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Long noncoding RNAs (lncRNAs) are regulatory molecules which have been traditionally considered as “non-coding”. Strikingly, recent evidence has demonstrated that many non-coding regions, including lncRNAs, do in fact contain small-open reading frames that code for small proteins that have been called microproteins. Only a few of them have been characterized so far, but they display key functions in a wide variety of cellular processes. Here, we show that TUNAR lncRNA encodes an evolutionarily conserved microprotein expressed in the nervous system that we have named pTUNAR. pTUNAR deficiency in mouse embryonic stem cells improves their differentiation potential towards neural lineage both in vitro and in vivo. Conversely, pTUNAR overexpression impairs neuronal differentiation by reduced neurite formation in different model systems. At the subcellular level, pTUNAR is a transmembrane protein that localizes in the endoplasmic reticulum and interacts with the calcium transporter SERCA2. pTUNAR overexpression reduces cytoplasmatic calcium, consistent with a possible role of pTUNAR as an activator of SERCA2. Altogether, our results suggest that our newly discovered microprotein has an important role in neural differentiation and neurite formation through the regulation of intracellular calcium. From a more general point of view, our results provide a proof of concept of the role of lncRNAs-encoded microproteins in neural differentiation., Work in the Abad lab is supported by VHIO, Fero Foundation, La Caixa Foundation (HR18-00256), Asociación Española Contra el Cancer (AECC), Cellex Foundation, Mutua Madrileña Foundation and by grants from the Spanish Ministry of Science and Innovation (SAF2015-69413-R; RTI2018-102046-B-I00). M.A. was recipient of a Ramon y Cajal contract from the Spanish Ministry of Science and Innovation (RYC-2013-14747). E.S. was recipient of a AECC Postdoctoral Fellowship. L.H-M. also acknowledges funding from grants SAF2017-88019-C3-1R y PID2020-116927RB-C21 from the Spanish Government. MG is supported by the advanced ERC grant NeuroCentro and the German Research Foundation (SFB870; SPP2202; SPP2306; SYNERGY; TRR274). DT is supported by the Ramón y Cajal program (RYC-2017-23486/RTI2018-095580-A-I00). MMA acknowledges funding from the Spanish Ministry of Science and Innovation PGC2018-094091-B-I00 co-funded by FEDER.

Background Optical technologies, relying on spectral analysis, are more and more implemented in portable devices for food analysis. Thereby, each food safety, quality or authenticity provision as well as each technology requires the generation of a dedicated spectral database with reference data. Currently, knowledge on how these databases might be connected or transferred across food commodities, targeted compounds or devices are very limited. Hence, repetitive work is conducted and technologies are not optimally used. Scope and approach This perspective focuses on the currently available technologies and approaches for data handling and database transfer across miniaturized devices and technologies for food safety, quality and authenticity assessments. Key findings and conclusions For almost every food commodity or target compound a miniaturized spectroscopic device can be applied with the respective database to compare findings. Recent developments in optical spectroscopy allow more possibilities for their use as well as facilitate the production of portable devices. A multifunctional device hyphenating several sensors and broadening the application range is still not marketed. Newly developed software architecture, accessing and extracting data, helps to overcome sample heterogenicity or spurious measured data. In addition, several data fusion approaches using machine learning and deep learning strategies are available to fuse spectroscopic data with itself or other non-spectroscopic data. Following the research results presented in this field, spectral data can possibly be re-used and shared across instruments and locations, highly increasing the applicability of data sets. Thereby, obstacles such as policy or confidentiality are taken into account.

