The long noncoding RNA RNCR2 directs mouse retinal cell specification

<p>Abstract</p> <p>Background</p> <p>Recent work has identified that many long mRNA-like noncoding RNAs (lncRNAs) are expressed in the developing nervous system. Despite their abundance, the function of these ncRNAs has remained largely unexplored. We have investigated the highly abundant lncRNA RNCR2 in regulation of mouse retinal cell differentiation.</p> <p>Results</p> <p>We find that the RNCR2 is selectively expressed in a subset of both mitotic progenitors and postmitotic retinal precursor cells. ShRNA-mediated knockdown of RNCR2 results in an increase of both amacrine cells and Müller glia, indicating a role for this lncRNA in regulating retinal cell fate specification. We further report that RNCR2 RNA, which is normally nuclear-retained, can be exported from the nucleus when fused to an IRES-GFP sequence. Overexpression of RNCR2-IRES-GFP phenocopies the effects of shRNA-mediated knockdown of RNCR2, implying that forced mislocalization of RNCR2 induces a dominant-negative phenotype. Finally, we use the IRES-GFP fusion approach to identify specific domains of RNCR2 that are required for repressing both amacrine and Müller glial differentiation.</p> <p>Conclusion</p> <p>These data demonstrate that the lncRNA RNCR2 plays a critical role in regulating mammalian retinal cell fate specification. Furthermore, we present a novel approach for generating dominant-negative constructs of lncRNAs, which may be generally useful in the functional analysis of this class of molecules.</p

NETME: on-the-fly knowledge network construction from biomedical literature

Background: The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling. Results: We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks

Interprofessional Communication Team for Caregivers of Patients Hospitalized in the COVID-19 Wards: Results From an Italian Experience

Background: During the COVID-19 pandemic, emergency restrictions did not allow clinician family meetings and relatives' visits. In Molinette Hospital, a new communication model between healthcare providers and families of COVID-19 affected patients was developed by a team of physicians and psychologists. The study's aims were to investigate caregivers' distress and to analyse their satisfaction with the communications provided. Methods: A cross-sectional study was conducted among caregivers of patients of Molinette Hospital COVID wards. Between April and June 2020, all caregivers were contacted 2 weeks after the patient's discharge/death to assess their satisfaction with the communications received through an online survey. Results: A total of 155 caregivers completed the survey. Caregivers' distress level was found to be higher in women than men (p = 0.048) and in caregivers whose relative died compared to the caregivers whose relative was discharged (p < 0.001). More than 85% of caregivers defined communication “excellent”/“very good”; being male was associated with higher satisfaction levels than women (β = −0.165, p = 0.046). Besides daily communication, 63 caregivers (40.6%) received additional support from a psychologist of the team. Conclusions: To our knowledge, this is the first study presenting, in an emergency, a new model of communication provided by a team of physicians and psychologists, and analyzing satisfaction with it. This model was highly appreciated by caregivers and it limited the discomfort caused by the restrictions on relatives' visits. It would be interesting to further evaluate the possibility of extending a communication model that includes doctors and psychologists in routine clinical practice

Status and perspectives of the 4 pi charged particles multidetector CHIMERA

The construction of the multidetector CHIMERA designed to detect and identify charged particles and fragments emitted in heavy ion reactions at intermediate energy is in progress and is coming to an end. The construction of this multidetector is presented in this paper as well as the status of the project

Dynamic Chromatin Localization of Sirt6 Shapes Stress- and Aging-Related Transcriptional Networks

The sirtuin Sirt6 is a NAD-dependent histone deacetylase that is implicated in gene regulation and lifespan control. Sirt6 can interact with the stress-responsive transcription factor NF-κB and regulate some NF-κB target genes, but the full scope of Sirt6 target genes as well as dynamics of Sirt6 occupancy on chromatin are not known. Here we map Sirt6 occupancy on mouse promoters genome-wide and show that Sirt6 occupancy is highly dynamic in response to TNF-α. More than half of Sirt6 target genes are only revealed upon stress-signaling. The majority of genes bound by NF-κB subunit RelA recruit Sirt6, and dynamic Sirt6 relocalization is largely driven in a RelA-dependent manner. Integrative analysis with global gene expression patterns in wild-type, Sirt6−/−, and double Sirt6−/− RelA−/− cells reveals the epistatic relationships between Sirt6 and RelA in shaping diverse temporal patterns of gene expression. Genes under the direct joint control of Sirt6 and RelA include several with prominent roles in cell senescence and organismal aging. These data suggest dynamic chromatin relocalization of Sirt6 as a key output of NF-κB signaling in stress response and aging

