dataesr/scientific-paragraphs-categorization huggingface dataset:
Plates were incubated for 2 h at room temperature (RT). While resazurin is naturally blue, its oxidation is associated with a change to red colour. To measure the colour change a dual wavelength measurement was performed (reference wavelength 604 nm, measurement wavelength 570 nm) using a Tecan Safire 2 microplate reader. For each plate the Z´factor 26 was calculated and was above 0. 55 (Supplemental Figure  1A ). The data were normalized using the following formula: % viability = 100 x (sample value -average value of positive controls) / (average of negative controls -average of positive controls). Active compounds were defined as compounds causing <1% viability. Chemical families were defined using clustering methods based on similarity concept using the Tanimoto coefficient and the K-modes clustering algorithm  [27] [28] [29].
Hybridization was performed using the DIG Easy Hyb buffer (Roche Applied Science, Meylan, France), at 24°C, overnight, in a rotating Agilent hybridization chamber. The slides were washed in decreasing concentrations of SSPE buffer (10 mM potassium phosphate (pH 7. 4), 150 mM NaCl, 0. 5 mM EDTA, 0. 05% (w/v) Triton X100) down to 0. 2 x SSPE, dried and treated immediately with the Agilent Stabilization and Drying Solution to avoid ozone-induced degradation of the Cy5 fluorophore. Scanning was performed on a Genepix 4200AL scanner (Molecular Devices, Saint Grégoire, France) and the images were analysed using Axon Genepix Pro 7 (Molecular Devices). We filtered the data according to our previous estimates of the reliability of the microarray sig nal. 32 Filtered data were normalized using the Loess algorithm (R package marray included in Bioconductor  33  ) separately for signals coming from upstream or downstream barcodes.
Confirmation of selected S. cerevisiae and C. albicans mutants In order to confirm the data of the fitness screen we selected several strains of the homozygous and the heterozygous deletion mutant collections (Table  1 ) and retested them singularly. The respective strains were streaked on YPD plates and single colonies were used to isolate genomic DNA of the strains using the Epicentre MasterPure Yeast DNA purification kit (Tebu-bio, Le Perray en Yvelines, France). PCRs were performed using oligonucleotides binding to the up-stream region of the respective gene and on the KANMX4 cassette, which was used to delete the genes. Confirmed strains, i e those harbouring a correctly integrated marker cassette, were inoculated from an overnight culture to an OD 600 =0. 002 in YPD medium and grown with (1) no compound, (2) 0.
00125 mg/L aminocandin and 20 mg/L colistin. After 10, 30, 50 and 70 minutes growth at 30°C, 4x10 6 cells were harvested, washed with 1x PBS, resuspended in 1x PBS and incubated for 3 min with 5µg Propidium Iodide (PI; Sigma-Aldrich) in the dark. Subsequently, the cells were washed with 1x PBS, resuspended in 1x PBS and analysed using a Miltenyi Biotec MACS QUANT FACS machine (Miltenyi, Paris, France). Cells treated with 70% ethanol for 1 h were used as positive control while untreated cells were used as negative control. In order to analyse the data and to calculate the percentage of PI-stained cells, the software FlowJo version 7. 6. 5 (TreeStar Inc, Oregon, USA) was used. The raw data were analysed using the statistical test Chi2-Mantel-Haenszel.
The group of mice not treated received 200 µl of sterile saline i. p. At 3 days postinfection (p. i. ), mice were euthanized by CO 2 asphyxiation. Kidneys of each animal were removed, weighed and homogenized in 1ml of sterile 0. 15 M NaCl. Serial 10-fold dilutions of the homogenates were plated onto SD agar containing 50 mg/L ticarcillin and 10 mg/L gentamycin and incubated for two days at 30°C. The fungal burden was determined by counting the CFUs and analysed using the software GraphPad Prism5. Data shown in Figure  6  represent pooled results of two independent experiments.
Data were normalized as percentage of viability relative to positive and negative controls. The Z' factor  26  , which is a statistical test to define the quality of a largescale screen was above 0. 55 for all plates, indicating the robustness and reliability of the assay (Supplemental Figure  1A ). Five hundred and fifty five compounds were identified whose combination with aminocandin resulted in 100% growth inhibition (Supplemental Figure  1B ) and were clustered into chemical families. A hundred and fifty six compounds selected within the different families were reevaluated using MIC tests and chequerboard assays. Among these, 98 showed antifungal activity independently of the addition of aminocandin while 58 had no significant antifungal activity on their own but acted synergistically with aminocandin (data not shown).
