dataesr/scientific-paragraphs-categorization huggingface dataset:
Particularly, we looked for virulence genes described in Bouzid et al. , 2013  15  and the ProtVirDB database. Only four gene families were identified in our study out of all the genes already described and implicated in Cryptosporidium virulence. These families were as follows: mucins (cgd2_450 and other predicted mucins like cgd3_720, cgd5_340, cgd7_5440 and cgd8_660), ATP-binding cassette (ABC) transporters (cgd4_4440, cgd7_4510 and cgd7_4520), ATPase3 (cgd3_1110) and cysteine proteases (cgd2_3450; cgd8_1320 and cgd7_2760) encoding genes. All these proteins have been suggested previously to be implicated in attachment/invasion (mucins) or intra-cellular multiplication/survival (ABC transporters, ATPase3 and cysteine proteases) of Cryptosporidium developmental stages in the host  15. However, it is not obvious to establish whether Cryptosporidium proteins are specific for a well-defined developmental stage  26.
Indeed, when looking for the expression profiles of the 90 proteins of interest during the life cycle of the parasite (Widmer and Lippuner RNAseq datasets in CryptoDB), we showed that most genes were mainly overexpressed in the intracellular stages of Cryptosporidium, but that some of them were overexpressed in the oocyst or sporozoite stages (like cgd2_340 coding for a signal peptide large protein, cgd7_4510 coding for an ABC transporter and cgd3_720 coding for a mucin protein) (Suppl. Table  S5 ).
However, we used here an approved MDA method with ɸ29 DNA polymerase to limit these biases  27. Moreover, different verifications confirmed that this technique did not affect significantly the outcome of our comparative genomic analysis. First, the MICRA pipeline revealed that C. parvum IOWA genome was covered at >98% by reads of DID, TUM1 and CHR isolates. Furthermore, Sanger sequencing was performed on regions of randomly selected CDS encompassing SNVs of interest and confirmed the presence of these SNVs. Finally, all of these SNVs were already identified in CryptoDB database (http://cryptodb. org/) for other C. parvum isolates (like the anthroponotic C. parvum isolate TU114  7  and/or C. parvum UKP isolates 2 to 8  9  ). Unlike analysis of data from bacteria, there are no established pipelines for comparative genomic analyses of WGS data from parasites.
Besides, the three C. parvum isolates sequenced in this study differed from C. parvum IOWA in three genes coding for ABC transporters. Thirty three Cryptosporidium ABC transporters have been estimated  30  , 13 of which have been identified according to TransportDB database (http://www. membranetransport. org). These ABC transporters have been described by others as highly divergent genes  7. Members of this family are mainly recognized to be implicated in multidrug resistance (MDR) and resistance-associated protein (MRP). However, ABC transporters can also be involved in cellular processes like DNA repair, translation or regulation of gene expression  31. For bacteria, ABC transporters are associated with pathogenesis or virulence  32  and some of them could participate to the process of adhesion or invasion of cells  33.
In the first step of MICRA, a very fast mapper, SNAP  22  (version 0. 15), was used to identify the closest reference genome for the three strains, which resulted to be C. parvum IOWA. Then, the sensitive SHRiMP2 program 23 (version 2. 2. 0) or Bowtie 2  24  was used to map the complete set of reads against C. parvum IOWA genome and calculate various mapping statistics. At this step several files were generated: one file containing all SNVs and indels found between DID, TUM1 or CHR and C. parvum IOWA (generated after variant calling using the following parameters: minimum 5 mapped reads at the position of interest and minimum variant frequency of 0. 9); files containing the consensus sequences and consensus CDS sequences generated from mapping against C. parvum IOWA for DID, TUM1 and CHR isolates.
In a third step, an iterative mapping of DID, TUM1 and CHR reads against C. parvum, then C. hominis and finally C. muris genomes was performed in order to identify DID, TUM1 and CHR specific sequences that were not found in C. parvum IOWA reference genome but potentially present in C. hominis or C. muris genomes. The final step of the pipeline consisted in a de novo assembly of the remaining unmapped reads after the iterative mapping step using MIRA 57 (version 3. 9. 16). De novo contigs were then blasted against nr database to identify previously undetected genes. Complementary modules of the MICRA pipeline were finally used to quickly compare the lists of covered CDSs and the lists of variants between TUM1, DID and CHR isolates, allowing us to identify the common CDSs and variations between the three isolates.
