dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Counts of male and female sandflies trapped over time was analyzed by a generalized linear model for a Poisson distribution. The model included effects for group (sugar rich control, sugar rich bait, sugar rich vegetation, sugar poor control, sugar poor bait, sugar poor vegetation), day  (1) (2) (3) (4) (5) (6) , and the interaction of group and day. A negative binomial link was used to accommodate the overdispersion of the sandfly counts. Planned comparisons were made among the control, bait, and vegetation measures within the sugar rich and poor conditions at each time. Results are shown as plots of means and standard errors in Figures  1  and 2. Percent change was analyzed with a linear mixed model. The fixed effects were the same six groups discussed above, day (2-6 and one week), and the interaction of group and day.
Acknowledgements Special thanks are due to the personnel of the  Department of Agriculture in Marrakech, Morocco  and the local authorities for the valuable assistance. The research reported in this publication was supported by the  National Institute of Allergy And Infectious Diseases of the National Institutes of Health  under Award Number  R01AI100968  and the  United States Department of the Army, Space and Missile Defense Command ,  Deployed War Fighters Protection Project (DWFP)  grant#  W81R6091285002. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the  Deployed War Fighter program.
retical options for calculating PDF-dependent cross section predictions corresponding to the measurements. The framework covers a large number of the existing methods and schemes used for PDF determination. The data and theoretical predictions are brought together through numerous methodological options for carrying out PDF fits and plotting tools to help to visualise the results. While primarily based on the approach of collinear factorisation, HERA-Fitter also provides facilities for fits of dipole models and transverse-momentum dependent PDFs. The package can be used to study the impact of new precise measurements from hadron colliders. This paper describes the general structure of HERAFitter and its wide choice of options.
The HERAFitter structure The diagram in Fig. 1  gives a schematic overview of the HERAFitter structure and functionality, which can be divided into four main blocks: Data: Measurements from various processes are provided in the HERAFitter package including the information on their uncorrelated and correlated uncertainties. HERA inclusive scattering data are directly sensitive to quark PDFs and indirectly sensitive to the gluon PDF through scaling violations and the longitudinal structure function F L. These data are the basis of any proton PDF extraction and are used in all current PDF sets from MSTW  [16] , CT  [17] , NNPDF pp( p) → t X, pp( p) → t W X Jets pp( p) → jetsX NLOJet++ (APPLGRID), NLOJet++ (fastNLO).
QCD analysis: The PDFs are determined in a least squares fit: a χ 2 function, which compares the input data and theory predictions, is minimised with the MINUIT  [31]  program. In HERAFitter various choices are available for the treatment of experimental uncertainties in the χ 2 definition. Correlated experimental uncertainties can be accounted for using a nuisance parameter method or a covariance matrix method as described in Sect. 5. 2. Different statistical assumptions for the distributions of the systematic uncertainties, e. g. Gaussian or LogNormal  [32] , can also be studied (see  Sect. 5. 3). Results: The resulting PDFs are provided in a format ready to be used by the LHAPDF library  [33, 34]  or by TMDlib  [35]. HERAFitter drawing tools can be used to display the PDFs with their uncertainties at a chosen scale.
Fit methodology When performing a QCD analysis to determine PDFs there are various assumptions and choices to be made concerning, for example, the functional form of the input parametrisation, the treatment of heavy quarks and their mass values, alternative theoretical calculations, alternative representations of the fit χ 2 and for different ways of treating correlated systematic uncertainties. It is useful to discriminate or quantify the effect of a chosen ansatz within a common framework and HERAFitter is optimally designed for such tests. The methodology employed by HERAFitter relies on a flexible and modular framework that allows independent integration of state-of-the-art techniques, either related to the inclusion of a new theoretical calculation, or of new approaches to the treatment of the data and their uncertainties.