Subsequently, cells treated with the different compounds or combination of compounds were compared to non-treated cells in order to identify those mutants that showed increased or decreased fitness in one of the three conditions tested (Supplemental Tables  2  and 3 ). Selected mutants identified in this manner were then tested individually in order to confirm the data of the high throughput experiments (Supplemental Figure  2 ). [log 2 (treated/untreated) < -0. 7] or the combination of the drugs [log 2 (treated/untreated) < -1. 0]. Enriched functional annotations for haploid mutants showing hypersensitivity to drugs were evaluated using the generic GO Term Finder (http://go. princeton. edu/cgibin/GOTermFinder). "Glycosphingolipid biosynthetic process" was the only term significantly enriched (corrected p value 0.
Acknowledgements We thank  Neil Gow  and  Carol Munro  ( University of Aberdeen ),  David Perlin  ( University of New Jersey ),  Dominique Sanglard  ( Centre Hospitalier Universitaire Vaudois ) and  Rajendra Prasad  ( Jawaharlal Nehru University ) for providing strains. We thank  Bruno Didier  and  Marcel Hibert  ( Faculté de Pharmacie Strasbourg ),  Aurélien Lesnard  and  Sylvain Rault  ( CERM, Caen ),  Florence Mahuteau-Betzer  and  Daniel Dauzonne  ( Institut Curie, Paris ),  Francoise Gueritte  and  Olivier Pamlard  ( ICSN, Gif-sur-Yvette ) and  Yves Janin  ( Institut Pasteur, Paris ) for providing some of the selected compounds. We thank  Jean-Paul Latgé  for constant interest in this study.
Funding This work was supported by a grant from the " Conseil Régional d'Ile-de-France" (Chemical Library Project , grants n° I  06-222/R and I 09-1739/ R ) to H. M. -L. , a grant ( Programme Fungi ) from  Institut Carnot -Pasteur Maladies Infectieuses  to H. M. -L. and C. d'E. and a grant from " Agence Nationale de la Recherche " ( ANR-08-JCJC-0019-01 ,  GENO-GIM  project) to C. S. U. Z. and C. C were the recipients of post-doctoral grants in the framework of the  Programme Fungi. A. L. was the recipient of a post-doctoral fellowship from the " Conseil Régional d'Ile-de-France". Transparency declaration M. -E. B. has received grant for research from GILEAD and lecture fees from  Astellas. All other authors: none to declare.
Aminocandin 1 Combined 1 Description 2 1nvolved in establishing cell polarity and morphogenesis1Numbers represent log 2 of the ratio treated over control for each strain 2 Descriptions were retrieved from the Saccharomyces Genome Database 71 and simplified YLR372W YPL066W SUR4 -0. 6 0. 0 -0. 4 -0. 7 -1. 1 -1. 8 Fatty acid and sphingolipid biosynthesis Unknown function Golgi function YPL086C ELP3 0. 1 -0. 7 -0. 4 Subunit of Elongator complex -modifies tRNAs YER151C YPL158C UBP3 AIM44 -1. 1 0. 1 -0. 4 -0. 6 -0. 9 -1. 4 Ubiquitin-specific protease that acts in complex with Bre5 to co-regulate Unknown function YGR228W Glucan synthesis YKL126W YPK1 YKR001C VPS1 YFL001W DEG1 YDR372C VPS74 YLR140W RRN5* 0. 2 -1. 3 -0. 9 0. 3 -0. 8 0. 3 -0. 2 -1. 0 0. 0 -0. 8 0. 0 -1. 2 -1. 1 -1. 3 -0. 5 0. 1 -0. 7 0.
Participants We used data from the first wave of IMAGEN (n = 648). Individuals who had passed quality controls for genotyping, neuroimaging, and behavioral tests were included in the data set. Four hundred and fourteen adolescents passed the inclusion criteria for further analysis (190 boys, 224 girls). The mean age of the participants was 14. 4 years (SD:. 4; range: 13. 3-15. 6 years; Table  1 ). Participants were tested at eight IMAGEN assessment centres (London, Nottingham, Dublin, Mannheim, Berlin, Hamburg, Paris, and Dresden). Local ethics research committees at each site approved the study. On the day of assessment, written consent was obtained from the parent or guardian, and verbal assent was obtained from the adolescent. A detailed description of recruitment and assessment procedures and inclusion/exclusion criteria have been published elsewhere  (37).
