dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgement The icFRC (http://wwwicfrc. fr), LabEx CSC  and  Region Alsace  are gratefully acknowledged for a fellowship to JS and the  Ministry of Education and Research  for a PhD fellowship to SJ. Funding from the  Italian CNR  (Project  PM. P04. 010"MACOL" ),  MIUR  project  PRIN 2010CX2TLM ,  MIUR-CNR  project  Nanomax N-CHEM  and the  French "Ligue contre le Cancer " are gratefully acknowledged. Blandine Guignard  is thanked for her help during MRI exams. Dr. F. Manoli  ( ISOF-CNR ) is acknowledged for a loan of ADPA and  Dr. Lucia Flamigni  ( ISOF-CNR ) is thanked for helpful discussion.
g. (Noack et al. , 2011)). First important step of the model reduction in fluid dynamics is to form an appropriate reduced basis from a complete set of basis functions. The choice of particular basis functions may be problem specific and the derivation can be 'a priori' or 'a posteriori'. One can refer to  (Joseph, 1976) ,  (Noack and Eckelmann, 1994)  for some of the early works on 'a priori' formation of the basis functions. In the recent,  (Dumon et al. , 2013)  used 'a priori' derivation of the basis functions, in the context of Proper General Decomposition (PGD). Besides, the spectral discretization methods are often preferred over spatial discretization methods to gain accuracy for same computing time and space requirements. In 'a posteriori' formation, the basis functions are derived using existing solution datasets with methods such as Proper Orthogonal Decomposition (POD) (e.
Method of snapshots POD The POD or Karhunen-Loeve expansion was first introduced in fluid dynamics by  (Lumley, 1967)  for the analysis of coherent structures in turbulence. Following the development of POD,  (Sirovich, 1987)  introduced the method of snapshots for the experimental and numerical datasets. It allows further reduction of degrees of freedom, compared to the direct method of POD. The solution state vector s s s includes all variables varying in time and space. Let η be an operating parameter (e. g. Reynolds number). The state vector of the High Fidelity Model (HFM) solution can be defined as, s s s(x x x, t; η) =       ρ(x x x, t; η) v v v(x x x, t; η) p(x x x, t; η).       (4) Where ρ, v v v and p are the fluid density, velocity vector and static pressure respectively.
A posteriori error estimate and stability The High Fidelity Model (HFM) solution can be an accurate CFD solution to the full NSEs or the experimental datasets for the flow under consideration. The HFM solution state vector can be expressed in terms of POD basis functions by Equation (  6 ). The method of snapshots leads to an approximation (similar to Equation  8 ), s s s(x x x, t; η) hf ≈ s s s(x x x; η) pod + N pod � i=1 φ φ φ i (x x x; η) pod ã a a i (t; η) pod (23) The subscript 'hf ' stands for a high fidelity solution, while as the subscript 'pod' stands for quantities estimated using POD. A posteriori the error in POD discretization can be given by, � � � ps s s (x x x, t; η) = s s s(x x x, t; η) hf -s s s(x x x, t; η) pod (24) Where the subscript 'ps s s' stands for a POD based error in the solution state vector s s s.
Background: For clinical genomic studies with high-dimensional datasets, tree-based ensemble methods offer a powerful solution for variable selection and prediction taking into account the complex interrelationships between explanatory variables. One of the key component of the tree-building process is the splitting criterion. For survival data, the classical splitting criterion is the Logrank statistic. However, the presence of a fraction of nonsusceptible patients in the studied population advocates for considering a criterion tailored to this peculiar situation. Results: We propose a bagging survival tree procedure for variable selection and prediction where the survival tree-building process relies on a splitting criterion that explicitly focuses on time-to-event survival distribution among susceptible patients.
A simulation study shows that our method achieves good performance for the variable selection and prediction. Different criteria for evaluating the importance of the explanatory variables and the prediction performance are reported. Our procedure is illustrated on a genomic dataset with gene expression measurements from early breast cancer patients. Conclusions: In the presence of nonsusceptible patients among the studied population, our procedure represents an efficient way to select event-related explanatory covariates with potential higher-order interaction and identify homogeneous groups of susceptible patients.
