dataesr/scientific-paragraphs-categorization huggingface dataset:
| Quantification of thoracic trident pigmentation Flies were immersed in 75% Ethanol and pinned through the abdomen on a silicon substrate. Their thoraces were imaged with a binocular equipped with Leica DC480 digital camera using the Leica IM50 Image Manager software. An annular lamp was used to ensure homogeneous lighting. All pictures were taken with identical settings during the same session. To quantify the intensity of the trident, we delimited the trident using a polygon based on the suture anterior to the scutellum and the base of each dorsocentral macrochaetes using IMAGEJ  (Rasband, 2016)  (see Supporting information Figure  S7 ). We measured the mean intensity of the pixels included in this polygon with the function "measure" of IMAGEJ. The obtained values were subtracted from 255 to get values comprised between 0 (white) and 255 (black).
| In silico Pool-GWAS simulations The effect of the haplotype structure of the three variants in the tMSE on Pool-GWAS experiments was simulated using a custom script in R v. 3. 3. 2 (R Core Team, 2016). For each in silico experiment, five replicates with 1500 individual each were created with haplotypes reflecting the frequencies estimated in the pooled European base populations using paired-end read data (Supporting information Table  S4 ). Each of the five occurring haplotypes was assigned the median of the phenotypic values measured for the corresponding transgenic individuals as a base pigmentation score. Assuming a heritability of h 2 = 0. 2, a normally distributed noise term was added to the base pigmentation score and the individuals were ranked according to their pigmentation. The allele counts of the lightest 100 and the darkest 75 of each replicate were contrasted using the mantelhaen.
We conclude that the regulatory variants segregating with In(3R)payne have a stronger effect on trident than on female abdominal pigmentation and that some variants around ebony are exclusively affecting trident pigmentation. Apart from the variants within In(3R)Payne, we mainly find associations on the X chromosome. The high density of candidate regions on the X chromosome is remarkable. Since we only scored males, it is possible that hemizygosity of the X chromosome provides penetrance to low frequency recessive variants, which would be difficult to detect in females. A few of these regions overlap genes that have been connected to pigmentation phenotypes-though not in association studies-such as Crag, CG15772 and Megalin, illustrating the power of the Pool-GWAS approach. In our study, we combined data from two populations, which were maintained at two different temperatures.
The same CXCR4 targeting HDR template was used for both CXCR4 and scrambled gRNAs (for ssDNA sequence, see Supplementary Data 1). Two days following stimulation, T cells were harvested and resuspended in P3 electroporation buffer (Lonza) at a concentration of 1 million cells per 20 µL of buffer. Five microliter of RNP (100 pmols) were added to 20 µL of cells (1 million T cells) along with 1 µL of HDR template (100 pmols) were mixed and electroporated in a single well of a Lonza nucleofection cuvette on a Lonza Nucleofector 4D X-Unit device using pulse program EH-115. Immediately following electroporation, 80 µL of prewarmed culture media were added directly to the cuvette and the cells were allowed to rest in a 5% CO 2 37 °C incubator for 15 min in the cuvettes before being stimulated and transferred out for further culture (see human T-cell isolation and culture).
1 M NaCl, 10 mM Tris-HCl pH 8. 0, 1 mM EDTA) for 30 min at 55 °C, then for 20 min at 80 °C to inactivate Proteinase K. The genomic region around the CRISPR/Cas9 target site for CXCR4 gene was amplified by PCR with primers positioned outside of the HDR repair template sequence (positioned to avoid amplification of exogenous template) for ten cycles using KAPA HiFi Hotstart ReadyMix (Kapa Biosystems, KR0370) according to the manufacturer's protocol (PCR primers listed in Supplementary Data 1). Primers contained inline sample-specific barcodes. Barcoded samples from each plate were pooled to concentrate and remove mineral oil using Zymo DNA Clean and Concentrator Column (Zymo research, D4004). Excess PCR primers were removed by incubating with Exonuclease I (NEB, M0293S) in 1 × exonuclease reaction buffer (NEB, B0293S) for 1 h at 37 °C, followed by enzyme inactivation for 20 min at 80 °C.
