dataesr/scientific-paragraphs-categorization huggingface dataset:
The Little Ice Age, which according to our best-dated records, initiated at ∼ 660 cal yr BP, is seen as an episode of deteriorating conditions, with Arctic/Polar surface waters off western Svalbard and the western Barents Sea, possibly associated with severe sea ice conditions, and a strongly reduced AW flow. This strong cooling was interrupted in the eastern Fram Strait by a short resumed high flow of WSC from ca. 330 to 410 cal yr BP, whose magnitude was only surpassed by the one that characterizes the modern period. Our dataset not only confirms the high amplitude warming of surface waters at the turn of the 19th century off western Svalbard, it also shows that such a warming was primarily induced by an excess flow of AW, which stands as unprecedented over the last 3000 yr.
Acknowledgements. This work is a contribution to "The  Changing Arctic and Subarctic Environment" (CASE) Initial Training Network  funded by the  European Community 's  7th Framework Programme   FP7 2007/2013 ,  Marie Curie Actions , under Grant Agreement No. 238111. Thanks are due to the captain and crew of the R/V  Helmer Hanssen  and  S. Iversen  for core collection ( HH11-134-BC ). L. Rossignol  and  M. Georget  (EPOC) assisted with sampling preparation and micropaleontological investigations. We acknowledge support from the  ARTEMIS 14 C Accelerator Mass Spectrometry CNRS/MRT/CEA/IRSN/IRD Program. Antoon Kuijpers ,  Jochen Knies , and an anonymous reviewer are acknowledged for their inspiring comments on an earlier version of this manuscript. Edited by:  J. Knies The publication of this article is financed by  CNRS-INSU.
Data analysis Patient data, drug therapy, and clinical pharmacist recommendations were recorded into an MS Access database (version 2007; Microsoft Corporation, Redmond, WA, USA). Statistical analysis was performed by using SPSS version 19 (SPSS, Inc, Chicago, IL, USA). The interrater variability was assessed by the kappa statistics, whereby raters were compared two by two. A kappa value of. 0. 8 indicates nearly perfect interrater agreement; kappa values between 0. 6 and 0. 8 indicate substantial agreement, and kappa values between 0. 4 and 0. 6 indicate moderate agreement. The difference in MAI scores before and after the recommendation was calculated by the Wilcoxon matched-pairs signed-rank test. A P-value , 0. 05 was considered statistically significant.
Acknowledgments The authors are indebted to the participating geriatricians, professors  Dr N Van Den Noortgate ,  Dr A Velghe ,  Dr P De Smet , and  Dr K De Wilde ; to head nurse, H Van Doninck; and to the patients of the geriatric ward. Furthermore, we thank the clinical pharmacists ( S Deryckere  and  S Mertens ) for their evaluation of the recommendations. Useful comments for this manuscript were provided by  Professor Dr M Bogaert. This study was not funded.
4 hr in the riz filters in poor seeing conditions (Table  2 ). We analyze the data using the IRAF gemini package, and detect a single source within the enhanced XRT error circle in all three filters. To assess any potential fading of the source, we obtained a second set of observations at δt ≈ 2. 6 d, where the source is clearly extended. Digital image subtraction using the ISIS software package  (Alard 2000)  shows no residuals in all three filters (Figure  2 ). We therefore place 3σ limits of r 22. 6 mag, i 22. 7 mag and z 22. 8 mag on the optical afterglow at δt ≈ 12. 7 hr (Table  2 ). The GMOS zeropoints are determined by sources in common with late-time IMACS observations (see below), which are calibrated to a standard star field at a similar airmass. Our limits match the GROND limits reported at δt ≈ 12.
2 hr  (Nicuesa Guelbenzu et al. 2012). In addition, we obtained two epochs of J-band observations with the Persson's Auxilliary Nasmyth Infrared Camera (PANIC) mounted on the 6. 5-m Magellan/Baade telescope at δt ≈ 1. 6 and 6. 6 d. We analyze the data using standard procedures in IRAF. Digital image subtraction shows no evidence for fading, with a 3σ limit of J 23. 9 mag (photometrically tied to the 2MASS catalog and converted to the AB system) at δt ≈ 1. 6 d (Table  2 ). We obtained late-time griz observations of the the field of GRB 100625A with the Inamori Magellan Areal Camera and Spectrograph (IMACS) mounted on Magellan/Baade starting on 2010 November 14. 11 UT. We also obtained K s -band observations with the FourStar Infrared Camera mounted on Magellan/Baade on 2011 December 07. 16 UT (Table  2 ).
