dataesr/scientific-paragraphs-categorization huggingface dataset:
Availability of unique biological materials Access upon request of the infant faecal samples to third parties is not part of the consent granted by the parents upon enrollment into the COPSAC2010 cohort. Nor is such access compliant with Danish or EU regulations for safeguarding rights of underage human research participants. Materials might however be obtained as part of a scientific collaboration agreement with COPSAC, and queries for such may be sent to the COPSAC Data Protection Officer, Ulrik Ralkiaer, PhD (administration@dbac. dk).
Illustrations Teeth and skull photographs were taken using Nikon digital camera D 5500 coupled with AF-S Micro NIKKOR 60 mm and macro extension tubes. Photos were stacked using Helicon Focus 8. 1. 4 and edited in Adobe Photoshop CC 21. 1. 3. We have tried to show the intrageneric variability in dental anatomy by including photographs of several species in each genus description. Schematics drawings of rodent craniodental morphology, including illustrations of some distinctive features for identification, were realized with Adobe Photoshop CC 21. 1. 3 and Adobe Illustrator CC 21. 0. 0. Distribution maps of each species have also been included. These maps are based on distributional data published in Monadjem et al. [22] , Wilson et al. [12, 13] , data from IUCN (International Union for Conservation of Nature) red list database and range maps from various expert sources available on the Map of Life's website at https:// mol.
The depth of the WDR neurons selected for this study was >400μm, most probably corresponding to deep neurons from laminae V. Activities of WDR neurons were sampled at 20 kHz, band-pass filtered (0. 3-3 kHz) and amplified using a DAM80 amplifier (WPI, Europe), digitized with a A/D-D/A converter (1401 data acquisition system, Cambridge Electronic Design, Cambridge, UK), and recorded on a computer using Spike 2 software (Cambridge Electronic Design, Cambridge, UK).
Patch-Clamp and calcium experiments on HEK293 transfected cells HEK293 were prepared as described elsewhere  [52] , before being transfected using JetPEI (Polyplus transfection SA, Illkirch, France) with different plasmids encoding the rat clones of ASIC1a, ASIC2a and/or ASIC2b. Different mixtures of pCI-A1a alone or pCI-A1a + pCI-A2a (1:2 ratio) or pCI-A1a + pCI-A2b (1:2 ratio) with either pIRES2-EGFP (patch-clamp experiments) and/or pIRES2-HcRed (calcium experiments) were used for transfections. Fluorescent cells were then used for patch-clamp or calcium recordings 2-4 days after transfection. For patch-clamp experiments, the whole-cell configuration was used to record membrane currents at a holding potential of -50 mV, and recordings were made at room temperature using an Axopatch 200B amplifier (Axon Instruments) with a 2 kHz low-pass filter and a Digidata 1550 A-D/D-A converter (Axon Instruments) driven by pClamp software (version 11; Axon Instruments), with a sampling rate of 20 kHz.
Acknowledgments We thank  Drs A. Baron ,  S. Diochot ,  J. Noel , and  M. Salinas  for helpful discussions,  V. Friend  and  J. Salvi-Leyral  for technical support, and  V. Berthieux  for secretarial assistance. We also thank the  Centre National de la Recherche Scientifique , the  Institut National de la Santeet de la Recherche Medicale  and the  University Cote d'Azur  for their supports.
E. D. and E. L. received grants from the  Agence Nationale de la Recherche ,  ANR  ( ANR-13-BSV4-0009  and  ANR-17-CE16-0018 ), https://anr. fr/, and from the  LabEx ICST  ( ANR-11-LABX-0015-01 ). E. D. received grants from the  Association Francaise contre les Myopathies, AFM  (grant # 19618  and grant# 23731 ), http://www. afmtelethon. com/,and from the  Institut Analgesia/ SFETD. E. D. and R. V. received a grant from the  Neuromod Institute of the University Cote d'Azur (UCA ), https://neuromod. univ-cotedazur. eu/. E. D. and L. P. have been supported by the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This type tends to occur in the cold season (September-April) in an environment characterized by strong forcing and low instability. Convective systems of this type generally propagate more slowly than progressive derechos. Various criteria have been employed to delineate a sequence of downburst clusters as a derecho, using observational data such as wind gust reports and weather radar data. For instance,  Johns and Hirt (1987)  proposed the following criteria: (i) a concentrated area of convective wind gust with a speed greater than or equal to 26 m s -1 or -when windspeed measurements are not available -the presence of damage following downbursts. The major axis length must be at least 400 km. (ii) The convective gusts must have an identifiable spatio-temporal progression. (iii) At least three gusts greater than or equal to 33 m s -1 must be measured or assessed on the basis of damage within the area covered by the episode, and these reports must be separated by at least 64 km from each other.
