dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, the cutoff for the restoration of motility was assigned to > 50% and identifies 59 Hits. Data information: For each drug, circles represent individual larvae (n = 4) and red lines show the medians for the Normalized Percent Activation. S5 ) reveals that only five drugs are able to rescue the common parameters regarding NMJ (parameters (1) and (  2 ) in Fig  7 ). These five drugs are divided into the three functional categories described above: two Adrenergic agents (15-Oxymetazoline Hydrochloride, 16-Phentolamine hydrochloride), one CA agent (19-Trichlormethiazide), and two Hits with Unknown target (1-Aceclidine Hydrochloride, 7-Digitoxigenin). Interestingly, the Aceclidine Hydrochloride, previously characterized as Unknown Hits according to the DrugBank database, turns out to be a well-described muscarinic AChR agonist  (Ehlert et al, 1996).
Aceclidine Hydrochloride is a well-known parasympathomimetic that stimulates muscarinic receptor but also potentiates cholinergic activity through its action as cholinesterase inhibitor (AChEI). Thus, it is largely conceivable that the effect of this Hit on the restoration of locomotion is due, directly to the potentiation of ACh effect due to a decrease in the rate of AChE-catalyzed hydrolysis of the neurotransmitter and its increased availability at NMJ enhancing neuromuscular transmission  (Fig 8B). Interestingly, some AChEI are currently used in therapy to ameliorate neuromuscular disorders caused by deficiency in synaptic AChRs, such congenital myasthenic syndromes (CMS;  Finsterer, 2019)  or Myasthenia Gravis (MG;  Maggi & Mantegazza, 2011). The AChEI pyridostigmine is also under investigation (Phase II clinical trial Identifier: NCT02941328) for spinal muscular atrophy  (Stam et al, 2018).
A phase II clinical trial (Identifier: NCT02466074) is ongoing to evaluate how the CAI Acetazolamide affects the way in which newly formed Multiple sclerosis lesions evolve and whether tissue repair is improved. Above all, CAIs are known to regulate acetylcholine receptor endocytosis and have been recently identified as a potential therapeutic approach for myasthenia gravis  (Du et al, 2017). In the literature, some studies demonstrated that CAIs had also effective AChE inhibition properties  (Aslan et al, 2019)  and could be used in the same way as AChEI cited above. Thus, the three different pharmacological families of the identified Hits share a common action in promoting NMJ maintenance at the synaptic compartment. Unveiling a specific role of the NMJ in the pathogenesis of GAN in zebrafish, we suggest to carefully examine its architecture in patients.
Miniaturized locomotion assay Five-day-old treated embryos were redistributed in four 96-well assay plates (Nunclon, Nunc™, ThermoFisher) with a single fish per well, following the same plate design than in the "treatmentplate. " The 64 "reading-plates" were used to carry out the locomotion assay. The spontaneous motility of individual larvae was recorded for 1 h in the dark using the Zebrabox system (Viewpoint Life Sciences, Lyon, France), and the total distance was quantified using the tracking mode of ZebraLab software.
Data analysis (Screen & Rescreen) The observer was blinded to the treatment. First, locomotion data were filtered out from recorded videos to exclude empty wells and wells containing embryos for which movement corresponds to a false-positive or false-negative output. Second, a quantitative scoring of movement was applied to the data using R programming language (R Core Team, 2019) and robustness and reproducibility of the test was evidenced by the calculation of the Z 0 -factor  (Zhang et al, 1999)  using the following equation: Z 0 À factor ¼ 1À 3 σ p þ σ n À Á μ p Àμ n where μ p and σ p are the median and median absolute deviation values of the positive controls (or alternately, the wild-type samples) and μ n and σ n are those of the negative controls (or alternately, the gan samples).
Filter I: System biology analysis To recover the protein targets associated with each drug, we used DrugBank (version 5. 1. 8), a freely available web resource containing detailed drug-target interactions for FDA-approved drugs ([DATABASE] DrugBank;  Wishart et al, 2018). We identified 93 targets for the 59 common Hits. We next seek the functional interactions between these 93 targets in the STRING v. 11  (Szklarczyk et al, 2019)  database, selecting interactions from all sources and keeping only high confidence interaction (score > 0. 7). This created a network of 89 nodes and 427 edges. We integrated and visualized the drug-target and target-target interaction with Cytoscape v. 3. 6  (Shannon, 2003). Target node sizes were scaled according to the number of drugs targeting the node. Filter II: Imaging analysis.
