dataesr/scientific-paragraphs-categorization huggingface dataset:
However, small-to-moderate shifts in the substantive conclusions are not a concern and should just be reported along with the findings and subsequently addressed in the discussion section with respect to learning something about the robustness of results under different prior settings. Note that the original prior settings are not modified during the sensitivity analysis process. Instead, sensitivity analysis results are presented, and they may be used as evidence that priors should be shifted in some way in a future analysis on another dataset. For transparency reasons, it is important to keep the original prior and not change it because of something that was unveiled in the sensitivity analysis. Doing so would be an instance of Bayesian HARKing (hypothesizing after results are known;  Kerr, 1998) , which is just as questionable as frequentist HARKing.
SENSITIVITY ANALYSIS IN ACTION: AN INTERACTIVE APP To illustrate the importance and use of prior sensitivity analysis, we created an interactive application using rstan (Stan Development Team, 2020), Shiny  (Chang et al. , 2020) , and RStudio (R Core Team, 2020; RStudio Team, 2020). The App can be accessed online at https://ucmquantpsych. shinyapps. io/ sensitivityanalysis/. Alternatively, it is available for download on the Open Science Framework 7. To run the App on your personal computer, open the ui. R and server. R files in RStudio and press the "Run App" link in the top-righth and corner of the R Script section of the RStudio window. For more information about Shiny Apps, we refer to RStudio Team (2020). Our App consists of seven different tabs, with each containing information that will help a user understand how to assess the substantive impact of prior selection.
Using confocal microscopy, the study consists of analysis of dendritic spines together with comparative analysis of soma areas and axonal projections between co-transplanted CTR and ASD cells from a male patient with a frameshift mutation in SHANK3 referred as L1142Vfs*153 located in the proline-rich domain of SHANK3 protein 1. (c) Primary dendrite segment of human pyramidal neuron at 50 days post-transplantation with corresponding 3D reconstruction. Scale bar = 4 μm. Dendrite segment (green color) are endowed with four categories of spines: Filopodia (pink color), Long Thin (blue color), Stubby (red color) and Mushroom (green color). (d) Comparative quantitative analysis of spine morphological parameters using the Imaris Software as described in Materials and Methods. In vitro data are from our previous study 1 of pyramidal neurons kept for 45-50 days in culture.
A higher dendritic spine straightness may result from increased actin dynamics and developmental plasticity within a 3D Figure  2. Integration of human iPSC-derived neurons into mouse brain ventricles and maturation pattern of axonal projections. (a) Camera lucida drawings of the pattern of axonal projections 50 days after transplantation within mouse ventricles. Abbreviations are according to Allen Brain Atlas (https ://mouse. brain -map. org/). Image credit: Allen Institute. Neuron location, migration and densities are depicted using a code-color schematic representation. Results are from two independent mice, using 72 brain slices of 50 μm each. The illustrated data are from one representative brain. (b) Visualization of GFP-positive cells with their cell bodies, dendrites and axons (green color) within the ipsilateral ventricle.
We have also reported a strong vascularization of transplants from NPC by two-photon imaging  23. This suggests that vascularization may have a strong protective effect as well. We also observed a decrease in the densities of cortical axonal projections from ASD neurons as compared to CTR neurons, as illustrated for the infra-limbic and piriform cortical areas. Our data are in agreement with Figure  3. Integration of Human iPSC-derived neurons into mouse brain frontal cortex and maturation pattern of axonal projections. (a) Camera lucida drawings of the pattern of axonal projections 50 days after transplantation within mouse brain frontal cortex. Abbreviations are according to Allen Brain Atlas (https :// mouse. brain -map. org/). Image credit: Allen Institute. Neuron location, migration and densities are depicted using a code-color schematic representation.
1. Lentiviral vectors were prepared according to a published protocol  36. The map of the CMV vector is detailed in Fig. 4a. NPC were transduced with 99. 5 ng of lentivirus in a 6-well culture plate filled with 500 µl of fresh culture medium for 3 h. The volume was completed to 2 ml and incubated for the next 48 h, and then rinsed with culture medium. For co-transplantation, transduced NPC were initially purified by FACS in order to inject equivalent levels of green or red fluorescence intensities within cells. Labeled NPC were amplified using the same culture medium Comparative fluorescence proportions of mCherry and GFP labeled CTR cells among several graft sub areas (the number of values is indicated in the graph). Data are presented using a Box and whiskers graph with plotted "min" and "max" and median values.
