dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016)  of the statistical network properties, using the edgecomplete portions of the datasets. The ML predictions show that weak/long-range links in general are not predictable in either species, and that these links have little to no information about each other, at least in terms of link weights and projection distance. Moreover, they also show that overall, the mouse brain has a more predictable structure than the macaque (roughly by a factor of two in terms of errors). However, it is somewhat more difficult to predict the weakest connections in the mouse, than in the macaque (compare panels A and C in Figure  6 ). Accordingly, one could speculate that the longrange connections have less specificity in the mouse brain than in the macaque. It is, however, important to note that these predictability measures are all based on the features of link weights and projection distances.
2013). Machine learning methods may also be used as a guide to future neuroanatomical experiments. For example, if all predictors consistently suggest the existence or absence of a link where the data indicates the opposite, it may prompt the revision of the empirical data. Prediction results could also propose optimal injection sites based on the expected surprise that the data could reveal from such injections. Targets that generate in-links that are highly similar to the existing data do not add much novelty to the dataset, but areas with large deviations from the average link predictions may contain significant information about the specificity of the incoming links into that target. These could correspond, for example, to the appearance of a novel information processing modality in the brain, reflecting a significant evolutionary branching event in the history of the species.
Acknowledgments This work was supported by the  National Science Foundation (NSF)  grant  IIS-1724297  (to Z. T. and H. K. ),  ANR-17-FLAG-ERA-HBP-CORTICITY  (to H. K. , M. E. -R. , and Z. T. ),  ANR-19-CE37-0025-DUAL_STREAMS  (to K. K. ) and by the  CNCS-UEFISCDI  grant  COFUND-FLAGERA 2-CORTICTY  (to M. E-R).
Histological characterization of the tissue structure and cell density in the medusae bell tissue by light microscopy The bell tissue fixed for paraffin embedding underwent serial dehydration in ethanol, then xylene before infiltration and embedding in paraffin. Sections 4 µm thick of paraffin-embedded samples were cut and placed on a glass slide, dewaxed, and stained with hematoxylin and eosin (H&E) before being imaged by transmitted light microscopy with a Keyence VHX-7000 digital microscope. The number of host nuclei (stained in blue by the H&E staining) and the number of dinoflagellate cells in the mesoglea were assessed and standardized by the mesoglea surface area using ImageJ2/Fiji plugin Cell counter and Measure (v2. 9. 0, https://github. com/fiji/Cell_Counter/blob/Cell_Counter-3. 0.
Acknowledgements The authors would like to thank  N. H. Lyndby  for help with the maintenance of the Cassiopea culture tank,  K. Vernez  for the ammonium measurements of seawater samples,  J. Daraspe  and  D. De Bellis  for their advice on sample preparation for EM, and  C. Göpfert  for the advice on histology. Histological sample preparation was performed with the help of the  EPFL Histology Core Facility. GT, NR, GBP and AM were supported by the  Swiss National Science Foundation  grants  200021_179092  and  205321_212614. CP was supported by the  Junior Professorship Grant  'A  connected underwater world ' awarded by the  French National Research Agency  and an associated start-up grant by the  French National Centre for Scientific Research (CNRS).
Precooling in biofreezer For evaluation of the effects of precooling, seeds previously dried on silica gel until reaching a moisture content of 20% wb (protocol 1) or 17% wb (protocol 2) and seeds dried with saturated ammonium sulfate saline solution (protocol 5) were subjected to pre-cooling, which is an optional preparatory step before immersion in liquid nitrogen. Seeds that had been dried on silica gel were placed in trifoliate aluminum foil packages, and those that had been dried with saturated ammonium sulfate saline solution were packed in cryotubes, according to each protocol (Table  1 ). Pre-cooling was performed in an Icecube 14S-B biofreezer with SY-LAB software (Minitub of Brazil ® ) at a rate of -1 °C min -1 to -40 °C or -50 °C, as summarized in Table  1. To assess the physiological and biochemical effects of pre-cooling, the seeds of all treatments at this stage were reheated in a water bath at 40 °C for two minutes  (DUSSERT et al.
ACKNOWLEDGMENTS The authors express their thanks to  Embrapa , to  Universidade Federal de Lavras (UFLA) , to  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) , to  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) , to  Consórcio Pesquisa Café  and to  INCT Café  for funding this study and for granting scholarships. They also express their thanks to  Fundação PROCAFé , for offering the seeds for the studies.
