dataesr/scientific-paragraphs-categorization huggingface dataset:
CODALEMA-3 is currently the only radio experiment able to access such a wide band at once. The high frequencies could be of paramount interest to sample the Cerenkov ring. As shown in figure  2  from  [6] , close to the axis the expected Cerenkov signal in 120-200 MHz is much higher than in the band 30-80 MHz (with which it competes, giving a "doublepeak" structure), and furthermore its variation with the distance to the axis is faster. Unless a high antenna density is used (LOFAR case), it is difficult to correctly sample the electric field profile in 30-80 MHz, while using the information above 120 MHz could greatly help determining the latter with only few antennas spaced by a few tens of meters (CODALEMA-3 case). This is currently studied on CODALEMA-3 events simulated with the SELFAS code  [7].
Such features can be exploited to provide compact representations  1  An example of automata network (AN). Automata are represented by labelled boxes, and local states by circles where ticks are their identifier within the automaton-for instance, the local state a 0 is the circle ticked 0 in the box a. A transition is a directed edge between two local states within the same automaton. It can be labelled with a set of local states of other automata. Grayed local states stand for the global state a 0 , b 0 , c 0 of the reachable states in a concurrent system, taking into account the partial order of transition applications. Unfoldings, and more precisely their complete finite prefixes  [21] , allow computing efficiently such compact representations. T (a) = {t 1 = a 1 ∅ -→ a 0 , t 2 = a 0 b 0 -→ a 1 , t 3 = a 0 b 0 ,c 0 --→ a 2 } T (b) = {t 4 = b 0 ∅ -→ b 1 , t 5 = b 1 a 0 -→ b 0 } T (c) = {t 6 = c 0 a 1 -→ c 1 , t 7 = c 1 b 1 -→ c 0 , t 8 = c 1 b 0 -→ c 2 } 1 https://github.
ASP syntax and semantics Answer-Set Programming allows for automatic logical deductions thanks to an ASP model which declares variables, domains, and constraints, and to a solver which computes the solutions, possibly accounting for optimisation criteria. It is close to SAT (propositional satisfiability)  [22]  and known to be efficient for enumerating solutions of NP problems while providing a convenient language for specifying the model. We give a very brief overview of ASP syntax and semantics that we use in the next section. Please refer to  [10, 23, 24]  for an in-depth introduction to ASP. An ASP program is a Logic Program (LP) formed by a set of logical rules, composed of first order logic predicates, of the form: where a i are (variable-free) atoms, i. e. , elements of the Herbrand base, which is composed of all the possible predicates of the LP.
3 [31]  as ASP solver. This identification is exact but incomplete: some bifurcation transitions can be missed. Due to the additional approximations brought by (I3 # ) compared to (I3), it is expected that less bifur- cation transitions can be identified with this latter approach, but with a higher scalability. The computation times correspond to the total toolchain duration, and includes the local-paths computation, unfolding, ASP program generation, ASP program loading and grounding, and solving. Note that the LCG (see above "Background" and "Results" sections) computation (and ASP program generation) is almost instantaneous for each case. We implemented the three methods in the Pint software. 2 Models and instructions are provided in Additional file 2.
Introduction The sensitivity of the arterial baroreceptor-heart rate reflex or cardiac baroreflex sensitivity (BRS) is calculated from the ratio of the heart period changes subsequent to systolic blood pressure changes in msec/ mmHg. In a previous paper  (Westerhof et al. 2004 ), we described a method for BRS computation from spontaneous cardiovascular oscillations, using a cross-correlation technique over a 10 sec wide sliding window (xBRS-technique, see Methods). We showed that the time-averaged xBRS values are close to those of the Eurobavar data set which we compared it to, but with a larger number of measurements of BRS per minute. A method with a high time-resolution is useful to uncover and follow instantaneous variations in BRS. In further exploration, we found the standard deviation of the xBRS values to be proportional to its mean value.
