dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the embryos were carefully mounted whole in 80% glycerol on a glass slide and imaged using a confocal microscope (Andor Dragonfly). HCR image processing and quantification. We utilized the HCR images to quantify the proportion of presumptive NMPs based on the co-expression of sox2 and tbxta mRNA transcripts. The images were first manually cropped to isolate the region of interest. Next, we denoised the images using Aydin's auto-tuned Butterworth filter. Aydin 60 is a powerful and user-friendly image-denoising tool that can significantly improve the image quality of microscopy datasets (http://aydin. app). Images were then downscaled along the z-axis by a factor of two to obtain isotropic images. The cells were then segmented using the DAPI channel in two steps. Firstly, we detected cells against the background, and secondly, we split the detected regions into individual cell instances.
Each library was normalized to 4 nM and pooled for each NovaSeq sequencing run. The pools were sequenced with Novaseq run kits, using either single or dual index plates (Illumina 20028313), with a 1% PhiX control library spiked in. Sequencing was performed on the NovaSeq 6000 Sequencing System (Illumina). Read alignment and single-ell filtering. After sequencing, the obtained sequences were de-multiplexed using bcl2fastq (v2. 20. 0. 422). To align the reads to the reference genome, a reference genome was first created using CellRanger v. 4. 0. 0. Subsequently, the CellRanger v. 5. 0. 1 software was used for read alignment to the reference zebrafish genome. Filtering and clustering of single-cell RNA sequencing data. To filter out low-quality cells, we performed quality control by removing cells with counts below 10,000 or with a fraction of mitochondrial genes greater than 5%.
Integration of multiple time points. To create a comprehensive UMAP embedding that included all time points, we employed the integration pipeline from Seurat 63 (v3, see satijalab. org/seurat/), a software tool used for single-cell genomics. Typically, data integration pipelines are utilized to rectify batch effects, which can lead to an over-correction of actual biological differences. However, we anticipate that some differences between time points would correspond to genuine biological changes during development. To prevent overcorrection, we utilized the integration pipeline with reciprocal PCA. This procedure generated a PCA projection of all cells from all time points. Finally, we used the first 30 principal components of the integrated PCA space to produce 2-dimensional and 3-dimensional projections using the UMAP algorithm (as depicted in Fig.
Clusters annotation. To ensure a comprehensive representation of cellular diversity, we chose a higher clustering resolution than is typically required. We identified cell types based on the expression of cluster-specific enriched markers obtained from literature search and ZFIN database queries (see zfin. org). For each developmental stage, individual UMAPs were annotated using a primary zebrafish expert and then verified the annotations independently with a second expert. A third expert used zebrafish ontology terms from the database to homogenize cell-type naming across all time points. For the integrated UMAP centered around the NMPs population, we reannotated cell clusters following the same procedure. To aid interpretation, we also used coarser annotations of broader cell types, which are summarized in Supp.
Fig. 8a, scatterplot ). This null model analysis allowed us to assess the significance of observed differences in gene expression between individual embryos. RNA velocity. RNA velocity is a method that allows for the estimation of the rate and direction of changes in gene expression for individual cells. To perform RNA velocity analysis, we used scVelo (v0. 2. 5, scvelo. readthedocs. io). We started with the CellRanger alignment and identified spliced and unspliced variants for all genes using Velocyto 37 (v0. 17, see velocyto. org). Next, using scVelo's dynamic modeling approach  64  , we computed the RNA velocity for each gene and cell. This lets us estimate the rate and direction of changes in gene expression for each cell in our dataset. Finally, we projected the average velocity vectors as a stream plot onto the integrated UMAP to visualize the changes in gene expression over time.
