dataesr/scientific-paragraphs-categorization huggingface dataset:
In contrast, CV-A13 and EV-C99 detected in the stools of two adopted children and in wastewater did not cluster. Based on the structure of the sequence database and phylogenetic techniques used in our study, the sequence assignment rate was high (99. 6%) and enabled us to detect less abundant viruses. Two EV types with direct public health relevance (EV-A71 and PV-1 Sabin) were detected in wastewater and identified down to the strain level by analysis of sequence data. The low nucleotide variation in the wastewater PV-1 sequence compared with that of the vaccine strain suggested no or limited circulation in the population. Our French data are consistent with earlier results from the United States and other European countries showing the importation of vaccine PV strains into polio-free countries  (Benschop et al.
Acknowledgements We are grateful to  Nathalie Rodde  and  Emilie Leroy  for their excellent technical assistance in enterovirus genotyping. We thank  Jeffrey Watts  for revision of the English manuscript. This research did not receive any specific grant from commercial or not-for-profit sectors. The  Centre National de R ef erence des Ent erovirus-Parechovirus  is supported by an annual public grant from the  national Agency Sant e publique France.
, 2019; Thorne et al. , 2017; Zhang et al. , 2011) , because in many cases, these raw daily observations are not openly available. The ETCCDI facilitated several regional workshops, where representatives of National Meteorological and Hydrological Services (NHMSs) could attend and calculate the climate extremes indices from their daily data without needing to release those data publicly  (Aguilar et al. , 2009; Caesar et al. , 2011; Donat et al. , 2014; Peterson & Manton, 2008; Meteorological Service Singapore, 2019; Vincent et al. , 2011). This approach has ensured the availability of climate indices from regions where daily data are not readily available free of charge to the scientific community. The development of standard software has further simplified this process, allowing contributors to calculate the indices without needing to travel to a workshop.
The ETCCDI approach has now been adopted by other groups such as the WMO Expert Team for Sector-specific Climate Indices (ET-SCI) who have also run workshops and developed sector-focused (e. g. , health and agriculture) indices and software  (Alexander & Herold, 2015; Herold & Alexander, 2016). The first global data set to contain all 27 indices defined by the ETCCDI was HadEX  (Alexander et al. , 2006) , which covered the period 1951-2003. As a result of the way that part of the data were collected and processed for inclusion in HadEX (through the aforementioned regional workshops), this data set was released as a static product covering a fixed time period. A follow-up effort a few years later resulted in the updated HadEX2  (Donat, Alexander, Yang, Durre, Vose, Dunn, et al. , 2013) , which extended the length of the data set, from 1901 to 2010.
Data Regional data collections and contributions from individuals and organizations, as both indices and raw daily temperature and precipitation observations, have been combined to form HadEX3. Below we outline the characteristics of these collections. Major collections are as follows, with the data span included in HadEX3 given in parentheses at the end of each entry: • The European Climate Assessment and Dataset (ECAD, https://www. ecad. eu//indicesextremes/index. php, Klein Tank et al. , 2002; Klok & Klein Tank, 2009)  contains indices from around 5,000 temperature stations and almost 15,000 precipitation stations across Europe, western Asia, and northern Africa. The ECAD use slightly different definitions for some of their indices (TX90p, TX10p, TN90p, TN10p, R95p, and R99p). We adjust these to match the definitions in.
These three data sets contain precalculated indices referenced to the 1961-1990 period. As we also calculate some indices using a 1981-2010 reference period (see sections 2. 2 and 4. 3), we also use the daily station measurements from these collections (named EOBS, SAOBS, and LAOBS) for those calculations. • The GHCND  (Menne et al. , 2012 ) is a comprehensive data set of over 100,000 stations across the globe (https://www. ncdc. noaa. gov/ghcnd-data-access). We use the same homogeneous subset from  Peterson et al. (2008)  as was used in HadEX2, which are those National Weather Service Cooperative and first-order stations with reasonably long records over the United States. These were determined to be free from significant discontinuities after 1950. After selecting for data record (section 3.
