dataesr/scientific-paragraphs-categorization huggingface dataset:
| Mean differences Mean sex differences in brain structure variables were tested using t-tests (FDR corrected, see  (Benjamini and Hochberg 1995) ) and effect sizes were estimated using Cohen's d-value. A negative effect size indicates that the mean was higher in females, and a positive effect size indicates it was higher in males. The brain structure variables were adjusted for age and covariates described above. Graphs were created with R package ggseg  (Mowinckel and Vidal-Pineiro, 2019).
| Shift Function To assess the nature of the variability difference between males and females, shift functions were estimated for each brain measure that showed significant variance differences between males and females using quantile regression forests  (Meinshausen 2006; Rousselet, Pernet, and Wilcox 2017) , implemented in the R package quantregForest (see  Wierenga et al. 2018 ) for a similar approach). First, as described above, and females separately after aligning the distribution means. Let q be a probability between 0 and 1. The quantile function specifies the values at which the volume of a brain measure will be at or below any given q. The quantile function for males is given as Q(qj males) and for females as Q (qjfemales). The quantile distance function is then defined as: T A B L E 1 (Continued) D q ð Þ = Q qjmales ð Þ-Q qjfemales ð Þ A bootstrap method was used to estimate the standard error of the quantile difference functions, which was used to form approxi-.
For thickness, stronger male than female homogeneity was observed in 21% of the correlations, while stronger female correlations were observed in <1% of the correlations (Figure  6C ). This difference was significant, X 2 (1, N = 484) = 460. 300, p <. 001. In this study, we analyzed a large lifespan sample of neuroimaging data from 16,683 participants spanning nine decades of life starting at birth. Results confirmed the hypothesis of greater male variability in brain structure  (Forde et al. 2020; Ritchie et al. 2018; Wierenga et al. 2018 Wierenga et al. , 2019)). Variance differences were more pronounced for subcortical volumes and regional cortical surface area than for regional cortical thickness. We also corroborated prior findings of greater male brain structural variance at both upper and lower tails of brain measures  (Wierenga et al.
tion to LN. BIG -Nijmegen 1. 5T; BIG -Nijmegen 3T: The BIG database, established in Nijmegen in 2007, is now part of Cognomics, a joint initiative by researchers of the Donders Centre of Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud university medical centre, and the Max Planck Institute for Psycholinguistics. The Cognomics Initiative is supported by the participating departments and centres and by external grants, including grants from the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. The authors also acknowledge grants supporting their work from the Netherlands Organization for Scientific Research (NWO), i. e. the NWO Brain & Cognition Excellence Program (grant 433-09-229), the Vici Innovation Program (grant 016-130-669 to BF) and #91619115.
ACKNOWLEDGEMENTS  ADHD NF-Study: The Neurofeedback study  was partly funded by the project  D8  of the Deutsche Forschungsgesellschaft collaborative research center  636. Barcelona 1. 5T , Barcelona 3T: The  Marató TV3 Foundation  (# 01/2010 , # 091710 ). Barcelona-Sant Pau: Miguel Servet Research Contract  CPII16/0020  ( Spanish Government, National Institute of Health, Carlos III ); the  Generalitat de Catalunya  ( 2017SGR01343 ). Betula -Umea University: KA Wallenberg Founda-.
The living ancestor's asexual fitness We next sought to understand why the living ancestor and the Alpechin descendants coexist in nature, while intermediate stages have not been isolated. We therefore measured their clonal reproduction in many environments permitting mitosis, and their survival after nutrients depletion. The living ancestor was often fitter than both S. cerevisiae and S. paradoxus parent species and the experimental F1 hybrid (in 40, 83 and 28% of environments respectively, Fig. 4a , Extended Data Fig. 9a , Supplementary Table  7 ). Gametes derived from the living ancestor suffered widespread (77/82 environments) and severe (33% average increase in doubling time) defects in asexual reproduction. Extensive survival defects amplified, rather than compensated for, these defects (Extended Data Fig.
