dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Pairs of Phage Genomes with Recombinant Genes In order to separate events of recombination from homology due to shared ancestry, we used the relationship between the fraction of homologous proteins (over the average number of proteins for each phage pair) and the fraction of homologous proteins with very high identity (at least 80% identity, also over the average number of proteins for each phage pair). We used the ols function from the statsmodels package in Python (v2. 7) to generate a linear model describing the expectation of ancestry between phage genomes. To characterize common ancestry as robustly as possible, we restricted the data set to temperate-virulent pairs of phages, as this data set is expected to contain the lowest frequency of recombination events. Further, and in order to reduce the influence of the outliers associated with recombination (and thus maximize the fit of the linear model to cases of common ancestry), we analyzed only the pairs of phage with at least 50% of the proteins in their (average) genomes classified as homologous (meaning at least 35% identity and 50% coverage) and a minimum of one highly similar homologous protein (at least 80% identity).
Functional Annotation of Proteins We used HMMER v3. 1b2  (Eddy 2011 ) (default parameters) to search for genes matching the prokaryotic Virus Orthologous Groups (pVOGS) (Version May 2016;  Grazziotin et al. 2017)  database of hmm profiles (filters used were e-value < 1e-5 and profile coverage > 60%). Only pVOGs with a viral quotient (VQ) above 0. 8 were used (7,751 out of 9,518 pVOG profiles in total). The pVOG profiles were classed into seven functional categories: 1) structure, 2) lysis, 3) packaging, maturation/assembly, and DNA injection, 4) DNA metabolism, recombination, and regulation, 5) others, and 6) unknown by two approaches. First a profile-profile comparison was done using the HHsuite 2. 0. 9 (So ¨ding 2005) with phage-specific profiles from the PFAM (El-Gebali et al. 2019) and TIGRFAM  (Haft et al.
2003)  database (taken from  Fouts [2006] ). Applying a threshold of P value <10 À5 resulted in 711 profiles that cluster in 261 groups using the Louvain algorithm. The annotations of the PFAM and TIGFRAM profiles were used to assign one of the functional categories to a group. The remaining profiles were manually assigned considering the piled-up annotations of the pVOGs (supplementary file S3, Supplementary Material online). The identification of bacterial functions was performed based on the EggNOG database of hmm profiles (bactNOG Version;  Huerta-Cepas et al. 2019). In order to minimize the number of bactNOG entries that derive from prophages in bacterial genomes, we used hhsearch (HHSuite version 3. 2;  Steinegger et al. 2019)  to remove from bactNOG the profiles matching pVOG profiles.
The bactNOG profiles with matches in pVOGs with VQ above 0. 8 (P value <0. 0001) were discarded. This resulted in a reduction of 34% of the bactNOG profiles' data set. The remaining 135,814 bactNOG profiles were used to class the data set of putative recombinant genes (filters used were e-value < 1e-5 and profile coverage > 60%) in broad functional categories.
Detection of Phage-Like Recombinases in Phage and Bacterial Genomes The families of recombinases of phage were described in  Lopes et al. (2010) , for which we built profiles or recovered them from PFAM. To build the profiles, we retrieved the homologs given in the reference above and aligned them using default options with MAFFT (v7. 407)  (Katoh and Standley 2013). The alignments were used to build the profiles using hmmbuild from HMMER (default options). Our novel profiles are given in supplementary material (supplementary file S6, Supplementary Material online). We used HMMER to search for homologs of Sak (PF04098, -cut_ga), Sak4 (Sak4 from phage T7, minimum score ¼ 20), Erf (PF04404, -cut_ga), RecT (PF03837, -cut_ga), and gp2. 5 (gp2. 5 from phage T7, minimum score ¼ 20). An additional profile, UvsX, also matching RecA (PF00154, -cut_ga) was searched only in phage genomes.
