dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Claudia DeToma ,  Marine Largeau ,  Elodie Michel  and  Lauriane Chambolle  for their work and implication at the  Biological Resources center  and  Tumor Bank Platform PRB-HEGP  ( BB-0033-00063 ). This study was done with a grant from  Site de la Recherche Intégrée sur le Cancer (SIRIC) CARPEM.
1a ). To eliminate displacement, a fragment of bovine liver was selected that would fit or be slightly larger in size than the cavity in the silicone mold. Next, the mold with the liver was tightly wrapped with polyethylene to prevent displacement or loss of the liver during transport between the CT device and the operating table. Thus, ten phantoms were prepared for each of the two methods -manual and robotic, each of which contained two foam balls. Next, a CT scan was performed of several phantoms placed parallel to the table (Fig. 1c ). The 3D-model of the obtained samples was created. The data obtained from the CT were recorded and analyzed in the Multitrack navigation system software. The analysis allowed to determine the target points (centers of phantom neoplasms) and entry points to the phantoms.
These two points for each phantom neoplasm determined the trajectory of the manipulator. The deviation of the working tool was measured with respect to a straight line passing through the two key points. We conducted a series of experimental operations of radiofrequency ablation on a biological phantom of the liver with a tumor. All operations were divided into 2 groups. In the first group, the positioning of the instrument for the RFA in the liver parenchyma with a tumor phantom according to the data obtained by CT of the liver phantom was performed by a robotic manipulator. In the second group, the positioning of the instrument was performed directly by the surgeon manually, which requires a significant amount of training, hand-eye coordination, 2D to 3D extrapolation skills  20. At the first stage, a 3D-model of bovine liver with a phantom of the tumor (foam ball) was built on the basis of the CT data in the Multitrack software.
The first step was to calibrate the instrument so that the robot's tool center point (TCP) was at the tip of the electrode. This procedure was carried out by regular means of the iiwa robot controller. Then the instrument was registered in the Multitrack navigation system by rotating the instrument around the new TCP in various planes using the robot. It is a built-in function of the Multitrack software, which records the position of the frame with IR reflectors during movement, after which it determines the common center of rotation. Based on this data, the relative position of IR reflectors and TCP was established. The next step is to determine the coordinate transformation from the Multitrack system to the robot. The robot sequentially positioned the tool at several points with a known relative position, and the navigation system recorded these points in its coordinate system.
Based on the collected data, the coordinate transformation from the Multitrack system to the robot base was found using the singular value decomposition method  21. After these procedures, it became possible to transfer key points from the navigation system to the robot controller. Using the navigation system, the coordinates of the two key points were determined -the target and the entry point to the phantom of the internal organ. The surgeon solved this problem based on medical images obtained on CT, through the graphical interface of the Multitrack software. To compute the position of the target in robot space it is necessary to compute first the transformation between the CT image space and the robot space -this procedure is called the registration transformation. It is based on the alignment procedure using the key points.
These obtained coordinates were entered into the control program of the robotic system, which determined the trajectory of the robot's movement. The first group of experiments was carried out using a robotic manipulator. The control program guided the active part of the electrode mounted on the flange of the manipulator to the target point. Upon completion of the procedure, the robot was given a command to remove the electrode. The movement was carried out along the same trajectory while conducting the track ablation -heating the electrode input-output channel, to reduce blood loss. Then the radiofrequency ablation device was turned on manually (Fig. 3b ). The second group of experiments had a similar setup. The surgeon was provided with a graphical interface that is included in the software of the Multitrack system, used for brain biopsy.
During the manual movement of the electrode, the deviation from the trajectory, orientation, and target point were graphically displayed on the screen. It allowed the surgeon to adjust his movements in real-time. Choosing a position and making sure that the instrument is held in the correct position, the surgeon began his insertion into the liver parenchyma. The depth of insertion of the instrument and its location in the liver parenchyma was estimated according to the data displayed on the monitor. The location of the instrument was estimated by aligning the instrument axis with the target axes on the monitor screen, and the depth of its insertion by numbers showing the distance to a previously determined target point located behind the phantom of the tumor so that the instrument passes through the center of the phantom of the tumor and somewhat stands behind it.
