dataesr/scientific-paragraphs-categorization huggingface dataset:
Six neural network ensembles were developed, one for each variable (i. e. , three macronutrients: nitrates (NO - 3 ), phosphates (PO 3- 4 ) and silicates (Si(OH) 4 ), and three carbonate system variables: pH on the total scale (pH T ), total alkalinity (A T ), and dissolved inorganic carbon or total carbon (C T ), trained using a specific quality-controlled dataset of reference "bottle" data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0. 78 µmol. kg -  for NO - 3 , 0. 043 µmol. kg -  for PO 3- 4 and 0.
71 µmol. kg -  for Si(OH) 4 , 0. 014 units for pH T , 13 µmol. kg -  for A T and 12 µmol. kg -  for C T. A second validation on the ANTARES independent time series confirmed the method's applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data.
Matlab and R code are available at https://github. com/MarineFou/CANYON-MED/v2/. A correction has been made to Material and methods, ANN ensemble model, Paragraph 1: The robustness and reliability of an ANN can be significantly improved by combining several ANNs into an ANN ensemble model  (Sharkey, 1999; Linares-Rodriguez et al. , 2013). The construction of an ANN ensemble is done in two main steps. First, the individual members of the model are created (as described above in section "Multi-Layered Perceptron"). Second, the combination of the outputs of these members is averaged to obtain the unique ensemble output. Thus, for each neural network [NO - 3 , PO 3- 4 , Si(OH) 4 , A T , C T , pH T ], the ten best topologies were chosen according to their statistics (as defined in section "Validation Statistics Metrics").
The data used in the manuscript correspond to commit `c1812590b18366bce7bffd63a0cecbcb9d8e21cfh ttps://github. com/CSSEGISandData/COVID-19/tree/c18 12590b18366bce7bffd63a0cecbcb9d8e21cf. and should be copied in this directory before running theDEM_COVID_X` script. https://github. com/CSSEGISandData/COVID-19/tree/c18125 90b18366bce7bffd63a0cecbcb9d8e21cf/csse_covid_19_data/ csse_covid_19_time_series The time series tables from States in America used in this report are stored in files time_series_COVID19_confirmed_US. csv and time_series_COVID19_deaths_US. csv.
This work was supported by  Wellcome  through core funding to the  Wellcome Centre for Human Neuroimaging ,  UCL Queen Square Institute of Neurology  [203147]; and a  Principle Research Fellowship  grant to KJF [088130]. AJB is supported by a  Wellcome Trust  grant  [091681 ]. AR is funded by the  Australian Research Council  [Refs:  DE170100128  and  DP200100757 ]. CL is supported by an  MRC   Clinician Scientist award  [ MR/R006504/1 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
67-82. For example, seeing the person on the pavement affords the opportunity to circumnavigate them by 6 feet. 6 Available from https://github. com/CSSEGISandData/COVID-19. 7  We will use State to indicate a State of the United States and state to denote a level of a factor in the generative model. 8 For example, acute respiratory distress due to secondary bacterial pneumonia, which may itself be antimicrobial resistant: https://www. southcentre. int/wp-content/uploads/2020/03/RP-104. pdf. 9 Undocumented infections may be the source of infection for 79% of documented cases Li et al. , 2020. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science, eabb3221.
Meteorological data and surrounding pollution To assess the impacts of weather parameters on PM concentration levels measured at each site during the 3-day sampling period, temperature, wind intensity and direction are analyzed at local As expected, Korhogo is under the influence of northern trajectories whereas Abidjan is under the influence of southern air masses, associated to air masses coming from the Nigerian coast. In order to identify which aerosols could be advected into Korhogo and Abidjan using these of the study period. Associated with Figs. S1, this shows that Korhogo is under the influence of regional winds that carry desert dust and biomass burning aerosols coming from the north and south of the city. While at Abidjan these regional winds show that only marine aerosols are expected.
Measurement campaigns and equipment The first step of the methodology deals with intensive data collection through measurement campaigns conducted at sites in the two cities, as presented in previously. The measurement campaigns of PM10 and PM2. 5 concentrations were carried out with 4 portables PM measuring devices (Aeroqual monitor S500 with PM10/PM2. 5 sensor). Particulate pollution is measured over 3 days at each site, with 2 consecutive working days and 1 weekend day, because these 2 periods (working and weekends days) present different activities levels in urban areas. Indeed, studies have shown different levels of pollution at these 2 periods of the week  (Adame et al. , 2014 ; Doumbia et.