Using the on-the-fly image analysis, a global image of the whole well was created, and the "Find Image Region" module of the Harmony ® High-content analysis software (v4. 9, PerkinElmer) was used to set the appropriate intensity threshold to detect the fish. The "Determine Well Layout" module was then used to define a rescan magnification, of 20× with an overlap of 6% between fields, covering the entire object. Then, a second automated acquisition (Re-Scan) of this specific position was made in confocal mode at 20× magnification (z-stack 90 μm), allowing imaging of the whole larva and a complete image analysis. Well areas containing whole zebrafish larvae were automatically imaged in confocal mode at 20× magnification on green (znp-1) and red (α-bungarotoxin) channels. A z-stack of 90 μm (5 μm interval) was applied, creating an on-the-fly image analysis to obtain a global image with Maximum Intensity Projections (MIP).
From AChR-channel global image, the "Find Image Region" and "Select Region" modules allowed to detect fish body and subtract 7 pixels around to restrict the analysis to the region of interest (Fig 6A ). "Find Image Region" module was then applied on the green channel global image to detect and measure size and area of the Axonal region, that is, spine and axons (Fig  6B ). The "Find spots" module (method D) was applied with a specific intensity threshold to locate AChR clusters on the red channel global image (Fig  6B  and D (1)). For AChR quantification, AChR number, area, and intensity were measured using the "Calculate Position Properties -Cross population" module. The coefficient of co-localization was obtained using the same module in a fraction of the myotome to quantify NMJ  (Fig 6B and D(2) ).
Statistics The statistical significance of the differences between experimental groups was determined by the R programming language (R Core Team, 2019). The assessment of the normality of the distribution of the data was determined with the Shapiro-Wilk test, to apply either a parametric or nonparametric test. We used the Kruskal-Wallis test  (Hollander et al, 2014) , a nonparametric test to compare the difference between experimental groups for analysis of projection lengths, area AChR cluster, NMJ junction and overlap. When differences in variance between groups were significant according to the Kruskal-Wallis test, we performed post hoc tests (i. e. , pairwise comparisons using Mann-Whitney test) to identify the groups for which the differences were significant. For pairwise comparisons, significance values were adjusted with Holm correction for multiple testsv  (Holm, 1979).
Acknowledgements We thank the  Aquatic Model Platform of the CRBM institute (Univ. Montpellier, CNRS, Montpellier)  and  B. Delaval  for the coordination of housing and support,  P. Richard ,  M. Plays  for zebrafish husbandry. We acknowledge the  SFR Biosciences (UMS3444/CNRS, ENS, University of Lyon) zebrafish facility , specifically  L. Bernard  and  R. Renard  for their technical assistance. We are also grateful to the  IGF Institute (Montpellier)  and especially  C. Joplin  and A. Faucherre  for the provision of the Zebrabox. We acknowledge the imaging facility MRI, member of the national infrastructure France-BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 , " Investments for the Future "). We thank  J. L. Bessereau  and  M. Jospin  for discussion on pharmacology and for providing curare.
This work was funded by  l'Institut National de la Sant e et de la Recherche M edicale (INSERM)  to PB, and the  Fondation pour la Recherche M edicale  to LL (FRM, fellowship  PLP20170939065 ). PB is supported by grants from the  ATIP-Avenir program (INSERM) , The  French Muscular Dystrophy Association  ( AFM-T el ethon ) (grant  17654 ), and the  Fondation Maladies Rares (FMR). We are grateful to  H. Barzilai-Tutsch  ( INMG-MeLiS ) for critical reading and comments on the manuscript.
