dataesr/scientific-paragraphs-categorization huggingface dataset:
Bacterial assemblage comparisons To characterise differences in the bacterial assemblages between chicks and adults, we implemented Unifrac  [33]  to compare overall bacterial assemblage composition using phylogenetic information. All assemblage analyses were conducted on the ARISA data and without abundance weights. Using the cloning data may produce misleading results due to stochastic variation in the identity of sequences cloned for each individual. A 23S rRNA phylogenetic tree containing all sequences found in chicks and adults was used for the analyses. We then implemented Unifrac to test 1) whether bacterial assemblages cluster by age (i. e. adults and chicks) using both the environmental clustering analysis (setting 'number of sequences to keep' to five and 'number of permutations' to 100) and principal coordinates analysis, 2) whether any clustering is statistically significant using both the P test (which does not use phylogenetic information; 100 permutations) and the Unifrac test (which tests whether more evolutionary history of bacterial assemblages is shared within age classes than between classes; 100 permutations) and 3) which bacterial lineages are responsible for differences in assemblages between the age classes, via the lineage-specific analysis (setting 'minimum descendants' to four).
e. clone length minus ARISA peak size), the species identity of the most similar sequence in GenBank as determined by the BLAST algorithm, the maximum BLAST score, the most derived phylogenetic position of the OTU and the phylum to which the OTU belongs. In some cases, we could not determine which ARISA peak represented a particular clone (indicated by a blank space in the "ARISA peak" column). Alphaproteobacteria, 3. 1% Betaproteobacteria and 4. 7% Gammaproteobacteria). The most common genera found were Corynebacterium (10. 9%) and Lactobacillus (6. 6%). The majority of OTUs identified in the control samples belonged to the class Alphaproteobacteria (58. 3%). In addition, we also identified bacteria from the classes Betaproteobacteria (16. 7%) and Gammaproteobacteria (8. 3%) and from the phylum Firmicutes (16.
ARISA can potentially underestimate species richness, as eight percent of bacterial species are known not to have the 23S and 16S rRNA organised in an operon (i. e. they have no intergenic spacer region) or have very large IGS lengths that cannot be detected by ARISA  [29]. In addition, divergent bacteria may share the same IGS length and therefore be associated with the same ARISA peak [this study,  [29]. In contrast, some species have several operons in their genomes resulting in multiple ARISA peaks for single species and potentially leading to overestimates in diversity  [29]. In our dataset, we conservatively assumed that any two clones with the same 23S sequence derived from the same OTU. We consequently identified many OTUs with multiple operons (1-9 operons were identified per OTU).
Acknowledgements We thank  François Bailly  for field assistance,  Florian Sammer  for assistance with molecular work,  Anne Hloch  for assistance creating the figures and  Andrew Ramey  for comments on an earlier version of this manuscript. Experiments were carried out in accordance with United States' laws and under permits from the  U. S. Fish and Wildlife Service and State of Alaska. Field data collection was financed by the  French Polar Institute Paul-Emile Victor  (IPEV, program 1162). This laboratory component of this study was supported by the  University of Veterinary Medicine Vienna  and the  Austrian Science Foundation (FWF)  Project  20401  to RHW. The lab EDB, which hosts five of the co-authors (JW, TM, SL, PB and ED) is part of the  "Laboratoire d'Excellence (LABEX) " entitled  TULIP  ( ANR -10-LABX-41 ).
At each trial, the standard square (side length: 5 cm) and the variable stimulus were presented consecutively on a 14-inch computer monitor (interstimulus duration: 500 ms). In accordance with the constant stimulus method, each pair of stimuli was presented three times in random order using E-prime software (Psychology Software Tools, Sharpsburg, PA, USA). The participant was instructed to judge whether each variable stimulus was smaller than the standard stimulus, equal in size to the standard stimulus or greater than the standard stimulus by pressing the corresponding key on a keyboard. There was no limit on the participant's response time. We recorded the number of incorrect judgments under each condition. Spatial orientation tests. Body Z-axis and SV tasks were studied under tactile and visual conditions.
