dataesr/scientific-paragraphs-categorization huggingface dataset:
e. due to changes in the length of months or seasons over time, related to changes in the eccentricity of Earth's orbit and precession), using the PaleoCalAdjust tool  (Bartlein and Shafer, 2019). The mean state of the present-day tropical Pacific is determined using a climatology over the period 1971-2000 for both the historical simulations and observational datasets: the Global Precipitation Climatology Project (GPCP;  Adler et al. , 2003) , the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST;  Rayner et al. , 2003)  and the 20th Century Reanalysis (C20;  Compo et al. , 2011). For the transient abrupt4xCO2 and 1pctCO2 simulations, the climatology is computed over the final 30 years. For the other quasiequilibrium simulations, all available data are considered to create the climatology.
Code and data availability. The CMIP5 and CMIP6 model data used in this study are available from the Earth System Grid Federation. All the processed model output and scripts to produce the figures in this study are provided on the GitHub repository: https: //github. com/chrisbrierley/PMIP4-enso (last access: 8 July 2020; chrisbrierley, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-16-1777-2020-supplement.
, 2013; Paul and wa Gĩthĩnji, 2018) , could shed new insight on how and why farming systems change, with important implications for development policies and environmental adaptation priorities  (Adamson et al. , 2018). This paper introduces a new systems analysis approach to model trajectories of farm typologies, systems dynamics and socioecological drivers using the south-central coast of Bangladesh as a study area. This new approach is characterized by integrating a well-articulated theoretical frame of change observed using a temporal view of change processes and outcomes. We used twenty years panel data from 502 households and analyzed those utilizing multivariate statistics in conjunction with panel data models that operationalize our theoretical framework. Our approach estimates the dynamic change effects over time that cannot typically be estimated utilizing cross-sectional data set.
Data and sample We developed a balanced panel dataset at farm household level of 502 HHs in coastal Bangladesh in Barisal division, spanning 20 years from 1995 to 2015 as part of the multi-year Cereal Systems Initiative for South Asia (CSISA) project. This dataset is compiled from a farm household level primary survey, NGO records pertaining to household characteristics, farmer focus groups and secondary data sources. Sample were selected from a list of farm HHs provided by the NGO (Bangladesh Development Society) that keeps village level records of selected coastal districts in Southern Bangladesh. Two districts within polders (Patuakhali and Barguna) and a single district outside polders (Barisal district) were selected for the study (Table  1 ; Fig. 2 ). We selected these three districts of the total 27 districts in southern Bangladesh due to specific interest considering the potential for crop intensification and surface water irrigation in coastal Bangladesh (see  Krupnik et al.
These same HHs were surveyed in 2015 by the authors in order to develop a twenty year panel data set by combining the compiled information with the authors HH survey. Above panel data set used in this paper is made openly available by CIMMYT DataVerse for interested users here: http://hdl. handle. net/11529/10898. The variables in the panel data set developed included farm structural and functional characteristics, household resource endowment,  Note: In Bangladesh and several parts of South Asia including eastern India, rice is cultivated three times in a year differentiated by names "aus, aman or boro". The aman (broadcast and transplanted) rice is generally cultivated in mid-July to mid-November, sown with the onset of South-west monsoon rains, boro (irrigated rice) in Feb-May, and aus rice in mid-March to mid-July cropping seasons utilizing summer rains.
agricultural management information, and data on off-and on-farm income, in addition to biophysical and socio-economic attributes. Village-level population and demographic data for twenty years period used in this study was obtained from Bangladesh Bureau of Statistics. In addition to the above, gaps from missing data were filled by collecting and validating information through a combination of 9 farmer focus group discussions and presentation of raw data to household members and through secondary sources including household income and expenditure survey (HIES) by Bangladesh government. Graphical overviews of selected variables for years 1995 and 2015 are given in Figs. 3  and 4  for sample outside and within polders, respectively. Detailed summary statistics are provided in Table  2.
Analytical process Our analysis for farm HHs outside polders (OP) and within polders (WP) proceeded in three steps. First, we developed farm typologies for farm households outside and within polders separately. Distinct farm types were identified for 1995 and 2015. In the second step, we identified the most important variables contributing farm type changes across panel years. In the last step, we identified the multi-level factors driving farm type dynamics using panel data regression models. A historical review of literature was also carried out to complement our findings.
