dataesr/scientific-paragraphs-categorization huggingface dataset:
In our case, the focus is the QR management process in ASD. To understand how QRs are managed in a particular context, it is necessary to consider: 1. The contextual software development process that is being followed. From a particular process, it considers the method taken as a basis (base method, e. g. , Scrum, Kanban), its release frequency, the feature language used (e. g. , natural language), and its feature management artifacts (e. g. , product backlog). 2. The QR management practices that are being applied. A QR management practice is classified as an elicitation practice, specification practice, analysis practice, or validation practice. The aspects to be considered in each practice appear as attributes in the corresponding class. & Actor: The main actor in the theoretical model is the organization that manages the development of the software system.
M7 Multi-level CI for QR validation decreases the difficulty of analyzing QRs in cases of a large product with complex dependencies in ASD. EM7 For one of the companies, the challenge of analyzing QRs in the context of a large product with complex dependencies (C5) was mitigated by testing the system in a multi-level advanced CI instead of spending resources on analyzing the dependencies in advance. practitioners (e. g. ,  Alsaqaf et al. 2019) , users do not focus on software-internal QRs (e. g. ,  Aljallabi and Mansour 2015) , user stories are challenging for QRs  (Rodríguez et al. 2009) , and feedback regarding QRs is obtained too late  (Alsaqaf et al. 2019). A more comprehensive list of all the QR-related challenges reported in the scientific literature can be found in our SMS  (Behutiye et al.
This alignment has been the interest of many large software development organizations in the automotive and avionic domains  (Karhapää et al. 2017) , and QR management could benefit from it. Since  Alsaqaf et al. (2019)  is the most recent study, and since one of our companies operates in large-scale ASD, a brief comparison is warranted. We can see similarities in findings regarding QR challenges, however, we have treated these findings somewhat differently in our studies. For example, one of the companies in our study stated that "users do not pay attention to software-internal QRs," highlighting this as a challenge for which the company had developed a mitigation practice. However, in the study by  Alsaqaf et al. , "customers are not interested in internal QRs" was identified as a mechanism behind "QR elicitation challenges" rather than as a challenge on its own.
Five major events of adaptation to the gut in the Archaea The broad taxonomic coverage of the animal hosts and the use of archaeal specific primers allowed us to identify archaeal ASVs belonging to 19 described families, 10 orders, 6 classes, and 3 phyla. 84. 9% of these ASVs (94. 5% of the reads) share more than 95% identity with species in the Living Tree Project (LTP, v138) database  34  amended with characterized candidate species, and half of the reads share more than 99% identity with known species (Table  S2 ). Consistently, the vast majority (93. 7%) of the reads are affiliated to only six genera or families (Figure  2a ): Methanobrevibacter, Methanosphaera (Methanobacteriales),.
Methanobrevibacter, Methanosphaera and Methanomethylophilaceae had already been extensively reported in the gut microbiota of ruminants, human and termites  5, 15, 20. Our results show that they are also the main methanogenic lineages in a much wider range of animals. Methanobrevibacter members are by far the most dominant methanogens in our dataset -composing over a third of the total number of reads, followed by Methanomethylophilaceae members which accounted for 17. 5% of the total reads (Figure  2a ). The two others methanogen lineages that are the most prevalent in our dataset, Methanocorpusculum and Methanimicrococcus, have been less often reported in previous studies on the animal digestive tract  15, 35. Thaumarchaeota (this study, ASV4, ASV20 and ASV21, purple) and dominant soil archaea  37  (DSC1 and DSC2, brown).
ASV4/ASV20 are virtually similar to DSC2 representative sequence (only 1 indel in a 4/5Gs homopolymer region, which may be due to a 454-sequencing error in DSC2  38. ASV21 shares 99. 2% identity with the DSC1 representative sequence. The five dominant methanogen lineages in the gut have been rarely reported in open environments, as revealed by a meta-analysis of the archaeal 16S rRNA gene sequences present in the Silva database (Figure  2a ). They are also often dominant in samples with the highest archaeal absolute abundance (Figure  2c ). In contrast, Nitrososphaeraceae and rarer archaeal lineages (e. g. , Methanobacterium, Methanosarcina, Methanomassiliicoccaceae) are more often dominating in samples with the overall lowest archaeal absolute abundance (Figure  2b  and c ). Sequences from the rarer archaeal lineages have been generally reported from non-gut environments such as sediments and wetland soils  32, 39  (Figure  2a ).
