dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016; Palm et al. , 2016). In contrast, SOA formed from O 3 and NO 3 oxidation in a biogenic environment can be accounted for from ambient VOCs alone, indicating that S/IVOC precursors tend not to have C=C bonds  (Palm et al. , 2017). The measured SOA formation (at the eq. ages of maximum SOA production, as discussed above) from ambient air in the OFR during GoAmazon2014/5 is shown in Fig. 8 , for both wet (IOP1) and dry (IOP2) seasons and both OH and O 3 oxidation and with linear regressions shown for reference. The measured SOA formation is corrected for LVOC fate. The predicted SOA formation was estimated by applying typical chamber SOA yield values to measured ambient VOC concentrations, as described in Sect. 2. 5. OH oxidation of ambient air produced on average 6. 5-8 times more SOA than could be accounted for from ambient VOCs, which is consistently observed in either the averaged data or the linear regressions.
Data availability. The data sets used in this publication are available at the ARM Climate Research Facility database for the GoAmazon2014/5 campaign (https://www. arm. gov/research/ campaigns/amf2014goamazon). All data shown in the figures in this paper (including Supplement) can be downloaded from http: //cires1. colorado. edu/jimenez/group_pubs. html. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-18-467-2018-supplement.
Artificial tethering of ACF1 to multiple reporters in Drosophila cells Kc167 cells containing the barcoded reporter library were generated as previously described  (24). Plasmids for the expression of GAL4-ACF1 fusion and controls were derived from pAc5-Gal4-V5-HP1a-T2A-mCherry (24) by Gibson assembly. All constructs were validated using DNA sequencing and restriction digestion analysis. The artificial tethering, including sample preparation, sequencing, and data processing/analysis, was performed as described in reference 24, including GAL4-HP1 construct as a positive control. Two biological replicates for each condition were analyzed. Reporters with normalized counts equal to zero in at least one condition were discarded. Linear models were calculated using the lm function in R. For transient expression of Gal4-ACF1 fusion and controls, 3 × 10 6 Kc167 cells were transfected with 1 μg of the corresponding plasmid using the X-tremeGENE HP transfection reagent (Sigma, Cat.
Six replicates per genotype and stage were sequenced on an Illumina HiSeq1500 instrument. Paired-end RNA-seq reads were mapped against the reference genome (FB2016_01 dmel_r6. 09 with selected chromosomes) using STAR (version 2. 5. 0a) with quantMode Gene-Counts for counting reads per gene  (44). One replicate from the Acf C genotype (Stage 3) was excluded because of improper staging (data not depicted). Size factors for normalization were calculated by DESeq2  (45). PCA was carried out on selected genes with variance across samples between the 85 th and 99 th percentiles. Genes with a read count equal to zero in at least half of the samples were filtered out for further analysis. Differential expression (DESeq2) analysis (mutant versus wild type) was carried out by fitting negative binomial GLM independently for the two developmental stages  (45).
Cutoffs for adjusted P-values were defined at the 0. 1 level. Full lists of differentially expressed genes are presented in Tables  S1  and S2. Gene ontology analysis on significantly different genes was performed on the FlyMine online database  (46). Genes were assigned to five-state chromatin domains  (25)  by the nearest method from the GenomicRanges Bioconductor packages. Trends on MA plot were visualized by local polynomial regression fitting (loess). modENCODE histone modification signals (smoothed Mvalues)  (26)  were averaged over genes and low/high levels were distinguished by a cutoff based on the local minimum in the density of the H3K36me3 levels. Genes were classified as marked/ unmarked based on whether they carry high/low histone modification levels in all four marks investigated in the analysis.
