dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated dataset Average (over 10 runs) performance of the different regularizers on the simulated dataset are reported in Table  1  through AUC, sensor selection rate and F-measure. This latter criterion measures the relevance of the selected channels compared to the true relevant ones. F-measure is formally defined as F-measure = 2 |C ∩ C * | |C * | + |C| where C and C * are respectively the set of selected channels and true relevant channels and |•| here denotes the cardinality of a set. Note that if the selected channels are all the relevant ones, then the F-measure is equal to one. Most of the approaches provide similar AUC performances. We can although highlight that groupregularization approaches (GSVM-2,GSVM-p, GSVM-a) drastically reduce the number of selected channels   2 : Performance results for the 3 datasets the average performance (over subjects) in AUC (in %), the average percent of selected sensors (Sel) and the p-value of the Wilcoxon signrank test for the AUC when compared to the baseline SVM's one.
P300 Datasets Results for these datasets are reported in Table  2. For the EPFL dataset, all methods achieve performances that are not statistically different. However, we note that GSVM-2 leads to sensor selection (80% of sensor selected) while GSVM-a yields to classifiers that, on average, use 26% of the sensors at the cost of a slight loss in performances (1. 5% AUC). Results for the UAM dataset follow the same trend in term of sensor selection but we also observe that the mixed-norm regularizers yield to increased performances. GSVM-2 performs statistically better than SVM although most of the sensors (9 out of 10) have been kept in the model. This shows that even if few channels have been removed, the group-regularization improves performances by bringing sensor prior knowledge to the problem.
We also notice that GSVM-a performance is statistically equivalent to the baseline SVM one while using only half of the sensors and GSVM-p consistently gives similar results to GSVM-2. To summarize, concerning the performances of the different mixed-norm regularization, we outline that on one hand, GSVM-2 is at worst, equivalent to the baseline SVM while achieving sensor selection and on the other hand GSVM-a yields to a substantial channel selection at the expense of a slight loss of performances. A visualization of the electrodes selected by GSVM-a can be seen in Figure  2  for the EPFL dataset and in Figure  3  for the UAM dataset. Interestingly, we observe that for the EPFL dataset, the selected channels are highly dependent on the subject. The most recurring ones are : FC1 C3 T7 CP5 P3 PO3 PO4 Pz and the electrodes located above visual cortex O1,Oz and O2.
We see sensors from the occipital area that are known to be relevant  [22]  for P300 recognition, but sensors such as T7 and C3, from other brain regions are also frequently selected. These results are however consistent with those presented in the recent literature  [31, 30]. The UAM dataset uses only 10 electrodes that are already known to perform well in P300 recognition problem, but we can see from Figure  3  that the adaptive mixed-norm regularizer further selects some sensors that are essentially located in the occipital region. Note that despite the good average performances reported in Table  2 , some subjects in this dataset achieve very poor performances, of about 50 % of AUC, regardless of the considered method. Selected channels for one of these subjects (Subject 25) are depicted in Figure  3  and interestingly, they strongly differ from those of other subjects providing rationales for the poor AUC.
To this aim, we have plotted the average performance of the different classifiers as a function of the number of selected sensors. These plots are depicted in Figure  4  for the EPFL dataset and on Figure  5  for the UAM dataset. For both datasets, GSVM-a frequently achieves a better AUC for a given level of sparsity. For most of the subjects, GSVM-a performs as well as SVM but using far less sensors. A rationale may be that, in addition to selecting the relevant sensors, GSVM-a may provide a better estimation of the classifier parameters leading to better performances for a fixed number of sensors. As a summary, we suggest thus the use of an adaptive mixed-norm regularizer instead of an 1 -2 mixed-norm as in van Gerven et al. [34]  when sparsity and channel selection is of primary importance.
ErrP Dataset The ErrP dataset differs from the others as its number of examples is small (72 examples per subject). The same experimental protocol as above has been used for evaluating the methods but only 57 examples out of 72 have been retained for validation/training. Classification performances are reported on Table  2. For this dataset, the best performance is achieved by GSVM-2 but the Wilcoxon test shows that all methods are actually statistically equivalent. Interestingly, many channels of this dataset seem to be irrelevant for the classification task. Indeed, GSVM-2 selects only 30% of them while GSVM-a uses only 7% of the channels at the cost of 10% AUC loss. We believe that this loss is essentially caused by the aggressive regularization of GSVM-a and the difficulty to select the regularization parameter λ using only a subset of the 57 training examples.
