dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Clinical and epidemiological data were collected and analyzed by the National Arbovirus Surveillance programme implemented in January 2017 and coordinated by INIS, Ministry of Health, Angola. Between January 2017 and February 2019, we tested samples collected from dengue suspected cases from 13 provinces across the Republic of Angola (Luanda, Benguela, Bengo, Cuanza Sul, Cuanza Norte, Huambo, Huila, Cunene, Namibe, Cabinda, Uı ´ge, Zaire and Moxico) using molecular detection via RT-PCR. A total of 351 sera samples with complete epidemiological information from suspected dengue cases were tested for the presence of DENV, ZIKV and CHIKV RNA using RT-PCR assays  [31, 32].
and S1 Table A case of co-infection with DENV1 (CT = 26) and CHIKV (CT = 25) was detected in a 32-year-old male from Luanda in May 2017 presenting with fever, severe hemorrhage and fatal outcome (S1 Table ). One case of DENV1 (CT = 27) was identified in a 19-year-old male patient from the Luanda province presenting with fever, myalgia, headache and arthralgia in May 2018. We also detected five CHIKV RT-PCR positive cases (mean CT = 33, range: 27 to 35) in Luanda (n = 4) and Huila (n = 1) provinces between January and April in the rainy season. No DENV3 or DENV4 infections were identified during the study period. No ZIKV infections were detected in our dataset of suspected DENV cases; Zika infections reported during the study period and subsequent outbreak investigations were reported elsewhere  [27].
Ethics statement Clinical samples (serum and blood) and associated demographic data were processed and analysed at the INIS, following guidelines of the recently established National Arbovirus Surveillance Laboratory in Angola from the Ministry of Health, Angola. Residual anonymized clinical diagnostic samples, with no or minimal risk to patients, were used for research and surveillance purposes as part of the National Arbovirus Surveillance in Angola within the terms of the National Ethics Committee, Ministry of Health, Angola.
Laboratory confirmation of DENV suspected cases Based on the official MoH guidelines, 401 suspected DENV cases were reported to INIS between January 2017 and February 2019, of which only 351 cases had detailed epidemiological data. Only samples with epidemiological data were used in this study. Suspected cases were reported from 13 provinces (Fig  1 ). Serum samples from dengue suspected cases were screened for DENV RNA at INIS, Luanda. RNA was extracted from serum samples using QIAamp Viral RNA mini kits according to the manufacturer's instructions. Dengue clinical symptoms often overlap with those from ZIKV and CHIKV  [47]. Moreover, strong cross-reactivity in serological tests is often seen with other flaviviruses such as Zika, yellow fever, West Nile and Japanese encephalitis virus  [48, 49].
Portable on-site DENV nanopore genome sequencing Sequencing was attempted on 25 RT-PCR positive samples using the Oxford Nanopore Technology MinION sequencer, according to protocols that have been described in detail previously  [30, 50]. 24 DENV2 complete or near complete viral genome sequences were generated. In brief, cDNA was reverse transcribed and subjected to a multiplex PCR designed to amplify overlapping fragments of ~1000bp that span the whole DENV2 coding region. A library was prepared with adaptor-ligated, barcoded PCR amplicons, and sequenced on the MinION. Bioinformatic generation of consensus sequences was conducted according to previously described protocols  [30, 50]. Following generation of consensus sequences, all sequences were aligned using Muscle v3. 8  [51]  and using DENV2 genome (Accession Number LC121816) as reference genome.
Compilation and curation of DENV2 genomic datasets DENV2 genomes or partial genomes of at least 1,000 nucleotides in length were downloaded from GenBank in June 2019  [52]  using an in-house python script (named as the global dataset). The search retrieved n = 1,584 sequences which were then classified into genotypes using an online phylogenetic-based tool  [33]  and confirmed with manual phylogenetic subtyping with RaXML v8  [53]. For manual phylogenetic subtyping, the global dataset and the new genome sequences from Angola were appended to a previously curated DENV2 reference dataset (n = 42) described in detail elsewhere  [30]. Next, we prepared a separate dataset with all DENV2 sequences pertaining to the same DENV2 genotype as the one circulating in Angola. This resulted in a dataset of a total 38 complete genomes for subsequent analyses, including data from Angola (n = 25), China (n = 2), India (n = 10), and Ethiopia/Djibouti (n = 1).