A C C E P T E D M A N U S C R I P T 11 a comparison between kriging and IDW, and showed that IDW method is associated to lower uncertainty than kriging method. In addition, our measurement network (number of points and geographic locations) is developed on the basis of the network of  Bahino et al. , (2018) ; therefore, in this study, the IDW method is chosen and applied to the collected data. ArcGIS version 10. 3 software (ESRI Inc. ) is used because it combines geostatistical data processing and map edition. The shapefiles for map editing are available for free on OpenStreetMap (https://www. openstreetmap. org). Briefly, IDW interpolation allows to predict the concentrations around a sampled point from the following formula presented in Eq. (  1 ). = ∑ λ  *     =1 (1) where,    is the observed value at location i; form geometrical mean of measured values at each site during the different measurement campaigns.
Adventitious and lateral root phenotyping and growth conditions Seedlings were grown in adventitious root-inducing conditions as previously described  (Sorin et al. , 2005; Gutierrez et al. , 2009 Gutierrez et al. , , 2012;; Lakehal et al. , 2019a). Briefly, they were grown in dark conditions until their hypocotyls were 6-7 mm long then transferred to long-day conditions. Numbers of ARs (primordia and emerged) were counted under a binocular stereomicroscope 7 d after transferring the seedlings to light conditions. Numbers of emerged lateral roots were counted on the same day from scanned plates. The seedlings' primary root lengths were measured from scanned plates using ImageJ software  (Schindelin et al. , 2012) , and their lateral root densities (lateral root numbers to primary root length ratios) were calculated.
Quantitative RT-PCR Transcript amounts were quantified by quantitative real-time PCR (qRT-PCR), using triplicate reaction mixtures containing 5 μl of cDNA, 0. 5 μM of both forward and reverse primers, and 1× LightCycler 480 SYBR Green I Master (Roche) (final volume, 20 μl) according to the manufacturer's instructions. All these experiments were performed with at least two independent biological replicates. Relative amounts of transcripts of the target genes were calculated as previously described  (Gutierrez et al. , 2009)  and regarded as significantly down-or up-regulated if fold differences were ≤0. 75 or ≥1. 5, respectively, with P-values ≤0. 05. Reference genes were validated as the most stably expressed genes under our experimental conditions  (Gutierrez et al. , 2009)  using GenNorm software, and the most stable pair were used to normalize the qPCR data.
Dark-light transition triggers profound transcriptional reprogramming To uncover the transcriptional reprogramming events associated with ARI in Arabidopsis, we re-analyzed our publicly available transcriptome dataset  (Lakehal et al. , 2020a). This dataset was generated by sequencing RNA extracted from etiolated hypocotyls of wild-type (Col-0) seedlings at T0 (just before moving them into the light), at T9 (9 h after the transfer), and at T24 (24 h after the transfer) (Fig. 1A ). This analysis detected 3778 differentially expressed genes (DEGs), 2480 of which were up-regulated and 1298 down-regulated at T9 compared with T0 (Fig. 1B ; Supplementary Table  S2 ). It also detected 4709 DEGs between T0 and T24, 3001 of which were up-regulated and 1708 down-regulated at T24 compared with T0. Moreover, 859 were differentially expressed (433 up-regulated and 426 down-regulated) at T24 compared with T9 (Fig.
JA down-regulates CKX1 expression in a COI1-dependent manner Previous studies have shown that moving seedlings from dark to light reduces JA contents of etiolated hypocotyls  (Gutierrez et al. , 2012; Lakehal et al. , 2020a). It was also shown that both JA and CKs repress ARI  (Ramírez-Carvajal et al. , 2009; Gutierrez et al. , 2012; Mao et al. , 2019; Lakehal et al. , 2020a) , but not whether they directly interact during this process. In this study, we found that dark to light transition reduces CK contents of etiolated hypocotyls (Fig. 2A-F ; Supplementary Fig. S1A-E ), raising the possibility that interplay between JA and CK pathways provides a coherent developmental input for ARI. To test the hypothesis that JA might control CK content, we first searched publicly available gene expression datasets (using the Arabidopsis eFP browser) for CK-related genes that are transcriptionally affected by exogenous applications of JA or the JA derivative methyl-jasmonate (MeJA)  (Winter et al.
