dataesr/scientific-paragraphs-categorization huggingface dataset:
For some of the measurements performed with the highest concentrations, the steps were maintained for 60 s for the lowest shear rates. These steps were repeated with increasing shear-rate values. We have improved our previous data-analysis procedure  (13). The signals were automatically analyzed by a routine implemented in MATLAB. The average viscosity during one measurement was obtained by removing the zero-shear baseline measured from a linear fit based on the instrumental signal obtained before and after the corresponding applied shear. The error bars in Fig. 3  correspond to the rms values of the signal for each independent measurement. The error bars in Fig. 4  represent the reproducibility of measurements performed at the same bacterial concentration but for different experimental campaigns-i.
Cell bodies showed up as bright features, and 2D tracks, in the focal, or (x, y), plane were extracted by using Trackmate (ImageJ)  (40). The 100 to 1,000 tracks in each dataset remaining in the (x, y) plane for longer than 0. 1 s were extracted. Velocities along the x and y axes were measured by deriving smoothed trajectories (smoothing over 0, 1 s using a third-order Savitzky-Golay filter). PIV. We recorded phase-contrast movies (≈ 40 s-long, Plan Fluor 10×, N. A. 0. 3) with large field of view (≈ 700 µm × 700 µm) near the middle of a 400-µm-height capillary to avoid the wall effect. Approximately 10 to 15 movies were recorded over a period of ≈ 15 min shortly after adding the serine and filling and sealing the capillary. Each movie was analyzed independently with PIV, yielding one velocity correlation function per movie.
We found no systematic time dependency of these correlation functions, and thus we averaged these together. The error bars presented in Fig. 4K  represent ±1 SD. We used a standard MATLAB PIV software adapted from the PIVLab toolbox  (41, 42). The time scale between two successive images was 0. 2 s (20 frames). The final metapixel box was 32 × 32 pixels 2 (≈ 42 × 42 µm 2 ) with an initial halfbox-size spatial shift. Nonmotile fraction of the bacterial solutions was negligible ( 10%), and thus PIV analysis was mostly based on motion of motile cells. Data Availability. The research data underpinning this publication are available on the Edinburgh DataShare repository  (43).
ACKNOWLEDGMENTS. The work was funded by  CNRS/Royal Society  Grants  PHC-1576  and  IE160675 ;  European Research Council  Grant AdG  340877-PHYSAPS ;  EU-FP7-Infrastructures European Soft Matter Infrastructure Grant CP & CSA-2010-262348 ; L' Agence Nationale de la Recherche  " Bacflow " Grant  ANR-15-CE30-0013 ; and by  "Investissements d'Avenir" LabEx PALM  ( ANR-10-LABX-0039-PALM ). We thank  Christophe Manquest  for technical support for viscosity measurements.
Drain and source steps consisted of a Ni deposition by EBD and patterned by lift-off in acetone, assisted by ultrasonication. The final step was a 200 nm Al EBD deposition, which was etched in Cl 2 plasma (Corial 200IL) for pads and metal path realization. An ohmic drain could be realized, but required additional processing steps. Electrical characterization of the MMTs and FGMMTs was performed on a Wentworth probe station. Measurements were taken using a Keysight B2902A source/measure unit (SMU) controlled by Keysight Quick IV Measurement Software v. 4. 0. 1648. 5970 with an additional Weir 413D voltage source as a constant voltage supply for either CG1 or CG2, depending on which variable was being swept. For the summing FGMMT with two CG1 inputs (CG1 A and CG1 B ), two Keysight B2902A SMUs were used to supply sine waves of different frequencies on each CG1.
Acknowledgements E. B. and R. A. S. contributed equally to this work. This work was partly supported through  EPSRC  grants  EP/R511791/1  and  EP/R028559/1  and  Research Fellowship   10216/110  from the  Royal Academy of Engineering of Great Britain. Device fabrication had been performed on the NanoRennes platform. The authors thank  Dr. Brice Le Borgne  for initial liaison and process discussions,  Prof. John M. Shannon  for on-going advisory meetings,  Prof. Craig Underwood  for reviewing the manuscript,  Dr. David Cox  and  Mr. Mateus Gallucci Masteghin  for assistance with the SEM images.