Hosts were retrieved from the GenBank file of each phage, and all the genomes in the database belonging to that host's species were used for the calculation. For example, if the host species of a given phage is described as E. coli, we calculated the mean number of recombinases in all E. coli genomes. The values shown in the table are the mean of these means for phages with recombinant genes and the remaining phages. If recombinases were found within the coordinates of a prophage, they were associated with prophage regions. Note that a particular phage host can be included multiple times in the distribution, if subsequent phages have a similar host, and can even result in the host being included in the data set of recombinant and nonrecombinant phages simultaneously. Although this results in repeated sampling, it represents the likelihood that hosts of recombinant or nonrecombinant phages encode for recombinases.
Detection of Genes Encoding Proteins Involved in NHEJ in Bacterial Genomes We used HMMER to search for homologs of the Ku protein (PF02735. 16) in the data set of bacterial genomes, using the -cut_ga parameter. We did not require the presence of a neighboring ligase to consider the system complete, because such a ligase is often absent  (Bernheim et al. 2019). The set of bacterial genomes assigned as host of a given phage was asserted as described in the section above. The fraction of hosts that encode proteins involved in NHEJ was calculated as the number of genomes where at least one homolog of Ku was found, divided by the total number of genomes considered for a given phage. The use of the presence/absence of Ku as the key variable, and not the number of Ku genes, is due to three reasons: 1) the numbers were always low (most often 0 or 1), 2) some genomes encoding multiple NHEJ systems express them during different times, and 3) some bacteria seem to encode heterodimeric Ku where two genes are necessary for the process  (Bertrand et al.
Calculation of the Patristic Distance between Bacterial Hosts We used the 16S rRNA of the bacterial genomes identified in the RefSeq annotations, corresponding to the species level of the identified phage hosts. We selected the first entry of each genome and aligned them using the secondary structure models with the program SSU_Align version 0. 1. 1  (Nawrocki et al. 2009 ). Poorly aligned positions were eliminated with the ssu-mask. The alignment was trimmed with Bacteriophage Diversification via Genetic Exchanges. doi:10. 1093/molbev/msab044 MBE trimAl version 1. 2 (Capella-Guti errez et al. 2009) using the option -noallgaps to delete only the gap proteins but not the regions that are poorly conserved. The 16S rRNA phylogenetic tree was inferred using maximum likelihood with IQTREE version 1.
6. 5  (Nguyen et al. 2015)  under the best-fit model automatically selected by ModelFinder  (Kalyaanamoorthy et al. 2017) , and with the options -wbtl (to conserve all optimal trees and their branch lengths) andbb 1,000 to run the ultrafast bootstrap option with 1,000 replicates. The patristic distances among the taxa in the 16S trees were calculated from the tree (weighted by the edge distances) with the dendropy package in Python  (Sukumaran and Holder 2010) , using the functions phylogenetic_distance_matrix and patristic_distance, with the default parameters.
Acknowledgments We acknowledge support by the  PRESTIGE Program  ( PRESTIGE-2017-1-0012 ), the  ANR  grants ( ANR-16-CE16-0029 ), the  Fondation pour la Recherche M edicale (Equipe FRM   EQU201903007835 ), and the  Laboratoire d' Excellence IBEID  ( ANR-10-LABX-62-IBEID ). We thank  Mireille Ansaldi ,  Marta Lourenc  ¸o, and the members of the  Microbial Evolutionary Genomics laboratory  by discussions and comments on earlier versions of this manuscript. This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris.
In OWL API  [50]  , there are four types of methods for creating bridging axioms; each one is dedicated to a particular type of entities (classes, object properties, data properties, and individuals). For example, to create an equivalence axiom between two classes class1 and class2 , we should call the following OWL API method: datafactory. getOWLEquivalentClassesAxiom(class1, class2). The same goes for creating equivalence axioms between two object properties, two data properties, or two individuals. However, in an RDF alignment, it is impossible to directly identify the type of the matched entities because they are only expressed by their original full IRIs-as they were defined in their original ontology. We cannot identify whether a given entity IRI represents a class, a property, or an individual.
AROM In this subsection, we introduce the Alignments Reuse for Ontology Merging (AROM) algorithm. The latter aims to automatically build a full-merge ontology among several input ontologies. Fig. 6  shows the general steps of the AROM process. The current AROM framework includes two different versions which provide respectively a non-refactored output (based on original IRIs) and a refactored output (based on modified IRIs). Both versions take into input the ontologies (in OWL) to be integrated, as well as the alignments (in RDF) among them. AROM source code and other associated resources are freely available on GitHub 11. The AROM algorithm comprises the following steps: 1. Loading two or more input ontologies ( owl files) and alignment(s) between them ( rdf file(s)); 11 https://github. com/inesosman/AROM 2.
