dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding was provided by the  National Institute on Aging ,  NIH  Contract No. NO1-AG-1-2109  to the  SardiNIA ('ProgeNIA') team. SHIP SHIP is part of the  Community Medicine Research net of the University of Greifswald, Germany , which is funded by the  Federal Ministry of Education and Research  (grants no. 01ZZ9603 ,  01ZZ0103 , and  01ZZ0403 ), the  Ministry of Cultural Affairs  and the  Social Ministry  of the  Federal State of Mecklenburg-West Pomerania. Genome-wide data have been supported by the  Federal Ministry of Education and Research  (grant no. 03ZIK012 ) and a joint grant from  Siemens Healthcare, Erlangen, Germany  and the  Federal State of Mecklenburg-West Pomerania. The  University of Greifswald  is a member of the ' Center of Knowledge Interchange' program of the Siemens AG.
This work was also funded by the  German Research Foundation  (DFG:  GR 1912/5-1 ). STR The STR was supported by grants from the  Ministry for Higher Education , the  Swedish Research Council  ( M-2005-1112  and  2009-2298 ),  GenomEUtwin  ( EU/QLRT-2001-01254 ;  QLG2-CT-2002-01254 ),  NIH  grant  DK U01-066134 , The  Swedish Foundation for Strategic Research  (SSF;  ICA08-0047 ), the  Swedish Heart-Lung Foundation , the  Royal Swedish Academy of Science , and  ENGAGE  (within the  European Union Seventh Framework Programme ,  HEALTH-F4-2007-201413 ). VIS The  CROATIA-Vis study  was funded by grants from the  Medical Research Council (UK)  and  Republic of Croatia Ministry of Science, Education and Sports  research grants to I. R. ( 108-1080315-0302 ). We would like to acknowledge the staff of several institutions in Croatia that supported the field work, including but not limited to The  University of Split and Zagreb Medical Schools, the Institute for Anthropological Research in Zagreb and Croatian Institute for Public Health.
Introduction Real-Time highway traffic management and development of Intelligent Transportation Systems (ITS) remain an important area of intensive research in order to mitigate the daily problem of congestion and ensure safe and less polluting transportation of goods and people. One of the prerequisites for continuous prediction is an efficient estimation of the real-time traffic conditions, using only a limited amount of data. Currently, traffic data is usually obtained via a set of sensors, such as inductive loop detectors which provide occupancy rate and traffic mean speed measurements from which other macroscopic quantities can be derived when accurate vehicle length is available. These macroscopic quantities include vehicles density (number of vehicles per unit length of highway), mean speed (unit length of highway per unit of time) and flow.
Traffic state estimation Herein we consider the same freeway segments shown in Fig. 5. It is the same section used by the Super-Twisting Sliding Mode (STSM) observer which let us compare these two observers. At first, the initial condition for the state variables 0 and for 0 matrix must be introduced. According to the quantity of the noise in the measurement and process data, and should be tuned, respectively, to produce the satisfied result, (31) 0 0 0 0 0 T , (32) 0 diag 0 0 0 0 , (33 ) 0 0 , Table  1. For the simulation purpose, MATLAB programming language and Simulink have been used. As a control algorithm, ALINEA is used. It is a first feedback control used in freeway traffic control  [37, 38]. It is very well known by the highway traffic community. Here, ALINEA is only used to check the stability and convergence of the two observers (STSM and EKF).
Performance To investigate the efficiency of the proposed STSM observer and compare it to EKF, it proves to be more informative to perform a number of experimental scenarios and verify the observers performance in real-life conditions rather than present simulation results. For this purpose we considered data collected on the M42 motorway in the United Kingdom. The identified values for the parameters of the system, given in  Table 1,    The accuracy of EKF's performance depends significantly on the accuracy of Q and R matrices, as these matrices determine the Kalman gain and the final value of the estimation error covariance matrix. It must be noted that the time step of the real data is 6 minutes which is relatively large. The accuracy of STSM can be improved by decreasing the step time.