ARI repression by JA and CKs involves synergistic induction of RAP2. 6L To obtain insights into the mechanism whereby JA-CK interplay represses ARI, we searched our transcriptomic database for potential transcription factors that are differentially expressed during the early stages of the process (Fig. 1A ). This analysis detected 180 differentially expressed transcription factors, 91 of which were up-regulated and 79 down-regulated at T9 compared with T0 (Supplementary Table  S5 ). It also detected 230 differentially expressed transcription factors between T0 and T24, 143 of which were up-regulated and 87 down-regulated at T24 compared with T0. Moreover, 63 were differentially expressed (39 up-regulated and 24 down-regulated) at T24 compared with T9 (Supplementary Tables  S6, S7 ). Interestingly, we retrieved several transcription factor genes with a potential role in de novo organogenesis and stem cell regulations, one of which (RAP2.
Numerical experiments In this section, we report on some numerical experiments with the intent of illustrating the performances of our proposed variants of the CDIIS in the context of the electronic ground state calculations considered in Section 4. All the computations presented were performed using tools provided by the PySCF package  [54]. The source code of our implementation of the CDIIS variants can be found in the following repository: https://plmlab. math. cnrs. fr/mchupin/restarted-and-adaptive-cdiis/.
Acknowledgement The authors wish to warmly thank  Antoine Levitt , for providing them with encouragements, useful references, and insightful remarks on a first draft of the manuscript, and  Qiming Sun , for his help with the PySCF package. They also thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  MDFT No 725528 of Mathieu Lewin ).
Biochemical analysis of caffeine and theobromine contents. Biochemical analyses were carried out on young leaves to characterize the chemical composition of the coffee species C. canephora, C. arabica and C. humblotiana using three different accessions for each. The results show significant contrasting quantities of caffeine and chlorogenic acids (CGA) among these three coffee species (Table  3  and Supplementary Table  S3 ). The data confirms the absence of caffeine and the lower quantities of CGA in C. humblotiana (Table  3 ). These findings confirm previous studies based on biochemical analysis on seed  10  , which underlined that the biochemical diversity is related to the species analyzed. In this regard, the confirmed absence of caffeine in C. humblotiana was the main interest for our study.
bioin forma tics. babra ham. ac. uk/ proje cts/ fastqc/). Truseq sequence adapters were removed using Trimmomatic V. 0. 39  52. Genome assembly. The size and heterozygosity of the C. humblotiana genome were estimated via the 21-mer depth distribution of Illumina reads. In detail, the k-mers were counted using Jellyfish  53  and the k-mer count histogram was analyzed using Genoscope (http:// qb. cshl. edu/ genom escope/). A de novo genome assembly was performed using the FALCON assembler  54. PBJelly 55 was used for scaffolding and gap-closing the FALCON primary contigs and finally, assembly-polishing was performed using the Illumina data produced to correct potential errors in low-quality regions. Ninety-four percent of the Illumina reads map to the PBJelly2 contigs, 89% of those with the correct orientation and within the expected insert-size range.
This bed-file was converted into a GFF3 format and filtered for junctions with a minimum coverage of 10. From the resulting GFF3 file intron features were given as hints while the remaining exon bits on either side of the introns were given as an exon part (source = E). The accompanying parameter file for extrinsic data for AUGUSTUS was adapted to include these hints as well as to mask the genomic sequence. AUGUSTUS was run as augustus -species = $species -hintsfile = $hint -extrinsicCfgFile = extrinsic. MPE. cfg -softmasking = 1 $fasta. The resulting gene predictions from AUGUSTUS were further curated with EvidenceModeler 59 using the same extrinsic data. EVM managed this way to clean a few predicted genes with ambiguous structure and no support. The final predicted gene set (32,874) was subsequently processed to be uploaded into ORCAE (https:// bioin forma tics.
The pulsed light source was a tunable Ti:Sapphire laser (Chameleon, Coherent Laser Group, Santa Clara, CA, USA) used at 900 nm and 80 MHz, providing a 13 ns temporal window for lifetime measurements. The system was equipped with the TCSPC from Becker and Hickl (Berlin, Germany) and fluorescence decay curves were obtained using single spot mode of SPCM software (Becker and Hickl). Experiments with the PSD-95-12-derived FRET reporter system were performed using the frequency domain analysis (LIFA) method a Leica DMI6000 (Leica Microsystems, Wetzlar, Germany) equipped with a confocal Scanner Unit CSU-X1 (Yokogawa Electric Corporation, Tokyo, Japan). The FLIM measurements were done with the LIFA fluorescence lifetime attachment (Lambert Instrument, Roden, Netherlands), and images were analyzed with the manufacturer's software LI-FLIM software.