Maximum likelihood phylogenetic analysis Maximum likelihood (ML) phylogenetic trees were generated for the global and the Cosmopolitan datasets using RaXML v8  [53]. Statistical branch support was obtained using 1000 bootstrap replicates for the Cosmopolitan dataset. Analysis of temporal signal of this dataset was conducted using TempEst by regressing collection dates (format yyyy-mm-dd; a precision of 1 year or 1 month was used when exact dates were not available) and root-to-tip genetic distances  [54]. One outlier sequence with an unusually low divergence for its sampling date (Gen-Bank MH822954) was removed from subsequent analyses. Given the strong correlation between root-to-tip divergence and sampling dates for the Cosmopolitan dataset (Fig 2A ), we conducted subsequent time-stamped Bayesian phylogenetic analyses using this dataset.
Acknowledgments We thank  Wenzhe Su ,  Department of Virology and Immunity, Guangzhou Center for Disease Control and Prevention, China , for permission to use two dengue virus genome sequences from China deposited in GenBank ahead of publication. We thank  Gilberto Santiago  and  Jorge Munoz-Jordan  ( Centers for Disease Control and Prevention, Division of Vector-Borne Diseases ,  Dengue Branch ,  San Juan ,  Puerto Rico ) for donating reagents for dengue molecular diagnostic. We thank  Patricia Alvarez Baptista  and  Antonio Gomes Pinto Ferreira  ( Fundacão Oswaldo Cruz, Instituto de Tecnologia em Imunobiologicos Biomanguinhos, Rio de Janeiro ) for donating reagents for dengue detection. We thank  Luiz Alcantara ,  FioCruz, Brazil  for help with logistics.
The research of T. B. has been supported by ANR project ESQUISSE (ANR-20-CE47-0014-01) of the French National Research Agency (ANR). The research of T. B. and C. R. has been supported by ANR project QTraj (ANR-20-CE40-0024-01) of the French National Research Agency (ANR). C. R. acknowledges the support of the Munich Center for Quantum Sciences and Technology, as well as the Humboldt Foundation. The research of L. H. was partially supported by the Swiss National Science Foundation (Grant No. P2EZP2-188093).
Furthermore, the 100 m resolution is ideal for optically-thin high-level ice clouds with maximum detectable range of 24 km. This radar is calibrated using the approach proposed by  Toledo et al. (2020)  based on corner reflectors. Another product developed by combining three modes providing optimized radar reflectivity, velocity and mask indicating the valid signal from noise is also developed. This level 2 (L2 here onwards) processing is a new vertical grid derived by combining several modes (vertical and temporal resolution) at the same time resolution in order to make the most of each mode. The largest range resolution data is used for their higher sensitivity, and background noise is removed. In this study, 39 cloud cases with L2 product of BASTA measurements at SIRTA location are used.
HATPRO microwave radiometer at SIRTA and SOFOG-3D A 14-channel HATPRO (Humidity And Temperature Profiler) MWR manufactured by Radiometer Physics GmbH (RPG) is operational at SIRTA observatory. HATPRO MWR is a passive instrument, converting the naturally emitted downwelling radiative energy emitted from the atmosphere within two spectral bands: the first one focuses on the 22. 24 GHz water vapor absorption band up to 31 GHz while the second one is centered on the 60 GHz oxygen complex band (51-59 GHz). Through the use of calibration coefficients, detected intensities are then directly converted into brightness temperatures. A retrieval technique is then needed to convert the brightness temperature spectra into vertical profiles of temperature, humidity as well as liquid water path. In general, statistical methods (linear, quadratic regressions or neural networks) trained from simulated MWR observations from a database of radiosoundings or model analyses are used  (Cimini et al.
, 2006). Optimal estimation retrievals combining an a priori estimate of the atmospheric state with observations through an iterative process can also be used  (Martinet et al. , 2020). In this study, LWP retrievals based on MWR observations have been retrieved through quadratic regressions trained from a database of radiosoundings for SIRTA while for SOFOG3D, neural networks trained from AROME short-term-forecasts have been used. MWRs are only sensitive to the total liquid water content present in the vertical profile  (Ware et al. , 2002). Humidity profiles can be retrieved with a limited vertical resolution due to the smoother weighting functions for K-band channels. Temperature profiles show a better vertical resolution, which can be improved through the use of different elevation angles (generally from 90 to 5.
