dataesr/scientific-paragraphs-categorization huggingface dataset:
In summary, our methods achieved better accuracy of 87. 5 %, especially the Group 2 algorithm where accuracy was of 88. 17 % with respect to Jaccard Coefficient. CNV automatic quantification on OCT-Angiography devices is strongly needed in clinical research and practice. That is why recent works have extensively focused on the role of the CNV quantification in the follow-up of neovascular AMD patients. Amoroso et al  [25]  have evaluated the reproducibility and the interuser agreement of the CNV lesion size manual measurements on OCTA using the measuring tool embedded in the AngioVue software. Although this work has shown that these measurements are reproducible, it has also revealed the importance of automatic CNV segmentation and quantification to avoid manual implication of clinicians for CNV segmentation.
Miere et al  [26]  have studied the changes of the CNV after anti-VEGF therapy by quantifying the CNV size at baseline and at three monthly follow-up visits. Their report showed that treatment naïve and treated eyes with CNV secondary to neovascular AMD responded differently to anti-VEGF therapy. Coscas et al  [27]  have analyzed quantitative OCTA biomarkers, such as blood flow area, fractal dimension and vessel density, for neovascular AMD patients under-treatment and in remission. Through this study, the authors have shown that studying these biomarkers on OCTA could become a valuable technique in assessing the CNV response to anti-VEGF agents and helping clinicians to develop personalized treatment and follow-up cycles. Other works on the assessment of anti-VEGF treatment for CNV on OCTA have used the AngioVue software to quantify the CNV area and the flow area using the embedded algorithm "AngioAnalytics" according to the manual delineation of two qualified technicians  [28].
V. Conclusion In this study, we developed two segmentation and quantification algorithms of CNV on outer retinal flow images acquired with OCT-Angiography, which is a new and very promising non-invasive imaging technique. These algorithms allow quantifying three biomarkers of neovascular AMD according to the CNV topology. Our segmentation and quantification results, obtained with the automatic algorithms showed a good agreement with the embedded software "AngioAnalytics" measures, calculated after the expert manual delineation of the CNV total area. The proposed methods thus allow automatically obtaining quantitative parameters of the CNV lesion, which can bring a precious aid for clinicians in the follow-up of patients with AMD disease and save time in clinical routine. For future works, further studies need to be performed in order to overcome the limits of the proposed algorithms and complementary validations have to be carried out to compare our method's accuracy to the embedded software "AngioAnalytics".
Statistical analyses. Differences in TL and telomerase expression between samples collected from within the CEZ and the control areas were analyzed using generalized linear models (GLM) in R v. 3. 4. 3  43  , using sample area (CEZ, control) and sex as fixed factors. Intra-individual correlations for telomere length were examined for CEZ and control individuals separately, and pairwise comparison for all tissues were performed (Pearson's correlation in R). REST 2. 0. 13 software  44  was used for comparing general levels of telomerase expression among tissues.
Acknowledgements PCW and TM are funded by  Academy of Finland  (project numbers 287153 and 268670 respectively). TAM and APM were supported by the  Samuel Freeman Charitable Trust , the  CNRS , and the  University of South Carolina. ZB is supported by the  Portuguese Foundation for Science and Technology  ( SFRH/BPD/84822/2012 ). KK is supported by the  Emil Aaltonen Foundation  (Grant No. 160107 ). We gratefully acknowledge  Igor Chizhevsky ,  Serhii Kireyev ,  Anatoly Nosovsky  and  Maksym Ivanenko  for logistic support and help in organizing fieldwork in Ukraine. We thank  Juho Niva  for laboratory assistance.
NT controls were frequency-matched on age, sex ratio, body mass index and major vascular risk factors. The study was approved by the local Ethics Committee (Committee for the Protection of Persons of Nord-Ouest I), and all participants gave written informed consent. The study was conducted according with the Principles of Good Clinical Practice and the Declaration of Helsinki and was registered at https://www. eudract. ema. europa. eu under the unique identifier RCB2007-A001-10-53.