Custom bioinformatics pipeline. In parallel, a custom bioinformatics pipeline has been developed, that was used in a complementary way to MICRA to reinforce the results obtained for the Ion Torrent DID and TUM1 reads and Illumina CHR reads. First, raw sequencing reads were cleaned and quality controlled with Trimmomatic 0. 36  58  and FastQC  59  , respectively. To note, FastQC was used with 'Q = 20′ on Ion Torrent reads and with default parameters on Illumina reads. Then, Kaiju 60 was used to detect microbial contaminants in whole genome sequencing data. Corresponding reads were removed after being mapped on the bacterial genomes found by Kaiju with Bowtie 2 v2. 2. 6  24. For all three datasets, the non-contaminated reads were mapped to C. parvum IOWA reference genome (http://cryptodb. org/common/downloads/release-35/CparvumIowaII/).
The SNVs of interest were classified as either non-synonymous or synonymous, SNVs eliminating start codon, causing premature termination codon or eliminating termination codon. PROVEAN software was also used to determine if non-synonymous variants are predicted to be functionally important  63. Then, the set of SNV-associated genes (proteins) was evaluated for a variety of functional characteristics. Bio-informatic analyses of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for their target genes were conducted with Blast2GO 4. 1. 9 program 64. SNV-associated genes without GO terms were further evaluated using blastx and blastp homology searches from NCBI's RefSeq database. Functional domains were predicted using SMART http://smart. embl-heidelberg. de/ and pfam http://pfam.
xfam. org/  65, 66. The presence of signal peptides and trans-membrane (TM) domains was inferred using the SignalP program V4. 1 (http://www. cbs. dtu. dk/services/SignalP/)  67. GPIsom (http://gpi. unibe. ch/) was used to detect GPI-anchored proteins (both C-and N-terminal signal sequences)  68. SNV-associated genes of interest were also assessed for their identity with the already described putative Cryptosporidium virulence factors;  11, 15  (ProtVirDB database (http://bioinfo. icgeb. res. in/protvirdb/)). The expression profiles of the variants proteins during the Cryptosporidium life-cycle were collected at CryptoDB (https://cryptodb. org/) in the Transcriptomics section according to RNAseq datasets provided by Giovanni Widmer Christoph Lippuner. Finally, distribution of SNVs in the 3 C.
Acknowledgements We thank  Emilie Fréalle  and  Laurence Delhaes  ( Centre Hospitalier Régional et Universitaire de Lille ) for providing the human C. parvum isolates CHR and DID, respectively, and  Donna Akiyoshi  and  Saul Tzipori  ( Tuft University, Boston, USA ) for kindly donating the oocysts of C. parvumTUM1 isolate. Grant  PEGASE-Biosciences 2012 ;  Bilille, the bioinformatics service platform of Lille, University of Lille and Inria ; the  Programme Orientations Stratégiques  from the  University of Lille , the  Centre National de la Recherche Scientifique , the  Institut Pasteur de Lille. DM was supported by the projet PARACAN-15-RCA funded by the  Epigenetics and Cancer Plan 2015, Inserm, France.
Verification of the Multi-Criteria Evaluation Method of Product-Level Circularity Strategies The proposed circularity strategies' evaluation method is verified through two illustrative examples with two case studies. Secondary data collected from two companies is used to verify the application of the proposed method to evaluate alternative circularity strategies. More details about the case study companies can be found from Kaddoura, Kambanou, Tillman, and Sakao (2019)  [81]. In the following sections, the application of the proposed method to each case company is presented.
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions.
However, whether a diagnosis of MDD is associated with the multivariate metric of brain aging in a large dataset, and which clinical characteristics further impact this metric, remains elusive. Accumulating evidence from studies suggests that, at the group level, MDD patients follow advanced aging trajectories, as their functional (e. g. , walking speed, handgrip strength)  [16]  and biological state (e. g. telomeres, epigenetics, mitochondria)  [17] [18] [19] [20]  reflects what is normally expected at an older age (i. e. , biological age "outpaces" chronological age)  [21]. It is important to examine whether biological aging findings in depression can be confirmed in a large heterogeneous dataset consisting of many independent samples worldwide, based on commonly derived gray matter measures.