The patient's covariates Z 2 j are dropped down each tree. Then, the prediction is obtained as the weighted average of the estimated CHF over the learning datasets with the same membership terminal node assignment than the new case: ˆ (t| Z j ) = 1 B B b=1 L(b) l=1 1 W (b) jl =1 e αZ 1 j ˆ b l (t) where L(b) is the number of leaves nodes of the tree T b.
For prediction Table  1  Simulation scenarios for the evaluation of the importance scores and the prediction accuracy For each scenario, we have generated 50 data sets. The bagging procedure with 400 trees was then applied to each data set with the two proposed splitting criteria. We then obtained 50 estimates of the Out Of Bag Integrated Brier Score for each method and each scenario. We considered an additional scenario designed to mimic a data set that would reflect a situation, such as the one presented in our example, where variables are functionally related through groups (e. g. biological pathway). In practice, we generated correlated variables divided in five blocks of various sizes (ranging from 10 to 30 %) with correlations ranging between -0. 2 to 0. 3. We considered a situation with 500 individuals, a proportion of nonsusceptible patients of 25 %, a rate of censoring observations within the susceptible population of 10 and 25 % and two different values for the number of non-informative covariables (100 and 500).
Analysis of breast cancer data Description of the data We used bio-clinical data extracted from two genomic datasets (GSE2034, GSE2990) publicly available on the GEO (Gene Expression Omnibus) website (http://www. ncbi. nlm. nih. gov/geo/). The GSE2034 dataset corresponds to the expression microarray study conducted by Wang et al. [28]  and the GSE2990 dataset to the one conducted by Sotiriou et al. [29]. Both studies investigate the prognostic effect of gene expression changes on the outcome of patients with primary breast cancer. For gene expression analyses, Affymetrix Human Genome U133A Array were used in both studies and estrogen-receptor (ER) status (positive/negative) was available. The clinical outcome considered was the distant metastasis-free survival. Distant metastasis-free survival was defined as the interval from the date of inclusion to the first occurrence of metastasis or last follow-up.
For these two early breast cancer series, surgical resection can be considered as effective at eliminating the tumor burden for a non-negligible proportion of patients whereas, for the others, it leads to a lower tumor burden and thereby prolonged survival without distant relapse. Thus, a nonsusceptible fraction exists, and having a large number of patients followed up more than a decade after the primary treatment allows for an interpretable time sequence for tumor relapse. For this work, we decided to investigate the impact of estrogen-related genes in predicting metastasis among patients with ER-positive tumors. The gene expression datasets of the two series were analyzed after a joint quantile normalization. Here, we focused on estrogen-related genes that were defined as those demonstrating, on the whole dataset, a significant gene expression changes between ER-positive and ERnegative for a familywise error rate of 1 % (Bonferroni Fig.
5  Box-plot of the Out Of Bag Integrated Brier Score on simulated data set for the additional scenario with correlated variables divided in five correlated blocks of various sizes procedure). This selection led to the selection of 1,265 genes. We then selected patients with ER-positive tumors with a total of 294 patients (209 from GSE2034 and 85 from GSE2990) and investigated the effect of estrogenrelated genes on the occurrence of distant metastasis. In order to take into account the difference in the proportion of nonsusceptible patients between the two series, we included this variable as a confounding variable.
Results Figure  11  displays the Kaplan-Meier estimate of the metastasis-free survival for the two series. The five year metastasis-free survival was 68. 4 % ([ CI 95 % : 62. 3 -75. 0]) and 84. 8 % ([ CI 95 % : 77. 2 -93. 1]) for the GSE2034 and GSE2990, respectively. It shows that the survival curve eventually reaches a plateau at seven years of 61. 3 % ([ CI 95 % : 55. 0 -68. 3]) for GSE2034 and 75. 7 % ([ CI 95 % : 66. 4 -86. 3]) for GSE2990. We applied our proposed bagging survival procedure (with LR and R2 criterion) with 400 trees on the joint dataset presented just above. As can be seen from Fig. 12 , the two splitting criteria lead to two different set of variables with very few overlap. As expected from the simulation results, for each splitting criteria, IIS, DIIS and PPIS give quite similar results.