The secondary goal is to detect potential alterations in synoptic conditions and environmental convective parameters, including CAPE and vertical wind shear, linked to past warmseason derecho-producing MCSs in France. The study also seeks to evaluate the contributions of climate change and natural variability to these observed changes. In Sect. 2, we delve into the methodological aspects of our work and introduce the datasets employed. Specifically, Sect. 2. 1 outlines the methodology and observational datasets used for detecting past derecho events over France, along with associated limitations. Additionally, Sect. 2. 2 introduces the methodology for detecting and attributing changes in synoptic conditions, accompanied by the presentation of the reanalysis and observational datasets. Moving on to Sect.
To circumvent the limited geographical coverage of wind reports in France, we use severe wind gust reports from three additional sources: (i) the Integrated Surface Database (ISD)  (Smith et al. , 2011)   (Dotzek et al. , 2009)  whenever the associated MCS track extends into other countries. The ESWD provides detailed and quality-controlled reports from severe convective events in Europe including severe wind gusts, heavy rain, hail, tornadoes and damaging lightning from a variety of sources. For our study, we only retained reports at minimum level quality QC1 ("report confirmed by reliable source"). These reports originate from various channels, including weather stations providing specific wind gust speeds and wind gust damage reports, such as those involving fallen trees. However, the latter type of report lacks precise wind gust speed information, impeding the accurate estimation of derecho intensity.
In cases where reports are insufficient, we also consider days on which derechos have been reported over France by  Gatzen et al. (2020). Then we use the Python FLEXible object TRacKeR (PyFLEXTRKR) algorithm developed by  Feng et al. (2023a)  to systematically detect and track potential associated MCSs for each previously selected day. This algorithm has notably been used to build a global MCS database  (Feng et al. , 2021)  using satellite imagery data, namely brightness temperature and precipitation. It has also been used to track MCSs in convection-permitting simulations  (Feng et al. , 2023b)  or convective cells from radar data  (Feng et al. , 2022). To detect and track MCSs, the algorithm uses brightness temperature thresholds to identify cold cloud systems (T b < 241 K) with an additional size constraint of an area greater than 4 × 10 4 km 2.
Precipitation data are used in addition to enable a more robust identification of MCSs by requiring that an intense precipitation feature (criteria include rain rate greater than 3 mm h -1 and major axis length greater than 100 km) is embedded within the cold cloud system. These criteria must be met for at least 4 h to define a cold cloud system as an MCS. We specifically use the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) V06 precipitation database  (Huffman et al. , 2019)  and the NOAA NCEP/CPC Globally Merged IR (MERGIR) brightness temperature (T b ) database  (Janowiak et al. , 2017) , as in  Feng et al. (2021). Both datasets are available from the year 2000 and cover the area between 60°S and 60°N at a time resolution of 30 min. The MERGIR database has a finer resolution (4 km) than IMERG (10 km or equivalently, 0.
1°), so we regrid T b data to a resolution of 0. 1°using the xESMF Python package  (Zhuang et al. , 2023)  prior to applying the tracking algorithm. If PyFLEXTRKR detects an MCS for a specific selected date, we conduct a visual comparison, aligning the detected MCS structure, as illustrated on brightness temperature and precipitation rate maps, with the severe wind gust reports in both time and space. For illustration, a snapshot of T b and precipitation rate, highlighting the contour of the detected MCS using PyFLEXTRKR, is provided in the Supplement (Fig. S1 ). Subsequently, we retain only those days when the reports exhibit a discernible match with an MCS along a minimum distance of 400 km. Additionally, we assign to each detected derecho an intensity following the criteria set by  Gatzen et al.
, 2003; Trapp et al. , 2007; Brooks, 2013; Púčik et al. , 2015; Taszarek et al. , 2020b; Battaglioli et al. , 2023). We use data from ERA5  (Hersbach et al. , 2020) , the latest climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) as part of the implementation of the EU-funded Copernicus Climate Change Service (C3S). It provides hourly data on atmospheric, land surface and sea state parameters from 1950 to the present. The ERA5 data are available from the C3S Climate Data Store on regular latitude-longitude grids at a horizontal resolution of 0. 25°× 0. 25°. We opted for ERA5 data in this study primarily due to the dataset's remarkable consistency over an extensive time span (73 years), facilitating the detection of changes in large-scale dynamics. The global nature of ERA5 also mitigates issues related to combining datasets from diverse national weather services, ensuring a uniform spatial and temporal coverage.
Nevertheless, it is important to acknowledge certain caveats arising from the long-term advancements in observation instruments, including satellites. These advancements may impact the uniformity of the quality of the dataset and have the potential to introduce spurious trends  (Thorne and Vose, 2010; Hersbach et al. , 2020). In addition, parameterizations can bring errors in the estimation of convective parameters such as CAPE and modelled precipitation and wind gusts  (Taszarek et al. , 2021c). Despite these considerations, ERA5 continues to be regarded as one of the best reanalyses currently available. For each period, we examine all daily averaged Z500 maps and select the 37 best analogues, i. e. the maps minimizing the Euclidean distance to the event map itself. The number of 37 corresponds approximately to the smallest 8 ‰ Euclidean distances in each subset of our data.
We then examine composites of the analogues in each period for daily averaged 2 m temperature and wind speed, daily cumulative precipitation, daily maximum CAPE, and DLS all computed from ERA5 hourly fields. For comparison, we also consider daily mean 2 m temperature and cumulative rainfall from the E-OBS observational dataset v27. 0  (Cornes et al. , 2018)  (available from https://www. ecad. eu/download/ ensembles/download. php, last access: 19 March 2024) which interpolates measurements from land weather stations across Europe on a regular grid at 0. 1°resolution. To determine significant changes between the two periods, we adopt a bootstrap procedure which consists of pooling the dates from the two periods together, randomly extracting 37 dates from this pool 1000 times, creating the corresponding difference maps and marking as significant only grid point changes above 2 standard deviations of the bootstrap sample.
Wei et al. (2021)  found an influence of PDO on northwestern Europe extreme rainfall. Similarly,  Casanueva et al. (2014)  found a significant role of SCAND in autumn and spring in extreme precipitation in Europe. In practice, we examine the association of the analogues with the aforementioned factors of natural variability (ENSO, AMO, PDO, NAO, EA, SCAND). We perform this analysis using monthly indices from NOAA ERSSTv5 data that are retrieved from the Royal Netherlands Meteorological Institute (KNMI) Climate Explorer (accessible at https://climexp. knmi. nl/selectindex. cgi, last access: 19 March 2024). In particular, the ENSO index is computed in region 3. 4 as defined by  Huang et al. (2017) , and the AMO index is calculated as described in  Trenberth et al. (2015). To assess the possible association of these different indices with circulation changes between factual and counterfactual periods, we compare the distributions of each index for the analogues of the two periods, and we evaluate any significant changes between factual and counterfactual distributions by performing a two-tailed Cramér-von Mises test  (Anderson, 1962)  at the 0.
To evaluate the significance of potential changes in analogue seasonality between the two periods, we apply the same statistical test that we use to compare the distributions of indices of natural variability. Additionally, we extend our analysis by computing the best 8 ‰ analogues for the entire Z500 dataset from 1950 to 2022 without segregating it into factual and counterfactual periods. Subsequently, we estimate a linear trend for this global quantile, recognizing that the total number of analogues in all decades for this specific quantile is 91. To evaluate the significance of these trends, we calculate the confidence interval using the Wald method  (Stein and Wald, 1947). Following  Faranda et al. (2022) , we define certain quantities that support our interpretation of analogue-based assignment.
5a  and b. Upon scrutiny, we observe no significant changes in the circulation pattern (Fig. 5c ). Notably, there is a pronounced increase in 2 m temperature across most of Europe, particularly over the Mediterranean Sea. Concurrently, there is a substantial reduction in precipitation over northern Italy, con-trasting with a significant increase in Eastern Europe and certain regions of the Mediterranean Sea (Fig. 5g, h, i ). These findings are consistent with results from the E-OBS dataset for temperature and precipitation (Figs. S2  and S3  provided as Supplement). Analysis of daily maximum CAPE (Fig. 5m, n, o ) reveals a significant surge over the Mediterranean, aligning closely with the exceptionally high values observed on 18 August 2022. It is worth noting that ERA5 values for CAPE exhibit an unrealistic spike for the 2022 derecho (local values exceeding 5000 J kg -1 in Fig.
