dataesr/scientific-paragraphs-categorization huggingface dataset:
A, overall views of the crystalline Fas2-mAChE complex (left) (PDB code 1KU6) and the 1029-Å 2 buried interface at the mAChE surface in this complex (center) (left and right mAChE molecules are oriented 90°f rom each other). Fas2 is displayed in light green, and mAChE Trp 286 is in light blue (other color codes as in Fig. 3A ). Right, spatial alignment of the key interacting aromatic and positively charged side chains in BfAChE-bound Fab410 (plain labels; color codes as in Fig. 5 ) and mAChE-bound Fas2 (italicized labels; green side chains) overlaid onto the Fas2 backbone (light green ribbon). Their similar positioning along with the absence of a Fab410 residue mimicking Fas2 Met 33 in its interaction with Trp 279 (displayed in light blue on the right) is evident. B, overall views of the theoretical model of the Fab410-EeAChE complex (left) and the 1017-Å 2 buried interface at the EeAChE surface in this complex (right) (left and right EeAChE molecules are oriented 90°from each other).
); 0. 82 Å for 520 C␣ atoms), acetylcholine/acetylthiocholine-mAChE complexes (PDB codes 2HA4/2HA5 (8); 0. 83/ 0. 79 Å for 521/518 C␣ atoms), decamethonium-TcAChE complex (PDB code 1ACL (62); 0. 86 Å for 525 C␣ atoms), BW284C51-TcAChE complex (PDB code 1E3Q (63); 0. 89 Å for 527 C␣ atoms) and unliganded DmAChE (PDB code 1DX4 (64); 1. 47 Å for 490 C␣ atoms) and comparison of the bound Fab410 structure with the theoretical model of unbound Fab410 (Ref. 32; 1. 8 and 2. 6 Å for 208/203 C␣ atoms for the L and H chains, respectively) used Coot (55). Figs. 3-6 were generated using PyMOL (65).
Acknowledgments-We are grateful to  Cassian Bon  (deceased) ( Institut Pasteur, Paris, France ) and  Jacques Grassi ,  Didier Boquet , and  Christophe Créminon  ( Commissariat à l'Energie Atomique Saclay, Gif-sur-Yvette, France ) for the kind gift of biological materials, unpublished data, and enthusiastic support;  Bernard Saliou  ( Institut Pasteur, Paris ) and  Marianick Juin  and  Sandrine Conrod  ( CNRS/ Aix-Marseille Université (AMU), Marseille, France ) for assistance in protein preparation and analysis;  Silvia Spinelli  ( Architecture et Fonction des Macromolécules Biologiques, Marseille ) and the  ID14-EH3  staff ( European Synchrotron Radiation Facility, Grenoble, France ) for crystallographic data collection;  Christopher Weise  ( Freie Universität, Berlin, Germany ) and  Xavier Cousin  ( Institut National de la Recherche Agronomique, Rennes, France ) for sharing the unpublished complete sequence of N.
e. SH3, DH, PH, CH, Drf and Eth domains), identified using Hidden Markov Model (HMM) search  [41]  against the PFAM database  [42]  (see Supplementary Methods). We observed that the ohnologs are particularly enriched in genes with autoinhibitory protein folds (amounting for just 1. 4% of non-ohnologs but up to 9-12% of ohnologs, i. e. a 6. 4-8. 6 fold increase, p = 4. 4 × 10 -150 Fisher Exact Test)  [5].
A hyperlink from this page directs to the details on its ohnolog families and its possible association with human diseases points to GENECARDS  [43]  and COSMIC  [44]  databases. This page also contain links to details in UniProt and Entrez databases if available. If the gene exists in our analysis, and is an ohnolog, users are directed to the details about ohnolog families for each statistical confidence levels (i. e. , strict, intermediate and relaxed criteria), S13 Fig. Alternatively, users can also generate ohnolog families using our multi genome comparison analysis, for any of the six available vertebrate genomes using an arbitrary, user-defined, quantitative criteria for the outgroup and self comparisons. The default values correspond to the strict criteria. The result pages display all the pre-calculated or custom generated families, which can also be downloaded.