Analysis of the method when microwave radiometer is available This section describes the analysis of retrieval when applied to various cloud types. As detailed in section 3, the retrieval technique is applied to reflectivity data from 95 GHz BASTA radar with LWP estimates from co-located RPG HATPRO microwave radiometer for various cloud cases from SIRTA. Between November 2018 to May 2019, 39 cloud and fog cases at SIRTA observatory are selected to address the algorithm's implementation on warm clouds. The data set contains a relatively large number of cloudy cases, including fog and light drizzle. A detailed discussion of retrieval and algorithm implementation is elaborated for a typical example of cloud in the next subsection.
Therefore, the in-situ measurements must be collected from a homogeneous cloud to compare with the retrievals. Particularly for fog, more continuous DSD measurements as well as the vertical profiles during distinct fog episodes are required to produce more significant results. The primary objective of this statistical analysis is to derive a climatology of LWC and lna in order to allow the algorithm to be able to retrieve LWC for fog and low-level liquid clouds even when additional measurements are not available. As presented in section 5. 1, when the retrieved LWC is compared with in-situ LWC measurements for fog and stratus cloud from the SOFOG-3D experiment. Therefore, the climatology is developed from the retrieval technique discussed in section 3. 5 using the larger data set from SIRTA measurements for a variety of cloud and fog incidents.
Summary and conclusions An algorithm for LWC estimation of warm clouds is proposed using a vertically pointing radar and microwave radiometer synergy. The algorithm also accounts for attenuation due to liquid cloud droplets. This algorithm is based on the hypothesis that LWC is related to reflectivity with a power-law fit, and one of the constants of the Z-LWC relationship is allowed to vary Although this climatology is developed using measurements from SIRTA observatory for limited cloud scenarios, a more extensive data collection from several measurement locations might be used to generate a more robust climatology of scaling factor. Furthermore, the retrievals are compared against in-situ measurements for a fog and cloud case collected during the SOFOG-3D field campaign. The comparison of LWC values estimated using this synergistic retrieval algorithm revealed that the fog and clouds were clearly distinct.
Our work therefore aimed at providing a tailored assessment of potential climate risk for the forage resource at the individual scale of each alpage in the French Alps. To this end, we developed agro-climatic indicators based on atmospheric and snow cover data accounting for geographic and topographic conditions, and applied them to a database providing unique spatially explicit information at the alpage level. For the first time, we introduce a description of agro-climatic conditions and provide a classification of agro-climatic profiles of alpages in the French Alps, ranging from low to high potential risk for the forage resource, mainly following a North-South gradient combined with altitude. We also bring insights on the evolutions of the climate risk with climate change and discuss management implications for agro-pastoral livestock systems using alpages.
The agro-climatic approach we present here represents the first step to a holistic assessment of the effective risk due to climate change on the alpages, which additionally needs to account for the degree of ecosystems and human responsiveness  (Deléglise et al. 2019). In this study, we introduce a specific set of agro-climatic indicators designed to be meaningful regarding the impact of the three climatic factors listed above (i. e. , water availability, temperature accumulation, and frost events after snow meltout) on the forage resource and pastoral management in the alpages. Their computation at the alpage-level accounts for the influence of both the regional climate and local topographic conditions, by using atmospheric and snow data covering the entire French Alps with respect to elevation, slope, and orientation, in combination with a database providing unique geographical and topographic information for a total of 2779 alpages referenced in the French Alps.
However, for precipitation-based indicators, we did not consider the intra-alpage spatial heterogeneity because of (i) the risk of over-interpreting the underlying climatic model for phenomena not or poorly integrated (e. g. , localized rain showers) and (ii) the lack of relevance for management (e. g. , possible intra-alpage spatial heterogeneity of water balance indicators favoring grass regrowth at high altitudes will be challenged by the risk of early snow and therefore might not be exploitable for grazing). Data was processed using PgAdmin 4. 28 (PostgreSQL 9. 6. 6 and Postgis 2. 3. 3); indicators were computed using Python version 2. 7 and R version 3. 6. 1 (R Core Team 2019).