Amplicon pools were re-amplified by PCR for 15 cycles using a universal primer to add the sequencing adaptor and secondary barcodes to allow parallel sequencing of multiple amplicon pools (Primer sequences are listed in Supplementary Data 1). PCR products of the expected size were isolated with Select-A-Size DNA Clean and Concentrator (Zymo research, D4080) as sequencing libraries. Pooled barcoded libraries were sequenced with 300 bp paired-end reads on a MiSeq (Illumina) instrument using the 300 cycles v3 reagent kit (Illumina). Sequencing data analysis and HDR/indel identification. All computational and statistical analysis were performed using Python 2. 7 and Unix-based software tools. Quality of paired-end sequencing reads (R1 and R2 fastq files) was assessed using FastQC (http://www. bioinformatics.
babraham. ac. uk/projects/fastqc). Reads with sample-specific inline barcodes were demultiplexed using our home-brew python script for FASTQ files splitting. Reads were then mapped on both the wild-type sequence and the expected HDR-edited sequence of CXCR4 using bwa version 0. 7. 15  17  with default parameters. Alignments files were sorted and indexed using samtools version 1. 3. 1  18, 19. Variants were called using freebayes version 1. 0. 2  20 , a Bayesian haplotype-based polymorphism discovery tool. Genotypes were determined for each colony based on the number of reads matching either the wild-type sequence, the HDR sequence or containing variants to these two sequences with a quality above 30. Python scripts implementing the demultiplexing, alignment, and genotyping are available from the authors upon request.
Acknowledgements We thank  Berkeley Lights  employees  Yelena Bronevetsky  and  Natalie Marks  for help with the establishment of T-cell culturing conditions on chip. We thank  Vincent Pai  and  Kai Szeto  for help with optimization of the split export process. We thank members of  Marson lab. Al. M. holds a  Career Award for Medical Scientists  from the  Burroughs Wellcome Fund  and is a Chan Zuckerberg Biohub Investigator. We relied on the Flow Cytometry Core at  UCSF , supported by the  Diabetes Research Center  grant  NIH P30 DK063720. T. L. R. and J. H. were supported by the  UCSF   Medical Scientist Training Program  ( T32GM007618 ). T. L. R. was supported by the  UCSF   Endocrinology Training Grant  ( T32 DK007418 ).
Data availability. The custom Python script used for this study is available for download at https://github. com/abhik/crispr-analysis. Sequencing data have been deposited at The National Center for Biotechnology Information (NCBI) under BioProject number PRJNA444104 (SRA Study: SRP136206) and are available at https://www. ncbi. nlm. nih. gov/Traces/study/?acc = SRP136206.
One source of uncertainty in TBM simulations is due to fixed (often uncertain) parameter values. Significant progress has been made in using carbon cycle-related observations to constrain TBM parametric uncertainty via Bayesian data assimilation (DA) methods  [5] [6] [7] [8] [9] [10]. These datasets have included eddy covariance measurements of net ecosystem exchange (NEE) 11 , satellite-derived measures of vegetation dynamics  [12] [13] [14]  , and ground-based atmospheric CO 2 concentration data. However, while there is considerable improvement in the simulation of leaf phenology and/ or NEE in these studies, there is often a remaining model-data discrepancy in the gross C fluxes. Satellite-derived measures of sun-induced chlorophyll fluorescence (SIF) offer a promising new direction to constrain simulated GPP at multiple scales 15.
5  for PFT acronym descriptions). For these PFTs, the mean reduction per PFT corresponds to ~40% of the prior temperate forest mean annual budget, ~60-110% of the prior boreal mean annual budget, and ~16% of the prior tropical forest mean. Overall, the optimisation resulted in a ~32% increase in the ratio of productivity per unit area between the tropical and extra-tropical (temperate + boreal) KG biomes (Supplementary Table  S2 ). When considering total PgC, the increase in the productivity ratio was 24% (Table  1 ). This value increased to ~50% in the ratio per unit area between tropical and boreal-only KG biomes (~40% in total PgC). The posterior ratios (1. 28 in total PgC for tropical: temperate + boreal biomes; 2. 75 for tropical:boreal) better match the same ratios derived from JUNG dataset (1.
