dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, NK subset 2 cells were enriched in genes such as STAT3 and GPR65 (Extended Data Fig. 10b ). Next, using ADTs (CD14, CD16, CD11c, CD123 and CD1c), we identified three myeloid cell subclusters (Extended Data Fig. 10c ). Cluster 0 expressed CD16 and CD11c ADTs and cluster-defining genes included macrophage markers CD68 and MAFB. Clusters 1 and 2 were marked by CD14 and CD1c, respectively. Cluster 1 was further defined by genes including CD32, VCAN, SERPINB2 and S100A8 in addition to CD14, whereas cluster 2 was defined by CD40, CCR7 and CCL22. We then assessed gene expression induced by Gag peptide stimulation. The number of cells in DMSO (6,530 cells) and Gag peptide (7,800 cells) stimulation conditions were comparable (Fig. 4c ). The remaining 241 of the total 14,571 cells profiled by CITE-seq could not be reliably assigned to a treatment group.
The standard in both anti-trimer and total IgA assays was the anti-CD4 binding site rhesus b12 dimeric IgA monoclonal antibody (NHP Reagent Resource). The standard in anti-trimer and total IgG assays was IgG purified from sera of SHIV-infected macaques and a pool of normal rhesus macaque sera, respectively. Neutralizing antibody assay by the Emory Vaccine Center. The HIV-1 Env clone BG505. W6M. Env. C2 (GenBank accession code DQ208458) that had been modified to contain the N332 glycan (T332N)  9  was used to measure autologous neutralization for the majority of experiments. The SHIV. BG505. 332N. 375Y virus stock, prepared by transfection into HEK293T cells, provided by G. Shaw was used directly to assess autologous neutralization. The TZM-bl neutralization assay used has been described previously  [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66].
Single-cell RNA-seq analysis. Sequencing output was processed with Cellranger v. 3. 0. 2 (10X Genomics). Briefly, the output bcl2 file was converted to FASTQ format using mkfastq. The FASTQ files were then aligned with default settings with Cellranger-count, resulting in a transcript and ADT read-count matrix for each sample. Cells were filtered out if they expressed <100 genes or had a mitochondrial gene content >4 s. d. above the median. Post-processing of raw counts was performed using the Seurat package v. 3. 0. 0 in R 3. 5. 1. Gene and ADT counts from each sample were merged into a single Seurat object. Counts were log-transformed after normalization by the total counts for that cell and multiplied by 10,000. Principle components (PCs) were then calculated for the dataset using Seurat.
Subclustering was performed on innate immune cells using Seurat with the same parameters as for all cells. Individual subclusters of innate cells were visualized with uniform manifold approximation and projection with the uwot package v. 0. 1. 3. To identify differentially regulated pathways between Gag and DMSO-treated cells within innate cells, MAST  73  was used to fit a linear mixed model to adjust for gene detection rate. The resulting model was used to perform gene-set enrichment analysis with blood transcriptional gene modules  74.
Statistical analysis. The difference between any two groups at a time point was measured using a two-tailed nonparametric Mann-Whitney unpaired rank-sum test. The difference between time points within a group was measured using a Wilcoxon matched-pairs signed-rank test. The statistical differences in Kaplan-Meier survival curves were analyzed by a log-rank test. All correlations were Spearman's correlations based on ranks. All the analyses were performed with GraphPad Prism v. 8. 2. 0. The statistical tests for CITE-seq analysis were performed using the stats package v. 3. 5. 1 in R. Bayes generalized linear model (GLM) was used to model the probability of infection as a function of neutralizing antibody titers 2 weeks before challenge. The discrete probability of infection was connected to continuous neutralizing antibody titers using the logit link function and the infection probability was computed for each animal through Bernoulli distribution.
