dataesr/scientific-paragraphs-categorization huggingface dataset:
Three benchmarks to test DAMA time complexity on long proteins The TitinLikeDB was constructed by considering three Pfam families (PF00041: fn3, PF09042: Titin_Z, PF07679: I-set; Titin_Z is known to strongly co-occur with I-set and fn3 with I-set). From these families, we selected proteins with at least 1000 amino acids: we included all Titin_Z proteins (53), randomly selected 750 proteins from fn3 and 750 from I-set. The number of proteins (1553) was determined by disk space limitations, since, on these large sequences, hmmscan was executed with permissive parameters and it produced a large amount of domain hits. The SilkWormSet was constructed from the SilkWormDB  (Xia et al. , 2005)  and it collects 324 proteins whose architecture is characterized by consecutive domain block repeats. For instance, these are proteins of the form DE[ABC] 13 FD, where ABCDEF are domains and where the block of domains ABC is repeated 13 times consecutively  (Moore et al.
, 2013). SilkWormSet contains all proteins with such repeated patterns found among the 14 623 proteins of the silkworm genome. A third dataset is constituted by long generated sequences. From the 14 831 domain families constituting Pfam 27 , we built hundred random sequences with n domains, for each n ¼ 3. 99 taken by steps of 3. Namely, for each random sequence to be generated, we randomly selected n domains within the Pfam 27 set, used HMMEmit  (Eddy, 1998)  to generate n sequences close to the original ones and concatenated the n generated sequences following the order of domain extraction. For each n, DAMA has been run on the concatenated random sequences, the average computational time has been evaluated for each n and plot in Supplementary Figure  S4. Over the three datasets, experiments were performed on a one core singleuser machine as indicated in Section 2.
Parameter settings for the optimization step To find the best domain architecture for a given query sequence, the algorithm (described in Section 3) generates a set of feasible architectures based on domain co-occurrence constraints and finds the most likely one by optimizing the five objective functions F 1. F 5 above. The tolerance values d j for each function F j (j 4) in Equation (  6 ) are set by the user depending on the dataset considered. For the d 1 value, aimed to introduce some flexibility on the selection of the domains, the idea is to evaluate not only domains with best E-value score but also those with good E-value score that are well supported by known co-occurrence. This required to set d 1 ¼ 0 for the two datasets we analyzed, one based on CATH and the other on Pfam. Then, for each domain database, the notion of 'higher confidence domain' for the optimization function F 1 was modeled accordingly.
For CATH, we wanted the model to fit the spirit of the database by allowing a selection of several potential hits for the final architecture, among the ones suggested by multiple probabilistic models (for a given domain, these hits overlap and are characterized by different boundaries and E-value scores), and we set d 1 ¼ 40 to expand the region of optimal solutions for our PDB benchmark. This permissive d 1 value allows to select a large number of overlapping hits with acceptable E value among which to find the one that accommodates best the other domains of the architecture. In contrast, for Pfam, the higher confidence domain is modeled by the domain hit with the best score. Because of this, we set a much less permissive d 1 ¼ 10 to assure hits with the highest E-value scores to belong to the final architecture for the P.
Measuring prediction accuracy As was done for MDA in  Yeats et al. (2010) , a predicted MDA is considered correct if the superfamily types and the number of occurrences of domains are predicted correctly, irrespective of whether the boundary positions are correct. The overall accuracy is then calculated over the whole dataset. If too many domains of the correct superfamilies (the CATH level H corresponding to Homologous superfamilies is used) are predicted then this is termed a 'semi-false positive', while if the extra domains belong to an incorrect superfamily, it is termed a 'false positive'; if DAMA is missing a domain, it is a 'false negative'. Note that the false negative rate is due to the E-value cut-off 0. 001, used both in DAMA and MDA. A single predicted MDA can potentially contain errors in all three classes.
