dataesr/scientific-paragraphs-categorization huggingface dataset:
RICAP-2014 close to the endpoint of the pp-induced recoil spectrum, E max R =264 keV. This underlines the importance of a careful assessment of pileup events, both 14 C-14 C and other pile-up. The pile-up component can be determined using an independent, data-driven method which we called synthetic pileup. Real triggered events with no cuts are artificially overlapped with random data obtained from the ends of real trigger windows, uncorrelated with the triggering event. By overlapping four random data samples with each physical trigger, we obtain a sample of synthetic events that has four times the exposure of the real data set used. The synthetic events are then reconstructed using the same software used for real events, and selected with the same criteria. Using this method, we obtained the true rate and spectral shape of pile-up in our detector.
This method naturally accounts for the effect of fiducialization on pile-up events. With the 14 C and pile-up rates fixed, we obtained the pp rate by fitting the data spectrum in a window of 165-590 keV against the known spectral shapes. The fit was performed with a tool named spectral-fitter, previously used in 7 Be  [5]  and pep  [7]  analyses. The tool was improved to work on any Borexino analysis. In particular, we included a treatment of the scintillator energy response to mono-energetic electrons at high statistics; a modified treatment of the energy resolution at low energy; and the introduction of synthetic pile-up. In addition to pp, 14 C and pile-up, other species included in the fit are other solar neutrinos ( 7 Be, CNO and pep), 85 Kr, 210 Bi, 210 Po and 214 Pb. The 7 Be rate was fixed to the rate previously obtained by Borexino  [5] ; CNO and pep rates were fixed to the values predicted by the highmetallicity Standard Solar Model; remaining background rates are left free in the fit, except for 214 Pb, which is fixed to the value measured by looking at 214 Bi-214 Po coincidences.
The scintillator light yield and two energy resolution parameters are free to vary in the fit. The energy spectrum after cuts, together with the best fit, is shown on Fig. 3. The uncertainties shown in the figure are statistical only. We considered also an alternative method to account for pile-up, which we called the convolution method. Random trigger with 16 s-long samples of data are regularly collected by Borexino. These data are sliced into time windows of the same duration as the window used for our energy estimator. By computing the energy estimator inside each of these small windows, we generate the distribution shown in Fig. 2 -right. This distribution is the spectrum collected by Borexino without a trigger threshold, and it includes electronic noise, as well as all physical events generated by signal and background species.
Search for resonances A limited luminosity already allows to search for heavy resonances decaying into a pair of jets, by analyzing the di-jet invariant mass spectra  [4]. This analysis has been already performed with the 8 TeV dataset  [6]. At the Run 2, it is based on H T trigger with a threshold of 800 GeV. Then, "wide jets" are reconstructed : starting from the 2 highest p T jets ( reconstructed with a cone of ∆R = 0. 4, and satisfying p T > 30 GeV) used as seeds, other close by jets (within ∆R <1. 1) are added to form 2 large jets. To avoid trigger bias, an offline cut on the di-wide-jet invariant mass is applied M j j > 1. 1 TeV. Wide-jets are further required to satisfy |∆η| < 1. 3, in order to reject QCD t-channel events. The corresponding di-jet invariant mass is presented in Fig.
ACKNOWLEDGEMENTS This work was funded by the  Direction Générale de l'Armement (DGA). We acknowledge  S Ferré  for his help on the carrier and photon lifetime calculation,  P Adam  for his reflection on the applications of chaotic QCLs, as well as  G Maisons  and  C Gilles  for their advice on the manuscript. FG and KS acknowledge the support of the  French National research Agency (ANR)  through the  Nanodesign  Project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. M. S. acknowledges the financial support of  Préfecture de Lorraine and SGAR for the projects PHOTON (FEDER)  and  APOLLO (FEDER and FNADT). M. S. also acknowledges the support of  Conseil Régional de Lorraine ,  Fondation Supélec ,  ANR  through the  TINO  project ( ANR-12-JS03-005 ), and the  Inter-University Attraction Pole Program IAP   VII P7/35  'Photonics@be'.