All recordings were performed using an EPC10 patch clamp amplifier operated with Patchmaster software (HEKA Elektronik). Data was acquired at 10 kHz and filtered at 3 kHz. Membrane capacitance was monitored frequently throughout the experiments and only cells with a series resistance <10 MΩ were analyzed. Data were collected and stored on computer for off-line analysis using a software developed in-house (Detection Mini) to detect miniature synaptic events using a variable threshold. The amplitude and frequency of miniature excitatory postsynaptic currents (mEPSCs) were obtained for a minimum of 500 events. Statistical values are given as mean ± SEM. Statistical significances were performed using GraphPad Prism software (San Diego, CA). Normally distributed data sets were tested by Student's unpaired t-test for two independent groups.
Fluorescence experiments were carried out in an extracellular solution containing (in mM): NaCl (120), KCl (3. 5), MgCl2 (2), CaCl2 (2), D-glucose (10), HEPES (10) (pH 7. 4, ~270 mOsm), and transfected neurons were observed through a 63x oil objective (Leica, HC PL APO CS2, NA 1. 4). GFP fluorescence was illuminated with 491 nm laser light using a high-speed spinning disk confocal scanner unit (Yokogawa CSU22-W1) and emission was captured with a sCMOS camera (Prime 95B, Photometrics). Microscope hardware was controlled with MetaMorph (Molecular Devices, v7. 1. 7) and ILAS2 system (Roper) softwares. For FRAP experiments, the following protocol was used: 1) prebleaching (20 images at 3 s interval); 2) photobleaching of the regions of interest (10-15 ROIs per field of view, ROI=10 pixels, eq.
to 2. 3 µm), 3) fast recovery (40 images at 0. 5 s interval) and 4) long-term recovery (10 mins recording at 3 s interval). For photobleaching, we used a 5 ms pulse of 488 nm laser light sufficient to reduce fluorescence by at least 50 %. Experiments where fluorescence dropped more than 20% in non-bleached regions during acquisition were discarded. FRAP experiments were analyzed using an in-house developed macro to the ImageJ freeware (http://imagej. nih. gov/ij/). The source code is freely available from GitHub (https://github. com/fabricecordelieres/IJ-Macro_FRAP-MM), accompanied by a documentation and an example dataset. Briefly, as part of the macro, the FRAP region for each spine was imported from the Metamorph software to ImageJ's ROI Manager using the Metamorph Companion plugin (https://github.
com/fabricecordelieres/IJ-Plugin_Metamorph-Companion). From the data extracted by the macro, average intensity within the ROI was collected for each timepoint (Ft), at first timepoint (pre-bleach, Fpb) and immediately after the bleaching (F0). Simple normalization was performed as follows: FRAPt =Ft-F0 /Fpb-F0. The mean spine FRAP curve of each cell was subsequently fitted to a mono-exponential model using GraphPad Prism software. STED Fixed neuronal cultures (DIV21) expressing GFP-tagged Xph20 were imaged with a glycerol immersion objective (Plan Apo 93x NA 1. 3 motCORR). Cells were immunolabeled with MAP2 (Synaptic systems 188006 and anti-chicken AF594, ThermoFisher A11042) to identify dendritic draft. A 660-nm wavelength laser was used for GFP depletion. Acquisition parameters were: 20 nm pixel size, 4 times accumulated average per line and 200 Hz scan speed.
SMLM analysis Localization and tracking reconnection of ATTO-647N (uPAINT) or mEos3. 2 (PALM) signals were performed using homemade software developed as a MetaMorph plugin and kindly provided by J. B. Sibarita (Interdisciplinary Institute for Neuroscience) (Kechkar et al. , 2013). Singlemolecule fluorescence could be identified by occurrence of fluorescence in the red channel and the defined minimum duration of fluorescence. Trajectories were reconstructed by a simulated annealing algorithm (Racine et al. , 2006), taking into account molecule localization and total intensity. Diffusion coefficients were calculated by linear fit of the first four points of the Mean Square Displacement plots. PALM clusters analysis was performed using SR-Tesseler software as previously described (Levet et al.