Subjects Patients with ASD and ABCR were ascertained through two sources: 1) 12 patients from the Paris Autism Research International Study (PARIS) family dataset  [17] , and 2) 6 patients referred by the network of French cytogeneticists. A summary of the clinical and cytogenetic data of the patients is shown in Table  1. Sixteen patients fulfilled DSM-IV criteria for autistic disorder, while two subjects (patients 9 and 13) had a previous diagnosis of ASD but could not be formally evaluated for ASD for this study. The Autism Diagnostic Interview-Revised (ADI-R) was performed in 14 subjects; in 1 individual, the Diagnostic Interview for Social and Communication Disorders, tenth revision (DISCO-10) was used instead. In addition, five subjects were assessed with the Autism Diagnostic Observation Schedule (ADOS).
Patient 1 This boy was referred for evaluation at the age of 2 years and 9 months because of developmental delay and ASD   1A ), absent from the parents. Both rearrangements were confirmed by qPCR and FISH. The deletion was confirmed by FISH with the 4p subtelomeric probe RP11-2H3. Using probes RP11-296G16 (4p16. 3) and RP11-265012 (4p16. 2), the FISH findings were consistent with an inverted duplication (data not shown), indicating a chromosome 4p inverted duplication deletion. SNP genotyping indicated that both rearrangements occurred on the paternal chromosome. The deletion includes eight RefSeq genes; the duplication encompasses 70 genes and overlaps the Wolf-Hirschhorn critical region (Figure  2 ).
18p11. 22p11. 31 deletion In patient 2, with a de novo translocation (5;18), we identified an interstitial deletion of 4. 22 Mb in the 18p11. 22p11. 31 region. The 18p deletion syndrome, associated with terminal deletions of variable size, is characterized by typical facial features, growth retardation, and ID  [45]. Our patient does not have any dysmorphic features or growth retardation. Pure interstitial deletions in this region have not been reported in the literature so far. The DE-CIPHER and ISCA databases include several patients with partially overlapping interstitial deletions of variable sizes (Figure  3 ); although several are inherited from a normal parent, two are de novo, a 9 Mb deletion in a patient with autism (290283) and a 3 Mb deletion (286638, no phenotype information). The deletion in patient 2 overlaps 24 known genes; only 1 has a disease-associated OMIM entry, NDUFV2, involved in autosomal recessive mitochondrial complex I deficiency characterized by early-onset cardiomyopathy and encephalopathy.
The 2q14. 1q14. 2 duplication includes 20 genes; no similar imbalances are reported in DECIPHER, ISCA, or the Database of Genomic Variants (DGV) so its clinical significance is unknown. Five smaller duplications with a common duplicated region of 440 kb (chr2:120,126,884-120,567,392) have been reported in DECIPHER; four are inherited from normal parents (one unknown inheritance), suggesting that they are probably benign.
Acknowledgements We are grateful to the patients and families who participated in this study. We thank the  Clinical Investigation Center of the Robert Debré Hospital  and the DNA and cell bank of the  Pitié-Salpêtrière Hospital. This research was supported by  Fondation de France ,  INSERM , and  Institut de France. MP and ED were supported by a PhD fellowship from the  French Ministry of Research. This study makes use of data generated by the  DECIPHER  Consortium, funded by the  Wellcome Trust. A full list of centers who contributed to the generation of the data is available from http://decipher. sanger. ac. uk and via email from decipher@sanger. ac. uk.
Each contributing study ran a linear regression model on age at menarche, adjusted for birth year and principal components derived from genotypes, using the skatMeta/seqMeta package in R. Studies with family data included a random effect to account for relationships. Alleles were aligned to a common reference file before association testing (SNPInfo_HumanExome-12v1_ rev5. tsv. txt available at http://www. chargeconsortium. com/main/exomechip/) and variants with MAF45% in the meta-analysis were excluded. We performed gene-based testing (within seqMeta) for low-frequency variants using fixed effect burden tests, which assume that all rare variants have the same effect direction and size (scaled by a weight determined by allele frequency), and SKAT tests, which assume that rare variant effects are random and can contain a mixture of null, protective and risk rare alleles.