, 2013). Time series were quality controlled used RClimDex and extra QC checks. The temperature time series were subjected to homogenization by using the RSNHT software, whereas RHtestV3  (Wang & Feng, 2013)  was used to test homogeneity on the precipitation series (1950-2017). • A further set of around 350 temperature and precipitation stations from 1961 from the Regional Climate Center for the South of South America was also included. These data were quality controlled using a suite of quality control  (Podestá et al. , 2013; Veiga et al. , 2014 Veiga et al. , ) (1950 Veiga et al. , -2017)). • Twenty-two temperature and precipitation stations over Spain, which have been quality controlled using the RClimDex-extra QC (http://www. c3. urv. cat/softdata. php) and homogenized at the daily scale following procedures described in  Brunet et al.
Indices The ETCCDI indices have a long heritage in their use for monitoring, analyzing, and projecting moderate extreme events and absolute values  (Alexander et al. , 2019; Zhang et al. , 2011). They are calculated from daily maximum and minimum temperature as well as daily precipitation amount. In many cases, these indices have been calculated by data contributors using the RClimdex, climdex. pcic, or FClimdex software  (Zhang & Feng, 2004). When data have been submitted as daily observations, we have used the Climpact2 v1. 2. 7 software  (Herold & Alexander, 2016)  to calculate the indices. Climpact2 is the most recent instance of code used at regional workshops to calculate the ETCCDI and ETSCI indices and builds on the earlier codes (e. g. , climdex. pcic). It is available from https://github.
, 2014)  suggest that ERA5 may show reasonable spatio-temporal correlations with HadEX3. We calculate the ETCCDI indices from ERA5 by aggregating the hourly temperature and precipitation values to daily values, obtaining the maximum/minimum and sum, respectively, for each 00-23Z period and passing these through the Climpact2 software. Then, for each year (and month if appropriate) in HadEX3, the ERA5 fields (for the matching calendar month if appropriate) are selected. These ERA5 fields are regridded to match the resolution of HadEX3 and then are masked by the spatial coverage of the HadEX3 field, and the global average (cosine-weighted) is calculated. The residuals between the masked and unmasked averages are taken, and the standard deviation of this distribution of residuals is used as the spatial coverage uncertainty in the global average time series in section 4.
Trends in Precipitation Indices As in HadEX2, the lower correlation length scales of precipitation accumulations and events results in fewer stations falling within the search radius for the ADW gridding and reduced spatial coverage compared to temperature indices, despite having more stations contributing. Furthermore, when showing the maps of trends in Figures  7 8 9 , we require 66% completeness, and so intermittent grid-boxes are excluded from this overview analysis. Generally, these indices show spatially more heterogeneous patterns than the temperature indices, also a result of a smaller DLS. The relative magnitude of the coverage uncertainty on the global time series is also consequently larger, in many cases being comparable to both the long-and short-term variability. Heavy precipitation days (R10mm, Figure  7 ) show very little change globally over the entire period of the data set, though there are indications in the last decades of a gentle increase.
, 2008; Sillmann & Croci-Maspoli, 2009). We have already noted how the definition and construction of these indices can affect how the gridded product behaves with a changing station network, and especially so when comparing products derived from the two reference periods outlined above. However, despite these extra considerations, the agreement between the "global" HadEX3 and other products is very good, and the uncertainties arising from the incomplete coverage frequently encompass all of the products. This increases our confidence in the changes seen over time. The gridded HadEX3 product presented and analyzed here is one of a wide number of possible versions of a data set of gridded climate extremes indices. A specific set of values, methods, and algorithms has been chosen for good reasons, but other combinations could be equally valid.
These parametric and structural uncertainties, as assessed for HadEX2 in  Dunn et al. (2014) , are yet to be fully explored for HadEX3. We expect that changes to the methods used in the creation of HadEX3 (parametric) would have a smaller effect than the use of different methods entirely (structural). Although the underlying station data would remain the same, the spatial representation resulting from the gridding method used herein (ADW) compared to others or by inverting the order of operation is very likely to be different, especially at local and regional scales. Investigations into the effects of different gridding and interpolation methods show that the method used for HadEX3 has a high level of smoothing, resulting in loss of detail, especially in topographically varying areas, and the order of operation changes the spatial patterns in the final grids  (Avila et al.
