dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-validation results on our training datasets The evaluation of our method is conducted through a 5-fold cross-validation on Human and Drosophila datasets. Because we have large sets of data, we also performed a 10-fold crossvalidation, and the results are similar to the ones obtained with the 5-fold cross-validation. The experiment was repeated five times, considering at each time a matrix containing 7500 (respectively 5000) positive sequences and 7500 (respectively 5000) negative sequences for Human (respectively Drosophila). The value of in the Gaussian kernel (see Section 2) is estimated to 1. 73 in Human and 1. 79 in Drosophila for the first kernel (k-mer kernel), to 8. 73e-17 in Human and 4. 67e-17 in Drosophila for the second one (position kernel) and to 4. 48e-13 for Human and 5.
21e-12 for Drosophila for the third one (k-nearest neighbors kernel). To evaluate the relevance of the defined kernels, we tested our method using each of the three kernels, and then the combination of the three kernels by the kernels mean method and by the SPG-GMKL method (see Section 2. 2). The cross-validation results obtained on our training datasets of Human and Drosophila are given in Table  1. Km represents the kernel implementing the k-mer and the uridine features (see Section 2. 1. 1), Kd represents the kernel implementing the distance of the sequences from pericentromeric and subtelomeric regions on the chromosome (see Section 2. 1. 2) and Kn represents the kernel implementing the k-nearest neighbors sequences (see Section 2. 1. 3). The tool developed by Zhang and collaborators  (Zhang et al.
, 2011) , based on the k-mer method, was tested and compared with our method. To test it under the same conditions as our tool, it was re-trained on our datasets, and a 5-fold cross-validation was performed. The results are also given in Table  1. The classification results are evaluated using the measures given above i367 (Section 3. 2), and the best score for each measure is given in bold. As shown in Table  1 , the results obtained by piRPred with the SPG-GMKL method as well as with the kernels mean method are 40. 8 in almost all measurements for both Human and Drosophila. Our results are clearly better than the ones reported by Zhang et al. Their tool fails on our training datasets, particularly on Human data. The accuracy is close to 0. 5, the value of a random classification. Besides, the sensitivity is 50.
1. 1), which shows that the percentage of sequences containing a uridine at the first position is higher in Human than in Drosophila (79. 68 and 65. 93% respectively), and on another hand with the results published in  (Zhang et al. , 2011) , where a better performance of the k-mer method on Human than on Drosophila was shown. Surprisingly, however, the results obtained by Zhang et al. method, when we retrain it on our data, give completely opposite results. This is probably because our Drosophila training dataset is bigger than the one used by  Zhang et al. (composed   Finally, the combination of the three kernels presents an advantage over each single kernel in almost all measures in both Human and Drosophila, showing a certain relevance of their combination. Besides, using the SPG-GMKL method for combining the kernels is clearly more beneficial than performing a simple mean of the kernels.
Predictive sensitivity on new sequences For both Human and Drosophila, we investigated our algorithm on 2500 piRNA sequences distinct from the ones used in the training step. Again, this was done in comparison with the Zhang et al. method, for which we tested both the online tool available on the web server (http://122. 228. 158. 106/piRNA/ana lysis. php), as well as the model obtained after re-training on our datasets. The results are given in Table  2. The prediction results obtained by piRPred and by Zhang et al. method are in agreement with those obtained in the cross-validation. They show clearly superior performance of our algorithm. The re-trained version of Zhang et al. method fails completely to predict the given sequences as piRNAs. However, when using the web server version, the results are better.
For this classification problem with large training dataset, accuracy, training and testing speed and   memory usage are the main concerns. One solution is to combine the SVM to a Nystr€ om methods  (Gittens and Mahoney, 2013; Zhang et al. , 2012)  commonly used to obtain good-quality lowrank approximations of large kernel matrices. In the present study, we have proposed a new k-nearest neighbors kernel, which represents the cluster structure in the training set. To improve the classification results, we will extend these cluster structure to the test set, using transductive or semi-supervised learning algorithms  (Weston et al. , 2005). We can also use the labels of the neighbors by exploiting collective classification  (Sen et al. , 2008)  approaches. Finally, in the present version of piRPred, the input is a set of sequences, with the position of each sequence in the genome, including the '+' or '-' strand, the chromosome ID and the position on the chromosome.