The griz zeropoints are calculated using a standard star field at a similar airmass, while the K s -band zeropoint is determined from point sources in common with 2MASS. Our afterglow limit and host galaxy photometry are summarized in Table  2. We obtained a spectrum of the putative host galaxy with the Low Dispersion Survey Spectrograph 3 (LDSS3) mounted on the 6. 5-m Magellan/Clay telescope on 2011 October 21. 27 UT. A dithered pair of 2700 s exposures was obtained with the VPH-ALL grating, which has a wavelength coverage of 4000 -10000 Å and a spectral resolution of ≈ 8 Å. We used standard tasks in IRAF for data reduction, HeNeAr arc lamps for wavelength calibration, and observations of the smoothspectrum standard star EG131 for flux calibration. We discuss  the spectral features and redshift determination in §4.
6 mag at δt = 1. 7 hr. Finally, to complement our early optical/NIR observations, we obtained imaging of the putative host galaxy in the gz-bands with LDSS3 starting on 2011 January 12. 15 UT, and in the K s -band with FourStar on 2011 December 07. 24 UT. Our limits for the afterglow and photometry of the putative host galaxy are summarized in Table  2. We obtained spectroscopic observations of the host on 2011 January 2. 25 UT using GMOS on Gemini-North at a mean airmass of 1. 2. We obtained a set of 4 × 1800 s exposures with the R400 grating and an order-blocking filter, OG515 in the nod-and-shuffle mode, covering 5860 -10200 Å at a spectral resolution of ≈ 7 Å. We used standard tasks in IRAF for data reduction, CuAr arc lamps for wavelength calibration, and archival observations of the smooth-spectrum standard star BD+28 4211 for flux calibration.
X-ray Observations XRT commenced observations of the field of GRB 110112A at δt = 76 s and located a fading X-ray counterpart with a UVOT-enhanced positional accuracy of 1. 6 ′′ radius  (Evans et al. 2011; Goad et al. 2007; Evans et al. 2009 ; Table  1 ). We extract the XRT light curve and spectrum in the manner described in Section 2. 1. 1, requiring a minimum S/N = 3 for each bin, and use the Galactic absorption in the direction of the burst of N H,MW = 5. 5 × 10 20 cm -2  (Kalberla et al. 2005). The light curve is characterized by a short plateau for δt 200 s, followed by a steady decline (Figure  5 ). Performing χ 2 -minimization, we find the XRT light curve for δt 200 s is best fit with a single power law characterized by index α X = -1. 10 ± 0. 05 (χ 2 ν = 1. 0 for 17 d. o. f. ). Our best-fitting spectral parameters over the entire data set, where there is no evidence for spectral evolution, are Γ = 2.
Optical Afterglow Discovery UVOT commenced observations at δt = 80 s, and no corresponding source was found within the XRT position to a 3σ limit in the white filter of 21. 3 mag using data over δt = 4400 -6100 s (uncorrected for Galactic extinction; Breeveld & Stamatikos 2011). We obtained i-band observations with ACAM mounted on the 4. 2-m William Herschel Telescope (WHT) at δt = 15. 4 hr. In a total exposure time of 600 s (Table  2 ), we detect a single source within the enhanced XRT error circle with i = 22. 77 ± 0. 29 mag, where the zeropoint has been determined using sources in common with the SDSS catalog (Figure  6 ). To assess any fading associated with this source or within the XRT position, we obtained i-band imaging with LDSS3 starting on 2011 June 27. 83 UT and no longer detect any source within the error circle to i 24.