Multi-task Learning We now evaluate the impact of the approach we proposed in Equation (  9 ) and  (10)  on the P300 datasets. We expect that since multi-task learning allows to transfer some information between the different classification tasks, it will help in leveraging classification performances especially when the number of available training examples is small. Note that the ErrP dataset has not been tested in this MTL framework, because the above-described results suggest an important variance in the selected channels for all subjects. Hence, we believe that this learning problem does not fit into the prior knowledge considered through Equation  (10). We have followed the same experimental protocol as for the single task learning except that training and test  The poor performances of the SVM-Full approach is probably due to the high inter-subject variability in this dataset, which includes impaired patients.
For the UAM dataset, results are quite different since the SVM-Full and MGSVM-2s shows a significant improvement over the single-task learning. We also note that, when only the joint channel selection regularizer is in play (MGSVM-2), multi-task learning leads to poorer performance than the SVM-Full for a number of trials lower than 500. We justify this by the difficulty of achieving appropriate channel selection based only on few training examples, as confirmed by the performance of GSVM-2. From Figure  8 , we can see that the good performance of MGSVM-2s is the outcome of performance improvement of about 10% AUC over SVM, achieved on some subjects that perform poorly. More importantly, while performances of these subjects are significantly increased, those that performs well still achieve good AUC scores.
In addition, we emphasize that these improvements are essentially due to the similarity-inducing regularizer. For both datasets, the MTL approach MGSVM-2s is consistently better than those of other single-task approaches thanks to the regularization parameters λ r and λ s that can adapt to the inter-subject similarity (weak similarity for EPFL and strong similarity for UAM). These are interesting results showing that multitask learning can be a way to handle the problem related to some subjects that achieve poor performances. Moreover, results also indicate that multi-task learning is useful for drastically shortening the calibration time. For instance, for the UAM dataset, 80% AUC was achieved using only 100 training examples (less than 1.
sets have been formed as follows. We first create training and test examples for a given subject by randomly splitting all examples of that subject, and then gather all subject's training/test sets to form the multi-task learning training/test sets. Hence, all the subjects are equally represented in these sets. A 3-fold nested cross-validation method is performed in order to automatically select the regularization terms (λ r and λ s ). Performances of the different methods have been evaluated for increasing number of training examples persubject and are reported in Figure7. We can first see that for the EPFL dataset, MGSVM-2 and MGSVM-2s yield a slight but consistent improvement over the single-task classifiers (SVM-Full being a single classifier trained on all subject's examples and SVM being the average performances of subject-specific classifiers).
GenBank accession numbers are indicated in parentheses. Sequences were aligned using CLUSTAL X (V2), and phylogenetic inferences obtained using the maximum-likelihood method within the MEGA 5 software  [36]. Numbers at the nodes are percentages of bootstrap values obtained by repeating the analysis 1,000 times to generate a majority consensus tree. Brevibacillus brevis was used as out-group. The scale bar represents a 2% nucleotide sequence divergence. Different growth temperatures (25, 30, 37, 45°C) were tested. No growth occurred at 45°C, growth occurred between 25°and 37°C, and optimal growth was observed at 37°C. Colonies were bright grey with a diameter of 1. 0 mm on 5% blood-enriched Columbia agar. Growth of the strain was tested under anaerobic and microaerophilic conditions using GENbag anaer and GENbag microaer systems, respectively (BioMérieux), and under aerobic conditions, with or without 5% CO2.