Bioinformatics CRAC samples were demultiplexed using the pyBarcodeFilter script from the pyCRAC utility suite. Subsequently, the 3′ adaptor is clipped with Cutadapt and the resulting insert is quality trimmed from the 3′-end using Trimmomatic rolling mean clipping (window size = 5, minimum quality = 25). At this stage, the pyCRAC script pyFastqDuplicateRemover is used to collapse PCR duplicates and ensure each insert is represented only once. Each unique insert in our library is associated with a six nucleotides random tag within the 5′ adaptor. The resulting sequences are reverse complemented with Fastx_reverse_-complement (part of the fastx toolkit), and mapped to the R64 genome (sgd) with bowtie2 (-N 1 -f)  [38]. Read counts were normalized relative to reads derived from an S. pombe spike-in.
The S. pombe spike cells contain a non-relevant protein tagged with the same HTP tag that was co-purified with the S. cerevisiae sample. We then performed a metagene analysis using a package available for Python, ngsplot. We obtained the scatterplots, boxplots and density plots by counting the number of reads within the ORF of a gene using HTSeq, and then plotting this data in R. IGV  [39]  was used to directly compare reads at different positions in the genome. AfterQC v0. 9. 6  [40]  was used with default parameters to do the quality control, filtering and trimming of the paired-end sequencing data obtained from MNase. The cleaned sequence data was aligned to the S. cerevisiae (R64) reference genome with bowtie2-2. 3. 3. 1  [38]. Once mapped, the resulting SAM files were analysed using DANPOS v2.
2. 2  [41]  in order to determine the nucleosome localization, occupancy and fuzziness across the whole genome in all the analysed strains. DANPOS pools the replica of the same condition and can use naked data as a background correction. For BioGRO-seq samples, Trimmomatic  [42]  was used to clip sequencing adapters, trim reads with low quality scores and filter reads shorter than 20 bases. High quality reads were then aligned to the S. cerevisiae R64 reference genome with TopHat2. Normalized average density plots around genomic features were generated with the ngs. plot software  [43]. The RNA-seq mode and the statistical robustness parameter, which filters out 0. 5% of genes with the most extreme expression values, were applied to all calculations. For high-resolution polyA-site mapping, we used Trimmomatic to remove sequencing adapters, 5ʹ-end barcodes, and 3ʹ-end bases that fell below a minimum quality of 3.
In order to confirm that the reads conforming the detected CRAC peaks corresponded to nascent RNA, all reads that contain a deletion (indicating the presence of a crosslink) were selected and a dataset containing only the position of the deletions (instead of the whole read) was generated. Genes showing prominent peaks were examined and showed patterns identical to those seen with all the reads (Figure  S10 ). This accumulation of RNA pol II at the 3ʹ was also detected in metagenes after fast depletion of Xrn1 (Fig. 3E, S11A ), as it happened in almost every gene (Fig. 3F, S10, S11B ). We found, also in this case, that the maximal differential CRAC signal mapped 40 bp upstream of the pAS (Fig. 3G, S11C ). These results strongly indicate a functional influence of Xrn1 in late elongation, that is not due to the slow growth phenotype of xrn1∆.
Accession numbers All seq data are stored in the GEO repository (accession numbers GSE153037, GSE153072 and GSE158548). To review GEO accession go to https://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE153037token: irixekwyrpkhnkf to https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE153072 token: cpkroosstvobdql and to https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE158548.