The coarse-resolution hydrological modelling grid Regional gridded hydrological models are often implemented on 1km resolution grids, aligning with the typical resolution of operational radar-based quantitative precipitation estimates. In this context, the 1km × 1km hydrological modelling grid was herein generated by upscaling the 50m flow direction grid from the BNBV database using the IHU method  (Eilander et al. , 2021) , see figure  6  to visualize the upscaling results. The IHU method incorporates principles from previous upscaling methods  (Döll and Lehner, 2002; Fekete et al. , 2001; Olivera et al. , 2002; Paz et al. , 2006; Wu et al. , 2011)  and has demonstrated superior performance compared to the benchmarks methods, e. g. DMM method  (Olivera et al. , 2002)  and EAM method  (Yamazaki et al.
An alternative approach that could circumvent the challenges faced is to use hydrological models structured based on vectorial objects instead of regular grids. These models preserve the topology of river networks and allow seamless integration of observational data. However, vector-based modeling also introduces its own challenges related to data and computational requirements and the need for accurate input data. for performing the area-based method. Financial support. This research was performed within the framework of the MUFFINS project (ANR-21-CE04-0021-01).
Acknowledgments The authors acknowledge the financial support from the  Slovenian Research Agency  young researcher scheme,  ARRS  research projects  N2-0214  and  N2-0165 , research programs  P2-0423  and  P2-0145 ,  Ministry of Education, Science and Sport (MIZS)  for funding M. Era-net project  InsBioration  (call 2021) and  Honda R&D Germany. https://doi. org/10. 26434/chemrxiv-2023-xbhbf Content not peer-reviewed by ChemRxiv. License:  CC BY 4. 0.
Acknowledgements We are thankful to all participants of the study for sharing their valuable time. Special thanks also to  Sophie Müller ,  Megan Evans  and  Moussa Douno  who helped review the KAP questionnaire. Special thanks as well to  René Umlauf  and  Carlos Rocha  for qualitative and theoretical insights. We are also very appreciative for the contribution of  Patrick Mirindi  in planning the training content.
The data selected according to a data quality criterion from four stations on the Kou (Koumi, Nasso-milieu, Badara, Confluence Niamé-Baoulé) and three stations on the Mouhoun (Guéna, Banzon, Samendéni) were used to define these boundary conditions (water levels) (Figure  1 ). On the northern and southern limits of the area, specified steady state head boundaries are defined from the observed groundwater levels and the values are extracted from the reference potentiometric map. Groundwater level data from 100 observation points (87 piezometers and boreholes intersecting the phreatic aquifer, and 13 springs) for the period 1995-1999 were used to draw this reference potentiometric map for the lowflow period of this time interval. Streams in the Kou basin are represented using a Robin boundary condition, i.
Daily climate data from the Bobo-Dioulasso meteorological station (rainfall, temperature, Penman-Monteith potential evapotranspiration; 1961-2014) and monthly rainfall data from five other rainfall stations (Nasso, Farakoba, Vallée du Kou, Orodara, and Samoroguan; variable periods between 1961 and 2014) were used. The latter were completed using Bobo-Dioulasso time series with which they are correlated at over 0. 8 (R 2 )  [Tirogo et al. , 2016]. These data were used to assess groundwater recharge during the period 1961-2014 for both steady state and transient modelling. The infiltration was evaluated from the average basin rainfall of the whole modelled area calculated from the Thiessen polygon method and the data from the above-mentioned stations. A time-varying runoff coefficient in the range of 3%-9% is derived from the hydrometric data of the Kou River for the period 1961-2014.
Model calibration Considering the availability of data, the steady state model is calibrated under low-flow conditions, using data from 68 observation points between 1995 and 1999. The discharge data of the main springs of the area and river flow were also used to calibrate the model. The input parameters for the steady state calibration were recharge, hydraulic conductivity, and river and drain conductances. The steady state calibration consisted in adjusting these parameters to reproduce the observed groundwater level and the flows of the Kou River, the drains (temporary tributaries), and the springs. The a priori and uniform value of the recharge was adjusted following a sensitivity analysis, performed with a range of recharge values calculated using maximum available water contents between 100 and 200 mm and runoff coefficient values from 3% to 9%.