Mean values are also indicated as "+". Statistical analysis was performed using a Wilcoxon signed rank test to compare medians to the theorical value "50". p values are indicated in the graph. Right: Graphic camembert illustrating the percentages of mCherry-labeled control cells and of GFPlabeled control cells. Similar data were obtained between 2 transplants from 30-day old mice. (d, e) confocal images of isolated human pyramidal neurons after transplantation of CTR (green) and ASD (red) NPC from 30-day old mice. (f) Representative axonal projections of human GFP-labeled CTR neurons and mCherrylabeled ASD neurons in the piriform cortex (left panel) and in the infralimbic cortex (right panel) at 30 days after transplantation. Image credit: Allen Institute. Scale bar = 50 μm. ◂ described above supplemented with brain-derived neurotrophic factor (20 × 10 -3 μg/ml), epidermal growth factor (10 × 10 -3 μg/ml) and fibroblast growth factor (10 × 10 -3 μg/ml), then aliquoted in cryotubes using FBS with DMSO (10%) and frozen in liquid nitrogen.
An additional incubation with DAPI diluted in DPBS was performed for 10 min at room temperature. image quantitative analysis. We have previously reported a stepwise protocol for imaging of dendritic spines after immunofluorescence in a video journal  37. Briefly, confocal imaging was performed using a confocal laser-scanning microscope with a 40X oil NA = 1. 3 objective, a 488 nm laser for GFP excitation, and a 555 nm laser for mCherry excitation. A Z spacing ranging from 150 to 300 nm was used for Z-stack acquisitions. A semiautomatic tracing of dendrites and automated segmentation of spines was performed using the Filament Tracer module of Imaris 9. 5 software (Bitplane AG, Zürich). Spine categories were defined by their morphology as follows: Stubby: length < 1 μm; Mushroom: Length (spine) < 3 μm and Max width (head) > mean width (neck) × 2; Long thin: Mean width (head) ≥ Mean width (neck); Filopodia-like: length ≤ 4 μm (no head).
Statistics. Statistical analysis was performed using GraphPad Prism Version 6 software (GraphPad, San Diego, California, USA). Statistical analyses are reported in each figure legend. 95% confidence levels have been used. Dendrite segments are endowed with four categories of spines classified as: Filopodia (pink color), Long Thin (blue color), Stubby (red color) and Mushroom (green color). Number of neuronal dendrites is indicated in the graph. Data are presented using a Box and whiskers graph with plotted "min" and "max" and median values. Mean values are also indicated as "+". Statistical analysis was performed using a Mann-Whitney test. For the measurement of spine deviation angle, the statistical analysis was performed using a Wilcoxon signed rank test to compare medians to the theorical value "90".
Figure2. Integration of human iPSC-derived neurons into mouse brain ventricles and maturation pattern of axonal projections. (a) Camera lucida drawings of the pattern of axonal projections 50 days after transplantation within mouse ventricles. Abbreviations are according to Allen Brain Atlas (https ://mouse. brain -map. org/). Image credit: Allen Institute. Neuron location, migration and densities are depicted using a code-color schematic representation. Results are from two independent mice, using 72 brain slices of 50 μm each. The illustrated data are from one representative brain. (b) Visualization of GFP-positive cells with their cell bodies, dendrites and axons (green color) within the ipsilateral ventricle. Structures from the olfactory system (Bregma 2. 445 mm; 0. 145 mm), lateral septal nucleus (Bregma 0.
