dataesr/scientific-paragraphs-categorization huggingface dataset:
2-0. 6 s after the stimulus presentation. We imported from Brainstorm into Field-Trip, the forward model and lead field model of each subject, which describe how activity in the source space translates into activity in the sensor space. We discretized the source space into a 3-dimensional grid with a 1 centimeter isotropic resolution. We then estimated, using all trials together, the beta power on each grid point using a beamformer (Dynamical Imaging of Coherent Sources), by computing a filter that projects sensor cross-spectral density data onto source space. This same filter was then used to project separately each condition (high vs. low confidence). We then computed the paired difference of power in source space, normalized the sources anatomically across subjects, and computed a group-level t-test for the condition difference.
Pupil size recording and pre-processing Eye gaze and pupil size were monitored using EyeLink 1000. Blinks were delineated (adding a margin of 50 ms before and after) and the data within them linearly interpolated; the signal was then low-pass filtered (5 Hz) and epoched within -0. 5 to 1. 5 s relative to the stimulus onset. Epochs whose total blink duration exceeded 20% were excluded. Finally, the data was zscored per subject.
Regression models All multiple regression models included a constant and z-scored predictors. The significance of regression coefficients were assessed with group-level t-tests. Regressions were estimated with Matlab's regstats. m function (based on QR decomposition), or custom code derived from it. We often used the "residual" Bayes-optimal confidence as a predictor, which was computed by linearly regressing out several effects related to the prediction itself. More precisely, we estimated a multiple linear regression of the Bayes-optimal confidence, using as predictors the prediction itself (i. e. the Bayes-optimal estimate of the transition probability of the next stimulus, p(A)), its square and logarithm, log(p(A)) and log(p(B)). Using the square and logs enables to capture non-linear effects.
Cross-validated predictive accuracy In order to estimate whether the subject's confidence could be predicted from the pre-stimulus power across different frequencies, we used a cross-validated ridge regression. The power was averaged within -0. 5 to 0 s relative to the stimulus onset and z-scored across questions. We used a ridge penalty of 0. 01, but the results are quite robust to the choice of this parameter. We adopted a cross-validation approach: the data were split into 20 distinct sub-sets of inter-leaved trials and at each iteration, the ridge regression was estimated on all sub-sets but one, and its parameter estimates were used to predict subjective confidence on the left-out sub-set. We assessed the accuracy of this prediction as the Pearson correlation between predicted and actual confidence at the subject's level.
, 2003). On the other hand, we envisage that  (2)  the study duration should be less relevant when the spatial structure of the metacommunity is maintained through time (low temporal variation; Figures  1C, D ). Dispersal limitation can create permanent differences among localities and therefore spatial patterns would acquire more relevance (with high spatial variation; Figure  1D ). Similarly, environmental conditions could also create spatial or temporal patterns in those metacommunities depending on the environmental variation, increasing the importance of the temporal scale when the environmental conditions vary through time. These two predictions can be considered two extremes of a continuum between unique temporal or spatial variation. Additionally, we analyze empirical data sets of aquatic invertebrates from a temperate setting to compare our expectations as resulting from the simulations with the field data.
Empirical Metacommunities We used three databases of aquatic invertebrates from previous works, which were here reanalyzed with other purposes. The first database had 10 localities in a stream sampled on 11 occasions for 19 months (the first months were sampled seasonally and the last ones monthly;  Mezquita et al. , 1999; Rueda et al. , 2002). The other two databases correspond to two groups of interdunal ponds, with 9 and 13 localities, respectively, sampled monthly during 12 months  (Valls et al. , 2013; Rueda, 2015). All the data originate from the eastern Iberian Peninsula, with an intraannual fluctuation of temperatures and precipitations typical of Mediterranean regions (i. e. , mild winters, hot and dry summers, and rainfalls concentrated in autumn months). We selected four groups of arthropods (Ostracoda, Odonata, Coleoptera, and Diptera), each one corresponding to a taxonomic group with similar trophic features for the encompassed species, which potentially compete in a region for similar resources  (Hubbell, 2001).
