dataesr/scientific-paragraphs-categorization huggingface dataset:
We generated a model for the KEOPS-tRNA (PDB: 3L0U) complex by rigid global docking with Patch-Dock  (53) , specifying that A37 of tRNA directly contacts His 141 /His 145 /Asp 344. The resulting model orients the anticodon stem loop of tRNA toward the active site of Kae1 and shows that all the subunits except Cgi121 of KEOPS are potentially involved in the binding of tRNA (Figure  5 ). We realize that tRNA probably undergoes important structural rearrangements during the modification reaction, precluding more rigorous modeling.
Figure5. Docking model for KEOPS-tRNA complex by PatchDock. The A 37 of tRNA and active site (H141, H145 and D344) of Kae1 are highlighted and denoted. The orientation is the same as for Figure3. We have also represented the sulfate ions that were present in the yeast Bud32-Cgi121 structure (green spheres) and the tungstate ions present in the P. abyssi Kae1 structure (yellow spheres taken from PDB: 2IVO) were superposed onto the yeast Kae1 model.
FUNDING The  French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-01 ]; the ' Lidex-BIG' Project Funding [IDEX Paris-Saclay   ANR-11-IDEX-0003-02 ]; 2009  CSC Ph. D. Fellowship  [to W. Z. ]. Conflict of interest statement. None declared.
70 34. 0 31. 0 29. 0 66. 0 64. 0 RMSD from standard stereochemistry Bond length ( Å) 0. 010 Bond angles ( • ) 1. 28 0. 010 1. 29 0. 005 0. 98 0. 008 1. 08 0. 011 1. 48 0. 014 1. 70 Ramachandran plot statistics Favored (%) 90 98 97 98 98. 3 98. 4 A l l o w e d ( % ) 1 0 2 3 2 1. 7 1. 6 D i s a l l o w e d ( % ) 0 0 0 0 0 0 PDB code 4WWA 4WW5 4WW9 4WW7 4WX8 4WXA a The AMPPNP moiety without ␥ -phosphate. The numbers in parentheses are for the highest resolution shell. and the SAXS-MoW method available at http://www. if. sc. usp. br/∼saxs/. The model of S. cerevisiae.
Web parameters Photos of the webs were analyzed with Image) software  (Schneider et al. 2012). Only 84 webs were analyzed; the other webs were destroyed when we opened the frames. Some parameters were assessed directly on the web; for that, we defined four sectors (Fig. 1 ) in four directions (high, low, right and left). In each sector, we manually counted the number of spiral turns, and measured the length of the intemal and external radii that are the threads joining the hub to supports (Fig. 1 ). Web structure was then characterized according to investment in silk and its general shape by computing several parameters. The length of the capture thread (CTL) measured the silk investment  (Venner et al. 2001) , where Nh, Nr are the number of spiral turns in the four sectors (h = high, 1 = low, le = left, r =right) (Fig.
Data analysis Data were first tested for homoscedasticity and normality using the Shapiro-Wilk method and Levene test. Only the web symmetry data failed to meet assumptions of normality even after log-transformation, and in this case, we used a nonparametric Kruskal-Wallis test. All the other parameters fitted the parametric conditions and we used an ANOVA. As weight is known to influence web characteristics  (Venner et al. 2003) , we used a one-way. ANOVA to test for differences in spider mean weight between groups (doses) for each replicate (week) separately. Mortality, defined as the number of dead spiders, was analyzed at two different time scales: short-term (after 72 h) and long-term (after 4 weeks). Mortality was compared between the four groups with a z2 test. The same statistical method was used for the number of webs built (the first built by each spider) and the number of prey captured.
The promotion of biodiversity in agricultural areas involves actions at the landscape scale, and the management of cropping patterns is considered an important means of achieving this goal. However, most of the available knowledge about the impact of crops on biodiversity has been obtained at the field scale, and is generally grouped together under the umbrella term "crop suitability. " Can field-scale knowledge be used to predict the impact on populations across landscapes? We studied the impact of maize and rapeseed on the abundance of skylark (Alauda arvensis). Fieldscale studies in Western Europe have reported diverse impacts on habitat selection and demography. We assessed the consistency between field-scale knowledge and landscape-scale observations, using high-resolution databases describing crops and other habitats for the 4 km 2 grid scales analyzed in the French Breeding Bird Survey.