Conclusion This paper presents a comprehensive comparison of two kinds of observers: STSM and EKF. The simulations are established with simulated and real data. The real data are collected on M42 motorway in the United Kingdom. For the simulation, a second order traffic flow model METANET has been used. The STSM, which behaves like a full order observer, and the structure of EKF have been presented. The following conclusions can be stated: STSM can perform as well as EKF. The robustness of the STSM to external noise can be guaranteed. In contrast, the EKF should be tuned with respect to the external noise. It means that EKF is not robust with respect to the external noise, for accurate values of the input and measurement noise covariance matrices and are required. The performance of EKF in the sense of convergence and stability is better than STSM when the covariance matrices ( and ) are well tuned.
Computer program operation Our homemade computer tool, named EPIMIC (for EPIdemiological surveillance and alert based on MICrobiological data) was implemented using the Microsoft Excel software. Data were split into several files accessible via a shared drive to any PC computer in the laboratory (S1 Table ). Each of these files encompasses a dozen parameters, fitting the capability of our standard PC computers to open and run them; parameters from a given file are related to a given clinical syndrome or technological platform. These files can be accessed through hyperlinks from a Microsoft PowerPoint slide that presents our entire surveillance activity, which is split into various infectious syndromes or technological platforms (Fig  1 ). Laboratory data are collected weekly, either manually or automatically from our laboratory computer system, then entered manually into the different Microsoft excel files by a medical biology resident.
Detection of abnormal events An abnormal event corresponds to an increase above, or a decrease below threshold values calculated based on the mean of historical data plus or minus 2 SD, respectively. While entering weekly data, conditional formatting from the Excel software automatically changes the font to red if numbers are above the mean+2 SD and to blue if they are below the mean-2 SD. These automatically calculated thresholds can be replaced by others chosen by the user. Computed data are presented at least once a week during medical meetings, and interpreted by microbiologists. Confirmed alerts are reported to clinicians, and, depending on their nature, to a committee for the control of nosocomial infections, to the health regional agency, or to other French sanitary surveillance institutions.
Statistical analysis of antibiotic-resistance surveillance data Statistical analyses were performed for the surveillance of antibiotic-resistance patterns using linear models and the LOESS (locally weighted polynomial regression) regression curve to determine whether the proportion of isolated bacterial strains presenting a particular resistance profile monitored by EPIMIC significantly increased or decreased throughout the surveillance period. The tests were two-sided, p-values < 0. 05 being considered as statistically significant, and were performed using the R program (Auckland, New-Zealand).
EPIMIC datasets Between November 2002 and October 2013 (11 years), 293 items were surveyed weekly, including 38 clinical samples, 86 pathogens, 79 diagnosis tests, and 39 antibacterial resistance patterns (S3 Table ). The mean duration of surveillance was 7. 6 years (range, 1 month-10. 9 years). A total of 108,427 Microsoft Excel file cells were filled with counts of clinical samples, and 110,017 cells were filled with counts of diagnoses. EPIMIC was used at our laboratory by 15 senior biologists and 30 residents in medical biology per year; the training period for each new person was approximately 10 min. In addition, as Microsoft Excel is a widely used and easy to operate software, EPIMIC could be created, run, maintained and repaired without the need of a resource person with high levels of computer skills.
Almost half (n = 36) of these 76 surveillance systems only surveyed one pathogen or topic (e. g. , nosocomial infection, antimicrobial resistance, or invasive diseases). Nine systems (12%) surveyed between 2 and 13 pathogens or topics. Finally, 31 systems (41%) surveyed an undefined number of pathogens or topics. In contrast, during the study period EPIMIC surveyed 293 pathogens or topics. The mean (±standard deviation) duration of surveillance of the 76 surveillance systems was 10±10 years (range, 1-60 years), whereas mean duration of surveillance with EPIMIC was 11 years. Finally, only one third  (25)  of the 76 laboratory-based surveillance systems surveyed pathogens in real-time, and in a large majority of cases they focused on a single pathogen. By contrast, EPIMIC allowed the real-time surveillance of our entire clinical microbiology laboratory dataset.
These findings allowed us to report not only to clinicians from our institution, but also to regional and national institutions, and several of these findings were worthy of publication. EPIMIC was also an interesting educational tool for students, through objective assessment of the actual incidence and prevalence of IDs and pathogens. Other automated laboratory-based surveillance systems were described as efficient to identify rises in IDs. Nevertheless, compared to these systems, EPIMIC continuously surveys and alerts on a more comprehensive dataset including clinical samples and tests, and not only pathogens. Moreover, EPIMIC does not focus on specific infectious threats during specific periods but performs surveillance without a priori, which is a prerequisite to detect unexpected events.