Step 1 : Loading Input Ontologies & Alignments This step is the same step used in OIAR ( See Section 4. 1. 1 ). Step 2 : Disambiguating the Input Alignments (Optional) This step is the same step used in OIAR ( See Section 4. 1. 1 ). Step 3 : Generating a New Name for each Set of Equivalent Entities to be Merged Algorithm 2 parses the equivalence correspondences of the input alignments after being trimmed, and returns a HashMap whose "key" contains the original IRI of a given entity, and "value" contains its new name (or code) in our future merged ontology. By doing so, we associate a unique code to each set of equivalent entities. We will use this unique code as a short name for the merged entity resulting from the merge of a set of equivalent entities in the merged ontology. The resulting merged entity will also have new labels that are actually the short original names of the entities that were merged into it.
Methods Fourteen patients with mitral valve prolapse were included in this study. The MR protocol, performed on a 3T MR scanner, included MOLLI sequences for T1 maps acquisition and a standard SR-turboFlash sequence for dynamic acquisition. DCE data were acquired for at least 120s. We implemented a full DCE analysis pipeline with a pre-processing step using an innovative motion correction algorithm (RC-REG algorithm) and a post-processing step using the extended Tofts Model (ECVETM). Estimated ECVETM maps were compared to standard T1-based ECV maps (ECVT1) with both a Pearson correlation analysis and a group-wise analysis.
Acquisition of DCE data and T1 maps MR data of 14 patients, with suspected or diagnosed mitral valve prolapse were extracted from the STAMP (STretch and Myocardial Characterization in Arrhythmogenic Mitral Valve Prolapse) study (NCT02879825), and were analyzed for the purpose of this study. STAMP was approved by the local committee of ethics, wherein all participants provided written informed consent. Every patient had a single measure of hematocrit (hct) that was used for ECV calculation. The CMR protocol was performed on a 3Tesla (T) MR scanner (Prisma, Siemens Healthineers, Erlangen, Germany). Injection consisted in a 0. 15mmol/kg bolus Gd-DOTA (Dotarem ® , Guerbet, France) at least 6 seconds after the beginning of the dynamic acquisition at a rate of 4mL/s. The pre-bolus T1 map (T10 map) was acquired with a single shot MOLLI set of 8 TI samples (5(3)3) on 8 slices covering the heart from the left atrium to the mid ventricular area (TR/TE: 283/1.
ECV mapping using extended Tofts modelling The processing pipeline of this study (Figure  1 ) was implemented in Matlab® (The Mathworks, Natick,MA, USA). The first step consisted in the registration of the magnitude images of the DCE series using our homemade registration algorithm (RC-REG). DCE acquisitions were normalized using a pre-scan calibration in order to attenuate signal inhomogeneity from the coil profile across the slice. The specific difficulty met with DCE images registration is the contrast change from one image to another, especially during the bolus arrival. The originality of this registration algorithm was the use of two reference images for the successive registration of pre-bolus and post-bolus acquisitions. Registration of DCE images was based on a 2-dimensional non-rigid algorithm using b-spline deformation model with gradient descend optimization using the residual complexity (RC) as similarity measure included in the Medical Image Registration Toolbox developed by Myronenko et al.
[25]. RC-REG algorithm is further described in Figure  2. The algorithm was set to iterate until both the mean and standard deviation (SD) of the Structure Similarity (SSIM)  [26]  scores of the registered DCE series reached empirically determined cutoff values (mean > 0. 81 and std < 0. 028). Thus, the algorithm kept iterating using the newly registered DCE images as a starting point. This allowed the algorithm to manage DCE series with large respiratory motion, which leads to blurry averaged DCE series after the first iteration. The T10 map was resized and downsampled to fit the DCE series resolution and FoV. Then, T10 map was aligned to the registered DCE series averaged image using the Matlab® multimodal 2-dimensional rigid registration algorithm (function imregister). A pixel-wise signal to CA concentration conversion on the DCE series was then performed.
