dataesr/scientific-paragraphs-categorization huggingface dataset:
net/Multiview-Reconstruction) according to the following steps: 1. Preprocessing: Image data acquired on Zeiss Lightsheet Z. 1 were saved as an array of czi files labeled with ascending indices, where each file represented one view (z-stack). czi files were first renamed into the 'spim_TL{t}_Angle{a}. czi' filename, where t represented the time-point (e. g. 1 to 192 for a 1 day recording) and a the angle (e. g. 0 for left view, 45 for ventral-left view, 90 for ventral view, 135 for ventral-right view and 180 for right view), and then resaved as tif files. 2. Bead-based spatial multi-view registration: In each time-point, all views were aligned to eachother and to an arbitrary reference view fixed in 3D space (e. g. views 0, 45, 90, 135 aligned to 180) using the bead-based registration option  (Preibisch et al.
In each time-point, the deconvolved fused image was calculated on GPU in blocks of 256 Â 256Â256 pixels with 7 iterations of the Efficient Bayesian method regularized with a Tikhonov parameter of 0. 0006. 4. Bead-based temporal registration: To correct for small drifts of the embryo over the extended imaging periods (e. g. due to agarose instabilities), we stabilized the fused volume over time using the segmented beads (sigma = 1. 8 and intensity threshold = 0. 005) for temporal registration with the affine transformation model using an all-to-all matching within a sliding window of 5 time-points. 5. Computation of spatiotemporally registered fused volumes: Using the temporal registration parameters, we generated a stabilized time-series of the fused deconvolved 3D images. 6. 4D rendering: The Parhyale embryo was rendered over time from the spatiotemporally registered fused data using Fiji's 3D Viewer.
Lineage reconstruction with the Massive Multi-view Tracker (MaMuT) MaMuT was developed as a tool for cell lineaging in multi-view LSFM image volumes by enabling to track objects synergistically from all available views. This functionality has a number of advantages. Raw views do not have to be fused into a single volume, which is computationally by far the most demanding step  (Preibisch et al. , 2014). The users also preserve the original redundancy of the data, which in many cases like in Parhyale allows capturing cells from two or more neighboring views that can be interpreted independently for a more accurate analysis. Finally, MaMuT allows users to analyze sub-optimal datasets that cannot be fused properly or may create fusion artifacts. Of course, combining the raw views with a high-quality fused volume is the best available option, especially when handling complex datasets with high cell densities.
While offering multi-view tracking, MaMuT delivers also other important functionalities. First, it is a turnkey software solution with a convenient interface for interactive exploration, annotation and curation of image data. Any image acquired by any microscopy modality that can be opened in Fiji can be also imported into MaMuT. Second, MaMuT offers a highly responsive and interactive navigation through multi-terabyte datasets. Individual z-stacks representing different views, channels and time-points of a multi-dimensional dataset can be displayed independently or in combinations in multiple synced Viewer windows. Third, objects of interest like cells and nuclei (spots) can be selected synergistically from all available Viewers and followed over time to reconstruct their trajectories (tracks) and lineage information.
Fourth, the created spots and tracks can be visualized and edited interactively in the Viewers and the TrackScheme lineage browser, and animated in the 3D Viewer. For visual interpretation of the data, annotations can be colored based on the primary lineage information or derived numerical parameters. Fifth, lineages can be reconstructed in a manual, semi-automated or fully automated manner followed by manual curation if necessary. Sixth, all spot and track information can be exported from MaMuT to other interfaces for more specialized analyses. Seventh, decentralized annotation by multiple users has been made possible by also developing a web service for remote access to large image volumes stored online. Following on the tradition of the Fiji community for open-source distribution of biological image analysis software, MaMuT is provided freely and openly to the community, it is extensively documented and can be customized by other users.