Muscle pH and dry matter content The muscle pH and dry matter content data are summarized in Table  2. In unstressed fish, there was no difference between HR and LR lines in muscle pH at slaughter, 72 h pm, or for cooked fillets. In fish from both HR and LR lines slaughter stress significantly lowered muscle pH (a mean difference of -0. 31 pH units) as measured within two hours after death (p<0. 001). No effect of slaughter stress was observed on muscle pH measured at 72 h pm or on cooked fillet, and no interaction between selection lines and stress was observed. The postmortem decrease in muscle pH was lower for stressed fish compared to unstressed fish (p<0. 001). Dry matter content of LR fish was higher than those of HR fish in both raw and cooked fillet (mean differences of +1. 1% and +1.
Fillet color The color of fillets was evaluated on raw fillet within 2 hours after slaughter and at 3 days post-mortem (pm), and on cooked fillet. Data obtained at slaughter are presented in Table  3 , and similar results were measured at 3 days pm. Raw fillets from HR and LR fish exhibited differences in all color parameters: lightness (L*), redness (a*) and yellowness (b*). However when body weight was considered as a covariate in the analysis of variance, some of those differences were no longer significant. The mean L* of raw fillets from LR fish was lower than that of HR (44. 7 vs 46. 5, p<0. 001 at slaughter time), whereas a* and b* tended to be higher in LR fish than HR fish. For cooked fillets, differences between lines for L* were less pronounced but higher values of a* and b* were still observed for fillets from LR fish compared to HR (p<0.
Mechanical resistance of raw and cooked fillets The data for fillet mechanical resistance is summarized in Table  4. For raw fillets, the mechanical resistance of HR fish was higher than that of LR fish whatever the parameter considered (p<0. 01 +19. 8% for specific resistance parameter for example). A significant decrease in mechanical resistance due to slaughter stress was measured for specific resistance (-12. 5%) and force at 80% of deformation (p<0. 05 and p<0. 001, respectively). We noted that the effect of selection line observed for resistance at low (20%) deformation tended to be due to NS fish (interaction p=0. 09). Moreover, for mechanical resistance at high (80%) deformation, the effect of stress tended to be due to HR fish (interaction p=0. 09). For cooked fillet, opposite results were observed for both specific resistance and work.
Acknowledgements The authors thank  Dave Abel  ( CEH ) for fish rearing and technical assistance during slaughter procedure and measurements, and  Gilles Paboeuf  for technical assistance for fillet quality measurement. This work was funded by the  European Commission  through the  Aquafirst  project ( FP6-STREP-2004-513692 ) and co-funded by the  French National Institute for Agricultural Research (INRA)  and the  Natural Environment Research Council (NERC). The help of  Dr Jean-Charles Gabillard  was appreciated for good advice and helpful discussion during preparation of the manuscript.
Xu et al. (Xu et al. , 2012)  demonstrate the effectiveness of such abstraction via a context rules based classification in the urban environment. Mallet et al. (Chehata et al. , 2009)  demonstrate satisfying performance of the random forest machine learning method in feature based classification of photogrammetry data. Mattausch et al. (Mattausch et al. , 2014)  introduce a unsupervised machine learning method for segmenting similar objects in indoor scenes. They perform a planar patch detection as preprocessing and categorize the patches into vertical and horizontal patches. A small set of geometric features is computed per patch and a similarity matrix is constructed, considering pairwise similarity between patches that share similar neighborhoods. Clustering under consideration of the similarity matrix yields a segmentation of patches into similar objects across the datasets.
Third, exploring the relationships between the planar shapes yields invariance to orientation and scale. Contributions. We contribute a novel supervised machine learning method for the classification of objects acquired in indoor scenes. The key novelty of our approach is to derive from a multiscale planar abstraction a so-called feature vector. These features, extracted from a pre-labeled dataset of CAD models, are used to train a random forest classifier  (Breiman, 2001)  and to evaluate the performance. We then demonstrate the performance of our classifier on point data acquired from indoor scenes. Our approach improves over previous work on two main aspects: • Robustness: Performing the planar abstraction at different scales makes it possible to detect dominant properties at low scales while being robust to defects and variations in the acquisition process, as well as to capture the discriminative role of details at finer scales.
