BUSCADOR RECOLECTA

Extra details of the mean-field dynamics of the trimer and an explanatory note on cascaded quantum systems., Peer reviewed

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This is a supplementary file that includes all figures and tables that complement the information provided in the manuscript. Contents of this file: Figures S1 to S10 and Table S1., Peer reviewed

[Description of methods used for collection/generation of data] We generated raw TE libraries, or used the ones already available, for the six species analyzed, with three de novo tools, namely RepeatModeler2 (RM2; Flynn et al., 2020), EDTA (Ou et al., 2019) and TEdenovo from REPET (Flutre et al., 2011). To generate the libraries the following assemblies were used: GCA_002050065.1 for D. melanogaster, GCF_000738735.6 for C. cornix, and GCA_000002035.4 for D. rerio, the RGAP assembly version 7 for O. sativa (available at https://phytozome-next.jgi.doe.gov/info/Osativa_v7_0; Ouyang et al., 2007), and the T2T assemblies GCA_022117705.1 for Z. mays, and GCF_009914755 for H. sapiens. For the for C. cornix, Z. mays and H. sapiens RM2 raw libraries, we generated them using the TEtools Docker image available at https://github.com/Dfam-consortium/TETools. For for D. melanogaster, O. sativa and D. rerio we used the raw libraries available at: https://github.com/jmf422/TE_annotation/tree/master/benchmark_libraries/RM2. For EDTA libraries, we generated them for the six species using the EDTA pipeline v.2.0 with --anno 0 and the other parameters by default. Regarding the REPET libraries, for the D. melanogaster genome, we ran the TEdenovo pipeline using default parameters and followed the recommended steps in the user's guideline (https://urgi.versailles.inra.fr/Tools/REPET/TEdenovo-tuto). For the D. rerio genome, we created a subset of 300 Mb using the PreProcess.py script available with the REPET package and then we ran TEdenovo pipeline using default parameters as for the D. melanogaster genome (Jamilloux et al., 2016). The REPET group kindly provided us with the output generated with the O. sativa genome available in the RepetDB database (Amselem et al., 2019). They also provide us with the libraries for C. cornix and Z. mays. As the reference libraries, we used the Berkeley Drosophila Genome Project (BDGP) dataset (Kaminker et al., 2002) and the Manual Curated TE library (MCTE) provided in Rech, et al., (2022) for D. melanogaster, the library published by Ou et al. (2019) for O. sativa (referred to as "standard library" by the authors), the MClibrary available at Weissensteiner et al., (2020) for C. cornix, the manual curated TE models available in Dfam release 3.7 (J. Storer et al., 2021) and in Repbase version 20181026 for D. rerio, MTEC curated by Ou et al (https://github.com/oushujun/MTEC) for Z. mays, and curated TE models in Dfam 3.7 for H. sapiens. For the rice "standard library”, Dfam and Repbase libraries, we unified the LTR sequences with their corresponding internal part before performing further analysis, using in-house scripts. MCHelper v.1.7.0 was executed with default parameters (to see customizable parameters see Github: https://github.com/GonzalezLab/MCHelper). In the False positive filtering step, MCHelper requires a gene set to detect homology between the consensus sequences and multicopy gene families. We used the following BUSCO gene sets: for D. melanogaster, we used the diptera set (diptera_odb10), for O. sativa and Z. mays, the viriplantae set (viriplantae_odb10), for C. cornix, the aves set (aves_odb10), for D. rerio, the actinopterygii set (actinopterygii_odb10), and for H. sapiens, the mammalian set (mammalia_odb10). To accommodate MCHelper's expectation of a single file with all the hidden Markov models (HMMs), we concatenated all the available HMM files into a single file for each species. For the annotation we ran RepeatMasker v.4.1.2-p1 (Smit et al., 2015) on each of the three genomes with the raw, reference and MCHelper libraries using the -lib parameter, and the following additional parameters: -gff -nolow -no_is -norna. We then used the OneCodeToFindThemAll script (Bailly-Bechet et al., 2014) in each genome annotation to defragment the copies, and we used the “--strict” parameter for D. melanogaster, D. rerio, and C. cornix. [File List] this dataset is composed of six folders, each one corresponding to a species. The species are: D. melanogaster (fruit fly), O. sativa (rice), C. cornix (crow), D. rerio (zebrafish), Z. mays (corn) and H. sapiens (human). The folder for each species contains the assembly used, the BUSCO gene dataset (except for Z. mays because the BUSCO dataset is the same as for O. sativa), a folder for each the de novo program used to generate the TE library (RM2 (RepeatModeler2), EDTA and REPET), plus a folder with the files for the reference library. Each de novo program folder contains the raw library (produced by that program), a CSV containing the length by order of the TE consensus sequences, the output data of the MCHelper execution (in the folder curation_ite16), as well as the annotation (in the folder RM_annot). The REPET folder has an extra file describing the classification and structural features (ending in denovoLubTEs_PC.classif). Note that REPET was not run for the H. sapiens genome., This work was supported by grant PID2020-115874GB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by grant 2021 SGR 00417 funded by Departament de Recerca i Universitats, Generalitat de Catalunya awarded to J.G. P.S. is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956229. R.D. acknowledges funding from Wellcome grant 207492., Peer reviewed