However, to the east of the study area, it was necessary to introduce a low conductivity layer in order to maintain sufficiently high groundwater levels while maintaining hydraulic conductivities appropriate for a surface aquifer and consistent with the results of pumping tests. The presence of such low-permeability layers locally dividing the multilayer reservoir is recognised, particularly at the interface between the GKS and the Gfg [e. g. ,  Derouane, 2008]. A modern automatic parameter estimation method, rather than the two-stage trial-and-error calibration procedure used in this study, would also help to quantify the degree of uncertainty associated with a given prediction of the calibrated model. It would provide valuable feedback on the strengths and weaknesses of the input datasets, allowing future data collection to be optimised.
Acknowledgements This work was carried out with the financial support of  Institut de Recherche pour le Développement (IRD)  through the  Programme Doctoral International " Modélisation des Systèmes Complexes" (PDI/MSC ) , of  Office National de l'Eau et de l'Assainissement (ONEA)  and  SIREA France. The realization of this work was possible thanks to the numerous data made available by the  National Meteorological Agency (ANAM) ,  Direction Générale des Ressources en Eau (DGRE) in Burkina Faso, ONEA , and the  Regional Directorate in  charge of water in " Hauts Bassins ". We would also like to thank the three reviewers for their insightful comments, which have helped us to improve the quality of this article.
ACKNOWLEDGEMENTS We thank  Stéphane Pêtres  (Production and  Purification of Recombinant Proteins Facility, Institut Pasteur, 75015 Paris, France ) for his advice on protein purifications. This work was funded by  Fondation Janssen Horizon , the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  Institut Pasteur. LH is a doctoral fellow of  Sorbonne Université. DS was a recipient of a poste d'accueil 2017  Institut Pasteur -Assistance Publique des Hôpitaux de Paris (APHP). Work in the G. Gorochov's team is supported by  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Sorbonne Université ,  Fondation pour la Recherche Médicale (FRM) ,  Paris, France , program '' Investissement d'Avenir' ' launched by the  French Government  and implemented by the  Agence Nationale de la Recherche (ANR)  with the reference  COFIFERON   ANR-21-RHUS-08 , by  EU Horizon   HLTH-2021-DISEASE-04   UNDINE  project, by programme  DIM Ile de France thérapie cellulaire et génique , by  Fondation pour la Recherche Médicale, Paris, France  ( Programme Equipe FRM 2022 ) and by the  Département Médico-Universitaire de Biologie et Génomique Médicales (DMU BioGen) ,  APHP, Paris, France.
Thus, we can now move beyond fingerprinting to understanding the genealogy and evolution of the mineral constituents of an ophiolite and raise understanding to a new level. This study presents extensive mineralogical, geochronological, and geochemical datasets for zircon and two TiO 2 polymorphs, rutile, and anatase, extracted from harzburgite and podiform chromitite of the Peridotite Nappe in New Caledonia. These minerals exhibit distinct colours and morphologies. They display a wide range of geochemical signatures and U -Pb ages, providing a comprehensive dataset for understanding the geological history of the region. Notably, xenocrysts demonstrate a robust affinity to terranes that were subducting beneath a developing intra-oceanic island arc system. The occurrence and isotopic system of these xenocrysts provide important information about the depth at which recycling processes took place within the forearc wedge.
Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) mineral analysis Mineral grains were mounted in 2. 54 cm epoxy resin discs and polished to reveal their crystal cores. Backscatter and cathodoluminescence (CL) SEM images and EDX data were obtained using a TESCAN Integrated mineral analysis (TIMA) Field Emission SEM at the Central Analytical Research Facility (Queensland University of Technology) or a Hitachi SU3500 at the Centre for Microscopy and Microanalysis (UQ). These data were used to examine mineral morphology, texture, fractures, and inclusions. CL images were obtained with a 10 Kv acceleration voltage and 12 nA beam current (Refer to Appendix 1 A for CL images). For identification of mineral phases on the TESCAN TIMA, the dot mapping approach was employed with 25 Kv acceleration voltage, 1 μm/pixel spacing for backscatter imaging, 3 μm/pixel EDS dot spacing, and 1000 counts per spectrum per pixel analyzed.