Conclusion In the presence of a mixed population with nonsusceptible patients, our results show that our bagging survival procedure with the proposed splitting criterion has good performance for prediction and variable selection. For measuring variable importance, we recommend the use of either the proposed Index Importance Score or the Depth and Index Importance Score. The proposed tree-building process, which relies on a model-based splitting criteria, can be considered as a convenient hybrid solution that combines multiplicative intensity model and tree-structured modeling. We believe that the proposed survival bagging procedure is very appealing for many clinical genomic studies in which a fraction of nonsusceptible individuals is commonly encountered. This procedure has been implemented in a R package called iBST (improper Bagging Survival Tree) and will be available soon on the CRAN repository.
Acknowledgements The research leading to these results was conducted as part of the  ABIRISK consortium (Anti-Biopharmaceutical Immunization: Prediction  and analysis of clinical relevance to minimize the risk). For further information please refer to www. abirisk. eu. We thank the members of the Abirisk  WP4 ,  Julie Davidson  and  Agnès Hincelin-Mery  for their great support. The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement n o [115303], resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution.
This work was supported by the  LABEX ITEM  ( ANR-10-LABX-50-01 ) within the  "Investissements d'Avenir" programme  operated by the  French National Research Agency (ANR). Introduction. A project in search of social validation: conflict as a resonant space 1 politicians and regional public opinion. project have appeared and gathered momentum. The " Lyon-Turin " project is not new. The first technical proposals go back to.
Materials and methods All 68 JJ stents analyzed in the present study were from Tenon Hospital. We followed the usual procedures. All participants gave their verbal consent, documented by the researchers, for use of the material. Samples were examined without knowledge of the patient's name or other identifying data. Ethical approval was obtained from the ethics committee of Tenon Hospital. The investigation conformed to the principles of the Declaration of Helsinki. All concretions on the JJ stents were characterized by FTIR (Vector 22; Bruker Spectrospin, Wissembourg, France) according to a previously defined analytical procedure  [47, 48]. Data were collected in the absorption mode between 4000 and 400 cm À1 with a resolution of 4 cm À1. An FEI/Philips XL40 environmental FE scanning electron microscope was used for characterization at the mesoscopic scale.
Data processing was accomplished with the STARe software package. The DSC instrument was calibrated for both, temperature and enthalpy using open-porous metallic standards (In, Pb, Zn). About 1-5 mg of the respective aerogel was weighed into a 160 ll aluminum DSC pan and soaked with o-xylene in slight excess to ensure both quantitative filling of the aerogels voids and presence of some free solvent to allow the measurement of its crystallization upon cooling. Crystallization curves were used instead of melting curves, but reversibility of the transitions was confirmed by cycling the system. All measurements were carried out in ambient atmosphere and included the following steps: • Cooling from 25 °C down to -70 °C at a rate of 0. 7 °C min -1 ; • Heating from -70 °C up to T, at a rate of 0.
On shore, the positions of the acoustic receivers are calculated by measuring the ToF (Time Of Flight) of the LBL beacons' signals on the acoustic receivers, thus determining, via multi-lateration, the position of the acoustic receivers with respect to the geo-referenced LBL. The synchronized and syntonized electronics and the data transmission/acquisition allows for calculating the latencies of the whole data acquisition chain with an accuracy of better than 100 ns. The APS, in combination with compass and tilt, pressure, current and sound velocity data, is expected to measure the positions of the digital optical modules in the deep sea with an accuracy of about 10 cm. Since data are continuously transmitted to shore and distributed to the local data acquisition network at the shore station, acoustic data are available also for Earth and Sea science users.