Another possible explanation is an overestimate of the prior error. The high prior uncertainty (and therefore strong reduction in uncertainty) in tropical regions is likely related to conservative priors (high uncertainty) for many of the TrBE photosynthesis parameters (PFT 2-1 st column in Fig. 4 ). The considerable amount of noise in the GPP uncertainty reduction (Fig. 1f ) is likely due to spatial heterogeneity in sub grid-cell PFT fraction. At global-scale, the continental scale spatial patterns of GPP simulated by the ORCHIDEE model match both the SIF data and the independent JUNG dataset (Figs  1  and 2 ). However, we can determine if the optimisation had an impact on the finer-scale spatial distribution of simulated GPP by examining the spatial gradients between the prior and posterior in different regions (Figs  1  and 2 ).
The posterior GPP corresponds more closely to JUNG in three regions: the northern extratropics (between ~30-75°N); in the tropics between ~10°N and 10°S; and in the southern hemisphere between 30 and 50°S (Fig. 2 ) -the same regions which show a strong reduction in The maps were created from the ORCHIDEE model simulations performed in this study, GOME-2 SIF data, and the JUNG product, using the Python programming language v2. 7. 13 (Python So ware Foundation -available at http://www. python. org) Matplotlib (v2. 0. 2) plotting library54 with the Basemap Toolkit (http://matplotlib. org/ basemap/). GPP. In particular, both the north-south and east-west spatial gradient in the posterior GPP simulations across northern Eurasia appear to better approximate the SIF and JUNG products (Fig. 1 ). Due to the reduction in GPP in the Amazon, the posterior spatial gradient between the rainforest and Cerrado ecozones in South America is more consistent with SIF data (Fig.
3a ). A smaller decrease in GPP magnitude is observed across the year for tropical and arid biomes (Fig. 3b, c ). The seasonality in tropical regions does not match that of the data-driven JUNG estimate; however, this could not have been corrected by the optimisation due to the lack of an evergreen phenology module or time-varying physiological parameters in the current version of ORCHIDEE. Finally, the optimisation did not result in any discernible change in the long-term (1990-2012) global trend of increasing C uptake, which is ~0. 64 PgCyr -1. The optimisation also did not change the global-scale or biome level long-term inter-annual variability (IAV) magnitude (3% reduction in global IAV standard deviation) or phase (global-scale correlation with JUNG decreased from 0. 44 to 0. 4).
Our results are contrary to Verma M. et al. 26  and Koffi E. et al. 32  who both found limited sensitivity of V cmax to SIF using the SCOPE (Soil Canopy Observation,. Red triangles mark the location of the optimisation sites. The maps were created from the ORCHIDEE model simulations performed in this study using the Python programming language v2. 7. 13 (Python Software Foundation -available at http://www. python. org) Matplotlib (v2. 0. 2) plotting library  54  with the Basemap Toolkit (http://matplotlib. org/basemap/). See Section on Data Availability for the ORCHIDEE model licence information and ORCHIDEE code availability. Photochemistry and Energy fluxes) model  33, 34. The slope and intercept of the SIF-GPP linear relationship (SIF a and SIF b parameters) were also highly constrained (>70% reduction in uncertainty) across all PFTs.
These studies used the same DA system, the same version of ORCHIDEE, and a similar number of sites. The only differences from previous assimilations were the number of parameters included in the optimisation (given the different processes being optimised) and the site locations (given the different datasets). The parameter prior values, uncertainties, and parameter bounds were the same. A comparison of the global GPP resulting from these assimilations shows that the SIF optimisation appears to result in larger decrease in global mean annual GPP than either of the aforementioned datasets (compare Supplementary Fig. S2d  with Fig. S2e  and f ) due to a greater reduction in overall GPP magnitude worldwide and a decrease in peak growing season GPP in the northern extratropics (Supplementary Fig.
Datasets. Solar-induced fluorescence (SIF). In this study, we used near-infrared SIF derived from data acquired by the Global Ozone Monitoring Experiment-2 (GOME-2) instrument on board EUMETSAT's polar orbiting Meteorological Operational Satellite-A (MetOp-A). The retrieval, described by Köhler P. et al. 44  , essentially disentangles the SIF emission from various spectral features related to atmospheric absorption, scattering, and surface reflectance. In particular, we use the monthly aggregated SIF data between 2007-2011 with a spatial resolution of 0. 5° × 0. 5° in our analysis.