To compute the cross-spectrum of beat-to-beat IBI or systolic pressures with xBRS, we wrote a specialpurpose Matlab â program. This computed xBRS-values as described and assigned each value that fulfilled the criteria to the middle heartbeat of the 10-sec window. Missing values were interpolated; these files were analyzed with Matlab â DFT. We adopted the nomenclature proposed by the TaskForce (1996) for the various frequency bands: ultralow frequencies (ULF): 0-0. 003 Hz; very low frequencies (VLF): 0. 003-0. 04 Hz; low frequencies (LF): 0. 04-0. 15 Hz and high frequencies (HF): 0. 15-0. 4 Hz. From the Phe/SNP-estimates, reference BRS was obtained with the "tangent to sigmoid method" published earlier (TG-BRS  (Parlow et al. 1995) ). Rescaled nitroprusside (SNP)phenylephrine (Phe) response data allow the generation of a sigmoidal cardiac baroreflex curve.
Due to the lack of a reliable respiration signal in the present data set, we are unable to substantiate this claim; the present experiments were not designed to look into the relation between blood pressure and respiration. Slow oscillations of baroreflex gain have also been observed by  Eckberg and Kuusela (2005)  and  Eckberg et al. (2013) , using short-time Fourier analysis of blood pressure and heart rate recordings. Those authors used a sliding window of (the shortest) 15 sec duration to estimate BRS from the cross-spectrum between systolic pressure and heart period in the LF band (0. 04-0. 15 Hz). Shifting the window by 2 sec, they obtained BRS time series with oscillations concentrated in the VLF band (around 80 sec, as reported in their 2005 study) or in the LF band (around 18 sec in the 2013 study).
In our imaging configuration, laser-induced plasma was generated continuously while scanning the sample surface over the region of interest. The surface scanning was performed line by line in a raster scan mode with the use of a computer-controlled x-y-z stage. An emission spectrum was recorded for each sampling position. The travel range of the X motorized stage was 100 mm, while the travel range of the Y and Z stages was 50 mm. The X and Y ranges impose a limit on the maximum surface area that could be analysed in one scan (i. e. , 50 cm²). The step size could be adjusted by changing the speed of the X stage over a range from a few µm to 100 µm (i. e. , considering the maximum stage speed of 10 mm/s and the laser frequency rate of 100 Hz). We developed software in the LabVIEW environment that controlled all system components and allowed us to set automated sequences in order to scan regions of interest with the specified resolution.
After completion of the 2-D scanning, elemental images were created for each element of interest by selecting their specific emission lines (see Supplementary Table  1 ). Their relative abundance can be obtained and displayed in false-colour images, in which an arbitrary colour is assigned to each element (Fig. 1c ). In the present manuscript, a typical imaging result represents approximately 3 million pixels (i. e. , individual laser pulses) and an amount of data of approximately 50 GB (~25 GB/spectrometer). At a scanning rate of 100 Hz, the duration of such an analysis is approximately 8 hours (360 000 pixels/hour). Dedicated software was developed to handle the large amount of data collected and to extract elemental images from the raw spectra datasets (Fig. 1d ). This software, named LasMap, includes different atomic and molecular databases that enable the fast identification of the emission lines observed in the raw spectra.
After the line identification step, different algorithms can be used to extract the net line intensities from raw spectra. Once the spectral lines associated with the different elements of interest are selected, the software extracts the corresponding intensities spectrum by spectrum to create the elemental images associated with each element. This operation requires typically less than 7 minutes for a spectra dataset of 3 megapixels. To match the standard methodologies generally used in paleoclimate studies, the signal retrieved from the elements of interest (i. e. , Mg and Sr) were normalized, pixel by pixel, by the Ca signal (i. e. , the signature of the calcium carbonate phase). Although quantitative analysis was not required in the present case, the Sample single-pulse emission spectra covering spectral ranges between 245 and 310 nm (spectrometer 1) and between 400 and 420 nm (spectrometer 2) recorded in three different regions of the speleothem sample.
These 3 spectra have been vertically shifted for better clarity. The characteristic emission lines of calcium (Ca), carbon (C), silicon (Si), iron (Fe), magnesium (Mg), aluminium (Al), manganese (Mn), and strontium (Sr) are clearly detected. (c) Sample relative-abundance images of Mg (red), Sr (cyan), Fe (violet), Si (yellow), Al (orange) and Mn (blue) represented using false colour scales for a 3000 × 1000 pixel sequence (resolution of 15 µm). (d) Principle of data analysis using the LasMap software. limits of detection of Mg and Sr were estimated at, respectively, 5 ppm and 7 ppm for single-pulse analyses using a reference sample of calcium carbonate performed in the laboratory. The samples analysed herein include a ~25 cm long calcite speleothem collected from a cave (Cueva Mayor, Sierra de Atapuerca karst system, Spain) that is being investigated for paleoclimate studies and an ~8 cm-long sample from a recent cold-water scleractinian coral, Dendrophyllia ramea, that was retrieved from the Bay of Cadiz (on the Atlantic coast in southwestern Spain).
