dataesr/scientific-paragraphs-categorization huggingface dataset:
We collected additional information from product websites, and via teleconferences and web-based demonstrations with the product creators, and from free trials to determine the degree to which each is (1) customizable in its information presentation and data capture capabilities, (2) private and secure, and (3) accessible (easy to use, economical, and open source). Customization is important to ensure that content, language, and presentation can be adapted and updated to meet the specific needs of a given population of intended end users, and to expand the scope of possible dimensions to assess, analyses to perform, and inferences to make. Privacy and security is a central concern, especially as we conduct research involving a triply vulnerable population of end users: (1) child and adolescent (2) patients with (3) mental health and learning disorders.
Development of the MindLogger Platform MindLogger  [59]  is intended as an open ecosystem to create, edit, share, and administer mobile or web applets for data collection and content delivery. We use the term applet to refer to a customized collection of activities within the MindLogger app administered to target end users. Our focus with MindLogger is to easily create and edit digital mental health assessments and interventions and administer them to users via mobile or web applets. The 3 key innovations we sought to accomplish with MindLogger were customizability of content, response options, and appearance; an extensive library of applets built using open standards (open, reusable parts defined by an open protocol), and distribution as a single app that appears differently to different user groups.
Roles and Permissions Figure  4  shows a schematic of MindLogger, where an administrator selects, edits, or creates an applet; administers the applet to end users; and views or makes use of their data. Figure  5  outlines the different roles (owner, manager, coordinator, editor, user, or reviewer) and their permissions with regard to administration, content, use, and data management. For instance, a principal investigator of a research study could be the owner of a new applet as well as its editor and might assign a laboratory manager who recruits participants (applet users) to be the manager of the applet, and 2 data analysts to be the reviewers of all deidentified participants' data. As a second example, a clinical director of a pediatric mental health clinic could be the owner of a copy of an applet containing assessments, assigns a clinical coordinator who manages patients (applet users) to be a manager of the applet, and assigns parents to be reviewers of their child's responses.
All user data are stored in a MongoDB database hosted in an Amazon Web Services  [88]  cloud instance with password-based encryption. Specifically, user response data are encrypted using their own password on the client side so that only managers or reviewers can view their data using an applet password; other sensitive information (name and email) is encrypted on the server side. We have Health Insurance Portability and Accountability Act compliance agreements with Amazon Web Services, Google Cloud Platform  [89] , and MongoDB Atlas  [90] , and the software permits installation on an arbitrary backend server (eg, on a university or hospital server and not on any cloud service provider's servers). For improved performance, MindLogger uses a Redis  [91]  instance as a temporary storage for data caching.
MindLogger uses Firebase Cloud Messaging to send notifications from the backend server to the end user's mobile device. All additional data are consumed from the backend API through the HTTPS requests. The applets, activities, and items are described using ReproSchema  [92] , an emerging standard for capturing and harmonizing cognitive, clinical, and behavioral assessments and responses in a provenance-preserving manner. The schema uses JSON for linking data (JSON-LD)  [93]  as its representation format and captures, as a connected graph of information, the details of the questions, presentation logic on the basis of responses or scheduling, computation of scores, and interface hints for applications such as MindLogger. The schema uses GitHub to maintain versions and provide persistent Uniform Resource Identifiers for applets and activities, supports multilingual applets, and uses World Wide Web Consortium provenance specifications  [94]  to establish provenance between the response, the responder, and the applet.
MindLogger is cross-platform (iOS, Android, and web browser compatibility) and has open-source code for apps and applets, and data are end-to-end encrypted. The browser-based administration panel enables user management, easy creation, and customization of one's own mobile or web applications without programming or design experience, scheduling of applets and notifications per activity per user or group of users, and visualization and export of data. Figure  7  shows screenshots of the MindLogger mobile phone app features. We have created applets to remotely administer assessments as well as therapies and are currently constructing a public library with over 100 mental health and cognitive assessments that have open licenses for general use.
Thus, the children most in need are the least likely to have access to evidence-based treatment. The EDUCATE study, funded by the National Institute of Child Health and Human Development, is a collaborative effort between researchers at the University of Connecticut and the Child Mind Institute. This clinical trial will examine the effectiveness of an at-home, game-based intervention for reading disorders. Parents of participants in this study are completing daily and weekly assessments in MindLogger, which will allow researchers to assess the home environment and compliance with the intervention protocol throughout the study period. These data are critical for evaluating the impact of this clinical trial.