This implies that the same residue content and the same length are preserved. The idea behind this last model is that protein sequences are made by small fragments of residues playing a relevant structural and functional role and that small k-mers approximate these potential patterns in a protein. Both random models were applied to each sequence 20 times, and the random reshuffling was realized with the perl function List::Util::shuffle(). If P is the original set of protein sequences and S its associated shuffled sequence, let P þ S be the set of concatenated sequences. Note that the set P þ S is a set containing 20 times more sequences than P because from each sequence in P, we generated 20 sequences. Then, we computed the number of domain predictions within the P-portion (saying R) and the number of predictions within S-portion (saying A) of the P þ S sequences and set the FDR ¼ A=R for the dataset.
Computational tools used for comparisons We compare DAMA to four domain architecture prediction tools: hmmscan, CODD, dPUC and MDA. hmmscan was run with default parameters and curated inclusion thresholds. The option -cut_ga, for model-specific thresholding (using profile's GA gathering cutoffs to set all thresholding), was used. hmmscan is included in the HMMER 3. 0 package (http://hmmer. janelia. org/software). We also used hmmscan (with permissive thresholds) to generate the set of potential domains required as input for DAMA, CODD, dPUC and MDA. For that, we used the following command line: hmmscan -F1 0. 1 -F2 0. 1 -F3 0. 1 -domZ 1 -Z 1 -E 0. 1 -domE 0. 1 -o dev/null -domtblout.
Results We evaluated the performance of DAMA on two datasets: a benchmark containing multi-domain proteins and the genome of the malaria parasite P. falciparum. The PDB benchmark was proposed in  Yeats et al. (2010)  to measure the performance of the MDA approach, and we used it to compare DAMA and MDA (Section 4. 1). This benchmark is useful for optimizing parameters and evaluate the robustness of the algorithm, but it does not constitute a realistic case study, made of a large set of highly divergent sequences, as often encountered in the annotation of new genomes. To address these difficulties, we considered the set of P. falciparum proteins, known to be hard to annotate and we compared the performance of DAMA, MDA, dPUC and CODD on its domain annotation (Section 4. 2).
Annotation of the P. falciparum proteins To account for a realistic dataset, made of a large pool of highly divergent sequences, we tested DAMA, MDA and two more available tools, CODD  (Terrapon et al. , 2009)  and dPUC  (Ochoa et al. , 2011) , on the P. falciparum genome. Since we do not know the exact number of domains present in proteins of P. falciparum, it is not possible to measure the rate of true positives, false positives and false negatives for computing standard measures (precision, recall and Fmeasure) as done for the PDB benchmark. However, we can estimate the proportion of false positives, i. e. the FDR, among domain predictions obtained for P. falciparum and then plot the number of domain predictions against the FDR at various thresholds. We have computed the FDRs according to two different strategies, over domain predictions (Section 2.
Results for the three experiments are reported in Table  2. See also Supplementary Figure  S5  for boxplots of run time on P. falciparum proteins. For TitinLikeDB and SilkWormSet, all known domain architectures could correctly (for domain positioning and number of repeated domains) be reconstructed by all systems in a very reasonable computational time, starting from the same set of domain hits. DAMA is the fastest, yet comparable to MDA and CODD. To investigate further DAMA behavior on small and large architectures, we constructed a dataset of generated sequences containing a progressively large number of domains, from 3 up to 99. DAMA time performance is reported in Supplementary Figure  S4. Note that, this performance evaluation agrees with the one reported in Table  2 , and it provides an estimation for much larger protein architectures.
In summary, the process of architectures formation appears to follow evolutionary rules that need yet to be unraveled. To test the performance of DAMA and other tools in architecture reconstruction, we looked for a realistic dataset of sequences that could represent well the difficulties encountered in genome and metagenome annotation. We decided to consider the set of P. falciparum protein sequences, knowing that these sequences diverged sufficiently from those that were used to construct probabilistic models employed for domain identification. This set of sequences represent in a fine way the protein world, compared with the benchmark based on CATH domains used in the first evaluation. Other genomes could be used instead. On comparable FDRs, computed from the two artificial datasets (1-mer and 4-mer) constructed by reshuffling sequences of the P.