Random forests overcome this issue by creating a large number of decision trees. For each decision tree a random subset of the training data is chosen and on each node only a random subset of the features are used. Additionally, the maximum depth of the trees can be limited. The classification is performed as a voting. The feature vector of an unknown object is evaluated on each tree and the predicted label corresponds to the most voted label. Random forests aim at providing the highest prediction performance for the training data set. Choosing an imbalanced training set, where the number of training samples for each object class varies, can lead to a poor prediction performance for the underrepresented classes. The classifier can afford or sometimes even exhibit a higher prediction performance by neglecting the minority classes.
EXPERIMENTS We implemented our approach in C++ using the CGAL Library (CGAL, 2012), OpenCV and the efficient RANSAC approach implemented by Schnabel  (Schnabel et al. , 2007). The size of the feature vectors are as follows: 8 bins for the area fragmentation distribution, 10 bins for the pairwise orientation and pairwise adjacent orientation distributions and 5 bins for the orientation and transversality distributions. We achieved the best results for three different scales. This sums up to a feature vector size of dimension 115, including the oriented bounding box ratio. Object Databases. We perform the evaluation of our classifier on a subset of the Princeton Shape Benchmark (The Princeton Figure  4 : Benchmark. We compared the performance of our method with the performance of the D2 by Osada et al.
The ESF shape descriptor reveals more sensitive to noise and outliers as the precision drops quickly with increasing amounts of defects: (c) 72, 5%, (f) 55% and (i) 45%. Shape Benchmark, 2004), see Fig. 4. A subset of the full dataset is used as many objects do not belong to the indoor environment. We select 100 objects from 8 different object classes that are common to indoor scenes: Bottle, Chair, Couch, Lamp, Mug, Shelf, Table  and  Vase. Each model in the object database is sampled into a point cloud by ray shooting, and oriented into a random direction to evaluate invariance to orientation. The calculated set of features is split into two sets: 60% for training and 40% for evaluation. To avoid a bias towards overrepresented classes, we remove samples until every class is represented evenly.
A further addition of noise challenges especially the ESF shape descriptor whose performance falls to 45%, while D2 reaches 62, 5% and our method still provides the strongest performance with 70% precision. Our method shows an advantage of our feature set over the other shape descriptors by not just showing a higher precision but also through higher robustness against defect-laden data. The abstraction of point data by planar shapes provides robustness towards noise and outliers. Point-based shape descriptors provide some robustness against noise as shown by Osada et al. , however, they are prone to outliers as our experiments records. Performance. As recorded by Tab. 2, feature computation is the most compute-intensive operation of our approach. The timing is however reasonable as only a few minutes are necessary to compute all the features of the hundred objects of the Princeton dataset, which represent a total of 25M input points.
7 mm dia. pin. The assay plates were 96-well clear flat-bottom microplates containing 2% χ1666 E. coli bacteria in 0. 5X Liquid Nematode Growth Media (LNGM) plus cholesterol (Bacteria were mass-grown in rich media, and harvested by centrifugation to produce a thick paste of bacterial cells that was kept frozen at -20°C. Cells were resuspended at 2% W/V in 0. 5X LNGM to produce liquid worm food). Using a COPAS Biosort (Union Biometrica), two L4 to young adult stage VC2010 worms were added to each well of the plate in a final volume of 45 μL. After five days of exposure to the compounds, the plates were scanned and the data analyzed. A five day time period was chosen to be able to observe effects over all stages of the life cycle and through at least one round of replication. The initial proof-of-principle primary screen was performed manually using a dissecting microscope.
Phenotypes scored included decreased motility, reduced brood size, and/or death as described previously  [45]. For high throughput screening of the Chembridge and Maybridge compound libraries we used the program WormScan  [46] , which was adapted for screening 96-well flatbottom LNGM plates. Using WormScan it was possible to screen four 96-well plates at a time. Plates were scanned twice, at a resolution of 1200 dots per inch, 16-bit grayscale, producing a jpeg image. The light intensity produced by the scanner was sufficient to cause negative phototaxis equivalent to a physical stimulus for mortality determination. The time required to scan four 96-well plates using an Epson Perfection V700 Photo Scanner was less than ten minutes. The time to screen a single plate was less than 1. 2 minutes, with less than ten seconds between scans.