Principal Findings In this paper, we reviewed customizable, mobile experience sampling products for configurable data collection and content delivery, summarized the motivation for and development of a new mobile platform called MindLogger, and described an initial use case that applies a MindLogger version of the NIMH Family Study app as part of the Healthy Brain Network study. Our review returned an initial set of 392 products, of which 59 appeared to be in current use, had Android and iOS mobile apps, and were capable of scheduling questionnaires and notifications for a group of respondents. Of the 47 companies that responded to our inquiries and did not require a legal agreement, we identified 10 products that satisfied our primary criteria (administrator interface for creating and scheduling recurring, customized questionnaires, where an arbitrary number of users can receive and respond to scheduled notifications on their mobile devices).
Products are also adding features over time, and some companies or organizations offer paid services to build the desired features. Our comparison should be seen as a snapshot of the current state of a subset of features provided by software products intended for experience sampling. We successfully developed MindLogger, a new platform that meets most of the stated needs of our collaborators around the world, who desire an open source, mobile mental health platform to inform, assess health, acquire data, and administer therapies. We prioritized clinicians' and researchers' needs and users' experience during development, aligned technologies to meet these priorities, and are on track to achieve the full feature set we set out to include. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features.
Acknowledgments This project was supported by the  Hearst Foundations , the  Intramural Research Program  of the  National Institute of Mental Health  (grants with principal investigator KRM: ZIA MH002954 Motor Activity Research Consortium for Health and ZIA MH002804-15  Family Study of Affective and Anxiety Spectrum Disorders ); the  Centre for Research and Interdisciplinarity, Université de Paris ; the  Blanca and Sunil Hirani Foundation ,  Massachusetts Institute of Technology  through the laboratory of SSG (SSG and SAA were supported by grants:  NIH   P41 EB019936 ) and  Joseph Healey. The authors thank the companies and organizations that responded to the questions and provided demonstrations of their products. The authors thank  Rangle. io  for their help in refactoring MindLogger's mobile app code base and  Alpine Security  for conducting penetration tests of the entire code base.
Design: Retrospective multivariate analysis using a modified adaptive platform design. Methods: A computerized sampling using respiratory related key words from a blinded dataset yielded 178 patients with respiratory complaints or pain in the chest area. Additional statistical analysis using parametric and non-parametric tests evaluated the difference between each treatment protocol and the rest of the treatments provided. Results: Several protocol combinations and one individual protocol passed the study criteria. Cardiac vascular venous thoracic (CVVT) protocol was used most frequently within these combinations (7), followed by Urinary Drainage (UD) (4). Other protocols in this group were Cardiac Cervical Cranial Vascular (CCCV), Venous Thoracic Cardiopulmonary (VTCP), and Diaphragm Cranial Sinus (DCS).
Sampling and testing The sampling strategy meant to emulate an adaptive platform design  (Kaizer et al. , 2018). In this model, the protocol or protocol combinations were treated in the same manner that investigative drugs or drug combinations are treated in this design. The performance of each protocol or combination of interest was compared to the overall performance of all other interventions and the most effective ones are identified. All sampling was done by automated queries in a Microsoft Access database. Testing was done using MedCalc software (https:// www. medcalc. org) Fig. 1  provides a schematic illustration of the sampling and testing process.
Population sample: The study population included 1551 patients with a total of 26,585 treatment visits (1015 female, 536 male, age range six months to 92 years, average age (calculated at the start date) was 58. 1 years, SD 18. 0). Records were imported from the primary database dating from April 1, 2015 to March 27, 2020 and were completely stripped of identifiable information. 2. Study sample: This sample was created by querying the population sample for patients that listed in their patient identified problem (PIP) items that included the keyword: "respiratory," "breathing," "COPD," "asthma," "breath," "chest," "ribcage," or "rib cage. " Each patient record was individually evaluated and differentiation was done between chronic and acute presenta-   4 ) that had in it one of the 45 protocol or protocol combinations from group 3.