Acknowledgments We thank all animal staff at the  Yerkes National Primate Research Center at Emory University  for help with the macaque study and  B. Wehrle  and  Z. S. Momin  for technical assistance in processing samples. We thank  J. Yewdell  and  D. Barouch  for providing VV-Gag and Ad5-Gag viral vectors, respectively. We thank  A. Dey  of the  International AIDS Vaccine Initiative  for providing us with BG505 SOSIP. 664 for vaccinations. We thank  CFAR Immunology/Emory Vaccine Center Flow Cytometry Core , especially  K. Gill  and  Stanford Shared FACS Facility , especially  M. Rieck , for the support with flow cytometry. We thank  S. Liang  and  S. Wang  at the  CFAR Immunology Core  for viral load assays. We thank the  Genome Sequencing Service Center  by the  Stanford Center for Genomics and Personalized Medicine , especially  K.
Code availability Codes used to analyze data and generate figures are available on Github (https:// github. com/scottmk777/Rhesus_CITE-seq). Data exclusions At week 82, serum could not be collected from 2 animals in the SOSIP/3M-052 group. Therefore, 2 samples (RBi16 and RSf16) were not available for any analysis from that time point. As a quality control, cells were filtered out from the CITE-seq analysis if they expressed fewer than 100 genes or had a mitochondrial gene content greater than 4 standard deviations above the median.
Free ATP was quantified replacing DMSO with the same volume of sterile saline solution. Blank tests were performed by substituting the sample with the same volume of extraction reagent (for cellular ATP tests) or of saline sterile solution (in the case of free ATP). The cellular ATP values reported in the results were obtained subtracting the free ATP and the blank values to the registered cellular ATP. All the tests were performed on three aliquots of the sample, and the results are reported as the average value of the three readings. The bioluminescence was registered as relative light unit (RLU) through a luminometer GloMax ® 20/20 (Promega) combined with GloMax ® Spreadsheet Interface Software (GLOMAX SIS v1. 10. 0) using the default setting. The possible influence of the EOs on the ATP quantification was evaluated by adding 1 µL of the three EOs to 99 µL of standard ATP (REF: F203A, Promega) at concentration 10,000 pg/mL.
B). Sequencing was performed by Miseq (2 × 300 paired-end, 600-cycle) Illumina platform (Illumina, San Diego, CA, USA). Raw fastq files were imported for preprocessing and OTU picking into qiime2  [75] , the quality-check and clustering were done using DADA2  [76] , representative sequences were aligned using mafft  [77] , uninformative positions were masked, and a phylogenetic tree was built with fasttree  [78]. Taxonomic assignment for 16S rRNA and ITS data were performed using the SILVA and UNITE database  [79, 80]. Files containing the OTU tables, the phylogenetic trees, and the metadata, along with the beta-diversity distance matrices, were exported from qiime2 and imported into calypso for downstream analyses  [81]. To determine the taxa which had differential abundance among the treatment we used the linear discriminant analysis (LDA) effect size (LEfSe)  [82].
Alpha and beta diversity values were calculated in qiime and plotted using the calypso interface. Data are available in SRA database (project PRJNA679775, in particular 16S data are available in SUB8581970 and ITS2 data are available in SUB8582450). Cinnamon essential oil, selected based on the results obtained in the previous experimentations, was tested as preservative during one of the phases of the restoration project entitled "Restauro del relitto E dagli scavi del porto antico di Napoli. Due metodi di consolidamento a confronto"  [83]. The tests were performed on wooden poles recovered from the excavation area of Piazza Municipio in Naples. The poles were treated into four tanks containing a lactitol-trehalose consolidation bath. Two of the tanks were transparent, the others opaque. After 1 month from the beginning of the consolidation treatment a conspicuous microbial growth was observed in all the containers, so it was decided to treat the baths with a biocide.
