dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that S. pneumoniae serotypes 6B, 15B/C, 19A, 35F and 35B were the best biofilm producers. Previously, serotypes 14, 6B, 15B/C, and 11A were found efficient in producing biofilm  [25, 40] , whereas Domenech et al. , reported serotypes 35B and 11A, 19A as efficient  [24]. Therefore, these results support the validity of our method because the 5 serotypes we found as the best biofilm producers were those previously described. Analysis of a large database of AOM serotypes revealed that serotype 19A has the highest disease potential for AOM  [44]. The high production of biofilm may be one explanation for this phenomenon. Our study has several limitations. The first is the lack of direct evaluation of biofilm in biological samples in middle ear fluid (MEF) with alternative methods such as confocal microscopy after live/dead staining  [45, 46].
Dynamics of ecological interaction networks The dataset was split according to the effects previously described (cropping season, seasonality, soil-contact conditions). Six ENA were performed per experimental year, corresponding to residue samples in contact with the soil and aboveground residues, collected in October, December, and February (Fig. 5 ). The networks for July are presented in Additional file 7: Figure  S4. The mean number of interacting nodes in the network (120. 3 +-41. 8) increased over the season (Additional file 1: Table  S1 ). Overall, networks were sparse, with a mean node degree of 2. 76 ± 0. 43. For each network, the positive/ negative edge ratio decreased over time, reaching 1. 0-1. 5 in February. Most nodes were common to October, December, and February. Zymoseptoria ASV was one of the fungal ASV with the largest number of degrees and greatest betweenness (measurement of centrality in a graph based on the shortest paths) for aboveground samples in October.
Discussion By sequencing the microbial communities of 420 samples of wheat residues, we obtained a total of 996 bacterial ASVs and 520 fungal ASVs. Using this large dataset, we estimated the potential interactions occurring between a plant pathogen (Z. tritici) and the members of microbial communities associated with crop residues in field conditions. By combining two approaches-LDA and ENA-we were able to demonstrate an effect of pathogen infection, even after the disappearance of the pathogen, on the structure and composition of the microbial communities during residue degradation.
Sequence processing Runs were analyzed separately. Primer sequences were first cut off in the fastq files with Cutadapt  [68]. Files were then processed with DADA2 v. 1. 8. 0  [69]  according to the recommendations for the "DADA2 Pipeline Tutorial (1. 8)" workflow  [70] , with quality trimming adapted for each run (Additional file 3: Table  S3 ). A mock sample consisting of equimolar amounts of DNA from known microorganisms was included in each run (see Additional file 9: Figure  S6 ) to establish a detection threshold for spurious haplotypes. At a threshold of ≤ 0. 3‰ of the size of the library, amplicon sequence variants (ASVs) were considered spurious and were removed from the sample. We used the naive Bayesian classifier on RDP trainset 14  [71]  and the UNITE 7. 1 database  [72]  to assign ASVs.
2. [74] ). PERMANOVA was performed with the "margin" option, to test the effect of each factor on communities (adonis2 function, vegan package  [75] ). Since the July samples were derived from living plant tissues (greenhouse), we carried out a PERMANOVA to test the effects of inoculation (for fungi and bacteria) and season (for fungi only; Table  1 ), and a PERMANOVA for the other sampling dates together to test the effects of inoculation, season, and contact with soil. A linear discriminant analysis (LDA) implemented in Galaxy  [76]  (LefSe, http://huttenhower. org/galaxy) was used to characterize the differential abundances of fungal and bacterial taxa between each soil-contact condition and each Z. tritici inoculation condition. In this analysis, differences in the relative abundance of taxa between treatments were evaluated with a Kruskal-Wallis test; a Wilcoxon test was used to check, by pairwise comparisons, whether all subclasses agreed with the trend identified in the Kruskal-Wallis test.
