histones. Taken together with other results, our data show that the MTA proteins act as scaffolds for NuRD complex assembly. We further show that the RbAp48-MTA1 interaction is essential for the in vivo integration of RbAp46/48 into the NuRD complex

High resolution mapping and positional cloning of ENU-induced mutations in the Rw region of mouse chromosome 5

<p>Abstract</p> <p>Background</p> <p>Forward genetic screens in mice provide an unbiased means to identify genes and other functional genetic elements in the genome. Previously, a large scale ENU mutagenesis screen was conducted to query the functional content of a ~50 Mb region of the mouse genome on proximal Chr 5. The majority of phenotypic mutants recovered were embryonic lethals.</p> <p>Results</p> <p>We report the high resolution genetic mapping, complementation analyses, and positional cloning of mutations in the target region. The collection of identified alleles include several with known or presumed functions for which no mutant models have been reported (<it>Tbc1d14</it>, <it>Nol14</it>, <it>Tyms</it>, <it>Cad</it>, <it>Fbxl5</it>, <it>Haus3</it>), and mutations in genes we or others previously reported (<it>Tapt1</it>, <it>Rest</it>, <it>Ugdh</it>, <it>Paxip1</it>, <it>Hmx1, Otoe, Nsun7</it>). We also confirmed the causative nature of a homeotic mutation with a targeted allele, mapped a lethal mutation to a large gene desert, and localized a spermiogenesis mutation to a region in which no annotated genes have coding mutations. The mutation in <it>Tbc1d14 </it>provides the first implication of a critical developmental role for RAB-GAP-mediated protein transport in early embryogenesis.</p> <p>Conclusion</p> <p>This collection of alleles contributes to the goal of assigning biological functions to all known genes, as well as identifying novel functional elements that would be missed by reverse genetic approaches.</p

STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse

Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STAT egra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra include

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor packag

Preoperative Brain Tumor Imaging:Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16–54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5–15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.publishedVersio

Cysteamine Attenuates the Decreases in TrkB Protein Levels and the Anxiety/Depression-Like Behaviors in Mice Induced by Corticosterone Treatment

OBJECTIVE: Stress and glucocorticoid hormones, which are released into the circulation following stressful experiences, have been shown to contribute significantly to the manifestation of anxiety-like behaviors observed in many neuropsychiatric disorders. Brain-derived neurotrophic factor (BDNF) signaling through its receptor TrkB plays an important role in stress-mediated changes in structural as well as functional neuroplasticity. Studies designed to elucidate the mechanisms whereby TrkB signaling is regulated in chronic stress might provide valuable information for the development of new therapeutic strategies for several stress-related psychiatric disorders. MATERIALS AND METHODS: We examined the potential of cysteamine, a neuroprotective compound to attenuate anxiety and depression like behaviors in a mouse model of anxiety/depression induced by chronic corticosterone exposure. RESULTS: Cysteamine administration (150 mg/kg/day, through drinking water) for 21 days significantly ameliorated chronic corticosterone-induced decreases in TrkB protein levels in frontal cortex and hippocampus. Furthermore, cysteamine treatment reversed the anxiety and depression like behavioral abnormalities induced by chronic corticosterone treatment. Finally, mice deficient in TrkB, showed a reduced response to cysteamine in behavioral tests, suggesting that TrkB signaling plays an important role in the antidepressant effects of cysteamine. CONCLUSIONS: The animal studies described here highlight the potential use of cysteamine as a novel therapeutic strategy for glucocorticoid-related symptoms of psychiatric disorders

Corticosterone Alters AMPAR Mobility and Facilitates Bidirectional Synaptic Plasticity

Background: The stress hormone corticosterone has the ability both to enhance and suppress synaptic plasticity and learning and memory processes. However, until today there is very little known about the molecular mechanism that underlies the bidirectional effects of stress and corticosteroid hormones on synaptic efficacy and learning and memory processes. In this study we investigate the relationship between corticosterone and AMPA receptors which play a critical role in activity-dependent plasticity and hippocampal-dependent learning. Methodology/Principal Findings: Using immunocytochemistry and live cell imaging techniques we show that corticosterone selectively increases surface expression of the AMPAR subunit GluR2 in primary hippocampal cultures via a glucocorticoid receptor and protein synthesis dependent mechanism. In agreement, we report that corticosterone also dramatically increases the fraction of surface expressed GluR2 that undergo lateral diffusion. Furthermore, our data indicate that corticosterone facilitates NMDAR-invoked endocytosis of both synaptic and extra-synaptic GluR2 under conditions that weaken synaptic transmission. Conclusion/Significance: Our results reveal that corticosterone increases mobile GluR2 containing AMPARs. The enhanced lateral diffusion properties can both facilitate the recruitment of AMPARs but under appropriate conditions facilitate the loss of synaptic AMPARs (LTD). These actions may underlie both the facilitating and suppressive effects of corticosteroid hormones on synaptic plasticity and learning and memory and suggest that these hormones accentuate synaptic efficacy

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental AE tiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount