dataesr/scientific-paragraphs-categorization huggingface dataset:
The development dataset consisted of 575 bacterial strains, including 440 Shigella strains (86 S. boydii; 90 S. dysenteriae; 139 S. flexneri 1-5, X, and Y; 22 S. flexneri 6; and 103 S. sonnei strains) (TableS1). All Shigella serotypes are represented within this dataset. In addition, 45 EIEC and 90 non-Shigella/EIEC genomes -70 E. coli, six Escherichia albertii, two Escherichia marmotae, two Campylobacter jejuni, one Enterobacter hormaechei, one Enterococcus faecium, two Klebsiella pneumoniae, three Listeria monocytogenes, two Salmonella enterica and one Staphylococcus aureus -were used as a control group in the development dataset (Table.
All supporting data, code and protocols have been provided within the article or through supplementary data files. One supplementary figure and thirteen supplementary tables are available with the online version of this article. 000961 https://www. ebi. ac. uk/ena/) under study accession numbers PRJEB44801, PRJEB2846 and PRJEB2128. Other whole-genome sequences analysed during the study are available from ENA, NCBI RefSeq (https://www. ncbi. nlm. nih. gov/refseq/), DDBJ (https://www. ddbj. nig. ac. jp/index-e. html) and GenBank (https://www. ncbi. nlm. nih. gov/ genbank/). All the accession numbers of the genomes used in this study are listed in Supplementary Data.
of ShigaPass and other in silico tools with the development dataset The development dataset consisted of 575 bacterial strains, including 440 Shigella strains (86 S. boydii; 90 S. dysenteriae; 139 S. flexneri 1-5, X, and Y; 22 S. flexneri 6; and 103 S. sonnei strains) (TableS1). All Shigella serotypes are represented within this dataset. In addition, 45 EIEC and 90 non-Shigella/EIEC genomes -70 E. coli, six Escherichia albertii, two Escherichia marmotae, two Campylobacter jejuni, one Enterobacter hormaechei, one Enterococcus faecium, two Klebsiella pneumoniae, three Listeria monocytogenes, two Salmonella enterica and one Staphylococcus aureus -were used as a control group in the development dataset (Table.
e. ERA5 and MERRA2) capable of realistically representing the low clouds present in WEA? (ii) are the low clouds of this region a mere extension of the large-scale low cloud deck found over the Southeast Atlantic Ocean? (iii) do CMIP6 GCMs underestimate low clouds as in the previous model generations, and is there any difference between atmospheric and coupled simulations? (iv) how does the low cloud cover relate to oceanic and atmospheric forcings, in models and reanalyses? (v) do these relationships help to understand the models' systematic biases in their reproduction of low clouds? To answer these questions, low cloud cover will be compared between CMIP6 historical simulations (both coupled and atmospheric only), both reanalysis products, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite data and in situ synoptic observations for WEA.
The focus will be on the austral winter dry period (June-September) during which most of the low clouds develop over the region, but insights will also be given into the annual cycle. Besides the examination of mean fields, interannual variability will be considered, in particular the relationship between low cloud variability and SST over the Equatorial Atlantic Ocean, which is suspected to play a significant role in (or least interact with) low cloud occurrence. Section 2 presents the different types of data and the methods used in the study. The climatology of low level clouds is then examined (section 3. 1), followed by an analysis of the interannual variations of low clouds (section 3. 2). Finally, their relationships with oceanic and atmospheric fields are studied in section 3. 3. Section 4 summarizes the results.
CALIOP CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) is the lidar device aboard the CALIPSO satellite. CALIPSO (Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations) is a Franco-American mission launched in 2006 with the objective of studying the radiative impacts of clouds and aerosols on climate. CALIOP is a two-wavelength backscattering lidar (532 and 1064 nm) that provides high-resolution vertical profiles of clouds and aerosols  (Chepfer et al. , 2008). The lidar detects all types of clouds but low clouds can only be detected when they are not covered by optically thick higher clouds. The data used in this study are not those obtained directly from the lidar but come from the GCM-Oriented CALIPSO Cloud Product (GOCCP) (see below). The spatial resolution of the GOCCP low cloud data is 2° in latitude and 2° in longitude, i.