Acknowledgements We thank  K F örstemann  for sharing reagents and cell lines for gRNA testing. We thank  S Krebs  and  H Blum  and the  Netherlands Cancer Institute Genomics Core Facility  for outstanding sequencing service. PB Becker  and  A Scacchetti  are funded by the  European Research Council (ERC) , MSCA-ITN-2014-ETN No. 642934. F Schnorrer  is funded by the  ERC  [(FP/2007-2013); grant  310939 ], the  Centre Nationnal de la Recherche Scientifique , the excellence initiative  Aix-Marseille University AMIDEX , the  Agence Nationale de la Recherche , and the  LabEX-INFORM. B van Steensel is supported by  National Institutes of Health  grant  U54 DK107965.
This feature is in agreement with the fact that all the fragments were in a monomeric state in solution. In all cases, except for R23, the P(r) function plots presented additional peaks, notably one peak at around 40 -50Å which represents the distance correlation between adjacent domains (i. e. repeat length). In summary the resulting data are characteristic of rather rigid but non-linear macromolecules. These features could be attributed to specific kinks located at linker regions, and delimiting the coiled-coil structured repeats, that would disrupt a purely rod-shaped filamentous molecule. Three-dimensional analysis evidences a tortuous and complex structure of the dystrophin central domain. We used the SASFit program  (24)  to obtain values for radius, contour length and Kuhn length according to three different models: long cylinder model, worm-like chain model and Kholodenko worm model (Figure  S3 ).
This analysis suggests that the Kholodenko model is the most appropriate to describe the physical characteristics of the dystrophin fragments in solution. The radius of about 10. 8 Å obtained for all fragments is in accordance with the R1 monomer radius  (18). Contour length higher than D MAX values calculated for the Kholodenko models indicate that the dystrophin fragments are shorter than a theoretical linear extended rod. This implies a dystrophin filament shape distinct from the rodlike structure previously assumed for the central domain of dystrophin  (2, 15). Twenty ab initio models, i. e. low-resolution molecular shapes, were simulated by the GASBOR method  (25)  for each protein fragment. All the models were superimposed using DAMSUP and analyzed by DAMAVER programs (ATSAS suite, 26).
Finally, despite high χ2 values, a problem already described and related to lowerror detectors we used (28), experimental SAXS scattering curves and the CRYSOL theoretical curves produced from these structural models are in a range of less than 8% discrepancy for high q values that fall below 2% in low q values (Figure  2A  and Figure  S5 ). Consequently, these structural models suggest with confidence that the observed kinks are relevant features of the dystrophin central domain in solution (Figure  2B ). All models have been successfully deposited in the SASBDB database, an international curated repository for small angle scattering data and models (29) (http://www. sasbdb. org/, see Supporting Information). Similar to the crystal structures of spectrin and α-actinin, the B helices are generally bent in their middle  (30, 31).
Small-Angle X-ray Scattering (SAXS) experiments. SAXS measurements were conducted either at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) on the ID14-eh3 beamline (sample R11-15) or at the French synchrotron SOLEIL (St. Aubin, France) on the SWING beamline (all other samples). All experiments were performed at 15°C. For both set-ups, the scattering vector is defined as q = 4 π/λ sinθ, where 2θ is the scattering angle. Data were collected in a q-range of 0. 005-0. 5 Å -1. ID14-eh3 set-up. The data were collected on a 2D Pilatus 1M detector at a distance of 2. 43 m (λ= 0. 933 Å). For R11-15 a series of 5 concentrations (1. 1, 2. 1, 4. 6, 6. 4 and 10. 8 mg/ml) was prepared prior to injection into the SAXS capillary. Ten frames of 1. 5 seconds each were collected with alternating TNE buffer (Tris 20 mM at pH 7.
5, NaCl 150 mM and EDTA 0. 1 mM) and R11-15 protein samples at increasing concentrations. All frames were then averaged for each data-set after visual inspection. SAXS data were directly evaluated using the Primus software, as implemented in the ATSAS 2. 3 suite  (26). The experimental SAXS data for all samples were linear in a Guinier plot of the low q region. These observations indicated that the sample did not aggregate. The radius of gyration R g for R11-15 was derived from the Guinier approximation I(q) = I(0) exp (-q 2 R g 2 /3) for qR g < 0. 8. Data collected at high q using high protein concentrations and at low q using low protein concentrations were then merged, with the aim to minimize the particle interference occurring at low q and to maximize the signal/noise ratio at high q.