Landscape data For the identification of landscape factors affecting farmland birds, we carried out a literature review based on studies using data from French and UK breeding bird surveys  (Chamberlain & Gregory, 1999; Devictor & Jiguet, 2007; Siriwardena, Cooke & Sutherland, 2012)  or studies focusing on single factors, such as roads  (Reijnen, Foppen & Meeuwsen, 1996). The variables used in this study are shown in Table  1. These variables were obtained from three national databases: the Land Parcel Identification System (LPIS) 2007-2010, used for the administration of the Common Agricultural Policy (CAP), the BD topo ® from Institut Géographique National, and Corine Land Cover 2006 (Table  2 ). These geographic data were integrated into a single database, with priority given to the data with the best spatial resolution: the BD topo ® , followed by the LPIS and finally Corine Land Cover, mostly to cover the gaps in non-agricultural areas.
There is often more than one way to select tonal adjustment for a photograph, and different individuals may prefer different adjustments. However, selecting good adjustments is challenging. This thesis describes a method to predict whether a given tonal rendition is acceptable for a photograph, which we use to characterize its range of acceptable adjustments. We gathered a dataset of image "acceptability" over brightness and contrast adjustments. We find that unacceptable renditions can be explained in terms of over-exposure, under-exposure, and low contrast. Based on this observation, we propose a machine-learning algorithm to assess whether an adjusted photograph looks acceptable. We show that our algorithm can differentiate unsightly renditions from reasonable ones. Finally, we describe proof-of-concept applications that use our algorithm to guide the exploration of the possible tonal renditions of a photograph.
This thesis describes a method for predicting the acceptable set of tonal adjustments for a given photograph. There are four main contributions of this work. 1. We gather a crowd-sourced dataset of labeled photographs that allow us to analyze the notion of acceptability. This dataset is available online at http://projects. csail. mit. edu/acceptable-adj/. 2. We find, from this dataset, that in a two-parameter adjustment space, photograph corruption can be categorized into three forms: over-exposure, under-exposure, and low contrast.
Chapter 2 A Crowd-sourced Dataset In order to address the photographic acceptability problem, we created a dataset of photographs adjusted to various configurations, and crowd-sourced acceptability judgements from human observers. This chapter will address the critical issue of how we define and communicate the concept of "acceptability" to our experiment participants, and will give the full details of the considerations we have taken when we collected our data. We will also present some observations we had about the data that will be crucial to how we predict the acceptability of a photograph.
We collected a small set of data that allowed such renditions to be accepted, but there was not enough agreement among the participants to create a meaningful signal. Thus, in the final version of the survey, we excluded these effects, and instructed our participants to reject such renditions. Figure  2 -1: Extreme adjustment that might be seen as interesting by some users. Because answers were not consistent on those, we guided users to reject them. Crowd-sourcing subjective judgement has several drawbacks such as the lack of control for viewing conditions. This meant that the acceptability ratings we collected would contain nonnegligible amount of noise regardless of the precision of our instruction. However, people typically view photographs on their computers in an uncontrolled environment.
The Zero Configuration and the Adjustment Model For this data set, we used photographs from the MIT-Adobe FiveK dataset because it provides a variety of high quality photographs in both edited and raw format [3]. We randomly selected photographs and manually adjusted the selection to ensure a variety of natural scenes and conditions, e. g. , day vs. night, indoor vs. outdoor. There were 500 photographs in total. In this work, we focused on tonal adjustments and sought to minimize any color-related effects. In particular, we found that using the as-shot white balance and saturation settings can sometimes make a photo unacceptable independently of tonal settings. To avoid this issue, we applied the white balance and saturation settings of one of the expert retouchers of the MIT-Adobe FiveK dataset Although two-dimensional adjustment space is of limited use, it allows us to sample densely while keeping the size of our data manageable.