We present a 1:15,000 geomorphological map of the Maladeta massif in the Central Pyrenees. Our methodology includes fieldwork, the analysis of aerial photographs from 1956 to 2015, and the use of drone flights from 2020 to 2023. The study area consists mainly of granodiorite but also has outcrops of limestone, shales, and quartzites at lower elevations. The landscape of the Maladeta massif is primarily shaped by glacial and periglacial processes, with fluvial, karstic, and hillslope dynamics manifesting in the lower regions. The assessment of moraine thickness has facilitated the determination of maximum glacier thickness during the Little Ice Age (LIA) and has shown that there is no correlation between ice thickness and glacier extent. After the LIA, the massif experienced a continuous glacier retreat. Currently, only 21.4% of the glacier area observed in 1956 remains., This research was made possible through the support of the Interreg-POCTEFA project OPCC ADAPYR, as well as the projects MARGISNOW (ref PID2021-124220OB-I00) and SNOWDUST (ref TED2021-130114B-I00). Ixeia Vidaller acknowledges financial support from the grant FPU18/04978., Peer reviewed

Fig. S1 Location of study plots in the Middle Ebro Valley (Spain). See Table 1 for further details. Fig. S2 A) Number of sheep droppings (mean ± SE) per m2 for each grazing intensity and location. Different letters indicate significant differences among grazing intensities (post-hoc Tukey HSD test; p < 0.05). Significance of differences was tested by fitting a generalized linear mixed model (GLMM) in which grazing intensity and location were set as fixed factors, and study plot was set as a random intercept effect. A negative binomial distribution of errors was specified in the model to deal with overdispersion. B) Schematic representation of study plots and sampling quadrats. Table S1 Mean (± SE) for the variables of interest at the plot level. See Materials and Methods section for further details. Fig. S3 Initial path diagram with the hypothesized causal relationships among variables. Grazing intensity and aridity level are exogenous variables, while woody index, diversity, plant cover, production, plant C:N ratio and fiber index are endogenous variables. A total of four path diagrams were generated, one for each final variable (i.e., plant cover, forage production, forage C:N ratio and forage fiber index). Directions of the arrows indicate the causal relationships between variables. Dashed arrow indicates a non-hypothesized missing path added during the evaluation of the overall models fit. See Materials and Methods section for further details. Fig. S4 Spearman correlation coefficients among all variables included in the SEM analysis. Blue and red colors indicate positive and negative correlations respectively, with more intense colors denoting higher correlation values. Visual representation of the correlation matrix was created with the GGally package in R 4.0.3. Fig. S5 Direct (dark colors) and indirect (light colors) effects of grazing intensity (solid bars) and aridity (dashed bars) on community plant cover, forage production, C:N ratio and fiber index, community structure and diversity at A) p < 0.05 and B) p < 0.1. Red bars indicate negative effects. Blue bars indicate positive effects. See Materials and Methods section for further details. Table S2 List of plant species found at the study plot level, their labels, forage value (FV) and plant cover (%)., Peer reviewed