Results The data obtained through the surgical navigation system were transformed for further analysis in the MATLAB software. The analysis was carried out according to the criteria described above  22. The first criterion revealed the following results: the standard deviation of points from a given linear trajectory was 2. 33 mm for a manual experiment and 0. 3 mm for a robotic experiment. Thus, it is obvious that the robot maintains a linear trajectory more stably than a surgeon  23  -the robot has surpassed the surgeon by more than 7 times. The gathered data revealed a 7. 99 mm maximum deviation of for the surgeon against 0. 55 mm for the robotic experiment, which is more than 14 times smaller. This difference is due to the fact that even for an experienced surgeon it is very difficult to maintain a straight-line trajectory of the input-output of the electrode, avoiding sharp displacements.
Agents able to modify microtubule dynamics are important anticancer drugs. The absence of microtubules resulting from drug-induced depolymerization is easy to detect. However the detection of a stabilized microtubule network needs specific assays since there is not a significant visual difference between normal and stabilized microtubule networks. Here, we describe a quantitative cell-based assay, suitable for automation, which allows the detection of stabilized microtubules without the need of microscopic examination. The rationale of this assay is based on the drug-induced resistance of the microtubule network to the depolymerizing agent combretastatin A4 and the subsequent detection of the residual microtubules by immunoluminescence. Using this assay to screen a kinase inhibitor library allowed the selection of seven known kinase inhibitors: selonsertib, masatinib, intedanib, PF0477736, SNS-314 mesylate, MPI0479605, and ponatinib.
Measurements of Microtubule Dynamics Using Videomicroscopy Fluorescence imaging of mRuby3-MT+tips of MTs in live HeLa cells maintained at 37°C, 5% CO 2 was performed using an inverted microscope (Axio Observer, Zeiss) coupled to a spinning-disk confocal system (CSU-W1-T3, Yokogawa) connected to a wide-field electron-multiplying CCD camera (ProEM+ 1024, Princeton Instrument). The microscope setup was controlled by MetaMorph software. Time-lapse microscopy imaging was performed after 2 h of treatment with 0. 2% DMSO (control) or drugs at a concentration of 10-20 µM. Acquisitions were performed every 1. 5 sec with a 100 msec exposure time during 2 min using a 63× NA 1. 46 oil-immersion objective. Kymographs of MT plus end dynamics were done using ImageJ software and a homemade KymoTool  (Ramirez-Rios et al.
Identifying Microtubule-Stabilizing Compounds in a Library of Kinase Inhibitors We performed a pilot screen of a commercially available chemical library of 187 known kinase inhibitors. In addition, the library was supplemented with the known microtubule stabilizing agents, PTX and epothilone  (Steinmetz and Prota, 2018). We also added a collection of 12 bis-aryl urea derivatives for which a LIM kinase (LIMK) inhibitory activity has been previously described in vitro in a cell-free system  (Yin et al. , 2015). Compounds were screened at a final concentration of 10 µM. Compounds that showed an activity superior to 50% of Pyr1 activity were considered as hits. Table  1  shows that different known kinase inhibitors-masatinib, selonsertib, intedanib, PF0477736, SNS-314 mesylate, MPI0479605, and ponatinibwere found positive using this screening assay.
, 2017). Immunofluorescence (Figure  3B ) showed that the amount of detyrosinated MTs was indeed clearly increased in cells treated with SNS-314 mesylate, masatinib,  and selonsertib, and moderately increased in cells treated with intedanib and ponatinib, when assayed at a concentration of 20 µM. A few scattered cells with detyrosinated MTs were found in the PF0477736 and MPI0479605 treated cell populations. As noticed in previous studies  (Prudent et al. , 2012; Martinez et al. , 2015)  the generation of detyrosinated MTs after treatment with MT stabilizing drugs was somehow heterogeneous among cells. The analysis of the detyrosination level of tubulin in whole cell lysates using Western blot confirmed the important detyrosination induced by SNS-314 mesylate and masatinib treatment (Figure  3C ).
