dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation with Independent Datasets Although the two-step random forest model showed good performance, especially for Spain (Pearson's correlation coefficient of 0. 90 for reservoir-based and 0. 98 for run-of-river generation from 5-fold cross-validation), this validation method usually gives optimistic results  [16]. One possible explanation is that despite being randomly permuted, the validation dataset in each cross-validation is still an average of multiple random samples from the same dataset, and thus the prediction is not.
Validation with Independent Datasets Although the two-step random forest model showed good performance, especially for Spain (Pearson's correlation coefficient of 0. 90 for reservoir-based and 0. 98 for run-of-river generation from 5-fold cross-validation), this validation method usually gives optimistic results  [16]. One possible explanation is that despite being randomly permuted, the validation dataset in each cross-validation is still an average of multiple random samples from the same dataset, and thus the prediction is not totally independent of the training set. As the model's skill for extrapolation is important in producing historical (and eventually seasonal forecast, and projections) data, we conducted further tests with additional datasets from Réseau de Transport d'Électricité (RTE) for French hydro power generation in 2013-2014, and from Open Power System Data (OPSD)  [17]  data for Finland and Sweden in 2010-2014 (Figure  5 ).
totally independent of the training set. As the model's skill for extrapolation is important in producing historical (and eventually seasonal forecast, and projections) data, we conducted further tests with additional datasets from Réseau de Transport d'Électricité (RTE) for French hydro power generation in 2013-2014, and from Open Power System Data (OPSD)  [17]  data for Finland and Sweden in 2010-2014 (Figure  5 ). Considering that the model only uses climate data and does not include any power plant information, these reconstructed time series show very encouraging results (see correlation Considering that the model only uses climate data and does not include any power plant information, these reconstructed time series show very encouraging results (see correlation coefficient indicated on each subplot).
Summary and Discussion We developed a new dataset of hydro power generation for reservoir-based and run-of-river generation using only two essential climate variables-temperature and precipitation-and a machine learning methodology, based on random forests, selected for its flexibility and accuracy. Model training was performed over the period 2015-2019. The reconstruction dataset was then developed for the historical period from 1979 to present, corresponding to the available period of the ERA5 climate reanalysis dataset (it will also be extended to seasonal forecast and climate projections). Snow melt has an important impact on river flow and, thus, on hydro power generation, but it is generally not available in seasonal climate forecast or climate projection outputs. Therefore, an approach was suggested using lagged values of temperature and precipitation to replace snow depth in the model.
For instance, a newly installed wind farm might make the operator reduce electricity generated from a reservoir, but this effect is not captured by our model as it is not weather driven. In addition, due to the complexity of hydro power plants, a statistical model cannot outcompete a physical model, especially when extrapolating to periods with climate characteristics outside of the observed range. Further, in our case, the training period is relatively short with five-year data for the period 2015-2019. In any case, one should keep in mind that the primary objective, developed in the context of C3S Energy is to provide a realistic method which can be readily implemented with publicly accessible datasets such as ENTSO-E. For example,  [19]  has produced a time series of reservoir-based generation in China using basin and power plant information.
However, it is the first time a dataset of hydro power, which is also complemented by electricity demand, wind and solar power, has been produced at the European scale for both historical and future periods  [20]. It is important to emphasize that the approach taken by C3S Energy is to model electricity demand and power generation over the ca. 40 year period, 1979 to present, assuming a fixed EU energy system based on available power data, which normally cover the last several years. From this dataset, actual energy generation and demand can be derived using simple arithmetic to rescale mean energy demand and generation based on actual installed capacity and annual energy consumption. In terms of data quality, there is confidence in the model's input data:  [21]  shows that precipitation and temperature variables from the ERA5 dataset can produce results equivalent to observational data in hydrological modelling from North-American catchments;  [6]  on the ENTSO-E dataset also highlights that it is the most ambitious global open source dataset for energy data.