Image acquisition and analysis Labeled sections were examined under fluorescent illumination with a Leica DM-4000 automated research microscope (Leica, Dollard-des-Ormeaux, QC, Canada) equipped with a Lumenera InfinityX-21 digital camera using Infinity Capture software (Lumenera Corporation, Ottawa, ON, Canada) or analyzed by confocal microscopy using an Olympus Fluoview 1000 (FV1000) laser-scanning IX81-ZDC inverted microscope (Olympus Canada, Markham, ON, Canada). For the quantitative analysis of the number of Na v 1. 8-positive neurons, three immunofluorescence stained non-consecutive sections (90 μm apart from each other in the z axis) were imaged per ganglion. The data were collected from three animals at each time point (3, 8, and 14 days following CFA injections). Three agematched sham rats were used as controls to set standard acquisition parameters (laser power, HV gain, offset).
Data analysis The peak inward current values at each potential were plotted to generate I-V curves. Conductance (G) was determined as I/(Vm-Vrev), where I is the current, Vm is the potential at which current is evoked, and Vrev is the reversal potential of the current. Activation was fitted with the following Boltzman equation: G = Gmax/[1 + exp[(V 1/2 -Vm)/k]], where Vm is the test pulse voltage potential at which current is evoked, Gmax is the calculated maximal conductance, V ½ is the potential of half activation or inactivation, and k is the slope factor. The normalized curves were fitted using a Boltzmann distribution equation: I = Imax/[1 + exp[(V 1/2 - Vm)/k]], where Imax is the peak sodium current elicited after the most hyperpolarized prepulse, Vm is the preconditioning pulse potential, V 1/2 is the half maximal sodium current, and k is the slope factor.
Sodium currents were recorded using a Digidata 1440 A data acquisition system (Molecular devices) digitized at 10 kHz, low-pass filtered at 2 kHz and captured using pClamp software (v10. 2, Molecular devices). For current density measurements, the currents were divided by the cell capacitance as read from the amplifier. The offset potential was zeroed before patching the cells and leakage current was digitally subtracted online using hyperpolarizing potentials, applied after the test pulse. Curves were plotted and fitted using Origin software (OriginLab Corporation, Northampton, MA, USA).
Acknowledgements The authors would like to thank  Mr. Alexandre Parent  for his assistance in the preparation of primary cultures of DRG neurons. This work is supported by a grant from the  Canadian Institutes of Health Research  awarded to PS and SMP. MBe, MAD, and PT are respectively supported by the  Lavoisier ,  Sir Frederick Banting  and  Dr. Charles Best Canada , and  Natural Sciences and Engineering Research Council Graduate Scholarships. PS is a recipient of the  Canada Research Chair in Neurophysiopharmacology of Chronic Pain, director of the Sherbrooke's Neuroscience Centre , and member of the  FRQS-funded Centre de Recherche Clinique Étienne Lebel.
De Novo Fat Pad Formation in Nude Mice and Immunohistochemistry Animal studies were approved by the Ethics Committee in Animal Experiment Charles Darwin (agreement no. Ce5/ 2011/015). 3T3-F442A preadipocytes were grown to near confluence and trypsinized, and cells in PBS were injected subcutaneously (3 3 10 7 cells per site) in 6-week-old BALB/C NU/NU athymic mice (Charles River Laboratories). Four weeks after implantation, mice on a standard diet received drinking water containing 30% sucrose. At 14 weeks, mice were killed by cervical dislocation and fat pads were excised. Small pieces of adipose tissues were either fixed with paraformaldehyde 4% or homogenized in lysis buffer for Western blotting. Adipose tissues were processed for immunohistochemistry as previously described  (8). Adipocyte diameters were measured using ImageJ software.
Caveolin-1 Expression Correlates With Fat Cell Hypertrophic Response to Overfeeding in Healthy Humans These data strongly suggest that caveolin-1 expression participates in the setting of adipocyte expandability, a key parameter in physiological response to nutrient overload. To address this, we assessed caveolin expression in adipose tissue frozen biopsies obtained from healthy human subjects enrolled in a trial designed to investigate metabolic effects of overfeeding. Thirty-five young subjects (6 females and 29 males) participated in the 8-week overfeeding protocol, and caveolin-1 protein expression showed no significant correlation with fat cell size at baseline (data not shown). During overfeeding, all the subjects gained weight (from 4. 4 to 10. 7 kg, mean value 7. 5 kg) and increased fat mass as well as nonfat mass (Fig.
Acknowledgments. Kai Simons  ( MPI-CBG, Dresden, Germany ) provided adenovirus encoding canine Cav1. Plasmids encoding Cav1-RFP, PTRF-eGFP, and SRBC-eGFP were generously provided, respectively, by  R. G. Parton  ( Institute for Molecular Biosciences, University of Queensland, Brisbane, Australia ) and  R. G. Anderson  ( Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX ).