Then, in an attempt to rank these compounds, we tested the MT stabilizing effect of different concentrations of these compounds, using the above-described quantitative luminescence assay. As shown in Figure  4A , SNS-314 mesylate, masatinib, and intedanib were found more active than Pyr1. Selonsertib was as active as Pyr1, whereas MPI0479605, ponatinib, and PF0477736 showed a lower stabilizing activity. Finally, we also analyzed the effect of the four most potent compounds-i. e. selonsertib, intedanib, masatinib, and SNS-314 mesylate-on MT dynamics, by following the fast growing plus ends of MTs using time-lapse fluorescence microscopy. To this end, we infected HeLa cells with a Ruby-construct that presents the Ser-x-Ile-Pro (SxIP) motif, which binds to the endogenous MT plus-end binding protein EB1  (Honnappa et al.
, 2005; Prudent et al. , 2012; Mardilovich et al. , 2015; Prunier et al. , 2016). We thus assayed the effect of the compounds on cellular LIMK activity by analyzing the phosphorylation of its major substrate cofilin. We found that upon cell treatment with SNS-314 mesylate and PF0477736, the intracellular cofilin was less phosphorylated, indicating that these compounds likely target upstream components of the pathway leading to cofilin phosphorylation (Figure  6A ). An in vitro analysis of the direct inhibitory effect of the compounds on recombinant LIMK activity, using also recombinant cofilin as substrate (Figure  6B ) showed that PF0477736 is indeed able to directly inhibit LIMK. PF0477736 was originally described as a Chk1/Chk2 inhibitor. The MT stabilizing effect of this compound could thus be ascribed to a combination of its inhibitory action on LIMK and on Chk1/Chk2, which have been shown to be able to phosphorylate and inactivate the MT-associated protein Tau  (Mendoza et al.
We have also found that the MT stabilizing effect of two of the selected compounds, SNS-314 mesylate and PF0477736, whose primary targets are Aurora Kinase and Chk1/Chk2, respectively, could be due to their inhibitory activity on LIMK, which has not been previously described. The important MT stabilizing effect that we have observed for SNS-314 mesylate can be the source of the in vivo synergy with paclitaxel that has been described for this compound in a colon carcinoma model  (VanderPorten et al. , 2009). Surprisingly, we have discovered a strong MT stabilizing activity for selonsertib, masatinib, and intedanib and a moderate one for ponatinib. Selonsertib is currently evaluated in Phase 3 clinical studies for the treatment of non-alcoholic steatohepatitis (https://www. clinicaltrials. gov).
FUNDING This work was supported by  INSERM, Universite ́Grenoble Alpes, CNRS , and by l 'Institut National du Cancer (INCa ,  PLBIO16186 ), and  Association Le Cancer du Sein-Parlons-en , to LL; by the  LabEx GRAL  ( ANR-10-LABX-49-01 ), supported by the  University Grenoble Alpes   graduate school  ( ANR-17-EURE-0003 ) to M-OF.
5 to ∼ 3. 0 GeV, depending on the data-taking period). The software trigger requires a two-, three-or four-track secondary vertex, with a significant displacement from any primary pp interaction vertex. At least one charged particle must have significant p T and be inconsistent with originating from any PV. A multivariate algorithm  [32]  is used for the identification of secondary vertices consistent with the decay of a b hadron. The analysis is performed using a data sample corresponding to 3 fb -1 of pp collision data collected with the LHCb detector at a centre-of-mass energy of 7 and 8 TeV (Run 1) and 1. 7 fb -1 at a centre-of-mass energy of 13 TeV collected during 2016 (Run 2). Samples of simulated Λ 0 b → pK -µ + µ -, Λ 0 b → pK -e + e -, Λ 0 b → pK -J/ψ(→ µ + µ -) and Λ 0 b → pK -J/ψ(→ e + e -) decays, generated according to the available phase space in the decays, are used to optimise the selection, determine the efficiency of triggers, reconstruction and signal event selection, as well as to model the shapes used in the fits to extract the signal yields.