Primary ciliary dyskinesia (PCD) is a rare inherited disease characterised by malfunctioning cilia leading to a heterogeneous clinical phenotype with many organ systems affected. There is a lack of data on clinical presentation, prognosis and effectiveness of treatments, making it mandatory to improve the scientific evidence base. This article reviews the data resources that are available in Europe for clinical and epidemiological research in PCD, namely established national PCD registries and national cohort studies, plus two large collaborative efforts (the international PCD (iPCD) Cohort and the International PCD Registry), and discusses their strengths, limitations and perspectives. Denmark, Cyprus, Norway and Switzerland have national population-based registries, while England and France conduct multicentre cohort studies.
Based on the data contained in these registries, the prevalence of diagnosed PCD is 3-7 per 100 000 in children and 0. 2-6 per 100 000 in adults. All registries, together with other studies from Europe and beyond, contribute to the iPCD Cohort, a collaborative study including data from over 4000 PCD patients, and to the International PCD Registry, which is part of the ERN (European Reference Network)-LUNG network. This rich resource of readily available, standardised and contemporaneous data will allow obtaining fast answers to emerging clinical and research questions in PCD.
Danish PCD Registry The Danish PCD Registry, founded in 2006, is hosted by the Danish PCD Centre (Copenhagen, Denmark) and governed by the head of the service. The registry is national and includes all patients diagnosed with PCD in Denmark since 1976. Patients are identified during diagnostic work-up after referral from other departments across the country ( primarily from paediatrics, adult pulmonology, ear, nose and throat (ENT), cardiology, and neonatology) and occasionally from abroad (Sweden and Norway), although the latter are not followed-up after diagnosis and are not included in the counts on prevalence. The registry collects diagnostic information from several years of repeated testing and longitudinal lung function tests (more than 6000 tests). Clinical data are available by searching the EPIC electronic patient records.
Others Several other countries in Europe have cross-sectional or longitudinal datasets on PCD patients treated in specialised centres, without having a national registry or cohort study. These include Belgium, Italy and the Netherlands. Most of these contribute either to the iPCD Cohort or the International PCD Registry, or both. As they are not population based, however, data cannot be used to estimate incidence or prevalence and it is unclear if they are representative for all patients in the respective country.
Collaborative studies: the iPCD Cohort and the International PCD Registry The two large collaborative studies were both set up in BESTCILIA (2013-2016) in the EU FP7. Work package 1 of BESTCILIA assembled available datasets on PCD in a retrospective international cohort study, the iPCD Cohort [31], and work package 2 set up a prospective International PCD Registry  [36]. Both datasets were further expanded through the COST Action BEAT-PCD into large, still growing datasets  [25, 28, 29]. They have different strengths and limitations, and the datasets are overlapping because many centres contribute to both initiatives (table  4 ).
The iPCD Cohort The iPCD Cohort is hosted by the University of Bern and governed by an international advisory board including representatives from all contributing centres  [31]. It aims to answer questions on clinical presentation, severity, prognosis and treatments in patients with PCD. The iPCD Cohort was originally set up as a retrospective international cohort, combining existing datasets on patients with PCD, but now also collects prospective follow-up information. Since 2019 the iPCD Cohort has been linked with FOLLOW-PCD, the standardised clinical form for documenting patient encounters in PCD clinics  [79]. Eligible datasets and centres were initially identified by a systematic literature search for studies describing clinical manifestations in patients with PCD [80], through personal contacts from the ERS PCD Task Force  (2006) (2007) (2008) (2009)   [15, 27]  and from the BEAT-PCD network.
New centres that wish to contribute data can always join. The iPCD Cohort collects basic patient information, results of diagnostic tests, baseline clinical history, and data on growth, lung function, microbiology, imaging, clinical manifestations and treatments. Data is contributed on a standard format that was developed in 2013, in discussion with all contributors. This was based on the data available in the local datasets and therefore not very detailed. With the standardised PCD follow-up form (FOLLOW-PCD), the iPCD Cohort now has the possibility to include detailed physician-reported prospective data and patient-reported information on symptoms, health behaviour and environmental exposures. Currently, the iPCD Cohort includes data from 3824 patients from 22 countries in Europe, the Middle-East, North and South America, and Australia.