In practical terms, for lineaging purposes, the Parhyale multi-view LSFM raw views were registered spatiotemporally and the image data together with the registration parameters were converted into the custom HDF5/XML file formats utilized by the BigDataViewer and MaMuT Fiji plugins. The MaMuT reconstruction of the Parhyale T2 limb described in this article required about 10 weeks of dedicated manual cell tracking by an experienced annotator. The raw image data were displayed in Viewer windows and each z-stack was visualized in any desired color and brightness, scale (zoom), translation (position) and rotation (orientation). All Viewer windows were synced based on the calculated registration parameters and shared a common physical coordinate system; upon selecting an object of interest (spot) in one Viewer, the same spot was identified and displayed in all other windows, and its x, y, z position was mapped onto this common physical space.
Analysis of gene expression Parhyale decapentaplegic (Ph-dpp), Dorsocross (Ph-Doc), engrailed-2 (Ph-en2) and H15 (Ph-H15) genes were identified by BLAST analysis against the Parhyale transcriptome and genome  (Kao et al. , 2016)  using the protein sequence of Drosophila orthologs as queries. Sequence accession numbers are KY696711 for Ph-dpp, KY696712 for Ph-Doc, and KY696713 for Ph-en2. Phylogenetic tree construction was performed with RAxML using the WAG + G model from MAFFT multiple sequence alignments trimmed with trimAl  (Stamatakis, 2014). In situ hybridizations were carried out as previously described  (Rehm et al. , 2009). Stained samples were imaged on a Zeiss 880 confocal microscope using the Plan-Apochromat 10x/0. 45 and 20x/0. 8 objectives. Images were processed using Fiji and Photoshop CS6 (Adobe Systems Inc).
JYT and SS were supported by intramural funding from the Pasteur Institute and ANR Program Grandes Investissement de l'avenir France BioImaging Consortium. SP was supported by core funding from the Max Delbru ¨ck Center. TP and PT were supported by the core funding from Max Planck Society and the European Research Council Community's FP7 grant agreement 260746. ES, LG, PJK and AP were supported by the Howard Hughes Medical Institute. AP was also supported by an EC Marie Skłodowska-Curie fellowship. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Acknowledgements We are grateful to  Michael Akam  and  Gerhard Scholtz  for actively supporting the early phases of this project,  Stephan Saalfeld  for assistance with image rendering,  Fernando Amat  for his help with importing TGMM annotations into MaMuT,  Damian Kao  for assistance with phylogenetics,  Michalis Averof  and  Matthew Benton  for comments on the manuscript, and  Angeliki-Myrto Farmaki  for designing the MaMuT logo. CW was supported by the  Einstein Foundation Berlin  grant  A-2012_114.
(2007)  main-sequence. Alternatively, we fit a second order polynomial to the subsample of 10 10 -10 11 M ⊙ Hα members and compute the amount of dust attenuation A V , as a function of M ⋆ , required to reproduce the  Whitaker et al. (2014)  main-sequence at z = 1. 5. We find that the required dust attenuation A V is monotonically increasing with stellar mass and ranges from 2 mag at 10 10 M ⊙ to 4 mag at 10 11 M ⊙. These values are significantly higher than typical dust attenuation for massive galaxies, both at low redshifts and in colorselected massive galaxies and comparable redshift to the CARLA sample. Specifically, using a sample of 90, 000 star-forming galaxies from Data Release 7 of the Sloan Digital Sky Survey  (Abazajian et al. 2009 ),  Garn & Best (2010)  find Hα dust attenuations ranging from 0.
7. Finally, these results highlight both the strengths and weaknesses of shallow HST grism spectroscopy for confirming high-redshift galaxy clusters. With -2 sec -1 ) col14 f OIII [O III]λ5007 flux (erg cm -2 sec -1 ) col15 f OIII ERR [O III]λ5007 flux uncertainty (erg cm -2 sec -1 ) col16 f OII [O II] flux (erg cm -2 sec -1 ) col17 f OII ERR [O II] flux uncertainty (erg cm -2 sec -1 ) Note. -The catalog is published in its entirety in the machine-readable format. Meta-data are shown here for guidance regarding its form and content. an extremely efficient strategy of just 2-orbits per field, we are able to confirm most of these candidate high-redshift clusters and demonstrate the robust selection efficiency of the CARLA Spitzer imaging program. However, by design, this strategy fails to confirm member galaxies lacking star formation and therefore fails to identify the interesting sample of massive, evolved, early-type galaxies in these distant structures.