The artificial  dataset that we constructed from P. falciparum sequences, allowed to verify again that DAMA performs the best followed by MDA. The difference between MDA and DAMA with CODD and dPUC is striking, especially when considering the high number of domains certified by the tools. New improvements, leading to a higher accuracy, might be envisageable by looking at specific failures of DAMA, by replacing some of the optimization functions F i with some alternative definition, by redefining tolerance parameters d i as functions of specific architecture characteristics (as the number of domains) or by integrating other architecture characteristics like the high frequency of certain pairs of domains that was ignored in this version of the tool.
Acknowledgements We thank  A. Ochoa  and  N. Terrapon , authors of dPUC and CODD, for having made accessible to us their implementations (for use with Pfam 27 ) and for their help in running the tools. We thank the referees for making very insightful suggestions and remarks. Experiments were carried out using the computing grid Grid'5000 (https://www. grid5000. fr)and the  UPMC MESU machine  ( ANR-10-EQPX-29-01 ).
Funding This work was undertaken (partially) in the framework of CALSIMLAB, supported by the public grant  ANR-11-LABX-0037-0  from the ' Investissements d'Avenir' program  ( ANR-11-IDEX-0004-02 ) (to A. C. and J. S. B. );  Institut Universitaire de France  (A. C. ) and  Brazilian National Research Agencies CNPq ,  CAPES ,  FAPERJ  and  FACEPE  (to F. R. J. V. and G. Z. ).
Data collection Data were collected by teams of 2 to 5 researchers and veterinary students. Interview team members were previously trained using participatory epidemiology approaches. In the first phase of the study, semi-structured interviews and proportional piling (PP)  (Mariner and Paskin, 2000)  were conducted with focus groups of poultry farmers to identify the names of the poultry diseases that farmers perceived as causing the highest and quickest mortality in poultry flocks. Proportional piling consisted in asking participants to draw circle corresponding to items like disease names and distributing 100 counters in each circle (according to rate of mortality and rapidity of the disease). Participants were then asked an estimate of the rate of mortality and duration of diseases which were given the highest scores.
Data analysis. Directed weighted networks were built using categories of actors as nodes and exchanges of information about suspicions as links between nodes  (Jackson, 2008). Links were weighted according to the results of the PP on the relative probabilities of information sharing between the different categories of actors. The category which received the highest score was considered to be contacted with certainty whereas the others were given relatively less priority. All scores were divided by the highest score, such that the category which ranked first then had a score p of 1, and other categories had a score ranging from 0 to 1. Several individuals or groups of individuals from a given category were separately interviewed, generating several estimations of the relative probability of information-sharing between 2 given categories of actors.
Computing material Data analysis was performed using R. 2. 15. 3 software (R core team, 2014). Network graphs were built and alpha centralities were calculated using the igraph package  (Csardi and Nepusz, 2006). Graphical displays of quantitative outputs were made using ggplot2 package  (Wickham, 2009). Spatial representations were made using ArcMap 10. 2 software (ESRI, 2011).
Acknowledgements This study was carried in the framework of the  REVASIA  project funded by the  French Development Agency (AFD). The study was officially supported by the  National Institute of Veterinary Research and the Department of Animal Health of Vietnam. The authors thank all the participants involved in the different field studies and the sub-departments of agriculture and  Sub-Departments of Animal Health of Hải Dương and Đồng Nai province  for their support.
Role of funding source The ICASA Foundation (www. adhdandsubstanceabuse. org) developed the IASP study, and arranged with its participating institutes that each of these institutes would seek funding for their regional process and data sampling efforts. The ICASA Network sought funding for the central organization costs. These central costs included: -Organizing meetings for the network; -Site visits for training and monitoring (the last author (VdG G) visited all of the institutes at least once, the European institutes were visited twice); -Building a data base fit for remote data storage at the University of Amsterdam; -Obtaining the right for use of the CAADID interview; -Translating the instruments in the necessary languages; -Cleaning the data; -Analysing the study results and coordinating publishing; In the period of development of the study  (2005) (2006) (2007) (2008) (2009) (2010)  the ICASA network received unrestricted grants from the following pharmaceutical companies: Janssen Cilag, Eli Lilly and Company, Shire.