Ostracods are benthic microcrustaceans which are predominantly omnivorous and they spread passively among ponds  (Mesquita-Joanes et al. , 2012). The other three groups have active dispersal with winged adults, although they have different dispersal capabilities and body sizes  (Schmidt-Kloiber and Hering, 2015). Dragonflies and damselflies (Odonata) are predators, as well as the coleopterans (here, only family Dytiscidae). However, these two groups have different prey preference, attack strategies and life histories. Finally, dipterans (only families Stratiomyidae, Psychodidae, Ephydridae, and Dixidae) present aquatic larvae mostly consuming detritus. The criteria for selecting these groups were based on their abundance and species richness in the datasets. Overall, we analyzed nine datasets, separating the invertebrate groups in each landscape setting (3 in each landscape).
Sampling Metacommunities We sampled the simulated and the empirical metacommunities across a gradient of study durations (i. e. , temporal extents), maintaining the number of temporal sampling points or frequency (therefore varying the lag between samples; Figures  2B, C ). In the simulated data, we sampled the metacommunities varying the temporal extent from 20 to 2,000 timesteps (Figure  2B ), fixing the frequency at 10 times. In a similar way, empirical metacommunities were sampled at different study durations maintaining the number of temporal points at a frequency of 6 times (Figure  2C ). After being sampled, the simulated and the empirical data have the same arrangement in matrices, and we applied the same analytical routine for both types of datasets, as follows.
Metacommunity Variation Partitioning The empirical and the simulated data (after sampling) were analyzed by means of variation partitioning between environmental, spatial and temporal variables  (Borcard et al. , 1992; Anderson and Cribble, 1998; Peres-Neto et al. , 2006). Environmental variables in the empirical dataset were previously log-transformed, and later we carried out a PCA to use the principal components as orthogonal environmental variables with all the environmental variability measured. In the simulated data, we used directly the simulated environmental variable (E). We used Moran Eigenvector Maps (MEMs;  Dray et al. , 2006)  to model spatio-temporal variables (see Supplementary Material). For this purpose, we created a three-dimensional network with the sampling points and the links among them, distributing them across space (x and y axes) and time (z axes).
, 2008a). We applied a Bonferroni correction for multiple testing to the p-values of the selected variables, i. e. , those with adjusted p-values lower than 0. 05. All the simulations and metacommunity analyses were performed in R 3. 6. 3 (R Core Team, 2020), using the packages vegan  (Oksanen et al. , 2019) , adespatial  (Dray et al. , 2020) , spdep  (Bivand and Wong, 2018) , and RandomFields  (Schlather et al. , 2015). All the codes are available in https://github. com/andreucastillo/TemporalScales Metacommunities.
In the present work, the morphology of the models from  Hemond et al. (2008)  was simplified using an algorithm that used circuit theory to combine compartments connected in series and in parallel to create simplified equivalent circuit representations of complex morphologies  (Marasco et al. , 2012). The algorithm was used in the current work to construct simplified models while preserving the firing behavior exhibited by the original models (Figure  2C ). The simplified models contained eight compartments corresponding to a compartment for each layer upon which input is received and reduced simulation times by a factor of 20. Model parameters are summarized in the Supplementary Tables  1 2 3 4. The computational models were simulated using NEURON v7. 5 and scripted using Python 2. 7. The entorhinal-dentate network used in the present study was the same as described in  Yu et al.
Anatomically-Constrained Mesoscale and Microscale Connectivity The major intrahippocampal afferents to CA3 pyramidal cells were considered in this study: the lateral perforant path input, the medial perforant path input, mossy fiber input, and the associational input. Anatomical data was used to define and constrain the topography of the various projections. The topography describes the regional mapping between layers/subfields of the hippocampal formation. Relevant data include anterograde and retrograde tracer injection studies which can reveal the relation between position within a hippocampal subfield and the region within the postsynaptic area to which axons are sent (anterograde) or the region within the presynaptic area from which axons are received (retrograde). Another crucial aspect of the anatomy is the spatial distribution of the axon terminal field within the postsynaptic area.