Tera-Wassenburg and Kernel density diagrams were plotted with 2σ error using the Isoplot-R software  (Vermeesch, 2018). For U -Pb isotope measurements, rutiles and anatases were ablated using a 38 μm spot size beam in 22 cycles of 1 s, a repetition rate of 8 Hz and fluence of 3 J/cm 2. The primary and monitoring standards were R632  (Axelsson et al. , 2018)  and DXK rutiles  (Shi et al. , 2012) , respectively, and the precision and accuracy of these standards were < 1% (Appendix A). Raw data were processed using Iolite software  (Paton et al. , 2011). Common Pb was corrected using the R632 standard and the "VizualAge UcomPbine" package  (Chew et al. , 2014)  with the 208 Pb (no Th) correction. Out of the 230 grains analyzed, only 11 displayed Th values above 1 ppm, and most grains contained Th abundances below the detection limit.
Consequently, the 208 Pb measured in these grains was assumed to be non-radiogenic  (Zack et al. , 2011). Common Pb correction of unknown samples was achieved by grouping the samples based on their 206 Pb/ 238 U ages and plotting them in Tera-Wasserburg diagrams using 238 U/ 206 Pb and 207 Pb/ 206 Pb ratios (2σ propagated error) with Isoplot-R software  (Vermeesch, 2018). Rutile ages presented in this study plots near the Concordia line, indicating low levels of common lead in these grains. Concordia and lower intercept Discordia ages exhibit a concordance rate of over 97%, facilitating age calculations. Final regression lines in the Tera-Wasserburg plots were unanchored for common Pb. To validate the common lead correction, three methods -nominal (0. 9), Stacey-Kramer, and Isochron -were tested.
Trace element abundance Analyses of zircon trace element isotopes ( 91 Zr, 45   232 Th, 238 U) were performed within the same analytical session as those for U -Pb isotopes measurements. NIST612 glass standard was used as the primary standard, with NIST614 glass measured for quality control. Raw data were reduced using the Iolite software  (Paton et al. , 2011)  with Zr as internal normalization standard. Rutile and anatase were analyzed for specific elemental abundances ( 25 Mg, 27 Al, 45 Sc, 49 Ti, 51 V, 53 Cr, 55 Mn, 57 Fe, 59 Co, 60  Pb, 232 Th, 238 U) with the same Thermo Fisher iCAP RQ quadrupole ICP-MS with an ASI RESOlution SE 193 nm laser, but in a different analytical sessions as those for U-Pb isotope analyses. Trace element abundances of rutile and anatase grains were acquired using a 50 μm spot size beam that overlapped the U-Pb laser holes.
Measurements were performed in 29 cycles of 1 s at a frequency of 6 Hz and fluence of J/cm 2. NIST 612 was measured as primary standard, while NIST and R632 (natural rutile) were used as monitoring standards. Raw data were processed using the Iolite software  (Paton et al. , 2011)  with Ti as the internal normalization standard. Trace elemental data acquired on the monitoring standard (R632 rutile) closely match the values reported, indicating an accurate analytical session (A. Fig. 17 ). For more information on analytical procedures, data acquisition, reduction, and validation, refer to Appendix A. Data are presented on a spreadsheet in Appendix B.
U -Pb ages Zircon grains can be grouped based on their U-Pb ages (A. (Vermeesch, 2018) , and the ages of the peaks were calculated using the software Density plotter  (Vermeesch, 2012). from 50 to 2764 Ma. U/Pb age populations can be visualized in kernel density plots (Fig. 4c, d ). Phanerozoic ages predominate, with the most prominent age populations being Late Cretaceous (90-120 Ma; Th/U avg = 0. 87; n = 27), Early Jurassic to Cretaceous (125-170 Ma; Th/U avg = 0. 58; n = 51), Late Permian to Early Triassic (225-260 Ma; Th/U avg = 0. 61; n = 33), and Late Ordovician to Early Devonian (405-465 Ma; Th/ U avg = 0. 38; n = 46). Some zircons have ages dating from the Neo-, Meso-, and Paleoproterozoic eras, and one Archean zircon is noted (refer to Appendix B for individual grain ages). The largest Proterozoic age cluster lies within the Stenian-Tonian periods (ca.
U-Pb ages Ages of red prismatic rutiles from both chromitite and harzburgite samples have been reliably determined and range from Devonian to Eocene. U -Pb age determinations for red rutile grains (n = 82) from the chromitite sample exhibit well-defined populations, including an Eocene cluster at ~47 Ma (as determined by the lower intercept age; n = 36; Fig. 6a, c   (Vermeesch, 2018) , and the ages of peaks were calculated using the software Density plotter  (Vermeesch, 2012). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) identified at ca. 400  n = 8)  in the red rutile from the harzburgite.