Although random forest is known for its robustness against outliers, this feature reduces its ability to extrapolate to extreme events or to values outside of the training dataset, as shown in Section 3. 2. In addition, when input variable deviation is large compared to their mean value, it is more difficult to differentiate between signal and noise, hence the lower performance in countries with smaller installed capacity such as run-of-river generation in Switzerland or both hydro power types in Slovakia. Nevertheless, the model performance is encouraging, considering its simplicity and versatility. The presented results will be available in the C3S Climate Data Store later in 2020 and can serve as a benchmark for further studies on the impact of climate variability on hydro power.
Funding: This work was funded by: (i) the  European Climatic Energy Mixes (ECEM)  contract, supported by the  Copernicus Climate Change Service  ( C3S ), a programme being implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission  (contract number:  2015/C3S_441_Lot2_UEA ); (ii) the C3S Energy service contract, supported by the  C3S  (contract number:  2018/C3S_426_Lot1_WEMC ); (iii) the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  776868  ( SECLI-FIRM  project).
Compilation of reports on cholera outbreaks We searched the peer-reviewed literature for further identification and reporting on cholera outbreaks. Peerreviewed articles were sourced from PubMed/MEDLINE using a date-specific keyword search ("country AND cholera"). Given that only a small number of outbreaks are reported in the scientific literature, we searched the gray literature, including epidemiological summaries, national cholera preparedness and response plans, and non-peer-reviewed studies. The sources included the following: (1) Reliefweb (https://reliefweb. int/, a repository of documents and data from humanitarian crises) using a date-specific keyword search ("country AND cholera"; "UNHCR AND cholera") and (2) regional outbreak bulletins and journals including the  WHO   [16, [20] [21] [22] [23].
The Program for Monitoring Emerging Diseases (ProMED) database of disease observations from media sources was used to fill in missing information on dates, but was not used as the primary source of information  [24]. When little information was available from the sources above, websites of ministries of health and crisis-specific surveillance systems (e. g. , early warning alert and response systems or networks (EWARS/EWARN) or disease early warning systems (DEWS)) were searched. An example includes the EWARN of the Syrian Assistance Coordination Unit for Northern Syria  [25].
Inclusion criteria and data extraction Outbreaks were included if at least two of the following dates were available: (1) dates of symptom onset of the primary case, and/or case presentation, and/or outbreak detection, and (2) dates of investigation and/or response. If the date of symptom onset for the primary case was missing, it was estimated as 5 days before the date of case presentation (equal to the median delay for outbreaks with available date of symptom onset), or date of outbreak detection if date of case presentation was unavailable. If the date of case presentation was unavailable, it was replaced by the date of outbreak detection. The earliest dates of (1) symptom onset for the first identified primary case, (2) case presentation to a health facility, (3) detection of outbreak/alert raised, (4) investigation by local health authorities, (5) response, and (6) laboratory confirmation by culture were extracted (Table  1 ).
We defined the date of response as the earliest date by which a cholera-specific control measure was applied to the outbreak-affected area (e. g. , water, sanitation, and hygiene (WASH) activities, setup of case management, active case-finding, community-based activities, and delivery of cholera kits). The starting month and year of the outbreak, geographical context (i. e. , urban, rural, or displacement camp), type of crisis or fragility (i. e. , armed conflict, fragile state, natural disaster, refugee setting, non-fragile state bordering a fragile state), and the WHO region were extracted. Any additional information on factors that may have contributed to the observed delay, including presence of an early warning function, was extracted. Details on the signal type for outbreak detection were recorded, if available, as a (1) formal alert detected by health workers reported immediately within the surveillance system, (2) informal alert from community members or a non-governmental organization (NGO) reported immediately, or (3) weekly data analysis of surveillance trends.