In addition, historical data in EPIMIC are available over more than a decade, which is a longer duration than for most of the other systems. Importantly, EPIMIC automatically generates weekly alerts that are managed in real-time. Finally, our surveillance tool is user-friendly and can be used by any microbiologist as it operates using Microsoft Excel and requires no specific computer skills. Over the study period, EPIMIC was used by 300 residents and biologists trained within minutes. Furthermore, it can be implemented in any setting including unsophisticated ones because it can operate using basic PC computers with no specific cost. Thus, EPI-MIC can be shared easily; a ready-to-use EPIMIC file is freely-available from our institution website. Some limits of our surveillance computer tool are, notwithstanding, related to its absence of sophistication.
The Cancer Genome Atlas data We accessed to the Head and Neck Squamous Cell Carcinoma (HNSC), Lung Squamous Cell Carcinoma (LUSC) and Lung Adenocarcinoma (LUAD) components of the TCGA data (TCGA Project Number #3230 and #2731). This data is accessible using the dbGAP via the TGCA (https://tcga-data. nci. nih. gov/tcga/). Data were downloaded either from https://cghub. ucsc. edu/ for exome sequencing or directly from https://tcga-data. nci. nih. gov/tcga/ for the RNA sequencing, methylation and genotype data. Exome sequencing. We accessed TCGA exome sequencing "level 1" (unprocessed) data for 363 HNSC and 459 LUSC TCGA individuals and completed bioinformatics analysis of their sequence data using Picard, GATK, MuTect and Somatic Indel detector (Methods A in S1 File). Subsequently we used in house bioinformatics pipelines (Methods A in S1 File) to determine the highest quality variant calls.
Somatic point mutations were exonic functional variants defined as either truncating, impacting splicing or missense variants predicted as deleterious by SIFT/POLYPHEN2  [16, 17]. Copy Number Variation. Samples were hybridized using the Genome-Wide Human SNP Array 6. 0 platform at the Genome Analysis Platform of the Broad Institute. We retrieved level 3 TCGA data of 374 HNSC, 464 LUSC and 476 LUAD individuals containing normalized log 2 ratios of the fluorescence intensities between the target sample and a reference sample. We only included in our analysis individuals for whom both tumor and corresponding normal calls were available. For a segment, we considered log 2 (ratio) < -0. 5 to be an indication of a loss, and a log 2 (ratio) > 0. 5 to indicate a gain. Segments with log 2 (ratio) of between -0.
5 and 0. 5 were not retained as somatic copy number alterations. Annotation was done adding the genes contained in each of the remaining segments using EnsEMBL databases  [18]. RNA sequencing. RNA sequencing (RNA-seq) TCGA data was retrieved the "level 3" data for 263 HNSC, 223 LUSC and 125 LUAD individuals. Normalization of this data is further detailed within the statistical methods section. Methylation. TCGA methylation data was analysed on the Illumina Infinium Human-Methylation 450K BeadChip assay. We accessed TCGA methylation "level 2" data for 263 HNSC, 223 LUSC and 125 LUAD individuals. We estimated the methylated level of each CpG site by calculating the M-value (log 2 (ratio of methylated and unmethylated probes)) using TCGA level 2 data  [19]. Methylation level 2 data is already background-corrected.
Statistical methods Association analysis. The association between the variants and UADT cancer risk was estimated by odds ratio (ORs) and 95% confidence intervals (CIs) per allele under the log-additive model and genotype derived from multivariate unconditional logistic regression, with sex and study specific country of origin included in the model as covariates (S1 Table ). Heterogeneity of ORs was assessed using the Cochran's Q test. Statistical analyses were performed using SAS version 9. 3 (SAS Institute, Cary, NC, USA). To control for potential ethnic heterogeneity between cases and controls, we performed a principal components analysis using the EIGENSTRAT package of the EIGENSOFT 5. 0 software  [20]  using 12,898 markers in low linkage disequilibrium  [21]. We used the resulting 12 statistically significant eigen vectors (as defined by the Tracy-Widom statistics) in the sensitivity analysis (Table A in S1 File).