5 s and a dead time between frames of 0. 5 s. One hundred frames accounting for buffer scattering were collected before the void volume. The averaged buffer scattering was then subtracted from the protein signal. SAXS curves displaying a constant R g in a R g versus frame number plot were averaged (Figure  S1A ) and were used for further characterization. Data reduction to absolute units and frame averaging and subtraction were performed in FOXTROT  (7).
SAXS data analysis. All subsequent data processing and analysis were conducted with the Scatter (http://www. bioisis. net/), SASFit  (45)  or PRIMUS software and other programs of the ATSAS suite. The SAXS data have been successfully deposited in the SASBDB database  (29)  and were validated following a expert quality control procedure (http://www. sasbdb. org/, see Supporting Information). Guinier plots assess that all samples were not aggregated (Figure  S1B  and S7A ). The distancedistribution function P(r) and the maximum particle diameter (D MAX ) were calculated using the GNOM program  (46). This approach offers an alternative calculation of R g , which is based on the entire scattering spectrum (Table  S2 ). Within a margin of error, these R g values match those deduced from the first Guinier approximation.
e size-exclusion chromatography multi-angle light scattering (SEC-MALS) for two of our constructs (R1-3 and R11-15), the Porod volume-based method and that proposed by  Rambo and Tainer (21). SEC-MALS experiments were performed with an HPLC system (Agilent) equipped with an UV detector coupled with light scattering (miniDAWN Treos, Wyatt) and refractive index (Viscotek, Malvern) detectors. The refractive index increment value (dn/dc) of the proteins used to determine their molecular weight was 0. 185 mL/g. Data were collected using a customized Bio SEC3 column (Agilent) equilibrated with TN buffer (Tris 20 mM pH 7. 4, NaCl 150 mM) with a flow rate of 0. 3 mL. min -1. Data were processed with the ASTRA software v6. 1. For the Porod volumebased method, we assume that the Porod volume in nm 3 is about 1.
6 times the MW in kDa  (46). The method developed by  Rambo and Tainer (21)  was used to assess the monomeric state of all protein fragments employing the volume of correlation (Vc). Structural models derived from the SAXS data. The overall ab initio models of the protein fragments were obtained from the SAXS experimental data using the GASBOR program  (26). Following the standard procedure and using 50 harmonics, the scattering profiles were fitted to a q max = 0. 45 Å -1 for all samples -except for R16-19 and R16-21Δ45-47, q max = 0. 3 Å -1 (Figure  S4A ). Twenty independent GASBOR computations were performed for each scattering profile (each protein sample) and were superimposed using the SUPCOMB and DAMAVER programs (Figure  S4B  and S4C ). This allowed to identify general structural features over the models of a given fragment, and thus, the result of the GASBOR computation with the smallest χ 2 was finally conserved as the unique ab initio model of each dystrophin fragment.
ACKNOWLEDGEMENTS This research was supported by research grants from the  Association Française Contre les Myopathies, AFM-Téléthon , the  Conseil Régional de Bretagne ,  RTR Biologie-Santé  of the  Université Européenne de Bretagne  and the  Grand Equipement National de Calcul Intensif -GENCI program  ( DYSIM  project). AEM was recipient of a fellowship from  AFM-Téléthon. AN was recipient from a  Centre National de la Recherche Scientifique (CNRS)  fellowship. We thank  Dr Nick Menhart  for providing purified R16-17 protein. We also thank the synchrotron facilities SOLEIL (St. Aubin) and  ESRF (Grenoble)  for allocating regular beam time and their dedicated staffs for technical help with the beamlines SWING and  ID14-EH3 , respectively. We thank  UMS BioSit  for access to the spectroscopy facility.