Statistical analysis To characterize past and recent agro-climatic conditions on alpages, average values (30-year average) and interannual variability (30-year standard deviation) of indicators were computed per pastoral unit over the 1961-1990 reference past period and the 1991-2018 recent period. To identify prominent agro-climatic profiles for the recent period, we performed a principal component analysis (PCA) with pastoral units as objects and indicators (average values and interannual variability for the recent period) as variables. Average physical variables derived from pastoral units' rasters (see section 2. 5) were plotted as supplementary variables in the analysis using R statistical software version 3. 6. 1 (R Core Team 2019) with the packages FactomineR and factoextra. To analyze the spatial distribution of indicators' evolutions, the variations of the indicators' values between past and recent periods were mapped at the spatial resolution of pastoral units across the study area using QGIS software version 3.
6. We then tested the effect of period (past, recent), geographical region (Northern Alps, Central Alps, Southern Alps, Extreme Southern Alps), and the period × region interaction, on 30-year indicators' values, across all pastoral units (n=2779), by using a linear mixed-effect model with pastoral units set as a random effect. Assumptions for linear modeling (normality of the data and homogeneity of variances) were checked prior to the analysis. When tests revealed significant effects of factors period and/or region, post hoc tests (Tukey's honest significant difference method) were performed to assess contrasts among factor combinations (8 groups) and after computing estimated least-squares means in linear models. These analyses were carried out using R statistical software version 3. 6. 1 (R Core Team 2019) with the packages nlme, lme4, and emmeans.
Acknowledgements We thank all of the experts involved in the " Alpages Sentinelles" research program  who participated in the co-construction of agro-climatic indicators:  Fabien Candy ,  Laurent Garde ,  Simon Vieux ,  Julien Vilmant  (pastoralist experts at the  ADEM [Association Drômoise d'Economie Montagnarde], CERPAM [Centre  d'étude et de réalisations pastorales Alpes-Méditerranée] and  FAI [Fédération des Alpages de l'Isère]) ,  Muriel Della-Vedova  and  Guy-Noël Grosset  (in charge of summer pastures management in the  Ecrins National Park and Vanoise National Park ),  Christophe Chaix  (climate expert at  AGATE [Agence Alpine des Territoires]) , and  Philippe Choler  and  Sandra Lavorel  (researchers at the  LECA [Laboratoire d'Ecologie Alpine, CNRS]). The S2M data was provided by  Météo-France -CNRS, CNRM Centre d'Etudes de la Neige , through AERIS (Vernay et al.
https://geoservices. ign. fr/documentation/diffusion/ telechargement-donnees-libres. html#bd-alti-25-m & The French Alpine pastoral inventory dataset is available (under convention) on the dedicated website: https://enquete-pastorale. inrae. fr/ & The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request Code availability & Preliminary metrics: code available on reasonable request from raphaelle. samacoits@meteo. fr code available on reasonable request from.
5% and 1. 1% of patients with moderate-to-severe and severe SMR. In the subgroup of patients with HFrEF (LVEF <50%), 3. 1% underwent a mitral intervention  [2]. According to the STS database, isolated MV surgery for NICM and ICM represents 2. 9% and 1. 3% of all MV surgery performed  [42]. This conservative approach might be partially explained by the lack of evidence that surgical MV repair or replacement can improve survival in SMR and the excessive risks associated with surgical treatment in an elderly and sick population. Most of the patients assessed had associated significant comorbidities that might preclude the surgery (hypertension 61%, CAD 50%, diabetes 26%, atrial fibrillation 30%, cerebral vascular disease 18%, and peripheral vascular disease 24%). In all randomized-controlled trial assessing MV surgery for SMR, patients with papillary muscle rupture, recent myocardial infarction, severe comorbidities such chronic kidney failure, liver disease, life expectancy <12 months, LVEF <30%, or a previous surgery were excluded (Table  2 , Ref.