We removed excess liquid by drying plates in a laminar air-flow in a sterile environment. Pre-cultured populations were mitotically expanded until stationary phase (2 million cells; 72 h), were again subsampled (~50. 000 cells; short pins) and transferred to experimental plates, containing the medium of interest (Supplementary Table  7 ). Synthetic grape must (SGM) was prepared as previously described  48. We tracked population size expansion using the Scan-o-matic system (v1. 5. 7, https://github. com/Scan-o-Matic/scanomatic). Plates were maintained undisturbed and without lids for the duration of the experiment (72 h) in high-quality desktop scanners (Epson Perfection V800 PHOTO scanners, Epson Corporation, UK) standing inside dark, humid and thermostatic cabinets with intense air circulation.
3. 0) to obtain filtered subreads. The assembly was processed with the LRSDAY (v1. 0. 0) pipeline  52. We performed de novo assembly using Canu (v1. 5)  53  by setting the overall (diploid) genome size to 24 Mb. We then aligned the raw PacBio reads to our Canu assembly by pbalign (distributed with GenomicConsensus v2. 0. 0) and polished the assembly by Quiver (distributed with GenomicConsensus v2. 0. 0)  54  , correcting sequencing errors. We further mapped the Illumina reads to the polished genome assembly for another round of error-correction, using Pilon (v1. 22)  55. Scaffolds were manually visualised and inspected as dot-plots using the program MUMMERplot included in the suite MUMmer (v3. 1)  56. We additionally used the genome sequence of 25 living ancestor gametes to phase the living an e o ' genome an to determine its karyotype structure.
Phylogenetic analysis of the living ancestor's subgenomes We performed de novo genome assembly based on deep (~200X) Illumina sequencing data for the living ancestor and 32 representative strains sampled from the 1002 Yeast Genome Project 5. The genome assembly was performed by ABySS (v1. 9. 0)  51  wi h he op ion '-k 64'. All a em le scaffolds shorter than 1 kb were removed. The 33 assemblies were subsequently annotated using LRSDAY (v1. 0. 0)  24, 52. For the living ancestor, we further partitioned its annotated genes into two subsets: the S. cerevisiae-like genes and the S. paradoxus-like genes, based on the tblastx (v2. 2. 31+) search against our curated yeast proteome database containing genes from 7 S. cerevisiae and 5 S. paradoxus representative strains  24. Each gene was classified as S.
Mapping LOH and introgressions' boundaries We annotated LOH and introgression regions based on the allelic fraction at a defined set of single nucleotide markers derived from the alignment of DBVPG6765 (Wine/European S. cerevisiae) and CBS432 (European S. paradoxus) genome assemblies. We generated a list of 1059399 reliable markers between the two assemblies using the software nucmer included in the suite MUMmer (v3. 1) 56 wi h he op ion "-C" o ex l e ma ke wi h am ig o po i ion a ignmen. The e markers reside within the core chromosome regions where near-perfect synteny is observed between the S. cerevisiae and S. paradoxus genomes. Because synteny is lost within subtelomeres  24  , these regions were excluded from our analysis. The over 1M markers distributed over 11210867 bp of aligned genome gives a very dense map of polymorphic markers at an average distance of ~10 bp.
We mapped Illumina reads on the S. cerevisiae DBVPG6765 assembly using BWA (v0. 7. 12). Read mapping runs were stored in the Sequence Alignment/Map (SAM) format and its binary form (BAM) and post-processing steps were performed by picard tools (v2. 8. 0). Sorting, indexing and per-position coverage calculation were performed using SAMtools (v1. 2). We used the software freebayes (v0. 9. 5) to call markers from the BAM files. Results were stored in Variant Call Format (VCF) files. We examined the fraction of the two possible alleles at every marker and used in-house Perl scripts to assign a genotype with the following distinguishing criteria: i) If the allelic fraction is equal to 0. 5 the marker is in heterozygosity, ii) If the allelic fraction equals 0 or 1 the marker is in homozygosity for the S.
cerevisiae LOH), giving a total of 15. 2% of genome in LOH (9. 7% for S. paradoxus and 5. 5% for S. cerevisiae). We genotyped 77955 introgressed markers in the Alpechin clade, distributed along 902805 bp (with a range of 467265 -583627 bp in individual strains), giving a total of 8% of introgressed genome (ranging from 4% to 5% in individual strains). Subtelomeres were excluded from the genome size estimations. We intersected the genomic coordinates of S. paradoxus LOH in the living ancestor with the genomic coordinates of introgressions in the Alpechins, using the program intersectBed included in the suite bedtools (v2. 17). We retrieved the genomic positions in which S. paradoxus LOH and introgressions overlapped, for a total of 534736 overlapping bp (containing 43872 markers). We calculated the expected random number of overlapping markers (8221) as the total number of markers (1059399) multiplied by the percentage of genome in S.