Xph20-SNAP was then visualized with Cy3b-labelled DNA imager strands, added to the Ludin chamber at variable concentrations (2-5 nM), as previously described (Schnitzbauer et al. , 2017). Cy3B-labelled strands were visualized with a 561 nm laser (Cobolt Jive). Fluorescence was collected by the combination of a dichroic and emission filters (dichroic: Di01-R561, emission: FF01-617/73, Semrock) and a sensitive sCMOS (scientific CMOS, ORCA-Flash4. 0, Hammatasu). The acquisition was steered by Metamorph software (Molecular Devices) in streaming mode at 6. 7 Hz. GFP was imaged using a conventional GFP filter cube (excitation: FF01-472/30, dichroic: FF-495Di02, emission: FF02-520/28, Semrock). Super-resolution DNA-PAINT reconstruction and drift correction were carried out as described before, using the software package Picasso (Schnitzbauer et al.
Calcium signaling imaging Imaging of GCaMP6f and Xph-GCaMP6f was carried out in rat hippocampal dissociated cultures nucleoporated with the appropriate DNA on the day of the culture. Neurons were imaged at 13 to 18 DIV using a Nikon inverted microscope (Ti Eclipse) with an EMCCD camera (Evolve 512, Photometrics) controlled by Metamorph software (Molecular Devices) and equipped with a 60x/1. 49 N. A. oilimmersion objective (Nikon). Images were acquired at a rate of ∼50 Hz. The imaging chamber (Ludin Chamber, Life Imaging Services) was perfused with extracellular buffer containing (in mM): 130 NaCl, 2. 5 KCl, 3 CaCl2, 0. 1 MgCl2, 10 glucose, 10 HEPES, 0. 001 TTX, 0. 05 PTX, (pH adjusted to 7. 4 with NaOH and osmolarity adjusted to 280 mOsm) at room temperature. The fluorophores were excited with 488nm laser lines, and imaged with the appropriate filters.
E18 rat hippocampal neurons were electroporated with GCaMP7f or Xph20-GCaMP7f and Homer1c-Dsred using the 4D Nucleofection system (Lonza) at DIV0, seeded on 18 mm glass coverslips, and cultured for 15 days. Imaging was performed by placing coverslips in a Ludin observation chamber (Life Imaging Services) in Mg²+-free Tyrode's solution (15 mM D-glucose, 108 mM NaCl, 5 mM KCl, 2 mM CaCl2 and 25 mM HEPES, pH 7. 4) containing 20 µM glycine inside a thermostatic chamber (37 °C) placed on an inverted microscope (Nikon Ti-E Eclipse) equipped with an EMCCD camera (Evolve 512, Photometrics) controlled by Metamorph software (Molecular Devices) and equipped with a 60x/1. 49 N. A. oil-immersion objective (Nikon). Fluorescence was collected using a mercury lamp (Nikon Xcite) and appropriate filter sets (SemROCK).
Acknowledgments This research was financially supported by grants from the  Centre National de la Recherche Scientifique , the  Conseil Régional d'Aquitaine , the  France BioImaging national infrastructure  (grant  ANR-10-INBS-04 ), the  Agence Nationale de la Recherche  ( CheMoPPI ,  ANR-13-BS07-0019-01 ,  OptoXL ,  ANR-16-CE16-0026 ) to C. P and M. S, the  European Research Council  to D. C. , the  Labex BRAIN  ( ANR-10-LABX-43 ) to C. R. ,  Fondation pour la Recherche Médicale  fellowship to B. C. We also thank the IINS cell culture facility and  Emeline Verdier  for technical assistance, the  Biochemistry and Biophysics Core Facility  of the Bordeaux Neurocampus both funded by the  Labex BRAIN  ( ANR-10-LABX-43 ) and  Y. Ruffin  for technical assistance. Financial support from the  IR-RMN-THC Fr3050 CNRS  for conducting the research is gratefully acknowledged.
Optimal parameterization of SEAPODYM during historical period. The parameterization of SEAPODYM for each tuna species is highly sensitive to ocean forcing; that is, in its average state it is free from systematic biases, and it represents interannual variability and ENSO correctly. This sensitivity enables the model to reproduce observed variability within large, geo-referenced datasets of tuna catches and length distributions reflecting changes in fish abundance  12. The environmental forcings in this study were obtained from the historical NEMO-PISCES reference simulations using a realistic atmospheric reanalysis based on a consistent set of atmospheric observations. Historical fishing datasets used to achieve model optimal parameterizations were compiled from the combination of data provided by the Pacific Community for the WCPO and by IATTC for the EPO.