ISD ISD (Integrated Surface Database) is a global database comprising hourly synoptic surface observations made at meteorological stations  (Smith et al. , 2011). It is a database managed by the United States NCEI (National Centers for Environment Information). It includes over 35,000 stations around the world, with early data dating back to 1901, but many series beginning in the 1970s. Several parameters are recorded in the database such as temperature, wind, cloudiness, precipitation. In Gabon and Congo-Brazzaville, there are 30 stations with cloud data (sky cover, cloud ceiling, types of clouds -low, middle and high) (figure  1a ). Only the synoptic hours (i. e. GMT 0000, 0300, 0600 etc. ) are retained. Unfortunately, the records are rather incomplete. Only 15 stations have more than 25% of non-missing 3-hourly records over the period of study 1979-2014.
EECRA EECRA (Extended Edited Cloud Report Archive) is a global cloud database  (Hahn and Warren, 1999). These data are surface observations from various sources which have been processed, edited after quality control, from reports made by observers on ships or on the continent, day and night. Li et al. (2015)  found an excellent agreement at global scale between EECRA cloud data and various satellite cloud products. Data from land stations cover the period 1971-2009, from which the sub-period starting in 1979 has been extracted. Low cloud fraction is available at three-hourly resolution. There are only 25 stations in the study area (figure  1b ). The overall amount of missing data is close to that of the ISD data set.
This definition was retained for the ISD dataset only. Reanalysis products provide LC fraction data but the tropospheric depth over which low clouds are considered differ between ERA5 and MERRA2. In ERA5, low clouds are defined as the integration of all clouds found at pressure levels greater than 0. 8 times the local surface pressure, i. e. below about 2 km assuming a standard atmosphere. Importantly, ERA5 LC fraction does not distinguish stratiform from vertically developed clouds, hence it is expected that in seasons when convection is active (outside the JJAS season in the core study area) ERA5 LC fraction is positively biased relative to our station estimate described above. In MERRA2, the LC fraction ("CLDLOW" variable) is described as the maximum of the total cloud fraction (convective and large scale) of any layer at or below 700 hPa (about 3km in a standard atmosphere).
For CALIOP satellite data, the low cloud definition is the one retained in the GCM-Oriented CALIPSO Cloud Product (GOCCP)  (Chepfer et al. , 2010). GOCCP was built from an algorithm designed to evaluate the representation of clouds in climate models. Three categories of clouds are differentiated based on pressure level (P): low level (P> 680 hPa), medium level (680 <P <440 hPa) and high level (P <440 hPa). The retrieval steps are described by  Chepfer et al. (2013). In this product, low clouds are characterized by the cloud fraction at elevations below 3. 2 km (P> 680hPa). The LC fraction is calculated from the CALIPSO simulation included in the second version of the Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP)  (Webb et al. , 2017). Although these data can handle multi-layered clouds, the presence of optically thick high clouds and those associated with deep convection within the Intertropical Convergence Zone may partly mask low clouds  (Chepfer et al.
, 2010; Vaughan et al. , 2009). For this reason, the difference with the definitions retained in synoptic observations not only lies in the altitude of the top of the lower level, but also to the fact that in one case the clouds are somehow observed "from below" and in the other case "from above". The data from CALIOP, ISD and EECRA will serve as initial references for the evaluation of CMIP6 and reanalyses. Each of these reference datasets has its own flaws: ISD and EECRA records have lots of gaps and are only available at a limited number of stations, and in CALIOP stratiform low clouds can sometimes be undetectable since they can be hidden by opaque higher clouds of convective origin.
Methods Most analyses focus on the JJAS season, which is the dry season in most of WEA and shows the highest frequency of stratiform low clouds  (Dommo et al. , 2018). JJAS long-term means at individual stations are computed from all available observations (using only stations with at least 300 three-hourly records). Both night-and daytime observations are used, since the CMIP6 data do not enable to investigate the diurnal cycle. Although low clouds are more frequent at night, we checked that both night-and daytime observations were present at the stations we used (i. e. , the stations did not close during the whole night), and that the general results of our study were not affected by mixing nightand daytime records. However, observed ISD and EECRA low cloud monthly and seasonal means may be slightly negatively biased because more observations are generally available at daytime (63% of the records are within the time-span 0600-1500 UTC).
