dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Labeling results on a synthetic dataset Figures 9 show the results for a synthetic sub-dataset. In Felzenszwalb's segmentation, there are too many boundaries that do not fit the ground truth label. The results of SVM-MRF and CRF are not qualitatively and quantitatively better than the results of the proposed method. The performances of the MRF-and CRF-based approaches are highly dependent on the unary term. In other words, failures by SVM and TextonBoost have too much impact on performance. For this kind of small dataset, MRF-and CRF-based methods are not the best choice for achieving good performance. In contrast, the proposed method gives reasonable labeling results and better performance in terms of the Dice coefficient. Note that since 42,169 nodes (or samples) are used for training, the primal solver for SVM training is necessary.
The horizontal and vertical axes show the threshold and corresponding cost function value, respectively. (a -f) Convergence with different scale parameter values. The initial value θ 0 = 1000 is indicated as vertical green lines. Different colors are used for different iterations; the first iteration is in blue, and the final one is in red. λ = 10 -3 provides a small threshold value, and the boundary between foreground and background disappear. Smaller values λ = 10 -7 and 10 -8 , provide larger threshold values, and foreground objects becomes smaller. A better labeling result can be obtained when an appropriate value of λ, in this case 10 -5. The value should be tuned as the accuracy of labeling results is sensitive to it. Results for other sub-datasets are shown in Figure  12  to 14. In these results, MRF and CRF provide some successful results but the proposed method is stable and better in most of the cases.
For quantitative evaluation, we compute Dice coefficients over different numbers of training images. In this experiment, we used a sub-dataset containing ten images by adding five images to the sub-dataset of Figure  4. Figure  15  shows the box plots of Dice coefficients for different numbers of training images. The overall Dice coefficients of MRF and CRF are lower than those of the proposed method. Even when nine images are used for training, the median of the Dice coefficients of MRF and CRF are approximately 0. 6. Some of the Dice coefficients of CRF are extremely low. In contrast, the proposed method works effectively and provides adequately high Dice coefficients, even when only one training image was used. Here, we show some failure cases of the proposed method, such as the examples shown in Figure  16.
In these results, CRF outperforms the other methods. Two textures in the subdataset are of pebbles that are similar to each other, and the SITA feature used in the proposed method is invariant to this difference between geometrical, scale, and contrast changes. Therefore, the subtrees cannot be classified correctly, and we  Figure  17  shows further results wherein all of the methods do not work well. The possible reason for this failure is that the number of nodes n i is relatively small compared to that used for the successful results, such as in those in Figure  9. In this result, we observe that the number of training samples or nodes in the tree of shapes must be greater than at least 10,000 per image in order to obtain satisfactory labeling results. Regarding the computational time, our Python implementation of the proposed method takes approximately 200 s for training and approximately 30 s for labeling an image in the above experimental condition.
C. Labeling results on the MSRC-21 dataset Hereafter, we show labeling results on the MSRC-21 dataset  [31]. This dataset consists of 591 images with ground truth labels of 21 object classes. It has 20 subsets according to main objects of the image shown in the center, such as cow, sheep, tree, car, and building. To evaluate the proposed method with a few training images, we selected two subsets having rich  texture contents; subset 2 (tree, grass, and sky) and 9 (sheep and grass). In each subset, the label of the main object of the subset (trees of subset 2, and sheep of subset 9) is used as foreground, and the others are as background. In natural images, color is an important cue for segmentation. To demonstrate the proposed method for color natural images in this experiment, we use the following tree of shapes and histogram features.
VI. CONCLUSION In this study, we proposed a labeling method for texture image segmentation that works with a few training images. Our method is based on a tree of shapes and histogram features derived from the tree structure and selects optimal  discriminative subtrees for tree node classification. This is formulated as a joint optimization problem for estimating the threshold and classifier parameters and is solved using iterative block-coordinate decent. Then, images are labeled using the estimated parameters and the tree of shapes by classifying each node from the root node to leaf nodes and then mapping classification results into the corresponding pixels. We evaluated the proposed method on the two datasets: a synthetic texture image datasets based on the UIUC database and the MSRC-21 dataset.
tuberculosis strains. Activation of NF-κB, evaluated using SEAP activity, following incubation with 500 ng/ml or 50 ng/ml of crude extracts recovered from either bacterial cells or culture medium from the indicated M. tuberculosis strains. PBS 1× or PAM 3 CSK 4 (10 ng/ml) were used as negative and positive controls respectively. Data are presented as the mean +/-SD of three independent experiments. (D) NF-κB activation in HEK-TLR2 cells incubated with LprG/LAM complex purified from bacterial cells or culture medium of WT or Δpmt M. tuberculosis strains. HEK-TLR2 cells were incubated with 10 ng/ml or 1 ng/ml of LprG-His and co-purified LAM. SEAP activity was quantified after 24 h. PBS 1× or PAM 3 CSK 4 (10 ng/ml) were used as negative and positive controls respectively. Data are presented as the mean +/-SD of at least five independent experiments.