Statistical analysis All analyses were performed with Statistica software (version 7. 1, Statsoft Inc. , Tulsa, OK, USA). Demographic and clinical data with non-normal distributions and non-homogenous intergroup variances were studied with non-parametric Mann-Whitney and Spearman tests. In spatial orientation tests, the respective influences of group, body orientation and hand used were studied in an analysis of variance (ANOVA). An analysis of covariance (ANCOVA) was used to explore the influence of physiological and psychological measures on spatial tasks. The validity of the tests' conditions of application (i. e. normality and homogeneity of variances) had been demonstrated previously.
This paper deals with the implementation of Activity-Tracking (AT) paradigm in the DEVSimPy environment. DEVSimPy has been developed at the SPE UMR CNRS 6134 (University of Corsica) in order to facilitate the modeling and the simulation of discrete-event systems described with the DEVS formalism. The AT paradigm is increasingly being used by DEVS modelers for example to improve structural aspects of models (modeling level) or to make simulation more efficient using distributed or parallel algorithm (simulation level). Although DEVSimPy can be used as a software providing some features reusable for the implementation of AT paradigm (simulation profiling or step-by-step simulation), the object oriented way used to develop DEVSimPy makes it a powerful tool to implement AT paradigm in a generic manner.
The first one (MCC) is defined at the simulation level but computed before simulation while the second one is defined also at the simulation level but is computed during the simulation. The aim of this paper is twofold: (i) on the one hand it formally introduces the two new activity notions called MCC and CPU; (ii) on the other hand it proposes an evolution of the DEVSimPy framework  [4]  involving Activity Tracking (AT) concepts -this evolution will allow to compute the following four activity notions: QA, WA, MCC and CPU. Furthermore, we will describe in a discussion part the relations that can be pointed out between the four activity concepts and a potential evolution of the DEVSimPy framework based on activity tracking-driven simulation. This framework is being developed at University of Corsica using the Python language.
DEVSimPy (http://code. google. com/p/devsimpy/) is an open source project under GPL V3 license and the SPE research laboratory team supports its development. It uses the wxPython User Interface library, the Python Language and the PythonDEVS API  [3]. The DEVSimPy aims to facilitate the modeling and the simulation of dynamic systems described with the DEVS formalism  [12]. Currently DEVSimPy can be used as an AT software providing some features as simulation profiling or step-by-step simulations. Furthermore, DEVSimPy has been developed in an object oriented way using as much as possible design patterns. This feature allows an easy introduction of new concepts or functionalities in order to extend the DEVSimPy framework towards a real AT software for DEVS systems. The rest of this paper is organized as follows: the next part introduces the four activity notions.
Activity-Tracking with DEVSimPy DEVSimPy is an open source project initiated by the Modeling and Simulation team of the SPE (Sciences Pour l'Environnement) laboratory (University of Corsica). The aim of this software is to provide developers a collaborative modeling and simulation (M&S) framework in the Python language  [4]. It uses the wxPython library which is a blending of the wxWidgets C++ class library with Python. The DEVSimPy M&S kernel is based on the PythonDEVS  [3]  API which offers a consistent and coherent set of classes in order to construct a modular system and to achieve its hierarchical simulation. The philosophy of DEVSimPy is to be an open, extensible and participative environment for the developers. A plugin manager is proposed in order to expand the functionalities of DEVSimPy allowing their enabling/disabling through a dialog window.
05% 3,3Ј diaminobenzidine tetra-hydrochloride, 0. 025% cobalt chloride, 0. 02% nickel ammonium sulfate, and 0. 01% H 2 O 2 to yield a black section product. In cases in which two tracers was injected into a single animal (PHA-L and LY), adjacent sections were treated for each antibody protocol. Sections were mounted onto gel-coated slides, dehydrated, defatted in xylene, and coverslipped with Permount. Data analysis. Cortical injections limited to superficial or deep layers or in which the tracer had leaked to adjacent areas were not included in the analysis. Injection sites and labeled terminal fields in the striatum were delineated in each experimental case using a light microscope (Laborlux S; Leitz) driven by Neurolucida software (MicroBrightField). Thin labeled fibers containing boutons were charted.