Representation of χ 2 The PDF parameters are determined in HERAFitter by minimisation of a χ 2 function taking into account correlated and uncorrelated measurement uncertainties. There are various forms of χ 2 , e. g. using a covariance matrix or providing nuisance parameters to encode the dependence of each cor- Fig. 5  The gluon PDF as extracted by various groups at the scale of Q 2 = 4 GeV 2 , plotted using the drawing tools from HERAFitter related systematic uncertainty for each measured data point. The options available in HERAFitter are the following: Covariance matrix representation: For a data point μ i with a corresponding theory prediction m i , the χ 2 function can be expressed in the following form: χ 2 (m) = i,k (m i -μ i )C -1 ik (m k -μ k ), ( 18 ) where the experimental uncertainties are given as a covariance matrix C ik for measurements in bins i and k.
However, additive treatment of uncertainties is also possible in HERA-Fitter. During the χ 2 minimisation, the nuisance parameters b j and the PDFs are determined, such that the effect of different sources of systematic uncertainties can be distinguished. Mixed form representation: In some cases, the statistical and systematic uncertainties of experimental data are provided in different forms. For example, the correlated experimental systematic uncertainties are available as nuisance parameters, but the bin-to-bin statistical correlations are given in the form of a covariance matrix. HERAFitter offers the possibility to include such mixed forms of information. Any source of measured systematic uncertainty can be treated as additive or multiplicative, as described above. The statistical uncertainties can be included as additive or following the Poisson statistics.
Treatment of the theoretical input The results of a QCD fit depend not only on the input data but also on the input parameters used in the theoretical calculations. Nowadays, PDF groups address the impact of the choices of theoretical parameters by providing alternative PDFs with different choices of the mass of the charm quarks, m c , mass of the bottom quarks, m b , and the value of α s (m Z ). Other important aspects are the choice of the functional form for the PDFs at the starting scale and the value of the starting scale itself. HERAFitter provides the possibility of different user choices of all this input.
Bayesian reweighting techniques As an alternative to performing a full QCD fit, HERAFitter allows the user to assess the impact of including new data in an existing fit using the Bayesian Reweighting technique. The method provides a fast estimate of the impact of new data on PDFs. Bayesian Reweighting was first proposed for PDF sets delivered in the form of MC replicas by  [104]  and further developed by the NNPDF Collaboration  [109, 110]. More recently, a method to perform Bayesian Reweighting studies starting from PDF fits for which uncertainties are provided in the eigenvector representation has also been developed  [106]. The latter is based on generating replica sets by introducing Gaussian fluctuations on the central PDF set with a variance determined by the PDF uncertainty given by the eigenvectors.
Both reweighting methods are implemented in HERAFitter. Note that the precise form of the weights used by both methods has recently been questioned  [111, 112]. The Bayesian Reweighting technique relies on the fact that MC replicas of a PDF set give a representation of the probability distribution in the space of PDFs. In particular, the PDFs are represented as ensembles of N rep equiprobable (i. e. having weights equal to unity) replicas, { f }. The central value for a given observable, O({ f }), is computed as the average of the predictions obtained from the ensemble as O({ f }) = 1 N rep N rep k=1 O( f k ), ( 23 ) and the uncertainty as the standard deviation of the sample. Upon inclusion of new data the prior probability distribution, given by the original PDF set, is modified according to the Bayes Theorem such that the weight of each replica, w k , is updated according to w k = (χ 2 k ) 1 2 (N data -1) e -1 2 χ 2 k 1 N rep N rep k=1 (χ 2 k ) 1 2 (N data -1) e -1 2 χ 2 k , ( 24 ) where N data is the number of new data points, k denotes the specific replica for which the weight is calculated and χ 2 k is the χ 2 of the new data obtained using the kth PDF replica.
Given a PDF set and a corresponding set of weights, which describes the impact of the inclusion of new data, the prediction for a given observable after inclusion of the new data can be computed as the weighted average, O({ f }) = 1 N rep N rep k=1 w k O( f k ). ( 25 ) To simplify the use of a reweighted set, an unweighted set (i. e. a set of equiprobable replicas which incorporates the information contained in the weights) is generated according to the unweighting procedure described in  [109]. The number of effective replicas of a reweighted set is measured by its Shannon Entropy  [110] , N eff ≡ exp ⎧ ⎨ ⎩ 1 N rep N rep k=1 w k ln(N rep /w k ) ⎫ ⎬ ⎭ , ( 26 ) which corresponds to the size of a refitted equiprobable replica set containing the same amount of information. This number of effective replicas, N eff , gives an indicative measure of the optimal size of an unweighted replica set produced with the reweighting/unweighting procedure.