Funding. Funding from European Community FP7/2007-2013 LipidomicNet grant agreement no. 202272  and from  NORC  grant no. P30DK072476  ( National Institutes of Health-National Institute of Diabetes and Digestive and Kidney Diseases)  is acknowledged. S. L. L. is funded by a grant from  "Region Pays de la Loire. "Duality of Interest. No potential conflicts of interest relevant to this article were reported.
, 2007]. The mantle drag is applied to the plate via a set of dashpots, and the mechanical energy conservation equations are solved inside the oceanic plate using a Lagrangian-Eulerian finite element software, ABAQUS  [Hibbitt and Sorensen, 2000]. The oceanic plate of length L and thickness h is modelled by a Lagrangian grid of 250 or 500 (for longer plates) by 20 nodes, with constant node spacing along the plate and a refinement of vertical spacing towards the plates top. The four-node elements are bilinear in velocity and constant in pressure. The subduction behavior obtained with these models is The slab of thickness h, density q L , Young's modulus E, viscosity g L , and in some cases yield stress r Y is freely sinking into an infinite mantle of density q M and viscosity g M , achieving a subduction velocity U p , deflection w and slab dip h.
Acknowledgments This work was funded by  NERC  (grant  NE/G004749/1 , awarded to SG). We thank  Cl ement Narteau  for insightful discussions about b-values calculations and interpretation. We thank one anonymous reviewer and  Susanne Buiter  for their thorough and constructive comments that helped us improve our work and presentation. The data for this paper are available upon request to the corresponding author. Geochemistry, Geophysics, Geosystems  10. 1002/2014GC005535.
) Figure  4  Modulation of the cytokine secretion in THP-1 cells in response to fondaparinux and rivaroxaban. A: Cytokine Array. B: Data of the cytokine assay. Non-activated and LPS-activated THP-1 cells were incubated in defibrinated plasma with PBS (control), 0. 15 μg/ml fondaparinux or 0. 35 μg/ml rivaroxaban. After 24 h, supernatants were assayed for cytokine production. All spots are in duplicate. a1, a2, and d12: positive controls. a3, a4 and d11: negative controls. C: the quantification by Image J of cytokines secretion by LPS-activated THP-1 cells. Two independent experiments were performed. Figure  5  ELISA quantification of IL-8 and TNFα in THP-1 cells. Non-activated or LPS-activated THP-1 cells were incubated in defibrinated plasma in the presence of 0. 35 μg/ml rivaroxaban or 1.
5 μg/ml fondaparinux. After 24 hours, IL-8 in the supernatants was measured by ELISA. The data present is means ± SEM of 3 independent experiments. A: ELISA for IL-8. Statistic significance (p < 0. 05) was found for the differences between non-activated cells versus control plasma; between non-activated versus LPS-activated cells; and between the activated cells treated by fondaparinux versus LPS-activated cells alone. B: ELISA for TNFα. Statistic significance (p < 0. 05) was found for the differences between activated cells versus the control plasma; between activated cells treated by fondaparinux versus LPS-activated cells alone. prothrombin times (PT) in a concentration-dependent manner without antithrombin  [36]. This differs from indirect FXa inhibitors, which only marginally prolonged aPTT and PT.
In contrast, very few MerTK + CD64 + cells in naı ¨ve Cx3cr1 gfp/+ mice were GFP + (Fig. 3B ). These data are consistent with a previous report that GFP-positive macrophages could be isolated from the livers of schistosome infected Cx3cr1 reporter mice  [23]. In other settings of type 2 immunity due to helminths, macrophages have been reported to robustly incorporate BrdU in situ within 3 hours of BrdU injection as they proliferate in response to IL-4  [5]. We asked whether macrophages were proliferating in the liver during schistosomiasis by injecting mice with BrdU and then using flow cytometry to detect BrdU in MerTK + CD64 + macrophages at times thereafter. We additionally investigated BrdU incorporation into monocytes. The pulse-chase experimental design allowed us to effectively identify cells proliferating at the time of BrdU injection, and then to ascertain whether cells labeled within this period had differentiated into other cell types, not labeled within the first 4 h, but which were.