Variants from both studies were required to either pass genotyping QC (23andMe only, described below) or have imputation quality score 40. 4. X-chromosome follow-up was performed in the deCODE study alone. All participants in all published studies provided informed consent, and the research protocol of each study was approved by their local research ethics committee  1. 1000G X-chromosome discovery meta-analysis. X-chromosome SNP data were generated in up to 76,831 women of European ancestry from the 23andMe study  17, 27  , with questionnaire-reported age at menarche between the ages of 8 and 16 years, and who were genotyped on one or more of three GWAS arrays that also included customized content on human pathogenic variants (Supplementary Table  1 )  28. 23andMe participants provided informed consent to take part in this research under a protocol approved by the AAHRPP-accredited institutional review board, Ethical and Independent Review Services.
Before imputation, we excluded SNPs with Hardy-Weinberg equilibrium Po10 À 20 , call rate o95% or with large allele frequency discrepancies compared with European 1000 Genomes reference data. Frequency discrepancies were identified by computing a 2 Â 2 table of allele counts for European 1000 Genomes samples and 2,000 randomly sampled 23andMe customers with European ancestry, and identifying SNPs with a w 2 Po10 À 15. Genotype data were imputed against the March 2012 'v3' release of 1000 Genomes reference haplotypes. Age at menarche was assessed by questionnaire and recorded in 2-year-age bins, which were rescaled to 1-year effect estimates post analysis. The validity of this approach was confirmed by the lack of significant heterogeneity between rescaled 23andMe menarche estimates for the 123 previously identified signals and their reported effects 1.
Methods Yield data. Yield time series were retrieved from the Food and Agriculture Organization statistics (FAOSTAT) database 2. These data had already been used in several studies for analyzing past yield trends and anomalies  [40] [41] [42] [43]. Crop yield time series were collected from four European regions (i. e. , Western, Eastern, Northern and Southern Europe) and for two American regions (i. e. , North and South America). Supplementary Table  1  lists countries belonging to the studied regions and aggregated by the United Nations Statistics Division. In general, the database time interval spans from 1961 to 2013 (i. e. , n = 53 years). There are only two exceptions, for which time intervals are shorter (i. e. , soybean in Western Europe from 1973 to 2013 with n = 41 years and lupin in Northern Europe from 1992 to 2013 with n = 22 years).
We compiled branch xylem data across 335 angiosperms and 89 gymnosperm species, making this study five times larger than any previous work. We asked first whether there was evidence for a broad tradeoff between stem hydraulic efficiency (K S ) and safety (P X ) across angiosperm or gymnosperm species. Plotting efficiency against safety could result in several possible patterns (Fig. 1 ). If natural selection maximizes both traits within the limits of a tradeoff between them, we might expect a negative monotonic relationship (feature 3 in Fig. 1 ), and also unoccupied niche space outside of this optimum zone (features 1 and 2 in Fig. 1 ). Secondly, we investigated why so many species appeared to lie outside of the optimum zone, having both low efficiency and low safety, as reported in previous analyses  (Maherali et al.
The dataset The dataset used in this study (Xylem Functional Traits Database; XFT) can be accessed from the TRY Plant Traits Database (https://www. try-db. org/TryWeb/Home. php) and was an outcome from a working group assembled through the Australia-New Zealand Research Network for Vegetation Function, Macquarie University, Australia. Most data came from previously published reports  (Choat et al. , 2012) , but the dataset also included 31 unpublished safety-efficiency observations (Supporting Information, Data References). We included species in these analyses only when safety and efficiency were measured on small branch samples (ca. 0. 4 -1. 0 cm diameter) because these were the sizes most commonly measured in the literature and because stem diameter is known to correlate with conduit structure  (Jacobsen et al.
, 2003). In addition, we combined leaf and stem xylem Ψ data, as these were statistically indistinguishable from one another in this dataset  (Choat et al. , 2012). Although leaf Ψ must be lower than xylem Ψ for water transport to take place (towards the stomata), variation in Ψ among species and habitats is likely much greater than the error associated with this simplification. Data were mainly taken from naturally occurring plant populations, but glasshouse and common garden experiments were included, providing that safety and efficiency values were both measured in these studies. For climate comparisons, only naturally occurring plant locations were included in the analyses. Climate data were taken from the original reports, where available, and otherwise extracted from the Worldclim (elevation, temperature, precipitation) and the Climatic Research Unit (number freezing days) databases  (New et al.