Only the corrected results are shown in the text. The best results were obtained for C. A percent decrease of 100% on water and 99. 9% on wood was registered from the comparison of ATP values of this oil's solution with untreated control. WT produced a vitality decrease by 89% on water and 96. 7% on wood. The worst results were obtained with CT, for this oil's solution the residual microbial vitality was 1. 5% for wood and almost 27% for water. Control tests carried out on the ethanol treated sample showed that the ATP values registered for both water and wood were higher with respect to the untreated control. These data confirm what was observed in in vitro tests, ethanol has fungistatic but not fungicidal effect. NGS analyses performed on treated and untreated wood and storage water allowed to evaluate the effect of the tested oils on the microbiota composition.
Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO 2 ) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed.
The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means.
Two involve many crop models but only one crop (wheat)  (Fronzek et al. , 2018; Ruiz-Ramos et al. , 2018)  and cover only one to four individual sites. Snyder et al. (2019)  analyzes five crops over ∼ 1000 sites with individual site-specific crop models, and extrapolates in space to estimate mean latitudinal responses. In this paper, we describe a set of globally gridded crop model emulators developed from the new parameter-sweep dataset of the Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 effort. GGCMI Phase 2, a part of the Agricultural Model Intercomparison and Improvement Project (AgMIP)  (Rosenzweig et al. , 2013 (Rosenzweig et al. , , 2014)) , provides the first near-global-coverage systematic parameter sweep of multi-model crop simulations consisting of up to 756 combinations in CO 2 , temperature, water supply, applied nitrogen, and two different assumptions on growing-season adaptation (A0: none; A1: retaining growing-season length) (CTWN-A,  Franke et al.
, 2020a; Minoli et al. , 2019b). The experiment is designed to allow diagnosing the impacts on crop yields of both individual factors and their joint effects, and to allow construction of crop model emulators. In Sect. 2, we describe the training dataset, including the GGCMI Phase 2 experimental protocol and model participation (Sect. 2. 1) and the models' differing year-to-year and climatological-mean responses (Sect. 2. 2). Section 3 describes the statistical model used for emulation, Sect. 4 evaluates measures of emulator fidelity, and Sect. 5 shows examples of preliminary results.
The GGCMI Phase 2 dataset The GGCMI Phase 2 simulations are described in detail in  Franke et al. (2020a) , but we summarize briefly here. The experiment involves nine different globally gridded crop models, each simulating multiple crops (maize, rice, soybean, and spring and winter wheat) across a systematic parameter sweep of as many as 756 combinations, each driven by a historical climate time series with systematic perturbations to CO 2 , temperature, water supply, and nitrogen application (CTWN). The simulation protocol involves four levels of atmospheric CO 2 , seven of temperature, nine of water supply, and three of applied nitrogen, and simulations are repeated for two adaptation scenarios: A0 simulations assume no adaptation in cultivar choice, so that growing seasons shorten in warmer climates, and A1 simulations assume that adaptation in cultivar choice maintains fixed growing seasons.
e. , the change in aggregated mean yield in each 30year simulation. Emulation then becomes relatively straightforward since changes in time-averaged yields are considerably smoother than those in year-to-year yield response. In the GGCMI Phase 2 simulation output dataset, year-toyear responses to weather are also often quantitatively distinct from responses to climatological shifts, with the discrepancy especially strong in wheat and rice. The difference in behavior is illustrated in Fig. 1 , which shows irrigated and rainfed maize and wheat in representative locations. When discrepancies are large, year-to-year responses are generally stronger than climatological ones, but exact responses differ by crop and region and even by model within GGCMI Phase 2. While differences in responses at different timescales can arise for many reasons, including memory in the crop model or lurking covariates, the most likely explanation here is that the regressors used, mean growing-season temperature or precipitation, do not fully describe the conditions that affect crop yields.