Each contributor ensures that the delivered data are pseudo-anonymised and in accordance with the national/local data protection laws. Local principle investigators are responsible for obtaining local ethical approval and patient informed consent, and the process is supported by the iPCD Cohort management team. Identifying information linking the data to the patients is kept by the centre and not included in the iPCD Cohort. Agreements for data delivery and publication leave all rights with the data contributors. For each planned analysis, eligible data are validated, cleaned and standardised. So far, the iPCD Cohort has published data on growth, nutrition, lung function and PCD diagnostics  [30] [31] [32] [33] [34] [35] 50]. Centres that wish to participate to the iPCD Cohort can contact pcd@ispm.
unibe. ch to receive detailed instructions on how to proceed. Each contributor signs a data delivery agreement and receives a password to access the online software REDCap to enter data directly  [81]. Alternatively, data that are already in the standard format can be contributed as a data file, e. g. as an export from a national registry or the International PCD Registry. Centres keep constant access to their datasets and can export them in various formats for local analyses. Researchers wanting to use the iPCD Cohort dataset can propose a topic and a concept sheet describing the planned analyses and publication. Participating centres can always decide if they want to contribute with their data to a planned analysis and be part of a particular project. Nested studies using part of the iPCD dataset and collecting additional data are encouraged with methodological input and support by the iPCD Cohort team.
Such data will be added to the iPCD Cohort to enrich it after each project. Currently, the International PCD Registry includes 920 patients from 23 centres in 16 countries in Europe, the Middle-East, Canada and North America. After written informed consent by the patient or his/her legal guardian(s), individual patient data can be entered directly. The platform can also integrate data from local databases, avoiding the need to enter data twice. Informed consent remains with the referring centre to preserve pseudo-anonymity. Withdrawal of authorisation is possible at any time and all information will then be destroyed. Healthcare providers can include patients after they obtained local ethical approval. Publication of the data from individual centres is only possible when the participating centre consents.
Centres that wish to participate in the International PCD Registry need to meet all necessary legal and ethical requirements. This process is guided by the registry management team. Informed consent forms and protocols for data collection have been approved by the Ethical Review Committees of the Medical Council Westfalen-Lippe and the Medical Faculty of the Westphalian Wilhelms University Muenster. Each referring centre only has access to its own patients' data. Only the administration team has insight into data of all patients in an anonymised way. Information linking subject identifier (ID) with personal data identifying the individual is kept only in the referring centre and is not collected in the registry. To get access to registry data or recruit participants for research studies the registry steering committee comprising members of the ERN-LUNG PCD Core group has to be contacted via PCDRegistry.
Differences between the iPCD Cohort and the International PCD Registry Despite their similarities, these two independent initiatives present some important differences. The iPCD Cohort's inclusion criteria are broader than those of the International PCD Registry, including also patients with probable or clinical diagnosis of PCD, which results in a larger number of patients included, but also in less detailed and standardised data. However, the iPCD Cohort is from its set-up more research oriented, resulting in many publications. In contrast to the International PCD Registry, patient data are not always included at regular time intervals, but relevant data are added at irregular intervals, on the occasion of planned analyses. Lastly, by including more detailed data on diagnostic evaluations, the International PCD Registry can be used to identify eligible patients for randomised clinical trials studying specific diagnostic groups.
Data exchange and collaboration Figure  1  illustrates data flows between national registries, the iPCD Cohort and the International PCD Registry. All national registries and clinical cohorts described in this review contribute data to the iPCD Cohort and the International PCD Registry. Some national registries have started to use the standard clinical form FOLLOW-PCD to collect detailed clinical encounter data. Interested researchers may request data from the iPCD Cohort and the International PCD Registry to answer specific questions. Each patient has a unique iPCD-ID number for the iPCD Cohort as well as the original dataset ID. The iPCD Cohort and International PCD Registry are independent of each other, but collaborate. As the datasets for the iPCD Cohort and the International PCD Registry were developed together, many variables are common to both datasets and have the same format, permitting easy data transfer between the two databases.