Measurement of AWV AWV was estimated from the pressure-diameter relationship as previously described  [1, 4, 19, 20]. WE was assessed for each cardiac cycle by integrating the pressure-diameter area during the loading phase i. e. , from diastolic to systolic pressure, and is thus graphically, bounded by the area under the pressure-diameter relationship, the pulse pressure and the pulse diameter (Supplementary figure  1 ). The area of the hysteresis loop of the pressure-diameter relationship obtained during the loading and unloading phases, which has similarly a dimension of energy, corresponds in fact to the energy dissipated in viscous work (WV) by the arterial wall during one cardiac cycle. The loop area was measured by image analysis software (ImageJ). Energies are expressed in joules per square meters during one cycle.
a b s t r a c t Nowadays, camera networks are part of our every-day life environments, consequently, they represent a massive source of information for monitoring human activities and to propose new services to the building users. To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 single videos) recorded using 6 indoor cameras deployed on multiple spaces. The video sets represent more than 1 h of video footage, include 77 people tracks and captured different human actions such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting.
Moreover, each video has been manually and automatically annotated to include people detection and tracking meta-information. The automatic people detection annotations were obtained by using different complexity and robustness detectors, from machine learning to state-of-art deep Convolutional Neural Network (CNN) models. Concerning the contextual information, the Industry Foundation Classes (IFC) file that represents the environment's Building Information Modeling (BIM) data is also provided. The BIM/IFC file describes the complete structure of the environment, it's topology and the elements contained in it. To our knowledge, the WiseNET dataset is.
Data The WiseNET dataset was created using an indoor network composed of 6 smart cameras. The network was deployed on the third floor of the Institut Marey et Maison de la M etallurgie (I3M) building located in Dijon, France (see Fig. 1 ). The smart cameras, presented Fig. 2 , have been designed specifically for the experiment to embed the selected processing and enable the synchronization of the different video flows. The dataset consists of three main elements: (1) video sets, (2) information of the environment and context and (3) annotations for people detection and people tracking. 1. The video sets were recorded using from 5 to 6 cameras simultaneously. The features of the 11 sets are described Table  1. The videos captured different human actions such as walking around, standing/ sitting, motionless, entering/leaving a space and group merging/splitting.
Value of the Data The data presented are synchronized video streams which were acquired in a multi-space indoor environment. The proposed data could be used as a benchmark for people detection, as well as for Multi-Target Multi-Camera (MTMC) tracking [2e8], thanks to the given automatic and manual annotations as in Ref. [5]. The WiseNET dataset includes the complete information of the indoor environment, as well as relevant contextual information. This differentiate our dataset to the state-of-the-art ones [4e7]. The environment information is given as an Industry Foundation Classes (IFC) file 11 that represents the environment's Building Information Modeling (BIM) data. While the contextual information includes a semantic relation between real object (e. g. , cameras, spaces) with some enter/exit regions of interest (i.
e. , doors). The proposed video sets could be used for human-action recognition such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting. Moreover, they could be also be used for office-objects detections such as tables, monitors, chairs, etc. Furthermore, one camera view only includes shadows of people moving around. Each frame was timestamped and annotated using a JSON format, making the meta-data easy to read, understand and reuse. 2. The dataset includes the IFC file of the I3M building (referred as I3M-IFC) and a camera-calibration file for each camera node. From the IFC file, different data could be extracted, such as the building's topology and 2D/3D view, as depicted in Fig. 3. Furthermore, the dimensions of the different building elements could also be extracted from an IFC file, as presented in Table  2.
Additionally, a cameracalibration file including contextual information is also provided, an example is shown in Listing 1. 3. The dataset also includes people detection manual and automatic annotations (PD-MAN and PD-AUT respectively), as well as people tracking manual annotations (PT-MAN). Figs. 4  and 5  Furthermore, the use of automatic people annotation aims not only to propose an alternative to the time-consuming manual annotation but also to evaluate the complexity of each video (in terms of difficulty to detect people) using state-of-art people detectors. Therefore, Fig. 6  enables users to select video sets according to their "challenging" level.