We assume that SIF a and SIF b absorb biases in the SIF retrieval algorithm, mismatches in magnitude between the model and data due to temporal and spatial averaging, and ORCHIDEE model structural issues (for example lack a fluorescence module and clear sky vs diffuse radiation effects). Data assimilation system description. The ORCHIDEE Data Assimilation (DA) System (http://orchidas. lsce. ipsl. fr) is based on a variational DA system that has been described in detail in previous studies  8, 13, 14. It follows a Bayesian framework, in which the optimal parameter vector x can be found by minimizing the following cost-function J(x), assuming that the probability distribution functions (PDFs) of the model parameter and observation uncertainties are Gaussian 47 : where x b are the a priori parameter values, P b the a priori uncertainty matrix of the parameters, y is the observation vector, H(x) the model outputs, given parameter vector x, and R the uncertainty matrix of the observations (including observation and model errors).
Acknowledgements This work was supported and co-funded by the  ESA FLEX-Bridge Project  (http://www. flex-photosyn. ca/FB_HOME. htm), the  Copernicus Atmosphere Monitoring Service  ( CAMS41  project) implemented by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Commission , and the  CNES TOSCA Flu-OR  Project. LG and PK were funded by the  Emmy Noether Programme of the German Research Foundation  ( GU 1276/1-1 ).
Data availability. The GOME-2 level 1B product, containing necessary radiance spectra and daily solar irradiance measurements, was obtained from EUMETSAT's Data Centre (https://www. eumetsat. int/website/home/ Data/DataDelivery/EUMETSATDataCentre/index. html). The SIF retrieval method using the GOME-2 level 1B product is described in  Köhler et al. (2015). The 6-hourly CRU-NCEP v5 meteorological fields at 0. 5 × 0. 5° resolution are available from https://esgf. extra. cea. fr/thredds/catalog/store/p529viov/cruncep/V5_1901_2013/ catalog. html. The GPP MTE data (referred to as the 'JUNG' product in this study) were downloaded from the GEOCARBON data portal (FileID 66) in compliance with the Data Usage Agreement (https://www. bgc-jena. mpg. de/geodb/projects/Home. php).
'' That is the region participants ''struggle'' to name, and where the nonbasic terms peach and tan occur  (Boynton & Olson, 1987, p. 104). Boynton and Olson's experiment was in turn extended by  Sturges and Whitfield (1995)  who used the Munsell system. In this study, a subset of 446 samples from the Munsell system was used. Like Boynton and Olson, Sturges and Whitfield observed an overwhelming use of BCTs; only ''a relatively small portion of the names given were nonbasic'  ' (p. 366). Despite the interesting insight into naming behavior across several levels of chroma that these studies provide, there are some limitations that leave the question of desaturated categorization without a satisfying answer. Although in  Sivik and Taft's (1994)  study the colors used unambiguously ranged across all three dimensions of the NCS, their stimuli cannot be directly compared with data collected with Munsell stimuli.
Method Participants. There were 1,870 participants in this study. All participants were from Australia, Canada, Great Britain, Ireland, New Zealand, or the United States. They were recruited via Amazon's Mechanical Turk service, where they were directed to the Qualtrics platform via which the study was administered. Participants were financially compensated for their cooperation. Repeat participation was prevented through the Qualtrics software. We first removed data from 381 participants who had submitted incomplete response sets. Next, we removed data from the fastest and slowest 2. 5% participants, as well as from participants who indicated that they were nonnative speakers of English, color blind, or who answered in the negative to a question of whether they had taken the task seriously (by adding this question, we followed a recommendation of  Aust, Diedenhofen, Ullrich, & Musch, 2013).
This left us with 1,338 participants. From those participants, we further removed the ones who failed a color-sorting task that was presented at the end of the survey and served as a quality check (see  Douven, Wenmackers, Jraissati, & Decock, 2017). This left us with 1,177 participants for the final analysis. These participants spent on average 693 seconds on the survey (AE318 seconds); 735 of them were female; 972 indicated that they had a college degree, 188 indicated that they had high school as their highest education level, and 17 indicated a lower education level. Although the sorting task served as a data quality check, we are aware of the importance of individual differences in color perception  (Lindsey & Brown, 2014; Witzel & Gegenfutner, 2013)  and the fact that such differences will almost certainly have affected our results.