Ecological interaction network analyses For characterization of interactions within the different wheat residue microbial communities, we performed ecological network analyses (ENA) with SPIEC-EASI  [77]  for combined bacterial and fungal datasets  [78]. The same parameters were used for all networks. The nonnormalized abundance dataset was split on the basis of sampling date and soil-contact condition. Each of the datasets included 15 inoculated and 15 non-inoculated samples. This choice was based on the following considerations, (1) differences in relative abundance of Z. tritici were thus maximal in each dataset, (2) the variability between samples induced by inoculation was shown to be relatively lower comparatively to sampling date and soilcontact condition, (3) loss in specificity in networks was established to occur because networks are unable to distinguish whether a statistically significant co-occurrence is due to an interaction or rather to a shared habitat preference  [79] , and (4) the specificity of networks was established to increase with an increasing number of samples until it plateaued at about 25  [79].
Infrequent ASVs were filtered out by defining a threshold of a minimum of six occurrences, to increase the sensitivity of the ENA  [79]. We used the neighborhood selection as graphical inference model (Meinshausen and Bühlmann MB method) with SPIEC-EASI, as this method has been shown to outperform most of the other available methods (e. g. , CCREPE, SPARCC, SPIEC-EASI (glasso))  [77]. The StARS variability threshold was set at 0. 05. Networks were then analyzed with the igraph package (version 1. 2. 2. [80] ). Scripts for network construction and analysis are available from GitHub (see the "Availability of data and materials" section).
Acknowledgments This study was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr/),supported by  France Génomique National Infrastructure , funded as part of the " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). We thank  Angelique Gautier  ( INRA BIOGER ) for preparing the mocks,  Sandrine Gelisse  ( INRA BIOGER ),  Nathalie Retout  ( INRA BIOGER ), and  Christophe Montagnier  ( INRA Experimental Unit, Thiverval-Grignon ) for technical assistance,  Anne-Lise Boixel  ( INRA BIOGER ) for assistance with statistical analyses, and  Marie-Hélène Balesdent  ( INRA, UMR BIOGER ) for her comments and suggestions to improve the manuscript. We thank  Julie Sappa  for her help in correcting our English.
Study assessment At each session, the nurse in charge of the patient will search for signs indicating the presence of a TCI or dysfunction using a check list (comprising clinical signs of infection and bacteriological samples, antibiotic treatment, TC lines inversion and fibrinolytic treatments) according to the usual practice of the dialysis centres and clinical practice guidelines  [26, 28]. Data will be collected weekly in an electronic case report form specifically developed for the study. Data for adjudication of the primary outcomes and serious adverse events will be recorded continuously.
Coordination and conduct of the trial Before the start of the study, all health-care workers and clinical research assistants in the 14 participating study centres will attend a formal training session on the study protocol, patient randomization and data collection in the Clinsight Clinical Data Monitoring Software (CDMS) electronic case report form. CDMS is a secure, interactive, web response system available at each study centre. The data manager will have access to the dataset. He will send login with limited access to each investigators ans local clinical research assistant. All documents required for the study will be available in each dialysis centre. Data monitoring in each center will be made by the same coordinating clinical research assistant independent from each study center. The principal investigator and a clinical research assistant will be available to discuss any problem concerning data collection, treatment invoices and protocol compliance and to assess study progress.
General consideration The statistical unit of the primary analysis and of most secondary analyses will be the TC. When a new TC is inserted in a patient participating in the study, the new TC will be analysed for the occurrence of a TCI or TC dysfunction only from the time when the patient fulfils the eligibility criteria. This will rule out ongoing TCI or early dysfunction of the TC due to poor positioning at the time of insertion or an anatomical abnormality of the patient. The principal analysis will be performed on the intention-to-treat principle with Stata software (ver-sion13, StataCorp, College Station, TX). The tests will be two-sided, with a type I error set at α = 0. 05. Quantitative variables will be presented as mean ± SD when normally distributed (assumption of normality studied by Shapiro-Wilk test), and for non-normal distributions as median, quartiles and range.