Background High density genotyping data are indispensable for genomic analyses of complex traits in animal and crop species. Maize is one of the most important crop plants worldwide, however a high density SNP genotyping array for analysis of its large and highly dynamic genome was not available so far. Results We developed a high density maize SNP array composed of 616,201 variants (SNPs and small indels). Initially, 57 M variants were discovered by sequencing 30 representative temperate maize lines and then stringently filtered for sequence quality scores and predicted conversion performance on the array resulting in the selection of 1.2 M polymorphic variants assayed on two screening arrays. To identify high-confidence variants, 285 DNA samples from a broad genetic diversity panel of worldwide maize lines including the samples used for sequencing, important founder lines for European maize breeding, hybrids, and proprietary samples with European, US, semi-tropical, and tropical origin were used for experimental validation. We selected 616 k variants according to their performance during validation, support of genotype calls through sequencing data, and physical distribution for further analysis and for the design of the commercially available Affymetrix(R) Axiom(R) Maize Genotyping Array. This array is composed of 609,442 SNPs and 6,759 indels. Among these are 116,224 variants in coding regions and 45,655 SNPs of the Illumina(R) MaizeSNP50 BeadChip for study comparison. In a subset of 45,974 variants, apart from the target SNP additional off-target variants are detected, which show only a minor bias towards intermediate allele frequencies. We performed principal coordinate and admixture analyses to determine the ability of the array to detect and resolve population structure and investigated the extent of LD within a worldwide validation panel. Conclusions The high density Affymetrix(R) Axiom(R) Maize Genotyping Array is optimized for European and American temperate maize and was developed based on a diverse sample panel by applying stringent quality filter criteria to ensure its suitability for a broad range of applications. With 600 k variants it is the largest currently publically available genotyping array in crop species.

Passive systems are being implemented in several nuclear plants already constructed, under construction or under design. The purpose of these systems is to increase the inherent safety of the plant in any transient condition, so they can provide a higher safety level in case of an accident. Best estimate thermal-hydraulic system codes, widely adopted in the nuclear field to perform deterministic safety analyses, need to be validated against the phenomena typical of passive systems (e.g. natural circulation, thermal-hydraulic behavior of large pools, low pressure phenomena, etc.). An experimental test facility called PERSEO (in-Pool Energy Removal System for Emergency Operation) was built at SIET laboratory in Piacenza (Italy) modifying the existing PANTHERS IC-PCC facility. It is a full-scale test facility aimed at studying a new passive heat removal system and it can be used to analyze the capability of system codes to simulate the related phenomena. In the present analysis, Test 9, which was performed in the PERSEO facility at around 4.1 MPa to study the activation and the heat removal capability of the proposed system, has been simulated with the RELAP5-3D code to assess its capability to predict the dominant phenomena of the transient. Particular attention has been dedicated to the analysis of condensation heat transfer coefficient computed by the code, since in previous analyses it has been found an evident underestimation of the power exchanged by the passive system. Alternative correlations for the condensation heat transfer coefficient have been identified and a correction factor for the heat transfer coefficient calculated by RELAP5-3D has been computed. After the application of the correction factor, the set composed by Nusselt-Kutateladze-Chen correlations provided the better agreement with the experimental data. Finally, the accuracy of the code calculation has been evaluated qualitatively and quantitatively by adopting the Fast Fourier Transform Based Method.

Among the Postulated Initiating Events in nuclear fusion plants, the Ingress of Coolant Event (ICE) in the Plasma Chamber is one of the main safety issues. In the present paper, the best estimate thermal-hydraulic system code TRACE, developed by USNRC, has been adopted to study the ICE, and it has been qualified based on experimental results obtained in the Integrated ICE facility at JAERI. A nodalization has been developed in the SNAP environment/architecture, using also the TRACE 3D Vessel component where multidimensional phenomena could occur. The accuracy of the code calculation has been assessed both from a qualitative and quantitative point of view. In addition, an Uncertainty Analysis (UA), with the probabilistic method to propagate the input uncertainties, has been performed to characterize the dispersion of the results. The analysis has been carried out with the DAKOTA toolkit coupled with TRACE code in the SNAP environment/architecture. Results show the adequacy of the 3D nodalization and the capability of the code to follow the transient evolution also at a very low pressure. Response correlations have been computed to characterize the correlation between the selected uncertain input parameters and the Plasma Chamber pressure.