Non-methanogenic lineages are components of the animal gut archaeome The family Nitrososphaeraceae (Thaumarchaeota), which gathers 15% of the total reads in our study, has rarely been reported in previous gut microbiomes studies (great apes and humans) and only when archaeal specific primers coupled with high-throughput sequencing (or nested PCR) were used  16, 41, 42. Conversely, this lineage is common in terrestrial environments such as soils (Figure  2a )  37, [43] [44] [45]. The large presence of these obligate aerobes in the gut is somehow surprising, but oxygen is available in some gut sections and in proximity of the gut wall in other sections dedicated to fermentation  46  and some animal host more aerobic communities than others  47. The three thaumarchaeotal ASVs that gather the largest number of reads in our dataset (ASV4/ASV20/ASV21) are also highly distributed among animal species, ASV4 being the most widespread archaeon in our samples (present in 65 animal species from 8 classes).
In our dataset, hydrogenotrophic methylreducing methanogens constitute almost 40% of the overall methanogen reads (Figure  7a ; Table  S4 ) and represent a large fraction of the methanogens in many animals (Figure  3e ; Supplementary text). This contrasts with many non-host environments (e. g. sediments, peat bog), where hydrogenotrophic methyl-reducing methanogens constitute a minor fraction of the overall methanogens  39, 85. It also reinforces the hypothesis that the gut environment is particularly propitious for this kind of methanogenesis, which could have led to the transition from methylotrophic and CO 2 -reducing methanogenesis to methyl-reducing methanogenesis in Methanimicrococcus and Methanosphaera, respectively  35. Other methane metabolisms (based on dismutation of methyl-compounds or acetate) are almost absent from the animal gut and may occur in the few Methanosarcina members (0.
6 % of the total reads) identified in our dataset (Figure  7a ). The few data on methanol concentration and H 2 partial pressure in the gut highly contrast with those observed in marine sediments where dismutation of methyl-compounds is prevalent (Figure  7b ). Gut conditions with relatively high partial pressure of hydrogen are generally more favourable for CO 2 -reducing hydrogenotrophic methanogenesis but can vary to be equally favourable for methyl-reducing hydrogenotrophic methanogenesis or even for dismutation of methyl-compounds (Figure  7b ). Why methanogens having this latter metabolism are not more prevalent in the gut is unclear, it might be related to the conditions generally favouring other metabolisms but this could also be related to the lower affinity of their enzymes for methanol and H 2 than methyl-reducing methanogens  46.
In our dataset, the low abundance of bacteria in the gut microbiota of bird supports the hypothesis of Hammer et al. ,  94  and extend the observations of Song et al. ,  9  on the particularity of the gut microbiota of these animals. However, we found that concentrations of faecal bacteria in other animals proposed to rely less on their gut microbiota, such as Carnivora species  94  , are as high as in other mammals (Figure  5b ).
Microbial Diversity Analyses Reads were processed and assigned to amplicon sequence variants (ASVs) using the DADA2 software (v1. 12. 1) in R (v3. 6. 0). Briefly, reads were trimmed and quality-filtered using the standard parameters -maximum expected errors for forward and reverse reads = 2, quality score = 2, and trimming length = 273 and 170 base pairs for forward and reverse reads, respectively. Forward and reverse reads were merged with a 20 base pair overlap, ASVs were generated, and chimeras were discarded. ASV annotation was performed using the Silva 16S rRNA database (v132). Assignment of ASVs to a main type of methane metabolism (hydrogenotrophic CO 2 -reducing, hydrogenotrophic CH 3 -reducing, acetoclastic and methylotrophic (methyl-dismutation)), was done based on their taxonomic affiliation, since all members of almost all methanogen genera/families have the same dominant type of methane metabolism (Table  S4 ).
Methanosarcina is the main exception, as species from this group can have one or several types of methane metabolisms. All ASVs that were not annotated as archaea were removed from the archaeal-specific primer generated sequences, and ASVs annotated as archaea or bacteria were kept from the universal primer generated sequences. Samples from the same species were merged by summing ASV abundances. These approaches resulted in 1307 archaeal ASVs from the archaea specific primers, as well as 140 archaeal ASVs and 19,145 bacterial ASVs from the universal primers. To estimate the novelty of the archaeal ASVs (obtained with the archaea-specific primers), we compared them using BLAST to 16S rRNA genes of isolated archaea retrieved from the SILVA Living Tree Project LTP database  99  plus additional sequences of candidate species belonging to Methanomassiliicoccales and Thaumarchaeota.