Data Collection Protocol With the notion of acceptability and the definition of zero configuration in mind, we describe the full detail of our data collection, and all further considerations we have made to ensure the integrity of our data. Of the 500 photographs in our dataset, we divided our dataset into five photo sets of 100 photos each. In each trial, photographs in a selected photo set were adjusted randomly. The participants looked at the photographs one by one, and provided a binary label (acceptable/unacceptable). We provided them with an instruction that consisted of text explanation and examples of acceptable and unacceptable photographs. The guideline for what was considered acceptable was presented in the beginning of each trial, and was available throughout. Since our input are natural photographs, we chose to describe acceptable as "natural-looking" in our prompt instead of "acceptable.
During our test trials, we collected data on a small number of images with up to 1,200 adjustments each. We then subsampled this data to determine the optimal amount of sampling needed. We found that ~600 adjustments produced sufficiently consistent shape, and location of the cloud of accepted points. In our final run, there were 26 brightness x 26 contrast stratified adjustments per image. After discarding low quality trial, we had 500 -600 data points per photographs, and approximately 1,200 participants in total (some participants worked on more than one image set).
Toy Predictors In order to characterize the variations in our data, we create two toy predictors that only use the adjustment value as their feature: the average-adjustment predictor, and the per-image predictor. These toy predictors define performance bounds for the machine-learning algorithm that we describe in Section 3. 2. The average-adjustment predictor is trained purely on adjustment value and ignored image dependency completely. This corresponds to a naive form of machine learning where the prediction is always the same and equal to the average of the training set. While a random predictor would be worse, we nonetheless expect any sophisticated predictor to perform better than this simple technique. The per-image predictor, on the other hand, is a collection of classifiers, each of which was trained on one of the images from the dataset.
Going beyond Brightness and Contrast Because our algorithm is agnostic to the adjustment model, it generalizes beyond the brightnesscontrast adjustment space. To verify this, we collected a small dataset with highlight and shadow acceptability, it seems, has a lot to do with semantic understanding of the scene. As most of our features are low-to mid-level, there are many cases where it fails. This section will try to comprehensively list all those cases. The most striking problems we encounter seem to be related to texture and color, which we describe as follows.
Chapter 6 Conclusion We collected and studied a dataset of acceptable adjustments over brightness and contrast adjustment. To characterize our dataset, we describe two adjustment value-based toy predictors: the averageadjustment and per-image predictors. We find a significant performance gap between these two toy predictors, which suggests that image-dependent analysis is needed. Our dataset shows that unacceptable renditions can be explained by over-exposure, underexposure, and low contrast. We design a machine-learning feature based on this observation, and show that it performs significantly better than the average toy predictor and that it generalizes to higher-dimensional tonal adjustment models. Through the range of acceptable adjustment predictions, we demonstrated three proof-of-concept applications that facilitate exploration of adjustment space.
We hope that these applications will create better user experience with complex photographic adjustments, allowing users to work faster, while keeping the quality of their photographs high. Our dataset is available online at http://projects. csai1. mit. edu/acceptable-adj/. provided some guidelines and examples below. Please use your best judgement. Some (non-exhaustive) guidelines to consider when deciding: 1. NATURAL-LOOKING images are the ones that look like something you might have seen with your own eyes, OR taken with a camera with reasonable camera settings. Note that natural-looking images need NOT be images of nature. It could be man-made objects as long as it looks like something that you would see with naked eyes. NATURAL-LOOKING: it could be man-NATURAL-LOOKING: it could be namade ture 2.
Compiled into a regular 1-min grid (1. 852 × 1. 852 km), this dataset covered 22 % of the island. The studied rainforest species exhibited high environmental tolerance; 56 % of them were not affiliated to a substrate type and they exhibited wide elevation (average 891 + 332 m) and rainfall (average 2. 2 + 0. 8 m year 21 ) ranges. Conversely their spatial distribution was highly aggregated, which suggests dispersal limitation. The observed species richness was driven mainly by the density of occurrences. However, at the highest elevations or rainfalls, and particularly on UM, the observed richness tends to be lower, independently of the sampling effort. The study highlights the imbalance of the dataset in favour of higher values of rainfall and of elevation. Projected onto a map, under-represented areas are a guide as to where future sampling efforts are most required to complete our understanding of rainforest tree species distribution.
