dataesr/scientific-paragraphs-categorization huggingface dataset:
In the fifth analysis (four scenarios), New Zealand could be originating either from Europe, Asia, North America (east) or North America (west). Finally, the sixth analysis (five scenarios) aimed at deciphering the origin of the Australian population by testing as source population New Zealand, North America (west), and admixtures between New Zealand and either Europe, Asia or North America (west). All scenarios of all analyses are detailed in Table  1  and Fig S3. In our ABC analysis, historical and demographic parameter values for simulations were drawn from prior distributions defined from historical data and demographic parameter values available from empirical studies on Pieris rapae  [11] [12] [13] 29  , as described in Table  S5. Si ulated and observed datasets were summarized using the whole set of summary statistics proposed by DIYABC  54  for SNP markers, describing genetic variation per population (e.
We simulated a 10,000 SNPs datasets for each competing scenario using the standard Hudson's algorithm for minor allele frequency (i. e. , only polymorphic SNPs over the entire dataset are considered), so the number of used markers ranged between 13,974 and 17,116 depending on the analysis (Table  1 ). We then grew a classification forest of 1,000 trees based on the simulated datasets. The random forest computation applied to the observed dataset provides a classification vote (i. e. , the number of times a model is selected among the 1,000 decision trees). The scenario with the highest classification vote was selected as the most likely scenario, and we then estimated its posterior probability by way of a second random forest procedure of 1,000 trees 57. To evaluate the global performance of our ABC-RF scenario choice, we computed the prior error rate based on the available out-of-bag simulations and conducted the complete scenario selection analysis with two additional datasets with different sub-populations (dataset 2 and dataset 3) representative of the same populations as dataset 1  58.
We then performed a posterior model checking analysis on a full final scenario including all 8 populations (dataset 1), to determine whether this scenario matches well with the observed genetic data. Briefly, if a model fits the observed data correctly, then data simulated under this model with parameters drawn from their posterior distribution should be close to the observed data. The lack of fit of the model to the data with respect to the posterior predictive distribution can be measured by determining the frequency at which the observed summary statistics are extreme with respect to the simulated summary statistics distribution (hence defining a tail-area probability or p-value, for each summary statistic). We simulated 100,000 data sets under the full final scenario (17,609 SNP and 928 summary statistics), and then obtained a 'posterior sample' of 5,000 values of the posterior distributions of parameters through a rejection step based on Euclidean distances and a linear regression post-treatment  53.
We simulated 1,000 new datasets with parameter values drawn from this "posterior sample", and each observed summary statistic was compared with the distribution of the 1,000 simulated test statistics, and its p-value, corrected for multiple comparisons with the false discovery rate procedure  59  , was computed. Finally, 10,000 simulated datasets of the full final scenario were used to infer posterior distribution values of all parameters, and some relevant composite parameters under a regression by random forest methodology  60  , with classification forests of 1,000 trees. The simulation steps, the computation of summary statistics, as well as the model checking analysis were performed using DIYABC v2. 1. 0. All scenario comparisons and parameter estimations were carried out in R using the package abcrf (v1.
0. 4 using the De Novo Assemble tool with default settings. The find heterozygotes tool (peak similarity set to 50%) was used to find and discard any sequences found to be heterozygous. Reads were trimmed to 502 bp and aligned (error probability limit of 0. 001) with sequences from GenBank and Barcode of Life databases using MUSCLE Alignment in Geneious with default settings. To evaluate whether we were adequately sampling mtDNA haplotype diversity, we plotted rarefaction curves (estimates of haplotype richness by sampling effort) for each population using iNEXT  62  and predicted the total haplotypes for each population assuming 1,000 sampled individuals. A median-joining haplotype network was created using POPART  63  for all populations and for each population separately. In an effort to further pinpoint whether the introductions in North America came from western (i.