Non-Coding RNAs in Retinal Development

Retinal development is dependent on an accurately functioning network of transcriptional and translational regulators. Among the diverse classes of molecules involved, non-coding RNAs (ncRNAs) play a significant role. Members of this family are present in the cell as transcripts, but are not translated into proteins. MicroRNAs (miRNAs) are small ncRNAs that act as post-transcriptional regulators. During the last decade, they have been implicated in a variety of biological processes, including the development of the nervous system. On the other hand, long-ncRNAs (lncRNAs) represent a different class of ncRNAs that act mainly through processes involving chromatin remodeling and epigenetic mechanisms. The visual system is a prominent model to investigate the molecular mechanisms underlying neurogenesis or circuit formation and function, including the differentiation of retinal progenitor cells to generate the seven principal cell classes in the retina, pathfinding decisions of retinal ganglion cell axons in order to establish the correct connectivity from the eye to the brain proper, and activity-dependent mechanisms for the functionality of visual circuits. Recent findings have associated ncRNAs in several of these processes and uncovered a new level of complexity for the existing regulatory mechanisms. This review summarizes and highlights the impact of ncRNAs during the development of the vertebrate visual system, with a specific focus on the role of miRNAs and a synopsis regarding recent findings on lncRNAs in the retina

Dependence of atmospheric muon flux on seawater depth measured with the first KM3NeT detection units: The KM3NeT Collaboration

KM3NeT is a research infrastructure located in the Mediterranean Sea, that will consist of two deep-sea Cherenkov neutrino detectors. With one detector (ARCA), the KM3NeT Collaboration aims at identifying and studying TeV–PeV astrophysical neutrino sources. With the other detector (ORCA), the neutrino mass ordering will be determined by studying GeV-scale atmospheric neutrino oscillations. The first KM3NeT detection units were deployed at the Italian and French sites between 2015 and 2017. In this paper, a description of the detector is presented, together with a summary of the procedures used to calibrate the detector in-situ. Finally, the measurement of the atmospheric muon flux between 2232–3386 m seawater depth is obtained

The Control Unit of the KM3NeT Data Acquisition System

The KM3NeT Collaboration runs a multi-site neutrino observatory in the Mediterranean Sea. Water Cherenkov particle detectors, deep in the sea and far off the coasts of France and Italy, are already taking data while incremental construction progresses. Data Acquisition Control software is operating off-shore detectors as well as testing and qualification stations for their components. The software, named Control Unit, is highly modular. It can undergo upgrades and reconfiguration with the acquisition running. Interplay with the central database of the Collaboration is obtained in a way that allows for data taking even if Internet links fail. In order to simplify the management of computing resources in the long term, and to cope with possible hardware failures of one or more computers, the KM3NeT Control Unit software features a custom dynamic resource provisioning and failover technology, which is especially important for ensuring continuity in case of rare transient events in multi-messenger astronomy. The software architecture relies on ubiquitous tools and broadly adopted technologies and has been successfully tested on several operating systems

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain.The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.French National Research Agency (ANR) ANR-15-CE31-0020Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund)European Union (EU)Institut Universitaire de France (IUF)LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001Shota Rustaveli National Science Foundation of Georgia FR-18-1268German Research Foundation (DFG)Greek Ministry of Development-GSRTIstituto Nazionale di Fisica Nucleare (INFN)Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN)Ministry of Higher Education, Scientific Research and Professional Training, MoroccoNetherlands Organization for Scientific Research (NWO)National Science Centre, Poland 2015/18/E/ST2/00758National Authority for Scientific Research (ANCS), RomaniaMinisterio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44Severo Ochoa Centre of ExcellenceJunta de Andalucia SOMM17/6104/UGRGeneralitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034La Caixa Foundation LCF/BQ/IN17/11620019EU: MSC program 71367