Primary analysis To evidence the efficacy of the Ethenox lock solution in preventing TCIs in comparison with the reference lock solutions (UFH 5000 U/mL or Citrate 4%), the censored data will be estimated by the Kaplan-Meier method. The comparison between groups regarding the primary endpoint will be performed with a marginal Cox proportional hazard model. Results will be expressed as hazard ratios and 95% confidence interval. Second, owing to the possible non-random censoring process the proposition to right-censor patients at the time of TC removal is debatable. The previous models will be performed with the Inverse Censoring Probability Weighting estimator. Finally, because of possible competing events, which prevent an event of interest from occurring rather than just preventing it from being seen to happen (censoring), Fine and Gray's approach will be used.
Funding This study is funded by a grant from the  French Ministry of Health  ( Protocole Hospitalier de Recherche Clinique  PHRC national 2015). It is a public funder which has no role in protocol writing, data collection, analysis of results and their publication. Trial Sponsor:  Centre Hospitalier Universitaire de Clermont Ferrand , 58 rue Montalemenberg 63000 Clermont Ferrand Study sponsor code: PHRC N 2015 ANIORT.
Interestingly, such bi-patterns may also be defined in the directed case when considering subgraphs in which a single pattern is associated to each of the in or out vertex roles. Finally we will see that the methodology we propose may also apply to undirected networks as far as we may dynamically define two different roles in the network, namely here considering in one hand high degree nodes and in the other hand their neighbours. Note that in oder to properly define bi-pattern mining we also need to extract cores from subgraphs induced by vertex subset pairs. This also means defining cores made of two vertex subsets, which goes beyond generalized cores definition. On the computational side, we adapt the general core extraction algorithm for our new core definitions and we propose a closed bi-pattern enumeration algorithm that we have implemented within the minerLC software 1.
Bi-pattern enumeration We focus now on abstract closed bi-pattern enumeration. Building the bi-concept lattice has therefore to be a post-processing step. The enumeration follows the same process as abstract closed pattern enumeration, i. e. the efficient divide and conquer scheme described in  Boley et al. (2010)  as implemented in the MinerLC software. The adaptation is straightforward: the closure operator is now f A = int • p • ext where p is the interior operator as defined above. To perform enumeration of abstract closed bi-patterns we specialize each abstract closed bi-pattern (q 1 , q 2 ) by adding either an element of I 1 to q 1 or an element of I 2 to q 2. The algorithm bi-patterns is described below with the following notations: Let q = (q1, q2) be a bi-pattern, i) add(i, q) returns either (q 1 ∪ i, q 2 ) when i ∈ I 1 or (q 1 , q 2 ∪ i) when i ∈ I 2 , ii) minus(I, q) returns the set of items which belong neither to the left part nor to the right part of the bi-pattern q = (q 1 , q 2 ), i.
Experiments The first experiment concerns an original two-mode network, the second concerns a well-known directed social network available on the minerLC web page while the third one is an attributed undirected bibliographical network. The actual implementation, as part of the minerLC suite, relies on a pre-processing of the dataset that transforms the original network into a new network. Closed bi-patterns are then represented as single patterns whose items are prefixed by a role. Note that in this section there is no comparison with other programs or methods, as the task of bi-pattern mining is new as far as we know. However regarding the second dataset, we display a single pattern core subgraph, obtained in a previous work, together with a bi-pattern core subgraph sharing some nodes with the former.
h-a BHA bi-patterns in a two-mode network of epistemological data We are currently investigating a two-mode network concerning data related to a MNHN-IRD program (called MUSORSTOM then Tropical Deep-Sea Benthos) of expeditions exploring the deep-sea in the Indo-West Pacific region, since 1976  (Bary 2018). In this network 596 edges relate 74 campaigns (V 1 ) to 268 participants (V 2 ). Campaigns are described following their date and location, the type of fishing gear (dredge, trawl), the objectives of the campaign as well as species described during the campaign. Regarding Fig. 4  The search tree developed by minerLC during the bi-pattern enumeration of Example 4. Each box represents on the first line a bi-pattern q together with its support set pair e in the current vertex set, and on the second line the core p(e) of the subgraph induced by this support set pair, preceded by the associated abstract closed bi-pattern int • p(e).