The line diameter of the charted fibers was increased and a minimal surface area was defined to threshold automatically the areas of significant density. The same parameters were applied for all cases. For each case, stained sections were digitized using a scanner (Epson V 750 Pro; Seiko Epson) with a resolution of 3200 dpi (8 m/pixel) in 16-bit gray scale. A stack of 2D coronal sections was created using IMOD software (Boulder laboratory for 3D Electron Microscopy of Cells, University of Colorado, Boulder, CO)  (Kremer et al. , 1996). As previously described by  (Mailly et al. , 2010)  sections were aligned using manual rigid body transformations. The Neurolucida chartings were converted into the IMOD format using a specific program developed for this purpose. A 3D reconstruction that contained structures of interest (striatum, projections fields, cortex, injection sites, and structures of interest such as corpus callosum and anterior commissure) was created for each case separately.
4) for 5 min. The cells were washed with ice-cold buffer (twice), lysed with 1 N NaOH, and neutralized with 10 N HCl, and radioactivity was counted in a liquid scintillation counter. Uptake was expressed in term of fmol/mg protein/5 min (protein was determined in cell digest by Bradford method; Bio-Rad). Mock refers to endogenous transport (where ARPE19 cells were transfected with vector alone) and used as reference to induce uptake in cells transfected with the PfPAT construct in the same vector. The data are means Ϯ S. E. of five separate experiments (each containing three or four separate points).
S2 ), suggesting that it might have been acquired following the second endosymbiotic event at the origin of alveolata  (34). PfPAT encodes a 565-amino acid polypeptide with 11 predicted transmembrane domains (supplemental Fig. S3 ). Also, except for a 15-amino acid asparagine-rich domain, which is only present in PfPAT, orthologs in other Plasmodium species share high degree of identity and similarity throughout the length of the protein sequence (supplemental Table  S1 ). No significant sequence similarity exists between PfPAT and the human multivitamin transporter, hSMVT (supplemental Fig. S4 ). Furthermore, no homologs of the human hSMVT could be found in the Plasmodium databases. PfPAT Is Localized to the Parasite Plasma Membrane-To determine the localization of PfPAT in P. falciparum, transgenic parasites expressing PfPAT fused to GFP, in a vector harboring the TgDHFR-TS positive selectable marker, were selected following transfection of the wild type P.
). At the end of the incubation, 2 ml of PBS/EDTA was added and the cells were centrifuged (300 g for 5 min). Cells were incubated with the secondary antibodies conjugated with phycoerythrin (1:200) for p75 (BD Biosciences, San Jose, CA). At the end of the incubation, 2 ml of PBS/EDTA was added and the cells were centrifuged (300 g for 5 min). Then, cells were resuspended in 500 ml of PBS/EDTA. The total number of 10,000 events was analyzed on a FACSCalibur flowcytometer (BD Biosciences) directly after the staining procedure. Data were processed using the FACSdiva program (BD Biosciences). Proportion of p75 positive cells in the various samples was determined by measuring the fluorescence intensity dot plot compared to the total amount of non-labeled cells. Flow cytometry analysis allowed to determinate that OB primary cultures contained approximately 70% of p75 positive cells (Figure  2A ) and OB purified cultures contained approximately 97% of p75 positive cells (Figure  2B ), whereas in the same time OM cultures contained about 15% of p75 positive cells (Figure  2C ) and OM purified cultures contained approximately 98% of p75 positive cells (Figure  2D ).
GFAP and neurofilament antibodies were conjugated with Alexa FluorH 488. Images were analyzed and captured with a fluorescence microscope equipped with apotome (1006; Axioscope; Zeiss). Then, immunodensitometry were performed by outlining areas measuring 1. 410 6 pixels (2. 15 mm 2 ) for GFAP, neurofilament, neurocan and ED1 stainings using ImageJ software (NIH software). For this, number of positive pixels was counted using the measure tool of ImageJ based on the threshold defined with control animals (without surgery) as described by Stamegna et al. [19]. To address the localization of transplanted OM-OECs into the spinal cord, GFP-Neurofilament co-staining was performed using anti GFP antibody (Santa Cruz Biotechnology, CA). Stainings were performed as described previously. MRI. Experiments were performed 7 days after spinal cord transection in 4 other rats for Md, primary OB and purified OM treated groups (140-160 g) on a 7 Tesla Pharmascan magnet (Bruker, Germany) in anesthetized rats (1.