Acknowledgements: We would like to thank all the participants in the Pieris Project, without their help this research would not have been possible. We would also like to thank Arthur Shapiro for his extraordinary insights into this system and to the many researchers who contributed specimens (see full list here). A special thank you to Sang-guy Park for donating specimens from his private             Notes: Index i stands for the number of the invasive population, i. e. 3, 4, 5 or 6 for North America (east), North America (west), New Zealand or Australia respectively. Index j stands for the number of the ancient putative native population, i. e. 1, 2, 7 or 8 for Asia, Europe, Africa or Russia (east) respectively. ND and NA = stable effective population size (number of diploid individuals) of the ancestral native population respectively before and after a demographic expansion event (NG < NA); Nj, Ni = stable effective population size (number of diploid individuals) of the putative native and invasive populations; NFi = effective number of founders during a bottleneck lasting BDi generation(s) for population i; ta = time of the demographic expansion in the ancestral native population; tj = merging time of the putative native populations into the ancestral one; ti = introduction time of invasive populations i with bounds xi fixed from dates of first observation of established population; t4old corresponds to the particular case of an old introduction hypothesis of the North American (west) population in ABC analysis 4; Nia, Nib, NFia, NFib, BDia and BDib, tia, tib and tmix are the parameters associated to an admixture event leading to the formation of invasive population i; ari = admixture rate.
Prior and posterior distributions of all parameters and several composite parameters of the full final complete scenario (Fig 2d) performed with dataset 1. BNsevi = bottleneck severity of population i computed as [BDi × Nparental population of population i) / NFi], with parental populations being populations 2, 3, 4, 5, 2 and 1 for populations 3, 4, 5, 6, 7 and 8 respectively.
Asia and Europe are respectively the sources of Russia and Africa Results are provided for all three datasets. For each ABC analysis a forest of 1,000 trees was grown. The lines in bold characters corresponds to the selected (most likely) scenarios. 602 676 521 0. 7020 0. 7549 0. 6352 S2: Asia and Africa are respectively the sources of Russia and Europe 162 180 146 - - - S3: Africa and Russia are respectively the sources of Europe and Asia 56 30 76 - - - S4: Europe and Russia are respectively the sources of Africa and Asia 180 114 257 - - - Analysis 3 -North America east (NAE) -51 summary statistics; 16,753 SNPs 32. 82% 31. 83% 31. 82% S1: Asia is the source of NAE, 1 introduction 0 9 2 - - - S2: Europe is the source of NAE, 1 introduction 576 553 559 0. 5010 0. 6136 0. 5064 S3: Asia is the source of NAE, 2 introductions 6 4 2 - - - S4: Europe is the source of NAE, 2 introductions 418 434 437 - - - Analysis 4 -North America west (NAW) -116 summary statistics; 17,049 SNPs 11.
Sounds and light stimulations were randomly presented at 2. 5 s and each repeated 10 times. Single unit spikes were detected and sorted from multi-unit spikes using the Phy Suite (https://github. com/kwikteam/phy). Light stimuli consisted of small and large disk (360 µm and 720 µm diameter) presented at different positions of a two-dimensional grid centered on the probe location (Supplementary Fig. 5 ). For small disks, the grid included 5 × 8 locations with a regular Δx = Δy = 360 µm spacing. For large disks, the grid included 3 × 4 locations with a regular spacing of 480 µm. Population firing rate elicited by a single disk was estimated as the integral P Locations r x;y ΔxΔy of the responses r x,y to the disk centered at locations that were within in the extent of the smaller of the two grids (large disks: all locations, extent 1440 × 1920 µm; small disk: 4 × 5 locations, spanning 1440 × 1800 µm).
Acknowledgements We thank  K. Kuchibhotla ,  E. Harrell , and  M. Stüttgen  for comments on the manuscript,  P. Pindi ,  S. Sikirić , and  L. François  for help with behavioral and imaging experiments. We thank the  GENIE  Project,  Janelia Farm Research Campus , and  Howard Hughes Medical Institute  for GCAMP6s constructs. This work was supported by the  Agence Nationale pour la Recherche (ANR "SENSEMAKER") , the  Fyssen foundation , the  DIM "Region Ile de France" , the  Marie Curie Program  ( CIG 334581 ), the  International Human Frontier Science Program Organization  ( CDA-0064-2015 ), by the  Fondation pour l'Audition  ( Laboratory grant ), the  École Doctorale Frontières du Vivant (FdV)-Programme Bettencourt  (support to A. K. ), the  European Research Council  ( ERC CoG DEEPEN) , the  DIM Cerveau et Pensée and Ecole des Neurosciences de Paris Ile-de-France  (ENP, support to S.
Data availability All datasets, analysis software, and codes for running the simulations of our model are freely available on the Dryad Digital Repository (http://datadryad. org/) https://doi. org/ 10. 5061/dryad. 47h8t87 or on https://www. bathellier-lab. org/downloads. The source data underlying Figs. 1c, 3d, and 6e, f are provided as a Source Data file.