Special issue statement. This article is part of the special issue "Resolving uncertainties in solar geoengineering through multimodel and large-ensemble simulations (ACP/ESD inter-journal SI)". It is not associated with a conference. Acknowledgements. Andy Jones would like to thank the Met Office team responsible for the managecmip software, which greatly simplified data discovery and handling, and Neal Butchart for useful comments. Andy Jones, Jim M. Haywood and Adam A. Scaife were supported by the Met Office Hadley Centre Climate Programme funded by BEIS. Andy Jones and Jim M. Haywood would like to acknowledge funding provided by SilverLining through its Safe Climate Research Initiative. Jim M. Haywood and Matthew Henry were part-funded by the Natural Environment Research Council Exeter-NCAR (EXTEND) collaborative development grant (NE/W003880/1).
The IPSL-CM6A-LR experiments were performed using the high-performance computing resources of TGCC under the allocations 2019-A0060107732 and 2020-A0080107732 (project gencmip6) provided by GENCI (Grand Equipement National de Calcul Intensif). IPSL benefited from French state aid managed by the ANR under the "Investissements d'avenir" programme with the reference ANR-11-IDEX-0004-17-EURE-0006. Support for Ben Kravitz was provided in part by the National Science Foundation through agreement CBET-1931641, the Indiana University Environmental Resilience Institute, and the Prepared for Environmental Change Grand Challenge initiative. The Pacific Northwest National Laboratory is operated for the US Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830. Roland Séférian and Pierre Nabat particularly acknowledge the support of the team in charge of the CNRM-CM climate model; supercomputing time was provided by the Météo-France/DSI supercomputing centre.
Roland Séférian thanks Christophe Cassou for his fruitful discussion on the paper. CNRM-ESM2-1 simulations were supported by the European Union's Horizon 2020 research and innovation program via the H2020 projects CRESCENDO (grant agreement no. 641816) and CONSTRAIN (grant agreement no. 820829). Ulrike Niemeier was supported by the Deutsche Forschungsgemeinschaft Research Unit VollImpact (FOR2820 (grant no. 398006378)) and used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bm0550. We acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) for the ERA5 data. Financial support. This research has been supported by the Department for Business, Energy and Industrial Strategy, UK Government; SilverLining, USA; the Natural Environment Research Council, UK (grant no.
NE/W003880/1); the Agence Nationale de la Recherce, France (programme reference ANR-11-IDEX-0004-17-EURE-0006); the National Scicence Foundation, USA (agreement CBET-1931641); the Indiana University Environmental Resilience Institute, USA; the Prepared for Environmental Change Grand Challenge initiative, USA; the Horizon 2020 research and innovation program, European Union (grant agreement nos. 641816 and 820829); and the Deutsche Forschungsgemeinschaft Research Unit VollImpact, Germany (grant no. 398006378).
cinerea French populations into two genetic clusters on grapevine (G1 and G2 populations) and another, single cluster on tomato that diverged from the G2 population (T population; Sup. Fig. S1 ;  Mercier et al. , 2021). These genomic data also allowed to investigate the molecular differences underlying host specialization. By characterizing singlenucleotide polymorphisms in Illumina short-read data, genes with footprints of positive selection and/or divergent selection in the genomes of populations specialized to different hosts were identified. These candidate genes that represent possible determinants of host specialization were enriched in genes encoding PCWDEs and transporters and in genes involved in the oxidative stress response. The same study also highlighted a limited number of candidate genes that are populationspecific including a couple of PCWDE-coding genes and one secondary metabolism gene cluster  (Mercier et al.
Results The genome architecture of the Botrytis cinerea strains Sl3 and Vv3 differ from the B05. 10 strain in their accessory chromosomes Genome assemblies based on PacBio Sequel sequencing data identified 16 core chromosomes  (CCs)  and two accessory chromosomes (ACs) in both Botrytis cinerea strains Sl3 and Vv3 collected on tomato and grapevine. Sequencing reads were assembled de novo using a combination of HGAP4 (SMRT-LINK v5. 0. 1) (https://github. com/PacificBiosciences/) and CANU v1. 6  (Koren et al. , 2017). Both Sl3 and Vv3 assemblies consisted of 18 main contigs. The genome assemblies were estimated to be 43. 2 Mb (Sl3) and 44. 9 Mb (Vv3), which is close to the 42. 6Mb of the B05. 10 genome  (van Kan et al. , 2017). Contigs of the new genome assemblies were ordered and oriented as the previously described chromosomes of B05.