HERAFitter code organisation HERAFitter is an open-source code under the GNU general public licence. It can be downloaded from a dedicated webpage  [10]  together with its supporting documentation and fast grid theory files (described in Sect. 4) associated with data files. The source code contains all the relevant information to perform QCD fits with HERA DIS data as a default set. 1 The execution time depends on the fitting options and varies from 10 min (using "FAST" techniques as described in Sect. 4) to several hours when full uncertainties are estimated. The HERAFitter code is a combination of C++ and Fortran 77 libraries with minimal dependencies, i. e. for the default fitting options no external dependencies are required except the QCDNUM evolution program  [22]. The ROOT libraries are only required for the drawing tools and when invoking APPLGRID.
Drawing tools built into HERAFitter provide a qualitative and quantitative assessment of the results. Figure  7  shows an illustration of a comparison between the inclusive NC data from HERA I with the predictions based on HERAPDF1. 0 PDFs. The consistency of the measurements and the theory can be expressed by pulls, defined as the difference between data and theory divided by the uncorrelated error of the data. In each kinematic bin of the measurement, pulls are provided in units of standard deviations. The pulls are also illustrated in Fig. 7. In HERAFitter there are also available cache options for fast retrieval, fast evolution kernels, and the OpenMP (Open 1 Default settings in HERAFitter are tuned to reproduce the central HERAPDF1. 0 set. Multi-Processing) interface which allows parallel applications of the GM-VFNS theory predictions in DIS.
Data processing DISC data were processed with the open-source software package discusr (https://github. com/jiho/discusr) modified from  [33]. The camera produced 5 megapixels images of the fish larva in the chamber at 5 seconds intervals. The position of the larva was recorded on each image by clicking on it through a graphical user interface. Each position was converted to polar coordinates relative to the axis from the center of the chamber to the top of the picture. The angular part of the coordinate was converted to a bearing relative to the North by subtracting the bearing of the top of the picture, recorded by the digital compass. These bearings were the base data to detect cardinal orientation. In addition to cardinal orientation, the influence of various environmental cues was investigated.
This solar index ranges from 0 at sunrise to 1 at sunset; 0. 5 is the time when the sun is the highest in the sky. It combines the effect of the zenith (angle from the vertical) and azimuth (bearing) of the sun, which are highly interdependent. Wind, waves, and cloud cover may affect the solar signal. Meteorological data were obtained from Nice airport, located 8 km SW of the sampling zone. They included hourly averages of cloud cover (number of 1/8 th of sky occupied by clouds), wind bearing and speed (m. s -1 ). The drift direction and speed of the DISC were compared with larval orientation direction and strength to confirm that larvae did not simply orient into the current. Finally, the effects of other potentially confounding factors were considered, such as larva size, number of ships cruising by and presence of predators on the pictures.
However, such models cannot be implemented without knowledge of the pelagic life traits of the simulated larvae. Here, we gathered the first empirical data on larval fish orientation in the Mediterranean Sea. We demonstrated that settlement-stage larvae were capable of keeping a bearing and orienting, similarly to what has been observed in coral reef species. A collection of evidence points toward the sun as an orientation reference, which would enable larvae to orient over large scales during their pelagic phase. To better understand, model and predict connectivity, larval behavior should therefore be considered together with ocean currents, genetic population structure and adult population dynamics; it has received considerably less attention so far.
