Impact of Cage Size and Enrichment (Tube and Shelf) on Heart Rate Variability in Rats

Rats respond physiologically and behaviorally to environmental stressors. As cage conditions can be a stressor, it is important that experimental results acquired from caged rats are not confounded by these responses. This study determined the effects of cage size and cage enrichment (tube and shelf) on heart rate variability (HRV) in rats as a measure of stress. Electrocardiogram data were collected from 5 male Sprague-Dawley rats, each implanted with a radio-telemetric transducer to assess the ratio of the low to high frequency components of the HRV power spectrum (LF/HF). This ratio reflects the degree of sympathetic versus parasympathetic nervous activity and increases with decreasing HRV. Rats were housed for 3 weeks in each of the following cage conditions: small un-enriched, small enriched, large un-enriched and large enriched. Cage enrichment and/or larger cages did not significantly alter LF/HF values compared to the small, un-enriched cage condition, when considered independent of the sleep/wake cycle. However, when results were pooled for all cage conditions, LF/HF significantly increased during the wake cycle compared to the sleep cycle. Further analysis showed that this difference was only statistically significant for the un-enriched cage condition. Thus the presence of a tube and a shelf in a rodent cage can alter the diurnal rhythm of HRV in rats and this should be taken into account when designing experiments in which HRV is an outcome

The coming decade of digital brain research: a vision for neuroscience at the intersection of technology and computing

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales— from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, to identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research

Identification of mutations in CUL7 in 3-M syndrome.

Item does not contain fulltextIntrauterine growth retardation is caused by maternal, fetal or placental factors that result in impaired endovascular trophoblast invasion and reduced placental perfusion. Although various causes of intrauterine growth retardation have been identified, most cases remain unexplained. Studying 29 families with 3-M syndrome (OMIM 273750), an autosomal recessive condition characterized by severe pre- and postnatal growth retardation, we first mapped the underlying gene to chromosome 6p21.1 and then identified 25 distinct mutations in the gene cullin 7 (CUL7). CUL7 assembles an E3 ubiquitin ligase complex containing Skp1, Fbx29 (also called Fbw8) and ROC1 and promotes ubiquitination. Using deletion analysis, we found that CUL7 uses its central region to interact with the Skp1-Fbx29 heterodimer. Functional studies indicated that the 3-M-associated CUL7 nonsense and missense mutations R1445X and H1464P, respectively, render CUL7 deficient in recruiting ROC1. These results suggest that impaired ubiquitination may have a role in the pathogenesis of intrauterine growth retardation in humans

Multiple Miscarriages Are Associated with the Risk of Ovarian Cancer: Results from the European Prospective Investigation into Cancer and Nutrition

While the risk of ovarian cancer clearly reduces with each full-term pregnancy, the effect of incomplete pregnancies is unclear. We investigated whether incomplete pregnancies (miscarriages and induced abortions) are associated with risk of epithelial ovarian cancer. This observational study was carried out in female participants of the European Prospective Investigation into Cancer and Nutrition (EPIC). A total of 274,442 women were followed from 1992 until 2010. The baseline questionnaire elicited information on miscarriages and induced abortions, reproductive history, and lifestyle-related factors. During a median follow-up of 11.5 years, 1,035 women were diagnosed with incident epithelial ovarian cancer. Despite the lack of an overall association (ever vs. never), risk of ovarian cancer was higher among women with multiple incomplete pregnancies (HR >= 4vs.0: 1.74, 95% CI: 1.20-2.70; number of cases in this category: n = 23). This association was particularly evident for multiple miscarriages (HR >= 4vs.0: 1.99, 95% CI: 1.06-3.73; number of cases in this category: n = 10), with no significant association for multiple induced abortions (HR >= 4vs.0: 1.46, 95% CI: 0.68-3.14; number of cases in this category: n = 7). Our findings suggest that multiple miscarriages are associated with an increased risk of epithelial ovarian cancer, possibly through a shared cluster of etiological factors or a common underlying pathology. These findings should be interpreted with caution as this is the first study to show this association and given the small number of cases in the highest exposure categories

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

The coming decade of digital brain research - A vision for neuroscience at the intersection of technology and computing

<p>Brain research has in recent years indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modeling at multiple scales – from molecules to the whole system. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain integrates high-quality basic research, systematic data integration across multiple scales, a new culture of large-scale collaboration and translation into applications. A systematic approach, as pioneered in Europe's Human Brain Project (HBP), will be essential in meeting the pressing medical and technological challenges of the coming decade. The aims of this paper are</p><ul><li>To develop a concept for the coming decade of digital brain research</li><li>To discuss it with the research community at large, with the aim of identifying points of convergence and common goals</li><li>To provide a scientific framework for current and future development of EBRAINS</li><li>To inform and engage stakeholders, funding organizations and research institutions regarding future digital brain research</li><li>To identify and address key ethical and societal issues</li></ul><p>While we do not claim that there is a 'one size fits all' approach to addressing these aspects, we are convinced that discussions around the theme of digital brain research will help drive progress in the broader field of neuroscience.</p><p><strong>As the final version 5 has now been published, comments on this manuscript are now closed. We thank everyone who made a valuable contribution to this paper.</strong></p><p>This manuscript has been developed in a participatory process. The work has been initiated by the Science and Infrastructure Board of the Human Brain Project (HBP), and the entire research community was invited to contribute to shaping the vision by submitting comments. </p><p>All submitted comments were considered and discussed. The final decision on whether edits or additions was made to each version of the manuscript based on an individual comment was made by the Science and Infrastructure Board (SIB) of the Human Brain Project (HBP).</p><p><strong>Supporters of the paper</strong>: Pietro Avanzini, Marc Beyer, Maria Del Vecchio, Jitka Annen, Maurizio Mattia, Steven Laureys, Rosanne Edelenbosch, Rafael Yuste, Jean-Pierre Changeux, Linda Richards, Hye Weon Jessica Kim, Chrysoula Samara, Luis Miguel González de la Garza, Nikoleta Petalidou, Vasudha Kulkarni, Cesar David Rincon, Isabella O'Shea, Munira Tamim Electricwala, Bernd Carsten Stahl, Bahar Hazal Yalcinkaya, Meysam Hashemi, Carola Sales Carbonell, Marcel Carrère, Anthony Randal McIntosh, Hiba Sheheitli, Abolfazl Ziaeemehr, Martin Breyton, Giovanna Ramos Queda, Anirudh NIhalani Vattikonda, Gyorgy Buzsaki, George Ogoh, William Knight, Torbjørn V Ness, Michiel van der Vlag, Marcello Massimini, Thomas Nowontny, Alex Upton, Yaseen Jakhura, Ahmet Nihat Simsek, Michael Hopkins, Addolorata Marasco, Shamim Patel, Jakub Fil, Diego Molinari, Susana Bueno, Lia Domide, Cosimo Lupo, Mu-ming Poo, George Paxinos, Huifang Wang.</p&gt