dataesr/scientific-paragraphs-categorization huggingface dataset:
The segmentation of these respondents led to four clusters. It would be unreasonable to draw strong conclusions regarding the clusters with relatively small sample sizes. The findings from the cluster analysis should be taken for what they are worth that is, indications about the presence of segments and some sources of variation between these segments. In any case, further actions such as increasing the number of respondents are needed. In the future, more investigations will be dedicated to the tricky problem of determining the appropriate number of clusters when performing CLUSCATA. So far, the decision has been made on the basis of a visual examination of the dendrogram but a more formal choice based, for instance, on a hypothesis testing framework would be more indicated. The analyses discussed in this paper with the exception of the permutation tests are available in the R package ClustBlock  (Llobell, Vigneau, Cariou, Qannari, 2019).
In this study, we present the serendipitous discovery of a N-rich star confirmed to be in a binary system with a compact object. The new object associated to 2M12451043+1217401 is a nitrogen-enhanced and carbon-depleted metal-poor star with abundances of Al and Mg mildly discrepant from that of normal RGB stars. We hypothesise that this star is likely to be an example of the result of mass transfer from a binary companion, which is now in the white dwarf stage of stellar evolution. The discovery of such stars helps guide models that attempt to explain the unusual elemental abundances over a wide range of metallicities. This paper is outlined as follows. In Section 2, we present details and information regarding our serendipitous discovery in the APOGEE-2 dataset. In Section 3, we describe and discuss the behaviour of the measured light and heavy elements.
Stellar parameters and chemical-abundance measurements The first estimates for stellar parameters for 2M12451043+1217401 we adopted in the present work are (T eff , log g, ξ t , [M/H]) = (4750. 2 K, 2. 24, 1. 86 km s -1 , -1. 09), taken from Payne-APOGEE catalogue. With this set of parameters, we performed an abundance analysis using the LTE abundance code BACCHUS  (Masseron et al. 2016 ), using the plane-parallel, one dimensional grid of MARCS model atmospheres  (Gustafsson et al. 2008). The results are listed in Table  1. The metallicity listed in column 2 of Table  1  is the average abundance of selected Fe I lines, and is in acceptable agreement with that determined from the ASPCAP and Payne-APOGEE pipelines. Here, we explore whether or not our chemical-abundance measurements have the ability to chemically tag stars associated with globular clusters by performing a chemical-tagging analysis.
As can be seen in Figure  1 , this star occupies the same region of Mg-Al abundance space as globular cluster stars, without strong evidence of MgAl cycles. The Si abundance is also moderately enriched, which is typical for globular clusters. While the abundance pattern of 2M12451043+1217401 seems consistent with globular cluster stars in the [N/Fe]-[Fe/H], [Mg/Fe]-[Fe/H], [Al/Fe]-[Mg/Fe], and [Al/Fe]-[N/FE], it is distinct from the overall APOGEE data set, which contains bulge, disk and halo stars. We also see some distinction in carbon between 2M12451043+1217401 and the main body of N-normal stars and globular cluster population, with the N-normal stars and GCs stars typically having lower [C/Fe] (for a given metallicity). In Figure  1  we also compared the chemical-composition of a giant star in the main body of N-normal stars with similar stellar parameters and similar metallicity that 2M12451043+1217401, this clearly shows that our object display approximately the same [Ce/Fe] (see below), [Mg/Fe], [Si/Fe], and [Fe/H] abundance ratios that a normal giant star, but show the most significant differences in their [X/Fe] ratios, for α-elements, N, C, and Al.
Concluding remarks In this work, we communicate the serendipitous discovery of an unusual red-giant star that show significantly enhanced [N/Fe] among metal-poor field stars. Based on high-resolution NIR spectroscopic data from the APOGEE-2 survey, we determined the atmospheric parameters, spectrostopic distance, radial velocity variabitity, abundances of light elements (C, N, O, Mg, Al and Si), and the elements created by the s-process (Ce II) for 2M12451043+1217401. Combining the large radial velocity variation, and nitrogen over-abundance, we hypothesise that an possible AGB-binary system may produce a [N/Fe] over-abundance in some N-rich stars within the Milky Way, since mass transfer happened in the past, and the star is an normal RGB star where we see the effects due to the pollution of a companion that has undergone its AGB phase and dumped shell-nucleosynthesis processed material onto the observed star, i.