Indole production was negative. G. massiliense was sus-ceptible to ticarcillin, amoxicillin, tobramycin, imipenem, vancomycin and rifampin but resistant to ceftazidime (Caz 30), colistin (CT50) and metronidazole. When compared with representative species from the family Paenibacillaceae  [38] [39] [40] [41] [42] , G. massiliense gen. nov. , sp. nov. strain G5 T exhibited the phenotypic differences detailed in Table  2. Matrix-assisted laser-desorption/ionization time-of-flight (MALDI-TOF) MS protein analysis was carried out as previously described  [15]  using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany). Twelve distinct deposits were done for strain G5 T from 12 isolated colonies. The 12 G5 T spectra were imported into the MALDI BioTyper software (version 2. 0, Bruker) and analyzed by standard pattern matching (with default parameter settings) against 6,252 bacterial spectra used as reference data, in the BioTyper database.
A score enabled the pre-sumptive identification of the isolated based on the following heuristicpecies: a score > 2 with a validated species enabled the identification at the species level, a score > 1. 7 but < 2 enabled the identification at the genus level; and a score < 1. 7 did not enable any identification. For strain G5 T , a significant score was not obtained, suggesting it was not a member of any known species or genus. We incremented our database with the spectrum from strain G5 T (Figure  4 ).
Genome project history The organism was selected for sequencing on the basis of its phylogenetic position and 16S rRNA similarity to other members of the family Paenibacillaceae, and is part of a "culturomics" study of the gorilla flora aiming at isolating all bacterial species within gorilla feces. It was the 81 st genome of the Paenibacillaceae family and the first genome of Gorillibacterium massiliense gen. nov. , sp. nov. A summary of the project information is shown in Table  3. The Genbank accession number is CBQR000000000 and consists of 176 large contigs. Table  3  shows the project information and its association with MIGS version 2. 0 compliance  [43].
Genome annotation Open Reading Frames (ORFs) were predicted using Prodigal  [44]  with default parameters but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched against the GenBank database  [45]  and the Clusters of Orthologous Groups (COG) databases using BLASTP. The tRNAScanSE tool  [46]  was used to find tRNA genes, whereas ribosomal RNAs were found by using RNAmmer  [47]  and BLASTn against the GenBank database. ORFans were identified if their BLASTP E-value was lower than 1e-03 for alignment length greater than 80 amino acids. If alignment lengths were smaller than 80 amino acids, we used an E-value of 1e-05. To estimate the mean level of nucleotide sequence similarity at the genome level between G. massiliense and another 2 members of the family Paenibacillaceae and Brevibacillus brevis, we use the Average Genomic Identity of Orthologous gene Sequences (AGIOS), a custom application we developed.
The Myeloma IX Study The MRC Myeloma IX trial recruited 1,970 newly diagnosed patients. The trial design and results have been reported previously  (Morgan et al. , 2012). In brief, the trial included two treatment pathways: the intensive treatment pathway for younger and fitter patients and the nonintensive pathway for older and less fit patients. The intensive pathway comprised high dose melphalan and autologous transplantation after induction with cyclophosphamide, thalidomide, and dexamethasone (CTD) or cyclophosphamide, vincristine, doxorubicin, and dexamethasone. The nonintensive pathway consisted of a randomization to either attenuated CTD (CTDa) or melphalan and prednisolone. All patients were subsequently randomized to thalidomide maintenance or no thalidomide maintenance. The trial was approved by the MRC Leukaemia Data Monitoring and Ethics committee (MREC 02/8/95, ISRCTN68454111).
Array Analysis GeneChip Mapping 500K Array set (Affymetrix) were performed as previously described. Twenty additional samples in this study had also been analyzed using SNP6. For mapping array data, the SNP inferred copy number were obtained using GTYPE and dChip, as previously described. Data have previously been deposited as GEO accession number GSE21349. Homozygous deletions were identified as having an inferred copy number less than 0. 7, hemizygous deletions between or equal to 0. 7-1. 4, and gains > 2. 4 for 500K arrays. Similarly, for SNP6, homozygous deletions were defined as a ratio less than 1. 6, hemizygous deletions as a ratio between or equal to 1. 6 and 2. 4, and gains if the ratio was greater than 2. 4.