The stability of the fit is evaluated with a large number of pseudoexperiments before proceeding to the final fit to data. The moments of the pull distributions of the R -1 pK and r B parameters are examined and the estimators are observed to be unbiased. The results of the fit to data, where candidates are accumulated over all the trigger and data-taking categories, are shown in Fig. 3. In total, 444 ± 23 Λ 0 b → pK -µ + µ -and 122 ± 17 Λ 0 b → pK -e + e -decays are observed, where the uncertainties are statistical only. The four electron datasets, two trigger categories in two run periods, have similar numbers of signal decays. The same applies to the two muon datasets.
ACKNOWLEDGMENTS We thank  Jan Traas  and our colleagues for their comments and feedback on this manuscript, as well as  Geoff Wasteneys  for triggering this collaboration in 2014. This work was supported by the  AgreenSkills+ fellowship program , which has received funding from the  European Union 's  Seventh Framework Program  under Grant Agreement  FP7-609398  (AgreenSkills+ contract to S. T. ), by  Human Frontier Science Program Long-Term-Fellowship  ( LT000891/2018-L)  to S. T. , by the  European Research Council  ( ERC-2013-CoG-615739  ''MechanoDevo'') to O. H. , by  Human Frontier Science Program  grant  RGP0023/2018  to O. H. , by  JSPS KAKENHI Grants  ( 16K07403  and  19K06709  to H. M. ;  16J03501  to S. T. ), by the  NOVARTIS Foundation (Japan) for the Promotion of Science  to H.
However, the fact that these strains share the same characteristics at the S protein level highlights its importance in the FCoV S biology and suggest a key role in the biology of this type of virus. We also performed an in silico modeling of the S protein from six FCoV strains (three FCoV I and three FCoV II), following the methods described in  [35]. Briefly, FCoV S protein models were predicted based on the structure of the HCoV-NL63 S protein (PDB ID: 5SZS). We first performed a pairwise alignment of amino acid sequences of the HCoV-NL63 S and each of the to-be-modeled FCoV S. Then, we used the Modeller tool (Modeller, v. 9. 23, University of California) within the Chimera software package (v. 1. 13. 1, University of California) to build a distance-comparison model of each FCoV S protein.
In vivo tumor experiments Mouse Hepa1-6 liver cancer cells were injected (10 7 cells transplanted subcutaneously (s. c. )) to grow tumors in C57BL/6 mice (male, 5-6 weeks old, weighing 20-22g). For the orthotopic tumor model, subcutaneous Hep1-6 tumors were cut into cubes (1mm 3 ) under aseptic conditions. Single cubes were then inoculated into the liver parenchyma of C57BL/6 mice anesthetized using xylazine. This study was approved by the Shanghai Medical Experimental Animal Care Committee and performed according to the National Institutes of Health "Guide for the Care and Use of Laboratory Animals". In order to mimic the antitumor effect of TLR9 agonist combined with immunotherapy accurately, we searched Clinical Trials. gov and found that a Class A TLR9 agonist (CMP-001) is used to combine with anti-PD-1 or anti-PD-L1 monoclonal antibody in melanoma (NCT02680184), colorectal cancer (NCT03507699) and lymphoma (NCT03618641).
Statistics Results are expressed as mean ± SD and all statistical tests were performed as 2 sided. For data normally distributed, we performed Student's t test, and the nonparametric exact Wilcoxon's signed-rank test was used to compare data not normally distributed. Cumulative survival time was estimated by the Kaplan-Meier method, and the log-rank test was applied to compare the groups. P < 0. 05 was considered statistically significant. No animal data were excluded.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  ( 81871924 ,  81572301 ,  81502487 ,  81802893 ,  81572844 ,  81972829 ) and  Shanghai Sailing Program  ( 19YF1407600 ) and the  Natural Science Foundation of Guangdong Province of China  ( 2017A030310641 ,  2018A030313762 ).