4. 3, we confirm that an emulator trained on the GGCMI Phase 2 dataset can successfully reproduce yield changes under a full climate model projection. Note that even though distributions of climate variables are unchanged in the GGCMI Phase 2 simulations, the spread in annual yields still becomes wider in highly impacted climate states, because of the nonlinearity of yield responses (Fig. 2 ). In the GGCMI Phase 2 dataset, all crops except rice show greater year-to-year yield variance in conditions of extreme climate stress. (Rice is typically irrigated and experiences no water stress in simulations. ) Increased variance has been noted in previous studies. For example,  Urban et al. (2012)  used statistical models trained on present-day yields to find a projected future increase in yield variance of US maize of 20 % per Kelvin unit temperature rise.
3, where n is the number of samples and k is the number of features): R 2 adj = 1 - (n -1) • (1 -R 2 ) n -k. (3) We use the implementation of the Bayesian ridge estimator from the scikit-learn package in Python  (Pedregosa et al. , 2011). An additional diagnostic of fit quality is the distribution of residuals: normally or near-normally distributed residuals imply that errors around the fit are random and unbiased. When fitting Eq. (  1 ) to the GGCMI Phase 2 dataset, the distribution of the residuals depends on the number of features included in the regression, the method for estimating the parameters, and the target distribution in the training set. The residuals are only normally distributed (p value > 0. 05 in the Shapiro-Wilk test) for a single model, PEPIC, for any specification tested here, but their skew is relatively small except in a single case, EPIC-TAMU maize (Fig.
In GGCMI Phase 2, the effect is manifested in the higher normalized errors for soybeans across all models, which result not because soybean yields are difficult to emulate but because models agree more closely on yield changes for soybeans than for the other crops. 2. Out-of-sample validation. We provide a second, more stringent test of emulator performance via a 3-fold cross val-idation (also termed an out-of-sample validation). In this test, the GGCMI Phase 2 dataset is split randomly into two parts, with 90 % of the data used to train (calibrate) the model and the held-out 10 % used to test (evaluate) the fidelity of the resulting emulator. The procedure is repeated three times; in each case, we calculate the root mean square error (RMSE) between the emulated (predicted) and actual simulated test set values, and then average the three results.
The result is a single metric for each grid cell for each model-crop combination. As a last step, we normalize the error metric for each grid cell by dividing by its maximum yield change over the entire CTWN dataset. (Since all models have submitted the extreme T +6 scenario, this normalization choice is not problematic. ) Note that this validation exercise is independent of the procedure for generating the final published emulator values, which are generated using the full CTWN dataset. The resulting error metric is generally low. Table  3  shows the yield-change-normalized RMSE for rainfed crops in all models over currently cultivated land, both in selected major producing regions and in the global average. We include all simulations in the CTWN space and report the average error value in Table  3.
Emulation of realistic climate projections Finally, we test the ability of an emulator based on the GGCMI Phase 2 perturbed mean training set to reproduce the response of a crop model driven by a realistic, evolving climate scenario. Our emulators are trained only on growingseason means, and the GGCMI Phase 2 exercise involved only changes in means. We therefore seek to assess whether changes in the higher moments of temperature and precipitation distributions in a climate projection might have effects that lead to significant emulator error. Note that we are not asking whether year-to-year climate variability matters to crop yields; this point is well established  (Ray et al. , 2015). The question instead is whether a realistic future climate projection involves changes in variability large enough that they compromise an emulator based on the GGCMI Phase 2 dataset.
Discussion and conclusions In this work, we describe a new class of global gridded crop model emulators for five crops (maize, soybean, rice, and spring and winter wheat) and nine process-based crop models, based on the GGCMI Phase 2 dataset, a set of crop model simulations run with systematic perturbations to carbon, temperature, precipitation, and nitrogen (CTWN). The goal of this project is to provide a lightweight tool that reproduces the output of large numerical simulations of processbased crop models. The resulting emulators should provide useful tools both for diagnosing crop model behavior and https://doi. org/10. 5194/gmd-13-3995-2020 Geosci. Model Dev. , 13, 3995-4018, 2020 for climate impacts assessment, at least of large-scale timeaveraged responses. Specific findings of this work include the following: -In crop models, the climatological-mean yield responses to uniform perturbations in growing-season mean temperature and precipitation are very distinct from responses to historical weather fluctuations associated with the same mean differences.