RC-REG is a 2D registration algorithm (like MOCO), which implies that it is intrinsically unable to register through-plane motion leading to lower similarity metric scores from one image to another. It is also a post-processing algorithm and therefore dependent on the quality of the ECG gating during the prospective image acquisition. This acquisition technique is known to be sensitive to irregular heart rates  [35] ,  [36]  and even if DCE images were correctly aligned, the apparent thickness of the left ventricle may vary if such mistriggered data are present. This issue was not address in this work but it is visible on supplementary material 1. Such isolated mistriggered acquisitions could be potentially discarded from the DCE dataset and replaced using temporal interpolation with very low impact on the final analysis.
Transcriptome Analysis during hiPSC Differentiation into Otic Neurosensory Lineage Undifferentiated hiPSCs in monolayer cultures were treated with SB431542 (TGFβ inhibitor) and Dkk1 (WNT inhibitor) until day 6 in vitro. They were then challenged with WNT3A for an additional 7 days (Figure  1A ). Genes associated with pluripotency were progressively repressed, consistent with our previous data showing a marked difference between undifferentiated hiPSCs and the transcriptome of otic sensory cells at day 13 in vitro (  [4] ; (ENA) database (E-MTAB-6679)). The hiPSCs treated cultures underwent rapid and profound morphological changes leading to homogeneous colonies suggesting their progressive differentiation (Figure  1B-E ). The differentiated cells in treated-cultures were analyzed by immunocytochemistry for specific cell type markers along the human otic lineage specification  [3, 4, 8, 12, 13].
Evaluating Relationships among Differentially Expressed Genes through Network Building Ingenuity pathway analysis (IPA) software was used for the functional assessment of genes that were differentially expressed in differentiated cells at day 13 compared to those of undifferentiated hiPSCs at day 0. Enriched gene ontology (GO) terms revealed that some functional annotations were more represented in cellular and tissue development, cellular movement, and cell-to-cell signaling pathway categories (Figure  S2 ). Bar charts represent the relative gene expression levels obtained for distinct panels of markers featuring members of otic/placodal, HMG/CHD7, SOX and RA gene families, respectively. Noticeably, results confirm the upregulation of otic/placodal (DACH1, BMP7, etc. ) HMG (HMGA1, HMGA2, etc.
RNA-Seq Data Analysis Our publicly available RNA-seq data were downloaded from the European Nucleotide Archive (ENA) database (E-MTAB-6679). The RNA extraction procedure, libraries construction, and sequencing are detailed in our previous study  [4]. Briefly, the RNAseq libraries (2 × 100 bp; Paired-end) were constructed according to Illumina's protocol, using 2 µg of to-tal RNA. Sequencing analysis and base calling were performed using the Illumina Pipeline, and sequences were obtained after purity filtering. The studies of the RNAseq experiments were performed with RNAseq data treatment, TopHat (http://tophat. cbcb. umd. edu/, accessed on 1 August 2021) and Cufflinks (http://cufflinks. cbcb. umd. edu/, accessed on 1 August 2021) tools, with adapted and specific parameters (E-MTAB-6679, released on November 2020).
Positive controls (i. e. , brain and pancreas) are supplemented to each experiment. Negative controls are acquired by alternating the primary antibodies with reaction buffer or substituting them with isotype-matching immunoglobulins. All sections were digitally examined using a Zeiss AxioVision 4. 1 software coupled to an AxioPlan2 microscope (Zeiss, Jena, Germany). The immunostained sections were analyzed at x20 magnification utilizing a TissueFaxs ® Plus System coupled onto a Zeiss Axio Imager Z2 Microscope (Jena, Germany). Analyzed areas were then acquired using the TissueFaxs ® (TissueGnostics, Vienna, Austria). Fluorescence immunohistochemistry was performed using a Ventana Roche Discovery XT Immunostainer (Mannheim, Germany). The sections were incubated with the primary and secondary antibodies for 1 h.