h-a BHA bi-patterns in a Lawyer Advice directed network This dataset concerns a network study of corporate law partnership that was carried out from 1988 to 1991 in New England  (Lazega 2001). It concerns 71 attorneys (partners and associates). The vertices 1 to 36 represent partners while vertices 37 to 71 represents associates, i. e. attorneys with a lower position in the firm. In the Advice network 3 , each attorney is described using various attributes, and 892 directed edges xy relate attorney x who goes to attorney y for basic professional advice. This network was investigated in  Soldano et al. (2017b)  applying the abstract closed pattern methodology using the HAcore definition. We use here the same attributed network as found in the minerLC web page (see above). There may be many bi-patterns when considering a single mode network as their number is quadratic in the number of single patterns in the same network.
Star-Satellite bi-patterns in a bibliographical network We also investigated the co-authoring network DBLP. E extracted from the DBLP database. DBLP. E is part of a family of networks used in various experiments on graph mining  (Galbrun et al. 2014). To build the vertices description, first the terms in the titles of the author's articles were gathered and stemmed. Stop-words as well as terms that occur with more than 60% of the authors were then removed. Finally, each researcher is labelled by the terms whose occurrence count si higher than one percent of the total volume of terms for that researcher. The network is the ego-network of radius 2 of co-authors of George Karypis and has 721 authors connected by 1427 undirected co-authoring links. The maximum vertex degree is 68 and the average vertex degree is 3.
Event-free survival was determined between date of evaluation and date of event or date of last known status. Prognostic factors of event were determined using a Cox model after verification of the proportionality of risk hypothesis, first in a univariate analysis, and, if appropriate, in multivariate analysis including factors found significant in univariate analysis and adjusted on age. The statistical tests were two-tailed and the level of significance was set to 5% (p<0. 05). Statistical analyses were conducted using SAS v9. 4 (SAS Institute Inc, Cary, NC, USA).
3 × 4. 5 mm and was not bounded by external borders, hence cells could swim in and out the viewing field. Supplemental Information 1 gives additional information about the experimental protocol and material used. Images were treated using an objective and automated image analysis workflow to count individual cells and record morphology descriptors (cell size and cell shape); this workflow is based on ImageJ  (Schneider, Rasband & Eliceiri, 2012)  and was carefully validated and extensively optimised to produce accurate and repeatable results  (Pennekamp & Schtickzelle, 2013). Dispersal rate of a genotype was estimated as the ratio of density in the target tube to the overall density (start + target), that is the proportion of cells in the target. Individual cell trajectories were obtained from the digital videos in a standardised and automated fashion with a workflow that was later transformed into the R package BEMOVI  (Pennekamp, Schtickzelle & Petchey, 2015)  and was successfully used in previous studies extracting movement characteristics from video sequences  (Banerji et al.
, 2017)  to fit a hierarchical family of correlated velocity models, basically continuous-time equivalents of the widely applied correlated random walk, with biologically intuitive parameters such as movement speed and the velocity autocorrelation timescale (a measure of the decay in directional persistence). For each genotype, we randomly subsampled 23 trajectories per replicate and tube resulting in a total of 6,072 trajectories. The subsampling was necessary because analysis with continuous time movement models is computationally demanding due to the model selection procedure involved. Subsampling also ensured the same number of data points per genotype. For each trajectory, we fitted four models: an unbiased correlated velocity model (UCVM), an advective correlated velocity model (ACVM), a rotational correlated velocity model (RCVM) or a rotational advective correlated velocity model (RACVM).
Statistical analyses To address our first question, activity and movement metrics (speed and linearity) were compared among genotypes and among dispersal status (disperser vs. resident cells) using a three-way ANOVA, with genotype and dispersal status as crossed and fixed effects, and replicate as random effect nested in genotype but crossed with dispersal status. Genotype was considered as a fixed effect, despite its common consideration as a random effect  (Crawley, 2007). This is because the set of genotypes cannot be considered as a random sample of the genetic variation exhibited by the species in the wild (some genotypes were selected due to previous results or based on their phenotypic characteristics, some others were created by inbreeding in the laboratory). Dispersal status was crossed with replicate because the data for the two statuses (disperser and resident, i.