Dataset The synthetic environment is implicitly defined by  { ( 𝑥 𝑦 𝑧 ) 𝑇 ∈ R 3 , 𝑥 2𝑝 + 𝑦 2𝑝 + (𝑧 -𝑥 tan 𝛾) 2𝑝 = 1}, (17) 𝐧 𝛾 = ( -sin 𝛾 0 cos 𝛾 ) 𝑇. ( 18 ) One of these two faces plays the role of a slanted ground and four others are approximately vertical and orthogonal walls. The search space 𝑆 (Eq. (  1 )) meets 𝐯 0 = 𝐧 𝛾 , as if the rough estimate 𝐯 0 of the vertical is computed by PCA of a camera motion whose height is constant (A1). The ground truth of the vertical is (0 0 1) 𝑇. The triangulated surface is defined by a regular sampling of the spherical coordinates (60 longitudes, 30 latitudes plus two poles). Furthermore, we disrupt this surface using a noise 𝛿: add to every coordinate of every vertex an uniformly distributed random value in [-𝛿, +𝛿]. The darker the gray level, the greater the vote.
Dataset (Basalt and City) The 3D model ''Basalt'' is reconstructed from a 360 video taken using a helmet-mounted GoPro Max camera while walking for 27 minutes around a geological-interest site. (See Fig. 4. ) The two sides of the path are composed of basalt prisms that range from 5 m to 20 m in height and that have been eroded and degraded by vegetation and high humidity. The texture is favorable for photogrammetry, except  at a few locations with low light due to narrow canyon (less than 1 m) or where the vegetation is very close to the camera. The path is mostly composed of rocks, that can be slippery and are bordered by small vegetation. Its slope angle is less than 𝜋∕4, except at a few small sections where walking is replaced by climbing with hands or by sliding on the buttocks. This path was thoughtfully selected before the acquisition as it offers a complicated test (a kind of labyrinth with obstacles and dangerous sections) and as the trajectory has to include closed loops for the Structure-from-Motion step to reduce the drift.
Experiments #4: toward limits of our method Here we compare the methods based on transformations of the dataset to make it more difficult to estimate the unoriented vertical direction than in Section 5. 3. 2. Let CityR be a set of 100 sub-models of City with R a real number. A sub-model is the part of City (both triangles and trajectory) inside a sphere whose radius is 𝑅 and whose center is a camera location 𝐥 𝑖. (𝑖 is evenly spread in its range. ) We define several datasets, as follows: City20, City50, City100, Basalt50, Basalt100 and Basalt200. According to Table  4 , for each scene and each method, the rule is: the smaller the radius of a dataset, the greater the degradation of the estimated verticals. Degradation is measured by the angle between a vertical estimated for a sub-model and the vertical estimated by PCA+H1' for a complete model.
Dataset (Bremen and Kapelle) Two point clouds  (Borrmann et al. , 2023) , Bremen and Kapelle, are taken by a scanner (Riegl VZ400) and shown in Fig. 10. Since they have high numbers of points (about 200M) without normals, we first downsample them and then compute normals (by PCA of 6 nearest points) in a similar way to  Liu et al. (2022). Bremen is an urban environment that meets the Atlanta world assumption. It has 1. 8M normals and a vertical of about 𝐠 𝑇 = ( 0 0 1 ). Kapelle is a mixture of natural and man-made environments: a small chapel surrounded by trees, cultures and a town in the background. It has 1. 6M normals and a vertical of about 𝐠 𝑇 = ( 0 1 0 ). In this context, the H1' method is limited to the computations in Section 3. 2 and it takes these normals that share a same vote weight directly as input, as in  Liu et al.
Experiments Table  5  compares the vertical directions estimated by 100 runs of each method applied on the scanner dataset with noisy initialization 𝐯 0. The Bremen30 notation (and likewise Kapelle30 and Kapelle5) means that we experiment on Bremen with a noise such that the angle between 0 and 𝐠 is less than 30 •. For Bremen30, all verticals (except that of PCA2) are similar to 𝐠 with discrepancies below 1 • in most cases. H1' gives some of the best results among methods R-BnB-Urban methods that are purpose-designed for this kind of dataset. For Kapelle, a smallenough noise of 𝐯 0 is needed to obtain discrepancies of about 2 • -3 • (except BnB which fails). Fig. 11  shows the votes of H1' for Bremen30 and Kapelle5 using 𝛼 = 𝜋∕4 and 𝑠 = 100 (𝛽 is unused).