Only a handful of studies have investigated brain-PAD in people with psychiatric disorders, showing older brain-PAD in schizophrenia (SCZ), borderline personality disorder, and first-episode and at-risk mental state for psychosis, yet findings were less consistent in bipolar disorder (BD) (for an overview, see Cole et al. [22] ). Only three studies to date specifically investigated machine-learning-based brain aging in MDD-using relatively small samples of <211 patients, with inconsistent findings of a brain-PAD of +4. 0 years vs. no significant differences  [23] [24] [25]. The current study is the first to examine brain aging in over 6900 individuals from the ENIGMA MDD consortium (19 cohorts, 8 countries worldwide), covering almost the entire adult lifespan (18-75 years). Our additional aim was to build a new multisite brain age model based on FreeSurfer regions of interest (ROIs) that generalizes well to independent data to promote brain age model deployability and shareability.
Samples Nineteen cohorts from the ENIGMA MDD working group with data from MDD patients and controls (18-75 years of age) participated in this study. MDD was ascertained using the clinician-rated the 17-item Hamilton Depression Rating Scale (HDRS-17) in one cohort and diagnostic interviews in all other cohorts. Details regarding demographics, clinical characteristics, and exclusion criteria for each cohort may be found in Supplementary Tables  S1-3. Because the literature suggests differential brain developmental trajectories by sex  [26] , we estimated brain age models separately for males and females. Sites with less than ten healthy controls were excluded from the training dataset and subsequent analyses (for exclusions see Supplementary Material). In total, we included data from N = 6989 participants, including N = 4314 controls (N = 1879 males; N = 2435 females) and N = 2675 individuals with MDD (N = 986 males; N = 1689 females).
Training and test samples To maximize the variation of chronological age distribution and scanning sites in the training samples, and to maximize the statistical power and sample size of patients for subsequent statistical analyses, we created balanced data splits within scanning sites preserving the chronological age distribution, Fig. 1a. The full motivation to our data partition approach can be found in the Supplementary Material. Structural brain measures from 952 males obtained from 16 scanners and 1236 female controls obtained from 22 scanners were included in the training samples. The top panel in Fig. 1b  shows the age distribution in the training sample. A hold-out dataset comprised of controls served as a test sample to validate the accuracy of the brain age prediction model; 927 male and 1199 female controls from the same scanning sites were included.
Likewise, 986 male and 1689 female MDD patients from the corresponding scanning sites were included in the MDD test sample. The two bottom panels in Fig. 1b  show the age distributions across the test samples. feature extraction (N = 153) across multiple sites (http:// enigma. ini. usc. edu/protocols/imaging-protocols/). Cortical parcellations were based on the Desikan/Killiany atlas  [27]. Briefly, the fully automated and validated segmentation software FreeSurfer was used to segment 14 subcortical gray matter regions (nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus), 2 lateral ventricles, 68 cortical thickness, and 68 surface area measures, and total intracranial volume (ICV). Segmentations were visually inspected and statistically examined for outliers.
FreeSurfer brain age prediction model To estimate the normative brain age models, we combined the FreeSurfer measures from the left and right hemispheres by calculating the mean ((left + right)/2) of volumes for subcortical regions and lateral ventricles, and thickness and surface area for cortical regions, resulting in 77 features (Supplementary Table  S5 ). Using a mega-analytic approach, we first estimated normative models of the Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group association between the 77 average structural brain measures and age in the training sample of controls (separately for males and females) using ridge regression, from the Python-based sklearn package  [28]. All brain measures were combined as predictors in a single multivariate model.
Several methodological differences may underlie the inconsistencies or differences in magnitude of brain age effects in MDD, including, but not limited to (1) the use of high-dimensional features such as whole-brain gray matter maps in the  Kout   4 ) heterogeneity of MDD and differences in patient characteristics between the studies. The inconsistencies between brain-PAD findings in MDD might be due to any (combination) of the sources of variation outlined above and precludes a direct AD antidepressant, FDR false discovery rate, MDD major depressive disorder. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group comparison of these studies. Unfortunately, a methodological comparison is beyond the scope of our study and beyond our capability given data access limitations within ENIGMA MDD.