Analysis of delays and their predictors For each outbreak, median delays and their interquartile ranges (IQR) were calculated by subtracting the date of symptom onset of the primary case from the dates of (1) case presentation, (2) outbreak detection, (3) investigation, (4) earliest response, and (5) laboratory confirmation. For each outbreak, the dates were graphed on a timeline. To investigate the association between the observed delay from symptom onset of the primary case to response and potential predictor variables, a multivariate ordinary least-squares regression model was used. Delay to response was log-transformed to produce a normalized distribution. Extreme values in delay to response were judged to represent meaningful delays rather than data errors and were retained in the dataset.
The documentation of the occurrence and features of cholera outbreaks is currently very heterogeneous. A realtime global database using standard outbreak event reports maintained by WHO regional offices to prospectively log data and metrics from outbreaks of cholera, as well as other epidemic-prone diseases, would yield superior accuracy for the annual evaluation of timeliness. WHO AFRO's Weekly Bulletin on Outbreaks and Emergencies provides an existing template which can feed into such a global database  [20]. WHO AFRO has used this tool to provide annual metrics of timeliness in outbreak response for epidemic-prone diseases from 2017 to 2019, demonstrating reduced time from symptom onset of the primary case to outbreak detection (defined as alerting national authorities) from 14 (IQR 6-37) days in 2017 to 4 (IQR 1-11) days in 2019  [188].
DISCUSSION While several recent studies report on the heterogeneity of developing γδ T cells in the thymus  (Kernfeld et al, 2018; Sagar et al, 2020; Tan et al. , 2019) , there is little information on the diversity of CD27 + γδ T cells in peripheral organs and secondary lymphoid tissue of adult mice at the single cell level. The data described herein addresses this knowledge gap. Here, we show that CD27 + γδ T cells consist of two major transcriptional subsets, defined by naïve T cell markers and cytotoxic, effector-like markers. The transcriptional signatures of each subset were also validated at the protein level. We found that expression of Ly6C -a molecule normally associated with monocytes and neutrophils -stratifies these two subsets and associates with the T cell memory and activation marker, CD44.
The phenotype of CD27 + Ly6C + and CD27 + Ly6C -γδ T cells and their abundance was relatively similar between the lung and secondary lymphoid organs, suggesting the microenvironmental differences between these tissues have little impact on these cell subsets. However, the lung generally contained more effector memory-like γδ T cells. Our data support a recent study that labelled a population of γδ T cells expressing Ccr9 and S1pr1 mRNA without Cd44 mRNA in mouse thymus, blood and lymph node as naïve-like cells  (Sagar et al. , 2020). Although we did not detect Ccr9 transcript levels in our scRNAseq dataset, the previously identified Ccr9-/S1pr1-expressing cells are highly analogous to the CD27 + Ly6C -γδ T cells we describe, which express S1pr1 mRNA. Our findings corroborate the notion that these thymic-derived cells are immature and expand in secondary lymphoid organs after receiving some unknown stimulus.
Our data provides evidence that these innate-like lymphocytes can be imprinted with a memory-like phenotype and can be licensed to attack cancer cells. However, it is unclear which molecules are necessary for cancer cell recognition and induction of cancer cell death. Furthermore, the role of the TCR in this process remains unknown. The data presented here provide novel information on the degree of homology between mouse and human γδ T cells. Overlaying the gene expression signatures of mouse γδ T cell subsets onto a human PBMC and γδ T cell-enriched dataset revealed that mouse CD27 + γδ T cells share a great deal of transcriptional similarity with human circulating γδ T cells. The human dataset contained mature and immature γδ T cells that directly corresponded to CD27 + Ly6C + and CD27 + Ly6C -γδ T cells, respectively.
Because this dataset included γδ T cells from three individual donors  (Pizzolato et al. , 2019) , we believe that the comparison between species should be done with more human samples, when they become available. Nevertheless, it is clear that the activation status of γδ T cells is conserved across species, providing a greater rationale to use mice for the study of anti-tumorigenic γδ T cells. Our data showing that CD27 + γδ T cells acquire the ability to kill mammary cancer and lymphoma cell lines after ex vivo expansion corroborate several other studies. MMTV-PyMT mammary cancer cells, 4T1 mammary cancer cells, Lewis lung carcinoma cells, TRAMP-derived prostate cancer cells and B16 melanoma cells die when co-cultured with γδ T cells expanded in the presence of IL-2, IL-15 and/or TCR stimulation  (Beck et al.