All members are confirmed within ±1000 km s -1 and strongly peak at the same redshift as the RLAGN, which could argue against a very massive virialized cluster of larger velocity scatter/dispersion. We however determined a 123 flag for this confirmed structure (see Appendix B), which, following our analysis in Appendix B, would tend to support that CARLA J1103+3449, classified as PC, is probably a node of the cosmic web, as opposed to a filament. The RLAGN appears to possess at least one confirmed satellite member (#490). CARLA J1300+4009 (b) -Given its redshift, [O III] was the only strong line available in our grism data for confirming members of this CARLA cluster candidate. The 8 confirmed members have again very low velocity scatter, for a combined flag 122. Classified as PC, this would tend to support that CARLA J1300+4009 is most probably a node of the cosmic web at z = 1.
These sources are very promising targets for deeper spectroscopic observations and additional multi-wavelength follow-ups to reveal and study passive populations already present in a cluster core at z = 1. 65. A flag of 123 classifies CARLA J1052+0806 as PC. A bright nearby galaxy, falling on the West side of our HST field of view, additionally contaminated a significant fraction of our grism data in this field, likely hindering confirmation of additional star-forming members of CARLA J1052+0806. CARLA J1753+6310 (e) -We previously presented CARLA J1753+6310 in Section 4. 6. 2; see that section for details. We only confirm five members in our grism data, while additional Keck observations confirm an additional three members (A. Rettura et al. , in prep). CARLA J1753+6310 has a 223 flag, and  Cooke et al.
(2016)  demonstrated that this structure possesses a strong red-sequence of passive candidate members and that its core is dominated by passive galaxies. CARLA J1753+6310, classified as HPC, therefore appears as a very strong candidate for being a massive node of the cosmic web at z = 1. 58. The RLAGN likely possesses one confirmed satellite member (#470) at least. CARLA J2355-0002 (f) -We previously presented CARLA J2355-0002 in Section 4. 6. 1; see that section for details. With a total of 12 quality-A-only members at z = 1. 49, CARLA J2355-0002 appears as one of the most convincing spectroscopic confirmation from our HST data alone. Supporting data  (Collet et al. 2015)  additionally identifies z ∼ 1. 5 Hα emitters outside our HST field of view. CARLA J2355-0002 has a 122 flag and its velocity distribution shows some scatter.
One member (#442) appears as a probable satellite of the RLAGN. CARLA J0116-2052 (h) -We confirm 12 members at z = 1. 43, including six quality A sources, in the grism data of CARLA J0116-2052. As opposed to previously discussed confirmed structures, confirmed members of CARLA J0116-2052 appear to form two peaks around its mean redshift and are predominantly located to the North of the RLAGN. This might suggest that the RLAGN is not located at the center of the structure. With a flag of 122, our analysis however supports that this confirmed structure is a probable node of the cosmic web (it is classified as PC). CARLA J1017+6116 (i) -At a redshift of z = 2. 80, CARLA J1017+6116 is the highest redshift confirmed structure among our 20 fields. With seven confirmed members at this redshift, CARLA J1017+6116 also possesses the highest spectroscopic overdensity significance and galaxy contrast of our sample.
This work is also based in part on observations made with the 200-inch  Hale Telescope ,  Palomar Observatory , operated by the  California Institute of Technology. The work of P. E. and D. S. was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA. S. M. acknowledges financial support from the  Institut Universitaire de France (IUF) , of which she is senior member. D. W. acknowledges support by  Akbari-Mack Postdoctoral Fellowship. E. A. C. acknowledges support from the  ERC   Advanced Investigator program  DUSTYGAL  321334. N. A. H. acknowledges support from  STFC  through an  Ernest Rutherford Fellowship. Facilities: HST (WFC3; STScI), Spitzer (IRAC;  JPL/Caltech) ,  Palomar  (DBSP; Caltech).