Figure3. Integration of Human iPSC-derived neurons into mouse brain frontal cortex and maturation pattern of axonal projections. (a) Camera lucida drawings of the pattern of axonal projections 50 days after transplantation within mouse brain frontal cortex. Abbreviations are according to Allen Brain Atlas (https :// mouse. brain -map. org/). Image credit: Allen Institute. Neuron location, migration and densities are depicted using a code-color schematic representation. Results are from two independent mice, using 65 brain slices of 50 μm each. The illustrated data are from one representative brain. (b) Visualization of GFP-positive cells with their cell bodies, dendrites and axons (green color) within the ipsilateral regions. Axons are found mostly in cortical areas: motor and somatosensory cortical layers, anterior cingulate area (Bregma 1.
p values are indicated in the graph. Right: Graphic camembert illustrating the percentages of mCherry-labeled control cells and of GFPlabeled control cells. Similar data were obtained between 2 transplants from 30-day old mice. (d, e) confocal images of isolated human pyramidal neurons after transplantation of CTR (green) and ASD (red) NPC from 30-day old mice. (f) Representative axonal projections of human GFP-labeled CTR neurons and mCherrylabeled ASD neurons in the piriform cortex (left panel) and in the infralimbic cortex (right panel) at 30 days after transplantation. Image credit: Allen Institute. Scale bar = 50 μm.
Acknowledgements The authors are thankful to the  Clinical Investigation Center of Robert Debré hospital  for assistance with patient recruitment and to  Dr. Alexandra Benchoua  who performed the initial iPSC reprogramming. The  French Ministry of Education  and the  LabEx BioPsy  provided the funding for AV's PhD. Other funding for this study were provided by grants from the  French National Research Agency ANR  ( ANR-13-SAMA-0006 ; SynDivAutism), the  Bettencourt-Schueller Foundation , the  Cognacq Jay Foundation , the  Fondamental Foundation  and the  Fondation pour la Recherche Médicale  " FRM Equipe 2019 ". We gratefully acknowledge the  UtechS Photonic Bioimaging (Imagopole) ,  C2RT ,  Institut Pasteur , supported by the  French National Research Agency (France BioImaging ;  ANR-10-INSB-04 ; Investments for the future).
The strategy adopted for this TDR has been to hold the primary elements of the program to the highest standard of rigor, involving direct analysis of fully simulated data, using actual event reconstruction codes and analysis tools that could be applied to real data from DUNE FD modules. For other elements of the program, sensitivities use realistic beam and physics simulations, but employ parametric detector response models in place of full reconstruction. The implementation of this strategy comes with caveats and clarifications that are discussed in the corresponding chapters of Volume II. We mention some of these here. • In the case of the long-baseline oscillation physics program, this approach requires a combination of the full end-to-end analysis of simulated FD data with the concurrent analysis of simulated data from ND systems to capture in a realistic way the level of control over systematic errors.
CPV in the neutrino sector and precise oscillation parameter measurements The key strength of the DUNE design concept is its ability to robustly measure the oscillation patterns of ν µ and νµ over a range of energies spanning the first and second oscillation maxima. This is accomplished by a coordinated analysis of the reconstructed ν µ , νµ , ν e , and νe energy spectra in near and far detectors, incorporating data collected with forward (neutrino-dominated) and reverse (antineutrino-dominated) horn current polarities. The statistical power of DUNE relative to the current generation of long-baseline oscillation experiments is a result of many factors including (1) on-axis operations, (2) the LBNF beam power, (3) long-baseline and correspondingly high energy oscillation maxima and strong separation of normal and inverted neutrino mass ordering scenarios, (4) detector mass, and (5) event reconstruction and selection capabilities.
Searches for Beyond-Standard-Model physics The unique combination of the high-intensity LBNF neutrino beam with DUNE's ND and massive LArTPC FD modules at a 1300 km baseline enables a variety of probes of BSM physics, either novel or with unprecedented sensitivity. As examples of the potential impact of DUNE, we present results from the analysis of simulated data sets for two BSM scenarios, one with a sterile neutrino species participating in oscillations, and the other with anomalous "neutrino trident" events. From the sterile neutrino analysis, the DUNE sensitivities to the effective mixing angle θ µe (which depends on new mixing angles θ 14 and θ 24 ), from the appearance and disappearance samples at the ND and FD are shown in figure  2. 16. Considering a neutrino trident analysis in ND data, existing constraints and projected sensitivity to parameters of a Z boson resulting from the gauging of the difference between muon and tau lepton numbers, L µ -L τ , are presented in figure  2.