However, our analyses with the strain producing LprG T231A, which is no longer glycosylable by Pmt, strongly argues against a direct role of LprG glycosylation in the corresponding phenotype. It is possible that the activity of a protein acting in the same pathway as LprG is strongly influenced by O-mannosylation. An obvious candidate is the efflux pump encoded by the gene Rv1410c in M. tuberculosis H37Rv. This gene forms an operon together with lprG and most of the phenotypes described for the ΔlprG mutant were only complemented upon expression of both Rv1410c and lprG (this study  21, 22  ). We examined our mass spectrometry data in depth but found no evidence that the Rv1410c-encoded protein is O-mannosylated (data not shown). The impact of glycosylation on LprG activity thus remains an open question.
The LTQ-Orbitrap Velos was operated either in single MS or in data-dependent acquisition mode with the XCalibur software. MS scans were acquired in the 500-2000 m/z range with the resolution set to a value of 60,000. For top-down experiments, survey scan MS were acquired in the same way, with a resolution at 30,000. Precursor ions were selected from an inclusion list established thanks to previous MS analyses and were fragmented in ETD and the resulting fragment ions were analyzed in the Orbitrap, at a resolution of 60,000. Isolation width was set at 5 m/z and activation time was fixed to 20 ms for ETD.
tuberculosis strains. PBS 1× or PAM 3 CSK 4 (10 ng/ml) were used as negative and positive controls respectively. Data are presented as the mean +/-SD of three independent experiments. (D) NF-κB activation in HEK-TLR2 cells incubated with LprG/LAM complex purified from bacterial cells or culture medium of WT or Δpmt M. tuberculosis strains. HEK-TLR2 cells were incubated with 10 ng/ml or 1 ng/ml of LprG-His and co-purified LAM. SEAP activity was quantified after 24 h. PBS 1× or PAM 3 CSK 4 (10 ng/ml) were used as negative and positive controls respectively. Data are presented as the mean +/-SD of at least five independent experiments. *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001.
Acknowledgements We thank  Flavie Moreau  and  Pierre-Jean Bordignon  for the help with animal experimentation and bone marrowderived macrophages respectively. We are grateful to  Dr Agathe Subtil  ( Institut Pasteur ) for the generous gift of knock-out mice. The authors acknowledge support from the  Centre National de la Recherche Scientifique, University of Toulouse , the  National Research Agency  ( ANR-11-BSVB-016  and  ANR-11-EQUIPEX-003 ), the  Fondation pour la Recherche Médicale  ( ING20140129051  and  DEQ20160334879 ), the  French Ministry for Research  ( Investissement d'avenir, Proteomics French Infrastructure ,  ANR-10-INBS-08 ), The Bettencourt-.
For the present study, we selected older patients included in the OAK Registry with frailty and disability assessment and data related to vital status during the first 30 days after the index visit (Fig. 1 ). A brief geriatric assessment including frailty (Fried phenotype) and disability (Barthel index) was performed by a trained physician in each centre who was not responsible for the care of the patient during the first 12 hours of care in the ED on week days (Monday to Friday) from 8 am to 10 pm. Patients were asked about the presence or not of frailty criteria and disability one month before the ED visit.
The areas under the curve (AUC) of the risk models were compared using a non-parametric test. We considered differences to be statistically significant if the p value was less than 0. 05, and the 95% CI of the HR excluded 1 or 95% CI of AUC ROC excluded 0. 5. All the analyses were performed with SPSS 18. 0 and STATA 12. 0 statistical package.
This study was partially supported by grants from the  Instituto de Salud Carlos III  supported with funds from the  Spanish Ministry of Health  and  FEDER  ( PI15/00773 ,  PI15/01019 , and  PI11/01021 ) and  La Marató  de  TV3 (2015 ). FJMS received advisory/consulting fees from,  Novartis ,  MSD ,  Pfizer , The  Medicine Company ,  Otsuka  and research grants from the  Spanish Ministry of Health and FEDER, Novartis ,  Abbot and Orion-Pharma. PLL received advisory/consulting fees from  Novartis ,  MSD ,  BoehringerIngelheim ,  Pfizer  and  Orion-Pharma  and research grants:  Abbot ,  Otsuka ,  Cardiorentis  and  Novartis. PH received advisory/consulting fees from  Novartis  and research grants:  Abbot ,  Otsuka , and  Novartis. JJ received advisory/consulting fees from  Novartis  and research grants:  Abbot ,  Otsuka , and  Novartis.