Contextual information The contextual information of the WiseNET network is composed of two parts, the information of the environment where the network was deployed and the information concerning the camera nodes. The information about the environment is contained in the I3M-IFC file. IFC is a data representation standard, developed by the buildingSMART,  3  used to define architectural and construction-related data and to facilitate interoperability between the different agents involved in a building construction. The I3M-IFC contains large amount of information concerning the I3M building, e. g. , information about all the elements composing the building, their geometrical information, their position and their relation to other elements. Fig. 3  shows some examples of data that can be generated from the I3M-IFC file.
The environment's topology refers to the following tree structure: a building has a set of storeys, the storeys have a set of spaces and the spaces have a set of elements (e. g. , doors, windows and sensors). Another example of data that can be obtained from an IFC file are, the dimensions of the spaces where the cameras were installed and the dimensions of the doors they observe, as presented in Table  2. However, to extract information from an IFC file is not an easy task. A way to easily handle the IFC information is by converting the IFC file into an ontology, as presented in Ref. [1]  in order to obtain the building's topology. The I3M-IFC file was obtained from the company in charge of the construction of the I3M. Remark (ID of elements of interest). The dataset includes a file containing the ID of each element of interest, used for extracting (from the I3M-IFC file) the data presented in Table  2.
People detection annotations The people detection manual annotations (PD-MAN) were obtained by manually enclosing a bounding box (Bbox) around each person that appears in a video frame, assigning them a unique identifier (ID) and stating if they are around a ROI. The process was performed by using a software developed in Python 5  using OpenCV library  6 dthe code is provided but not supported. The PD-MAN annotation rules were as follows: 1. On each video set, a unique ID should be associated to each person. For example, the person "Mario" should have the same ID in the five videos composing the set 1. Moreover, if "Mario" appears in another set he might be assigned a different ID. 2. The Bbox should be created by selecting its top-left and bottom-right corners. 3. If only a person's limb is visible, then no Bbox should be drawn.
7 Specifically, we used the model with input image size of 512 Â 512 which was first trained on the COCO dataset (Common Objects in Context)  8  and then fine tune on the union of PASCAL VOC2007 and VOC2012 dataset. 9 We chose this detector due to its high precision  [11]. YOLOv3_608 uses a single neural network that predicts bounding boxes and class probabilities directly from full images. We used the pre-trained modeldconfiguration and weightsdprovided by the authors. 10 Specifically, we used the model with size 608 Â 608 which was trained on the COCO dataset. We chose this detector due its high precision and low inference time  [12] , which are two major factors for a real-time surveillance system. For the object detectorsdSSD and YOLOdwe only focus on the person class, i. e. , the rest of objects were simply ignored.
The process was performed by using a software developed in Python using OpenCV librarydthe code is provided but not supported. The choice of detectors differs in complexity and robustness, which we consider an interesting factor for evaluating the limitations of systems. Moreover, due to the automatic nature of the annotations, the rules presented for the PD-MAN cannot be considered, only the rule stating if a detection is around a ROI is considered (rule 7). The PD-AUT where obtained for all the frames. The resulting meta-data from the PD-MAN and PD-AUT annotations were stored using a JSON structure based on the logic that a video has a set of frames and some of those frames present a set of Bboxes (detections). A typical annotated frame is shown on Fig. 4  and the resulting meta-data is the Listing 2.
The JSON fields correspond to: video: name of the video file from which the meta-data was obtained. resolution: video resolution. frames: set of frames with BBoxes. The frames with no BBoxes are not considered. frameNumber: frame number. deviceID: ID of the smart camera observing the scene. inXSDDateTime: time stamp obtained from the smart camera. detections: set of BBoxes (detections) made in the same frame. • class: detection's class name. In our case "person". • imageAlgorithm: Algorithm used for detecting. For example, "YOLO" or "SSD". If it is a manual annotation, then the value is "groundtruth". • xywh: detection's top-left point (x; y), width (w) and height (h). • id: person's ID. • regionOfInterest: ROI's ID. If the detection is not around a ROI then the value is "null". • visualDescriptors: array of features describing the detection.