A typical energy of 900 µJ per pulse was used in all the experiments. Homemade software developed in the LabVIEW environment controlled the entire system. Construction of elemental images. An advanced spectrum treatment was developed to perform a rapid intensity extraction for each measurement site and for each species of interest. A single emission line was selected for each element of interest, and the algorithm defined a baseline fit using a polynomial function and subtracted it from the emission signal. Emission lines were selected based on two criteria: each selected line was required to be the strongest line of each element in the probed range and to be unaffected by any possible interference from other lines. With this software, a typical image of ~3 million of spectra was processed in less than 5 minutes (time estimated for 1 dataset).
A two-dimensional matrix containing the selected line intensity from a point on the sample surface was provided for each element and could be displayed using a false-colour scale to present a visual result in the form of elemental images. All images were then processed using ImageJ software (NIH, Bethesda, MD, www. nih. gov); the images were contrasted (if necessary, and only in a linear manner), slightly smoothed (using a 0. 5-pixel Gaussian smoothing), and converted from 16-bit to 8-bit images. Note that all the Mg, Sr and Na images presented in this manuscript have been normalized to Ca. If necessary, a mask image retrieved from Ca was used to apply the normalization. The selected emission lines and associated wavelengths are summarized in Supplementary Table  1.
We provide evidence that the West Paces strain belongs to genetic group II and that E. chaffeensis is a highly conserved species. We describe likely virulence traits (candidate type IV effectors) encoded by their genomes and some putative host cell targets. Most notably some strains lack one or two candidate T4Es, but show conserved intracellular adaptive strategies. Our results show that using our S4TE software and approach even for strains which are really close at the intraspecies level, enables the prediction of candidate type IV effectors that could be relevant for the study of bacterial pathogenesis.
Retrieval of Genome Sequences and Comparison of Genomes Complete genome sequences of E. chaffeensis strains were obtained from the National Center for Biotechnology Information (NCBI) database (ftp://ftp. ncbi. nih. gov/genomes/ Bacteria/). Eight complete genomes were used in this study. Orthologous groups of all E. chaffeensis genes were identified using the PanOCT program  (Fouts et al. , 2012)  with the following parameters: E-value 10 -5 , percent identity ≥ 30, and length of match ≥ 65.
Analysis of Type IV Effectome Distribution According to Local Gene Density To visualize in a single representation the distance between each gene and its closest neighbors on the five prime and three prime borders, we sorted genes into two-dimensional bins defined by the length of their 5 ′ and 3 ′ flanking intergenic regions (hereafter denoted 5 ′ and 3 ′ FIRs)  (Raffaele et al. , 2010). The gene density distribution is represented in R by a heat map. We used the median length of FIRs to distinguish between gene-dense regions (GDRs); in-between regions (IBRs); and gene-sparse regions (GSRs). Putative type IV effectors identified by S4TE software were plotted on this graph according to their 5 ′ and 3 ′ FIRs (Figure  2A ). The distribution of putative T4Es in each region was calculated for each strain (Figure  2B ).
Prediction of E. chaffeensis Type IV Effectors and Host Protein-Protein Interaction Networks Protein-protein interactions between human genomes and predicted type IV effectome of E. chaffeensis were predicted using the Host-Pathogen Interaction Database (HPIDB)  (Kumar and Nanduri, 2010)  with the identity and percentage query coverage set at 30%. Based on the homology approach, the HPIDB predicts protein-protein interactions from a plentiful template of eukaryotic-prokaryotic inter-species interactions available among 68 hosts and 602 pathogens. Subcellular locations of the host proteins interacting with putative T4Es of E. chaffeensis were predicted using the CELLO2GO algorithm  (Yu et al. , 2014). S4TE 1. 4 results were used to predict the location of T4Es  (Meyer et al. , 2013).