RNA velocity quantification along the mesodermal-neural axis. We used the velocity graph as previously described. For each NMP cell, we computed the graph's weighted sum of outcoming velocity magnitudes. Finally, to distinguish between the neuronal and mesodermal lineages, we defined velocity magnitudes toward neurons as "negative", and those towards mesoderm as "positive", and plotted the distribution of velocity magnitudes per time point for all NMPs (Fig 4i ). Pseudo-time trajectories. Based on the integrated embedding (Fig. 4a ), we found potential developmental trajectories for NMPs using Slingshot 48 (see github. com/kstreet13/slingshot). Briefly, Slingshot fits a principal curve in a high-dimensional space such that single cells map to a one-dimensional projection called pseudo-time.
As input for Slingshot, we used a 3-dimensional UMAP projection of the data and defined the root as the cluster with the highest number of NMPs. In agreement with our results from RNA velocity, Slingshot identified two main trajectories, one towards mesodermal cell types and another towards neuronal cell types. We verified that these two trajectories were composed of the expected cell populations with the corresponding differentiation markers (Fig. 3j, k ). As a control, we checked the correspondence of pseudotime to real-time and verified that the most differentiated cells present at the end of the trajectories came from the latest timepoint (24 hpf, see Fig. 3l ). Finally, to find differential gene expression across pseudo time, we used tradeSeq  65  (see github. com/statOmics/tradeSeq) and tested for differences between the neuronal and mesodermal lineages.
Aligned UMAPs of individual time points. The stochastic nature of the UMAP algorithm leads to variations in the orientation, scale, and layout of UMAP projections. This impedes the comparison of datasets, such as developmental stages (e. g. , Fig. 3h ), as it is challenging to distinguish between (i) genuine differences in the data, and (ii) differences caused by algorithmic stochasticity. To address this issue, we employed the aligned UMAP algorithm, which aims to align projections by utilizing pairwise anchors between different datasets. For each point (cell), the nearest neighbors in the previous and next developmental stages were used as anchors. To prevent over-alignment artifacts, we relaxed the strength of the alignment regularization parameter to 0. 001. The outcome is a series of UMAP projections, each corresponding to a time point, that exhibit conserved relative orientation and proportions with one another (Fig.
Adaptive multi-view multi-color light-sheet imaging and processing. The open-source repository at github. com/royerlab/opensimview provides all information for building a 2 detection and 2 illumination simultaneous adaptive multi-view multi-color microscope  29, 30. We provide all blueprints, a parts list, the control software, and image processing tools. Once acquired the dataset consists of multiple views which were fused, deconvolved, and stabilized using DEXP 66 (see github. com/royerlab/dexp). DEXP is an image processing and visualization library based on napari, CuPy, Zarr, and DASK designed for managing and processing light-sheet microscopy datasets. It consists of light-sheet imaging specialized image processing functions (equalization, denoising, dehazing, registration, fusion, stabilization, deskewing, deconvolution), visualization functions (napari-based viewing, 2D/3D rendering, video compositing and resizing, mp4 generation), as well as dataset management functions (copy, crop, concatenation, tiff conversion).
Photo-conversion experiments. For the photo-conversion, we used a python-based custom-made software (see photo-conversion arm in github. com/royerlab/opensimview), that allows the users to select a region of interest and photoconvert this area by controlling a two-axis galvo mirror actuator. We selected the embryo area to photoconvert based on our HCR mapping of the presumptive NMP territory (Supp. Fig. 12 ). Photoconversion was achieved using a 405 nm laser. Time and laser power were manually accessed based on the sample fluorophore state transition evaluation. Photo-conversion image analysis and quantification. The estimation of the proportion of neural and mesodermal nuclei on the photo-manipulated light-sheet data requires three steps: segmentation of the nuclei; selection of which nuclei were photomanipulated; nuclei label assignment to neural or mesodermal tissue.
