Resultados de investigación

Published as part of Battiston, Roberto, Ortego, Joaquín, Correas, José R. & Cordero, Pedro J., 2014, A revision of Apteromantis (Mantodea: Mantidae, Amelinae): A comprehensive approach to manage old taxonomic and conservation problems, pp. 65-77 in Zootaxa 3797 (1) on page 70, DOI: 10.11646/zootaxa.3797.1.8, http://zenodo.org/record/4915383, FIGURE 3. Variation in (a) female head and (b) male genitalia shape along the first principal component (PC1) extracted from Fourier analyses. The last three columns show the case where the score takes –2 standard deviations (S.D.), mean, and +2 S.D., respectively. The first column shows the overlapped drawing for these three cases., Peer reviewed

Se presentan los datos del catálogo de los ejemplares ibéricos del género Sphingonotus Fieber, 1852 (Orthoptera: Acrididae: Oedipodinae) conservados en la Colección de Entomología del Museo Nacional de Ciencias Naturales de Madrid (MNCN-CSIC). En esta colección se conservan desde los ejemplares más antiguos examinados y publicados por los pioneros en la ortopterología ibérica, hasta los más recientes que han servido para aportar interesantes novedades al conocimiento del género. Todos los ejemplares han sido revisados e identificados de acuerdo al conocimiento actual de este complicado grupo., Peer reviewed

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30 pages, 11 figures, 10 tables.-- This work is distributed under the Creative Commons Attribution 4.0 License, The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2022 is an update of the previous version, GLODAPv2.2021 (Lauvset et al., 2021). The major changes are as follows: data from 96 new cruises were added, data coverage was extended until 2021, and for the first time we performed secondary quality control on all sulfur hexafluoride (SF6) data. In addition, a number of changes were made to data included in GLODAPv2.2021. These changes affect specifically the SF6 data, which are now subjected to secondary quality control, and carbon data measured on board the RV Knorr in the Indian Ocean in 1994-1995 which are now adjusted using certified reference material (CRM) measurements made at the time. GLODAPv2.2022 includes measurements from almost 1.4 million water samples from the global oceans collected on 1085 cruises. The data for the now 13 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, chlorofluorocarbon-11 (CFC-11), CFC-12, CFC-113, CCl4, and SF6) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but converted to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For the present annual update, adjustments for the 96 new cruises were derived by comparing those data with the data from the 989 quality-controlled cruises in the GLODAPv2.2021 data product using crossover analysis. SF6 data from all cruises were evaluated by comparison with CFC-12 data measured on the same cruises. For nutrients and ocean carbon dioxide (CO2) chemistry comparisons to estimates based on empirical algorithms provided additional context for adjustment decisions. The adjustments that we applied are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1% in oxygen, 2% in nitrate, 2% in silicate, 2% in phosphate, 4μmolkg-1 in dissolved inorganic carbon, 4μmolkg-1 in total alkalinity, 0.01-0.02 in pH (depending on region), and 5% in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon and Acidification Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/oceans/GLODAPv2-2022/, last access: 15 August 2022). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones - the Arctic, Atlantic, Indian, and Pacific oceans - under 10.25921/1f4w-0t92 (Lauvset et al., 2022). These bias-adjusted product files also include significant ancillary and approximated data, which were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2022 methods and provides a broad overview of the secondary quality control procedures and results., Nico Lange was funded by EU Horizon 2020 through the EuroSea action (grant agreement 862626). Siv K. Lauvset acknowledges internal strategic funding from NORCE Climate. Leticia Cotrim da Cunha was supported by Prociencia/UERJ 2022-2024 and CNPq/PQ2 309708/2021-4 grants. Marta Álvarez was supported by IEO RADPROF project. Peter J. Brown was partly funded by the UK Climate Linked Atlantic Sector Science (CLASS) NERC National Capability Long-term Single Centre Science Programme (grant NE/R015953/1). Anton Velo and Fiz F. Pérez were supported by BOCATS2 (PID2019-104279GB-C21) project funded by MCIN/AEI/10.13039/501100011033 and contributing to WATER:iOS CSIC PTI. Funding for Li-Qing Jiang and the CODAP-NA development team (Simone R. Alin, Leticia Barbero, Richard A. Feely, Brendan R. Carter) comes from the NOAA Ocean Acidification Program (OAP, project number: OAP 1903-1903) and NOAA National Centers for Environmental Information (NCEI). Brendan R. Carter thanks the Global Ocean Monitoring and Observing (GOMO) program of the National Oceanic and Atmospheric Administration (NOAA) for funding their contributions (project no. 100007298) through the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CIOCES) under NOAA Cooperative Agreement NA20OAR4320271, contribution no. 2022-2012. Richard A. Feely and Simone R. Alin acknowledge the NOAA GOMO (project no. 100007298) and the NOAA Pacific Marine Environmental Laboratory. Henry C. Bittig gratefully acknowledges financial support by the BONUS INTEGRAL project (grant no. 03F0773A). Bronte Tilbrook was supported through the Australian Antarctic Program Partnership and the Integrated Marine Observing System. Matthew P. Humphreys acknowledges EU Horizon 2020 action SO-CHIC (grant no. 821001). Adam Ulfsbo was supported by the Swedish Research Council FORMAS (grant no. 2018-01398). Jens Daniel Müller acknowledges support from the European Union's Horizon 2020 research and innovation program under grant agreement no. 821003 (project 4C). Alex Kozyr and Li-Qing Jiang were supported by NOAA grant NA19NES4320002 (Cooperative Institute for Satellite Earth System Studies – CISESS) at the University of Maryland/ESSIC. GLODAP also acknowledge funding from the Initiative and Networking Fund of the Helmholtz Association through the project “Digital Earth” (ZT-0025) and from the United States National Science Foundation grant OCE-2140395 to the Scientific Committee on Oceanic Research (SCOR, United States) for International Ocean Carbon Coordination Project. The contribution of Leticia Barbero was carried out under the auspices of CIMAS and NOAA, cooperative agreement no. NA20OAR4320472, Peer reviewed