Reference samples were measured in transmission mode using ionization chambers (300 mm, FMB Oxford) filled with 95% N 2 and 5% Ar (I 0 ) and with 100% Ar (I 1 and I 2 ). Spectra were collected at 15 K using a closed cycle He cryostat with a large fluorescence exit window and a low vibration level (CryoVac). Photoinduced redox reactions were effectively prevented by the cold temperature, since XANES edges remained stable during short-term exposure (10 min) as well as during the EXAFS measurements which took up to 8 h. For energy calibration, a gold foil (K-edge at 11,919 eV) was chosen because of its greater inertness in comparison to Se. Data in the XANES region were collected in steps of 0. 5 eV, i. e. with higher resolution than the resolution of the Si(111) crystal at the given vertical divergence (1.
7 eV) and the broadening due to the core-hole lifetime (2. 3 eV). A comparison of single scans of the same sample showed an accuracy of better than 0. 5 eV. Dead time correction of the fluorescence signal, energy calibration and the averaging of single scans were performed with the software package SixPack  (Webb 2005). Normalization, transformation from energy into k space and subtraction of a spline background was performed with WinXAS using routine procedures  (Ressler 1998). The EXAFS data were fit with WinXAS using theoretical backscattering amplitudes and phase shifts calculated with FEFF 8. 2  (Ankudinov and Rehr 1997). This method provides a precision of ±0. 01 Å for shell distances and a resolution of about ±0. 1 Å for neighbouring shells. The error of coordination numbers is ±25%.
Identification of the selenium-containing species in the headspace of the cultures was achieved by matching the retention times of the standards together with mass spectra information stored in the instrument NIST library database. Using this approach, it was possible to detect methyl selenol (CH 3 SeH, MSeH), methylselenoacetate (C 3 H 6 OSe), dimethyl selenide (CH 3 SeCH 3 , DMSe), dimethyl diselenide (CH 3 SeSeCH 3 , DMDSe) and dimethyl selenenyl sulphide (CH 3 SeSCH 3 , DMSeS) in the headspace of selenite-amended Mc. capsulatus culture medium. In contrast, only two volatile selenium-containing species, DMDSe and DMSeS, were detected in the headspace of the Ms. trichosporium culture. It is noteworthy that with both culture media, these selenium species were detected soon after selenite addition.
Acknowledgements A. S. E. gratefully acknowledges financial support from the  Libyan Government  for a Ph. D. scholarship. The  European Synchrotron Radiation Facility (ESRF)  is acknowledged for the X-ray absorption spectroscopy measurements. We thank  Michael Cox  ( Sheffield Hallam University ) for assistance with the HPLC-ICP-MS measurements and  Jonathan Foster  ( Sheffield Hallam University ) for assistance with the GC-MS analysis.
53BP1 tandem Tudor domain is required to interact with TIRR Next, to map the binding site of 53BP1 with TIRR, interaction of recombinant fragments of 53BP1-FFR and recombinant TIRR was assessed at different salt concentrations. TIRR interacted with the Tudor-UDR as well as with the tandem Tudor alone (Extended Data Fig. 1b, c ) and it impaired the binding of the Tudor domain with an H4K20me2 peptide (Extended Data Fig. 1e, f ). Using isothermal titration calorimetry (ITC), we derived a dissociation constant (K d ) of 0. 79 μM for the interaction of Tudor with TIRR (Extended Data Fig. 1d ). The ITC results are consistent with a 1:1 stoichiometry for the Tudor-TIRR complex, most likely corresponding to two Tudor molecules binding to one TIRR homodimer.
TIRR impairs 53BP1 functions Next we tested whether TIRR interaction with the Tudor domains influences 53BP1 function. First, we observed that high TIRR expression almost completely abolishes 53BP1 foci formation (Fig. 3a ). Second, TIRR over-expression conferred resistance to the PARP inhibitor, olaparib (Fig. 3b ) and restored HR in MEFs carrying hypomorphic Brca1-Δ11 alleles (Extended Data Fig. 2a ). Furthermore, silencing 53BP1 in cells over-expressing TIRR showed the same extent of olaparib resistance as 53BP1-depleted cells, indicating that TIRR-induced olaparib resistance was likely mediated by loss of 53BP1 function. Finally we tested whether TIRR compromised 53BP1 function in CSR of primary B cells. CSR to IgG1 was significantly reduced by TIRR (Fig. 3c  and Extended Data Fig. 2b ), and was not caused by reduced levels of AID or expression of germline transcripts (Extended Data Fig.