For diversity analyses, rarefaction was performed to normalize sequencing depth to 3,000 reads, leading to 1,253 archaeal ASVs. Bacterial ASVs were normalized to a sequencing depth of 12,000 reads per sample. Observed richness (alpha diversity) was estimated and all beta diversity analyses were performed using the 'phyloseq' package in R (v1. 30. 0). Subsequent statistical analyses were performed using the base Rstudio 'stats' package (v3. 6. 0) as well as the R package 'vegan' (v2. 5-6). To test for significant differences using the various beta diversity metrics (Table  1 ) a permutational multivariate analysis of variation (PERMANOVA) from the R package 'vegan' (function adonis) was used. A pairwise Wilcoxon rank sum test with continuity correction from the R package 'stats' (function pairwise.
Placement of ASVs within Reference 16S rRNA gene trees All archaeal ASVs were filtered on a per sample basis, to keep only ASVs representing at least 1% of the total number of reads of the sample. Reference sequences > 1200 bp with a quality >95% were obtained from the Silva SSU 138 database 100 , RDP database 101 , and an in-house dataset. Redundancy was removed from reference sequences with a 98% or 97% sequence identity threshold using the VSEARCH software 102. For each archaeal order, long reference sequences were combined with the ASV sequences and were aligned using the G-IN-SI algorithm in MAFFT 103. Phylogenetic trees were generated using the GTR+G4+I model in the IQTREE software  104. The distribution of ASVs host orders were mapped using ITOL 105. Reference sequences were ultimately removed from the tree to only keep the ASVs sequences.
Co-occurrence of Archaea and Bacteria To identify co-occurrence signal between archaea and bacteria across Mammalia, Reptilia, and Aves, we integrated the sequences from both the Universal and Archaea specific 16S rRNA gene amplicon sequencing. Only bacterial reads were selected from the Universal 16S rRNA gene amplicon sequencing for this analysis. We used VSEARCH 102 to cluster ASVs into OTUs at 97% in order to reduce the size of the dataset and to filter out truly low abundance lineages of microbes. Then, to merge these datasets in a way that accurately represented the microbial community in terms of relative abundance between archaea and bacteria, we normalized the two datasets both in terms of sequence depth and in terms of archaea-bacterial ratiosinformation which was gathered through qPCR data.
OTUs that were present in less than 10% of the animal classes -Mammalia, Aves, and Reptilia independently-were removed. Following this, we implemented both the SPIEC-EASI (Spiec. Easi package v1. 1. 0, 106 ) and the SparCC algorithms 107 (part of the Spiec. Easi package (v1. 1. 0)) in Rstudio (v3. 6. 0) to determine cooccurrence trends between archaea and bacteria. Networks were calculated with 1000 iterations. The output from these analyses were filtered using a 0. 5 minimum threshold of edge stability (SPIEC-EASI) (Table  S3 ) and a p-value < 0. 05 (SparCC), independently. Only the co-occurrence patterns identified by both algorithms were further analysed.
Investigation of archaea distribution in the gut and other environment All archaeal 16S rRNA gene sequences from Silva database  99  longer than 800 bp and with more than 80% sequence quality, alignment quality and pintail quality were downloaded. Sequences from metagenomes were removed because their environmental origin was not clearly indicated. The annotation of each sequence was retrieved from GenBank and used to classify them as "Gut", "Environmental" or "Human built" origin. Sequences from sponge, animal environments (e. g. nest) or polluted sites (e. g. dump) were not included. The relative abundance of each category was mapped on a tree of archaea built with genomic sequences used in Borrel et al. ,  36  as well as additional DPANN sequences not present in this study.
Gibbs free energies of methanogenic pathways The following chemical reactions were considered for methanogenic catabolisms: For each catabolism Gibbs free energy (∆G cat ) calculations were performed using the R package CHNOSZ 108 considering C(methanol) between 10 -3 and 10 -7 mol/l, p(H2) between 1 and 10 -7 bar, T = 298 K, pH = 7 and p(CO 2 ) = p(CH 4 ) = 10 -1 bar. CH 3 -.