The inter-comparison of the data sets is first carried out over a broad area covering an extended Gulf of Guinea area and the neighbouring African land areas (15°W-30°E, 20°S-20°N, figure  2 ). Then more restricted analyses are produced focusing on WEA, using regional indices which will be defined below. This part of the analysis has some limitations in CMIP6, since the relatively coarse spatial resolution of the simulations (100-250km) do not enable a detailed spatial analysis. Comparisons between the data sets are made using standard methods. To assess the agreement in the spatial distribution of low clouds, pattern correlation coefficients are computed as well as bias and root-mean-square error (RMSE). To that end, all products are linearly interpolated to the same resolution, i. e. that of ERA5 (about 30 km).
The agreement in temporal variations is examined by plotting the mean annual cycle of low clouds and, for the atmosphere-only experiments, reanalyses and observations, by analysing interannual variations using Pearson's correlation coefficient. The latter is also used to relate low clouds and SST variations. In addition, Taylor diagrams are plotted, which enable to compare at the interannual time-scale the skills of different data sets (CMIP6 and MERRA2) with respect to a reference time-series (ERA5), by combining correlations, centered RMSE and standard-deviations. Two regional low clouds indices are extracted over Gabon, depicting regions where the dry season low cloud cover is the most persistent (figure  1 ), with coastal and inland locations respectively in order to depict the zonal LC gradient  (Dommo et al.
, 2018). Given the coarse definition of CMIP6 data and the limited availability of synoptic stations, it was not possible to define customized indices reflecting the exact topography, although the latter has some effect on LC patterns. The first index covers northwestern Gabon along the coast (9-11°E, 1°S-1. 5°N) and the second one is located further inland over north-eastern Gabon (11-13°E, 1. 5°S-1. 5°N). Each includes three stations from the ISD and EECRA databases, namely Libreville, Cocobeach and Lambaréné (NW-Gabon), and Makokou, Mitzic and Lastoursville (NE-Gabon). Seasonal (JJAS) means for individual years are computed first at station level (based on days with at least 4 three-hourly records, and provided that half the days in a season have data), then the time-series for each station is standardised, and finally a regional average is computed.
Climatology of low level clouds The JJAS mean low cloud fraction is first examined over the larger region (Gulf of Guinea and nearby African landmass) (figure  2 ). CALIOP data shows a major contrast between the oceanic areas, where a high to very high LC fraction is observed (40-85%), especially off the coast of Southern Angola, and the land areas where LC fraction is generally below 20%. As an exception to the land areas, the coastal regions from Sierra Leone to Northern Angola show a higher LC fraction, with a distinct maximum in Gabon (30-70%). This maximum decreases inland, but the LC fraction in the northern Democratic Republic of Congo is still above 20%. The two reanalysis products and the CMIP6 coupled simulations all display high pattern correlations with CALIOP (figure  2 , lower right corners).
This merely reflects the land-ocean contrast, always well replicated, but it is easy to see that both the oceanic and land patterns often notably differ from the CALIOP satellite estimates. In ERA5 for instance, while the oceanic LC fraction agrees relatively well with satellite data (as found by  Koseki and Imbol Koungue, 2021) , on the land areas bordering the Gulf of Guinea, the LC fraction is very high, and even over the inland (northern) part of the Congo Basin, it often exceeds 40%. This is likely due to the fact that in ERA5, low clouds include convective clouds with a low cloud base. MIROC also portrays a high LC fraction over the Congo Basin, with almost no difference with the coastal regions. By contrast, an overall negative bias is found in some products, especially MERRA2 reanalysis and CNRM.
Synoptic observations are now added and serve as reference for the other datasets. For NW-Gabon (figure  4a  For NE-Gabon (figure  4b ), quite similar conclusions can be reached. Satellite (CALIOP) and in-situ observations (ISD and EECRA) are very close to each other, with LC fraction / frequency around 60%, i. e. slightly less than in NW-Gabon. ERA5 shows a marginal negative bias, but MERRA2 cloud fraction remains very low (19%). Again, CMIP6 models generally underestimate LC fraction, although atmosphere-only simulations perform a bit better than coupled simulations. The ranking of the models in terms of skill is not the same as for NW-Gabon. For instance, the IPSL model shows a too strong inland decrease of LC fraction. However, the coarse spatial resolution of the models is not fully adequate to depict such gradients, partly controlled by topography, which is oversimplified in global models.