Modelling multi-level factors and drivers influencing farm change trajectories In the third analytical stage, we analyzed the effects of multi-level factors/drivers of change on dominant variables contributing to farm type dynamics in panel years (T) by employing panel data modelling  (Hsiao et al. , 2000; Baltagi, 2008). Let the panel dataset contain observations of the multi-level factors and drivers of change (independent variables), X 1 , X 2 , …, X k and the dominant variables contributing to farm type dynamics, whose identification is explained in Section 3. = + + Y X it it it (2) where i = 1, …, n and t = 1, …, T, where the first subscript, i, refers to the farm being observed, and the second term t, refers to the observational year (T = 5 in our case). In Eq. (  2 ), ε it is the error term and Z i ' is a set of farm household-specific micro-level factors (Table  2 ).
Conclusions and implications This study aimed to elucidate farm change trajectories and likely factors/drivers influencing changes in the farming systems of Bangladesh's central coast using a novel systems analysis approach. We presented a new framework for analyzing farm trajectories using longterm panel data. While we adopted both fixed and random effects models similar to  Baltagi (2008) , our approach was unique in that we employed data dimensionality reduction techniques, hierarchically clustered farm typology analysis, and examined how they changed over time. We also extracted the underlying latent factors that helped explain what drives change over time using panel data modelling. The conceptual model and the analysis provided here illustrate how it is meaningful to consider a wide range of socioecological system properties potentially influencing agricultural intensification, rather than singling out macro-drivers such as population pressure as the primary metric of agrarian change and intensification.
Acknowledgements This study was conducted as part of the 'Agro-ecosystem diversity and the Trajectories and  Trade-offs for Intensification of Cereal-based systems' (ATTIC)  project of the  Wageningen UR  funded by the  MAIZE CGIAR Research program  and  Cereal Systems Initiative for South Asia (CSISA)  project supported by the  United States Agency for International Development (USAID)  and  Bill and Melinda Gates Foundation (BMGF). Financial support for field research was also provided by the  Foundation Stiftung Fiat Panis in Germany. The authors thank  USAID ,  BMGF , and  Stiftung Fiat Panis  for their support, as well as  Dr. Johannes Schoberg ,  Dr. Andrea Fadani ,  ASM Kurishi  and  Sumona Shahrin  for early support. The content and opinions expressed in this paper are those of the authors and do not necessarily reflect the views of  USAID ,  BMGF , or the  Stiftung Fiat Panis.
Field data To train the linear model used in the calibration process, we relied on the variety testing network targeting released genotypes in France  (Mestries and Jouffret, 2002)  and conducted by the French technical institute in charge of references for oilseed crops and grain legumes (namely Terres Inovia). While this testing is conducted yearly, data is not in a readily usable format, and a major effort is needed to gather climate and soil data relevant to the tested locations. Here, we reused a dataset fully described in  Casadebaig et al. (2016a) , and summarized its main characteristics below. The target population represented by this dataset is sunflower cropping area in France with current recommended management. Trials were conducted in 52 locations during one year  (2009). Locations concentrating the most trials represented about 75 % of the cultivated sunflower area in France, i.
e. Poitou-Charentes (16 trials), Centre (9), Midi-Pyrénées (8) and Pays de Loire (7) regions. The network was split in three geographical zones, matching the operational areas of the technical institute and referred to as South (South and South-West France), West (Center and West France), and East (North and East France). At each location, one to four variety trials were conducted (differing by maturity group or fatty acid composition -oleic vs linoleic), for a total of 82 trials performed over the network. Only the locations that could be reasonably described (nearby weather station, sufficient information on soil depth, reliable information on crop management) were kept in the dataset (82 out of 99 trials in the MET). Furthermore, only genotypes with data from at least 10 trials were retained (28 genotypes).