These functions are plotted for our system A&A proofs: manuscript no. ms in the same figure. The green and yellow colours correspond to more probable regions of the space, which are crossed more frequently by the simulated orbits. We derive the distance to 2M12451043+1217401 using StarHorse 3  , a Bayesian distance estimator initially developed for APOGEE stars  (Queiroz et al. 2018). For completeness, we provide dynamical solutions and orbit calculations for both the StarHorse distance of 13 kpc and the Bailer-Jones et al. (  2018   Kernel Density Estimate (KDE) smoothed distribution of simulated orbits employing a Monte Carlo approach, showing the probability densities of the resulting orbits projected on the equatorial and meridional Galactic planes in the non-inertial reference frame where the bar is at rest.
Acknowledgements. We thank the referee for her/his work that improved the manuscript. J. G. F-T is supported by  FONDECYT  No. 3180210  and the  European COST   Action CA16117 (ChETEC)  project  No 41736. R. E. M. acknowledges project fondecyt  1190621 , and also acknowledges support by  VRID-Enlace 218. 016. 004-1. 0  and the  Chilean Centro de Excelencia en Astrofísica.
y  Tecnologías Afines (CATA) BASAL  grant  AFB-170002. M. C. acknowledges support by  Astronomical Institute of the Czech Academy of Sciences  through the project  RVO 67985815. O. Z. acknowledge support by the  MINECO  under grant  AYA-2017-88254-P. SzM has been supported by the  Premium Postdoctoral Research Program of the Hungarian Academy of Sciences , and by the  Hungarian NKFI  Grants  K-119517  and  GINOP-2. 3. 2-15-2016-00003  of the  Hungarian National Research, Development and Innovation Office. D. M. acknowledges support from  FONDECYT Regular  grant No. 1170121 , and the kind hospitality of the  Osservatorio di Capodimonte/INAF, Italy. T. C. B. and V. M. P. acknowledge partial support for this work from grant  PHY 14-30152 ;  Physics Frontier Center / JINA Center for the Evolution of the Elements (JINA-CEE ), awarded by the  US National Science Foundation.
APV acknowledges a  FAPESP  for the postdoctoral fellowship grant no. 2017/15893-1  and the  DGAPA-PAPIIT  grant  IG100319. B. T. gratefully acknowledges support from  National Natural Science Foundation of China  under grant No. U1931102  and support from the hundredtalent project of  Sun Yat-sen University. Funding for the GravPot16 software has been provided by the  Centre national d'études spatiales (CNES)  through grant  0101973  and  UTINAM Institute of the Université de Franche-Comté , supported by the  Région de Franche-Comté and Institut des Sciences de l'Univers (INSU). Simulations have been executed on computers from the  Utinam Institute of the Université de Franche-Comté , supported by the  Région de Franche-Comté and Institut des Sciences de l'Univers (INSU) , and on the supercomputer facilities of the  Mésocentre de calcul de Franche-Comté.
Algorithm 1: Terrestrial reference frame computation with gravity identification. Input : Recording of the system worn at the ankle Output : Inertial data projected in a terrestrial reference frame with gravity identification 1 foreach t ∈ [0, t f inal ] do 2 Computation of R t u by gyrometer integration during ∆T 3 Integration of R t u Γ(u) during ∆T that we assume to be close to gravity 4 Alignment of the computed gravity with the previous ones We now have access to the acceleration of the ankle for all t by removing the gravity (≈9. 81 m/s) from the Z terr axis: Γ Ankle B terr (t) = Γ B terr (t) -    0 0 9. 81   . ( 6 ) The advantage of the attitude filter is the efficiency of its computation. The pseudocode is given in Algorithm 1. This characteristic is necessary as we use Γ Ankle B terr to compute a pseudo-speed introduced in Section 3.
GBT Classifier for Candidate Interval Classification Section 2 described the theory of supervised statistical learning, especially the GBT algorithm. From the interval extraction presented in Section 3. 1. 2, a learning set was built from the recordings of a group of people of various ages and heights practicing several activities while wearing the system at the ankle. A binary label was assigned to each interval indicating if it is a stride (label 1) or not (label -1). Our database contains about 6000 positive intervals and about 6000 negative intervals. As the labeling is a time-consuming task, the number of labeled intervals is relatively small compared to other supervised problems. As a consequence, in order to help the GBT algorithm to provide a classifier with good performance it is crucial to extract relevant information from the data we have access to for each interval.