Discussion This article gives an overview of the European registries and collaborative studies for PCD. While a decade ago clinical research in PCD relied on small cross-sectional studies and case series, with information assessed at one time-point or from retrospective charts reviews, we now have large population-based registries and collaborative studies in Europe. This allows us to tackle open questions using standardised representative and prospective datasets. Population-based national registries aim to include every patient with PCD in a country, allowing us to assess incidence and prevalence, and to describe the full range of clinical presentation and prognosis. The summaries presented review the current state of the registries and problems they have to overcome. Registries are easier to lead in countries with centralised care, such as Cyprus or Denmark with only one national PCD centre.
In such a system, every resident diagnosed with PCD has a record in one of the dedicated clinics and a clinical registry is automatically completed. In countries with decentralised care, such as Switzerland, patients are seen in numerous clinics and private practices, and registries must actively search for new patients by regularly contacting all medical specialists likely to see PCD patients ( pulmonologists, ENT physicians, cardiologists, neonatologists and fertility specialists) and patient networks, which leads to high costs. Other countries with decentralised care, such as Norway, follow-up all known PCD patients at least annually in one centralised clinic using a shared-care strategy. Prevalence data (table  2 ) suggest, however, that only a fraction of adult patients are diagnosed and thus included in registries  [53].
The lack of funding to reimburse centres or doctors for data entry into national registries (from where international collaborations obtain their data too) may also reduce data collection completeness, especially in adults. This does not only lead to low precision of results for adult patients, e. g. on lung function  [30, 32, 36] , but also to biased and potentially wrong results. It is impossible to know whether registered adult patients represent the average patient, or rather the upper or lower end of the severity spectrum. Although numbers are rising and there are initiatives such as the ERS Task Force registry EMBARC (www. bronchiectasis. eu) and the UK Bronch-UK (www. bronch. ac. uk), adult pulmonologists remain massively under-represented in PCD task forces and networks. This will hopefully change with the establishment of PCD excellence centres for adults in the UK and elsewhere.
Two large collaborative studies, the iPCD Cohort and the International PCD Registry, combine data from registries and single-centre studies from Europe and beyond into large research datasets with sufficient statistical power and precision for different types of analyses. Both initiatives have key strengths that provide opportunities for different types of research. The iPCD Cohort includes more patients (nearly 4000), as it also includes older datasets. The International PCD Registry is smaller, but growing, and has potential as it now receives continuous support from the EU in the ERN-LUNG network. It contains detailed data on results from diagnostic evaluations, particularly genetics, and will allow genotypephenotype association studies to be performed in the future. Both the iPCD Cohort and the International PCD Registry collect a minimal core dataset of clinical information.
This can be overcome by focused collection of additional data in nested studies, which can be fed back to the original dataset to enrich it further. This approach has worked well for the iPCD Cohort where some national registries register data in iPCD continuously, while others contribute tailored datasets for specific analyses and publications. The introduction of FOLLOW-PCD, a standardised form for collecting data during routine care, will greatly improve the richness of the dataset. Outside Europe, there are other established or emerging studies on patients with PCD. An important resource is the PCD Foundation Clinical and Research Centers Network in North America, which has recently set up the PCD Foundation Registry collecting health and demographic data of PCD patients who are followed in the participating centres (https://pcdfoundation.
The most advanced and best-know examples of registries for rare diseases are cancer registries  [92]. A systematic registration of cancer (many of which are rare, including all childhood cancers) was set up in the 1950s by pioneer countries (e. g. the Nordic countries and the UK) and has expanded since then; coverage is now almost universal. Cancer registries include large numbers of patients and thus usually collect a limited dataset. Completeness of case registration is usually high (over 95% of affected patients), and therefore provides reliable data on incidence and survival. Time trends and regional differences in incidence allow us to investigate potential causes of cancer, while survival differences can be used to investigate differences in healthcare between countries. Cancer registries also serve as sampling frames to recruit patients into randomised clinical studies, which compare new treatment options with the current standards of care  [93].