The input variable which gave the smallest mean squared error (MSE), Z s was selected by comparing regression models. The model with linear correction is then f (X i ; θ) + â + bZ si where â and b are the estimated regression coefficients. A large number of input variables could be used for this empirical correction if we consider combinations of measured climatic variables by key crop phenological stages. Our proposal was to restrict this choice to a smaller number of candidates to limit selection bias. We therefore focused on variables readily available in climate datasets and representing the major abiotic factors known to affect sunflower growth and development. The four candidate input variables considered for the empirical correction are shown in table  2. They represent variables related to water (total precipitation or average water stress), radiation and temperature.
Software and data processing Experimental and simulated data were processed with the R software version 3. 5. 1 (R Core Team, 2018) with R packages dplyr (data processing,  Wickham et al. , 2018) , rsample (resampling,  Kuhn et al. , 2019 ), ggplot2 (visualization, Wickham, 2016) , and knitr (reporting,  Xie, 2015). The source code for the SUNFLO simulation model is available on INRA software repository [https://forgemia. inra. fr/record/sunflo. git]. The INRA VLE-RECORD software environment  (Quesnel et al. , 2009; Bergez et al. , 2013)  was used as simulation platform. Distributions of these variables tend to be bimodal, which can be explained by the spatial structure of the network, designed for testing late maturing cultivars in drought-prone locations (e. g. South-West of France), while early maturing cultivars were tested in colder and more humid environments.
Because of this spatial variability most variables displayed a strong coefficient of variation (∼ 35% for temperature and precipitation, ∼ 60% for water deficit). Radiation showed lesser variability (∼ 7%). The regressions between the candidate variables and model residuals are presented in figure  2. Each panel only shows those genotypes for which the indicated candidate variable was chosen. Supplementary figure  S1  illustrates these regressions in more detail for a subset of the dataset. Precipitation was the most-often chosen input to predict yield residuals (70 % of the population of genotypes) and temperature was most-often chosen for oil concentration residuals (55 % of the genotypes). For yield residuals related to precipitation, mean Pearson's coefficient of correlation was 0. 44 (ranging from -0.
Effect of empirical correction The SUNFLO model with parameter values based on phenotyping, before any correction, has an average relative RMSE of 18% (0. 63 t/ha) for yield and 5% (2. 7%) for oil content (figure  3 ). The initial model accuracy on this dataset was slightly lesser than previously reported studies in Argentina or Spain (8-15%,  Villalobos et al. , 1996 ; Pereyra-Irujo and Aguirrezabal, 2007)  or France (15%,  Casadebaig et al. , 2011). Even if the model correctly simulated phenotypic plasticity on this dataset and enabled applications for cultivar recommendation  (Casadebaig et al. , 2016a) , it is clearly of interest to try to improve model predictions. We analyzed how the two empirical correction methods that we tested impacted the predicted values for grain yield and oil concentration (figure  3 ).
Possible improvements The approach here uses a single environmental variable in the linear correction, chosen from among a small set of variables that are known to have a strong effect on yield and oil concentration. It is important use knowledge of the crop to limit the number of possible input variables. With very many possible variables there is a high risk of selection bias, where random error rather than true correlation determines the choice of explanatory variable  (Winship and Mare, 1992). When we tested this hypothesis on our dataset, goodness-of-fit was improved by allowing multiple explanatory variables, but prediction accuracy as evaluated by cross validation was decreased (figure  S3 ). We assume that the tested linear models contained more parameters than can be justified by the data (overfitting).
Acknowledgements Research grants were provided by the  French National Research Agency  ( ANR SUNRISE ANR-11-BTBR-0005 ). The authors are grateful to the staff from  Terres Inovia (Frédéric Bardy ,  Philippe Christante ,  André Estragnat ,  Pascal Fauvin ,  Emmanuelle Mestries ,  Jean-Pierre Palleau ,  Célia Pontet ,  Frédéric Salvi ) that helped to constitute the phenotypic and climatic database.
Upregulation of the FAO Transcription Program in BRAF(V600E)-Mutated Melanoma Persister Cells Melanoma persister cells are represented by a subpopulation of cells surviving the treatment with BRAFi/MEKi and regaining their initial sensitivity to the same drugs upon subsequent culture in a drug-free medium  (Shen et al. , 2019). To explore the underlying mechanisms of the metabolic state alteration in persister cells, we re-analyzed a single-cell RNA sequencing (scRNAseq) dataset of residual persister cells isolated from patient-derived melanoma xenografts grown in the presence of BRAFi/ MEKi  (Rambow et al. , 2018). This study defined four persistent cell states, including starvation-like melanoma cell (SMC) state, invasive cell state, neuro crest stem-cell-like (NCSC) cell state, and pigmented cell state.