Safety-efficiency data were analysed using the "smatr" package  (Warton et al. , 2006)  for R (R Core Team, 2014), which gave estimates of the standardised major axis (SMA) slope and also the percentage covariation between these traits (r 2 ). To evaluate if the correlation between safety and efficiency differed among plant clades, SMA models were fit to species belonging to individual clades (e. g. , family), provided at least eight species were present per clade. It is not well understood how P X relates to irreversible damage and mortality across species. As such, "safety" is a rather subjective concept. Although P 50 relates to the steepest part of the vulnerability curve, different points on the curve may reflect different aspects of the dehydration process. The initial decline in maximal conductivity (P 12 ) relates to the airentry tension and is likely coordinated with decreasing stomatal conductance and increasing hydraulic capacitance, i.
A c c e p t e d v e r s i o n The majority of species in our dataset did not sit close to the standard major axis trend-line (e. g. , feature 3 in Fig. 1 ), suggesting that safety or efficiency were trading off against other, as yet unknown variables. To test this hypothesis we assessed whether variation in other plant traits (e. g. , wood density) or climate variables (e. g. , temperature, precipitation) were correlated with variation orthogonal to the fitted safety-efficiency trendline. To do this, the residuals from the safety-efficiency SMA fit were saved and regressed against our "third" variables (plant traits, climate). Third variables were transformed as necessary to meet the assumptions of the analyses. In addition to investigating whether third variables modified the safety-efficiency relationship, we also wished to know whether or not these variables were more significantly aligned with either the safety or efficiency axis.
Comparing methods Hydraulic safety data were included in the analyses regardless of the methods employed to build vulnerability curves. We note that some work has questioned the validity of angiosperm safety data obtained by centrifuge-spinning short xylem segments for species with relatively long vessels  (Cochard et al. , 2013; Martin-StPaul et al. , 2014). Data obtained via air-injection have also been disputed  (Torres-Ruiz et al. , 2014). It is suggested that the centrifuge technique may incur an "open vessel" artefact, resulting in an exponential "rshaped" curve. However, others have found no evidence for a long vessel artefact and have found that r-shaped curves are valid when the standard centrifuge technique has been used  (Jacobsen & Pratt, 2012; Sperry et al. , 2012; Tobin et al.
, 2011). Similarly, different anatomical features could confer different levels of safety, although these mechanisms remain largely unstudied except for airseeding through pores in pit membranes  (Cochard, 2006; Jansen et al. , 2009; Lens et al. , 2011; Brodersen et al. , 2015). The second question that arises from these results is, why should any particular anatomical arrangement of the xylem result in both low efficiency and low safety? Given that efficiency should nearly always enhance fitness (result in higher rates of gas exchange or lower xylem construction and maintenance costs), the presence of so many species that have both low efficiency and low safety suggests that the anatomical basis for high efficiency may be trading off with important traits other than safety. We note that this observation holds whether we consider all species in the dataset or omit those exhibiting r-shaped vulnerability curves.
Acknowledgements The authors thank the  French ministry of Foreign affaires and the Conicyt for financial support of collaboration between French and Chilean research groups  under the framework of the  ECOSSUD-CONICYT C13U02  project. P. T. Ngo acknowledges funding under the framework of the project Fondecyt Posdoctorado  3150194. A. Crème  was funded by the  European community 's  Seventh Framework programme  ( FP2012-2015 ) under grant agreement no. 262060  ( ExpeER ) and the  Regional Council for Poitou-Charentes. Moreover, we received funding from the  French agency ADEME  under the framework of the  REACTIFF  project  AEGES.
Past and future climate data The study is based on temperature data recorded at the weather station of San Camilo (lat. 14°04' 24", long. 75°42' 40", alt. 398 m), which is well located in the centre of the Ica region, far enough from the coastal line (Figure  1 ). It has also the longest records of temperature data : 54 years. Future climate data were generated by the Atmospheric General Circulation model MRI/JMA TL959L60 of the Japanese Meteorological Agency  (Noda et al. , 2008) , which was validated with 20 stations of the coastal region of Peru. MRI simulations for the present days and future climates are performed by using the observed sea surface temperatures (SST) and their changes projected by atmosphere-ocean coupled models as the lower boundary conditions. With a 20-km grid, they reproduce fairly well the features of the general circulation in Peru, the climate trends, the seasonal cycle and the mean meteorological variables  (Blázquez and Nuñez, 2013).