Detecting a SNB poses different challenges: those of dealing with a high data rate and maintaining the high detector up-time required to ensure we do not miss one of these rare events. The signature of an SNB is a collection of MeV-energy electron tracks a few centimeters in length from CC ν e interactions, spread over the entire detector volume. To fully reconstruct an SNB, the entire detector must be read out, a data-rate of up to 2 TB/s, for 30 s to 100 s, including a ∼ 4 s pre-trigger window. A large volume of LAr is subjected to a strong E field of a few hundred volts per centimeter. Charged particles passing through the detector ionize the argon atoms, and the ionization electrons drift in the E field to the anode wall (called an APA array) on a timescale of milliseconds. The SP module APAs consist of layers of active wires strung at angles to each other to form a grid.
Finally, data from the ProtoDUNE detectors (section 1. 3. 2) will be invaluable in understanding the response and particle ID (PID) capabilities of the FD. Over time, the FD calibration program will evolve as statistics from cosmic rays and the LBNF beam amass and add to the information gained from the calibration hardware systems. These many calibration tools will work alongside the detector monitoring system, the computational fluid dynamics (CFD) models of the argon flow, and ProtoDUNE data to give us a detailed understanding of the FD response across the DUNE physics program.
Data acquisition The DAQ systems for the SP and DP technologies have been designed jointly and are identical except for the architecture of the detector readout electronics. The output format of the generated data is common, and both are synchronized to the same global clock signals. The DAQ architecture is based on the Front-End Link eXchange (FELIX) system designed at CERN and used for the LHC experiments. The DAQ is divided between an upstream section, located underground in the CUC, and a downstream DAQ back-end subsystem (DAQ BE) to be located above ground at the SURF. All trigger decisions are made upstream, and the data is buffered underground until the DAQ BE indicates it is ready to receive data; this controls the rate of data flowing to the surface. An end-goal of the DAQ is to achieve a data rate to tape of no more than 30 PB/year.
For the SP module, the 150 APAs are processed by 75 DAQ readout units (DAQ RUs); each DAQ RU contains one FELIX board. The PDs from the module will have a lower data rate because the PD electronics, unlike the TPC electronics, perform zero-suppression; therefore, the PDs of a module will be processed by six to eight additional DAQ RUs. The DAQ can be partitioned: it will be possible to run multiple instances of the DAQ simultaneously, so most of the detector can be taking physics data while other DAQ instances are doing test runs for development or special runs such as calibration runs. Two basic triggers will be operating. Beam, cosmic, and nucleon decay events will be triggered using the localized high-energy trigger that will open a readout window of 5. 4 ms, enough to read out the full TPC drift around an event.
CFD simulations help identify expected temperature, impurity, and velocity flow distributions and guide the placement and distribution of instrumentation devices inside the cryostat. The slow controls portion of CISC consists of three main components: hardware, infrastructure, and software. The slow controls hardware and infrastructure comprises networking hardware, signal processing hardware, computing hardware, and associated rack infrastructure. The slow controls software provides, for every slow control quantity, the central slow controls processing architecture, databases, alarms, archiving, and control room displays.
Computing consortium 6. 2. 1 Overview The mission of the DUNE CSC is to acquire, process, and analyze both detector data and simulations for the collaboration. This mission must extend over all the primary physics drivers for the experiment and must do so both cost effectively and securely. The CSC provides the bridge between the online systems for DAQ and monitoring and the physics groups who develop high-level algorithms and analysis techniques to perform measurements using the DUNE data and simulations. The CSC works with collaborating institutions to identify and provide computational and storage resources. It provides the software and computing infrastructure in the form of analysis frameworks, data catalogs, data transport systems, database infrastructure, code distribution mechanisms, production systems, and other support services essential for recording and analyzing the data and simulations.
Lessons learned The first ProtoDUNE-SP run has given us very valuable information for planning the full DUNE computing model. • Data and simulation challenges led to a reasonably mature and robust model for acquiring, storing, and cataloging the main data stream at design rates. • The experiment integrated several existing grid sites and used substantial opportunistic resources. This allowed initial processing of data within one month of the end of the run. • Prototype infrastructure was in place for provisioning, authentication and authorization, data management, networking, file catalog, and workflow management. • Reconstruction algorithms were available, permitting immediate studies of detector performance and calibration. • Beam information was successfully integrated into processing through an IFbeam database.