Analysis group: This group included 90 samples creating 45 dataset pairs, one pair for each of the samples in the protocol sample group (group 3). Each pair included data containing the samples from the frequency group ( tion (n > 1 month ¼ 126, n < 1 month ¼ 49, unknown ¼ 3) and respiratory problems (n ¼ 115) vs. chest pain or other symptoms (n ¼ 63). This sample yielded initially 178 patients with 2535 visits (125 females, 53 males, age range 7e92, average age 58 years, SD 19. 1). Twelve patients were excluded from further analysis because they never had a follow-up visit. The trimmed average for days between visits was 7. 63 days (Grubb's n ¼ 2395/2535, 95% CI 7. 49 to 7. 76). In this sample, 145 (6%) of the treatments were provided by doctoral physical therapy in- terns, 902 (36%) by PTA staff, and 1488 (58%) by licensed physical therapists.
Microarray experiments and analysis Each cell type studied was FACS sorted from spleen to over 98% purity in independent duplicate samples. Microarray experiments were performed as follow  (Robbins et al. , 2008). Briefly, RNA was extracted from between 10 5 and 3 3 10 6 cells for each cell subset with the Qiagen (Courtaboeuf, France) micro RNAeasy kit, yielding between 100 and 300 ng of total RNA for each sample. Quality and absence of genomic DNA contamination were assessed with a Bioanalyser (Agilent, Massy, France). RNA (100 ng) from each sample was used to synthesize probes, and hybridized to the GeneChip Mouse Gene 1. 0ST Array (Affymetrix, Santa Clara, CA, USA). Affymetrix DAT files were processed using Affymetrix Gene Chip Operating System (GCOS) to generate. CEL files. Quality control of array hybridization, and data normalization and analysis were performed using the affy and oligo packages in R (version 3.
Analysis of cell distribution and cell contacts For cDC1 distribution analysis and quantification of clusters in the spleen, we imaged half spleen sections, and randomly mixed them using the random number generator (www. dcode. fr). Three independent manipulators blindly counted clusters using Zen software with a fixed zoom of 30 and 40%. Cluster quantifications were normalized to the section area (mm 2 ). We quantified tdRFP intensity as a read-out of cDC1 presence. Each cluster was isolated by drawing a Region of Interest (ROI), which was then exported as unmodified uncompressed TIFF files. The tdRFP intensity was quantified in each ROI using ImageJ, and normalized to the ROI area (mm 2 ). To follow cDC1 relocalization in the three splenic areas marginal zone, bridging channel and T cell regions, we applied a high throughput acquisition of whole spleen sections using the Pannoramic Scan slide scanner (3dHistec), and quantified tdRFP intensity minus background in each these areas.
We identified them using MOMA-1 and B220 staining as shown in Figure  S4. Between 6 and 14 white pulp follicles were analyzed per mouse. The 3-dimensional (3D) reconstruction and the Video S1 were generated using the Imaris software (Oxford Instruments). cDC1/DETC contacts were quantified in the skin and normalized to the length of the skin analyzed in the section. The Icy software (http://icy. bioimageanalysis. org/) was used for some analysis as detailed in the figure legend. Other software used for image analysis are listed in the Key resources table.
ACKNOWLEDGMENTS We acknowledge  France Bio  lmaging infrastructure supported by the  Agence Nationale de la Recherche  ( ANR-10-INBS-04-01 ,  call ''Investissement d'Avenir' '), in particular Sebastien Mailfert,  Roxanne Fabre ,  Mathieu Fallet  and  Lionel Chasson  from the  CIML Immagimm  and histology core facilities for technical help. We thank  Eric Vivier  ( CIML ) for the Nkp46-Cre mice,  Sandrine Sarrazin  and  Michael Sieweke  ( CIML ) for Csfr2b-deficient mice, and  Mauro Gaya  ( CIML ) for CD1d tetramers. This work benefited from institutional supports from  CNRS  and  Inserm  and was funded by the  I2HD   CIML-SANOFI  project, by grants from  Fondation pour la Recherche Medicale  ('' Equipe FRM 2011 '', # DEQ20110421284  to MD) and from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013  grant agreement  281225 , to M.