HadEX3 has been designed to estimate long-term changes in observed extremes. The order of operations in going from daily station observations to the gridded product (calculate indices then grid) results in this data set comprising estimates at grid box centers. The output from climate models or reanalyses are most typically representative of the average over a grid box. Although this may not have a noticeable affect for some temperature indices, for maxima and minima (e. g. , TXx) and especially for precipitation indices, these values may differ (e. g. ,  Chen & Knutson, 2008). Therefore, comparisons with products where gridded values are the average value of the indices for that grid box (rather than being representative of the center and some region surrounding it) should be undertaken with care.
Summary We present the creation of the next version of a data set of extreme temperature and precipitation indices, HadEX3. Station data have been gathered from a number of open global and regional sources as well as from individuals from a large number of nations and territories. The ETCCDI extremes indices have been calculated from daily observations resulting in almost 37,000 stations of varying record length and completeness. For a gridded product, the stations with the longest records ending in 2009 or later have been selected and combined using an ADWinterpolation algorithm. The final gridded data set is available on a 1. 875°×1. 25°l ongitude-latitude grid from 1901-2018 inclusive, and some indices are available on a monthly as well as annual resolution. Analysis of the long-term changes of both temperature and precipitation indices agree very well with previous (HadEX2) and current monitoring (GHCNDEX) products.
As some indices use a reference period to derive threshold values, we have created versions using the original (HadEX2 and HadEX) period of  (which corresponds to the WMO period for long-term climate change assessments) and one using the current WMO reference period of 1981-2010, with fractionally different data submitted for each. Although the global results are qualitatively very similar, there are some intricate differences for the temperature percentile indices which will require careful thought by users. Despite our best efforts, there are still parts of the world where data are sparse or the temporal coverage is inadequate for a data set designed for long-term monitoring. This is partly a result of the approach to use regional workshops for data collection, which, although allowing access to the underlying data which cannot be shared openly, can result in station index time series for these regions ending much earlier than the data set and so limits their usefulness.
Van Den Besselaar et al. , 2015)om countries in Southeast Asia. We do not include SACAD stations in Australia as separate data sets were used for that region in combination with GHCNDEX. • The Latin American Climate Assessment and Dataset (LACAD, https://lacad. ciifen. org/indicesextremes/index. phpVanDen Besselaar et al. , 2015)contains indices from 200 temperature stations and 1,200 precipitation stations from countries in western South America.
Data Availability Statement The gridded data set is available at www. metoffice. gov. uk/hadobs/hadex3/ and at www. climdex. org. In addition, a version will be lodged on the CEDA archive (https://archive. ceda. ac. uk/). The underlying station indices will be made available on www. climdex. org where we are allowed to do so. For some collections, we are not allowed to make the underlying station data public under terms of their license, and we encourage readers to contact the appropriate co-author.
Van Den Besselaar et al. , 2015)om countries in Southeast Asia. We do not include SACAD stations in Australia as separate data sets were used for that region in combination with GHCNDEX. • The Latin American Climate Assessment and Dataset (LACAD, https://lacad. ciifen. org/indicesextremes/index. phpVanDen Besselaar et al. , 2015)contains indices from 200 temperature stations and 1,200 precipitation stations from countries in western South America. 10. 1029/2019JD032263 Journal of Geophysical Research: Atmospheres around.
Both aforementioned GWAS analyses on osteoporosis relied on the assessment of mouse knockout models to corroborate the proposed candidate genes  [9, 10]. This stressed the value of knockout mouse models for the true identification of genes controlling BMD. Beyond the assessment of individual knockout mice for data corroboration, a more systematic and comprehensive production and skeletal phenotyping of knockout mice has been part of the IMPC, an international consortium of mouse clinics (Fig  1A )  [12] [13] [14]. Work at the IMPC aims to individually ablate the function of every protein-coding gene in mice, and subsequently phenotype the mutated animals  [15] [16] [17] [18]. The IMPC phenotyping pipeline interrogates a range of organ and tissue types, including the skeleton. The skeletal exams encompass dual-energy X-ray absorptiometry (DXA)-based measures of the three important bone parameters bone area (BA), bone mineral content (BMC), and the resulting BMD.