Origin of the metadata Animal metadata were collected from various literature sources and online databases. Diet information for mammals and birds were downloaded from the EltonTraits database 109 , and information for other animal diets were annotated using the Animal Diversity Web database (Museum of Zoology, University of Michigan, https://animaldiversity. org/). Information on body weight were also gathered on this website. Information about coefficients of gut differentiation, pH, diet fibre content, and intestinal tract structure and mean retention time were gather from  3, 86, [110] [111] [112] [113] [114]. Information on methanol, H 2 CO 2 and CH 4 in the gut and in marine sediments originate from  18, 46, [115] [116] [117] [118] [119] [120] [121] [122] [123].
Acknowledgments C. T. is supported by a  PhD fellowship  from  Paris Sorbonne Université Science  and by funds from the doctoral school  Bio Sorbonne Paris Cité "BioSPC ". G. B. acknowledges funding from the  French National Agency for Research Grant Methevol  ( ANR-19-CE02-0005-01 ) and S. G. acknowledges  Archaevol  ( ANR-16-CE02-0005-01 ). This study has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative   Biology of Emerging Infectious Diseases"  (grant  n°ANR-10-LABX-62-IBEID ). We thank the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris. We also thank all people who generously collected and provided samples (or made it possible to do so) for this study:  Jérôme Fuchs  from the  Museum National d'Histoire Naturelle (MNHN) ,  Frédéric Delsuc  ( Institut des Sciences de l'Evolution, UMR5554 ),  Alexis Lécu  and  Olivier Marquis  from  Parc Zoologique de Paris ,  Baptiste Mulot  and  Hanae Pouillevet  from  Zoo de Beauval ,  Paola Dvihally  and  Dominique Gitton  from  Parc des Mammelles ,  Antoine Talarmin ,  Yann Reynaud ,  Stéphanie Guyomard ,  Matthieu Pot ,  Gaëlle Gruel ,  Séverine Ferdinand  from  Institut Pasteur de Guadeloupe ,  Thomas Godoc  from  Aquarium de Guadeloupe ,  Patrick Buisson  and  Sabine Collin  at  Palais de la Découverte ,  Jérôme Contignac  and  David Luis Garcias Warner  from  Nabau Projects/Grupo Atrox ,  Aude Bourgeois  from  Jardin des Plantes de Paris ,  Jérémy Sauvanet ,  Gaspard ,  Anaïs Tibi  and  Johnatan Aparicio.
These were derived from the field measurements within the IGAC/DEBITS/AFRICA (International Global Atmospheric Chemistry/Deposition of Biogeochemically Important Trace Species/AFRICA) project, in which some of our investigation sites were also included  (Agoufou, Banizoumbou, Djougou, and Katibougou). According to this dataset, the total wet deposition of N was estimated to be around 3. 2 kg, and dry deposition was estimated to be around 3. 6 kg N ha -1 yr -1 , with only little variation between Sahelian and Sudanian ecosystems  (Galy-Lacaux et al. , 2014).
1 ). (2015),  Bliefernicht et al. (2018)  grass/woodlands, the PSIM physiological simulation module  (Grote et al. , 2011a; Werner et al. , 2012)  was used, which has been widely applied for forests, including sites where ground vegetation needed to be considered  (Lindauer et al. , 2014; Dirnböck et al. , 2020) , as well as for savanna grasslands  (Grote et al. , 2009b). However, the model does not consider the production of fruits or similar. Therefore, we applied the PlaMo x module  (Kraus et al. , 2016; Liebermann et al. , 2020) , which has been developed particularly to described crop growth based on the same physiological processes as PSIM, for the agricultural plants. While parameters for agricultural crops were mostly defined in a species-specific manner in the model already, information about physiological properties of savanna grass and tree species were newly collected from literature.
Code availability. The LandscapeDNDC model source code for released versions of the model is permanently available online Data availability. All data regarding soil properties, climate, and management that were used to initialize and run the model, as indicated in the text, are provided in the Radar4KIT database (DOI: https://doi. org/10. 35097/437; Rahimi et al. , 2021)  and are freely downloadable. In addition, evaluation data were obtained from the FLUXNET (Pastorello et al. , 2020; https://daac. ornl. gov/, last access: 16 June 2021), WASCAL Data Discovery Portal  (Bliefernicht et al. , 2018 ; https://wascal-dataportal. org/wascal_searchportal2/, last access: 16 June 2021), CARBOAFRICA project (Bombelli  et al. , 2009; https://www. carboafrica. eu/data_en. asp/, last access: 16 June 2021), and AMMA-CATCH database  (Gallé and Feller, 2007 ; http://bd.