Acknowledgments This document was prepared by the DUNE collaboration using the resources of the  Fermi National Accelerator Laboratory (Fermilab) , a  U. S. Department of Energy, Office of Science, HEP User Facility. Fermilab  is managed by  Fermi Research Alliance, LLC (FRA) , acting under Contract No. DE-AC02-07CH11359. The DUNE collaboration also acknowledges the international, national, and regional funding agencies supporting the institutions who have contributed to completing this Technical Design Report.
, et al. Two cables (Shimano Blue Wing TM , 3. 2 kg, 0. 2 mm, k s = 140. 18 N) per actuator, instead of one, make the constraints on the pulley shaft more even, minimizing the frictions on the plastic sleeve bearing of the actuators. Each pair of cables is modeled as a single cable of specific stiffness k s = 280. 36 N. The robot controller, like the structure, is designed with a rapid prototyping approach for highly modular robotics applications. RPIt 1 is a Simulink toolbox, developed in our laboratory, that uses the code generation feature of Simulink Coder TM on a Raspberry Pi target. This toolbox provides all Simulink blocks needed for the experiment: multiple EV3 blocks, various IMU blocks,. A custom firmware has been developed for the EV3 brick called EV3. 14 2. It improves encoder resolution from 1 to 0.
Specimens were exposed to UV light (365 nm) for 30 s prior to mechanical testing (this is how the "sponge" is filled prior to testing) and the loading cycle started 10 min later, that is when the phosphorescence background emission became negligible in comparison with the EML intensity. The DFT calculations were performed using the VASP code (Vienna ab-initio Simulation Package; www. vasp. at)  13, 14 , considering the ambient pressure unit cell of the P2 1 space group SrAl 2 O 4 crystal, with a = 8. 447 Å, b = 8. 816 Å, c = 5. 163 Å and β = 93. 42°1  5. The VASP code allows the determination of the ground state properties for a given compound based on DFT calculation and is based on a plane wave based Projector Augmented Wave (PAW) approach. The semi-local Perdew-Burke-Ernzerhof (PBE) functional  16  on a (2a,2b,3c) supercell containing 336 atoms was used to identify the defect energy levels responsible for the detrapping process under mechanical loading and unloading.
A hereditary mechanical power (P m (t)) is calculated at time t from the whole loading history. The EML intensity is proportional to P m (t) and to the fraction of remaining trapped e -: I EML ∝ P m (t) (N 0 te -N de (t))/N 0 te , where N 0 te is the initial number of trapped e -(following UV irradiation) and N de (t) is the number of detrapped e -at time t (α stands for proportional). This latter equation can be readily integrated and the result is expressed as: I EML = C 1 -exp -C ′ t 0 P m (t ′ )dt ′ , where C and C' are positive constants. The dashed lines in Fig. 1  show the result of the modeling when the parameters of the Zener model are optimized to fit the experimental data using the Microsoft excel solver tool, and assuming that the isostatic part, σ h , of the stress tensor ∼ σ gives no contribution to the light emission peak occurring on unloading, which is hence solely governed by the deviatoric part, ∼ s , and by the shear characteristics.
ChMics and adherent FITC-labelled H. pylori were visualized by confocal laser scanning microscopy (CLSM, Leica SP5, Leica Microsystems) with excitation at 561 nm and 488 nm, respectively. For each microsphere, stacks from the whole microsphere were performed with a step size of 2. 98 µm, and images were afterward superimposed using the Leica Application Suite Advanced Fluorescence (LAS AF) software.
Statistical Analysis Statistical analysis was performed using SPSS  statistical software program (SPSS  24. 0, Microsoft, USA). In the in vitro cellular experiments, Kruskal-Wallis was used to verify statistical significance between groups (p < 0. 05). In the in vivo experiments, Mann-Whitney U-test was used to confirm that there were no significant differences between control groups (Hp+) of the two experimental setups (p > 0. 05), allowing groups Hp+, Hp+XL16, Hp+XL6 and Hp+XS16 of experimental setups 1 and 2 to be analyzed together. Nonparametric Kruskal-Wallis was used to confirm statistically significant differences between groups (p = 0. 011) and Mann-Whitney Utest with Bonferroni correction (p < 0. 0083) was used to evaluate differences between these groups. Comparison between Hp+ and Hp+XS6 groups was performed using the Mann-Whitney U-test (p < 0.