Normally, experts undertake this challenging and time-consuming process by collecting information from (i) generic or disease specific association databases (e. g. OMIM  (14) , GAD  (15) ), (ii) relevant literature or (iii) their own data and expertise (including for instance relevant patient records). Therefore, a tool that would support automatic identification of the training genes as an initial step to candidate gene prioritization would provide better usability to researchers interested in candidate gene prioritization  (16). Text mining is one popular strategy for automatically associating biomedical entities with each other  (17) (18) (19) (20) (21) (22) (23) (24). MeSHOP  (22)  and Genie  (24)  are two examples that associate genes with diseases. These tools can be used to rank a set of genes given a disease query, hence they could be used by the genetic experts to automatically search for the training input required by the gene prioritization tools.
To do this, we have extracted a list of experimentally validated disease-gene associations from the OMIM database. Then, for each disease in this list, we compared the associations returned by Beegle to the known associations from OMIM. In addition, we compared Beegle to MeSHOP, a similar tool, on a subset of the OMIM list using the same experimental setup. Second, we evaluated the suitability of the returned genes to serve as input to train genomic models and generate novel hypothesis. Here, we employed an evaluation methodology that mimics real discovery by using rolled-back data to generate the gene prioritizations, and then by testing on disease-gene associations that were reported after the training data were collected. For this we have used two benchmarks: one based on literature that has already been described  (10)  and a new one that we have generated from the OMIM database.
Annotating the literature We use the biomedical database MEDLINE as our source of literature. Offline, we have indexed every MEDLINE abstract using MetaMap  (25) , which identifies the UMLS concepts (26) within a given abstract text. UMLS is a large, multi-purpose and multi-lingual thesaurus that brings together many health and biomedical vocabularies and standards (e. g. MeSH and SNOMED CT). With this strategy we have associated each MEDLINE abstract to a list of UMLS concepts. This corresponds to 12 308 151 abstract-concepts entries. We report the corresponding list of MEDLINE ids in Supplementary Data 1. For every gene, we find the list of associated MED-LINE abstracts according to GeneRIF (downloaded in May 2012). Hence, we could generate a UMLS concept profile for all Entrez gene entries (16 493 genes in total, which we report in Supplementary Data 2).
Data sources and background corpus The public web interface of Beegle relies on the 2013 PubMed release 'downloaded on March 4th 2013'. Then it uses the 2013 (or the latest) version of the following data sources for the discovery phase: Gene Ontology (annotations for gene products) 'downloaded on May 15th 2013', Uniprot (protein sequence and functional information) 'downloaded on June 14th 2013', Text (MEDLINE literature) 'downloaded on March 4th 2013', STRING (genomic data integration) 'downloaded on June 10th 2013', Genetic Association Database 'downloaded on June 14th 2013', Rat Gene Database 'downloaded on June 20th 2013', gene predicted pathogenicity  (29)  and expression data  (30).
The literature-based benchmark The first benchmark we apply to mimic novel discovery is an existing validation set  (10) , which was previously used to compare the predictive performance of several publicly available prioritization tools. Briefly, it was manually prepared by reviewing the scientific literature to gather novel disease-gene associations. For more details about the construction of this validation set, we refer the reader to our previous work  (10). This set is composed of 34 queries and 42 annotations with at least one novel gene reported in 2010. In this benchmark, the training sets used for prioritization were manually extracted from the literature and dedicated databases.
This results in a refined list of disease-gene annotations (314 diseases and 2654 annotations) based on the OMIM database (version 2013). We call this list the OMIM-search validation set. To generate a benchmark that allows a validation that mimics novel discovery, we used two versions of OMIM (2010 and 2013) as follows: for both versions, we refined the list as described above. We then compared the output of Step 1 between both versions and discarded the diseases that did not appear in both lists. We also discarded diseases for which we could not find at least one 'novel' gene (i. e. reported in the 2013 version and not mentioned in the 2010 one). Finally, to avoid false positives, we verified the resulting entries by manually looking into the scientific literature. Our aim was to make sure the annotated genes were assigned to their correct disease concepts, and have indeed been reported in the correct period.