Monitoring and 3-D video recording through surveillance cameras, and processing of the collected information using Python software, constitute an essential part of the evaluation procedures in the artificial street. VICON Motion Systems Inc. , Los Angeles, 8 cameras, 120 Hz; Python (Python 2. 7. 6, www. python. org). All parameters are variable and can be set and modified remotely from a control room ( Figure  3  ). Several studies were performed in this platform. They allowed us to demonstrate that the precise evaluation of the ability of glaucoma patients to perform everyday life tasks is complex and may require both monocular and binocular VF tests. 15 Also, in glaucoma patients, not only is the quality of life altered but also their ability to perform common activities of daily life. 16 In Streetlab, we identified novel indicators of postural control interfering with spatial learning and navigation capabilities in the elderly, and we tested the hypothesis that navigation difficulties in elderly people are associated with deficits in processing and encoding spatial cues.
17 , 18 We also evaluated the age-related differences in functional and structural connectivity of spatial navigation. 19 , 20 Here, we shall report elements of our experience in the field of IRDs. Here, we report on novel in vivo high-resolution imaging methods that can assist disease staging and will help in evaluating treatment efficiency (i. e. , by evaluating rescue of photoreceptor structure and function); and we offer an overview of the concept, development, validation, and achievements of the innovative Streetlab platform, dedicated to assessing th real-life benefit of neuroprotection and optogenetic therapies for patients with retinal degenerative diseases. correlations between the different variables measured from standard fundus autofluorescence and OCT examinations, and functional data including VA and static perimetry, to highlight the structural severity criteria that predicted an important level of functional loss.
The study population consisted of 172 adult patients with RP (rod-cone dystrophy) without any other ocular or general complication or disease, selected from the cohort followed at the Rare Disease Reference Centre of the Quinze Vingts National Ophthalmology Hospital, and a group of 15 age-matched healthy controls. Functional data collected and analyzed were bestcorrected VA, central sensitivity, and mean sensitivity extracted from static perimetry.
Structural data Fundus autofluorescence (FAF) imaging pictures were acquired with the Heidelberg retina angiograph (HRA2; Heidelberg Engineering, Heidelberg, Germany). We measured the dimensions of the ring of increased autofluorescence in the macular region. The parameters analyzed were ( Figure  4  ): -Surface of the area delimited by the internal edge of the ring (mm 2 ) = FAF_ACI -Surface of the ring of hyper-autofluorescence (mm 2 ) = FAF_area-HAF -Horizontal diameter of the internal edge of the ring ( μm) = FAF_GAH -Horizontal width of the ring of hyper-autofluorescence ( μm) = FAF_HAFh -Vertical diameter of the internal edge of the ring ( μm) = FAF_GAV -Vertical width of the ring of hyper-autofluorescence ( μm) = FAF_HAFv OCT data were obtained from Heidelberg Spectralis ( Figure  5  ).
• HIGH-RESOLUTION ADAPTIVE OPTICS RETINAL IMAG-ING: Flood-illumination AO images from 10 eyes of 10 patients with rod-cone dystrophies were acquired and reviewed. Patients were included in a clinical trial on retinal imaging (registered in clinicaltrials. gov NCT01546181). This institutional clinical study was carried out according to the principles outlined in the Declaration of Helsinki. Approval of the ethics committee of Saint-Antoine Hospital (Paris, France) was obtained. Patients with rod-cone dystrophies from various genetic causes were examined. The image protocol was as follows: Flood-illumination adaptive optics (FIAO) imaging was performed with a commercially available FIAO camera (rtx1; Imagine Eyes, Orsay, France). The adaptive optics loop features a 750 nm superluminescent diode beam to measure the point spread function by a Shack-Hartmann wavefront sensor (40 × 32 array).
Images taken at different entry pupils were registered by rotation and size adjustment using either Adobe Photoshop 7. 0 (Adobe Corporation, Mountain View, California, USA) or i2k Align Retina software (DualAlign, LLC, Clifton Park, New York, USA) in combination. The effect of gaze orientation was analyzed by calculating the standard deviation of the ImageJ Z projection plugin (available in the public domain at rsb. inf. nih. gov/ij; National Institutes of Health, Bethesda, Maryland, USA). • ASSESSING FUNCTIONAL VISION: We report in this paper two case studies that assess functional vision in IRDs: (1) assessing mobility in visually impaired subjects with RP; and (2) assessing mobility in subjects treated with Luxturna R for RPE65 -LCA.