Methods: We developed a new mobile application, "MyFootCare", to engage people with DFU through goals, progress monitoring, and reminders in self-care. Key features included novel visual analytics that automatically extract and monitor DFU size information from mobile phone photos of the foot. A functional prototype of MyFootCare was created and evaluated through a user-centred design process with 11 participants with DFUs. Data were collected through semi-structured interviews discussing existing self-care practices and observations of MyFootCare with participants. Data were analysed qualitatively through thematic analysis. Results: Key themes were: (1) Participants already used mobile phone photos to monitor their DFU progress, but (2) had limited experience with using smartphone applications.
(3) Participants desired the objective DFU size data provided by the tracking feature of MyFootCare to monitor their DFU progress. (4) Participants were ambivalent about the MyFootCare goal image and diary features, commenting that these features were useful but also that it was unlikely that they would use them. (  5 ) Participants desired to share their MyFootCare data with their clinicians to demonstrate engagement in self-care and to reflect on their progress.
Conclusions: MyFootCare shows promising features to engage people in DFU self-care. Most notably, ulcer size data is useful to monitor progress and engage people. However, more work is needed to improve the usability and accuracy of MyFootCare, i. e. , by refining the process of taking and analysing DFU photos and removing unnecessary features. These findings open the door for further work to develop a system that is easy to use and functions in everyday life conditions, and to trial it with people with DFUs and their carers.
MyFootCare Prototype The overall goal of MyFootCare is to be a mobile application that optimizes the engagement of people with DFUs in their self-care away from the clinic. MyFootCare was conceived by the research team based on their experience in the treatment and study of people with DFUs (JvN, PL) and in the design and implementation of mobile health technologies (BP, RB). The team developed multiple features within MyFootCare to engage people with DFUs, including the ability to visualize personal goals, self-monitor their DFU through ulcer photos and ulcer size information, a diary to foster reflection, and reminders to enact self-care  [22]. The prototype presented in this article was the result of an iterative, user-centred design process. Multimedia Appendix 1 shows our initial prototype, which was implemented in Axure  [23] , a prototyping software to generate interactive screen mock-ups to gather feedback from prospective users.
Data Collection We conducted a qualitative evaluation through semi-structured interviews to explore how people with DFUs would use MyFootCare and to what extent the app could enhance their self-care practices. The interviews took place in a meeting room at the clinic where participants received their foot care and lasted 30 to 60 minutes per participant. Ethics approval was obtained from The Prince Charles Hospital's (TPCH) Human Research Ethics committee (#17/QPCH/14). The evaluation followed a standard procedure. First, a background interview was conducted to learn about their ulcer history, clinical care and self-care practices, and smartphone usage. Second, we conducted observations of patients exploring each of the MyFootCare features. The participants were given a mobile phone with the MyFootCare prototype.
Data Analysis The data were analysed qualitatively, following a thematic analysis approach  [35]. The authors read through all transcripts and coded the data to identify the various uses for each app feature as well as areas for improvement. Data were coded by the authors (BP, JvN, LdS) through SaturateApp, a web-based tool for collaborative qualitative analysis  [36]. In total, 54 codes were generated about the existing smartphone practices, 97 codes about MyFootCare features, and 57 codes about the potential use in daily life. These codes were collated into 5 themes that describe existing care and mobile phone practices and how MyFootCare could support them, and are presented in the Results section.
For example, participant 2, a 74-year-old man, stated, "the problem I have is my hands, my dexterity's not that good (. ) for me to type in the stuff it would take me 20 minutes or half an hour. " Furthermore, participants reported difficulties reading on smartphones, which is not surprising considering people with diabetes often also develop the diabetes-related complications of retinopathy and blindness. For instance, participant 9, said "that's too small an interface for my eyes because I've had retinopathy, I've had laser surgery on both eyes, I've had cataracts removed off both eyes. " Theme 3: Participants desire objective data from MyFootCare to monitor DFU progress Feedback regarding MyFootCare was largely positive. Seven out of 11 participants said that they would be interested to try out the app on their own phones for several weeks to support their self-care.