This paper presents the new global Mesoscale Eddy Trajectory Atlases (META3.1exp DT all-satellites, 10.24400/527896/a01-2021.001, Pegliasco et al., 2021a; and META3.1exp DT two-satellites, 10.24400/527896/a01-2021.002, Pegliasco et al., 2021b), composed of eddy identifications and trajectories produced with altimetric maps. The detection method used is inherited from the py-eddy-tracker (PET) algorithm developed by Mason et al. (2014), and is optimized to efficiently manage large datasets, and thus long time series. These products are an improvement on the earlier META2.0 product, which was produced by SSALTO/DUACS and distributed by AVISO+ (http://aviso.altimetry.fr, last access: 8 March 2022) with support from CNES, in collaboration with Oregon State University and support from NASA, and based on the Chelton et al. (2011) code. META3.1exp provides supplementary eddy information, such as eddy shapes, eddy edges, maximum speed contours, and mean eddy speed profiles from the center to the periphery. The tracking algorithm is based on overlapping contours, includes virtual observations, and acts as a filter with respect to the shortest trajectories. The absolute dynamic topography (ADT) field is now used for eddy detection, instead of the previous sea level anomaly (SLA) maps, in order to better represent the dynamics in the more energetic oceanic regions and in the vicinity of coasts and islands. To evaluate the impact of the changes from META2.0 to META3.1exp, a comparison methodology has been applied. The similarity coefficient (SC) is based on the ratio of the eddy overlaps to their cumulative area, and allows for extensive comparison of the different datasets in terms of geographic distribution, statistics on the main physical characteristics, changes in the lifetimes of the trajectories, etc. After evaluating the impact of each change separately, we conclude that the major differences between META3.1exp and META2.0 are due to the change in the detection algorithm. META3.1exp contains smaller eddies and trajectories lasting at least 10d; these were not available in the META2.0 product. Nevertheless, 55% of the structures in META2.0 are similar to META3.1exp, thereby ensuring continuity between the two products and their physical characteristics. Geographically, the eddy distributions differ mainly in the strong current regions, where the mean dynamic topography (MDT) gradients are sharp. The additional information on the eddy contours allows for more accurate collocation of mesoscale structures with data from other sources, and so META3.1exp is recommended for multi-disciplinary application., This research has been supported by the Centre National d'Etudes Spatiales (CNES) by a Developing Use of Altimetry for Climate Studies (DUACS) R & D contract., Peer reviewed