GDDI, also called Winkler index (WI), is expressed as : !"# ! ! !!! ! ! !!! ! !!!"#$%!!" !!!"#!! !!"!! ! Calculated over the same period as GST, from October to April, it indicates the suitability of growing grapevine in a given environment  (Winkler et al. , 1974). HI provides information on sugar potential and thus on grape quality  (Huglin, 1978)  : K is an adjustment coefficient for day length with the value of 1 for low latitudes (< 40°). Generally, this index is calculated on one month less than the other indices. It is maintained here in this form for ease of comparison with published data  (Jones et al. , 2010). CNI accounts for the average minimum night temperature of the month when ripening usually occurs, here in March : This index has some relevance in relation with grape and wine colour and aroma  (Tonietto and Carbonneau, 2004).
CONCLUSION Temperature data recorded in the Peruvian region of Ica show that there has been a slight increase during the recent years, mainly for maximum temperature. Future climatic data generated under the SRES scenario A1B indicate that this increase will continue during the 21 st century. Consequently, all the bioclimatic indices commonly used in grapevine studies (GST, GDDI, HI, CNI) have increased and will continue to rise. The phenology of four cultivars grown for pisco production was investigated during four consecutive years and the cumulative thermal times of their different phenological stages were determined. The entire cycle is completed within approximately 204 days, summing about 2630 degree-day for all the four grapevine varieties. From these results, it was inferred that during the recent years, cycle lengths have been shortened by about 2 days every ten years and that, according to the projected climate, the tendency will continue all along the 21 st century.
[2013]  reported that SOC varied sixfold between ESMs, from 510 to 3040 Pg C during  1995-2005. In their study, only 6 out of 11 model estimates were within the range of the Harmonized World Soil Database (HWSD)'s estimate of 1260 Pg C (with a 95% confidence interval of 890-1660 Pg C), and spatial correlation coefficients between modeled and empirical SOC estimates at 1°resolution were <0. 4, indicating that variations in the spatial distribution of SOC are not well represented by ESMs  [Todd-Brown et al. , 2013]. Global Biogeochemical Cycles 10. 1002/2014GB005021 Uncertainties in the modeled SOC estimates may arise from model structure, parameterization, and driving data sets such as climate, soil properties, and land use. One of the major uncertainties in model structure is the representation of the belowground C processes that vary significantly in different models  [Johnston et al.
First, climate conditions can affect enzyme activity with respect to activation energy but can also influence plant primary productivity, C entering the soil pool, substrate quality and accessibility, and nitrogen mineralization  [Davidson and Janssens, 2006]. Second, temperature and precipitation covary with other environmental constraints (e. g. , soil aeration, soil properties, and vegetation types), which influence the litter quality and the ratio of labile and recalcitrant SOC components. The modeled climate sensitivities of SOC decomposition are highly dependent on model structure and assumptions, which may, in part, reflect complex biogeochemical processes in the real world and be able to explain different model behaviors in simulating SOC stock change. In this study, we examined the global SOC stocks and Rh estimated by 10 terrestrial biosphere models (TBMs) with different structures, assumptions, and parameterization schemes driven by the same land use and climate forcing to explore the disagreement among models and the potential reasons responsible for model-data mismatch.
Methods and Data Our analysis included modeling results from 10 TBMs that participated in the North American Carbon Program Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP)  [Huntzinger et al. , 2013; Wei et al. , 2014b]  and that were released under MsTMIP version 1  [Huntzinger et al. , 2015]. The model ensemble includes Biome-BGC  [Thornton et al. , 2002] , CLM  [Mao et al. , 2012] , CLM4VIC  [Lei et al. , 2014] , DLEM  [Tian et al. , 2012] , GTEC  [Post et al. , 1997] , ISAM  [Jain et al. , 2009; El-Masri et al. , 2013] , LPJ  [Sitch et al. , 2003] , ORCHIDEE-LSCE (ORCHIDEE thereafter  [ Krinner et al. , 2005] ), VEGAS2. 1  [Zeng et al. , 2005] , and VISIT  [Ito, 2010]. The models were driven by the same environmental input database including climate, atmospheric CO 2 concentration, atmospheric nitrogen deposition, and land use and land cover changes during 1901-2010.