For each time point, we use two orthogonal oblique light sheets to improve coverage. Images were processed using DEXP 66 to (i) equalize brightness between views, (ii) remove background, (iii) correct for distortions between views, and (iv) temporally stabilize the timelapse to remove artifacts caused by sample movement or vibrations. Instructions to build and operate this instrument can be found are: github. com/royerlab/daxi Sample preparation for single objective light-sheet microscopy (DaXi). Embryos are first dechorionated in a glass-bottom cell culture dish (35mm Cell Culture Dish with Glass Bottom 20mm -Stellar Scientific) and then gently transferred into a 0. 7% solution of low-gelling-temperature agarose (Sigma, A0701). Embryos are then positioned at the correct position and angle for imaging using a custom-made capillary.
For the local movement estimation, we use radial neighborhood regression  71. For each time point, we fit a regression model that predicts the positions of cells in subsequent frames. The training data for these models are pairs of adjacent same-track coordinates at the time point of interest. Track endpoints (splits) are connected to the nearest starting point on the next frame. With these regression models, one for each time point, we can evaluate the movement of any cell at any location if they fall within the radius of an existing track. The tool is available at github. com/royerlab/in-silico-fate-mapping, and lets users predict the cells' fate by estimating their movement over time given a userselected starting point. This is analogous to a photo-manipulation experiment but in silico.
The DM method is ideal for quantifying morphogenesis as it reveals the intrinsic geometric feature of spatiotemporal trajectories and is robust to noise. For more details explanation see the Supp. Theory Note. 3D visualization & rendering. Visualization, interaction, and figuremaking of the available data were done using napari 52 (see napari. org), particularly the napari-animation plugin (github. com/napari/napari-animation). We also used rendering routines in DEXP, some of which use the Spimagine (see github. com/maweigert/spimagine) volume rendering code. Additionally, the light-sheet datasets were stored using zarr  73  to enable out-of-memory loading. 3D RNA velocity visualizations were rendered using the Houdini software. The sample chamber is designed to hold the sample (fish embryo) immersed in water and provide water-tight ports for two illumination and two detection objectives (allowing the objectives to move in 3 dimensions) aimed at the sample, and a watertight connection to the combined XYZ and rotation (XYZR) stage that moves the sample with those four degrees of freedom.
ACKNOWLEDGEMENTS We thank the whole  Data Science and IT  teams of the  Chan Zuckerberg Biohub  for helpful discussion and support. We are grateful for the support, feedback, and proofreading received from  S. Schmid ,  J. DeRisi , and  S. Quake. We thank  P. Keller  for the critical reading of the manuscript and for help with our variant of the SiMView microscope design. We thank  C. Guillot ,  L. Mahadevan , and  S. Darmanis  for initial discussions. We thank  J. Weissman  for his support and encouragement in pursuing zebrafish as the lab's model organism. We thank  A. York  and  Calico Life Science LLC  for developing and loaning the AMS-AGY V2. 0 objective. We also would like to thank  J. Maitin-Shepard  and  V. Jain  for their generous help with Neuroglancer. Funding for this work was provided by  Chan Zuckerberg Biohub  San Francisco (CZB SF).
Sample and setting In exploring our propositions, this study used a survey to collect data from managers working in SMEs and large manufacturing firms actively engaging in NPD activities. We began by assessing the content validity of a preliminary version of the questionnaire with managers from firms of various sizes. We then revised and administered it to a sample of NPD project managers in manufacturing firms who have managing roles and authority over the NPD process in their organisations. To enhance the reliability of our analysis, we incorporated two pre-screening questions into the survey. First, we inquired about the number of projects the participants were concurrently involved in, excluding those engaged in multiple projects to minimise instances of simultaneous team memberships (O'  Leary et al.
High technological turbulence indicates a fast-evolving technological landscape, necessitating organisations to be innovative, adaptable, and forward-looking  (Dayan and Elbanna, 2011). To estimate the underlying latent constructs representing the survey items, we relied on the statistical doctrine of congeneric approaches, which contend the increase in accuracy estimation and representativity of latent constructs  (McNeish and Wolf, 2020). In doing so, we employed the CLC Estimator software  (Marzi et al. , 2023a). Maximum likelihood estimation method has been selected for the weights imputation. The table below (Table  2 ) shows the full list of items, reliability and AVE. McDonald's Omega was always above 0. 70 and met the minimum requirement, confirming the internal consistency and reliability of the measures  (Groves et al.