HOST GALAXY PROPERTIES 4. 1. GRB 100625A The XRT position of GRB 100625A fully encompasses a single galaxy, which we call G1 (Figure  2 ). To assess the probability that the burst originated from G1, we calculate the probability of chance coincidence, P cc (< δR), at a given angular separation, (δR) and apparent magnitude (m) for galaxies within 15 ′ (the field of view of our images) of the burst position  (Bloom et al. 2002; Berger 2010a). For G1, we conservatively assume δR = 3σ XRT ≈ 3. 4 ′′ , and calculate P cc (< δR) ≈ 0. 04. The remaining bright galaxies in the field have substantially higher values of P cc (< δR) 0. 17, and a search for galaxies within 5 • of the GRB position using the NASA/IPAC Extragalactic Database (NED) yields only objects with P cc 0. 98. From these probabilistic arguments, we consider G1 to be the host galaxy of GRB 100625A.
We thank  F. di Mille  for observing on behalf of the  Berger GRB group at Harvard. The Berger GRB group is supported by the  National Science Foundation  under Grant  AST-1107973 , and by  NASA/Swift   AO6  grant  NNX10AI24G  and  A07  grant  NNX12AD69G. Partial support was also provided by the  National Aeronautics and Space Administration  through  Chandra Award Number   GO1-12072X  issued by the Chandra X-ray Observatory Center, which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of the  National Aeronautics Space Administration  under contract  NAS8-03060. This paper includes data gathered with the 6. 5 meter  Magellan Telescopes  located at  Las Campanas Observatory, Chile. This work is based in part on observations obtained at the  Gemini Observatory , which is operated by the  Association of Universities for Research in Astronomy, Inc.
Data sets We compiled datasets from aquatic invertebrate studies of 14 temporary rivers in Europe (seven rivers), North America (five rivers), and New Zealand (two rivers). The datasets consisted of matrices of invertebrate taxa, abundances and sampling dates at multiple sites within temporary and perennial river reaches. The average number of days between two consecutive sampling events on a given site was 146. 5 ±74. 5 d (SD?), and the average distance between sites was 3. 1 ± 2. 6 km. At each site, invertebrates were collected from riffle habitats using standardized and comparable sampling methods (Surber, Hess, and kick-net samplers with mesh sizes ranging from 250 to 500 µm) from at least three sites per river (Table  1 ). The datasets were classified by the spatial arrangement of perennial and temporary reaches (upper, middle and lower reach drying, Table  1 ).
Quantification of flow intermittence For each data set, annual flow intermittence (FI), the proportion of the year with zero-flow, was calculated for each sampling site. One of three procedures outlined below was used to calculate FI values for the sampling sites in each river, reflecting the type of flow measurement and quantity of discharge data available in each study. River discharge was standardized to L s -1. Hydrological modelling. For five datasets (Albarine, Asse, Little Stour, Orari, Selwyn), mean daily flow (including zero-flow) at sampling sites were estimated using the statistical model ELFMOD  (Larned et al. 2011). For each river, the input data consisted of manual measurements of discharge at sampling sites on ≥ nine dates, and continuous discharge from at least two permanent recorders that bounded the study reaches.
The discharge time-series from each recorder was ≥ 10 years long, and included the study period during which invertebrate samples were collected. Modelled mean daily discharge was used to calculate FI for each site each year. Direct measurement. For four datasets (Garden, Huachuca, Little Lusk, Sycamore), waterstate loggers were used to record the presence or absence of water during the invertebrate sampling period  (Fritz et al. 2006, Jaeger and Olden 2012). FI was calculated for each sampling site from the logged time-series. Estimates of FI based on ELMOD and water-state loggers at sampling sites along the Albarine River were highly correlated (r = 0. 93, P < 0. 001, n = 9). Direct observations. For five datasets (Alme, Ellerbach, Fish Brook, Menne, Sauer) FI was estimated at sampling sites using weekly to bi-monthly observations of flow state (flowing or completely dry) for three to 12 months.
Invertebrate variables For each sample in the invertebrate datasets, taxon richness (TR) was calculated as the number of taxa per sample. To account for differences in taxonomic resolution among datasets, TR was calculated at three levels of resolution: fine (TR1), medium (TR2), and coarse (TR3). The degree of taxonomic consistency among datasets increases and taxonomic precision decreases from TR1 to TR3. The taxonomic groups used at each level of resolution are shown in Table  2. To describe the communities in each dataset in terms of taxonomic composition and lifehistory traits, the relative abundances of Coleoptera, Diptera, Oligochaeta, and the sum of Ephemeroptera, Plecoptera and Trichoptera (hereafter 'EPT') were calculated in each sample. In addition, each taxon in each dataset was classified into one of four classes using life-history and biological traits: resistant, resilient, both resistant and resilient, or neither resistant nor resilient.