However, we must also appreciate a pragmatic approach for collating data from such a large number of scanning sites. Here, we developed a parsimonious model based on Free-Surfer features collected with standardized ENIGMA extraction scripts to promote model sharing. While pooling harmonized data from many sites increases (clinical) heterogeneity, it also makes predictive models less susceptible to overfitting and more generalizable to other populations  [57] , even though this might have come at the cost of lower accuracy  [58]. Finally, the large within-group variance regarding the brain-PAD outcome in both controls and MDD (Fig. 3 ), compared with the small between-group variance, renders the use of this brain aging indicator for discriminating patients and controls at the individual level difficult.
Acknowledgements ENIGMA MDD: This work was supported by  NIH  grants  U54 EB020403  and  R01MH116147. BiDirect-Münster: The study was supported by a grant from the  German Federal Ministry of Education and Research (BMBF ; grant  FKZ-01ER0816  and  FKZ-(CP16/00020 ). PGS reports funding by the  German Research Foundation  (DFG,  SA 1358/2-1 ) and the  Max Planck Institute of Psychiatry, Munich. LS was supported by a  NHMRC Career Development Fellowship  ( 1140764 ). JCS was supported by the  Pat Rutherford Chair in Psychiatry, UTHealth. PMT was supported in part by  NIH  grants  U54 EB020403 ,  RF1 AG041915 ,  RF1AG051710 ,  P41EB015922 ,  R01MH116147 , and  R56AG058854. SIT was supported in part by  NIH  grants  U54 EB020403 ,  RF1 AG041915 ,  RF1AG051710 ,  P41EB015922 ,  R01MH116147 , and  R56AG058854.
TTY was supported in part for this study by  NCCIH   R61AT009864 , the  National Center for Advancing Translational Sciences, National Institutes of Health , through  UCSF-CTSI  Grant Number  UL1TR001872 , the  American Foundation for Suicide Prevention (AFSP) ,  NIMH   R01MH085734 ,  NCCIH R21AT009173 ,  UCSF Research Evaluation and Allocation Committee (REAC)  and  J. Jacobson Fund , and the  Brain and Behavior Research Foundation  (formerly NARSAD). Amsterdam DIADE: The DIADE study was funded by  ZonMW OOG 2007 , the  Netherlands  (# 100002034 ). Cardiff: The Cardiff dataset was supported through a 2010  NARSAD Young Investigator Award  (ref:  17319 ) to XC. CIAM Cape Town: This work was supported by the  University Research Council of the University of Cape Town  and the  National Research Foundation of South Africa.
FIDMAG Barcelona : This work was supported by the  Generalitat de Catalunya  ( 2014 SGR 1573 ) and  Instituto de Salud Carlos III  ( CPII16/00018 ) and ( PI14/ 01151  and  PI14/01148 ). Galway: This work was supported by the  Health Research Board, Ireland  and the  Irish Research Council. Grenoble: This work was supported by research grants from  Grenoble University Hospital. Halifax: This work was supported by the  Canadian Institutes of Health Research  ( 142255 ). MOODINFLAME Groningen: This study was funded by  EU - FP7-HEALTH-222963 "MOODINFLAME"  and  EU-FP7-PEOPLE-286334  " PSYCHAID. "Oslo: Funded by the  South-Eastern Norway Regional Health Authority  (2014097) and a research grant from  Mrs. Throne-Holst. Paris: This work was supported by the  FRM (Fondation pour la recherche Biomédicale)  " Bio-informatique pour la biologie " 2014 grant.
Singapore: Funded by  Singapore Bioimaging Consortium   Research Grant  ( SBIC RP C-009/2006 ) and  NHG grant  ( SIG/15012 ). UNSW:  Australian NHMRC Program  Grant  1037196  and Project Grants  1063960  and  1066177 ; and the  Janette Mary O'Neil Research Fellowship  to JMF. VA San Diego Healthcare/University of California San Diego: This study was supported by  R01MH083968 ,  Desert-Pacific Mental Illness Research Education and Clinical Center , and the  US National Science Foundation (Science Gateways Community Institutes; XSEDE). Sweden: Funding for the Swedish St. Göran collection was provided by by grants from the  Swedish Research Council  ( 2018-02653 ), the  Swedish foundation for Strategic Research  ( KF10-0039 ), the  Swedish Brain foundation , and the  Swedish Federal Government  under the  LUA/ALF agreement  ( ALF 20170019 ,  ALFGBG-716801 ).