The key benefit of MyFootCare for participants was that the app could provide objective data to monitor the progress of their ulcers. Participants could clearly see how they could monitor progress by taking photos on a regular basis and by tracking the objective ulcer size information provided by the visual analytics feature. The participants highlighted that MyFootCare would make ulcer size more explicit: "It's so handy especially if you've got no idea. In my case I don't see a lot of the wound so knowing the size is handy because then I can tell whether it's actually a problem or becoming more of a problem than you know just going along and all of a sudden, and I've done it before, going along well and all of a sudden my wound's fifteen by three or something, which is not ideal. " (P5). Seeing progress through the app is particularly important, because ulcers often heal slowly.
" (P1). It is also important to note the limitations pointed out by participants. First, participants recognized that taking photos of the plantar side of their foot to provide such objective data may be difficult, but that the automated image taking feature contained in MyFootCare may provide a solution to this difficulty. Images need to be consistently taken at a certain angle and at a certain distance to provide accurate data: "I'd say with certain parameters within [the app] that recognises that OK you're holding it at this angle or that angle and that's why it's saying no take the photo again. Or you know it's supposed to be between ten and fifteen centimetres or what have you so it can do all the calculations" (P1). While the researcher could demonstrate the image-taking process during the interview, participants and their carers noted that taking an image at home might be difficult and that assistance from another person might be needed.
Furthermore, the personal benefit of the diary was not clear to participants: "From my point of view I don't see that as an advantage, probably might be for the healthcare worker" (P11). Theme 5: Participants desire to share MyFootCare data with their clinicians Nine out of eleven participants pointed out that MyFootCare data would be useful for discussion with their clinician. Even though this was not an explicit feature of the app, participants suggested that the information available through MyFootCare would be useful for consideration during consultations with their podiatrists and GPs: "That would be good because then I could show my doctor and say look this is the progress we're having. If I see another podiatrist, I mean I know it's in my file, but it's a nice easy way for them to look at it and go hey look OK right-o!" (P3).
Our application differs by being patient-focused, including a patient-oriented design, involving patients from the start of the research, and aiming to improve patients' motivation by developing an application for them to use, rather than keeping the app in the hands of the clinician. Patients perceived the main benefit of MyFootCare was its visual analytics feature that provides objective data about the size of ulcers from photos of the foot. This information was seen as valuable, because patients typically cannot feel or see their ulcer. And even if they could see their ulcer (on photos or in person), they could not detect if it was improving or deteriorating. Additionally, the participants regarded the information provided by the app as objective, and hence put more faith in this information than in their own or their carer's subjective accounts.
Now that we understand that patients desire objective data from MyFootCare to monitor DFU progress, our future research will focus on iteratively evaluating and improving the accuracy of the app. Evaluations will be performed by comparing MyFootCare measurements with measurements performed by clinicians using ruler measurements [14], wound tracings  [16] , or gold standard digital wound imaging instruments  [48, 49]. Accuracy will be improved by working with patients to assist them in controlling factors such as light, distance, and angle of the foot during image capture. We will continue to refine the voice assistant and also explore alternatives, e. g. , selfie sticks, light boxes  [16] , and 'ghost' outlines of the foot  [17]. A third limitation of this study lies in the ecological validity.
Conclusions MyFootCare, a mobile application that seeks to engage patients through goals, progress monitoring, and reminders, shows promising features to engage people in DFU self-care. The patients in this study expressed positive views on MyFootCare. The features perceived most useful were (1) taking photos of foot ulcers to assess healing and (2) the ability to monitor changes in the size of their ulcers through wound size data generated from such photos. More work is needed to improve the usability and accuracy of MyFootCare, i. e. , by refining the process of taking and analysing wound photos. This study enhances our understanding of opportunities and challenges for mobile health technologies, especially through medical photography, to support people with diabetes and DFUs. The findings open the door for further work to develop an application that is accurate, reliable, and easy to use in daily life, and to trial it with people with DFUs and their carers.