Further to this, planar radiographic images are used for detection of skeletal abnormalities. This study probed the IMPC data release 6. 0 and focused on genes controlling BMD. This measure was selected because in the clinical setting, in veterinary medicine, and in genetic mouse models low BMD is a hallmark of osteoporosis  [19] [20] [21]. The present study has been driven by the following three research questions: (1) does screening of mutant mice at scale identify novel genes controlling BMD?, (2) which BMD genes cause both BMD alterations and skeletal abnormalities?, and (3) is there evidence for functions of novel BMD genes in osteoblasts or osteoclasts? (Fig 1B). Addressing these questions, we report on a total of 200 BMD genes that, when deleted, caused alteration in BMD. Of those 200 genes, 59 loci (class 1) had a previously reported function in bone, while 141 (class 2) genes were not previously described as regulators of BMD in mouse or human.
With the exception of 4930591A17Rik, biological functions of all BMD genes had been previously reported to some extent. To determine if novel BMD loci were among the 200 genes, we discriminated between genes with a known (class 1) and unknown (class 2) function in bone biology based on previously published data. A total of 59 and 141 genes were assigned to classes 1 (blue) and 2 (yellow), respectively (Table  1 ). To quantitatively assess the change in BMD caused by the 200 genes, we plotted gene numbers against percentage of change in BMD  (Fig 2C). While decreases in BMD ranged from 2% to 49%, and increases from 2% to 52%, we observed that the majority of genes elicited a reduction in BMD between 5% and 10%. PhenStat, the statistical package used for the analysis of the high-throughput phenotype data, did not consider BMD changes between -2% and +2% to be significant phenotypes.
A distance range between 0 kilo bases (kB) and 250 kb was chosen. Of the 200 genes, 11, 38, and 52 genes matched the human GWAS derived genes at a 0 kB, 100 kB, and 250 kB distance range, respectively (S3 Table ). To take a closer look at genes with potential human relevance, we then utilized the GEFOS data set, which although smaller than the UK Biobank release, utilized X-raybased BMD acquisition. The six class 2 genes AP4E1, KIAA0825, ACSF2, PKP4, TTC28, PHF19 and the class 1 gene MACROD2 altered BMD in humans (S4 Table ). The direction of BMD change matched between mouse and human for the five genes ACSF2, PKP4, TTC28, PHF19 and MACROD2, and as described below the latter was found to be expressed in osteoblasts.
As a combination of altered BMD and skeletal abnormality has the potential to compromise the biomechanical stability of bone, we explored the biomechanical testing data collected as part of the OBCD project. For this, we facilitated the import of skeletal phenotyping data from the OBCD into IMPC. Then, we surveyed the bones of the 19 knockout mice for compromised mechanical stability, a functional hallmark of osteoporosis. Of the 19 genes with both a BMD and skeletal phenotypes, the three genes Hbs1l, Slc25a30 and Slc38a10 matched to the OBCD data, but only loss of Hbsl affected skeletal stability, specifically it resulted in a decrease in Homeostasis/metabolism was the primary co-phenotype detected. The size of the arcs connecting the phenotypes was proportional to the number of genes connected.
Diagram colors were assigned clockwise starting with the primary phenotype. (B) The majority of BMD genes presented with up to 3 co-phenotypes, while eight genes were limited to a skeletal phenotype. Class 1 (known function in bone biology) and 2 (unknown function in bone biology) genes are distinguished in yellow and blue color, respectively. (C) Analysis of the IMPC phenotyping data showed that 19 of the 141 class 2 genes were also associated with skeletal abnormalities in addition to changes in BMD. Vertebrae abnormalities were the most frequent skeletal abnormalities associated with the deletion of one of the 19 genes. (D) OBCD data showed a difference in vertebra stiffness for Hbs1l compared to wt. (E) OBCD data detected a difference in vertebra yield load for Hbs1l compared to wt. (F) OBCD data measured a difference in vertebral height for Hbs1l compared to wt.