5% isoflurane in O 2 / N 2 O 33%/67%). Sagittal T2-weighted images were acquired with relaxation enhancement (RARE) with an accelerating factor of 4 (TR = , 6000 ms, TE = 40 ms, slice thickness = 0. 5 mm, planar resolution of 100 mm * 100 mm, acquisition time , 35 minutes). Image analysis was performed using Image J software.
CatWalk gait analysis. This analysis revealed that for all time points analyzed there was no significant differences between all the groups, due to a high intra-group dispersion (data not shown). However, contingency analysis was performed. For this, we have counted all the runs performed by the animals during the three distinct periods (D15 to D60). Then, we have counted the number of successes and failures based on the ability of animals to have walking into the corridor and we have determined the ratio of successes and failures for each group. This analysis showed that Primary OB, Purified OB and Purified OM treated groups displayed a significantly higher rate of runs succeed in comparison to the Md treated animals. Indeed, 0% of the Md treated group has been walking during all the runs, in comparison, 21.
Acknowledgments This work was supported by the european research Council (PrISTINe-PD 269064 to P. B. ), the  Swedish research Council  ( K2010-61X-11286-16-3  to P. B. ), the  Parkinson Foundation (Sweden)  ( 416/11  to P. B. ), the  Human Frontier Science Program  (rgP0022/2009-C to P. B. ), the  Centre National de la recherche Scientifique , and the  Agence Nationale de la recherche  ( ANr-09-MNPS-013-01  to r. M. ) and a " Coup d'Élan à la recherche Française' ' award from the  Fondation Bettencourt Schueller. N. r, g. H. P. and P. B. are active in the Strategic research  Area Multipark  ( Multidisciplinary research in Parkinson's disease at lund University ), and N. r. is recipient of personal grants from this program. We thank  Alicja Flasch ,  Birgit Haraldsson  and  Marie Persson-Vejgården  for their technical assistance.
e. , using a so-called virtual director). The aim of the proposed system is to separate semantic objects in the low-level signals (like voices, faces) to be able to determine their number and location, and, finally, determine, for instance, who speaks and when. The underlying algorithms comprise continuous localisation of audio objects and its application for spatial audio object coding  [2] , detection, and tracking of faces, estimation of head poses and visual focus of attention, detection and localisation of verbal and paralinguistic events, and the association and fusion of these different events, which are performed on a per room basis. To quantitatively evaluate the individual algorithms as well as the whole realtime/low delay system, experiments have been carried out on two datasets containing high-definition audio and video data recorded in an unconstrained videoconferencing-like environment.
Datasets and Performance Measures. The experiments for objective evaluations were performed on two real life hand-labelled datasets: 3 h 50 min for Dataset 1 with enabled echo suppression  [35]  (the process of removing echo from a voice communication in order to improve voice quality on a teleconferencing call); 1 h 20 min for Dataset 2  [36]  with disabled echo suppression, lower SNR, and fewer frontal face views. Dataset 2 was made publicly available. The datasets follow the systematic description presented in  [36]  and contain 2 room recorded gaming sessions with enabled video chat of socially connected but spatially separated people. Each room was recorded and analysed separately and contained up to 4 people. The achieved results at different steps of processing are summarised in Figure  11.
In addition to face tracking, the person identification algorithm (described in detail in Section 4. 1) has been evaluated on the given datasets by measuring the amount of time with correctly and incorrectly assigned identifiers, respectively, where, for a given person, the longest continuous track determines the correct identifier. Then, precision and recall, shown in Figure  11 , are computed in a standard way. We also performed a visual focus of attention (VFOA) evaluation (see Figure  11 ) using a representative subset of the data, where we manually annotated for each frame and each person (if not ambiguous) whether the person is looking at table, screen, another person (ID) or none of them (unfocused). Nonannotated, ambiguous frames were not included in the statistics.