Magnetic Resonance Imaging Data Acquisition and Analysis Structural and functional magnetic resonance imaging (fMRI) data were acquired at eight IMAGEN assessment sites with 3T MRI scanners of different manufacturers (Siemens, Munich, Germany; Philips, Best, The Netherlands; General Electric, Chalfont St Giles, UK; Bruker, Ettlingen, Germany). The scanning variables were specifically chosen to be compatible with all scanners. The same scanning protocol was used at all sites. In brief, high-resolution T1-weighted three-dimensional structural images were acquired for anatomical localization and coregistration with the functional time series. Functional MRI BOLD images were acquired with a gradient-echo, echo-planar imaging sequence. For the MID task, 300 volumes were acquired for each subject.
For the SST, 444 volumes were acquired for each subject. For both tasks, each volume consisted of 40 slices aligned to the anterior commission/ posterior commission line (2. 4-mm slice thickness, 1-mm gap). The echo time was optimized (echo time ¼ 30 msec, repetition time ¼ 2200 msec) to provide reliable imaging of subcortical areas (Supplement 1). Functional MRI data were analyzed with SPM8 (Statistical Parametric Mapping; http://www. fil. ion. ucl. ac. uk/spm). We extracted regions of interest using the Marsbar toolbox (http://marsbar. sourceforge. net). The IFG opercularis was extracted based on the Montreal Neurological Institute Automated Anatomical Labeling. Because the VS is not available in Automated Anatomical Labeling, it was created using Marsbar based on the peak of the "anticipation high win versus no win" contrast (xyz ¼ Ϯ9, 11, À2, 9-mm radius; Figure  S2  and Table  S2  in Supplement 1).
This work was supported by the  European Union -funded  FP6   Integrated Project IMAGEN (Reinforcement-Related Behaviour in Normal Brain Function and Psychopathology ; Grant No. LSHM-CT-2007-037286 ), the  FP7 projects ADAMS (Genomic Variations Underlying Common Neuropsychiatric Diseases and Disease Related Cognitive Traits in Different Human Populations ) (Grant No. 242257 ), and the  Innovative Medicine Initiative Project EU-AIMS  (Grant No. 115300-2 ), as well as the  United Kingdom National Institute for Health Research Biomedical Research Centre Mental Health  and the  Medical Research Council   Programme Grant  " Developmental Pathways into Adolescent Substance Abuse " (Grant No. 93558 ) and the  Swedish Research Council. The authors report no biomedical financial interests or potential conflicts of interest.
In this paper, we characterize the incidence of AGN in star-forming galaxies at intermediate redshift (0. 3 < z < 1). The multi-wavelength dataset and galaxy samples are described in Sections 2 and 3, respectively. The AGN diagnostics are introduced in Section 4. Section 5 contains the results regarding the AGN fraction among star-forming FIR-selected galaxies ( §5. 1); the occurrence of active nuclei as a function of infrared luminosity ( §5. 3) and as a function of specific star formation rate (sSFRs; §5. 4); the absorption of AGN in relation to host galaxies ( §5. 5); and the influence of AGN emission on mid-to-far infrared color, probing the dust temperature on the warm side of the infrared SED ( §5. 6). The caveats are discussed in Section 6. 1, followed by the possible connections between AGN obscuration and host galaxies (Section 6.
MULTIWAVELENGTH DATASET The primary intermediate-redshift galaxy sample is based on observations from the Great Observatories Origins Deep Survey 19 (GOODS) and the All-wavelength Extended Groth strip International Survey 20 (AEGIS), specifically in the GOODS-North and the Extended Groth Strip (EGS) fields. 2. 1. Infrared Photometry Both fields were observed during the Far-Infrared Deep Extragalactic Legacy survey (FIDEL;  Dickinson & FIDEL team 2007) , yielding sensitive Spitzer observations at 24µm and 70µm (respective 3σ limiting fluxes of ∼20µJy and 2. 5 mJy). MIPS photometry was obtained using a guided extraction method, where images at shorter wavelength, less subject to confusion, are used to build a set of prior positions to fit for the same sources at longer wavelengths. In this case, IRAC 3.
X-ray Data We take advantage of the fact that the Chandra X-ray coverage is very deep: 2 Ms in GOODS-N  (Alexander et al. 2003 ), 200 ks (Laird et al. 2009; Nandra et al. 2005) , and 800 ks in the EGS  (Laird et al. , in prep. ;  reduced in a similar fashion as  Laird et al. 2009 ). In the EGS, we favor the 800 ks X-ray fluxes when available, and use the wider area 200 ks observations otherwise. The shallower EGS X-ray data (200 ks) are sufficient to detect moderate luminosity X-ray AGNs (L 2-10keV > 10 42 erg s -1 ) out to z ∼ 1. Furthermore, 21 http://tkserver. keck. hawaii. edu/tksurvey/ 22 http://deep. berkeley. edu/spec2d/, developed by the DEEP2 team at the University of California-Berkeley we can detect fainter X-ray galaxies (mostly starbursts) in GOODS-N and in the 800 ks portion of the EGS.