Estimation of the living ancestor's base substitution rate and phenotypic impact Read mapping and variant calling of the living ancestor before propagation (generation zero, G 0 ) an he m a ion a m la ion line we e pe o me a e i e in he pa ag aph "Va ian alling an NV anal i ". We il e e he VCF files to keep only SNVs with quality higher than 100 and coverage higher than 20, and we excluded SNVs in subtelomeric regions  24. We subtracted the SNVs present in the living ancestor G 0 from its derived mutation accumulation lines using the vcf-isec program included in the suite VCFtools (v0. 1. 16). Next, we searched for shared SNVs present in at least two mutation accumulation lines and removed them from the VCF files, based on the assumption that identical de novo mutations are highly unlikely to arise independently and derived from incorrect mapping or poor coverage.
87 million years ago (mya) with 95% central 95% probability range covering 4. 06 mya -5. 84 mya (based on previous estimate  68  ). -MCMC chain length: 100000000 with 1000 pre burn-in. -MCMC chain logging frequency: every 1000 steps. Two independent runs were performed and the convergence of these two MCMC runs was assessed by Tracer (v1. 7. 1) (http://tree. bio. ed. ac. uk/software/tracer/). TreeAnnotator (distributed with BEAST2) was used to generate the consensus tree with 10% burn-in. The resulting consensus tree was visualized with FigTree (v1. 4. 4) (http://tree. bio. ed. ac. uk/software/figtree/).
GO term analysis and annotation of LOH and introgressed genes Introgressed genes were annotated using the CBS432 and DBVPG6765 genome annotations (Supplementary Table  9 )  24. Standard GO term analysis was performed with the GO Term Finder tool available at Saccharomyces Genome Database (SGD). Significant GO terms were extracted by the algorithm implemented in the tool, with a False Discovery Rate (FDR) corrected α threshold of 0. 05 (Supplementary Table  8 ). respectively. Numbers below the bottom chromosome pair report de novo mutations that are shared between the living ancestor and the Alpechins, again partitioned into those occurring on the S.
Acknowledgements We thank  Anders Bergström ,  Gilles Fischer ,  Bertrand Llorente ,  Simone Mozzachiodi  and  Lorenzo Tattini  for critical reading of the manuscript. This work was supported by  Agence Nationale de la Recherche  ( ANR-11-LABX-0028-01 ,  ANR-13-BSV6-0006-01 ,  ANR-15-IDEX-01 ,  ANR-16-CE12-0019  and  ANR-18-CE12-0004 ) and the  Swedish Research Council  ( 2014-6547 ,  2014-4605  and  2018-03638 ). We thank the  Institut Curie NGS platform  for generating the sequence data. J. S. is a fellow of the  University of Strasbourg Institute for Advanced Study  and a member of the  Institut Universitaire de France.
Data availability The genome sequences generated in this study are available at Sequence Read Archive (SRA), NCBI under the accession codes BioProject ID PRJNA594913, Biosample ID SAMN13540515 -SAMN13540586. Re-analysed sequencing data are available as part of the project ERP014555 in the Sequence Read Archive (SRA). The phenotype data are available within the supplementary information files. The source data underlying Figs. 1b, 2a-d, 4a-d and Extended Data Fig. 1a-b, 2, 4a-b, 5a-d, 6a-d, 7b-e, 8a, c, d, 9a-b, 10a-c are provided in the Source Data File. All the strains generated in this work are freely available upon request.
