dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design and participants The GARFIELD-AF registry recruited patients from a range of representative care settings in each country. 6, 7  Investigator sites were selected randomly (apart from 18 sites, out of >1000). The study is non-interventional in nature, and treatment decisions were solely at the discretion of treating physicians, with no specific treatments, tests, or procedures mandated by the study protocol. Recruitment took place in five independent sequential cohorts. 6  Cohorts 3-5, prospectively recruited during April 2013-August 2016, were included in this analysis: the method of reporting concomitant medication (β-blockers and calcium channel blockers) changed between Cohorts 2 and 3 such that data on co-GDMT from Cohorts 3-5 are not comparable with data from Cohorts 1 and 2; in addition, the use of non-vitamin K oral anticoagulants (NOACs) was not common during the period when Cohorts 1 and 2 were recruited (December 2009-April 2013).
6, 10  Baseline characteristics collected at study entry included medical history, care setting, type of AF, date and method of diagnosis of AF, symptoms, antithrombotic treatment [vitamin K antagonists (VKAs), NOACs, and antiplatelet (AP)], and all cardiovascular drugs. Race was classified by the investigator in agreement with the patient. 6  Chronic kidney disease (CKD) was classified according to the National Kidney Foundation guidelines into moderate-to-severe (Stages 3-5), mild (Stages 1 and 2), or none. Data on components of the CHA 2 DS 2 -VASc and HAS-BLED risk stratification schemes were collected and calculated retrospectively. 9, 11  HAS-BLED scores were calculated excluding fluctuations in international normalized ratios. Collection of follow-up data occurred at 4-month intervals up to 24 months.
Covariates evaluated in the weighting scheme included country and year of enrolment, vitals, care setting, demographic characteristics, medical history, and lifestyle factors (see Supplementary material online, Table  S3 ). Treatment was defined as the first treatment received at the time of enrolment, approximating 'intention-to-treat'. Only complete cases are presented in descriptive tables. Multiple imputation was applied in the derivation for the modelling process for the estimation of the co-GDMT effect. Coefficients and standard errors for the risk models were obtained by combining estimates across five imputed databases. Single imputation was applied for the OAC vs. no-OAC analysis. Statistical analyses were carried out using SAS (version 9. 4).
13  Like our study, the ORBIT-AF study found underuse of co-GDMT, with only 33% of patients with AF receiving all recommended co-GDMT. The use of all co-GDMT was associated with a non-significant reduction in the rate of major adverse cardiac or neurovascular events and a trend towards a lower rate of all-cause mortality, which achieved statistical significance in CHF. One limitation of our study is that medication use is based on baseline (intention-to-treat) data so that any changes in medication over the 2-year follow-up period were not taken into account. Moreover, due to the observational nature of this registry, only categories of medication used for the various comorbidities were assessed; medication dosing and guideline-directed titration of medication were not addressed but may have a relevant impact, particularly in heart failure, hypertension, and statin use.
In this way, the method can be used by even non-expert practitioners. Our technique is flexible and requires only one pass over the full dataset, instead of repeatedly using the projections in an optimisation heuristic. Moreover, the Freund-Shapire technique comes with theoretical guarantees  [26] ,  [27]  as opposed to most meta-heuristic techniques such as swarm based optimisation methods. The rest paper is organised as follows. In section 2, the detail of the AwPCSD reconstruction algorithm, as well as the definitions of all the hyper-parameters, are explained. Section 3 presents the way the Hedge method is implemented to select the hyper-parameter, as well as the Cross-Validation algorithm which is used to compare the results. Numerical results are presented in section 4, and conclusions are presented in section 5.
The Adaptive-weighted Projection-Controlled Steepest Descent of Lohvithee et al. [21] The Adaptive-weighted Projection-Controlled Steepest Descent (AwPCSD) algorithm is a TV regularisation -based reconstruction method which was first proposed in  [21]. This algorithm is available in the TIGRE toolbox: a MATLAB-GPU toolbox for CBCT image reconstruction  [28]. The main idea in AwPCSD is to adapt the two-phase strategy from the ASD-POCS algorithm proposed by Sidky et al  [12]. These two phases are performed alternatingly until the stopping criterion is verified.