Further, we identified a number of genes producing greater than 10% decreased or increased BMD (Fig 2D  and 2E ), including novel class 2 genes, for example Nhlh2 and Dppa. A loss of BMD greater than 20% was recorded for Lepr, Bbs5, Cyp27b1, and Ghrh, while a gain in BMD of more than 20% was only noted for Lrrk1 (Fig 2C  -2E  and S1 Table ). These five class 1 genes are all known key players in bone biology, and their respective effect on BMD should be considered confirmatory. The human relevance of the presented mouse BMD genes was gauged by comparing the mouse IMPC data to human GWAS data derived from the UK Biobank. An overlap of up to 25% (S3 Table) was seen. Further, a more detailed GWAS analysis based on the GEFOS data set found that, of the 200 BMD genes, one class 1 and six class 2 genes were linked to corresponding human genes (S4 Table ).
Together, this showed that mouse-derived BMD data has relevance for human BMD pathologies, but it also demonstrated that the IMPC mouse data is complementary to GWAS-derived human data and has the potential for de novo gene discovery. Importantly and in contrast to GWAS studies, the IMPC data is based on targeted gene disruption, thus potentially offering causal validation of candidate genes found via SNP analysis in GWAS. We anticipate that future progress in the understanding of osteoporosis genetics of the 200 BMD genes. The central hubs of the network where identified based on the betweenness centrality index and extracted from the main network. All PPIs were presented as black lines. Blue and yellow nodes represent class 1 (known function in bone biology) and class 2 genes (unknown function in bone biology), respectively.
Osteoblastic bone mineral deposition: tissue-nonspecific alkaline phosphatase (Alpl), removal of mineralization inhibitor inorganic pyrophosphate (PP i ); ectonucleotide pyrophosphatase/phosphodiesterase 1 (Enpp1), production of PP i. Together, these proteins (red elliptic nodes) formed the PPI networks for osteoclastic resorption and osteoblastic formation during bone turnover. Connecting proteins distinct from the bone turnover genes are represented as pink elliptic nodes, while white nodes marked genes with an undefined role in bone. Black lines represent PPIs as determined by STRING or REACTOME analysis. Arrows indicated REACTOME-curated data. For validation, we used experimental gene expression data from osteoclasts and osteoblasts in combination with statistical testing. The resulting gaussian probability of interactions, i.
e. FDR-adjusted p-values (q-values) were superimposed on the STRING PPIs as follows: large line width, q<0. 0005; medium line width, q<0. 05; small line width, q>0. 05 or no q data. (B) Based on their cellular expression, the three class 2 low BMD candidate genes Rab31p, Ncald, and Arl4d were probed in the osteoclastic and osteoblastic compartments of bone turnover. Rab3ip and Ncald interacted via single intermediates with proteins degrading bone matrix and also mineral, while Arl4d, also via an intermediate, interacted with a regulator of the procollagen cleavage essential for bone matrix formation. https://doi. org/10. 1371/journal. pgen. 1009190. g006 hinges on the synergistic application of both human GWAS and the IMPC mouse phenotyping program. At present, the IMPC BMD data is similar in size to a skeletal phenotyping program that was carried out between 2000 and 2008 in a commercial setting at Lexicon Pharmaceutical  [23].
However, the IMPC program will continue to grow and thus the skeletal data presented here will expand in size and depth over time. A gender-specific action of BMD genes has been postulated previously but led to inconclusive results. On the one hand, a recent GWAS study dedicated to the assessment of gene-by-sex interaction, did not find such an interaction for BMD  [24]. On the other hand, a string of earlier studies using whole genome quantitative trait loci analyses identified chromosomal regions linked to sex-dependent regulation of BMD  [25] [26] [27]. Further, studies using individual gene deletion in mice, proved that genes cause gender-restricted effects on BMD  [28]. Lastly, it has been shown that the regulation of a gene can vary between the sexes  [29]. In the present study, which we believe is currently the most comprehensive analysis reporting BMD changes in both male and female mutant mice, we defined 90 genes that, upon deletion, caused a change in BMD that was restricted to either female or male animals (Fig 3A  -3C ).