Z. , supervision, conceptualization, methodology, investigation, formal analysis, writing-original draft, writing-review and editing. All authors have read and agreed to the published version of the manuscript. Funding: This research has been financially supported by EU-FP7, Grant/Award Number: 603029 to A. Z. This research has also been financially supported by the K-regio project "eVITA" (electrical Vestibular Implant Tirol Austria) sponsored by EFRE (Dieses Projekt wird aus Mitteln des Europäischen Fonds für regionale Entwicklung kofinanziert, www. efre. gv. at, accessed on 1 August 2021). The Austrian Science Fund (FWF Austria) projects I 4811 (Neurotrophins in developing human inner ear and in HNSCC) & project I 4147-B (Modelling electrical stimulation of the human cochlear nerve), also supported this research.
Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.
INTRODUCTION Digital measurements provided through clinical and consumer devices such as wearables, phones, and smartwatches are providing opportunities to monitor the disease, treatment effects, and the daily lived experience of disease through the collection of real-world evidence  1. While most efforts to incorporate these types of data have been in the context of exploratory and feasibility studies, we are increasingly seeing evidence of their use as digital endpoints in clinical trials  2. Interpretation of the data streams from these devices into sensitive 'digital biomarkers' and endpoints requires the development of sophisticated analytical algorithms, and vetting these algorithms requires extensive validation against quality datasets, using unbiased evaluation methods. Here we describe the use of an unbiased approach to benchmark multiple approaches for deriving clinically relevant features of disease through crowdsourcing and independent evaluation.
In short, the PDDB Challenge participants were provided with training data, which included sensor data, as well as disease status or symptom severity labels. They were also provided a separate test set, which contained sensor data only. Given raw sensor data from two studies, participating teams engineered features from the sensor data that were evaluated on their ability to predict disease labels in models built using an ensemble-based predictive modeling pipeline. The challenge leveraged two different datasets. In order to assess the ability to predict whether an individual has PD, we used mPower  7  , a remote smartphone-based study. While a portion of the mPower dataset had previously been released publicly, a second portion of the data remained private to the challenge organizers. This allowed challenge evaluations to be performed in a blinded, unbiased manner.
In this study, accelerometer and gyroscope data from a gait and balance (walking/standing) test in 4799 individuals (76,039 total measures) were provided to participants in order to engineer features to discriminate patients with PD from controls. In order to assess the ability to predict symptom severity, we used the Levodopa (L-dopa) Response Study  8, 9  , a multi-wearable clinical study that included symptom severity assessment by trained clinicians. This dataset had not been shared publicly at the time of the challenge. In this study, accelerometer recordings from GENEActiv and Pebble watches were captured on two separate days from 25 patients exhibiting motor fluctuations 10 (i. e. , the side effects and return of symptoms after administration of levodopa), as they were evaluated for symptom severity during the execution of short, 30 s, motor tasks designed to evaluate tremor, bradykinesia, and dyskinesia.
The Parkinson's Disease Digital Biomarker DREAM Challenge We developed four sub-challenges using the two datasets; one using data from the mPower Study and three using data from the L-dopa Response Study. Using the mPower data, we sought to determine whether mobile sensor data from a walking/standing test could be used to predict PD status (based on a professional diagnosis as self-reported by the study subjects) relative to agematched controls from the mPower cohort (sub-challenge 1 (SC1)) (Supplementary Table  1 ). The three remaining sub-challenges used the L-dopa data to predict symptom severity as measured by: active limb tremor severity (0-4 range) using mobile sensor data from six bilateral upper-limb activities (sub-challenge 2. 1 (SC2. 1)); resting upper-limb dyskinesia (presence/absence) using bilateral measurements of the resting limb while patients were performing tasks with the alternate arm (sub-challenge 2.
To assess whether the total number of tasks performed by a subject had an impact on predictive performance, we attempted to compare subjects that had performed more tasks with those that had performed fewer. However, we found that in the mPower dataset the number of walking activities performed was predictive in itself, i. e. , PD cases on average performed more tasks than the corresponding controls. We could therefore not conclusively determine whether having more data from walking activities on a subject increased the performance of the predictive models, though, related work has shown that repeatedly performed smartphone activities can capture symptom fluctuations in patients  3.