VR for the extended photogrammetric dataset Section 5. 5. 1 summarizes the extended photogrammetric dataset, which includes Basalt and City, and its VR applications (viewers). Section 5. 5. 2 focuses on a 3D model of this dataset that does not meet the assumption A2. The other sections provide informations describing the VR applications, which are useful for people who want to use VR to display photogrammetric reconstructions: common VR headsets  (Section 5. 5. 3) , compromise between rendering quality and framerate (Section 5. 5. 4), Locomotion (Section 5. 5. 5).
Compromise between rendering quality and framerate The PC VR applications render the models in Tables  2  and 6  with the relevant game engine's default setting. In the standalone mode, which has less computational resources, more things are needed to obtain a decent framerate. A decent framerate reduces the risk of VR sickness, and is also required for the Oculus Quest to store a VR application in a database (at least 60 frames per second for the App Lab, as summarized in the third line of Table  7 ). Here we explain how to increase the framerate for a given 3D model. First we enable monoscopic rendering: the two eyes see the same image. This halves the computations needed in comparison to the default stereoscopic rendering and doubles the framerate. If the framerate is still too low, we discard triangles in the far background during the rendering.
Conclusion The paper provides a new method for estimating the vertical direction of a triangulated surface reconstructed by photogrammetry from terrestrial imagery. The method is simple: first compute the unoriented vertical by principal component analysis of the acquisition trajectory followed by a Hough transform with votes of the triangle normals, then obtain the oriented vertical direction and the scale by projecting the trajectory onto the surface with respect to the two possible opposite vertical directions. We report experiments using the method in both man-made and natural environments reconstructed from consumer grade 360 videos with trajectory lengths of several hundred meters. Variants of the Hough transform and previous methods are compared on both synthetic and real datasets. We detail both our method assumptions and limitations.
This paper presents work intended to characterize air flow and convective heat transfers within a ventilated window. This window is a device that allows for the entry of fresh air into a building while simultaneously preheating it in order to satisfy requirements in terms of air quality and thermal comfort in inhabited spaces. Therefore, this essential component of the building envelope functions herein as a heat exchanger with its own geometric characteristics and exchange conditions. In this research, a dual numerical and experimental approach has been implemented in order to highlight the temperatures, velocities and heat flux fields both at the glazing surfaces and in the ventilated air gaps. Several turbulence models were tested using CFD software (ANSYS-FLUENT ® ); their results were compared with each other as well as with the experimental results.
Wei et al. [17]  developed an analogical model to consider two-dimensional heat transfer in a double airflow window system (Figure  1d ), then coupled this model to a building in the "EnergyPlus TM " software. Their model was validated using a test cell with a window ventilated under actual weather conditions before being applied to examine the energy performance of a small apartment installed with double-flow windows in five different climate zones across China. The average energy savings rates resulting from the application of airflow were approximately 25% for cold climates and 21% for warm climates  [18, 19]. More detailed studies have been carried out using computational fluid dynamics (CFD) for mechanically ventilated facades. Raffnsøe  [20]  compared two numerical models simulating a window with airflow through a U-shaped channel: one based on ISO 15,099  [21] , the other CFD-based.
This cutting was developed with the Ansys-Workbench tool. The window configuration was generated by means of the DesignModler component; this module offers several possibilities with respect to not only the creation of the geometry and the prior definition of model boundary conditions but also mesh generation and parameterization. In order to verify the independence of the numerical results, six grid variants with different densities were produced. The mesh density was varied from 46,000 to 338,100 cells, which served to ensure the stability of the digital solution obtained. Generally speaking, the mesh specification depends on the complexity of the model geometry. Figure  5a ,b present the architecture of the mesh generated in the supply air window, as observed by the elongation ratio of the component: a height-to-thickness ratio on the order of 25.
Abstract The Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a e-mail: leigh. howard. whitehead@cern. ch (corresponding author) a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations.