Solutions. All electrophysiological experiments were performed at 32 °C in a standard or perforated patch whole cell configuration. Recordings were made using an EPC-10 amplifier and Pulse software. The action potential-like voltage-clamp commands were constructed by averaging 12 and 15 action pontential in EndoC-βH1 and EndoC-βH2, respectively and used to elicit currents. Action potentials were also recorded from human primary beta cells to allow comparison with action potential shape in the two cell lines. For the perforated patch recordings and measurements of K + current, the extracellular medium was composed of (mmol/L) 138 NaCl, 3. 6 KCl, 0. 5 MgSO 4 , 0. 5 NaH 2 PO 4 , 5 NaHC0 3 , 1. 5 CaCl 2 and 5 HEPES (pH 7. 4 with NaOH). The extracellular medium was supplemented with 1, 6 or 20 mmol/L glucose and tolbutamide (0.
Proportion of SST/GLC positive cells were determined using FIJI software. Electron microscopy. EndoC-βH1 and -βH2 cells cultured either in flasks or on a 0. 4 μm polycarbonate membrane (Nunc, #137052) were fixed in 2. 5% glutaraldehyde, post-fixed in 2% uranyl acetate, dehydrated in graded methanol, and embedded in London Resin Gold (Agar Scientific, Stansted, UK). Ultrathin sections (70 nm) cut onto nickel grids were immunolabelled with anti-glucagon (Sigma, 1:100) or anti-somatostatin (Santa Cruz Biotechnology, #25262, 1:10) followed by anti-rabbit biotin (Vector Laboratories, Peterborough) and streptavidin gold 15 nm (British Biocell International, Cardiff, UK). Insulin was immunolabelled (DAKO, Ely, UK, 1:500) followed by anti-guinea pig gold 10 nm (British Biocell International). Sections were viewed on a Joel 1010 microscope (accelerating voltage 80 kV) with a digital camera (Gatan, Abingdon).
The analysis is detailed in the Supllementary material. Morphology. The granule density (N A ) can be converted to the volume density (N V ) using the equation (Eq. 3): = + - N N T D h ( 2) ( 3) V A where D is the measured averaged granule diameter, h is the smallest granule diameter and T the section thickness. From the average vesicle area, the average diameter (D) was estimated as 0. 2 μm. The smallest vesicle diameter detected was 0. 025 µm (h) which corresponds to Nv values of ~13 and 20 vesicles per μm 3 in EndoC-βH1 and βH2, respectively. RNA sequencing. For both cell lines, RNA was extracted using TRIzol and sequenced at the Oxford Genomics Centre (Wellcome Centre for Human Genetics, University of Oxford)  25, 26. These data are deposited at the European Nucleotide Archive (https://www.
2 mmol/L) (EndoC-βH1 p = 0. 52, EndoC-βH2 p = 0. 016) (Fig. 1b ). Transcriptomic analysis of these cell lines obtained by RNA-sequencing revealed that key genes involved in the beta-cell glucose sensing are expressed in these cell lines. The K ATP channels subunits KCNJ11 (Kir6. 2) and ABCC8 (SUR1) as well as SLC2A1 (encoding for the glucose transporter GLUT1) and GCK (glucokinase) are expressed at comparable levels in EndoC-βH1 and -βH2 (Supplementary Fig. 1  and Supplementary dataset). Whilst SLC2A2 (GLUT2) is expressed at levels approaching SLC2A1 in EndoC-βhH1 cells, its expression is very low in EndoC-βH2 cells (Supplementary Fig. 1  and Supplementary dataset). 2+  ] i in EndoC-βH1 and -βH2 cells. Insulin is secreted in response to an elevation in cytoplasmic Ca 2+ ([Ca 2+ ] i ). We correlated the insulin secretion data to changes in [Ca 2+ ] i in cells transfected with the genetically encoded Ca 2+ indicator GCaMP5 (Fig.