Notice that any type of visual features can be used to describe visually the detection. For all the PD annotations in the dataset we decided to use a localize 2D Hue-Saturation (HS) histogram as visual descriptor Vd m , where m is the size of the array and is defined by the number of bins in each channel as m ¼ H n Â S n. We used 9 bins per channel (H n ¼ S n ¼ 9) for PD-MAN which gives a visual descriptor of 81 features; and 8 bins per channel for PD-AUT which gives a descriptor of 64 features. In the PD-AUT we decided to use 8 bins instead of 9 because in future works, we plan to combine them with other types of visual features and to have 64 features allow us to easily give equal weight to all the types. Finally, the visual descriptor Vd was normalized using the [ 2 À norm (see Eq. (  1 )) in order to keep the relative contribution of the histogram bins regardless of their absolute contribution.
People tracking annotations People tracking manual annotations (PT-MAN) consisted in manually stating the space location of each person at all times, during a complete video set. This was done by considering the people's ID and the time they enter and leave each space. For each video sequence, the tracking information is given in the form of a space-time graph along with its meta-data stored in a JSON file. The space-time graph is an intuitive way of presenting the location and the changes of spaces of all people during a period of time. The tracking meta-data is stored in a JSON file, where the fields corresponds to: set: video set number from which the PT-MAN was obtained. tracks: set of people tracks. A track relates a person with a set of spaces at some periods of time. A track is divided into a set of tracklets.
Padilla with an Intersection Over Union (IOU) threshold of 50%. 11 Moreover, to use this implementation a script that extracts each detection in the JSON file and convert it to a text file was developed and is also provided. AP is a numerical metric, which simplifies the comparison of different detectors. Fig. 6  presents a comparison of the resulting APs for all videos in the dataset. The dataset provides all the PR-Curves from which the AP were computed. Notice that the videos without detection (i. e. , nobody appeared in the camera's view) were ignored during the evaluation (e. g. , the videos from camera 6 in sets 5e11). From the AP, is possible to evaluate the difficulty/challenge degree of each video in the dataset. It is important to notice that the results presented depend on the quality of the ground truth, which was done by multiple humans, thus is prompt to subjectivity and errors.
Moreover, there is some discrepancy between PD-MAN rules and the automatic annotations especially when the person torso is not visible (rule 5), which occurs when the person is much closed to the camera. Furthermore, to obtain the PD-MAN annotations is a time-consuming task. Therefore, for all those reasons we recommend the users of the database to use (if possible) the automatic detections instead of the manual, especially the YOLOv3_608 detections. Remark (Camera 6). Even though camera 6 did not record any person in the sets, it recorded shadows of people walking around space s2 (see Fig. 1 ). Thus, we decided to include the videos.
Description of video sets. FPS stands for frames per seconds; #Videos indicates the number of cameras used in the set; #Frames (Time) indicates the number of frames and recording time of a complete set; #People indicates the number of people present in the set; #PD-MAN refers to the number of manually annotated people detection bounding boxes. 1280 Â 720) or VGA (640 Â 480)ddifferent frames per second (FPS)d30 or 25dvarious recording timed40 seconds or 1, 2 and 4 minutesdand using two video codecsdMPEG-4 and Planar 4:2:0 YUV. The different recording characteristics lead to a richer and more diversified dataset.