Performance of GBT for Stride Detection A popular method to estimate the performance of a classifier is k-fold cross-validation  [41]. For this method the training set is divided into k subsets. One by one, a set is selected as validation set and the k -1 others are combined to form a training set and the error estimation is averaged over all k trials. During this process, every observation of the total dataset is in a validation set exactly once, and is in a training set k -1 times. Typically, the procedure is repeated for different model hyperparameters (or even different model types) in order to compute the best classifier. This provides a validation set with low bias, as we are using most of the data for fitting, as well as low variance, as most of the data are also being used in the validation set.
Empirically, k equals 5 or 10. In practice we set k = 10 and we obtained the best results for a GBT learning rate equal to 0. 25 and 100 decision trees. The results are presented in the following confusion matrix (Table  1 ). The mean error was less than 0. 3%. This score depends on the difficulty of the database. Many atypical strides and ankle movements (labelled -1) that looked like true strides from inertial data point of view were included in the database. As a result, the final score was slightly deteriorated but it led to a more robust classifier. In order to better analyze the performances of our algorithm, in the following we study the results of our stride detector in controlled environments, meaning we know where strides occurred in the recordings. Two kinds of error were considered: the false negatives (FNs) corresponding to missing strides, and the false positives (FPs) corresponding to non-stride intervals classified as "1".
Total -FN Total -FN Total -FN Total -FN Total -FN All walking strides were detected. We can see in Figure  5  that the MOCAP dataset presents diversified stride lengths and stride durations. This means that our detection achieved 100% accuracy for walking phases with various paces. Our algorithm did not detect all atypical strides, but showed good results while most existing methods described in the literature do not detect them. Some of the walking strides may appear very small, but this corresponds to half turns. Indeed, the foot ends very close to the starting point of the stride.
Our stride detector produced almost no mistakes during these situations, while existing methods in the literature do not consider them. Indeed, they evaluate FP errors only when the device is correctly worn (on the foot) and the wearer is walking (still generating FP). We can see in Table  3  that the most difficult situation is when the WATA device is manipulated in the hand. Some hand movements may look a lot like strides (displacing the system on a table) that even an expert can hardly differentiate by looking at the data we have access to. In Section 3. 2, we presented our stride detector's performance showing its robustness and reliability for use in clinical trials. A clinical outcome computed by Sysnav is based on the stride length. Thus, a trajectory reconstruction of each detected stride is needed.
GBT Learning Performances for Activity Recognition The goal was then to build a classifier that recognizes the activity of each detected stride. We tested several supervised learning algorithms for multi-class classification (five classes for five activities) on the database. Once again, GBT provided the best results using the 10-fold cross-validation. The confusion matrix is presented in Table  6. The global score was about 99. 4%. The difference between "atypical stride" and "walking" is difficult to define, especially for a small forward step. As even the labelling decision by the video viewer is difficult, it is not surprising that most errors were between these two classes.
The patient took 5 s to climb it for a small altitude variation. This kind of stride does not exist in the learning dataset "climbing stairs" (label 10), and it is not surprising that the GBT prediction function for activity recognition classified the stride as "atypical stride". In addition, several patients performed a run in a 10-m hospital corridor with the WATA device at the ankle. We adopted the same evaluation, by counting the number of missing running strides defined as classified as another activity or not detected (FN). The results are presentend in Table  8. The success rate was about 97. 4% less than the 99. 4% presented in Table  6. This is due to the disproportion of DMD strides in the HAR learning dataset compared to adult ones. Most of the labeled strides were performed by Sysnav employees.
The maximum number of switch reconfigurations is assumed to be 10 for both scenarios. Note that in while in step 2 of Figure  2b  the label chosen to be expanded is the one with the least cost, in step 2 of Figure  2d  the label (65,15) has to be discarded because the constraint on the maximum number of reconfigurations is violated. The solution paths are highlighted by the edges in bold in Figure  2a  and Figure  2c  respectively. A pseudo-code of the proposed heuristic is reported in Appendix B.