These results show that FAO signature is associated with a pejorative outcome in patients with a BRAF-mutated melanoma. However, it is not possible to differentiate between a negative prognostic factor and a pejorative predictive factor for response to BRAFi/ MEKi. We also studied tumor biopsies taken before and early during BRAF ± MEKi (baseline and day 14) (Table  S3 ) and evaluated the early expression of FAO master regulator PPARa and its potential correlation with future clinical outcome due to the inacceptable reactivity of ACOX1 antibody for immunohistochemistry. FFPE sections were stained with an antibody recognizing PPARa using an automated image analysis with CellProfiler  (Carpenter et al. , 2006)  (Figure  S7A ). To quantify the total numbers of PPARa + cells and PPARa À cells in each tumor biopsy, we used CellAnalyst software, which is designed to explore image-based data  (Dao et al.
Data and code availability This study did not generate unique code. Original data of in vivo experiment and metabolomics have been deposited to Mendeley Data: https://data. mendeley. com/datasets/cddh4thvw7/1; original data of uncropped western blot images and microscopic images have been deposited to Mendeley Data: https://data. mendeley. com/datasets/cddh4thvw7/2.
Patient samples Metastatic melanoma patients treated with dabrafenib or dabrafenib + trametinib at Gustave Roussy Cancer Campus (Villejuif, France) provided written informed consent for the collection of tissue and blood samples for research, as approved by the Gustave Roussy Review Board (BRF116613). Matched tumor samples from 14 patients and matched blood samples available from 25 patients were obtained at both baseline and after treatment. Patient clinical data are described in Table  S3.
Brightly fluorescent single cell clones were expanded and compared to parental populations using traditional proliferation measurements to ensure they were representative of the initial population. Imaging was performed on 6-well plate using a Nikon TiE automated microscope with 10x (0. 75 NA) objective. Images were acquired every 24 h using NIS element software. Nine adjacent images were captured at 0. 4 s exposure to comprise a single 3x3 montage from each well per experiment. Example montaged images are shown in Figure  S1. Cells were seeded at 3 3 10 5 cells per well initially and were allowed to grow overnight, then were treated with vemurafenib (1 mM) and cobimetinib (1 mM) for the indicated times. Images were imported into ImageJ (https:// imagej. net/Fiji) program and subjected to a macro image analysis  (Tyson et al.
Genome-wide transcriptome analysis Total RNAs were extracted using TRIzol (Sigma) according to manufacturer's procedure and were quantified by using RNA 2100 Bioanalyzer (Agilent Genomics). Exon array experiments were submitted to NGS platform (Institut Curie) and performed in triplicate using Affymetrix ClariomD human array (Affymetrix). Exon array raw data CEL files were processed with Affymetrix expression console software. Data were then normalized based on SST-RMA method using default settings. Principal component analysis on each replicate samples was performed to interrogate the reproducibility of the replicates. Gene expression counts based on exon alignment were used for statistical modeling of the transcriptomic data using R software. The Negative Binomial (NB) model was well fitted to the gene expression data.
Differential expressed genes and their false discovery rate were identified by the limma package. Genes with log2FC more than 1 (adjusted p < 0. 05) were defined as significant genes. The p values were further calculated by sample permutations (parental versus persister). The rotation gene set (ROAST) algorithm as implemented in the limma package was used to perform metabolic gene set analysis based on manually cited metabolic pathway gene sets from  Kalucka et al. (2018).
RNA isolation and Quantitative RT-PCR RNA was collected and purified using TRIzol (Sigma) according to the manufacturer's instruction. RNA was quantified using Nano-Drop 2000 instrument (Thermo Scientific), and was normalized to 20 ng/ml diluted in RNase-free water. Two hundred ng of RNA was then converted to cDNA using the SuperScript IV Reverse Transcriptase (Invitrogen) with random hexamer primers according to the manufacturer's instructions. Following the reverse transcription, mRNA abundance was determined by qPCR using Luminaris SYBR green PCR Master mix (Thermo Scientific) with the indicated primers. Data were analyzed by the threshold cycle (Ct) comparative method and normalized to the HPRT housekeeping gene.