In this study, we used a new-occurrences dataset to test whether the environment is the main force driving New Caledonian rainforest tree species diversity and individual species distribution. In other words, does the species assemblage in rainforests reflect a deterministic model (i. e. an environment or niche-based one) or a stochastic model (i. e. a null one)? We draw up a starting-point statement of knowledge of rainforest tree species distribution in New Caledonia by compiling tree occurrences from four distinct sources: (i) herbarium specimens, (ii) plot inventories, (iii) photographs and (iv) other observations collected over several decades. First, we evaluated the geographic and environmental representativeness of this occurrence dataset. Second, we analysed how tree species richness found on the main island (i.
Selection of tree species We focussed on the distribution of 702 woody tree species (i. e. excluding lianas, tree ferns and palms) reaching a diameter at breast height (DBH at 1. 3 m) of 10 cm, at least once in our set of 37 597 trees in the New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN). Intraspecific ranks (subspecies and varieties) were merged at the specific rank to ensure uniformity of identification across datasets. The nomenclature of tree species followed the New Caledonian taxonomic name reference Florical  (Morat et al. 2012). The NC-PIPPN inventory covers a surface area of 15 ha and comprises: (i) 220 plots of 0. 04 ha (20 × 20 m), with 30 221 inventoried trees (DBH. 5 cm) located across rainforests of Grande Terre on both UM (111 plots) and non-UM substrates (89 plots) along a wide range of elevations (5-1292 m on UM and 105-1187 m on non-UM) and rainfalls (1.
Compilation of tree occurrences Occurrences of the 702 tree species were compiled from four datasets: (i) the NC-PIPPN inventory (29 409 occurrences), (ii) herbarium specimens (22 715 specimens) compiled from the database of the herbarium of the IRD Centre of Noumea (NOU, http://herbiernoumea. plantnet-project. org), (iii) other observations (44 227 observations) from different unpublished inventories used for assessing the flora of areas under consideration for mining exploration or for conservation measures ([see Supporting Information]) and (iv) photographs acquired in the field (4326 photographs). We then checked datasets and removed inaccurate geolocations. We only retained herbarium specimens that were: (i) georeferenced with a Global Positioning System (GPS), (ii) collected by Hugh S.
MacKee (the principal contributor to the NOU Herbarium with 45 000 specimens) and estimated to have a horizontal accuracy of ,500 m according to his online gazetteer (http:// phanero. novcal. free. fr) or (iii) labelled with an elevation matching with a difference of +50 m the elevation extracted from a digital elevation model (DEM). From the 100 677 initial occurrences, we compiled a dataset of 38 936 ( 40 %) unique occurrences combining species with accurate geolocation: 11 845 from herbarium, 7420 from plots, 18 390 observations and 1281 photographs, used thereafter to describe the known distribution of tree species in New Caledonia. A total of 15 285 occurrences (i. e. ,40 % of the whole dataset) were found in rainforests while the remainder occurred in other vegetation types.
Statistical analysis Tree occurrences dataset. First, we subdivided the main island into 5546 cells of 1 min 2 resolution (1. 852 × 1. 852 km) and computed in each cell the number of occurrences (i. e. the occurrence density) and the number of species (i. e. the species richness or a-diversity). We then analysed the relative contributions of the four datasets and examined the occurrence and species patterns they produced. Geographical and environmental distributions of g-diversity. We then focussed on occurrences found exclusively in rainforests, and analysed their distribution as a function of environmental features including (i) the substrate type (UM or non-UM), (ii) elevation and (iii) mean annual rainfall. For each substrate, elevation and rainfall class (bands of 100 m and 0. 25 m year 21 , respectively), we computed the number of occurrences found in rainforests, the observed g-diversity and the theoretical g-diversity (calculated by considering that a species occurs between the minimal and maximal class where it had been recorded).