NMR spectroscopy The NMR data were recorded at 25°C using a Bruker Avance III 700 MHz spectrometer equipped with a cryoprobe. Protein samples were in 50 mM sodium phosphate (pH 7. 5), 500 mM NaCl and 10% D 2 O. 53BP1 tandem Tudor was  15  N-labeled using a previously reported procedure  15, 38. For the titration experiments, TIRR was at concentrations ranging from 20 μM to 50 μM and small aliquots of 3. 5 mM 53BP1 Tudor were added. All spectra were recorded using salt-tolerant shaped NMR tubes (Bruker). The reductive alkylation of TIRR to generate  13  C-dimethylated lysine residues followed a previously reported protocol making use of  13  C-enriched formaldehyde (Sigma)  46, 47. The lysine residues were assigned by individual mutation to arginine. The synthesis of  13  C-methyl alkylation reagent dimethylated 2-chloroethylamine and installation of dimethylated lysine analog followed previously reported protocols  47, 48.
Briefly, two of the three cysteines in TIRR were mutated into serines. Each lysine of interest (K10, K151 and K205) was replaced by a cysteine and converted to the dimethylated lysine analogs K C 10me2, K C 151me2 and K C 205me2. For the reductive alkylation of TIRR and the installation of dimethylated lysine analogs, excess reagent was eliminated by size-exclusion chromatography using a Superdex 75 10/300 GL column (GE healthcare) and 50 mM sodium phosphate (pH 7. 5), 500 mM NaCl running buffer. The NMR data were processed with NMRPipe  49  and analyzed with NMRView  50. PyMol was used to create the molecular representations [WF Delano, The PyMOL Molecular Graphics System, version 1. 3r1 (Schrodinger, LLC, New York, 2010)].
Extended Data Extended Data Figure  1. TIRR prevents the interaction of 53BP1 Tudor domain with a peptide derived from H4K20me2 a, Soluble nuclear proteins were prepared from HeLa and HeLa cells in which TIRR has been knock out using CRISPR/cas9 system (HeLaΔTIRR) undamaged or irradiated at 10 Gy for 90 min. Endogenous 53BP1 was next immunoprecipitated and immunoblotted using the indicated antibody. b, Coomassie staining of FLAG-tagged recombinant proteins purified from bacteria; "OD-Tudor" encompasses the oligomerization, the Tudor and UDR domains, "OD" the oligomerization domain and "Tudor-UDR" the Tudor and UDR domains. c, Recombinant TIRR-His protein was incubated with the indicated FLAG fusion protein in presence of 200 or 400 mM NaCl. Pull-down proteins were subjected to FLAG (top) and His (bottom) immunoblotting.
Acknowledgments We are grateful to  Lifeng Xu  ( University of California, USA ) for RIF1 antibody and  Simon Boulton  ( CRUK ) for RIF1 -/-MEFs. DC is supported by  R01 AI101897-01  ( NIAID ) and  R01CA142698-07  (NCI),  Leukemia and Lymphoma   Society Scholar Grant ,  Claudia Adams Barr Program for Innovative Cancer Research, DOD Ovarian Cancer Award, Breast SPORE Pilot Award, Robert and Deborah First Fund Award. GM is supported by  NIH  grants  R01 CA132878  and  R01 GM116829 , and a  Mayo Clinic Brain Cancer SPORE Program Pilot Award  ( P50 CA108961 ). JWH is supported by add  AG011085.
Reefscape composition and configuration Habitat suitability maps were developed from field data on T. maxima densities and from 30 m Landsat satellite imagery from the Millennium Mapping Project  [31]. Data on T. maxima densities came from Gilbert et al. [34] , Wantiez et al. [35] , and from Friedman et al. [36]. To avoid biases associated with combining data across multiple methods, we only considered studies that used belt transects. Overall, density estimates were available for 31 sites spread over the area. Each site was sampled with a number of stations spread over various shallow reef geomorphological units, and each station is a set of transects. Reef geomorphological units were defined at three levels (L1, L2, L3, Table  3  and Fig  3a  and 3c ). The geomorphological level L1 considered all reefs (shallow and variable depth reef areas confounded) as habitat for T.