WormScan was used to screen for the same phenotypes as in the manual screen (reduced brood size, reduced behavior/motility response and increased mortality). Two sequential scans for each 96-well plate were aligned to a reference region of interest (ROI)  [47]. Image analysis was based on a difference image score calculated for each ROI. The difference image score (WormScan score) was normalized using a "percent of control" method to derive a normalized WormScan score  [48]. For the primary screen the WormScan score was calculated using custom macro scripts written for Fiji  [49]. For secondary screening and any subsequent studies the WormScan score was calculated using a standalone Java program (See supplemental). Probit regression of mortality was calculated using Mathematica 8. 0.
Structure Activity Relationship (SAR) and cheminformatics A 3D similarity score was generated with the chemistry informatics tool Screen3D  [57]  using the atom matching algorithm and allowing the query and template molecules to be flexible. The compounds were categorized into similar groups using the Library MCS hierarchical clustering program Chemaxon (Chemaxon: Budapest H, Library MCS, version 6. 0. 1,2013). Singletons (molecules which did not belong to any cluster) were removed and all the remaining scaffolds were saved.
For sdhd-1, one of five MMP strains is resistant (Table  5 ). To better understand how these different alleles might alter drug effectiveness, we mapped all of these mutations plus gk3613 onto an X-ray resolved protein crystal structure of the mitochondrial complex II of Ascaris suum (PDB: 3VRB)  [74]  (Fig  3a ). The five MMP mutations that confer resistance to CID 2747322 are highlighted, as are the nucleotide positions that, when mutated, do not confer resistance (Fig  3b ). The gk3613 T66I mutation that confers resistance to CID 2747322 was found in the Burns study to confer resistance to the WACT-11 compound series  [34]  (note position of other WACT-11 resistance sites). Consistent with these observations, we note that resistance mutations for complex II inhibitors found in the wheat pathogen Mycosphaerella graminicola  [75]  have the homologous succinate dehydrogenase position highlighted on the C.
Modeling CID 2747322 binding to mitochondrial complex II quinone pocket We leveraged the fact that the antifungal compound flutolanil is an excellent inhibitor of A. suum mitochondrial complex II, which has previously been shown to bind within the quinone pocket of the crystal structure of A. suum complex II (PDB: 4YTM)  [76]  to further explore the compound-target interaction. CID 2747322 has structural similarity to the flutolanil analogue N-biphenyl-3-yl-2-(trifluoromethyl)benzamide (Fig  4a ). Furthermore, the A. suum mitochondrial complex II has conserved sequence homology with C. elegans (Fig 5 ), making it an excellent template for 3D homology modeling for C. elegans. To better understand how CID 2747322 interacts with the target site, we constructed a model of the C. elegans succinate dehydrogenase complex in which the flutolanil analogue is used to model the CID 2747322 3D position within the C.
elegans protein model (Fig  6A ). The docked position of CID 2747322 was found to form essential non-covalent interactions annotated in the structure of flutolanil and A. suum complex II (PDB: 4YTM)  [76]. The C. elegans homology structure shared the conserved TRP197 of SDHD-1, which forms a hydrogen bond with the amide group of CID 2747322. Additionally, the ARG89 of SDHB-1 can form a cation-π interaction with the right R While flutolanil is a potent A. suum complex II inhibitor (IC 50 0. 0245 μM), it has been shown to be a less effective inhibitor of the porcine complex II (IC 50 8. 61 μM)  [80]. The aligned vertebrate porcine protein structure of complex II lacks a hydrophobic pocket for the left R group of D19 (Fig  6B ). The key residues important for this binding pocket are the proline 63, tryptophan 68 and leucine 69 of MEV-1 in C.
Regional representativeness as seen by spaceborne measurements The observations conducted from the Minorca station are relevant to the local atmospheric column. In the following we put them in a more regional context using the measurements performed by SEVIRI (e. g. Bennouna et al. , 2009)  and the spaceborne instrument Moderate Resolution Imaging Spectroradiometers (MODIS;  Salmonson et al. , 1989; King et al. , 1992 ; http://modis. gsfc. nasa. gov). The MODIS data above land (few data are available above sea due to sun glitter) and the SEVIRI data above sea are combined in a single map to check the reliability in terms of continuity between sea and continent. The spatial resolution of the MYD04_L2 product of MODIS is 10 × 10 km 2 at nadir. The predicted uncertainty on the AOT at 550 nm over land is ±0.