We injected groups of infected CCR2-DTR and wild type (WT) mice with DT at week 6 of infection, which is just prior to the increase in blood monocyte numbers (Fig. 1D ). This treatment resulted in rapid weight loss in CCR2-DTR mice but not WT mice, such that by day 4 post injection CCR2-DTR mice had lost approximately 20% of their body weight (Fig. 5A ); the mice succumbed from infection by day 8 post injection. Naı ¨ve CCR2-DTR mice treated with DT remained healthy and did not lose any weight over the time period of the study (data not shown). To analyze the effects of CCR2 + cell depletion, we sacrificed mice at day 5 post initiation of DT treatment, and used flow cytometry to assess blood monocyte levels. As expected, we found that CD11b + Ly6C hi cells, which were also CD115 + (not shown), were lost in infected CCR2-DTR mice following DT treatment (Fig.
5B ). DT treatment also depleted LyC hi monocytes from the livers of infected CCR2-DTR mice (Fig. 5C ). Serum levels of the liver enzyme alanine aminotransferase (ALT), a marker of hepatocellular injury, were no higher in DT treated infected CCR2-DTR mice than in DT treated infected WT mice, and DT treatment per se did not lead to liver damage in infected WT controls, since serum ALT levels in these mice were no higher than in untreated infected mice (Fig. 5D ). We next used flow cytometry to assess the effect of acutely deleting monocytes on hepatic macrophage populations in infected mice. We found no overall change in the frequency of MerTK + CD64 + cells in DT treated infected CCR2-DTR mice compared to DT treated WT mice (Fig. 6A ), although there were substantially fewer hepatic leukocytes following monocyte depletion (data not shown).
Data were acquired on a Canto II (BD Biosciences) and analyzed with FlowJo v. 8. 8. 6 (Tree Star, Inc. ). Cells were sorted on a BD FACSAria (BD Biosciences). For morphologic characterizations, sorted cells were prepared on slides by cytocentrifugation at 1000 RPM for 5 min, and stained with HEMA-3 (Fischer Scientific). In vitro macrophage differentiation FACS-sorted Ly6C hi monocytes from blood and livers were treated with M-CSF (0. 02 mg/mL) in RPMI (Hyclone) supplemented with 10% FCS (Hyclone), 50 mM 2-Mercaptoethanol (Cellgro, Mediatech, Inc. , Va) and 100 U/mL Penicilin-Streptomycin (Cellgro, Mediatech, Inc. , Va). Cells (10 5 ) were plated in triplicate in 96-well round bottom plate (Costar, Corning Inc. , NY) and cultured in a humidified incubator at 37%, 5% CO2  [21]. Cells were harvested at day 7, and the expression of the surface marker F4/80, CD64 and MertK were determined.
Acknowledgments We thank  Drs. Eyal Amiel ,  P'ng Loke ,  Markus Mohrs ,  Erika Pearce ,  Emil Unanue , and  Pearce laboratory members  for valuable discussions, and  Yuliya Shakir ,  Shannon Nephew ,  Amanda Buck , and the staff of the  Department of Pathology and Immunology FACS facility  for technical assistance. Some of the work reported here was performed while MN, SCH, BE, and EJP were at the  Trudeau Institute , and the authors are thankful to the  Institute  for its support during that time. We are indebted to  Dr. Eric Pamer  for the generous gift of CCR2-DTR mice.
Macroscopic analysis and histology For macroscopic analysis, the investigated organ was excised and formalin fixed for 3 weeks. Post-fixation, the specimen was encapsulated in dual-component polyurethane foam formed into a cubic shape to enable mechanical slicing. The entire cube obtained after a 15 min polymerization period was sliced in 1-1. 2 mm thick slices using a commercially available slicer (Gemma 300, SIRMAN s. p. a. , Pieve di Curtarolo, Italy). Optical scans (600 to 2400 dpi) of each individual macroscopic slice were systematically recorded. Co-registration was further improved using in-house written Matlab code (The Mathworks Inc, US), if needed, and stack tiff-type images were generated for further integration and 3D postprocessing using Osirix Software (Osirix Foundation, Geneva, Switzerland).
Microscopic histology (frame l) revealed a clear difference between the ablated zone and healthy tissue, with normal cells showing clear striation by hematoxilin-eosin staining, as well as peripheral nuclei, while areas of coagulation necrosis appeared with lighter staining and showing a complete loss of lobular structure. A 3D reconstruction (Osirix Software) of the hepatic lobe containing the final lesion (same as in frame j) is shown in frame k, indicating the HIFU lesion and the RF marker at the periphery. The shortest distance between the centers of the two lesions was 5 mm. The calculated volume of the HIFU lesion was 1. 935 cm 3 , while the volume of the created marker was 0. 0235 cm 3 in the presented case.