6) and SNC9A (Na v 1. 7). SCN3A (Nav1. 3) is also highly expressed in EndoC-βH2 cells. Furthermore, SCN5A and SCN7A are expressed at relatively high levels but these encode TTX-resistant channels. Given that I Na in both cell types is TTX-sensitive, it appears that these transcripts do not give rise to functional channels. Of the β-subunits, only SCN3B is expressed at significant levels (Supplementary Fig. 2 , and Supplementary dataset). Ca 2+ channels. Voltage-gated Ca 2+ currents (I Ca ) were recorded in the presence of TTX. The relative contribution of P/Q-, L-and R-type Ca 2+ channels was estimated by sequential addition of ω-agatoxin, isradipine and SNX482 in presence of TTX to inhibit I Na+ (Fig. 4a, c ). Figure  4b ,d compares the voltage dependence of the total I Ca and the different (pharmacologically isolated) I Ca components (EndoC-βH1, n = 5; EndoC-βH2, n = 3 cells).
Moreover, CACNA1H (T-type) Ca 2+ channels are expressed at high levels and are likely to account for the shoulder between -50 and -20 mV in the current-voltage relationships (Fig. 4a, c ). Of the auxiliary subunits, high expression of CACNG4 (γ 4 ), CACNA2D (α 2 δ 2 ) and CACNB1-3 (β 1 -β 3 ) was observed (Supplementary Fig. 3 ; and Supplementary dataset). K + channels. Voltage-activated K + -currents (I K ) elicited during membrane depolarisations from -70 mV to membrane potentials between -20 and +70 mV in EndoC-βH1 and -βH2 cells are shown in Fig. 5a. The amplitude of I K increased linearly with the applied voltage and the density was ~50% larger in EndoC-βH2 (n = 3) than in -βH1 (n = 4 cells) (Fig. 5b ). We estimated the contribution of voltage-(K v 2) and Ca 2+ -activated (BK) channels to I K by sequential addition of their cognate blockers stromatoxin and iberiotoxin respectively (Fig.
5c ). Notably, BK channels played a more prominent role in EndoC-βH2 than in EndoC-βH1 during depolarisations to -10 and 0 mV (Fig. 5d ). Figure  5e  shows the contribution of K V 2 and BK channels to the outward current during voltage-clamp depolarisations based on the action potentials in EndoC-βH1 and -βH2 cells. This analysis reveals that BK current component activated rapidly during the action potential and at a time when no outward current was seen in the EndoC-βH1 cells (Fig. 5e , vertical dashed line). The transcript profile of the K channels expressed in EndoC-βH1 and -βH2 cells reveals high levels of the K V 2 channel genes KCNH6, H2, KCNB2, KCNQ2 and the BK channel genes KCNMA1 and KCNMB3 (Supplementary Fig. 4  and Supplementary dataset). Exocytosis. Depolarisation-evoked exocytosis was monitored as increases in cell capacitance.
6f, inset ). Transcriptomic analysis of genes encoding proteins involved in exocytosis revealed higher expression of SNAP25 in EndoC-βH1, than in EndoC-βH2 cells. The other SNAREs, VAMP2 and Syntaxin1A (STX1A) were expressed to the same extent (Supplementary Fig. 5  and Supplementary dataset). Other components of the exocytotic machinery, including munc18a-c (STXBP1-3), munc13b (UNC13B) and the complexins (CPLX1-2) were also highly expressed. Of the Ca 2+ sensors of exocytosis, the high-affinity Ca 2+ sensor SYT7 was the predominant synaptotagmin in both cell types but relatively high levels of SYT1, 5 and 9 were also detected in EndoC-βH1 cells.