Flow cytometry analysis Cells were kept in culture medium containing fluorescent probes for 24 h. For glucose uptake assay, cells were incubated with 20 mM 2-NBDG; For mitochondrial mass assay, cells were incubated with 200 nM Mitotracker FM Green; For mitochondrial membrane potential assay, cells were incubated with 100 nM TMRE. After 24 h incubation, cells were trypsinized and re-suspended in PBS buffer containing 0. 1% bovine serum albumin (BSA). Fluorescence was measured by flow cytometry LSRII cytometry instrument (BD Bioscience) and data were analyzed with Cytobank.
Immunofluorescence on human biopsies Sample sections (3 mm thickness) were de-paraffinized and antigen retrieval was performed for 64 min in cell conditioning 1 (CC1) Tris-based buffer at room temperature. The anti-PPARa antibody was incubated overnight at 4 C. An anti-rabbit AlexaFluor TM conjugate was incubated with the sections for 2 h at room temperature. The sections were then washed twice with cold PBS, and counterstained with Hoechst 33342 and BODIPY 493/503 for 10 min at room temperature. Slides were digitized as 4 random files for each sample using a VS120 virtual slide microscope (Olympus) at 20x magnification and visualized with OlyVIA software in the tumor area. Cell images were analyzed with Cellprofiler 2. 0  (Carpenter et al. , 2006)  to detect the total number of nuclei, PPARa + cells.
Gas saver mode was set to 15 ml/min after 5 min. The helium gas flowed at 1 ml/min through the column (J&W Scientific HP-5MS, 30 m x 0. 25 mm, i. d. 0. 25 mm, d. f. Agilent Technologies Inc). Column temperature was held at 60 C for 1 min, then raised to 210 C followed by a step to 230 C, and reached 325 C, and be held at this temperature for 5 min. The collision gas was nitrogen. Peak detection and integration were performed using the Agilent MassHunter software (B. 07. 01). Baseline corrected tracing data are uploaded to Escher-Tracer web server (https:// escher-trace. github. io/) (Kumar et al. , 2020). The molar percent enrichment (MPE) are calculated following the Escher-Tracer protocol.
ACKNOWLEDGMENTS We thank all members of the lab for helpful discussions and support. We thank  Dr Ye  ( FuDan University, China ) for the kind gift of ACOX1 expression plasmid. We are thankful for philanthropic financial support from  Sebastien Bazin  (to  Shensi Shen ) and  Fondation Carrefour  (to C. R. ). Life Science editors provided editorial assistance. This work was made possible by continuous financial support from  INSERM ,  CNRS , and  Gustave Roussy , and it was also funded by grants from  Fondation ARC pour la Recherche sur le Cancer  (project  PJA20161204588  to  Shensi Shen ),  Institut National Du Cancer  (grant  2013-1-MEL-01-ICR-1  to C. R. ),  Collectif Ensemble contre le melanome and Association Vaincre le Melanome  (to C. R. ), and  Les Sites de recherche Integres sur le Cancer (SIRIC) label Gustave Roussy  (to C.
After these control experiments, the concentration of the peptide inside the cell was measured again. It was found that at 20 • C, the heat of dilution of monomer was -187. 4 µcal for a final concentration of 273 µM. The critical concentration of GN-NQQNY was found to be ∼110 µM at 20 • C. Assuming that the heat of dilution scales linearly with final concentration, we subtracted -13µJ from each experimental data point at 20 • C. Furthermore, it was found that at 30 • C, the heat of dilution of monomer was -213. 1 µcal for an injected concentration of 305 µM, which yielded a final concentration of 53 µM. The critical concentration of GNNQQNY was found to be ∼154 µM at 30 •. We therefore subtracted -108 µJ from each experimental data point at 30 • C. The measured heats of crystal dissolution are then plotted against the concentration in the ITC cell at the end of the experiment.