1 degree resolution) using the rasterize function of the raster package in R 3. 1. 0, and the shortest path between each pair of cells was determined using the costDistance function of the gdistance package. Second, we tested the IBOD model by considering the probability for larvae to travel from habitat patch i to habitat patch j as a function of oceanic currents. Larval dispersal between habitat patches was simulated with Lagrangian particle tracking models. We modelled the drift of larvae for 9 days, as that is the typical time for T. maxima's Larval Precompetency Duration (LPD)  [37, 38]. During the competency period (after 9 days), we allowed larvae to settle as soon as they crossed a patch of favourable habitat  [39, 40]. We considered a competency period ranging from 9 to 19 days on the basis of Jameson  [37]  and Neo et al.
4  [49]  as implemented for online use (http://genepop. curtin. edu. au/). Significance levels for multiple comparisons of loci across samples were adjusted using a standard Bonferroni correction  [50]. Since null alleles were detected in our samples, genetic divergences among samples were estimated in FREENA  [51]  (i) using the F ST estimates of Weir & Cockerham  [52]  and the ENA method that provides unbiased F ST estimates, and (ii) using a direct estimation of pairwise values for the Cavalli-Sforza & Edwards  [53]  genetic distance following the so-called INA method  [51]  that decreases the bias in genetic distance estimation observed when null alleles are present. Genotypic differentiation among samples was further tested with an exact test (Markov chain parameters: 1000 dememorizations, followed by 1000 batches of 1000 iterations per batch) with the original dataset, and the P-value of the log-likelihood (G) based on the exact test  [54]  was estimated in GENEPOP version 3.
Giant clam observed genetic diversity and structure Raw microsatellite data can be found at https://figshare. com/s/a00f8be352aae916c147. Over all samples, the number of alleles per locus ranged from 11 to 40 (mean 26. 8). The observed heterozygosities ranged from 0. 493 to 0. 610, and the expected heterozygosities ranged from 0. 752 to 0. 831 (See S1 Table ). Significant deviations from HWE were observed in all samples, with multilocus estimates of F IS ranging from 0. 273 to 0. 402, showing in all cases heterozygote deficiencies. However, six loci were in HWE in nearly all samples, suggesting that heterozygote deficiencies in the remaining nine loci were caused by the occurrence of null alleles (confirmed by the presence of null homozygotes at those nine loci). The Efate sample (Vanuatu) was significantly differentiated from other sampled sites with pairwise Cavalli-Sforza & Edwards' genetic distance values ranging from 0.
The limited (but present) genetic structure observed for giant clams in our study (see Vanuatu, S2 Table  , Fig 5 ) is therefore representative of, and a useful example for, future studies in the seascape literature. Second, the multi-disciplinary approach performed here required extensive datasets that were not easy to collect and bring together. Habitat maps were readily available, but a typology adapted to the current knowledge on T. maxima density in the literature needed to be established (Table  3 ) to represent T. maxima habitats adequately  [31]. For example, comprehensive data on giant clam density in each habitat patch were not available at the wide geographical scale of our study. Specifically, we had to use the density of giant clams in each habitat type to calculate giant clam abundances for each habitat patch.
We used a dataset from the literature that itself involved a substantial sampling effort (n = 31 sites surveyed over 6 shallow geomorphological units), though many regions and species do not have such data readily available. Despite the effort, we still lacked fine density estimations for reefs of variable depths (Table  3 ). Our results nevertheless demonstrated that more accurate characterization of reefscape features enhanced our understanding of genetic structure. Third, the model used in this study projected gene flow on the basis of migration only (through larval dispersal). Other evolutionary forces (selection, mutation, and genetic drift) were neglected, but their influence on the results should be assessed in future research programs. Mutation is likely unimportant at the small spatial and temporal scales that are addressed in this study.
This work is part of  SVW  and JT PhD theses, supported by a fellowship from  "Ministère de l'Enseignement Superieur et de la Recherche " to Universitede la Polynesie francaise. Genetic analyses were co-funded by the  BeN-Co project (ZONECO program , New Caledonia, www. zoneco. nc) and the  TriMax  project ( LabEx CORAIL ,  Agence Nationale de la Recherche , www. labex-corail. fr). Sampling was conducted during cruises COBELO (http://dx. doi. org/10. 17600/13100100 )and BIBELOT (http://dx. doi. org/10. 17600/14003700 ) on board RV Alis, and during the New Caledonia leg of the  Global Reef Expedition of the Khaled Bin Sultan Living Ocean Foundation , on board RV  Golden Shadow  (www. livingoceansfoundation. org).