Product inhibition of EGs and CBHs by the main product, cellobiose, is alleviated by ␤-glucosidases (BGs), which cleave di-and oligosaccharides into glucose. In the Carbohydrate-active enzymes database CAZy (www. cazy. org [8] ), EGs are typically found in glycoside hydrolase (GH) families GH5 and GH9; CBHs in GH6, GH7, and GH48; and BGs in GH1 and GH3. The discovery of lytic polysaccharide monooxygenases (LPMOs) that cleave glycosidic bonds via oxidation and substantially boost the action of the aforementioned hydrolytic enzymes has greatly improved our understanding of microbial turnover of lignocellulose  (9) (10) (11) (12) (13). Cytophaga hutchinsonii and Sporocytophaga myxococcoides are two Gram-negative, aerobic, mesophilic, cellulose-degrading bacterial species belonging to the Bacteroidetes phylum  (14) (15) (16) (17).
RESULTS Carbohydrate utilization and carbohydrate-active enzyme repertoires. The carbohydrate-active enzyme (CAZyme) repertoire of C. hutchinsonii is available in the CAZy database (see Table  S1  in the supplemental material) and consists of 189 unique proteins. The genome of S. myxococcoides was analyzed using the CAZy pipeline  (8) , and the presence of 289 putative CAZymes, including putative cellulases (GH5, GH8, and GH9), was revealed (Table  S2 ). As expected, no putative CBHs or LPMOs were identified. S. myxococcoides encodes eight putative BGs (GH1 and GH3), which were predicted to be intracellular. The bacterium encodes nine putative GH5 enzymes (C. hutchinsonii encodes five); all but one of these were predicted to be extracellular enzymes. Also, genes encoding putative GH8 and GH9 enzymes were more numerous in the S.
No comprehensive growth data on polysaccharides other than cellulose are available in the literature; thus, both species were assayed for growth on multiple mono-and polysaccharides. Both C. hutchinsonii and S. myxococcoides were able to grow on filter paper  (20, 37, 43) , pectin, and mannose. To our knowledge, this represents the first reported study of C. hutchinsonii or S. myxococcoides growing on any polysaccharide other than cellulose. The C. hutchinsonii strain used (G2, glucose adapted) was, as expected, able to grow on glucose, but S. myxococcoides exhibited a lag phase of several days before growing on this monosaccharide (Fig. 1 )  (30, 31). Interestingly, despite the ability to metabolize mannose, neither organism was able to utilize mannan polysaccharides (galactomannan and glucomannan), and both were further unable to utilize xylose, xylan, or xyloglucan as carbon sources, despite the ␤-1,4-glucan backbone of the latter.
Peptides were prepared as described by Arntzen et al. (60)  and subjected to in-gel trypsinization as described by Shevchenko et al. (61). The dried peptides were analyzed by the use of NanoLC-Orbitrap tandem mass spectrometry (MS/MS) as described previously  (58). MS raw files were analyzed using MaxQuant v1. 5. 8. 3  (62)  for label-free quantification (LFQ) of proteins and the MaxQuant label-free algorithm  (47)  with both unique and razor peptides for protein quantification. Protein identification was done using the Andromeda database search engine  (63)  for searches against the UniProt proteome set for C. hutchinsonii (UP000001822; 3,771 sequences) and S. myxococcoides (UP000030185; 5,042 sequences). Identifications were filtered to achieve a protein false-discovery rate (FDR) of 1%.
The assays were incubated with shaking in round-bottomed tubes at 30°C and were stopped by the addition of 50 l 5 M NaOH. Products were determined using a Dionex ICS-5000 system equipped with a CarboPac PA-200 analytical column (Dionex, Sunnyvale, CA, USA) (3 by 250 mm) and a flow rate of 0. 5 ml/min. Eluent A consisted of water, eluent B of 300 mM NaOH, and eluent C of 1 M sodium acetate. The method used was as follows: 0 to 5 min, 15% eluent B; 5 to 15 min, a linear gradient of 15% to 33% eluent B; 15 to 25 min, 33% eluent B and a linear gradient of 0% to 10% C; 25 to 30 min, 33% eluent B and 66% eluent C. Products were detected by pulsed amperometric detection (PAD) using Chromeleon software v. 7 (Thermo Scientific, Bremen, Germany). ␤-Glucosidase activity was assayed using pNP-␤-Glc in 96-well plates and 200-l reaction mixtures containing 25 mM sodium citrate (pH 7.