Therefore, we expect that this category of AGNs will not be important for the sample selected here. In the GOODS-N field, there are deep Very Large Array (VLA) 1. 4 GHz observations reaching 3. 9 µJy beam -1 rms with a beam size of ∼1. ′′ 7  (Morrison et al. 2010). These data were used to identify radio-excess AGNs by Del  Moro et al. (2012)  for counterparts of 24 µm sources which have S/N>3 in the 1. 4 GHz observation (S(1. 4 GHz) > 12 µJy). Infrared SED fitting was used to compute FIR emission integrated over rest-frame ∼ 40-120 µm in order to apply a standard FIR-radio correlation 31. Only one galaxy in our GOODS-N subsample is selected as a radio-excess source by Del  Moro et al. (2012). This galaxy is also identified as AGN from all the other methods (X-ray AGN, IR AGN, and MEx-AGN) and therefore does not add a new AGN population.
The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The Keck Observatory acknowledges the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community and appreciate the opportunity to conduct observations from this mountain. Funding for the DEEP2 survey has been provided by NSF grants AST95-09298, AST-0071048, AST-0071198, AST-0507428, and AST-0507483 as well as NASA LTSA grant NNG04GC89G. The analysis pipeline used to reduce the DEIMOS data was developed at UC Berkeley with support from NSF grant AST-0071048. The TKRS was funded by a grant to WMKO by the National Science Foundation's Small Grant for Exploratory Research program. SJ was partially funded by a FQRNT fellowship (Fonds Québécois de recherche sur la nature et la technologie, Canada), a Philanthropic Educational Organization (P.
In this regard, one should note that small dams in South Africa are typically earth-fill dams, which are constructed as a simple embankment of well-compacted earth. In fact, in our database, more than 92. 1 percent of dams classified as small are earth-fill dams. Importantly, small earth-fill embankment dams are often built on ephemeral (seasonal) rivers (see  RAIN [2007]  and  Lasage et al. [2008] ) In contrast, such rivers are generally unsuitable for large dams, which, in South Africa, are almost exclusively buttress, arch, or gravity dams. 5 We thus use the interaction term of our policy variable with perennial (year-round) river gradients as instruments for both large and small dams, but we also use ephemeral length within a basin (interacted with the policy variable) to separately identify the potentially different effect of small dams on agricultural productivity.
5-3 percent (moderate), 3 -6 percent (steep), and over 6 percent (very steep) river gradient. ERLEGNTH i is the length of ephemeral rivers within a basin and is not used when sz ¼ LARGE. Our source for all geographical information on rivers is the Food and Agriculture Organization's Rivers of Africa geospatial database  (FAO 2000). To calculate all other geographical features, we use the GTOPO20 (USGS 2011a) data set. The interaction of year dummies, l t , with the river gradient variables is intended to capture country-wide, time-varying effects of river gradients that may affect dam construction, whereas M i is a vector consisting of river-basin-specific, time-invariant measures of elevation, gradient, river length, and river basin area. 7 5. More than 85 percent of large dams in South Africa are of the arch or gravity type, or both.
Dams To identify and geographically locate dams in South Africa, we use the Department of Water Affairs (DWA) dams database (DWA 2011), which collects detailed information on dams that fall within the dam safety permit system, essentially covering all known dams with at least 5 m height and a reservoir capacity of 50,000 m 3. 8 Importantly, the DWA dams database provides information on the exact latitude and longitude of the location, construction date, reservoir surface area and capacity, purpose, size classification, and, for many dams, height. Our purpose here is to distinguish between the effects of large and small dams in South Africa. A crucial component in this regard is the definition of dams as large versus small, a classification for which there is no global agreement. In fact, definitions differ widely.
Cropland Productivity Time-varying standard agricultural measures of cropland productivity, such as crop yields, do not exist for South Africa at a spatial resolution as fine as our hydrological regions. 9 We thus follow  Blanc and Strobl (2013)  and use two satellite data sources. The first is the Global Land Cover 2000 data set (GEM 2011), hereinafter called GLC 2000, which classifies land cover across the globe into 22 distinct land cover categories at a 1 km resolution based on images acquired by the SPOT 4 satellite during 2000. We use the land cover category "Cropland" to spatially capture agricultural crop areas within our basins, although we also experiment with including the categories "Mosaic of Cropland/Shrub or Herbaceous Cover" and "Mosaic of Cropland/Tree Cover/Other Natural Vegetation".