Acknowledgments The authors wish to thank the  Center for Biomedical Imaging (CIBM) -Switzerland  for access to their MR imaging infrastructure,  Dr. Erik Dumont  from  Image Guided Therapy SA, Pessac-Bordeaux, France  for technical support on operating the MRgHIFU platform, and  Mrs Aurélie-Djamila Baudat ,  Mrs Sylvie Roulet ,  Mr. Jean-Pierre Giliberto ,  Dr. Camille Doras ,  Prof Walid Habre  and  Prof. Dr. Denis Morel  from the  University Medical Centre, Geneva, Switzerland  for assistance with the in vivo experiments. This work was partly funded by the  Swiss National Foundation of Science  ( CR32I3_125499  and  CR33I3_143980 ) and La fondation pour la lutte contre le cancer et pour des recherches médicobiologiques, Geneva, Switzerland.
Coverslips with L-shaped fibronectin micropatterns were prepared and used as described previously  (Fink et al. , 2011). In brief, coveslips were first covered with poly-l-lysineg-polyethyleneglycol to passivate the surface. After UV illumination through a mask destroying the poly-l-lysine-g-polyethyleneglycol in the unprotected areas, fibronectin was added at 50 µg/ml for 1 h. Cells in C-terminal (aa 310-577). We used pcDNA3 (Invitrogen), pEGFP (Takara Bio Inc. ), and pCB6 (http://www. addgene. org/vector-database/2070/; a gift from M. Arpin, Institut Curie, Paris, France) backbone vectors for expression in mammalian cells (CMV promotor): peGFP-N1-ezrin T567D, peGFP-N1 empty, pCB6-ezrin T567D and pCB6 empty, and pcDNA3-myc SLK full length. Human SLK cDNA was purchased from Source BioScience and cloned into vectors using the Gateway technology (Invitrogen).
Statistics Data were analyzed, graphs were drawn, and statistics were done using Excel (Microsoft) and GraphPad prism (GraphPad Software). Data were compared with Student t test (Fig. 1, B  and G   S3 ) or with Kolmogorov-Smirnov test (Fig. 2 ; Fig. 3, F  and I ; and Fig. S1, E  and K ). Online supplemental material Fig. S1  shows spindle orientation defects in micropatterned cells and apical progenitor cells treated with a second, independent SLK siRNA or shRNA; it also shows the absence of visible defects on retraction fibers or spindle morphology after SLK or ERM depletion. Fig. S2  shows the localization of Gi, LGN, and NuMA in SLK#7-or ERM-depleted cells and in ezrin T567D-expressing cells. Fig. S3  shows NuMA and F-actin localization in control cells, in Latrunculin A-, or pertussis toxin-treated cells, and in SLK-or ERM-depleted mitotic cells.
4 NA Plan Apochromat). In each experiment, all images were acquired with the same exposure time and settings using Metamorph software (Molecular Devices) driving a charge coupled device camera (Coolsnap HQ; Photometrics). 16-bit images were converted into 8-bit images using ImageJ software (National Institutes of Health). Brightness and contrast of representative images were then adjusted to display equivalent background levels using Photoshop CS4 (Adobe) without changing the gamma. Intensity quantifications were all done on untreated images using ImageJ software. For endogenous SLK, Gi, LGN, and NuMA staining, images were deconvolved using Huygens software (Scientific Volume Imaging) with the microscope PFS (10 iterations, threshold 200, and signal-to-noise ratio of 40). Time-lapse microscopy and measurement of spindle orientation.
Machicoane  and  J. Fink ;  European Young Investigator award ,  Institut National de la Santé et de la Recherche Médicale ,  European Molecular Biology Organization Young Investigator Program  to  S. Garel ; and  Institut National du Cancer  grant  2011-1-PL BIO-11-IC  to  M. Piel. C. A. de Frutos  was supported by a  Marie Curie postdoctoral fellowship  and the  Investissements d'Avenir  ( ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02 PSL* Research University ). The authors declare no competing financial interests. Submitted: 13 January 2014 Accepted: 12 May 2014.
Ethics statement The Scottish samples on which this study was based were obtained from land belonging to the Scottish Crop Research Institute when one of the authors was a member of staff there and permission was granted. The French samples were given to the authors by Genevieve Grundman of the University of Lyon. However, no new sampling took place for this study. Only previously obtained data were used, most of which has already been published  [30] [31] [32]. The samples were taken from agricultural fields and did not involve any endangered species. Dataset with bacterial distributions is available from the authors upon request.