0), 5 mM CaCl 2 , 10 mM pNP-␤-Glc, and 1 to 10 g protein, and the plates were incubated at 30°C for 1 h. The reactions were stopped by addition of 100 l 0. 4 M glycine (pH 10. 8), and the products were quantified at 405 nm using a FluoStar Omega plate reader (BMG Labtech, Ortenberg, Germany) and a p-nitrophenol standard curve. M. T. planned and performed the experiments and data analysis and wrote the manuscript. J. L. designed and coordinated the overall study, planned the experiments, and wrote the manuscript. P. B. P. and M. Ø. A. advised with the bioinformatic analysis and mass spectroscopy. B. H. contributed to the annotation of the carbohydrate-active proteins and analyzed the S. myxococcoides genome. All of us read and approved the final manuscript.
ACKNOWLEDGMENTS We thank  Mark McBride , the  University of Wisconsin-Milwaukee , for constructive discussions. M. T. and J. L. were supported by a grant from the  Swedish Research Council  and the  Swedish Energy Agency  through the  Energy-Oriented Basic Research program  ( Dnr 2015-009561 ) and by the  Knut and Alice Wallenberg Foundation  through the  Wallenberg Wood Science Center. P. B. P. and M. Ø. A. were supported by  The Research Council of Norway  ( FRIPRO program 250479 ) and the  European Research Council  ( 336355 , MicroDE).
The model system chosen for this study is FeO/CoFe 2 O 4 core/shell NCs, as they have been shown to easily form crystalline SPs  17  and because the shape of these NCs can be tuned from spherical to cubic. 28 By comparison of our data with simulations on the crystallization mechanism of hard spheres,  17  it is clear that in the confined assembly of FeO/CoFe 2 O 4 core/shell NCs of more than 10 nm in diameter, attractions between the NCs must be taken into account. In Situ X-ray Scattering. We performed in situ WAXS and SAXS measurements at the ID02 beamline of ESRF in Grenoble. A scheme of the experimental setup is shown in Figure  1. The "oil droplets" of the oil-in-water emulsion form, in fact, a suspension themselves: they consist of FeO/CoFe 2 O 4 core/shell NCs of 10. 5 ± 1. 2 nm overall diameter (Figures 1a and S20-21) dispersed in cyclohexane, the initial NC volume fraction being 0.
■ ACKNOWLEDGMENTS The authors thank  F. T. Rabouw  and  W. van der Stam  for very fruitful discussions,  Jan Ilavsky  for support in the use of IRENA,  Jacques Gorini  for assistance during the scattering measurements, and  G. Fiorucci ,  M. Chiappini , and  G. Coli  for support in calculating crystal reflections. This work was supported by the  European Commission  via the  Marie-Sklodowska Curie action Phonsi  ( H2020-MSCA-ITN-642656 ). D. V. wishes to thank the  Dutch FOM  (program  DDC13 ),  NWO-CW (Toppunt 718. 015. 002 ), and the  European Research Council  under HORIZON 2020 (Grant  692691  FIRSTSTEP) for financial support. J. J. G. gratefully acknowledges financial support from the  joined UU and ESRF graduate program  (NWO grant number  022. 004. 016 ).
Similar results are obtained using an alternative areal threshold of 5,000 km 2 , and using images at 2100 and 0000 UTC (mature phase), though the frequency of MCSs decreases as the night progresses. Coverage of MCSs (%) is computed at the 1°scale, based on the fractional area containing pixels within contiguous cold cloud structures, while MCS frequency (per day) is computed from the average number of discrete MCSs centered within a specified larger region over a month. Months are excluded where five or fewer days of data are available; sampling at 1800 UTC, this amounts to only 4 out of 420 months. Previous work (T17) has shown that this measure of MCS frequency is robust to changes in measurement technology, notably the transition from Meteosat First to Second Generation, which occurs in July 2006 in the GridSat data set.
Moreover, the increase in intense storm frequency coincides with a shift to higher February temperatures in the Eastern Sahel (Figure  2b , inset; difference of 2. 2 °C between the two periods). The relationship between intense MCS frequency and monthly rainfall can be assessed using TRMM3B43 data from 1998 onward. Averaged over the Northern Congo region, February rainfall is well correlated (r = 0. 66, p < 0. 005) with intense MCS frequency sampled at 1800 UTC, becoming more so (r = 0. 74, p < 0. 001) when extending the sampling window from 1500 to 0300 UTC. This implies that the shift to more intense February MCSs from 1999 onward is likely associated with an earlier onset of the rainy season in the N. Congo. The length of wet and dry seasons is an important constraint on plant productivity for tropical forest, and the improvement in growing conditions in February may have partially offset the deleterious effects of the more recent drying trend during AMJ  (Zhou et al.