MLPA reactions, including internal quality controls and negative controls, were performed according to the manufacturer's instructions. The PCR products were analyzed using an ABI 3730 DNA analyser (Life Technologies, Paisley, UK) and Coffalyser. net software (MRC Holland, Amsterdam, The Netherlands). Copy number at each locus was estimated as described previously  (Schwab et al. , 2010). In summary, values above 1. 2, between or equal to 1. 2 and 0. 75, between or equal 0. 75 and 0. 25, and below 0. 25 were considered as gain, normal, hemizygous loss, and homozygous loss, respectively. Values above 1. 6 were consistent with amplification (more than three copies).
Comparison of MLPA and SNP Arrays Eighty six samples were assessable for MLPA, 500K arrays, and iFISH. When MLPA and iFISH were compared to 500K arrays, MLPA was associated with better sensitivity and specificity than iFISH for the different regions analyzed (Table  2 ). The sensitivity and specificity of MLPA in detecting del(17p) deletions were 100 and 99% versus 100 and 97% for iFISH, respectively. Given the small number of del(17p) in this dataset, an additional comparison was made between MLPA and a series of samples enriched for PC leukemia for which SNP6 data were available (n 5 29). Twelve samples had del(17p) in this dataset and the sensitivity of MLPA was 94% and the specificity 100% when compared to SNP6 arrays (Table  3 ). For CDKN2C, located at 1p32, losses were detected with a sensitivity and specificity of 100 and 97% versus 100 and 93% for iFISH.
In the expanded analysis using the SNP6 dataset, the sensitivity of MLPA for CDKN2C was 83% and the specificity 100%. The sensitivity of MLPA to detect WWOX deletions was 100% both for MLPA and iFISH but the specificity of MLPA was greater (92% vs. 84%). The loss of CYLD is not assessed by common iFISH probes but MLPA was able to detect them with good sensitivity (100%) and specificity (93%) when compared to 500K array. The sensitivity of detecting del(13q) by MLPA was lower than iFISH (97% vs. 100%) but MLPA yielded better specificity (77% vs. 71%). To facilitate comparisons, we defined hyperdiploidy in the three methods as being the gain of at least two chromosomes among chromosome 5, 9, and 15. The sensitivity of detecting hyperdiploidy was 100%, but MLPA lacked specificity (80%) like iFISH (75%) suggesting both methods currently overestimate hyperdiploidy.
MLPA for identification of gains and amplifications of CKS1B was associated with a negative impact on survival in this small dataset. Panel A: Distribution of CKS1B ratios. By applying k-mean clustering to 264 MLPA CKS1B raw values, we were able to identify normal patients with two copies of CKS1B (ratio range from 0. 83 to 1. 19, center 5 1. 02), patients with a gain or three copies of CKS1B (range1. 2-1. 59, center 1. 36), and a small subset of patients with CKS1B amplification (range 1. 63-2. 53, center 1. 86). Panel B and C: Survival analysis. Survival data were available for the 176 Myeloma IX samples and suggests that amplification is associated with a worse outcome in terms of both PFS (Panel B) and OS (Panel C). identify patients with two copies of CKS1B (ratio range from 0. 83 to 1.
19, center 5 1. 02), patients with a gain (three copies) of CKS1B (range 1. 2-1. 59, center 1. 36), and a small subset of patients with CKS1B amplification (range 1. 63-2. 53, center 1. 86). The specificity in detecting amplification using a ratio >1. 6, when compared to patients with more than three copies of CKS1B by iFISH, was good (96%). The sensitivity was nevertheless low (56%). Of note, all the false positives were in fact gained but one bore a subclonal amplification by iFISH (39% of cells). We were also able to show that amplification of 1q21 was present in 8. 5% of patients at diagnosis and associated with a negative impact on survival in this small dataset, Figure  1.
DISCUSSION Over the last 15 years, it has become clear that specific genetic lesions have an importance on the tumor biology of myeloma. Increasing evidence suggests that myeloma is not a single disease but a collection of diseases with distinct clinical behaviors. Understanding this heterogeneity will enable us to perform precision medicine where clinical decisions are based on molecular subtypes of disease. Identifying these groups and designing clinical trials for them will refine our current management strategies, avoiding overtreating or undertreating specific subgroups  (ClinicalTrials. gov, n. d. ). The first step toward precision medicine is applying a risk stratification approach based on prognostically important lesions. Translocations were first identified as being prognostic with more recent studies emphasizing the role of copy number changes such as gain(1q21), del(1p32), and del(17p)  (Avet-Loiseau et al.