This selection resulted in single cell transcriptomes of 458 CD27 + γδ T cells. The first 9 components of the principal component analysis (PCA) were used for unsupervised K-nearest clustering, and non-linear dimensional reduction using tdistributed Stochastic Neighbour Embedding (t-SNE) (  Van der Maaten & Hinton, 2008)  was utilised for visualization of the data. Then the data were analysed to determine the differentially expressed genes of clusters 0, 1 and 2. Genes from the murine cell clusters 0and 1-defining signatures were converted to their human orthologs with Ensamble database using the martview interface (http://www. ensembl. org/biomart/martview). Then, by using Single-Cell_Signature_Explorer  (Pont et al. , 2019) , these signatures were scored for each single cell of an already annotated dataset of ~10 4 human purified γδ T cells and PBMCs  (Pizzolato et al.
Cells were fixed and permeabilized for intracellular staining with Cytofix/Cytoperm (BD Bioscience), cells were stained with Zombie NIR Fixable Viability kit (Biolegend) (1:400 in PBS) for 20min at 4°C in the dark for dead cell exclusion. Antibodies to intracellular antigens were diluted in permeabilization buffer and incubated for 30min at 4°C in the dark. For intranuclear staining of Ki-67, samples were fixed and permeabilised using FOXP3/Transcription Factor Staining Buffer Set (eBioscience) according to the manufacturer's instructions. Antibodies used for respective panels are listed in Table  3. Samples were acquired using BD LSR II flow cytometer and Diva software. Data analysis was performed by using FlowJo (versions 9. 9. 6 and 10. 7. 1) and fluorescence minus one (FMO) controls to facilitate gating.
Cisplatin was used as positive control for cancer cell death (75μM for KP cells, 100μM for KB1P and YAC-1 cells). Cells were collected with trypsin, stained with anti-CD3-FITC (eBioscience; clone 145-2C11; 1:100) and DAPI (1μg/mL) was added to samples immediately prior to acquisition. Samples were acquired using BD LSR II flow cytometer and Diva software. Cancer cell death was measured by DAPI uptake.
The digitalized images were analyzed using the VisualSpeadsheet® software and classified manually into taxonomic categories. Considered living organism groups for the FlowCAM were copepods, nauplii, crustaceans, appendicularians, gelatinous, chaetognaths and other diverse zooplankton groups (polychaeta, ostracods etc. ). Non-organism particles were classified as detritus. Duplicates and bubbles were deleted. To calculate the number of particles in the sample, the following equation was used.
Data availability Gridded maps with all the temporal factors (monthly, weekly, daily, hourly) per sector and year are available as NetCDF files for the global domain at a resolution of 0. 1x0. 1 deg (CAMS-GLOB-TEMPOv2. 1, https://doi. org/10. 24380/ks45-9147, Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4. 0 License. Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4. 0 License. Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4. 0 License. Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Data Discussions Preprint. Discussion started: 4 August 2020 c Author(s) 2020. CC BY 4. 0 License. Data Discussions Data Discussions Data Discussions Data Discussions.
Data regarding molecular pattern in terms of reproductive and hormonal factor exposure in women is sparse, but a potential association between EGFR mutation and ER expression has been suggested  (6) (7) (8). The IFCT-1002 BioCAST study is a nationwide cohort aiming to collect LCINS cases over 1 year in France. Risk factor exposure was recorded via a standardized questionnaire investigating several variables regarding reproductive factors and the use of estrogen-based treatment. In this paper, we aim to examine whether exposure to reproductive factors and/or hormonal intake in women with LCINS modulates the somatic profile.