ESyS-Particle We triaxially compress simulated layered sedimentary rock using the parallel 3D discrete element method (DEM) simulation package ESyS-Particle  (Abe et al. , 2003). Similar to other DEM approaches (e. g. ,  Cundall and Strack, 1979)  ESyS-Particle simulations of fault gouge production produce realistic evolutions of fault gouge size and sliding friction  (Abe and Mair, 2005, 2009; Mair and Abe, 2008). The 3D implementation of ESyS-Particle enables out-of-plane motion, which has proven important to fault gouge production  (Hazzard and Mair, 2003; Abe and Mair, 2005; Mair and Hazzard, 2007). These DEM simulations provide additional information beyond that available from laboratory experiments, such as directed force networks, at fine spatiotemporal resolution throughout deformation (e.
Data collection Three teams comprised of 3 surveyors and 1 team leader conducted the data collection. Surveyors and team leaders were trained on the study procedures, GPS-use, sampling strategy, interviewing procedures and data collection. The questionnaire was piloted at the end of the training. Teams conducted face-to-face interviews after obtaining written consent. A second visit was organized later in the day or on the second day if the selected participant was absent. If during the second visit the household members could not be found or refused to participate, that household was skipped. Household visits, absent households and refusals were recorded. A standardized questionnaire was used to collect information about demographic data (age, sex and household size), having received a card and card availability at the time of the survey, vaccination status (self-reported and card-confirmed), reasons for non-vaccination (open question) and acceptability data (adverse events, taste and beliefs about the vaccine) using Kobo Toolbox software 1.
Data analysis OCV coverage of two doses (full course), with at least one dose, and single dose coverage (after the first and the second round), were calculated by dividing the number of individuals reporting being vaccinated by the survey population and expressed as a percentage. Vaccination coverage included both card-confirmed and verbally reported vaccination status. We estimated 95% confidence intervals (Fisher's exact test). In addition, we calculated the dropout rate between rounds as follows: Dropout rate (%) = (OCV first dose-OCV second dose/ OCV first dose) x 100. Data analysis was carried out using R statistical software, version 3. 2. 3, taking into account the sampling scheme.
Acknowledgments We would like to thank the field work team:  Melody Kalombo ,  George Kapapi ,  Christabel Nkunika ,  Evans Mwango ,  Ellen Imasiku ,  Michael Mwamba ,  Gwenie Nseluke ,  Mercy Mutambo ,  Muziya Chika ,  Tangu Ngoma ,  Nicholas Zimba  and  Edward Chipalo  for their intense and excellent work. And all people we interviewed for their patience.
Data were collected using TrueQuant software (PerkinElmer), and images were exported as high-quality jpeg files. This experiment was done twice with 50 filaria larvae inoculated each time. Isolation of soil-derived nematodes. Free-living and plant parasitic nematodes, were isolated from soil samples collected at the University of Chicago, the monitored location not visited by the wild, household, or farm animals. Actively moving nematodes were purified from the soil samples using the Baermann funnel method  56. Briefly, large particles were first removed from 100-g soil samples by mixing soil-samples with 300 ml of deionized water and passing the slurry through a funnel-shaped No. 20 sieve (850 μm mesh size). The same mesh sieve served as a screen after it was placed within a larger funnel fitted with 5 cm of rubber tubing, which was sealed with a hemostat.
Briefly, the architecture was designed with CleWin (WieWeb Software, Hengelo, NL) and printed on a chrome mask using a high-definition laser (DWL200, Heidelberg). The negative photoresist 1050 was spin-coated onto 100-mm silicon wafers and patterned using a mask aligner (MA6/BA6, Süss). A PDMS prepolymer and curing agent (Sylgard 184, Dow Corning) mixture was mixed according to the manufacturer's instructions, degassed, poured on the silicon master, and cured at 80 °C for 1 h. The PDMS cast was peeled off the wafer, and the fluidic ports were punched. The device was sealed with a glass coverslip after oxygen plasma treatment and filled with cell culture media (25 mM HEPES, bicarbonate-free RPMI 1640 with 10% FBS, Gibco). Larvae were pipetted into the C-S in bicarbonate-free DMEM buffered with 25 mM HEPES and allowed to spread throughout the network for 10 min before starting flow or imaging.