Acknowledgments The research leading to these results received funding from the  UK 's Natural Environment Research Council (NERC)/ Department for International Development (DFID)   Future Climate For Africa programme , under the  AMMA-2050  project (grant numbers  NE/M020428/1 ,  NE/M019950/1 , and  NE/M020126/1 ), and the  NERC -funded  VERA  project ( NE/M004295/1 ). Additional support is acknowledged by  Doug Parker  for a  Royal Society Wolfson Research Merit Award (Doug Parker ) and by  Andreas Fink  within the subproject "Prediction of wet and dry periods of the West African monsoon" of the  Transregional Collaborative Research Center SFB/TRR 165 "Waves to Weather " funded by the  German Research Foundation (DFG). We thank the providers of key data sets used here, notably Eumetsat,  ECMWF ,  CRU , and  NASA.
The data used in this study can be freely downloaded from the following locations: www. ncdc. noaa. gov/gridsat(GridSat), crudata. uea. ac. uk/cru/data/ hrg ( CRU temperature ), ghrc. nsstc. nasa. gov/home ( MSU temperature ), www. ecmwf. int/en/research/climate-reanalysis/era-interim(atmospheric reanalysis data), and disc. gsfc. nasa. gov/datasets/TRMM_3B43_V7/summary (TRMM 3B43 rainfall). We are very grateful for the helpful comments made by the reviewers.
Over the last 21 years, ISAAA has devoted considerable effort to consolidate all the available data on officially approved biotech crop adoption globally; it is important to note that the database does not include plantings of biotech crops that are not officially approved. The database draws on a large number of sources of approved biotech crops from both the public and private sectors in many countries throughout the world. The range of crops is those defined as food, feed and fiber crops in the FAO database, which totaled ~10 billion metric tons of production in 2010 (http://www. geohive. com. Charts/ag_crops. aspx). Data sources vary by country and include, where available, government statistics, independent surveys, and estimates from commodity groups, seed associations and other groups, plus a range of proprietary databases.
In the interest of uniformity, continuity, and comparability, wherever possible, ISAAA utilizes the same published data source annually; for example, for Brazil, the August biotech reports of Celeres are used; similarly, for the US, the USDA/NASS crop acreage reports published on 30 June annually are used. Published ISAAA estimates are, wherever possible, based on more than one source of information and thus are usually not attributable to one specific source. Multiple sources of information for the same data point greatly facilitate assessment, verification, and validation of specific estimates. The "proprietary" ISAAA database on biotech crops is unique from two points of view; first, it provides a global perspective; second, it has used the same basic methodology, improved continuously for the last 20 years and hence provides continuity from the genesis of the commercialization of biotech crops in 1996, to the present.
The database has gained acceptance internationally as a reliable benchmark of the global status of biotech food, feed and fiber crops and is widely cited in the scientific literature and the international press. Whereas individual data points make-up the data base, the most valuable information is the trends of adoption over time, for example the increasing dominance of developing countries which is clearly evident. Note that the words rapeseed, canola, and Argentine canola are used synonymously, as well as transgenic, genetically modified crops, GM crops, and biotech crops, reflecting the usage of these words in different regions of the world, with biotech crops being used exclusively in this text because of its growing usage worldwide. Similarly, the words corn, used in North America, and maize, used more commonly elsewhere in the world, are synonymous, with maize being used consistently in this Brief, except for common names like corn rootworm where global usage dictates the use of the word corn.
All biotech crop hectare estimates in this Brief, and all ISAAA publications, are only counted once, irrespective of how many traits are incorporated in the crops. Country figures were sourced from The Economist, supplemented by data from World Bank, FAO and UNCTAD, when necessary. The first 20 years of commercialization of biotech crops  (1996 to 2015)  has confirmed that biotech crops have delivered substantial agronomic, environmental, economic, health and social benefits to farmers, and increasingly to the consumers. The rapid adoption of biotech crops reflects the substantial multiple benefits realized by both large and small farmers in industrial and developing countries which have commercially grown biotech crops. In 20 years, an accumulated 2 billion hectares of biotech crops have been grown commercially comprised of 1.