The master clock frequency derived from the CLB is 25 MHz and the data sampling frequency is about 195. 3 kHz. The used Sigma-Delta ADCs permit amplitude quantization of 24 bit and possibility to digitize the signal with 2 different gains (high and low) using two independent transmission channels (stereo stream). Data from each DAR are sent and treated by the CLB, which time-stamps the received data and can select, via proper command from shore: 1) to propagate one or both data streams received from the DAR, 2) to reduce the number of amplitude bits to be sent to shore These options allow flexibility of the system, aimed at adjusting the dynamic range of the DAR for low and high amplitude signals and permit also the reduction of the data flow to shore. The use of AES/EBU (Audio Engineering Society/ European Broadcasting Union) protocol simplifies interface with CLB and permit to use standard audio libraries.
The absolute GPS time is stamped in the data stream by the CLB, tagging unambiguously the acoustic signal detection time. The CLB sends continuously the received acoustic data stream to shore, embedded in the main DOM data-stream. Since all DOMs of the detector are synchronous and phased with respect to the GPS time signal transmitted from shore, the whole acoustic receivers array is synchronized to the GPS master clock. Two kinds of technologies for acoustic sensors are available and will be installed aboard the DUs: an "internal" digital piezoelectric-sensor, installed inside the DOM and a digital "external" hydrophone, installed in each DU base.
DOM positioning procedure The Acoustic Positioning data analysis is performed on-shore by a farm of PCs. These PCs receive, on a intranet, data from the DARs, parsed from the main data stream. Data can be dispatched on shore either in native AES/EBU format (that permit easy interfacing with standard acoustic libraries and software) or in any other format that allows conservation of acoustic information (24 bits/channel, sampling frequency 195 kHz, absolute time header). The ToA of the acoustic signals detected by DARs are measured, analysing the DAR raw data flow using software algorithms based on matched filters. This step requires that the filtering algorithms have, as input, the characteristics.
PoS(ICRC2015)1169 of the signal emitted by each single active beacon, such as frequency and emission pattern. The waveform of the signal and the absolute time of emission (ToE) of each acoustic beacon to be emitted are set during run initialization and configuration, and are stored in the data base. Once a LBL-beacon pulse is identified, the code associates to it the detection GPS time (ToA). The distance between each beacon and each DAR will be then calculated taking into account the sound velocity profile along the water column. Knowing the location of the LBL beacons on the seabed, each DAR positions is recovered using algorithms based on spherical multi-lateration. At last, the calculated DARs positions are stored in the detector database and visualized by a dedicated Graphical User Interface.
Conclusions The expected accuracy of the KM3NeT APS for DOM positioning is estimated to be of about 10 cm, allowing a precise reconstruction of Cherenkov neutrino-induced events. Since all acoustic data are continuously transmitted to shore, DAR data could be shared with the Earth and Sea Science Community for several research items: marine monitoring, biology, geophysics, oceanography, etc. In addition, thanks to the synchronization of each DAR with the Master Clock of the telescope, the KM3NeT APS represents a good tool to study the feasibility of an acoustic neutrino detector and the possible correlation between acoustic and optical signals induced by neutrino events  [12].
Acquiring data Prior to using the tool, if tree species rankings are not already available, data should be collected in the following manner: 1. Select the region of implementation (for example: country, province, watershed, altitude or rainfall zone). 2. Identify issues and prioritize ES that are relevant to the farmer's livelihood context. This can encompass a broad range of ES, taking a full farmer's need approach or very specific ones, such as bee keeping for enhanced pollination of a particular crop or suppressive effects of certain specific pests and diseases depending on objectives of the development agent. 3. Select shade tree species based on inventories of local farms. 4. A specific population group can be targeted (e. g. women, youth) if it is relevant for the development agent.
5. Interview members of the target zone on tree knowledge and let them rank tree species for selected ES. 6. Review and compile scientific knowledge on selected tree species. For the development of the tool, a target zone is defined as an ecological zone where the rainfall and temperature pattern is homogeneous. Depending on the landscape, this can vary from less than 100 km 2 in mountainous areas to several 100 km 2 in a more uniform and flat area. In this context, databases have already been constituted for Mount Elgon that is an Arabica region and Greater Luweero that is a Robusta region in Uganda; and for the Western region for cocoa, in Ghana. In these regions, a list of 20 to 30 most prevalent shade tree species per target zone was made based on incidence in coffee or cocoa farms after a thorough tree inventory on around 100-150 farms.