The "pure" cropland cells constitute approximately 14 percent (56,000 km 2 ) of South Africa. According to the GLC 2000, 15 percent of the six-digit code basins did not contain any "pure" cropland cells. Of the 223 basins that did, 77 were located in the former homeland river basins. We depict the identified cropland areas within South Africa in figure  4. The largest areas of "pure" cropland are FIGURE 3. River Basins 9. Even more aggregate regional level agricultural data on any consistent basis only exists for the former nonhomeland areas prior to the fall of apartheid. located in the northern part of South Africa, mainly in nonformer homeland territories. 10 Most dams depicted in the same graph are located near cropland, except in the central part of the country. To measure the productivity of cropland areas within basins over time, we use as a proxy their net primary production (NPP) as measured by satellite images.
NPP quantifies the conversion of atmospheric carbon dioxide into plant biomass, and the resultant values can serve as a proxy of cropland productivity. Examples using NPP to proxy cropland productivity include  Heinsch et al. (2005)  and  (Lobell et al. 2002). As  Hicke, Lobell, and Asner (2004)  note, one of the advantages of using NPP to proxy cropland productivity over large areas and over time is that, unlike economic data, it provides a common metric among different crop types, thereby facilitating comparisons and aggregation over all types. NPP data for South Africa are the MOD17A2 measures derived from observations of the Moderate-resolution Imaging Spectroradiometer on the NASA Earth Observing System Terra satellite. The data are available at the 8 km spatial level on an annual basis for the period 1981 to 2000 and are given as grams of carbon per square meter (gC/m 2 /year).
We use the annual average of NPP to proxy cropland productivity. Although the Moderate-resolution Imaging Spectroradiometer data are at a greater resolution than the GLC 2000 data, this generally did not pose a problem in terms of masking out cropland given that most cropland cells were agglomerated at a much larger scale than 8 km. The use of the Moderate-resolution Imaging Spectroradiometer data as a proxy for actual cropland productivity has been validated in a number of studies  (Zhang et al. 2008; Turner et al. 2006 ). To roughly verify that this measure can be related to standard agricultural data, we aggregated its value for all of South Africa and correlated it with FAOSTAT (2011) data-derived measures of cropland productivity. The resultant Pearson correlation coefficient was 0. 74 and was significant at the 1 percent level, providing evidence that these measures are substantially positively related.
Most important, the joint F-test statistic for the river gradient interaction 14. We also attempted to run this specification for each year, excluding the time dummies. The results are qualitatively and, generally, quantitatively very similar across all years. 15. For a few large dams, our dam database lists other purposes in addition to irrigation. However, in many cases, internet searches on the dams that only listed irrigation as their purpose also indicated that these dams were used for other purposes, such as hydropower and water supply. Therefore, it is unlikely that the information on the purpose of dams allows us to truly distinguish between multipurpose and single purpose dams. Nevertheless, we reran our specification for large dams excluding the dams that listed more than one purpose.
Finally, one should note that underlying the identification of the IV strategy is the assumption that agricultural production would not have varied in response 17. Of our 223 basins, 960 also contained this type of vegetation. to the policy changes considered as function of land suitability in the absence of irrigation dam construction. An insightful placebo test in this regard would be to conduct a similar analysis using all nonirrigation dams in South Africa. More specifically, we employ the same IV strategy as above using nonirrigation dams from the DWA database. There were 90 large and 772 small nonirrigation dams in the data, the main functions of which differed widely, ranging from water and industrial supply to recreational use. The coefficients on the nonirrigation dams are reported in the last column of table 5.
I V. C O S T B E N E F I T A N A L Y S I S We now use our estimated coefficients to provide a cost-benefit analysis of large and small dams. Unfortunately, we do not have explicit information on the costs of construction of the dams in our database. However,  Alexander and van Wyk (2005)  provide estimates of costs for nine dams in South Africa according to their height and capacity, and we use these data to infer the costs of dams in our sample. More specifically, we regress their unit costs in terms of dollar per cubic meter of capacity on dam height and find the following relationship: Unit cost ¼ 0:4161921 À 0:0063412 Ã height ð4Þ where both coefficients are significant at the 1 percent level. Using the mean values of the capacity and height of small and large dams from our sample as given in table 1, these estimated coefficients suggest that large and small dams have average unit costs of $0.