Conclusions In summary, this study presents a 23-year climatology of warm-season derechos in France spanning from 2000 to 2022. To detect events, we used wind gust reports from weather station data, primarily sourced from Météo-France and supplemented by data from ISD, DWD and ESWD. Mapping was conducted using an MCS detection and tracking algorithm with satellite imagery, leading to the identification and analysis of 38 events. A comparative examination of their features was carried out, drawing parallels with climatologies in the USA and Germany. Derechos in France are notably less frequent than those in the USA and more comparable to those in Germany. Nationwide, an average of 1. 65 warm-season derechos occur per year, with the highest local frequency observed in northeastern France at 0.
While exploring changes in synoptic conditions and convective environmental parameters, we aimed to estimate the potential impact of specific internal variability factors on large-scale conditions associated with derechos. However, our analysis does not achieve a precise understanding of the individual contributions of these factors and anthropogenic climate change to the occurrence of derecho-producing MCSs. Additionally, the study does not consider potential influences from land use and land cover changes and other surface variables like SST, and it focuses exclusively on France, without extending its considerations to other regions. To address these limitations, future studies could benefit from radar data  (Huuskonen et al. , 2014)  and lightning datasets  (Schulz et al. , 2016)  to enhance detection accuracy and refine event definitions.
2. When x(t) contains all the variables of the system, the estimation of D based on extreme value theory has a number of advantages over traditional methods (e. g. the box counting algorithm;  Liebovitch and Toth, 1989; Sarkar and Chaudhuri, 1994). First, it does not require the volume of different sets to be estimated in scale space: the selection of s(q) based on the quantile provides a selection of different scales s which depends on the recurrence rate around the point ζ. Moreover, it does not require the a priori selection of the maximum embedding dimension as the observable g is always a univariate time series. The persistence of the state ζ is measured via the extremal index 0 < ϑ(ζ ) < 1, a non-dimensional parameter, from which we extract (ζ ) = t/ϑ(ζ ). Here, t is the time step of the dataset being analysed.
(ζ ) is therefore the average residence time of trajectories around ζ , namely the metric of persistence introduced in Sect. 2, and it has a time unit (in this study days). If ζ is a fixed point of the attractor, then (ζ ) = ∞. For a trajectory that leaves the neighbourhood of ζ at the next time iteration, = 1. To estimate ϑ, we adopt the Süveges estimator  (Süveges, 2007). For further details on the extremal index, see  Moloney et al. (2019). Data availability. ERA5 reanalysis data can be accessed through the Copernicus Climate Change Service (C3S) Climate Data Store (https://doi. org/10. 24381/cds. adbb2d47, Hersbach et al. , 2023a and https://doi. org/10. 24381/cds. bd0915c6,  Hersbach et al. , 2023b). The ERA5 data for attribution have been downloaded from the preprocessed data available at https: //climexp.
knmi. nl/selectdailyfield2. cgi?id=someone@somewhere (KNMI Climate Explorer, 2024). The E-OBS dataset, sourced from the EU-FP6 project UERRA (http://www. uerra. eu, UERRA, 2024) and the C3S, is accessible via the ECA&D project (https: //www. ecad. eu/download/ensembles/download. php#datafiles , ECA&D, 2024;  Cornes et al. , 2018). The GPM IMERG Final Precipitation L3 Half Hourly 0. 1 degree × 0. 1 degree V06 and the NCEP/CPC L3 Half Hourly 4 km Global (60°S-60°N) Merged IR V1 datasets are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC) at https://doi. org/10. 5067/GPM/IMERG/3B-HH/06  (Huffman et al. , 2019)  and https://doi. org/10. 5067/P4HZB9N27EKU  (Janowiak et al. , 2017) , respectively. Weather station data from Météo-France are accessible upon free request from https://portail-api.
meteofrance. fr/web/en/api/DonneesPubliquesPaquetObservation, (Météo-France, 2024). The Integrated Surface Database (ISD) from the National Oceanic and Atmospheric Administration (NOAA) can be accessed via https://www. ncei. noaa. gov/data/global-hourly/  (NCEI and NOAA , 2024). The European Severe Weather Database (ESWD) from the European Severe Storms Laboratory is accessible at http://www. eswd. eu/ (ESSL, 2024). Data from German Weather Service (DWD) weather stations can be accessed at https://cdc. dwd. de/portal/ (EDWD, 2024). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd-5-439-2024-supplement. Author contributions. LF performed the detection and tracking of derechos and the subsequent analysis. DF performed the attribution analyses.