Primary antibodies used for Western blot The following primary antibodies were used for Western blots: anti-Fis1 (LuBioScience GmbH, 10956-1-AP, diluted 1:2000), anti-Mff (Life Technologies, PA5-52765, diluted 1:500-1:1000), anti-Inf2 (Sigma-Aldrich HPA000724, diluted 1:2000), anti-alpha-Tubulin (Santa Cruz sc-5286, diluted 1:2000).
SIM imaging Single and dual-color SIM imaging was performed on an 3D NSIM Nikon inverted fluorescence microscope (Eclipse Ti; Nikon) equipped with an electron charge coupled device camera (iXon3 897; Andor Technologies). The microscope was equipped with a 100x 1. 49 NA oil immersion objective (CFI Apochromat TIRF 100XC Oil; Nikon). Live-cell imaging was performed at 37°C using a 488 and 561 nm laser. Acquisition setting were adapted to yield the best image quality with minimal photobleaching (laser power 0. 5-15%, Exposure time 30-100 ms). Images were captured using NIS elements software (Nikon) at temporal resolution of 1 s for single-color and 6-8 s for dual-color imaging. Imaging was performed at 37˚C in pre-warmed Leibovitz medium. Each sample was imaged for a maximum of 90 min.
Instant SIM (iSIM), Single and dual color instant SIM imaging was performed on a custom-built microscope setup as previously described  16, 17. Fluorescence was collected with a 1. 49 NA oil immersion objective (APONXOTIRF; Olympus), with 488 nm and 561 nm excitation lasers and an sCMOS camera (PrimeBSI, 01-PRIME-BSI-R-M-16-C, Photometric). Images were captured at 0. 5-5 s temporal resolution for both channels. All imaging was performed at 37˚C in Leibovitz media. Raw iSIM images were subsequently deconvolved using the Lucy-Richardson deconvolution algorithm  18  provided by Hari Shroff implemented in MATLAB, and were run for 40 iterations.
Confocal imaging Ratio-metric imaging of Grx1-rGFP was performed on an inverted microscope (DMI 6000; Leica) equipped with hybrid photon counting detectors (HyD; Leica). The sample was excited sequentially frame by frame with 408 nm and 488 nm with the detection set to 500-535 nm. Fluorescence was collected through a 63x 1. 40 NA oil immersion objective (HC PL APO 63x/1. 40 Oil CS2; Leica). Images were captured using the LAS X software (Leica). All imaging was performed at 37˚C in pre-warmed Leibovitz medium for maximum 90 min per sample. For confocal microscopy of fixed BrU samples the imaging was performed using a Leica TCS SP8 inverted microscope equipped with 405-, 488-, 552-and 638-nm lasers and a Plan-Apochromat oil objective (×63, NA 1. 4). The Lightning mode (Leica) to generate deconvolved images.
Microscope acquisitions were controlled by LAS X (v. 3. 5. 2) software from Leica. CLEM and Caspase 3/7 samples were imaged on a Zeiss LSM 700 inverted confocal microscope with equipped with a Plan-Apochromat oil objective (×63, NA 1. 40) and 488-nm and 555nm solid-state lasers and three photomultipliers. Acquisitions were controlled by the Zeiss Zen (v. 6. 0. 0) software.
Lysosome-mitochondria contact: A contact between mitochondria and lysosome was categorized as a close proximity (<500 nm) between lysosomes and the mitochondrial constriction site for at least three consecutive frames before fission. BrU quantification: Individual cells were selected manually, using Fiji's rectangular selection tool. Three consecutive slices to focus at the bottom of the cell were then chosen upon inspection & foci were detected automatically in both BrU, and FASTKD2 channels using a fixed threshold. Foci that occurred in both channels were then counted as relevant mitochondrial RNA-transciption granules, whereas nuclear transcription for instance, was excluded from the analysis. Multiple thresholds were tried, where 150 a. u. provided the most sensible results in control cells and are represented in Extended Data Fig.