Whole Exome Sequencing To identify the basic pathogenic variant leading to disease phenotype, we performed WES using the Illumina HiSeq 2000/2500 system (San Diego, CA, United States). We set up our sampling by following the Agilent SureSelect Target Enrichment Kit preparation guide (Capture Kit, SureSelect v. 6, Santa Clara, CA, United States). The libraries were sequenced utilizing the Illumina HiSeq 2000/2500 system. Diverse bioinformatics investigations were made to distinguish causative variant co-segregating for BRPF1 phenotypes in an autosomal dominant manner. Whole exome sequencing data generate the raw reads in the form of FASTQ format. Insertion, deletion, and copy number variation were distinguished by utilizing SAMtools 1. To use the BWA Aligner 2  , the crude information FASTQ files were adjusted.
The resulting VCF file contains 111,850 variants. The variants were clarified by using different parameters, such as quality, frequency, genomic position, protein effect, and pathogenicity. The resulting sequences were compared against the hg19 human reference arrangement (National Center for Biotechnology Information assembly GRCh37) 3. Furthermore, the obtained data were mapped against the information in the dbSNP 4  , population-based database such as genome aggregation database genomAD, Exome Variant Servise (ESP) and 1000 Genomes databases 5  and disease-based database such as ClinVar (database of assertions about the clinical significance and phenotype relationship of human variations), OMIM (database of human genes and genetic conditions that also contains a representative sampling of disease-associated genetic variants), and Human Gene Mutation (Database of variant annotations published in the literature).
The American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG). The variant detected here was classified as variant with unclear significance according to the most recent ACMG guidelines. In silico functional study was done for the current mutations to check deleterious effect and abnormalities caused by mutations. For the in silico predictions, we used software such as Mutation Tester 6  , 1000 Genomes 7  , PhyloP 8  , PhyloP GERP++ 9  , SIFT 10  , PhastCons 11  , CADD 12  , SiPhy 13  , and Exome Aggregation Consortium 14  , and PROVEAN and MAPP for protein structure/function and evolutionary conservation. All the software predict this variant as disease causing.
Sanger Sequencing Whole exome sequencing results were validated using Sanger sequencing analysis. As a result, data files were obtained in the AB1 sequence trace format. Each sequence trace file was aligned to the corresponding reference sequence using the BioEdit and FinchTV software packages. We searched for the observed variations in the National Center for Biotechnology Information SNP database. To validate the position of the variant, primers were designed to flank and amplify the c. 1054G > C in exon 3 of the BRPF1 gene. BRPF1_forward, 5'-ACAGCAATGTCATCCTCTTCTGTG-3'; BRPF1_reverse, 5-ATGTCACATGGAAAGCTGTGTA-3. Further, to rule out this mutation in normal population, we also sequenced this variant in 100 control people.
BFA, which disrupted the Golgi apparatus, inhibited the biotin-induced cellular release of SBP-GFP-mt1CALR and SBP-GFP-mt2CALR (Figures  S1C  and S1D ; Videos S4, S5, and S6). Moreover, upon addition of biotin without BFA, SBP-GFP-mt1CALR and SBP-GFP-mt2CALR transiently colocalized with GALT1 (with a peak around $30 min post-biotin) (Figures  1H-1J , S1E, and S1F), in line with the idea that CALR mutants are secreted via the Golgi, through a canonical pathway. The transient colocalization of SBP-GFP-mt1CALR and SBP-GFP-mt2CALR with the Golgi apparatus was confirmed by staining of the cells with antibodies specific for Golgi-sessile proteins (data not shown). Moreover, pretreatment with BFA prevented the biotin-induced release of SBP-GFP-mt1CALR and SBP-GFP-mt2CALR into the culture supernatant, as determined by immunoblot analyses  (Figures 2A and 2B)  or ELISA (Figures  2C-2E ).
p. Chemotherapy was repeated at day 2, and biotin was injected daily for 2 weeks. For the validation of biotin induced SBP-mtCALR release, tumors were excised 4 or 8 h after biotin injection and immediately fixed in 4% PFA before being embedded in O. C. T. and frozen at À80 C. The tissue was sectioned, permeabilized with 0. 5% Triton X-100 and stained with SBP antibody followed by Alexa Fluor 568-conjugated anti-mouse secondary antibody. Images were captured with an HR-SP8 confocal microscope (Leica Biosystems, Wetzlar, Germany). Image analysis was performed with the LAS X software (Leica). For tumor growth assessment, tumor surface was measured regularly and animals bearing neoplastic lesions that exceeded 250 mm 2 were euthanized. In vivo RUSH assay established through hydrodynamic tail vein injection For the in vivo expression of the RUSH elements Str-KDEL and SBP-ss-GFP, mice were injected intravenously with cDNA (90 mg pCDH-Str-KDEL for the expression of the hook, and 30 mg pCDH-SBP-ss-GFP for the expression of the reporter) in 37 C pre-warmed normal saline of a volume that equals 10% of the body weight (e.