Therefore the GISTIC scores represent significance levels and are expressed as q-values (significant below 0. 25). RNA sequencing normalization. Level 3 RNA sequencing tumor data that we accessed from the TCGA was already normalized to the kilobase per million reads (RPKM) standard which corrects for species length and read depth  [29] , but not for diversity of the RNA population. To control for this we applied TMM (Trimmed Mean of M-values) normalization  [30]  to the RPKM data. This possibly involves a loss of statistical efficiency relative to applying TMM to raw data, since the precision weighting in TMM will no longer function. However it should not add any bias and the loss of efficiency will be small if the read density is close to uniform. We used implementations in the EdgeR package of BioConductor  [31]  and the voom function of the Bioconductor limma package  [32].
6 ). Experiments. The mSR experiments have been performed at the low-energy muon beamline  [23] [24] [25]  of the Swiss Muon Source as described in the Supplementary Note 1. The experiments were carried out in zero applied field in the temperature range between 300 and 5 K. The sample was oriented so that the initial spin of the muons was parallel to one set of nanomagnets. At each temperature, 7. 5 million muon decay events were counted over a time of B50 min. lSR model. As the distribution of local fields generated by the nanomagnets in the Au capping layer is not trivial, we considered an empirical model based on exponentials, which includes both static and dynamic effects. The muon polarization is fitted using the MUSRFIT 26 software package and described by the following equation P t ð Þ ¼ 2 3 e À lTt ð Þ b T þ 1 3 e À lLt ð1Þ The first term accounts for the transverse spin relaxation and the second term accounts for the longitudinal relaxation.
Acknowledgements We thank  A. Weber ,  V. Guzenko ,  T. Neiger ,  E. Deckardt  and  M. Bednarzik  for their help. The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 290605  (COFUND:  PSI-FELLOW ) and from the  EPSRC  (grant  EP/J01060X ).
By a proper combination of computed distance functions we build a potential field which is backtracked in the steepest descent direction in order to get the cell trajectories. Finally we construct the cell lineage tree by detecting trajectories which merge together when going backward in time indicating mitosis and thus a branching node of the cell lineage tree. The paper is organized as follows. In the next subsection we present the zebrafish embryo development data acquired by the two-photon laser scanning microscope. Then we present all three steps of our approach, the image filtering, the cell center detection and the cell trajectories extraction. Together with the PDE based models, we outline the numerical approaches used in all three steps of our tracking method. Finally we discuss the numerical experiment devoted to tracking cells in a representative 3D video of the early zebrafish embryogenesis.
Many various quality datasets are available thanks to EC Embryomics and BioEmergences projects (http :/ /bioemergences. iscpif. fr). The data quality is related to a level of noise depending mainly on the size of the time step between acquisition of subsequent 3D images. The acquisition step ranges from 50 seconds to 5 minutes. A longer time step produces better 3D image quality, one has a good visual impression of what is happening during embryogenesis and such data is well suited for segmentation purposes e. g. for obtaining a shape of cells and other  their characteristics during the embryogenesis  [22, 18]. On the other hand, such data is not suitable for tracking since the cells move too far between single 3D images and consequently mother-daughters cell correspondences can be lost. In Fig.
1  we see the cell membranes visualization in the beginning and at the end of the imaging. The usage of membrane images for the cell tracking would be very natural, cf. [1] , but the quality of currently produced 3D membrane images is not sufficient for our purposes. In Fig. 2  we plot the cell nuclei visualization, again at the beginning and at the end of the imaging. Although nuclei images contain certain level of noise they are suitable for application of our tracking technique. In both figures we can observe how complex the development of organism in few first hours of embryogenesis is. It grows from an unorganized set of cells to various morphogenetic structures corresponding to future organs of the zebrafish adult. For the testing of the method and for the extraction of cell trajectories we use the representative dataset with acquisition step dθ = 67 seconds, N θ number of time steps and with dimension of every 3D image 512 × 512 × 104 voxels.