Since the ICASA Network is a formal foundation (September 2010) it operates independent from pharmaceutical funding. Since then funding was obtained via the following sources: -Participating institutes; -The Noaber Foundation; -The Waterloo Foundation; -The Augeo Foundation. The funding companies, institutes and foundations did not have and will not have influence on any aspect of the study, including research questions, data sampling, data management, data analyses and publishing results. The local institutes report the following funding sources: The Netherlands, Amsterdam: no external funding was obtained. The participating institute, Arkin, paid for the costs involved, and used funding from Fonds NutsOhra for this project. Norway, Bergen Clinics Foundation: Main external funding has been the Regional research council for addiction in West Norway (Regionalt kompetansesenter for rusmiddelforskning i Helse Vest (KORFOR)), funding a 50% position.
The remaining resources, with staff and infrastructure, have been from the Bergen Clinics Foundation. Norway, Fredrikstad: The IASP was funded by the hospital, Sykehuset Østfold HF, not with money, but with 50% of the salary of the participants, then by two sources outside the hospital: The Regional Hungary, Budapest: There was no direct funding, but the following grant was used: The European Union and the European Social Fund have provided financial support to the project under the grant agreement no. TÁMOP 4. 2. 1. /B-09/1/KMR-2010-0003. Australia: The IASP Screening Phase was funded by a strategic funding faculty grant from the Curtin University of Technology, Perth, Western Australia. USA, Syracuse: no funding was obtained. G. Van de Glind was on one occasion consultant for Shire, for which he refused payment.
Center of Dual Diagnosis and the Social and Health directory. Sweden, Stockholm: The study was funded by the Stockholm Center for Dependency Disorders. Belgium: Funding of the IASP-project in Belgium: private funding. France, Bordeaux: Research Grant PHRC (2006-2012) from the French Ministry of Health and the French Government Addiction Agency MILDT grant 2010 to M. Auriacombe and by a French National Research Agency PRA-CNRS-CHU-Bordeaux award(2008-2010) to M. Fatséas. Spain, Barcelona: Financial support was received from PlanNacional sobre Drogas, Ministerio de Sanidad y Política Social (PND0080/2011), the Agència de Salut Pública de Barcelona and the Departament de Salut. Government of Catalonia, Spain. Switzerland, Bern/Zürich: The IASP in Switzerland was funded by the Swiss Foundation of Alcohol Research (Grant # 209).
Results Titanium. All experimental conditions are listed in table  2. Titanium surfaces were irradiated by low energy (,45 eV) He ions with flux of   2-3x10 23 m 22 s 21 at surface temperatures of 400, 600 and 750uC for 10 minutes (samples (i), (ii), (iii)). Until around 450uC no clear surface modification was observed. Above that temperature voids start to appear on the surface. As seen in SEM images in (Figure  1. ), the areal density of these voids increased with the surface temperature. SEM images were analyzed by the Gwyddion software to specify the mean void size. 37 There is also a slight increase in the mean diameter of these voids, from 20 nm (88 voids were taken into analysis) to 25 nm (104 voids were taken into analysis) with an increase in surface temperature from 600uC to 750uC.
General Experimental Procedures UV and CD spectra were measured using a Jasco J-810 spectropolarimeter. Optical rotation was measured on a Perkin-Elmer 343 polarimeter, using a 100-mm microcell. IR spectra were recorded on a Bruker Tensor 27 spectrophotometer. NMR spectra were measured on a Bruker Avance 500-MHz spectrometer. Chemical shift values (δ) were reported in parts per million (ppm) relative to the appropriate internal solvent standard, and coupling constants (J-values) are given in Hertz and referenced to residual solvent signals (CDCl3 at δH 7. 26 and δC 77. 16). HPLC purifications were carried out on a Jasco LC-2000 system equipped with a PU-2087 Plus preparative pumping system and a UV-2075 Plus detector. HRESIMS data were conducted on an LTQ Orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA).