Introduction ProtoDUNE-SP  [1]  was a single-phase (SP) liquid argon time projection chamber (LArTPC) detector prototype for the Deep Underground Neutrino Experiment (DUNE) far detector  [2]. Installation of the detector at the CERN Neutrino Platform was completed in August 2018, and chargedparticle test-beam data were collected from August 2018 until the start of the CERN long shutdown period in December 2018. The primary engineering goal of the ProtoDUNE-SP detector was to prototype the production of large-scale LArT-PCs for use at the DUNE far detector (FD)  [3]. Alongside the validation of production and installation procedures, ProtoDUNE-SP had goals related to testing the event reconstruction and performing detector calibration in a controlled environment. The primary physics goals were measurements of the interaction cross-sections for various charged particle species on a liquid argon target that will be very valuable for modelling neutrino interactions at DUNE.
Pandora is a software package that has been developed for event reconstruction in high energy physics and is now in use at ProtoDUNE-SP  [4]. It consists of a framework, the Pandora Software Development Kit (SDK)  [5] , and a number of experiment-specific content libraries containing patternrecognition logic. Originally developed for event reconstruction at future linear e + e -colliders  [6, 7] , Pandora has since been successfully applied in LArTPC experiments, such as MicroBooNE  [8]. Pandora brings a multi-algorithm philosophy to LArTPC event reconstruction, applying over 100 algorithms to develop the reconstruction from the input hits to a hierarchy of fully-reconstructed particles. Each algorithm is designed to address a specific aspect of event reconstruction, and they collectively provide robust and sophisticated pattern recognition.
Pandora incorporates machine-learning techniques, such as boosted decision trees (BDTs)  [9, 10]  and support vector machines  [11] , to drive decisions made at certain junctions of the event reconstruction. The Pandora event reconstruction can be run standalone and it has also been interfaced with LArSoft  [12] , a common software framework used by the majority of LArTPC experiments. The contents of this paper are as follows: Sect. 2 describes the ProtoDUNE-SP experiment, Sect. 3 describes the Pandora reconstruction, Sect. 4 describes the simulated and experimental data samples, Sect. 5 provides an assessment of the cosmic-ray reconstruction, Sect. 6 examines the performance of the test-beam reconstruction, and Sect. 7 provides concluding remarks.
The Pandora pattern recognition was performed using LArPandoraContent version v03_15_02, which in turn depends on version v03_03_02 of the Pandora SDK. The experimental test-beam data samples considered in this article were collected from August 2018 to December 2018. Due to time constraints, test-beam data were collected only in the positive polarity mode at five different particle momentum settings: 1, 2, 3, 6 and 7 GeV/c. For this reason, only simulated interactions at these same five momentum settings are shown in this article. Both data and simulation events go through signal processing and hit finding stages, as described in Ref. [4]. The events are input into Pandora after the reconstruction of hits from the signals identified on the detector readout wires. The average time taken to reconstruct a full ProtoDUNE-SP event with Pandora using the LArSoft framework is approximately 40 s on an Intel Core Processor (Broadwell) 2.
Reconstruction performance for cosmic-ray data Reconstruction metrics for cosmic-ray muon data have also been evaluated. Figure  15  shows the number of reconstructed particles tagged as distinct cosmic-ray muons per event in ProtoDUNE-SP. For a cosmic-ray muon to be tagged as distinct it must deposit at least 100 hits in the detector. This cut is applied in order to define a substantial, distinct signal in the detector. Furthermore, applying this cut yields a minimum reconstruction efficiency of 90%, based on the simulated efficiencies in Fig. 12 , which ensures this metric gives an accurate reflection of the true number of distinct cosmic-ray muons entering ProtoDUNE-SP. Approximately 5% fewer cosmic-ray muons are reconstructed per event in data than simulation, with the data distribution peaking at 51.
16. The range of this distribution can be predicted by considering the readout time window (-250 µs to 2750 µs) and the time for charge to drift from the cathode to the APAs (2250 µs). The cathode-crossing cosmic-ray muons have t 0 values in the range -2500 µs to 500 µs: the lower value is the start of the readout window minus the drift time, and the upper value is the end of the readout window minus the drift time. For APA-crossing cosmic rays, the t 0 values fall only within the readout window. Thus, the total distribution spans the range -2500 µs < t 0 < 2750 µs. Good agreement is Fig. 16  The reconstructed t 0 distribution in ProtoDUNE-SP for cathode crossing and anode crossing cosmic-ray muons obtained from the Pandora stitching process in data and simulation. The distributions have been area normalised for comparison seen between data and simulation and the distributions fall within the expected time window predicted above.