We then compared the class distribution of available rainforest area, occurrences, observed and theoretical g-diversity using linear log -log models of correlation. Finally, we computed species rarefaction curves and Fisher's a diversity index at low, mid and high classes of elevation (,400, 400 -800 and. 800 m) and rainfall (,2. 5, 2. 5-3. 0 and. 3. 0 m year 21 ) to avoid sampling bias (see  McCain and Grytnes 2010). Occurrence-based rarefaction curves with 95 % confidence intervals were computed using 1000 random permutations (e. g. Gotelli and Colwell 2001). Geographical and environmental distributions of species. Finally, we calculated a basic index of aggregation as the ratio between the number of occurrences and the number of grid-cells intercepted by each dataset (i. e. the mean density of occurrences per grid-cell).
At the species level, we computed the Morisita index of aggregation (I mor ) in grid-cells as well as in the entire plots dataset (before removing species duplicates) and used the standardized I mst which ranges from 21 to 1. Species with I mst ≤ 20. 5 are equally distributed across grid-cells or plot inventories while species with I mst ≥ 0. 5 are aggregated in some grid-cells or plots. Geographic data processing was performed using Quantum GIS 2. 6. 0-Brighton (Quantum GIS Development Team, 2014) and statistical analyses using R 2. 15. 2 (R Foundation for Statistical Computing, Vienna, Austria), including the vegan package  (Oksanen et al. 2013).
Tree occurrences dataset The 702 tree species selected belong to 195 accepted genera and 80 families. At the family level, Myrtaceae were the most common, followed in decreasing order by Cunoniaceae, Sapindaceae, Araliaceae, Sapotaceae, Clusiaceae, Rubiaceae, Lauraceae, Primulaceae, Rutaceae and Apocynaceae. Together these represented 50 % of the total number of occurrences [see Supporting Information]. At the species level, the 124 species most-represented (92-491 occurrences per species) contributed to 50 % of the total occurrences. In contrast, the 434 species least-represented (1 -50 occurrences per species) contributed to ,25 % of the total number of occurrences. Tree occurrences were distributed into 1213 cells of a regular 1-min grid, which covers 22 % of New Caledonia's main island (Fig.
1 ). Occurrence density ranged from 1 to 1405 per grid-cell (32 on average, +2 standard error). Occurrences from the plot inventory dataset were the most aggregated data, occurring in only 68 cells with an average of 109 occurrences +18 per grid-cell. The next most aggregated dataset was of observations (340 gridcells and 54 occurrences +5 per grid-cell). This was followed by herbarium specimens (1073 grid-cells and 11 occurrences +1 per grid-cell) and, last, photographs (156 grid-cells and 8 occurrences +1 per grid-cell). Tree occurrences were substantially imbalanced among substrate types (Table  1 ). More than two-thirds of the occurrences were found on UM substrates while these substrates cover only one-third of the island (Fig. 1 ). This over-sampling was particularly high in the observation dataset.
Tree occurrences dataset We compiled occurrences from herbarium specimens, plot inventories, photographs and observations to draw up a first assessment of tree distribution and diversity in New Caledonian rainforests. These occurrences cover almost a quarter of the main island, Grande Terre, providing a fairly comprehensive view of the actual distribution of rainforest tree species. Herbarium specimens have long been used to study species and diversity distributions  (Lavoie 2013 ) and the biases associated with a taxonomic approach are widely recognized  (Ahrends et al. 2011). Species accumulation is more rapid when using herbarium specimens than when using plot inventories but their relative abundance is not reliable (e. g. Garcillan and Ezcurra 2011). To our knowledge, the use of field photographs is less common.
The development of GPS and photographic technologies now generates a huge amount of high-quality georeferenced and retrospectively verifiable information. In our study, even though this data source was less substantial, we stress that in the future it is likely to become a critical source of reliable data, by involving parataxonomists, in particular, through collaborative networks (e. g. Basset et al. 2004). Even if observations may be more doubtful than from other datasets (i. e. there is no way to check data a posteriori), these rapid   2. Linear log -log correlation between the available rainforest area (AREA), the number of occurrences (N), the observed g-diversity (R obs ) and the theoretical g-diversity (R theo ) along the elevation gradient and the rainfall gradient and on UM and non-UM substrates separately (*P , 0.