Starting with a label only on the first node, at each iterations the algorithm extends all the labels extending the corresponding path along all outgoing arcs. If δ + (i) is the set of outgoing arcs from node i, considering each arc (i, j) ∈ δ + (i), a label (W k i + w ij , C k i + c ij ) is saved on node j only if W k j = W k i + w ij ≤ W, and for each existing label l on node j is verified that W k j ≤ W l i and C k j ≤ C l i (we will refer to this last property saying that a label added to a node is efficient). When all the efficient labels have been generated, the algorithm stops and the solution path is the one idenfied by the label on destination node having minimal cost. The pseudo-code of the algorithm is given in Algorithm A1 where N is the set of nodes of the graph, L i denotes the set of labels in node i and T i ⊆ I i demote the index set of the labels on node i which have been handled.
Implementation of the menu techniques The 4 menu techniques were developed with the Epson Moverio BT-300, running on Android 5. 1. We chose this HMD because of its light weight (70g for the headset). Machine maintenance and surgery are physically and mentally tiring tasks, so minimizing the weight of the HMD was a determinant factor of choice. To be able to track the position of the HMD, we fixed three passive markers on it. The markers were tracked by a set of four Flex3 cameras monitored by the Tracking Tools software (version 2. 3. 1). This software was executing on a nearby laptop. The position of the MR glasses (and thus the participant's head orientation) was sent to the HMD through Wifi thanks to a custom Python script. The screen of the HMD was then updated according to where the participant was facing.
Experimental viral infection of O. tauri The host strain O. tauri RCC4221 (Roscoff Culture Collection, NCBI accession number txid70448) was grown in 60 mL of L1 medium  (Guillard and Hargraves, 1993)  in aerated flasks (Sarstedt), and incubated at 21. 5ᵒC (±0. 5ᵒC) with white light ~100 µE and a 10:14 hours photoperiod (light:darkness), until stationary phase (7. 16×10 7 ± 3. 57×10 6 cells mL -1 , estimated by 4′-6-Diamidino-2-phenylindole (DAPI) counts). Triplicate O. tauri cultures (20 mL) were infected with 1 mL of OtV5 inoculum (1. 3×10 7 ± 4. 3×10 6 viruses mL -1 , estimated by plaqueforming units), resulting in a 0. 01 MOI (multiplicity of infection). Non-infected triplicate O. tauri cultures (inoculated with 1 mL of L1 medium) were used as control. After OtV5 inoculation, samples (900 µL) were taken over 3 days at times 0, 8, 24, 48 and 72 h, and fixed with 100 µL of freshly filtered formaldehyde (3.
OtV5 probe design and synthesis For the detection of the OtV5 virus (NCBI accession number EU304328) we designed 11 dsDNA polynucleotide probes (300 bp each) using the software geneProber web service (http://gene-prober. icbm. de/). These 11 probes covered a total of 3998 bp of the OtV5 viral genome, offering sufficient sensitivity to detect single viruses (Table  S1 ), as it has been shown before  (Barrero-Canosa et al. , 2017). Each probe synthesis was done by obtaining the corresponding polynucleotides by PCR, and then all probes were mixed and labeled with the Alexa594 fluorochrome, based on the protocol from  Barrero-Canosa et al. (2017). The PCR was set up as follows: 10pg of OtV5 DNA were added to a reaction mixture containing 200 µM (each) deoxyribonucleoside triphosphates (Invitrogen), 1 µM of each primer, 1x PCR buffer (Invitrogen), and 5U of Taq DNA polymerase (Invitrogen).
Alignment of our designed viral probes against Prasinovirus genomes and the outgroup PbCV1 (Chlorovirus) using BLAST. Shadow cells indicate 664 identity higher than 80%, and coverage larger than 90%. Given than the probes are 300 bp long and all of them are combined for the hybridization, they may 665 serve to visualize the virus-host interactions of all the Ostreococcus virus described in the table, except for OtV6. Accession numbers in TableS3. Ostreococcus tauri virus; OlV, Ostreococcus lucimarinus virus; OmV, Ostreococcus mediterraneus virus; 669 MpV, Micromonas pusilla virus; BpV, Bathycoccus prasinos virus; PbCV, Paramecium bursaria Chlorella virus 1. 670.