The paper is organized as follows: In section Materials and Methods we shall describe our recently introduced UCN circuit  (Rozenberg et al. , 2019). We shall demonstrate how the basic behavior of the UCN can be very precisely captured by means of numerical simulations obtained with LTspice (LTspice R , 2020 that we validate against actual circuit measured data. Section Results contain the main results of the present work. In the first part, we exploit the simplicity of the simulation package capabilities to explore extensions of the basic UCN circuit block, searching for different types of biologically relevant dynamical behaviors. We shall demonstrate that small variants of a basic circuit allow us to capture at least 12 out of the 20 dynamical behaviors, including some inhibition ones. In the second part of this section, we use the simulation results to achieve the main goal of this work, namely to provide the explicit circuits and measure them to demonstrate that the thyristor-based UCN can realize the complex firing patterns observed in biological systems.
Validation of Numerical Simulations of the UCN Circuit We may exploit the fact that our UCN is simply implemented with standard electronic components to reproduce the spiking behavior using the standard electronic circuit simulation package LTspice, which is freely available (freeware) (LTspice R , 2020). In order to represent the actual SCR that we adopted in our circuits, we found convenient to modify the default parameters of the EC103D1 thyristor (SCR) model  (LITTELFUSE, 2019). Specifically, we changed the value of the parameter BF from 6. 10 to 2. 90, and all the other parameters were left unchanged (further details are provided in the Supplementary Material). The main results of the simulations of the basic UCN block are shown in Figure  1. The validation of the numerical data is done by comparison to the data measured in the actual circuit.
As can be observed in the figure, the agreement is excellent. We may note a minor difference in a transient effect when the input reached the threshold voltage to excite spikes. Besides the small difference, the remarkable agreement validates the LTspice package simulations as an efficient method to explore variants of the basic UCN circuit and search for biologically relevant dynamical behaviors. The results of the numerical exploration will then serve us to inform the actual circuit implementations, which is the ultimate goal of our work.
16  and used recently in vaccine response studies in human blood  18, 17, 26, 28  as well as in sheep blood. 20 The 334 BTMs were initially constructed from publicly available microarray data specific to human blood; they include cell-typespecific modules, allowing to point a cell type likely to be implicated in the gene expression modulation and we translated them into sheep BTMs (see Methods). The lists of the enriched BTMs induced by the three vaccines are provided in Supplementary Data Set 2 (iFMDV), 3 (Ad5-FMDV), 4 (Ad5-FMDV+ISA206V). The gene expression modulated between T4H and T0H did not retrieve interpretable functions. Interestingly at T24H, we retrieved several modules which were shared between the three vaccines (Fig. 5 , Supplementary Data Set 2-4) and include upregulated BTMs related to monocytes (M11.
ACKNOWLEDGEMENTS We thank  Pierre Sarradin  and  Edouard Guitton , directors of  Experimental Infectiology Platform (Nouzilly, France ), and the zootechnical staff, in particular  Maud Renouard ,  Olivier Boulesteix , and  Thierry Chaumeil  for expert and dedicated help with the sheep experiments. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) for providing help, computing and storage resources. We acknowledge the High-throughput sequencing facility of the  Institute for Integrative Biology of the Cell  for its sequencing and bioinformatics expertise. Many thanks to  Grégory Scapardini  for his technical assistance during the VNT analysis of the samples. We are grateful to  US-Department of Agriculture, Agricultural Research Service, North East Area, Plum Island Animal Disease Center, NY, USA , for providing Ad5-FMDV under CRADA No  58-3K95-1658M  ( Teresa de los Santos ) and to  Justin Widener  ( Boehringer Ingelheim ) for vaccine batch follow-up.
DATA AVAILABILITY All datasets, tools, and sheep samples developed by the authors are available to readers upon writing to the corresponding author. The RNA-seq data were deposited on Gene Expression Omnibus, NCBI series record GSE135609 (https://www. ncbi. nlm. nih. gov/geo/info/seq. html). The supplementary data sets are also available under https://doi. org/10. 5281/zenodo. 3545663.
In this work, we present a two-dimensional Finite Element Method (2D-FEM) model implemented on a commercial software, COMSOL Multiphysics, that is used to predict the high temperature behavior of surface acoustic wave sensors based on layered structures. The model was validated by using a comparative study between experimental and simulated results. Here, surface acoustic wave (SAW) sensors consist in one-port synchronous resonators, based on the Pt/AlN/Sapphire structure and operating in the 2. 45-GHz Industrial, scientific and medical (ISM) band. Experimental characterizations were carried out using a specific probe station that can perform calibrated measurements from room temperature to 500 • C. In our model, we consider a pre-validated set of physical constants of AlN and Sapphire and we take into account the existence of propagation losses in the studied structure.