Population We utilized data from the IFCT-1002 BioCAST cohort. The analysis was restricted to women who completed the questionnaire and were tested for at least one biomarker. The study design has been reported elsewhere, as is the case for the cohort overall description  (2). In short, BioCAST is a prospective, multicenter, observational study designed to describe the clinical, pathological, and molecular epidemiology of LCINS in a French population. It enrolled consecutive, newly diagnosed NSCLC patients claiming to be never-smokers (less than 100 cigarettes in their lifetime). Patients were surveyed using a standardized questionnaire during a phone interview with a study team member. This 17-page questionnaire requested information on demographics, and exposure to risk factors including hormonal, and reproductive factors (women only).
For EGFR mutations, we performed two additional multivariate models, setting all reproductive variables as continuous. The first model was adjusted for age at diagnosis, BMI, total duration of passive smoking exposure (3), age at menopause, parity, number of pregnancies, and age at first birth. The second model was additionally adjusted for family history of lung cancer (number of relatives), personal history of cancer (n), total duration of exposure to cooking oil fumes (years), exposure to fumes of solid fuels for cooking/heating (% of lifetime). All variables were continuous. Missing values were reported as such, and all tests were two-sided. All statistics were performed using the SPSS V20 software (IBM SPSS Statistics, New York, USA).
Estrogen directly stimulates transcription of estrogen-responsive genes in lung cell nucleus, but also trans-activating growth factor signaling pathways, such as the EGFR pathway  (19). A meta-analysis of five studies also found a positive association between high ERβ expression in lung cancer cells and EGFR mutation  (20). No correlation was determined between ERα expression and EGFR mutation by contrast to findings by Mazieres et al. and Raso et al. (7, 18). Nose et al. also reported a correlation between EGFR mutation and ERβ, and proposed that ER expression might be associated with a response to EGFR tyrosine kinase inhibitor  (6, 21). However, the IFCT-1003 LADIE trial (NCT01556191), aiming to test anti-estrogen in addition to EGFR TKI in EGFR mutated or naïve post-menopausal women with advanced stage lung adenocarcinoma, showed no benefit of the combo  (22).
ACKNOWLEDGEMENTS Authors thank  M. William Lebossé  and  Ms. Stéphanie Labonne , who performed interviews with patients;  M. Quan Tran  and  M. Antoine Deroy  ( Data Manager , IFCT);  Pascale Missy  (PhD, IFCT), who provided administrative support;  Ms. Gabrielle Cremer  for expert English rewording; the  French Ligue Against Cancer ; and all investigator in the 75 IFCT-1002 BioCAST participating centers; the patients and their families, who greatly contributed to this work by giving their time to prepare the questionnaire and participate in the interview.
Receiver compression It is advisable to design calibration experiments which avoid the appearance of compression effects. If this is not possible, compression must be considered in the data treatment so that the retrieved calibration remains valid in the receiver linear regime, where it usually operates during cloud sampling  (Scolnik, 2000). To study how these effects could affect our calibration, we retrieved the radar receiver power transfer curve. Receiver characterization was done by removing the radar antennas and connecting the emitter end to the receiver input with two attenuators in between. The first was a 40 dB fixed attenuator, while the second was a tunable attenuator covering the range between 50 and 1 dB of losses. The adjustable attenuator enabled the retrieval of the power transfer curve by varying the attenuation and sampling the power at the receiver end (digital processing included).
Here an adaptation of the effective RCS simulator would be necessary to account for the target type and different alignment protocol. Radar equivalent reflectivity dBZ Data availability. All data related to atmospheric conditions used in this study are hosted by the SIRTA observatory. Data access can be requested free of charge by following the conditions indicated in the SIRTA data policy (https://sirta. ipsl. fr/data_policy. html, last access: 3 December 2020, SIRTA, 2020b). The SIRTA observatory website can be accessed at https://sirta. ipsl. fr/ (last access: 3 December 2020, SIRTA, 2020c), and the data request form can be found at https://sirta. ipsl. fr/data_form. html (last access: 3 December 2020, SIRTA, 2020a). All radar data are distributed by the IPSL with the following identifier: https://doi.