Antimicrobial Activities Antimicrobial activities were evaluated by the capacity to inhibit E. coli (ATCC 25922), P. aeruginosa (ATCC 27853), S. aureus (ATCC 25923) and C. albicans (ATCC 10231) growth (OD 600nm). The crude extracts, VLC fractions, purified compounds and positives controls (ampicillin, bacitracin, chloramphenicol, oxytetracycline, amphotericin b and flumequine, from Sigma Aldrich, Canada) were prepared with sterile-filtered dimethylsulfoxide and stored at -20 °C. Tests were performed in 96-well plates at 37 °C for bacteria and 30 °C for fungi. Antimicrobial activity was expressed as IC50, defined as the concentration causing a 50% reduction of microorganism growth. 3. 6. 3. Data Analysis IC50 were calculated from the analysis of non-linear regression using GraphPad Prism program even Y = 100/(1 + 10 (X -Log IC50)) equation.
Acknowledgments This work was supported through funding provided by the  European Commission   Collaborative  Project  FP7  number  265896 -www. bammbo. eu). One of the authors (G. C. ) was partially supported by  CNRS  ( Côte d'Azur delegation-DR 20 ). J. -M. Guigonis  from the  Plate-Forme Bernard Rossi of Nice  is acknowledged for recording HRMS spectra.
However, these advances have not yet led to clinical applications, and the need to identify clinical IBC biomarkers to improve diagnosis and treatment persists. Gene expression changes and the coordination of cellular behavior depend on the activity of transcription factors (TFs) acting as master regulators (MR). In a number of cancer types, using context-specific network strategies has revealed the role of MRs. For example, the role of STAT3 and CEBP/B as responsible for the mesenchymal transformation in glioblastoma was evidenced by de novo reconstruction of the transcriptional network underlying the observed phenotype  [10]. Master regulators of FGFR2 signaling were recently identified in a similar fashion by using several datasets for discovery and validation  [11]. Gene networks and MRs have also been studied in stem cells  [12, 13].
Microarray gene expression dataset and supervised analysis To better understand the gene expression signature and identify potential transcriptional regulators of IBC aggressiveness, we used, as learning set, a previously published gene expression dataset  [7]  including 197 breast cancer samples from IPC Marseille-France (63 IBCs and 134 nIBCs) and available from the NCBI's Gene Expression Omnibus (GEO) portal (GSE23720) and the validation set  [9]  including 96 samples from the General Hospital Sint-Augustinus (Antwerp, Belgium; 41 IBCs and 55 nIBCs). The Affymetrix CEL files of both datasets were converted to normalized expression value using Robust Multi-Array Average (RMA) method provided by "affy" Bioconductor package. For the supervised analysis of gene expression profiles between the IBC and nIBC groups of the learning set, differentially expressed (DE) probe-sets were first filtered for absolute fold change ≥ 1.
5. Statistical analysis was then applied to these filtered probe-sets using the Student's t-test, with p ≤ 0. 05. The robustness of the resulting gene-list was tested in the Belgium independent validation set. A classifier of samples based on the expression of the differentially expressed probe sets was built by measuring the Pearson correlation (r thereafter designated as IBC score) of each sample with the IBC centroid, defined as the average corresponding expression profile of all IBCs from the learning set: samples with r > 0 were classified as "IBC-like" and those with r < 0 were classified as "nIBC-like". Gene Ontology (GO) term enrichment analysis of the resulting DE genes was conducted making use of Database for Annotation, Visualization and Integrated Discovery (DAVID) (http://david.
Construction of transcriptional regulatory network and master regulator analysis A transcriptional regulation network was inferred using the ARACNe algorithm  [15] ; it uses an information theoretic approach to dissect physical transcriptional interactions between TFs and their potential targets from mutual information (MI). The MI was estimated by using the "parmigene" package (PARallel Mutual Information calculation for GEne NEtwork reconstruction  [20] ), available on CRAN. The package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors' distances with default values of k equal to 3, and 10 -12 for random noise. Master Regulator Analysis (MRA) algorithm  [10]  was then applied to compute the statistical significance of the overlap between the regulon of each TF (that is, its ARACNe-inferred targets) and the differentially expressed gene list.