7  and Supplementary dataset). Notably, addition of the SSTR2 antagonist CYN154806 doubled glucose-induced insulin secretion in EndoC-βH2 cells (p = 0. 035) (Fig. 7f ). Transcriptome and foetal origin. The relative low stimulation of insulin secretion and the polyhormonality observed in EndoC-βH2 cells may be remaining features from their foetal origin  30, 31. To address this question,   we compared the publically available transcriptomic data from foetal and adult human pancreatic beta cells with the RNA sequencing datasets we generated from EndoC-βH1 and -βH2 cell lines. We analysed a subset of genes that were consistently lowly or not expressed in human adult beta cells but highly expressed in foetal beta cells  32, 33. Such genes encoded for either components of the early development (NGN3 (Neurogenin 3)) or the inflammatory response (PTGS2 (Prostaglandin-Endoperoxide Synthase 2), S100A9 (S100 Calcium Binding Protein), and DEFA4 (Defensin Alpha 4))  32.
Acknowledgements MvdB was supported by a  Novo Nordisk postdoctoral  fellowship run in partnership with the  University of Oxford. A. L. G. is a  Wellcome Trust Senior Fellow in Basic Biomedical Science. This work was funded by the  Wellcome Trust  ( 095531/Z11/Z  [P. R. ]; 095101, 200837, and  106130  [A. L. G]),  Medical Research Council  ( MR/ L020149/1 ) [A. L. G. , P. R. ], the  European Union   Horizon 2020 Programme  ( T2D Systems )[A. L. G], the National.
(2001). This area includes measurements from the SGP Central Facility in Lamont, Oklahoma (E13) and three of the extended facilities (E9, E11, and E15). The location of these four sites is shown in Figure  1. Surface precipitation data were also used from the ARMBE gridded 2-D product, which averages hourly precipitation data from the Oklahoma and Kansas Mesonet and the ARM standard rain gauge data to a 4 × 4 ∘ grid with a resolution of 0. 25 × 0. 25 ∘ , surrounding the ARM SGP Central Facility.
Radiation data are from the radiometers at the SGP Central Facility, and the Cloud information is from ARSCL. Data include the entire simulation period (April-August 2011) and the full hindcast time (T + 00 to T + 120) and are averaged over the entire diurnal cycle (day and night). Note that the observations are regridded to each of the model grids and hence vary slightly for each of the models. (W m -2 ) ( Wm -2 ) ( Wm -2 ) ( Wm -2 ) ( Wm -2 ) ( Wm -2 ) ( Wm -2 ) ( Wm - the coevolution of cloud and radiation errors, the total shortwave cloud radiative effect (tCRE) is calculated for each of the models and the observations as follows: tCRE i = (SW Di -SWCS Di )freq i = aCRE i ⋅ freq i (6) where the index i denotes the regime (from 0 to 7), SW Di and SWCS Di are the average all-sky and clear-sky downwelling radiation associated with regime i, and freq i is the frequency of regime i (as a fraction).
Analysis The presented results were obtained from ALICE measurements in Pb-Pb collisions at √ s NN = 5. 02 TeV during the Run 2 of the LHC. The recorded data represent an integrated luminosity of ∼ 225 µb -1. A complete description of the ALICE apparatus can be found at  [13]. Inclusive quarkonium measurements in the dimuon decay channel are performed at forward rapidity (2. 5 < y < 4) and down to zero p T in the muon spectrometer. The silicon pixel detector (SPD) provides the primary vertex and the event plane estimation for flow measurements. The raw number of reconstructed quarkonia were obtained by fitting the dimuon mass spectra with a superposition of signal and background functions. Systematic uncertainties on the extracted yield are obtained by varying the functions and fitting ranges and detector acceptances as well as efficiencies are taken into account by MC simulations.
However, a comprehensive description of the worldwide distribution of present day goat diversity is still lacking. The commercial goat 52 K single nucleotide polymorphism (SNP) chip has been available since approximately 5 years  [30] , and has already been used for national and multi-country goat diversity studies  [21, 31, 32]. Using this standardized tool for genotyping, the AdaptMap initiative  [33]  gathered a dataset that includes genotypes of 148 goat populations. The aims of this study were to (i) investigate the distribution of goat genetic variation around the world, (ii) compare the present-day diversity patterns to those observed in ancient goat samples and in other ruminant livestock species, and (iii) reconstruct admixture and migration events that have shaped goat post-domestication history.