This process resulted in a final OMIM list of 104 diseases that had annotations in both the 2010 and 2013 versions, and for which we had at least one novel gene. This corresponds to a total of 959 annotations reported in 2010, and a total of 277 annotations newly reported after 2010. We call these lists the OMIMdiscovery validation set. The interest of this setup is that for a version of Beegle limited to using information obtained prior to 2010, the discoveries made in the 2010-2013 period will serve as a prospective validation of the tool. Since we consider both the OMIM-search and the OMIM-discovery validation sets a secondary contribution of this work, we release the full lists in Supplementary Data 3-6. Hence, other researchers can use them for evaluating different gene prioritization methodologies.
Protein interactions are essential in all biological processes. The changes brought about in the structure when a free component forms a complex with another molecule need to be characterized for a proper understanding of molecular recognition as well as for the successful implementation of docking algorithms. Here, unbound (U) and bound (B) forms of protein structures from the Protein-Protein Interaction Affinity Database are compared in order to enumerate the changes that occur at the interface atoms/residues in terms of the solvent-accessible surface area (ASA), secondary structure, temperature factors (B factors) and disorder-to-order transitions. It is found that the interface atoms optimize contacts with the atoms in the partner protein, which leads to an increase in their ASA in the bound interface in the majority (69%) of the proteins when compared with the unbound interface, and this is independent of the root-mean-square deviation between the U and B forms.
Materials and methods We used the Protein-Protein Interaction Affinity Database  (Kastritis et al. , 2011)  containing 144 complexes along with the corresponding unbound structures, except for seven antibodyantigen complexes for which the unbound structure is not known. We thus considered 281 bound-unbound structure pairs. The unbound structure is designated U and the bound structure (isolated from its partner component) is designated B. The bound form of the component in the presence of its partner (i. e. in complex) is designated C. EMBOSS  (Rice et al. , 2000)  was used to perform the local alignment (using the Smith-Waterman algorithm) of the polypeptide chains constituting U/B pairs; 249 had a sequence identity of !96%, with the rest having values in the range 90-95%. Based on the sequence alignment, the interface residues as seen in the complex were mapped onto those in the unbound state using ProFit  (McLachlan, 1982)  and Biopython  (Cock et al.
Hydrogen bonds were assigned using HBPLUS  (McDonald & Thornton, 1994)  with default geometrical parameters. Secondary structures were determined using DSSP  (Kabsch & Sander, 1983). In the affinity data set, 17 unbound structures were determined by NMR, of which 13 have multiple models or conformations. For each of them the first model was used. However, in a control calculation, the surface parameters calculated using all of the models were found to be essentially the same as those employed in the reported calculations.
While the majority of complexes show an increase in ASA, 31% (88 of the 281 components) have a negative A value, indicating that the interface atoms are pulled back into the structure to facilitate the interaction with the incoming partner molecule: a 'partner accommodation' effect. Fig. 2  shows such an example with a A value (À10%) from the opposite side of the distribution. It is seen that for the core domain of the HspBP1 protein the effect of binding has been to pull the interface atoms, which were extended into the solvent in the U form, towards itself to allow a closer approach Hydrogen-bond geometries (distances shown) in -amylase (green) and tendamistat (cyan) between His201 NE2 and Tyr820 OH for (a) the pseudo-complex and (b) the experimental complex [PDB entry 1bvn  (Wiegand et al.
, 1995) ; PDB entries 1pig  (Machius et al. , 1996)  and 1hoe  (Pflugrath et al. , 1986)  are the U forms]. ÁASA for the participating atom and all of the interface atoms of the residues are À0. 6  and À3. 2 A ˚2,  respectively, for His,  and 4. 2  and 15. 5 A ˚2 , respectively, for Tyr. by the partner molecule (the Hsp70 ATPase domain). While the two component contributions in the complex are weakly correlated (Fig. 3 ), we note that the proportions of complexes in which the ÁASA contributions of the two partner proteins are both positive (65 complexes; 47%), both negative (15 cases; 11%) and mixed positive and negative (57 complexes; 42%) are consistent with a simple statistical model of independent component contributions (p 2 + = 47%, p 2 À = 10% and 2p + p À = 43% for p + = 0. 69 and p À = 0.