Phylogenetic Reconstruction and Genomic Plasticity Analysis For phylogenetic reconstruction, whole-genome nucleotide sequences of the eight E. chaffeensis strains were aligned using the progressiveMauve algorithm  (Darling et al. , 2010, http ://gel. ahabs. wisc. edu/mauve/). FastTree was used with default parameters to build the unrooted tree  (Price et al. , 2009). Mauve software was also used to characterize the genomic rearrangements between the eight genomes of E. chaffeensis by showing LCBs. In order to accurately align conserved regions in the genomes, the progressiveMauve algorithm was parameterized with a match seed weight of 15 and a minimum LCB score of 70. The seed size parameter sets the minimum weight of the seed pattern used to generate local multiple alignments (matches) during the first pass of anchoring the alignment.
RESULTS The Ehrlichia chaffeensis Genomes Are Highly Conserved In order to establish a whole genome-based phylogeny of these eight E. chaffeensis strains, we used the Mauve progressive alignment and FastTree to build the tree. Our results are in agreement with those of previous studies, with the eight strains being separated into three genetic groups. The Arkansas and Osceola strains were assigned to group I, and the Wakulla, Saint Vincent, West Paces, and Heartland strains were assigned to group II. We also assigned the West Paces strain to group II due to its phylogenetically close proximity to the Heartland strain (Figure  1A ). The Jax and Liberty strains were assigned to group III (Figure  1A ). With an average size of 1. 2 Mb, the genomic features of the eight strains used in this study are similar.
chaffeensis. We used PanOCT software to cluster the ortholog and compared the core and accessory genomes of the eight strains of E. chaffeensis. 1991 Sumner et al. , 1999 Paddock et al. , 1997 Sumner et al. , 1999 Sumner et al. , 1999 Paddock et al. , 1997 Sumner et al. , 1999 Cheng et al. , 2003  Human isolates of E. chaffeensis were classified in three genetic groups according to the 28-kDa major outer membrane gene cluster, the number of TRP32 repeats (variable-length PCR target gene, NCBI accession version # WP_011452439. 1) and the number of TRP120 repeats (120-kDa immunodominant surface protein, NCBI accession version # WP_011452362. 1). Data for pathogenesis in SCID mice for E. chaffeensis Arkansas, Liberty and Wakulla strains were obtained from  Miura and Rikihisa (2007)  and indicated.
Liberty, whereas ECHLIB_RS04640 was only absent in E. chaffeensis str. FIGURE 3 | Protein-protein interaction network between the E. chaffeensis str. Liberty effectome and the human genome. A sub-cellular location was predicted with the S4TE algorithm (http://sate. cirad. fr) for Ehrlichia candidate effectors (left) and with CELLO2GO software (http://cello. life. nctu. edu. tw/cello2go/) for human proteins (right). Blue and red circles represent predicted T4Es located in the cytoplasm and in the nucleus of the host cell, respectively. Blue, red, pink, green, purple, yellow, and turquoise hexagons represent the different locations of targeted human proteins in the host cell. Hexagons harbor several colors when CELLO2GO predicts several probable subcellular locations. West Paces. All the other predicted T4Es (94% of predicted effectors) are common to the four strains, revealing the low diversity of effector repertoires in E.
chaffeensis contains all the 47 predicted T4Es. Although, S4TE software was designed for optimal sensitivity  (Meyer et al. , 2013) , the prediction of false positives can occur and is inherent to any predictive computational approach. However in our study, the S4TE algorithm correctly predicted the two known type IV effectors in Anaplasmataceae family with E. chaffeensis mitochondrial effector ECH0825 (ECHLIB_RS01385) and the homolog of A. phagocytophilum nucleomodulin AnkA (ECHLIB_RS01940) (Table  S1 ). In addition, S4TE predicted effectors that are homologous to known effectors in other bacteria, including C. burnetii, L. pneumophila, and Brucella spp. S4TE also predicted some new candidate T4Es that were not easy to identify ab initio, based solely on the poor quality of automated genome annotations, especially for bacteria harboring 30% or more unknown hypothetical proteins like Anaplasmataceae.