g. , 2 mL for a mouse with 20 g body weight) via the tail vein. DNA injection was completed in less than 5 s. Forty-eight h after the hydrodynamic injection, mice were either treated with 15 mg/Kg biotin i. p. , or equivalent vehicle solutions, for 6 h before being euthanized to harvest the livers that were subjected to frozen sectioning as described above. Tissue sections were permeabilized with 0. 5% Triton X-100 and stained with an anti-streptavidin antibody followed by the Alexa Fluor 647-conjugated anti-mouse secondary antibody. Next, the stained samples were mounted with mounting media containing DAPI and Alexa Fluor 568 Phalloidin (Invitrogen). Images were captured with an HR-SP8 confocal microscope (Leica Biosystems). Image analysis was performed with the LAS X software (Leica). MDSC subsets from mouse peripheral blood mononuclear cells (PBMC) and spleen Phenotyping of M-MDSC and G-MDSC was performed by flow cytometry.
Software and Algorithms BD FACSDivaÔ Software v8. 0 BD Bioscience https://www. bdbiosciences. com/en-us/ instruments/research-instruments/researchsoftware/flow-cytometry-acquisition/ facsdiva-software FlowJo VX FlowJo, LLC https://www. flowjo. com Microsoft Office 2016 Microsoft https://www. microsoft. com/fr-fr/ R Studio RStudio https://rstudio. com GraphPad Prism 7 GraphPad Software https://www. graphpad. com MetaXpress V5 Molecular Device https://www. moleculardevices. com/ MetaXpress custom model editor Molecular Device https://www. moleculardevices. com/ ImageJ FIJI NIH https://imagej. nih. gov/ij/download. html Wave XFe Desktop V2 Seahorse Bioscience https://www. agilent. com/en/products/cellanalysis/cell-analysis-software/data-analysis/ wave-desktop-2-6.
QUANTIFICATION AND STATISTICAL ANALYSIS Data mining was performed with the Microsoft Office software package (Microsoft, Redmond, DC, USA). Statistical analysis was performed using GraphPad Prism V7 (GraphPad Software, San Diego, CA, USA). Unless otherwise specified, data are reported as mean ± SEM. Statistical significance was analyzed using the Student's-test. Statistical parameters including the n value, the definition of center, and dispersion, precision measures (mean ± SEM) and statistical significance are described in the  Figures and Figure  Legends. All p values < 0. 05 were considered statistically significant. In vivo tumor growth and tumor-free survival data was analyzed using the freely available TumGrowth software (https://github. com/kroemerlab/TumGrowth) (Enot et al. , 2018).
DISC Experiment The behavioral data were collected with the Drifting In Situ Chamber (DISC) observational system. The design of the DISC has evolved since it was first conceived  [41, 42]. The detailed description of the chamber's current version is presented in  [11]. In brief, the DISC is composed of a clear acrylic frame with a top and bottom plate connected at the perimeter of each plate by three vertical poles; a circular behavioral chamber is suspended between the plates and is made of a transparent acrylic bottom and 1-mm Nytrex ® mesh on the sides and a closing lid. A GoPro4 ® camera was placed on the bottom plate looking up into the behavioral chamber (Figure  1b ). One analog compass is set on each pole within the field of view of the camera and a Hobo ® environmental sensor was attached on top plate of the frame to record temperature and light measured every 30 s (Figure  1a ).