Conclusions and limitations This study presents an empirical investigation into the factors that influence NPD team learning. Specifically, by analysing both cases of project failures and project successes, this study highlights the relevance of the project, interpersonal team, and environmental characteristics in shaping NPD team learning outcomes. Our data suggest that failure and success lead to different pathways towards NPD team learning. Despite its contributions, this study has a number of limitations that need to be considered. One limitation is that it was conducted in the manufacturing industry, with a focus on SMEs, and therefore the generalizability of the findings to other industries may be limited. Selfreported data were used in the study, which may introduce bias and affect the validity of the results.
Acknowledgments We wish to express our sincere gratitude to the Editor-in-Chief  Wim Vanhaverbeke  for his guidance and invaluable support. We also extend our thanks to the handling editor for managing the review process with utmost professionalism. We deeply appreciate the anonymous reviewers for their insightful critiques, and for dedicating their time and technical expertise. Special thanks also go to the participants of the  R&D Management  and IPDMC conferences. Their constructive feedback on earlier versions of the study has been key in refining our research.
Observations and Data Reduction Simultaneous MIRI imaging in the F560W, F770W, and F1000W filters was obtained to improve the astrometric accuracy of the MRS data. These observations were taken using the SLOWR1 readout pattern (to reduce data volume), with 10 groups and three integrations, using the same dither pattern as the MRS data. To reduce our MRS data we used a development version of the JWST Calibration Pipeline  (Bushouse et al. 2023 ) retrieved on 2023 May 23, 30  with versions 11. 17. 0 and "jwst_1090. pmap" of the Calibration Reference Data System (CRDS) and CRDS context, respectively, and development versions of the FLT-5 MRS calibration files that include an improved photometric calibration derived from the Cycle 1 calibration program. We ran all raw data files through the Detector1-Pipeline to convert the ramp files into rate images.
Unfortunately, without dedicated background observations, this residual cannot be removed without risking the subtraction of real signal from SN1987A. The channel 4 cubes suffer from an increased signal toward the right-hand edge in the cube frame. This likely results from a more uncertain flux calibration near the slice edges. We omitted these regions from our analysis. Two final aspects of our data to contend with were the degradation in spatial resolution and increasing background from band 1A through 4C, both of which are evident in Figure  1. The former presents challenges in creating representative full-band MRS spectra from small regions of the SN 1987A system. These are discussed in more detail below. Since we had no dedicated background pointing, we were required to use regions within the sub-band FOVs to create a background that could be subtracted from our spectra.
All regions are shown in Figure  2. Spectral regions were defined using SAO Image DS9  (Joye & Mandel 2003)  and extracted using the aperture module in the Astropy Photutils package 33   (Bradley et al. 2022b ) by parsing the regions using the Astropy Regions package 34   (Bradley et al. 2022a ). We applied an additional post-pipeline residual fringe correction to our spectra to account for the high-frequency fringing in channels 3 and 4, thought to originate in the MIRI/MRS dichroics, which are not effectively removed in the pipeline. This correction is now included in the JWST Calibration Pipeline package under the extract_1d step. Finally, to produce the total, full-band MRS spectrum of SN 1987A we scaled the MRS sub-band fluxes for minor offsets by determining the median flux difference in the overlapping wavelength regions, starting from the band 1A.
Emission Line Spectral Properties To investigate the emission line properties of the ER and its adjacent regions, we analyzed the spectrum corresponding to the whole ring, shown in Figure  4 , as well as the spectra extracted for the four cardinal point sub-regions of the ER that are shown in the lower part of Figure  2. Emission line radial velocities, full-width half-maximum (FWHM) values, and integrated line fluxes were measured using the Gaussian emission line fitting (ELF) routines written by P. J. Storey within the DIPSO spectral analysis package  (Howarth et al. 2014). The results are presented in Table  2  for the whole-ER spectrum and in Table  3  for the four cardinal point spectra. The solid angle corresponding to each spectral extraction is listed in the table headers. Sixteen emission line detections are listed in each table.