CONCLUSION Using S4TE software, we predicted in silico the putative type IV effectors from available complete genomes among E. chaffeensis species. In particular, we searched for proteins with eukaryoticlike domains, signals for addressing organelles, structural features known to be involved in protein-protein interactions or type IV secretion, and homolog to known T4Es in other bacteria. We identified 47 candidate T4Es in E. chaffeensis (45 belonging to the core type IV effectome) with several of the abovecited features. Some presented homologies with known type IV effectors in other bacterial systems and others were annotated as hypothetical proteins with no predicted function. We revealed one strain to be a specific candidate effector in the Liberty strain. The majority of predicted T4Es belonged to plastic regions of the genome.
ACKNOWLEDGMENTS The authors acknowledge the financial support from  European project ,  FP7-REGPOT-2012-2013-1 , grant agreement No. 31598 , "EPIGENESIS, " One Health approach to integrate Guadeloupe research on vector-borne and emerging diseases in the ERA: From the characterization of emergence mechanisms to innovative approaches for prediction and control (financial support for CN). We are grateful to  T. Lefrançois  and  N. Vachiéry  at  CIRAD  for their confidence and initial input into this project. We thank the reviewers whose insightful comments helped improve our manuscript.
Visual activity recognition plays a fundamental role in several research fields as a way to extract semantic meaning of images and videos. Prior work has mostly focused on classification tasks, where a label is given for a video clip. However, real life scenarios require a method to browse a continuous video flow, automatically identify relevant temporal segments and classify them accordingly to target activities. This paper proposes a knowledge-driven event recognition framework to address this problem. The novelty of the method lies in the combination of a constraint-based ontology language for event modeling with robust algorithms to detect, track and re-identify people using color-depth sensing (Kinect ® sensor). This combination enables to model and recognize longer and more complex events and to incorporate domain knowledge and 3D information into the same models.
Moreover, the ontology-driven approach enables human understanding of system decisions and facilitates knowledge transfer across different scenes. The proposed framework is evaluated with real-world recordings of seniors carrying out unscripted, daily activities at hospital observation rooms and nursing homes. Results demonstrated that the proposed framework outperforms state-of-the-art methods in a variety of activities and datasets, and it is robust to variable and low-frame rate recordings. Further work will investigate how to extend the proposed framework with uncertainty management techniques to handle strong occlusion and ambiguous semantics, and how to exploit it to further support medicine on the timely diagnosis of cognitive disorders, such as Alzheimer's disease.
Moreover, since the proposed framework stores event models as human-readable templates, it is very convenient to change models by adding new features or by adding models for new activities. For instance, when transferring the proposed system from location A to B, most changes refer to the update of contextual objects, i. e. , the new location of semantic zones. Zone updates can be done in a couple of minutes using support software or be learned from labeled data. Supervised learning methods will require one to retrain all event classifiers once a new event class, input feature or dataset is considered. In the same situation, the proposed framework eases model addition and update, and by consequence, it facilitates knowledge transfer between different scenes (or datasets) with minimal changes.
Experiments This paper has evaluated the proposed framework on three datasets: CHUN, GAADRD and nursing home. The first two datasets have compared the proposed framework to three variations of a state-of-art baseline for visual action recognition. The third dataset, nursing home, has evaluated the performance of the proposed framework on a more unconstrained scenario: the continuous monitoring of a senior person in her apartment, a scenario where only depth signal data is available. Software development was carried out using C++ language (version 11). Both experiments and development have used a PC workstation equipped with an Intel Xeon E2609@2. 40 Gigahertz, with 16 GB of RAM memory and Fedora 19 as operating system. Next sub-sections describe with more details the activity modeling step, the baseline methods and the datasets used during the experiments.
Activity Modeling and Knowledge Transfer For each target activity in CHUN and nursing home datasets, we have defined a model containing at least two sub-events, one that fires when the most frequent posture for the given activity is recognized, and one that fires when the person is inside/close to the most relevant semantic zone for the activity. A temporal constraint was added to capture the time ordering between the sub-events. A time duration constraint was also added to delay the recognition of the target activity until the instances of concerned sub-events are recognized for a minimum period of time. The knowledge above described was obtained by watching video examples of each activity, except for duration constraints that were learned from training data. The ontology-driven approach followed by the proposed framework has made easy to port event models between the CHUN and GAADRD datasets, since they contain similar activities.