TO O L S E T U P The tool was developed in R and is freely available on request. Furthermore, an HTML version of the tool is in development, which will be available online as a web app. As shown in the flowchart (Figure  1 ), the end-users first pick the country, region and crop of interest. After that, they can choose one to several ES available and also give these ES varying weights according to the specific needs and locally perceived importance (weights range from 1: relatively unimportant to 5: very important). The tool links the recommended tree species to a tree library, associated with the tool, which links to other online databases such as the ICRAF Agroforestree library and the PROTA (4U) library, so that comprehensive information is available before a definitive choice is made.
Extension services, cooperatives and NGOs should use the recommended list of species as input for a discussion with farmers, and take into consideration knowledge about additional cropping systems as well as off-farm resources and revenues and other criteria (e. g. availability of local markets, food security of household, ease and cost of management, cost-benefit ratio, short-term benefits). This tool will be freely accessible on the websites of IITA and ICRAF. Research institutions, NGOs and extension services of cooperatives and governmental institutions are encouraged to add information and databases, widening its current geographical and crop scopes. Indeed, new zones, regions or countries can be added to the open-access library, under the condition that collection of local agroforestry knowledge, farmers' rankings of tree species and subsequent data analysis strictly follow the recommended protocols as proposed in literature  (Cerdan et al.
, 2012; Coe, 2002; Sinclair and Joshi, 2001; Smith-Dumont et al. , in press; Soto-Pinto et al. 2007). Clearly, other cropping systems could also be added, under the same conditions. In order to insure quality control of the data used by the tool, it is envisaged that IITA will have a supervising role over the tool, organizing a peer-review of new datasets before uploading them for public use.
Acknowledgements. This research was conducted under the  CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). We acknowledge  Ashiraf Mawanda ,  Hannington Bukomeko  for their help in the analysis and development of the tool and  Richard Asare ,  Willem-Albert Toose  and  Sander Muilerman  for advice on interpretation. We thank  Dr Richard Coe  ( ICRAF ) for his advices on Bradley Terry ranking analysis in R,  Federal Ministry for Economic Cooperation and Development of Germany (BMZ)  and the forests, trees and agroforestry ( FTA) program  from the  CGIAR  for financial support;  Dr John Beer  (CATIE) and  Emilie Smith-Dumont  ( ICRAF ) for their valuable review of the manuscript;  Franco Magnet ,  Wilberforce Wodada  and the team of  Agro-Eco  for data acquisition;  Theresa Liebig  for advice on pests and diseases.
For targeted panels, it is possible to differentiate somatic from germline variants in a single tumor sample, without a matched normal, using a set of heuristics. For example, variants that have been detected in healthy population screens (e. g. , 1000 Genomes Project, Exome Aggregation Consortium (ExAC)) are generally assumed to represent germline variants, while those found to be verified somatic mutations in the Catalogue of Somatic Mutations in Cancer (COS-MIC) database are often assumed to be of somatic origin. Additional criteria based on allele frequency distributions may also be applied. 10] RNA sequencing NGS methods Sequencing of RNA (RNA-Seq) is an extension of the methods developed for DNA sequencing that generally requires synthesis of cDNA from an RNA template (Table  1 ). RNA-Seq protocols differ in their methods for removing ribosomal RNA (rRNA), which is a critical step given that over 90% of a typical total RNA sample is made up of rRNA molecules.
41 In addition to these screening approaches, the oncology community is getting more information about rare genomic variants that impact targeted therapy response by applying NGS to exceptional responders. For example, the retrospective sequencing of one exceptional responder to everolimus identified TSC1 mutations as a predictor of exquisite sensitivity to mTOR inhibitors. 84  second type of study aims to prove the clinical utility of new technologies such as NGS by showing that application of the new technology improves a clinical end point in the context of a prospective clinical trial. As mentioned previously, there is no evidence yet that NGS testing of a broad panel of genes is clinically useful in the context of oncology. The prospective SAFIR02_Breast and SAFIR02_Lung trials (ClinicalTrials.