ACKNOWLEDGMENTS We thank  Cécilia Cottiny  and  Mariona Colomer  for experimental and technical assistance,  Mary-Claude Croisier  and  Graham Knott  from the  BioEM (EPFL)  for carrying out the electron microscopy,  Thierry Laroche  ( BIOP, EPFL ) for imaging support and  Mike Ryan  for the MEF lines. We thank  Thomas Misgeld ,  Jean-Claude Martinou  and  Pavan Ramdya  for feedback on the manuscript. Research in S. M. 's lab is supported by the  National Centre of Competence in Research Chemical Biology , the  European Research Council  ( CoG 819823 , Piko) and the  Swiss National Science Foundation  ( 182429 ). T. K. received funding from  European Molecular Biology Organization  ( ALTF-739-2016 ) and the  Munich Cluster for Systems Neurology (SyNergy). This work is supported in part by grants from the  Swiss National Science Foundation  to T.
All statistics were performed using OriginPro software. First, datasets were tested for normal distribution using D'Agostino-Pearson normality test (significance value of 0. 05). If a dataset failed this test, a nonparametric test was chosen to compare significance of means between groups (Mann-Whitney test for two samples). For normally distributed datasets, a t-test was chosen to compare two samples. For a detailed description of statistical tests used and exact P-Values, please see Supplementary Table1. Critical comparative datasets (Fis1 and Mff datasets, Drp1 intensity analysis, ER analysis) were partially re-analyzed or analyzed with an automated analysis pipeline to exclude observer bias. p values < 0. 05 were considered to be significant and indicated by " * "; p values < 0. 01 were indicated by " * * " and <0.
0 sequencing chemistry with 6 hour & 10 hour movie run times. 6mA modifications were detected using the PacBio SMRT analysis platform (pb_basemods package; smrtanalysis version: smrtlink/8. 0. 0. 80529). 6mA modifications were then filtered and methylated genes were identified following the methods described in  Mondo et al. , 2017. 26  The assembly was completed with Falcon (https://github. com/PacificBiosciences/FALCON) which generates better assemblies than competing methods likely due to an improvement in isolation of high molecular weight DNA and sequencing larger DNA fragments. 1 While annotating fungal genomes present a challenge due to the lack of anaerobic fungal gene content in existing databases, the genome was annotated using the JGI Annotation Pipeline, which employs a variety of gene modelers to discover genes.
RNA Sequencing and Data Analysis Stranded RNASeq library(s) were created and quantified by qPCR for both monoculture and co-culture samples. Stranded cDNA libraries were generated using the Illumina Truseq Stranded mRNA Library Prep kit. mRNA was purified from 1 ug of total RNA using magnetic beads containing poly-T oligos. mRNA was fragmented and reversed transcribed using random hexamers and SSII (Invitrogen) followed by second strand synthesis. The fragmented cDNA was treated with end-pair, A-tailing, adapter ligation, and 8 cycles of PCR. The prepared library was quantified using KAPA Biosystems' next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified library was then prepared for sequencing on the Illumina HiSeq sequencing platform utilizing a TruSeq paired-end cluster kit, v4.
Based on previous studies noting an increase in metabolites produced by the ATP-generating fungal hydrogenosome during co-culture with methanogens, we hypothesized that genes associated with hydrogenosomal function would be upregulated in methanogen co-culture. 70, 71 A list of genes associated with the fungal hydrogenosome of the C. churrovis strain was constructed based on homology with known hydrogenosome components, shown in Supplemental Table  3. FASTA sequences from known hydrogenosomal components identified in the fungal strain Neocallimastix lanati  4  were aligned to filtered model proteins of C. churrovis using the blastp alignment program in MycoCosm. One or more genes within the C. churrovis genome aligned to all listed hydrogenosomal enzymes found in N. lanati. Regulation of these genes in co-culture compared to monoculture was examined for each substrate.
Acknowledgements We thank the following for funding support: the  National Science Foundation (NSF , grant no. MCB-1553721 ), the  Office of Science (BER)  of the  US Department of Energy (DOE)  (grant no. DE-SC0010352 ), the  Institute for Collaborative Biotechnologies  (grant nos. W911NF-09-D-0001 ,  W911NF-19-2-0026 , and  W911NF-19-1-0010 ) from the  US Army Research Office , and the  Camille Dreyfus Teacher-Scholar Awards Program. The sequencing conducted by the  US DOE Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the US DOE  under contract no. DE-AC02-05CH11231. The authors thank  Patrick Leggieri ,  Stephen Lillington , and  Amy Eisenberg  for helpful discussions and revision of the manuscript.