LifeCLEF Lab Overview Accurately identifying organisms observed in the wild is an essential step in ecological studies. Unfortunately, observing and identifying living organisms requires high levels of expertise. For instance, plants alone account for more than 400,000 different species and the distinctions between them can be quite subtle. Since the Rio Conference of 1992, this taxonomic gap has been recognized as one of the major obstacles to the global implementation of the Convention on Biological Diversity  14. In 2004, Gaston and O'Neill  [14]  discussed the potential of automated approaches for species identification. They suggested that, if the scientific community were able to (i) produce large training datasets, (ii) precisely evaluate error rates, (iii) scale up automated approaches, and (iv) detect novel species, then it would be possible to develop a generic automated species identification system that would open up new vistas for research in biology and related fields.
Dataset and Evaluation Protocol The challenge is based on a dataset of 997 species mainly focused on the South America's Guiana Shield (figure  2 ), an area known to have one of the greatest diversity of plants in the world. The challenge was evaluated as a cross-domain classification task where the training set consist of 321,270 herbarium sheets and 6,316 photos in the field to enable learning a mapping between the two domains. A valuable asset of this training set is that a set of 354 plant observations are provided with both herbarium sheets and field photos to potentially allow a more precise mapping between the two domains. The test set relied on two highly trusted experts and was composed of 3,186 photos in the field related to 638 plant observations. Participants were allowed to use complementary training data (e.
A second metric was again the MRR but computed on a subset of observations of species that are rarely photographed in the field. The species were chosen based on the most comprehensive estimates possible from different data sources (IdigBio, GBIF, Encyclopedia of Life, Bing and Google Image search engines, previous datasets related to PlantCLEF and ExpertCLEF challenges). It is therefore a more challenging metric because it focuses on the species which impose a mapping between herbarium and field photos.
Participants and Results 68 participants registered for the PlantCLEF challenge 2020 (PC20) and downloaded the data set, and 7 research groups succeeded in submitting runs, i. e. files containing the predictions of the system(s) they ran. Details of the methods and systems used in the runs are synthesized in the overview working note paper of the task  [21]  and further developed in the individual working notes of most of the participants (Holmes  [7] , ITCR PlantNet  [54] , SSN  [46] , LU  [58] ). The remaining teams did not provide an extended description of their systems but Fig. 2 : Density grid maps of the number of species of geolocated plants in Plant-CLEF2020. Many species have also been collected to a lesser extent in other regions outside French Guiana, such as the Americas and Africa.
To explain that we have first to distinguish between approaches based on CNNs alone (typically pretrained on ImageNet and finetuned with the provided training data) and approaches that additionally incorporate an explicit and formal Domain Adaptation (DA) technique between the herbarium and field domains. As expected regarding the low number of field photos in the training set for numerous species, directly finetuned CNNs with the PC20 data obtained the lowest scores (ITCR Run 1, SSN Run 1&2, UWB Run 1). External training data on traditional CNNs did not really improve performances. CNNs can be improved by the use of external data, involving more field photos, as it is demonstrated with the UWB runs 2 & 3 and ITCR Run 2. All these runs extended the training data with the previous year's PC19 training data and the GBIF training data provided by  [49] ).
It should be noted that the LU team also used an adversarial approach but with less success. Mapping DA technique reached an impressive genericity on difficult species. While the adversarial DA technique used by the ITCR PlantNet team obtained the best result on the main MRR metric, the Neuon AI team obtained the best results on the second MRR metric focusing on the most difficult species in the test set. This last team used two encoders, one trained on the herbarium sheets in PC20 and a second one trained on the photos from the PC17 dataset. Then they learned a distance function based on a triplet loss to maximize the embedding distance of different species and at the same time minimize the distance of the same species. Performances measured from the Neuon AI Run 5, which is an ensemble of 3 instances of their initial approach, gave especially impressive results with quite high MRRs and above all similar values between the two MRR metrics.
This is the first time over all the years of PlantCLEF challenges that we clearly observe an important impact of the use of genus and family information to improve the species identification. Many species with few training data have apparently been able to benefit indirectly from a "sibling" species with many data related to a same genus or family. The impact is probably enhanced this year because of the lack of visual data on many species. To a lesser extent, self supervision auxiliary task such as jigsaw solving prediction task (ITCR Run 5 improved a little the baseline of this team (run 4), and the best submission over all this year challenge is an ensemble of all FSADA approaches, combining self supervision or not, upper taxons or not.