Emulators can faithfully reproduce the output of processbased crop models in both in-sample and out-of-sample tests. Emulation error is generally small other than in localized regions where crops are not currently grown: across all models and scenarios, errors over currently cultivated land never exceed 5 % of yield changes at either global or regional scale. -Emulators trained on the GGCMI Phase 2 dataset, which samples over uniform climate perturbations, can effectively reproduce the behavior of crop models driven by realistic future projections of future T and P changes. This result suggests that any projected changes in weather distributions (temperature and precipitation variability) have relatively little effect on model-crop yield responses relative to changes in means, at least on the regionally aggregated level.
The GGCMI Phase 2 models are not formally calibrated, so the emulators should be used for absolute projections only in combination with historical data. Third, neither growing-season specification tested in GGCMI Phase 2 (A0 and A1) accounts for a major potential adaptation pathway under climate change, a shift to earlier or later planting dates  (Waha et al. , 2012) , or generally different growing seasons  (Minoli et al. , 2019a). And finally, the emulator should not be used to predict individual yearly yields, as the forced climatological-mean yield response will not match the response to mean growing-season weather in a single year. The emulator cannot provide a measure of changing yield variance and should not be used to evaluate extremes. In summary, the GGCMI Phase 2 dataset and emulators invite a broad range of potential future avenues of analysis.
, 2018  for methods of constructing synthetic climate time series with altered variability. ) The GGCMI Phase 2 dataset can be used as a testbed for examining the ability of statistical models that use more detailed withinseason regressors to capture both year-to-year and climatological changes, and for more systematic studies of emulation itself, including evaluation of alternate statistical specifications or machine learning methods. In general, the GGCMI Phase 2 experiment demonstrates the promise and utility of systematic parameter sweeps for improving understanding of the factors driving crop responses and for evaluating and improving process-based crop models.
Acknowledgements. We thank  Michael Stein ,  Kevin Schwarzwald , and three anonymous reviewers, who provided helpful suggestions that contributed to this work. This research was performed as part of the  Center for Robust Decision-making on Climate and Energy Policy (RDCEP)  at the  University of Chicago , and computing resources were provided by the  University of Chicago Research Computing Center (RCC). This is paper number  36  of the  Birmingham Institute of Forest Research. Financial support. RDCEP is funded by  NSF  through the  Decision Making Under Uncertainty program  (grant no. SES-1463644 ). James A. Franke was supported by the  NSF NRT program  (grant no. DGE-1735359 ) and the  NSF Graduate Research Fellowship Program  (grant no. DGE-1746045 ). Christoph Müller  was supported by the  MACMIT  project (grant no.
Code and data availability. The polynomial parameters for crop model emulators are available at https://doi. org/10. 5281/zenodo. 3592453 (Franke, 2019)  and https://doi. org/10. 5281/zenodo. 3994593 (Franke et al. , 2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-13-3995-2020-supplement.
S3 ). In addition to the full TraCE-21 simulation, we used the four single-forcing sensitivity experiments (ORB, GHG, MWF, and ICE), with only one of the forcings mentioned above allowed to evolve through time, while the three others are kept fixed at their 19 ka value. Atmosphere decadal mean seasonally averaged and ocean decadal mean annually averaged datasets were downloaded from the website of the Earth System Grid: https://earthsystemgrid. org/project/trace. html (last access: 25 October 2020). They have been used to prohttps://doi. org/10. 5194/cp-16-1969-2020 Clim. Past, 16, 1969-1986, 2020 vide the same atmospheric and oceanic parameters simulated by the IPSL model, but over the last 26 kyr and with the exception of marine biogeochemical variables which are not computed in the CCSM3.