This paper presents a semi-automatic methodology proposed for 3D modeling of utility networks in GIS environment. The ModelBuilder in ArcGIS (ESRI) software is used for implementing this methodology, by developing two tools to automate the construction processes of 3D networks. The first presents a tool to create a 3D Manhole layer from points layer, and the second is a tool to create a 3D pipe layer. For both tools, a work algorithm has been built, in addition to designing user interfaces elements. These tools are stored in a Toolbox called "3D Manhole & Pipe. tbx". The two previous tools were tested and applied to spatial data for a proposed residential area. The final 3D model of the residential area includes the sewage and stormwater networks, as well as other spatial data such as buildings, parks, roads, etc.
Study area The study area is located in east of Tartous city centre (Syria) (about 3 km), between longitude 35. 9236 °E to 35. 93029 ° E, and latitude 34. 8976 °N, 34. 8901 °N (Fig. 1 ). It covers an approximately area of 16 hectares. It is a proposed residential area by  [1]. The residential area consists of 54 residential blocks. It includes also commercial area (with a floor area of 3600 m 2 , gardens, playgrounds and water bodies. Geodatabase was built by dividing the space into a group of layers: buildings, road network, malls, schools, kindergartens, parks, playgrounds, water bodies, etc. based on WGS 84 / UTM zone36N projected coordinate system.
ModelBuilder ModelBuilder is a visual programming language to create, modify, and manage models within ArcGIS. It is used to automate workflow using the geoprocessing tools available within ArcGIS (ArcToolbox)  [9]. There are two basic modes for creating ModelBuilder. The mode allows exploratory work to be performed. The other one is an application where generic tools can be built, reused and shared. In 2012,  [7]  designed two general models using ModelBuilder that can be used for geoprocessing linear features (calculate changes between two linear elements). Many researchers have also used ModelBuilder in global scientific research such as:  [8]  to calculate the change in night lights in China, and  [10]  who implemented a tool for facilitating the process of urban regional risk assessment.
Test and apply the developed tools The two previous developed tools were tested and applied to the spatial data presented previously (in 2. 4 paragraph), which contain the required and necessary fields. Firstly, the 2D Manhole layers were converted to 3D Manhole layers, then the 2D pipes layers were converted to 3D pipes layers. In the end, three-dimensional layers (x, y, z) were obtained where the cartographic representation and visualization are possible in an environment that reads this type of 3D data (such as ArcScene). Fig. 5  represents the final 3D model of the residential area including the sewer and Stormwater networks (Manholes & Pipes), as well as other spatial data such as buildings, gardens, roads, etc. Through this model, This model is able to spot the intersection points in the network, visually or using the 3D analysis available in the software, allowing us to identify problems to be processed and resolved before starting a project, leading consequently to time and cost savings, effort and money.
In the condition used, the MgtR peptide is not cytotoxic to J774 cells as shown by quantification of LDH release (Supplemental Figure  S2 ). We designed and synthesized a scrambled peptide that contained the amino-acids of MgtR in a scrambled order, thus retaining the hydrophobicity, but lacking a predicted membrane helix, as shown using the TMHMM program (Figure  3A ). As expected, the scrambled peptide exhibited a circular dichroism spectra that differs from the one of MgtR, indicative of a loss of the alpha-helix structure (Supplemental Figure  S1 ). Importantly, no significant decrease of intramacrophage survival was observed with this scrambled peptide used at 120 µM (Figure  3C ), suggesting that the biological effect of the synthetic MgtR peptide is somehow linked to sequence specificity and not only to overall hydrophobicity.
ACKNOWLEDGMENTS We are grateful to  Scott Rice  ( NTU, Singapore ) and  Sophie Bleves (Marseille, France)  for providing strains. We are grateful to  Pascal Verdié  from the SynBio3 platform for providing peptide synthesis facilities, to  Albert Meyer  for performing MALDI spectrometry analysis, both from the  Institut des Biomolécules Max Mousseron, Montpellier. This work is supported by  Vaincre La Mucoviscidose  ( RIF20170502042 ) and  Association Gregory Lemarchal. MM is supported by  Vaincre La Mucoviscidose  ( RIF20170502042 ) and PG is supported by the  Association Méditerranée Infection.