Furthermore, the authors acknowledge Météo-France for providing wind gust data from its weather station network. The authors are grateful for the support provided by the CEA programme Focus Numérique Frugal, which funded Lucas Fery's PhD. Financial support. This research has been supported by Horizon 2020 (grant nos. XAIDA-101003469 and EDIPI-956396), the Agence Nationale de la Recherche (grant no. ANR-20-CE01-0008-01), the Centre national de la recherche scientifique (CNRS) (grant no. LEFE-MANU-CROIRE), the France 2030 (PEPR TRACCS programme, grant no. ANR-22-EXTR-0005) and the Commissariat à l'énergie atomique et aux énergies alternatives (CEA) (grant FO-CUS Numérique Frugal). Review statement. This paper was edited by Peter Knippertz and reviewed by two anonymous referees.
Code availability. The code to compute the dynamical indicators of predictability D and persistence is available at https://fr. mathworks. com/matlabcentral/fileexchange/95768attractor-local-dimension-and-local-persistence-computation  (Faranda, 2021). The Python FLEXible object TRacKeR (PyFLEXTRKR) algorithm developed by Feng et al. (2023a)  is available at https://doi. org/10. 5281/zenodo. 7429446  (Feng, 2022). Other analysis codes and the database of warm-season derechos in France are available upon requests from the authors.
Fig. 4. Similarity of alphabets within Voynich manuscript. Alphabets are grouped into clusters according its similarity,  [2]. amongst the alphabets. Our analysis aims to test the method based on deep learning, for comprehensive overview see  [8] [9] [10] , in order to determine in future at least approximately to which family of languages the Voynich manuscript could belong. Thus, this paper is a proof of concept -a more extensive set of alphabets is needed to gain a ''final'' decision. This is an open research chance for anyone. Similar research in the application of networks on dialect identification has already been done also in  [11]  (on distinguishing similar today existing languages and dialects),  [12]  (Convolutional neural networks and language embeddings for dialect recognition -acoustic and linguistic features for the dialect identification task on the Arabic dialectal speech dataset),  [13]  (German dialect identification shared task including four German dialects: Basel, Bern, Lucerne and Zurich) or  [14]  (discuss multilingual encoding method for dialect identification using convolutional neural network dataset for Arabic and English language) amongst the others.
To demonstrate proof-of-concept, the Indian scripts and dialects as the Assamese (Fig. 19 ), Gujarati (Fig. 20 ), Hindi (Fig. 21 ), Khojki Jiva (Fig. 22 ), Konkani (Fig. 23 ), Panjabi (Fig. 24 ) and Urdu (Fig. 25 ) has been chosen. The reason is that ancient India is the home of the world's oldest written language systems. India and the Sanskrit-based scripts were selected as our starting points. Why India? The visual inspection of the Voynich Manuscript shows naked-eyes similarities between the alphabet of Voynich's manuscript and some of the ancient Indian dialects. The reason for choosing these dialects was thus relatively prosaic. The code is accessible at GitHub 8 for readers to use and expand our experiments.
Alphabets as graphical objects The experiments in this article are based on a simple idea of the similarity of letters as graphic objects. As has already been said, in every culture, the scriptures change more or less gradually. These changes can be considered as a graphic mutation, which can be used to identify the root of a given language, provided we have enough samples to capture these mutations. But on the other hand, there is a chance to identify the original 8 https://github. com/Vinmel24/On-Comparing-of-Voynich-Manuscripts- with-Alphabets-of-other-Languages-arXiv-bio-arXiv-. git  language even without these mutations, provided that the script has not changed radically. Therefore, the letters and alphabet similarity was evaluated at the level of similarity of graphic objects. Image similarity  [15, 16]  consists of finding features for adequately describing the image content and finding a suitable metric for assessing the similarity based on feature space.