ACKNOWLEDGEMENTS Virginie Thuillier  and  Linda Dhondt  provided valuable help during the experiment and data collection. F. P. Doerder kindly provided a collection of 22 wild type genetic lines of T. thermophila. We thank  Delphine Legrand ,  Emanuel Fronhofer ,  Staffan Jacob ,  Camille Turlure ,  Justin Calabrese  and anonymous reviewers for providing valuable comments on earlier drafts of the manuscript. This is publication BRC360 of the  Biodiversity Research Centre.
The Bluetooth Low Energy (BLE) protocol is being included in a growing number of connected objects such as fitness trackers and headphones. As part of the service discovery mechanism of BLE, devices announce themselves by broadcasting radio signals called advertisement packets that can be collected with off-the-shelf hardware and software. To avoid the risk of tracking based on those messages, BLE features an address randomization mechanism that substitutes the device address with random temporary pseudonyms, called Private addresses. In this paper, we analyze the privacy issues associated with the advertising mechanism of BLE, leveraging a large dataset of advertisement packets collected in the wild. First, we identified that some implementations fail at following the BLE specifications on the maximum lifetime and the uniform distribution of random identifiers.
However, it has been shown  [7, 8]  that some information can still leak from BLE devices. As other wireless technologies, BLE features a discovery mechanism called advertisement [24, Vol 3, Part C, sec. 11]. To enable this mechanism, devices periodically broadcast advertisement packets that are populated with a variety of information announcing their characteristics and available services. Those packets are transmitted in clear, opening new possibilities for passively tracking users in the physical world  [11, 20]. In this paper, we present an analysis of the privacy provisions provided by the LE Privacy feature as well as its limitations. Our study is based on a dataset of real-world observations of BLE advertisement traffic containing about 8 million packets associated to more than 53500 different device addresses collected over a period of 5 months.
For instance, some medical devices are broadcasting their types (hearing aid, glucometer, insulin pen, etc. ) trivially exposing a medical condition of the owner. The paper is organized as follows. First, a BLE background is provided in Section 2. Then, Section 3 defines our experimental methodology. We present observations on our dataset in Section 4. Section 5 gives a detailed inspection of the BLE device address randomization mechanism and we show how it can be defeated in Section 6. Section 7 shows the additional information leaked by the advertising data. Finally, we highlight the related work in Section 8 and Section 9 concludes the paper.
METHODOLOGY 3. 1 Data collection protocol This work is based on a dataset of advertisement packets and scan responses that have been collected over 5 months by the authors during commute, work and leisure times. To collect the data, we used a Raspberry Pi single-board computer equipped with a CSR v4. 0 Bluetooth USB dongle. In order to collect advertisement packets on BLE advertising channels, we developed a C software based on BlueZ libraries, the official Linux Bluetooth stack. Furthermore, as advertising data can be obtained from both advertisement packets and scan responses (see Section 2. 3), we designed our C software to collect advertisement packets and automatically send scan requests to obtain potential scan responses from discovered Peripherals.
Structure of the data Our dataset contains a set of records, each record corresponding to an advertising frame (advertisement packet or scan response). A record includes a timestamp along with a header and a payload. The data obtained from the data collection are stored as raw records that are organized as follows: • Metadata: timestamp; • Advertising PDU header: PDU type, BD_ADDR type, length of the advertising payload; • Advertising payload: BD_ADDR, advertising data; The advertising payload of each record is then parsed to extract AD structures and their various fields. To perform this task, we implemented our own Python parser based on online official Bluetooth SIG resources. The resulting data are then stored in a relational database comprising 179 attributes corresponding to the metadata, fields of the header and parsed AD structures.