9 ± 0. 7 km s -1 between the NE and NW positions, with the velocity of the position north of the ER measured to be in between those of the other two. We interpret these results as not providing strong support for a very large expanding ring interpretation. [ We have identified spectral features of both small and large spatial scales by manually searching through the spectral axis MRS sub-band datacube using small and large apertures at varying locations around the system. We then identified the emitting species associated with each feature before slicing a sub-cube around the feature using the Spectral-Cube 35  Astropy affiliated package  (Ginsburg et al. 2014). We carefully fitted and subtracted the continuum from each of the sub-cube spaxels using regions away from the spectral feature, and accounting for the variation in sampling and residual fringing.
Grains formed this phase are primarily oxygen-rich, due to CNO processes in the core resulting in low carbon abundances, with any remaining C atoms locked up in CO molecules. The additional benefit of our model is that the cooler temperature we obtain for our hot dust component can be reached by collisional heating under thermal equilibrium, thus eliminating the need for the more problematic stochastic heating which very small amorphous hydrocarbons from previous fits would require  (Dwek et al. 2010; Arendt et al. 2016). While beyond the scope of the present study, detailed modeling of the various components of the SN 1987A MRS spectra with laboratory data is an important step toward understanding the evolution of dust in SNe. Using Dorschner et al. 's (1995)  optical constants for amorphous silicates which  Jones et al.
There is, however, no indication in the MRS data of a warm dust component from the ejecta nor a re-formed dust component or substantial grain growth in the ring, given the consistency between the long-wavelength MIRI/MRS and Spitzer/IRS data. This suggests that dust reformation in the post-shock region of this system does not easily occur or occurs on much longer timescales given the low densities  (Biscaro & Cherchneff 2014). Our results support the survival of large grains in the shocks. The different line shapes between the [Fe II] 25. 99 μm line, on the one hand, and the [Fe II] 5. 340 μm and [Ne II] 12. 81 μm lines, on the other hand, show that the excitation of the outer parts of the ejecta, close to the ER, and the central region are different. The excitation energy of the upper level of the [Fe II] 25.
Acknowledgments This work is based on observations made with the  NASA/ ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with program #1232. This research made use of Photutils, which is an Astropy package for detection and photometry of astronomical sources (Bradley et al. 2022). O. C. J. acknowledges support from an  STFC  Webb fellowship. acknowledges from  European Research Council Advanced  Grant  694520  SNDUST. P. J. K. and J. J. acknowledge support from the  Science Foundation Ireland/ Irish Research Council   Pathway program  under grant No.
21/ PATH-S/9360. J. L. acknowledges support from the  Knut & Alice Wallenberg Foundation. J. L. and C. F. acknowledge support from the  Swedish National Space Agency. M. M. and N. H. acknowledge support through a  NASA/JWST  grant  80NSSC22K0025 , and M. M. and L. L. acknowledge support from the  NSF  through grant  2054178. M. M. and N. H. acknowledge that a portion of their research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). J. H. was supported by a  VILLUM FONDEN Investigator grant  (project number  16599 ). T. T. acknowledges financial support from the  UK Science and Technology Facilities Council  and the  UK Space Agency. R. W. acknowledges support from  STFC Consolidated  grant ( 2422911 ).
Acknowledgements This research has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the Specific Grant Agreement No. 945539  ( Human Brain Project SGA3 ,  Task 3. 7 ), an  ERC grant  ( 101052963  " NUMEROUS "), an  NWO Crossover grant   17619  " INTENSE ", " DBI2 " (a  Gravitation program  of the  Dutch Ministry of Science)  and  NWO-OCENW. KLEIN. 178. We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the  European Union 's  Horizon 2020 research and innovation programme  through the  ICEI  project under the grant agreement No. 800858.