Trait classification was undertaken at the coarsest taxonomic level (TR3) to maximize consistency across datasets although this reduced our ability to detect fine-scale patterns. We assigned the following traits to the resilient class: long adult lifespan, high female dispersal, strong adult flying ability, common occurrence in drift, and strong swimming ability. We assigned the following traits to the resistant class: presence of desiccation-resistance forms (e. g. , cysts, cocoons, diapause stages), body armouring limiting water loss (including the use of external cases), plastron/spiracle respiration, and low rheophily. We used published  (Tachet et al. , 2002 , Poff et al. 2006 , Bonada and Doledec 2011)  and unpublished (V. Archaimbault, personal communication) trait information to classify each taxon (Appendix S2).
Statistical analyses The invertebrate community variables described above were used as dependant variables in linear mixed-effects models with Gaussian error distributions. Analyses were undertaken using the lme4 package for R (R Development Core Team 2008). For each dependent variable, we tested for effects of FI and then tested if these effects differed between rivers and spatial drying patterns. We compared three nested mixed-effects models that progressively increased in complexity. The first model (null) was a null model with a random intercept. The second model (average) was an average model with a fixed effect of FI across all rivers. The third model (river-specific) was a model with a random effect of FI, which was allowed to vary among rivers  (Bolker et al. 2009). River was entered as a random effect in each model.
Acknowledgements. TD thanks  R. Corti ,  G. Le Goff ,  R. Mons ,  M. Philippe  and  P. Roger  for help in sample collection,  J. Rigaut  for invertebrate processing and  J. Mouthon  for mollusk determination. Funding was provided by the  Water Agency RM&C, Temporary River Ecology Programme. STL thanks  D. Arscott ,  L. Drummond ,  P. McCaskill  and  A. Savery  for help with sample.
and flow gauging, and P. Lambert for invertebrate processing and identification. Funding was provided by the New Zealand Ministry for Science and Innovation, Environmental Flows Programme (C01X0308). KMF thanks B. Johnson, L. Winters, E. Hammer for help in sample collection and B. Thoeny, C. Watson, and P. Weaver for invertebrate processing and identification. The views expressed in this article are those of the authors and do not necessarily represent the views of policies of the U. S. Environmental Protection Agency. MTB thanks D. A. Lytle and K. S. Boersma for help in sample collection and K. L. Jaeger for providing flow intermittence data. Funding was provided by a United States Department of Defense grant (RC-1724) to J. D. Olden and D. A. Lytle. PJW thanks M. D. Agnew and N. Wood for help with sample collection.
Funding was provided during the 1996-1997 drought by the Environment Agency of England and Wales. EIM thanks S. Baltzer, M. Drees, K. Gravelaar, S. Knoche, J. Meinhardt, S. Riederer and M. Sehmisch- Bartel for sample collection and A. Meyer, C. Göcke, N. Kaschek, H. W. Riss and C. Tödtmannul for help in invertebrate processing and identification. We thank J. Diettrich for hydrological analyses, D. B. Arscott and N. Lamouroux for stimulating discussions, and N. Lamouroux, V. Resh and B. Robson for comments and meticulous editing, which improved the final version of this paper. Literature Cited Arscott, D. B. et al. 2010. Aquatic invertebrate community structure along an intermittence gradient: Selwyn River, New Zealand. -Journal of the North American Benthological Society 29: 530-545.
Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations.