However, it has a number of limitations, the most important being the excessive damping (propagation losses) of surface waves when the operating frequency exceeds 1 GHz  [15]. Recently, efforts have been devoted to the development of a SAW sensor heterostructure for high and very high temperature applications such as AlN/Pt/LiNbO 3  [16]  and AlN/Sapphire  [17] [18] [19] [20] [21]. The AlN/Sapphire SAW structure can also operate at very high frequencies  [18]. To predict the behavior of a SAW devices in harsh conditions and to design high-performance devices, modeling techniques such as the δ function model  [22] , the coupling of mode (COM) model  [23] , the P-matrix model  [24, 25] , and the equivalent circuit model  [26, 27]  have become essential in their design. The Finite Element Method, which can be implemented using commercial software such as COMSOL Multiphysics, has an excellent ability to model and analyze SAW devices and has a simplicity advantage compared to other methods when it comes to multilayer structures, which are the focus of the present study (Pt/AlN/Sapphire).
We can access in COMSOL the modeled values of the electric potential as a function of the frequency and consequently obtain modeled module of impedance as a function of temperature. Figure  4  shows simulated (blue) and experimental (red) impedance as a function of the frequency at 23 • C. We can see a good agreement between the two curves. The simulated values of frequencies f r and f a are, respectively, 2. 4166 GHz and 2. 4349 GHZ. Sensors 2020, 20, x FOR PEER REVIEW 8 of 14 The electrical impedance is frequently used in SAW analysis. Its modulus makes it possible to highlight the resonance and anti-resonance phenomena associated with the frequencies (minimum value of module) and (maximum value of module). It can be defined by  [39, 40] : = ( 16 ) where is the electric potential, the angular frequency and the electric charge.
We can access in COMSOL the modeled values of the electric potential as a function of the frequency and consequently obtain modeled module of impedance as a function of temperature. Figure  4  shows simulated (blue) and experimental (red) impedance as a function of the frequency at 23 °C. We can see a good agreement between the two curves. The simulated values of frequencies and are, respectively, 2. 4166 GHz and 2. 4349 GHZ. The electrical impedance is frequently used in SAW analysis. Its modulus makes it possible to highlight the resonance and anti-resonance phenomena associated with the frequencies (minimum value of module) and (maximum value of module). It can be defined by  [39, 40] : = ( 16 ) where is the electric potential, the angular frequency and the electric charge. We can access in COMSOL the modeled values of the electric potential as a function of the frequency and consequently obtain modeled module of impedance as a function of temperature.
The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002-2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes.
Previous studies have performed manifold classifications of the Nhecolândia lakes using passive and active sensors at short-term periods or single images (see  [11, 12, 14, 26] ). Nowadays, Google Earth Engine (GEE) offers a new suitable cloud-based platform for environmental data analysis from local to planetary scales, with rapid access and processing of multiple orbital data from different missions  [27]. The data catalogue contains a variety of standard Earth Science raster datasets consisting of imagery, geophysical, climate and weather, and demographic data collections with widely used geospatial datasets, such as the entire Landsat archive. GEE has exponentially increased the feasibility and reliability of remote sensing analysis, processing large volumes of data of long-term environmental analysis.
Datasets that would previously have been analyzed as single, neighboring, or temporally sequential scenes can now be processed faster and mass bulked  [28]. Taking into account the capabilities of GEE, we hereby propose an estimative of water pH values for Nhecolândia lakes using long-term time-series analyses of satellite images. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere. Recent studies have highlighted that water pH is one of the key factors in controlling the biogeochemical functioning and, consequently, the methane, carbon dioxide, and nitrous oxide production and emission of water bodies  [29, 30]. In this context, the pH of surface water has been shown to be one of the key tools in controlling GHG emissions. Here, we create a new pathway to analyze the pH of the Nhecolândia lakes using a cloud-based time series (2002-2017) of Landsat TM/ETM+ and OLI images, with threefold objectives: (I) To estimate pH values, (II) to analyze spectral signature variations of lakes with changes in pH, and (III) to correlate the different types of lakes with relief landforms and drainage networks.