Interannual variability of low clouds The interannual variations of the JJAS low clouds in the period 1979-2014 are examined for the two Gabon regional indices, in the observation and in the ERA5 reanalysis. The MERRA2 reanalysis is not used given its strong underestimation of LC fraction during this season. Since CALIOP data are available over a short period of time  (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014)  only, with few years having simultaneous synoptic records, they are not used here either. The many missing observations in the ISD and EECRA data result in highly discontinuous time-series, especially for NE-Gabon. Nevertheless, ERA5 and observations display variations that are largely in phase (table 2). For NW-Gabon, ERA5 correlates at 0. 78 with ISD (significant at p<0.
Interannual variations in NE-Gabon are fairly similar (not shown), though the observed time-series are highly discontinuous. The NE-Index correlates with the NW-index at 0. 74 (ISD, 11 years), 0. 71 (EECRA, 17 years) and 0. 98 (ERA5, 36 years). In the next step, we examine the ability of the CMIP6 models to reproduce the interannual variations of the LC over the same two regions of Gabon. Solely the atmosphere-only simulations can be analysed since the coupled simulations all have their own internal variability. The ERA5 indices are used as the reference because as demonstrated above, they agree fairly well with observed variations, and they have continuous time-series over the period 1979-2014. The MERRA2 data have been included in the assessment. In NW-Gabon (figure  7a ), the CMIP6 models show contrasted skills.
Conclusions Stratiform low clouds are a recurrent feature of the Southeast Atlantic Ocean. In austral winter, they extend over southern West Africa and Western Equatorial Africa, and mean cloud fractions of more than 60-70% are found over Gabon and southern Congo-Brazzaville. This extensive low cloud cover during the dry season is suspected to play a key role in the presence of a rainforest over WEA, by reducing solar radiation, daytime temperatures and hence water demand. The present paper aimed at assessing the ability of reanalysis products (MERRA2 and ERA5) and CMIP6 models to simulate these low clouds and their drivers, by focusing on mean patterns and interannual variability. CALIOP satellite data and ground observations at synoptic stations were used as reference. Over the broader region (Equatorial Atlantic and Tropical Africa), the spatial patterns of mean low cloud occurrence as depicted by CALIOP data are reasonably well reproduced by CMIP6 models, ERA5 and MERRA2 reanalyses.
ERA5 correlations actually show that low cloud cover over WEA is not only controlled by SST. Surface zonal winds and 700 hPa temperature, partly independent of SST, exert secondary controls on interannual variations of low cloud occurrence. Only about half of the CMIP6 models reproduce these controls. Reanalysis data analysed by  Neupane (2016)  indicate that the strength of zonal circulation over WEA is controlled by west-east temperature gradients, hence by Congo Basin temperatures in addition to Equatorial Atlantic SST. Among other parameters, biomass-burning aerosols are suspected to play a role in low cloud fraction over the South-East Atlantic Ocean, likely through an additional radiative heating generated by smoke above the low-level clouds, which increases the stability of the low-level inversion, a feedback which is not well represented in many CMIP6 models  (Lu et al.
Acknowledgements This study is part of the project DYVALOCCA (https://dyvalocca. osug. fr/) funded by  ANR  and  DFG  under contracts  ANR-19-CE01-0021  and  DFG FI 786/5-1. Computations were performed using HPC resources from  DNUM CCUB (Centre de Calcul de l'Université de Bourgogne), Dijon, France.
Due to the improved brilliance after the extremely bright source upgrade  [37] , an exposure time of 20 ms per spot on the sample was sufficient to measure the 10-nm-thick QW-layer diffraction signal with a signal-to-noise ratio of >10. To sample a threedimensional (3D) volume of reciprocal space, diffraction maps were measured for a series of sample rocking angles, ω. In this way, from the diffraction patterns measured at every spot on the sample, maps of the local scattering vector were generated (see the Supplemental Material for details  [35] ). Measurement of an SXDM dataset for one Bragg reflection took about 6 h, including alignment overhead. Thus, the total data acquisition time per sample was about 18 h. No systematic degradation of the diffraction or fluorescence signal from the Si or Si 0.