By experiments presented in Figs. 9-11 we illustrate the behavior of our cell trajectories extraction method and test its implementation. First, we created a 4D dataset (3D volumes with 100 × 100 × 100 voxels in 40 time steps) containing few cells that are moving in space and time and performing several cell divisions. The dataset contains also partial cell movements starting and ending inside the 4D data. In fact, here we created directly the 4D segmentation, described in Section 4. 1, which gives us the 4D spatio-temporal tree structures corresponding to cells movements and divisions. The 3D projection of that 4D  segmentation is given in Fig. 9. Then we performed computations of constrained distance functions D l , l = 1,. , 40 together with building of the combined distance function D and computation of the constrained distance function D B.
If it exists, it means that we have already added a trajectory which has some part equal to the current one. And also that there exists a later time after which the trajectories differ. We skip all equal points which are already represented as nodes in the lineage tree structure. Only the first different point of the current trajectory is added to the structure, together with the mother ID of the last equal point from the previously added trajectory. Using such approach we obtain the whole cell lineage tree where each node exists only once and the nodes are logically linked together in the same manner as it is in the real cell mother-daughter relation. For illustration, in Fig. 12  we present a detail of the cell lineage tree reconstructed for a data set discussed in the next section. The visualization of the cell lineage tree is done by MovIT, the software built by Thiery Savy, École Polytechnique, Paris.
Numerical experiment on real zebrafish embryogenesis data We performed the cell tracking by the approach designed in Sections 2-4 on the dataset described in Section 1. 1 representing the zebrafish brain early embryogenesis. All 3D images were filtered by 10 steps of GMCF model and the cell nuclei identifiers were detected by 10 steps of LSCD algorithm. From several millions of cell identifiers we built the 4D segmentation and then the cell trajectories were extracted by the approach developed in Section 4. We present here Figs. [13] [14] [15] [16]  showing results of the tracking procedure. In Fig. 13  one can see the visualization of the cell trajectories in developing embryo. To that goal we built software, running on graphics card, where we can fluently zoom, rotate and animate in time a 3D scene even with a very high number of trajectories.
Multiplex ligation-dependent probe amplification (MLPA) MLPA was conducted according to the supplied manufacturer's specifications using the probemix P022-PLP1 (MRC-holland, Amsterdam, Holland). PCR products were then mixed with formamide (HiDi Formamide, Applied Biosystems, Foster City, CA) and fluorescent Genescan 500 LIZ size standard (Applied Biosystems) prior to analysis using an Applied Biosystems ABI 3130xl capillary sequencer (Applied Biosystems). Data collection and export used GeneMapper software (Applied Biosystems). Peak areas of each fragment were compared to those of a control sample to calculate the gene dosage of each amplicon, including those corresponding to each exon of the PLP1 gene.
X chromosome inactivation pattern The analysis of the X inactivation pattern was based on extend human androgen receptor (HUMARA) assay proposed Bertelsen et al. [8]. Briefly, 200 ng of leukocyte DNA was digested with HpaII (New England Biolabs, Ipswich, MA) at 37 °C for 12 h. PCR amplification of four loci (AR, PCSK1N, SLITRK4, and ZDHHC15) was performed on undigested DNA and HpaII digested DNA. PCR products were subsequently analyzed on an ABI 3130xl Genetic Analyzer (Applied Biosystems) using GeneMapper software (Applied Biosystems). The X-inactivation ratios were calculated as previously described and skewed X chromosome inactivation was considered if this ratio decrease below 20 % in the investigated blood sample  [8].
Microarray Creation of a custom array CGH design was performed using the Agilent software eArray (https://earray. chem. agilent. com/earray/), referred to the GRCh37/hg19 assembly to cover the PLP1 genomic region with maximum resolution. The microarray contained a total of 30,032 probes spanning a 20 Mb region surrounding the PLP1 gene (Agilent Technologies, Santa Clara, CA). Those 30,032 probes were distributed between three groups with various probe densities. The first group includes 15,032 probes encompassing 2 Mb (chrX: 102,000,000-104,000,000; corresponding to a 150 bp average probe spacing) around the PLP1 gene and includes most of the PLP1 duplications described so far  [10, 11]. A second, largest interval contained 10,000 probes surrounding 8 Mb (chrX: 98,000,000-102,000,000 and chrX: 104,000,000-106,000,000; 800 bp average probe spacing).