Proteincoding SNPs can be further divided into synonymous and non-synonymous (nsSNPs). nsSNPs, also called missense mutations, result in the alteration of the amino acid sequence of the encoded protein. nsSNPs have been linked to a wide variety of diseases; for example, by affecting protein function, by reducing protein solubility or by destabilizing protein structure. 2 These protein alterations are considered to be the primary molecular phenotype linked to the missense mutation, with a cascade of consequences that finally leads to the emergence of a genetic disease and the associated phenotype. The elucidation of the complex relationships linking genotypic and phenotypic variations is a major challenge in the postgenomic era. With the huge amount of information now available in various biological databases, including sequences, structures, functions, pathways, interactions and variations,  3  it is now feasible to develop in silico analysis tools to better understand and/or to predict the correlation between a missense mutation and the associated molecular phenotypes.
KDD is commonly defined as the "non trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data". 18 From an operational point of view, the KDD process is performed within a KDD system including databases, data mining modules, and interfaces for human interactions, such as editing and visualization. The KDD process involves 121 three main operations: selection and preparation of the data, data mining, and finally interpretation of the extracted units. Most data mining algorithms used in KDD accept as input a single database table where the data to mine are represented as objects displaying specific values for given properties. This constraint is very prohibitive in the case of biomedical data, where different types of objects are often stored in different tables.
This has led to the exploitation of logic reasoning approaches, such as Inductive Logic programming (ILP). 19, 20 ILP is a method by which a computer program can learn concepts, or rules, by example. It has been applied successfully to various bioinformatics problems including breast cancer studies,  21  protein structure prediction,  22, 23  gene function prediction,  24  protein-protein interaction prediction,  25  proteinligand interaction prediction  26  and microarray data classification. 27 ecently, we implemented the ILP method in the KD4v system  28  for the interpretation and prediction of the phenotypic effects of missense variants. The performance of the ILP prediction was shown to be comparable to SIFT and PolyPhen-2, the most commonly used methods in the field. The power of the KD4V approach has also been demonstrated in a number of recent studies devoted to specific human diseases, including multifactorial diseases (complete congenital stationary night blindness).
29, 30 n this paper, we describe a study dedicated to the impact of mutations on protein function in the context of human monogenetic diseases. We extracted background knowledge from the MSV3d 31 annotated mutation database, which integrates a large number of human mutations and phenotypes. We then applied ILP and a clustering analysis to learn a set of rules that can be easily interpreted by the biologist and reused for the prediction of the functional effects of a mutation.
Materials and Methods MSV3d The datasets used in this study were taken from the relational database MSV3d ("Database of human missense variants mapped to 3D protein structures", publicly accessible online at decrypthon. igbmc. fr/ msv3d). MSV3d is designed to facilitate the investigation of the structural and functional impacts of missense mutations with regard to their phenotypic effects in the context of human genetic diseases. Based on Multiple Alignments of Complete Sequences (MACS)  32  and 3D structures available in the SM2PH database,  33  MSV3d annotates each mutation with various parameters describing the physico-chemical changes induced by the amino acid modification, as well as the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model.
The human missense variants in MSV3d are mainly retrieved from the dbSNP  34  and UniProtKB databases,  35  but also from several Locus Specific DataBases 36 (LSDBs), such as the ALPL gene mutations database. MSV3d classifies these variants in two categories: mutations linked to known human diseases (deleterious) and those associated with the "polymorphism" term (neutral), in accordance with the nomenclature used in the UniProtKB database. MVS3d currently contains a total of 404,425 missense variants from 20,219 human proteins, among which 27,401 are considered as deleterious and 377,024 as neutral. Concerning structure modelling, the database contains 58,726 variants mapped to 3D structure, among which 7,113 variants are associated with 1,278 OMIM disease annotations. The database facilities exploration of the relationships between genetic variations and 3D structure via a unified access to databases, including SOAP web services, a Java API, simple queries and full or partial database download services.
Inductive logic programming (ILP) ILP  37  combines machine learning and logic programming. Given a formal encoding of the background knowledge and a set of examples, an ILP system will derive hypotheses which explain all the positive examples and none, or almost none, of the negative examples. In this approach, logic is used as a language to induce hypotheses from the examples and background knowledge. Briefly, the basic form of the ILP problem is defined as follows. Given • A background knowledge B which is the knowledge available before the learning. In comparison to other machine learning approaches, ILP has several advantages. Firstly, in data mining, ILP is able to discover knowledge from a multi-relational database consisting of multiple tables. Thus, ILP is also called multi-relational data mining.