Discrepancy between iFISH and MLPA probably relate to point mutations, cryptic lesions, or subclones, as previously shown in chronic lymphocytic leukemia  (V eronese et al. , 2013). The comparison of MLPA to mapping arrays demonstrates the power of this method, as for each lesion the sensitivity and specificity is better than when using iFISH. Importantly, MLPA can be used to determine the number of copies of each region, which is of clinical relevance as amplification of CKS1B is associated with disease progression  (Sawyer et al. , 2014). We show for the first time that amplification of CKS1B is present in approximately 8% of patients at diagnosis and has a negative impact on survival. No high amplification samples were seen in our dataset (maximum five inferred copies). As far as the prognostic impact of the other lesions is concerned the exact prognostic relevance of each individual lesion remains to be determined, but our analysis suggests that MLPA may be used in combination with expression assays to determine a molecular risk stratification in myeloma, thus providing a rapid and robust alternative to iFISH.
The high resolution of this two-axis diffractometer allowed a precise determination of the positions and occupation numbers of O atoms. All patterns were refined by the Rietveld method using the FullProf suite tools  (Rodrı ´guez-Carvajal, 2000). Details of the refinements are given in the supporting information. 1  The XRD and NPD room-temperature data were refined simultaneously in order to obtain complementary information. The Bond_Str (bondvalence parameters) and GFourier tools of FullProf were used for calculating the distances and angles in the crystal structures and for obtaining the Fourier maps, respectively. The oxygen content of samples was determined in situ as a function of temperature in air by thermogravimetry (TG) using a symmetrical thermobalance based on a Cahn 1000 electrobalance.
Background surface-mass-balance information from GLACIOCLIM-SAMBA and good logistical support from the nearby Dumont d'Urville research station were major assets in initiating a multi-year blowing-snow monitoring campaign. Blowing snow and meteorological observation systems have been deployed and maintained since 2010. Instruments were deployed from the coast to 100 km inland  (Trouvilliez et al. , 2014). Here, we concentrate on the data obtained at site D17, about 10 km inland from the coast, because this is where the most extensive observation system was deployed. This is described in the data and model section (Sect. 2). An analysis of the data in terms of the relationship of atmospheric moisture with the occurrence of blowing snow is made in Sect. 3. The inability of various models without blowing snow, to reproduce the observed atmospheric moisture, is also demonstrated in this section.
Observation data Site D17 (66 • 43 26 S, 139 • 42 21 E; ∼ 450 m a. s. l. ) is located ∼ 10 km inland from the coast of Adélie Land (Fig. 1 ). Access is relatively easy in summer, but the site is not accessible in winter. Thus, the bulk of the instruments deployed at D17 must run in an automatic mode. A 7 m mast was erected early in 2010 (Fig. 2 ). Profiles of wind, temperature and moisture are recorded along the mast. Temperature and moisture are measured using Vaisala HMP45 sensors in MET21 radiation shields. Because winds are remarkably persistent at D17, wind ventilation of the radiation shields that house the thermometers prevents warm biases, as reported by  Genthon et al. (2011)  on the Antarctic plateau. Texas Instrument NRG40C cup anemometers were initially used to sample wind. They proved to be insufficiently robust for the extreme Adélie Land environment and were later replaced with Vector A100 cup anemometers.
Information on blowing snow was obtained using IAV Technologies FlowCapt sensors 1. Although FlowCapts are very good at detecting blowing snow, the original design resulted in significant errors in estimating the blowing-snow fluxes  (Cierco et al. , 2007). The sensors at D17 are of a more recent design, which significantly improves, although not necessarily solves, problems with estimating blowing-snow fluxes  (Trouvilliez, 2013). Data are sampled with a 10 s time step, and the 30 min statistics are stored by a Campbell CR3000 data logger. The 30 min averaged data are used in the present work. All instruments were set up within manufacturer-stated operating range of temperature and wind at D17. The HMP45 are factory calibrated to report relative humidity with respect to liquid water rather than to ice, even below 0 • C.