Strong coupling between light and molecular matter is currently attracting interest both in chemistry and physics, in the fast-growing field of molecular polaritonics. The large near-field enhancement of the electric field of plasmonic surfaces and their high tunability make arrays of metallic nanoparticles an interesting platform to achieve and control strong coupling. Two dimensional plasmonic arrays with several nanoparticles per unit cell and crystalline symmetries can host topological edge and corner states. Here we explore the coupling of molecular materials to these edge states using a coupled-dipole framework including long-range interactions. We study both the weak and strong coupling regimes and demonstrate that coupling to topological edge states can be employed to enhance highly-directional long-range energy transfer between molecules., We.acknowledge financial support from the grants BICPLAN6G (TED2021-131417B-I00) and LIGHTCOMPAS (PID2022-137569NB-C41), funded by MCIU/AEI/10.13039/501100011033, “ERDF A way of making Europe”, and European Union NextGenerationEU/PRTR and from MCIU through predoctoral fellowship PRE2019-090689., Peer reviewed

Description of the project: The reference fire perimeters for Madagascar were obtained in the framework of the project "Fire regimes and ecosystem services in African biodiversity hotspots: can fire policies favoring climate change mitigation, biodiversity and local communities converge?" financed by the Swiss Network for International Studies (SNIS). Among the goals of the project is the accurate characterization of burned area and fire regimes at Sentinel-2 spatial resolution (20m). The burned area database has been produced for the period 2016-2022 in Madagascar, southern Mozambique, Eswatini and Eastern South Africa (Fernández-García et al., 2023: https://doi.org/10.5281/zenodo.8201841). The MGBAS2 reference data presented here is the validation data for the mentioned burned area database. The characterization of burned area is the first step of the project that also aims to analyze the drivers of fire regimes and their consequences on carbon dynamics, biodiversity and local livelihoods., This file contains four folders: - metadata: includes a .csv file for each year (2019 and 2021) containing the file name of all the reference files included in the dataset and their associated information and a read.txt file with a description of the fields in the csv file. - regions: includes a shapefile for each year (2019 and 2021) containing all the sample sites covered by the dataset. - sampling: includes a shapefile with the fraction of burned land in 2021 according to the MGBAS2 burned area database (0= low burned area 1=high burned area). - shapefiles: includes the validation reference shapefiles for each year (2019 and 2021)., The MGBAS2 reference data was derived from Sentinel-2 images over a set of 6 tiles (sampling units)sampled following a design customized for the Sentinel tiling grid system and to represent low and high burned area occurrence strata in Madagascar. Over each tile Sentinel-2 time series were defined based on a set of conditions for minimizing cloud cover and to guarantee series length and a minimum time lag between image pairs. Sentinel-2 image pairs were classified with a Random Forest (RF) algorithm to provide burned perimeters of depicting areas that burned between the two dates that were combined in a synthetic burned area reference dataset. This dataset represent for each unit burned and unburned polygons and masked areas. A detailed description of the dataset can be found in Fernández-García et al.(2023) ("Madagascar’s burned area from Sentinel-2 imagery (2016–2022): four times higher than from lower resolution sensors", in preparation)., This research has been funded by the project "Fire regimes and ecosystem services in African biodiversity hotspots: can fire policies favoring climate change mitigation, biodiversity and local communities converge" financed by the Swiss Network for International Studies (SNIS)., Peer reviewed