Results and Discussion All data and files for the analysis are available at https://osf. io/tujhb/?view_only= 423c03ec0a9f4b51b485a147303b3109. We here report the main results. Pointers to code in the Mathematica notebook and the R file that were used for the analysis are also given in the following and can be used to obtain further results by readers who have access to the requisite software packages. While R is open source, Wolfram's Mathematica is proprietary software. For readers who do not have access to Mathematica, the online materials include a computable document format (CDF) document. This document can be viewed in Wolfram's CDF Player, which is freely available at https://www. wolfram. com/cdf-player/, but which offers limited functionality. Descriptive statistics. There were in total 74,874 responses given to the 1,332 stimuli.
We then looked at the correlations between those mean chroma values and the levels of consensus. The results, reported in Table  2 , showed that, for the most part, there are moderately strong to very strong correlations between consensus and chroma. The correlations are also in the directions one would expect them to be; in particular, there are negative correlations for the achromatic colors. To investigate further the connection between chroma and consensus, we used the lme4 package  (Bates, Ma¨chler, Bolker, & Walker, 2014)  for R to fit a linear mixed-effects model with level of consensus as outcome variable and the mean chroma values as predictor variable, looking specifically at the chromatic colors. For control purposes, we included as a covariate the means of the Munsell values for the same levels of consensus for which we had mean chroma values, and we included chromatic BCTs as random effects, where we used the full random-effects structure, as recommend in  Barr, Levy, Scheepers, and Tily (2013).
AIC is the Akaike Information Criterion and BIC the Bayesian Information Criterion, two metrics that weigh model fit against model complexity. Their values are to be used comparatively, in that models with smaller values are taken to be predictively more accurate than ones with larger values. The ÁAIC and ÁBIC columns give the differences with the best model, according to AIC and BIC, respectively. The R 2 values were calculated using the r 2 function in the sjstats package (Lu ¨decke, 2017) for R, which follows the recommendations of  Nakagawa and Schielzeth (2013). The 2 column gives the results of the likelihood ratio tests, comparing the full model with the various other models; all 2 values were significant at a ¼. 0001. The Mathematica notebook contains additional machinery to visualize the relation between consensus level and chroma.
Chroma, consensus, and non-BCTs. We conducted a mixed-effects models analysis similar to the one reported in Chroma, Consensus, and Basic Color Terms section, and in light of the aforementioned observations, we were not surprised to find that neither Munsell chroma nor Munsell value was a significant predictor of level of consensus for non-BCTs. (The R code is in the Supplementary Materials, for readers interested in the details. We also fitted ordinary least squares models for the most frequently used non-BCTs individually. Plots of four of those models are shown in Appendix B, for comparison with the plots of the ordinary least squares models for the individual BCTs. Regression tables of the models for the ten most frequent non-BCTs can be found in the Mathematica notebook. ) Thus, in view of our data, the answer to  Kay et al.
Branched-chained amino acids (BCAA) are essential dietary components for humans and can act as potential biomarkers for diabetes development. To efficiently estimate dietary intake, we developed a BCAA database for 1331 food items found in the French Centre d'Information sur la Qualité des Aliments (CIQUAL) food table by compiling BCAA content from international tables, published measurements, or by food similarity as well as by calculating 267 items from Greek, Turkish, Romanian, and Moroccan mixed dishes. The database embedded in MEDIPAD software capable of registering 24 h of dietary recalls (24HDR) with clinical and genetic data was evaluated based on archived 24HDR of the Saint Pierre Institute (France) from 2957 subjects, which indicated a BCAA content up to 4. 2 g/100 g of food and differences among normal weight and obese subjects across BCAA quartiles.
We also evaluated the database of 119 interviews of Romanians, Turkish and Albanians in Greece (27-65 years) during the MEDIGENE program, which indicated mean BCAA intake of 13. 84 and 12. 91 g/day in males and females, respectively, comparable to other studies. The MEDIPAD is user-friendly, multilingual, and secure software and with the BCAA database is suitable for conducting nutritional assessment in the Mediterranean area with particular facilities for food administration.