The high-altitude aerosol event observed between 4 and 7 km a. m. s. l. above the western Mediterranean basin from 26 to 28 June is not usual in its nature since it results from a very turbid plume (AOT 550 >0. 6) arriving from the NE Atlantic as visible on the Bay of Biscay on 26 June in Fig. 10. Formenti et al. (2002)  have already documented with airborne measurements such an event of 10-day aged haze layers from Canadian fires over the eastern Mediterranean in August 1998. Seven-day back trajectories have been computed with the HYSPLIT model (Fig. 10 ). The back trajectories are superimposed on a MODIS AOT image combining data from 24 June 2013. Dense aerosol plumes appear all along a transport pathway over the North Atlantic, with a dark red colour associated with AOT 550 values larger than 1.
Acknowledgements. This work was supported by the  French space agency (CNES)  and the  Commissariat à l'Energie Atomique (CEA). The campaign at Minorca was also supported by  CNRS/INSU  and  Météo-France  through the  MISTRALS/ChArMEx programme , and by the  Spanish Ministry of Science and Innovation  and  FEDER  funds under the projects  TEC2012-34575 ,  UNPC10-4E-442  and  CGL2011-13580-E/CLI. Matthieu Jeannot  from  LPC2E , and  François Dulac  and  Sahar Hassanzadeh  from LSCE are acknowledged for their technical help during the campaign. Juan Ramon Moreta Gonzalez  and  Miguel Angel Heredia Jodar  from  AEMET  are acknowledged for AERONET data from Palma. Edited by:  O. Dubovik.
2013 , Martin et al. 2014 , Gatti et al. 2015). The CIGESMED SeasEra project aims to enhance understanding on the links between natural and anthropogenic pressures and ecosystem functioning to define and maintain the "Good Environmental Status" (GES) of the Mediterranean Sea, through the integrated study of coralligenous assemblages. In the framework of CIGEDMED SeasEra project, a specialized CS initiative was launched aiming to engage enthusiast divers in the study of this Mediterranean habitat in order to obtain information regarding its spatial distribution, enable a primary characterization of the basic structure of its assemblages, and monitor potential pressures and threats to the habitat. The initial development of this pilot CS project was based on previous experience of the IMBBC (Institute of Marine Biology, Biotechnology and Aquaculture, HCMR) research group and CIGESMED partners on citizen science pilots design and implementation, as well as on the human, hardware and software resources of the LifeWatchGreece Research Infrastructure, a landmark in the European Strategy Forum on Research Infrastructures (ESFRI) roadmap.
Preparatory phase and methodological concept Past experience from CS projects showed that it is relatively easy to design a CS approach and address volunteers but hard to ensure continuous production and flow of useful and reliable data  (Arvanitidis et al. 2011). In retrospect, a key aspect in the design of this pilot project was the collection of data that are: (a) relatively easy to acquire and report, based on the overall, underlying principle "keep it as simple as possible", and (b) reliable and failproof. In order to develop a CS protocol that would fulfill the objectives of CIGESMED project, we first created a list of the main types of data that can plausibly be gathered by citizen scientists, based on feedback from scientists that are actively involved in the study of coralligenous assemblages.
Instead the project's main target group is "dedicated divers", i. e. dive enthusiasts who dive at depths greater than 20 m, and exhibit a particular interest for underwater life and the conservation of their diving spots (e. g. local diving associations, naturalists and underwater photographers). According to the preliminary research and previous experience of CIGESMED partners, many divers fitting to this profile are active participants in existing marine life fora and networks (e. g. DORIS, BioObs), and are positively inclined towards using a platform that allows systematic sharing of their observations. Keeping these considerations aside, the designed CS module is openly addressing all amateur divers who are able and willing to contribute information on the occurrence of coralligenous bioherms, alongside specific information on conspicuous coralligenous species, existing pressures and threats, at any place they carry out their diving activities.