OAA was funded by the  Research Council of Norway  ( 223273 ,  248778 ,  273291 ),  NIH  (ENIGMA grants). CMB thanks the PERIS grant contract by  Departament de Salut CERCA Programme/Generalitat de Catalunya   SLT002/16/ 00331. JMG thanks the support of  CIBERSAM  and the Comissionat per a Universitats i  Recerca del DIUE de la Generalitat de Catalunya  to the  Bipolar Disorders Group  ( 2017 SGR 1365 ) and the project  SLT006/17/ 00357 , from PERIS  2016-2020  ( Departament de Salut ). CERCA Programme/Generalitat de Catalunya. TH was supported by the  Canadian Institutes of Health Research  ( 103703 ,  106469 ),  German Research Foundation  (DFG grants  HA7070/2-2 ,  HA7070/3 ,  HA7070/4 ),  Nova Scotia Health Research Foundation ,  Dalhousie Clinical Research Scholarship, Brain & Behavior Research Foundation  (formerly NARSAD) 2007 Young Investigator and 2015 Independent Investigator Awards.
ML was funded by the  Swedish  state under the  ALF-agreement  ( ALF 20170019 ,  ALFGBG-716801 ) and the  Swedish Research Council  ( 2018-02653 ). JR thanks the Miguel Servet contract by the  Spanish Ministerio de Ciencia, Innovacion y Universidades. JS was supported by the  National Institute of General Medical Sciences  ( P20GM121312 ) and the  National Institute of Mental Health  ( R21MH113871 ). MHS was supported by the funding agencies  CAPES, Brazil. DJS was supported by the  SAMRC. GMT's work was supported by the  National Institutes of Health , Grant  T35 AG026757/AG/NIA  and the  University of California San Diego, Stein Institute for Research on Aging. EV thanks the support of the  Spanish Ministry of Science, Innovation and Universities  ( PI15/ 00283 ) integrated into the  Plan Nacional de I+D+I y cofinanciado por el ISCIII-Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER) ; CIBERSAM; and the  Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya  to the  Bipolar Disorders Group  ( 2017 SGR 1365 ) and the project  SLT006/17/ 00357 , from PERIS  2016-2020  ( Departament de Salut ).
Research Foundation (DFG, grant  FOR2107 KR 3822/7-2  to AK;  FOR2107 KI 588/14-2  to TK and  FOR2107 JA 1890/7-2  to AJ). Leiden: EPISCA was supported by  GGZ Rivierduinen  and the  LUMC. Melbourne: This study was funded by  National Health and Medical Research Council of Australia (NHMRC)  Project Grants  1064643  (Principal Investigator BJH) and 1024570 (Principal Investigator CGD). Minnesota: The study was funded by the  National Institute of Mental Health  ( K23MH090421 ), the  National Alliance for Research on Schizophrenia and Depression , the  University of Minnesota Graduate School , the  Minnesota Medical Foundation , and the  Biotechnology Research Center  ( P41 RR008079  to the  Center for Magnetic Resonance Research) ,  University of Minnesota , and the  Deborah E. Powell Center for Women's Health Seed Grant, University of Minnesota.
This work was carried out in part using computing resources at the  University of Minnesota Supercomputing Institute. Münster: This work was funded by the  German Research Foundation  (DFG, grant  FOR2107 DA1151/5-1  and  DA1151/5-2  to UD;  SFB-TRR58 , Projects  C09  and  Z02  to UD) and the  Interdisciplinary Center for Clinical Research (IZKF)  of the medical faculty of Münster (grant  Dan3/012/17  to UD). Novosibirsk: This work was supported by  Russian Science Foundation  (RSF grant  16-15-00128 ) to LA. SHIP: The Study of Health in Pomerania (SHIP) is part of the  Community Medicine Research net (CMR)  (http://www. medizin. uni-greifswald. de/icm) of the  University Medicine Greifswald , which is supported by the  German Federal State of Mecklenburg -West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from  Siemens Healthineers, Erlangen, Germany  and the  Federal State of Mecklenburg-West Pomerania.