66 Ge 0. 34 virtual substrate. With respect to this average biaxial strain, in both datasets, we observe a spatial distribution of the lattice (a) (c) (e) (g) (i) (b) (d) (f) (h) (j) f (counts/s) f (counts/s) ε yy (%) ε zz (%) ε yz (%) ε xx (%) ε yy (%) ε zz (%) ε yz (%) ε xx (%).
ACKNOWLEDGMENTS We acknowledge the  European Synchrotron Radiation Facility  for the provision of synchrotron radiation facilities and the staff for assistance in using beamline ID01. Furthermore, the authors thank  Dr. J. Martin  from  IKZ  for fabricating fluorescent markers,  Dr. T. U. Schulli  from  ESRF  for setting up the fluorescence detector,  Dr. Y. Liu  from  IKZ  for proofreading the manuscript,  P. Muster  from  Infineon Technologies Dresden GmbH  for useful discussion,  Dr. I. Herman  from  Eurofins EAG GmbH  for providing nanoindentation measurements, and the cleanroom staff at  IHP  and at  Infineon Dresden GmbH  for technical support and processing wafers. This research was supported by the Leibniz association " High-Definition Crystalline Silicon-Germanium structures for Quantum Circuits " project (SiGeQuant, Project No.
Chi-square tests were conducted to determine specific differences (i. e. , sex, age, years practising, sector, context and country providing services) of those respondents preferring to use lithium as a first treatment option. Countries´ economic category were determined by the last edition (April 2022) of International Monetary Fund (IMF)  (International Monetary Fund IMF 2022). Lithium monitoring standards were adopted from the last recommendations of the ISBD/IGSLI Lithium Task Force  (Nolen et al. 2019). Statistical significance was established at p <. 05. The data collected were analyzed using SPSS version 28 (SPSS Inc. , Chicago, IL). For reporting purposes, we rounded up percentages if the next decimal was six or above, and down if it was five or less.
Acknowledgements The authors would like to thank all participants who completed the survey for this study. We want to express our gratitude for their help in the distribution of the link survey to  Mary Miller  and  Chad Daversa  from the  International Society for Bipolar Disorders  ( ISBD ) as well as to all representatives of local, regional, national and international professional associations. Allan H. Young 's independent research is funded by the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the  Department of Health. Eduard Vieta  thanks the support of the  Spanish Ministry of Science, Innovation and Universities  ( PI15/00283 ,  PI18/00805 ,  PI19/00394 ,  PI21/00787 ,  CPII19/00009 ) integrated into the  Plan Nacional de I + D + I  and co-financed by the  Instituto de Salud Carlos III (ISCIII)-Subdirección General de Evaluación.
Reference ranges provide a powerful tool for diagnostic decision-making in clinical medicine and are enormously valuable for understanding normality in pre-clinical scientific research that uses in vivo models. As yet, there are no published reference ranges for electrocardiography (ECG) in the laboratory mouse. The first mouse-specific reference ranges for the assessment of electrical conduction are reported herein generated from an ECG dataset of unprecedented scale. International Mouse Phenotyping Consortium data from over 26,000 conscious or anesthetized C57BL/6N wildtype control mice were stratified by sex and age to develop robust ECG reference ranges. Interesting findings include that heart rate and key elements from the ECG waveform (RR-, PR-, ST-, QT-interval, QT corrected, and QRS complex) demonstrate minimal sexual dimorphism.
Introduction Reference ranges are a powerful tool for diagnostic decision-making in clinical medicine and their use has become increasingly common  (Rijnbeek et al. 2001; Williams et al. 2020). Reference ranges are derived intervals containing a defined subset of values from a large and comparable population dataset. These values, designed to delineate the expected range of a given parameter, are used clinically to identify outlier values. Individuals presenting with values outside of a clinically defined reference range are considered abnormal and flagged for follow up clinical investigation. Looking beyond clinical applications, reference ranges are of enormous value in pre-clinical, basic scientific research using in vivo modelling  (Otto et al. 2016). They are used to define "normality" for a given genetic background, sex, and age of animals, such as inbred mouse strains.