Red-rutile U-Pb and trace element data constraints Red rutiles recovered from both harzburgite and chromitite yield U-Pb ages that cluster in the Eocene (ca. 47 Ma) and Late Triassic-Early Jurassic (ca. 205 Ma), with less prominent peaks at 171, 247, and 401 Ma (Fig. 6 ). Red rutile xenocrysts (> 53 Ma) exhibit geochemical signatures similar to those of mafic-ultramafic rocks and were likely originated through magmatic processes (Fig. 9 ). These findings indicate these xenocrysts were likely recycled from mafic units within the subducting slab. However, determination of the sources of TiO 2 xenocrysts remains challenging given the limited dataset for rutile U -Pb ages amongst potential source rocks in New Caledonia or other units along the Eastern Gondwana margin. These xenocrysts could have originated from the extensive mafic and intermediate rocks present in New Caledonia  (Maurizot et al.
Acknowledgements The authors acknowledge funding support from the  Australian Research Council  ( ARC DP190100814  Diamonds in  ophiolites: Recycling deep mantle into supra-subduction zones ). D. C. and J. C. A. thank  M. Henri Reuillard ,  M. Marc Akaro  and the  Tiébaghi Historical Society  for arranging access to the mine wash plant and to  Aileen  and  Elisabeth  for help in sample collection. The authors also thank two anonymous reviewers and Editor  Dr. Sonja Aulbach  for their valuable constructive feedback, which significantly improved this work.
Introduction Correlation estimation is integral to a wide range of applications, and is often the starting point of further analyses. However, data are often contaminated by noise. If data are additionally inherently divided into separate, and study-relevant groups, inter-group correlation estimation becomes all the more challenging. Such datasets are often encountered in spatio-temporal studies, such as single-subject brain functional connectivity network estimation, where voxel-level signals acquired via functional Magnetic Resonance Imaging (fMRI) are grouped into predefined spatial brain regions  (De Vico Fallani et al. , 2014). This work is relevant as well to other fields, such as organizational studies, where individuals are grouped by organization  (Ostroff, 1993). As such, we will be using the words group, region, and parcellation interchangeably.
Preliminaries From this point forward, and without loss of generality, we will focus on spatio-temporal contexts. In particular, we are motivated by an application to brain fMRI data where individual observed variables correspond to blood-oxygen-level-dependent (BOLD) signals that are assigned to voxels, and are grouped by regions. Nonetheless, the following results can be applied to any dataset of grouped measurements of a quantity. In this section we define our notation and model, together with the inter-and intra-correlation coefficients. Throughout this paper we consider two regions, generically denoted A and B. In reality, datasets will involve a potentially large number of regions but, for the purpose of correlation network construction, the correlations can be estimated in a pairwise fashion at the regionallevel.
Real-World Datasets Rat Brain fMRI Dataset We apply our estimator on fMRI data acquired on both dead and anesthetized rats  (Becq et al. , 2020a,b). In this paper we consider the following anesthetics: Etomidate (EtoL), Isoflurane (IsoW) and Urethane (UreL). The dataset is freely available at https://dx. doi. org/10. 5281/zenodo. 7254133. The scanning duration is 30 min with a time repetition of 0. 5 s. After preprocessing  (Becq et al. , 2020b) , 25 groups of voxels, each associated with its BOLD signal with a number of time points in the order of thousands, were extracted for each rat. They correspond to rat brain regions defined by an anatomical atlas obtained from a fusion of the Tohoku and Waxholm atlases  (Becq et al. , 2020b). Region sizes vary from about 40 up to approximately 200 voxels.
Synthetic Datasets We consider several synthetic datasets to evaluate our estimator. For each simulation, we simultaneously generate 800 independent samples of a pair of inter-correlated regions, containing each 60 intra-correlated variables that follow a multivariate normal distribution with a predefined covariance structure contaminated by Gaussian noise. The inter-correlation is constant across all pairs of voxels. The different parameters are chosen to ensure the population covariance matrix of the two regions is positive semidefinite. For instance, one cannot generate a covariance matrix where both intra-and inter-correlation values are low. Toeplitz Covariance Structure We first generate 1-dimensional data with a Toeplitz intra-regional covariance structure (later denoted 1D Toeplitz).