Speaker Match Results. The speaker match is evaluated (i. e. , temporal weighted scoring) based on different acoustic localisation approaches (see Figure  11 ), described in Sections 3. 2 and 4. 4. In case of the spatiotemporal fingerprints approach for speaker match  [7] , defined by block "spatiotemporal Fingerprints" in Figure  11 , the dependency between recall and precision for varying operating point is shown in Figures  12  and 13. Here, the fingerprint approach with algorithmic delay of about 112 ms is visualised. From these figures, it is clearly visible that for Dataset 1, the speaker match performs significantly better than for Dataset 2 since there are 4 participants in Dataset 2 within a sector of 100 ∘ , which is definitely going beyond the spatial resolution of the used microphone array.
Since the task is not detection but rather identification (of a speaker), the performance is measured in terms of accuracy (variable precision with a fixed recall of 100%). In the simplest case, the speaker match is based on mapping of direction of arrival to a corresponding detected face (using  (25) ). Achieved speaker (localisation) match accuracies are about 89. 9% and 77. 7% for Dataset 1 and Dataset 2, respectively. We have also carried out event-level based speaker match experiments by exploiting purely information extracted by a visual head motion analysis (see  Section 4. 3). This is defined by block "head motion based speaker detection" in Figure  11. The mean, estimated over the given speech interval, represents a confidence value of visual head motion for each individual speaker.
In the video modality, the mean confidences of visual head motion were exploited. These weights were furthermore modified by a prior which rather takes into account the audio stream against the video stream. Results, given in the block "speaker match" in Figure  11 , obtained after audio-visual combination show slight additional improvements (absolute accuracies of 90. 3% and 77. 8% for Dataset 1 and Dataset 2, resp. ) over the audio-only system (absolute accuracies of 89. 9% and 77. 7% for Dataset 1 and Dataset 2, resp. ), as shown in Figure  11. According to preliminary experiments on other additional data, we have discovered that the gain achieved by augmenting the visual information (i. e. , head motion estimation) is more significant in case of more noisy audio data. Known meeting-wise speaker error rates for CPUintensive state-of-the-art speaker diarisation techniques  [38]  are as low as 7.
Voice Activity Results. The block "voice activity detection" and derivative blocks (Figure  11 ) show precision and recall for the operating system's point performed on the output of the local far-field voice activity detection (more than 6 K manually annotated speech segments used). Although only Dataset 1 is echo cancelled, we were able to achieve reasonably good precision/recall levels for Dataset 2 (see Figure  11 ) after application of the "Directional filtering" block on semantic level within voice activity detector (a difference of 20. 2% in precision (92. 8% instead of 72. 6%) can be seen between corresponding blocks). The sector of interest in the final system for directional filtering was defined as [-110 ∘ , 110 ∘ ] with respect to the reference direction of 0 ∘ , defined as an imaginary arrow intersecting the camera and the centre of the microphone array, facing the participants.
This allows us to eliminate remote parties in case of disabled echo suppression (Dataset 2) and few echo cancelation artefacts in case of enabled echo suppression (Dataset 1). The block "directional filtering" shows precision and recall values of voice activity detection for the case when barge-in (break into a conversation) events are treated by temporal interruption detector, while the blocks "spatial interruption filtering" show precision and recall values of voice activity detection for the case when barge-in events are treated by a spatial interruption detector (i. e. , using azimuth of the stream). While the approach with spatial interruption detection shows slightly better performance using temporal weighted scoring, surprisingly, we have found that in case of event-level based scoring, the spatial approach has a lower performance.
e. , the number of speech segments comprised in the transcription) as well as nontargets (i. e. , the number of potential speech segments not comprised in the transcription but appearing in the detection output). The resulting DET curves are normalised in such a way that the number of targets and nontargets is set to be equal. For each operating point in DET curve, precision and recall values can be estimated. Thus, depending on a potential application, VAD can easily be tuned by considering different thresholds applied on confidence scores associated with each speech segment. Figures  16  and 17  show DET characteristics for detection of voice activity on Datasets 1 and 2. More specifically, 5 different audio-visual VAD systems were considered based on the input audio and a visual motion extracted from video stream.