This dataset contains the number and width of increments found in the RSS of the 10 M. longimana’s lower beaks used in this study. Further information related with the studied beaks such as beak measurements, origin and sample date can be found in the published article., In this work we used upper and lower beaks of Moroteuthopsis longimana collected from the diet of Antarctic toothfish Dissostichus mawsoni from the Pacific (Ross and Amundsen Seas) and Atlantic (South Sandwich Islands) sectors of the Southern Ocean to study the feasibility of using beaks collected from predators’ stomachs to study the age of Southern Ocean oceanic squid and to estimate the age and growth patterns of M. longimana. We analysed the presence of micro-increments in the rostrum sagittal section (RSS) and the lateral wall of both upper and lower beaks. Lower beak’s RSS was the only section where it is possible to observe and count a readable sequence of increments that allowed to estimate the age of oceanic squid using beaks collected from predators’ stomachs., This work was supported by the strategic program of Marine and Environmental Sciences Centre (MARE), financed by the FCT (UIDB/704292/2020). JPQ was supported by a FCT/MCTES through national funds (PIDDAC) and Portuguese Polar Program (PROPOLAR) and by FCT PhD scholarship co-financed by FSE (SFRH/BD/144320/2019)., Peer reviewed

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/metabo12050452/s1: Supplementary File S1, Peer reviewed

The following are available online at https://www.mdpi.com/article/10.3390/cells11020227/s1, Supplementary File S1: File S1: IDE position-specific scoring matrix. Supplementary File S2: Figure S1: Genotyping of IDE-KO mouse colony; Figure S2: Structural alignments of clan ME metalloendopeptidases; Figure S3: Gene architecture comparison between 26 IDE and NRD proteins; Figure S4: Controls for microscopy experiments; Molecular phylogeny data S2: Trees output (text format). Supplementary File S3: Table S1: Sequences used in Figure 1; Table S2: Sequences used in Figure 2A; Table S3: Sequences used in Figure 2C; Table S4: Sequences used in Figure 3A; Table S5: SignalP predictions results; Table S6: SignalP summary per Phylum; Table S7: DeepLoc results., Peer reviewed

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14030670/s1, Figure S1. Sanger sequencing results of the CYBA (upper panel) and TRPM4 (lower panel) wild type and mutant sequences; Figure S2. Splicing functional assays of TRPM4 variant c.25-1G>T. (A) Fluorescent fragment analysis by capillary electrophoresis of RT-PCR products generated by the wild type and mutant minigenes. The canonical and the aberrant ∆(E2p2) transcripts are shown as blue and red peaks, respectively, while the LIZ500 size standard is displayed as orange peaks. (B) Schematic representation of the canonical (above) and anomalous (below) transcripts. Variant c.25-1G>T is shown in red; acceptor sites are underlined; splicing events are shown as broken arrows; Figure S3. SiRNA knockdown of CYBA or TRPM4 lead to reduced mRNA and protein of the respective genes: mRNA (A) and protein (B) levels of CYBA and TRPM4 decreased after siRNA knockdown of CYBA or TRPM4 in LS174T cells; mRNA (C) and protein (D) levels of CYBA and TRPM4, decreased after siRNA knockdown of CYBA or TRPM4 in HT-29 cells. For real-time PCR assays, three independent experiments in triplicate were performed and means and standard errors of the means are presented on the graphs. For western blot two independent experiments were performed;Table S1. Summary of the sequencing coverage and quality statistics for each sample; Table S2. Loss-of-function variants segregated with the disease in 15 colorectal cancer families., Peer reviewed