Data Analysis DISC data were processed with the open-source software R package "discr", modified from  [41]. The camera produced timelapse images at 1-s intervals, each image of 7 megapixels. The position of the larva was recorded on each image by clicking on it through a graphical user interface. Each position was converted to polar coordinates relative to the axis from the center of the chamber to the top of the picture (Figure  1c ). The angular part of the coordinate was converted to a bearing relative to the North by subtracting the bearing of the top of the picture, recorded by the analog compasses (Figure  1d ). Combined, these bearings form the trajectory of each individual during the trial and constitute the base data to detect orientation behavior  [41, 42]. Two levels of behavioral data can be extracted from these trajectories.
, the top of the picture; r raw ) and in the cardinal reference after correcting the positions by the rotation of the DISC (r rotated ). As per the requirement when dealing with data generated with the DISC  [41, 42] , an individual is considered as "orienting" (i. e. , tracking a bearing) and its mean bearing assumed relevant if and only if its directionality was significant and higher in the cardinal frame of reference than in the DISC's frame of reference (r rotated > r raw ). Individuals that did not move in the chamber were considered as not orienting, and only the r rotated (hereafter r) was used for subsequent analyses. The swimming speed of an individual can be influenced by the sampling methodology  [45]. Here we measured the swimming speeds in cm s -1 and in body length per second (BL s -1 ) based on the average displacement of the larva in the chamber at each 1-s interval.
For each deployment, the reference direction used to correct the bearing was set as the mean direction of the left light source for the larvae and juveniles with a "left preference", and to the mean direction of the right light source otherwise. We then compared the statistics of the resulting distributions of bearings with the Rayleigh's and Wallraff's tests to detect if specific preferences emerged toward one of the above six different orientation scenarios among deployments. All analyses were conducted using the R software  [47] , with the packages "circular v0. 4. 93"  [48]  for circular statistics and "tidyverse v1. 3. 0" [49] , for data management and graphics.
Data Availability Data for this article may be accessed through the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) available at https://data. gulfresearchinititative. org, DOI: 10. 7266/HZB203BM. The software to process the DISC data is open-source released under the GNU General Public License v3. 0 and available at www. github. com/jiho/discr.
Acknowledgments: The authors would like to thank  Macy Sherman  and  Adèle Fleury  for their help with the DISC experiment. Martin Grosell  holds a Maytag Chair of Ichthyology. Data are publicly available through the  Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC)  at https://data. gulfresearchinitiative. org (DOI: 10. 7266/HZB203BM ).
patients and Methods Study design and patients. This is a retrospective study covering the period April to August 2018. All data were retrieved from the clinical database of the Centre Antoine Lacassagne (Nice, France). Tumor responses were evaluated after monotherapy according to RECIST 1. 1 criteria (complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD)). Objective response was evaluated as previously published  [33] [34] [35]. Immunerelated adverse events (irAEs) were evaluated according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE V5). Pre-baseline, baseline, and initial imaging results were recorded and were to calculate the TGK R (ratio of the slope of tumor growth before treatment and the slope of tumor growth during treatment), as previously reported  8.
The assay to screen the 17 SNPs was created by using Assay Design Suite v2. 0 (AGENA Bioscience online software) with the "Genotyping Design"option. We had created the assay to screen the 17 SNPs. Data were verified and compatible with DNA controls polymorphism for 15 SNPs; the remaining 2 SNPs had been eliminated (PD-L1 rs822339 and IDO1 rs34820341) because incompatible with DNA control polymorphism (https://www. coriell. org/1/NIGMS/ Collections/CEPH-Resources). For 15 SNPs minor allele frequency was ≥5% in Caucasians according to SNPpedia (http://www. snppedia. com) and the Ensemble database (http://www. Ensembl. org). All tested SNPs were in Hardy-Weinberg equilibrium (Table  2 ).
The identification of orthologs--genes in different species which descended from the same gene in their last common ancestor--is a prerequisite for many analyses in comparative genomics and molecular evolution. Numerous algorithms and resources have been conceived to address this problem, but benchmarking and interpreting them is fraught with difficulties (need to compare them on a common input dataset, absence of ground truth, computational cost of calling orthologs). To address this, the Quest for Orthologs consortium maintains a reference set of proteomes and provides a web server for continuous orthology benchmarking (http://orthology. benchmarkservice. org). Furthermore, consensus or-tholog calls derived from public benchmark submissions are provided on the Alliance of Genome Resources website, the joint portal of NIH-funded model organism databases.