Experiments with different datasets For the next part of the study, the best set of hyper-parameters obtained from the previous experiment are applied to the reconstruction of different datasets with the same context i. e. similar assumption about the x-ray measurements and materials/tissues. The aim is to observe whether the best hyper-parameters from the implementation of the proposed Hedge-based approach using one set of data can be readily applied to different datasets. For this purpose, we produced two new imaging data using also the digital XCAT phantom, but with different anatomical and motion parameters as shown in table  4. The two datasets, male and female phantoms, are generated to represent the cases of different patients with different genders, as well as anatomical structures of the bodies.
The parameters were specified randomly, aiming to generate as many differences as possible between the two datasets. The projection data were generated in the same way as in the first dataset. For the male phantom, the detector size is 512x512 pixels, with each pixel's size of 0. 8 mm 2. The image size is 128x128x70 voxels with the total size of 256x256x140 mm 3 and image voxel's size is 2 mm 3. For the female phantom, the detector size is similar to that of the male phantom. The image size is 128x128x48 voxels with the total size of 256x256x96 mm 3 and image voxel's size is 2 mm 3. The experiments with these two datasets were also conducted using 50 projection images, equally sampled over 360 •. Cross-sectional slices of the two phantoms are shown in figure  9. The reconstruction using the AwPCSD algorithm with the best set of hyper-parameters from the training data set (ng = 10, = 0, β = 1, β red = 0.
99 and δ = the 90 t h percentile of the histogram of the reconstructed image from the OS-SART algorithm which are 0. 0589 and 0. 0748 for male and female datasets, respectively) was implemented with the male and female datasets. In addition, the AwPCSD algorithm using random hyper-parameters (ng = 1, = 500, β = 1, β red = 0. 99 and δ = 0. 0589/male, 0. 0748/female) was also implemented for comparison, as well as the exact phantom image. Cross-sectional images of these 3 cases are shown in figures 10 and 11, for male and female datasets, respectively. the training using the proposed Hedge algorithm can be readily applied to different datasets within a similar imaging context.
However, with exposure time of 1 frame per second, one complete scan requires 52 minutes. This process is time consuming when there are significant number of samples to be measured. In order to reduce scan time, in the recent European Metrology Programme for Innovation and Research (EMPIR) project Advanced Computed Tomography for dimensional. and surface measurements in industry (AdvanCT), we have considered fast scan capability with less projection images. to the optimal cumulated incurred by the best hyper-parameter configuration, as a function of the number of data observed so far, a criterion which is not rigorously equivalent to finding the best configuration based on visual reconstruction assessment. This makes the TV-penalised algorithms, specifically the AwPCSD, more generalised to be implemented by a general user.
The results with the other two datasets of the same imaging context but different anatomical and motion parameters showed that the best set of hyper-parameters derived from the training data also produced a good quality reconstructed image. The main conclusion of this paper is that the proposed hyper-parameter approach using Hedge method provides a systematic way of choosing the hyper-parameters for the TV-regularised algorithm. Within the proposed framework, users can define a relevant range for the comparison of possible hyper-parameters configurations. Either the best performing hyper-parameters will be selected by the proposed method in the interior of the domain, or the selected hyperparameter values will lie on the boundary of the prescribed search domain, in which case the non-expert user will be able to relaunch the search within a larger domain in order to ensure that a relevant domain is explored.
Figures 10 and 11  show that the best hyper-parameters from the implementation of the proposed approach using one dataset can be applied for the reconstruction for different datasets and the images obtained via this approach are closer to the exact phantom image than the reconstruction obtained with random hyper-parameters. Depending on the task, the reconstructed images using the best hyper-parameters obtained from the training dataset may prove useful for a particular imaging application. The quality of the reconstructed image can be improved further by fine-tuning the set of hyper-parameters. It is very promising from the results that the set of hyper-parameters obtained from.
Figures 10 and 11 Figure 8. 118 Figures 10 and 11  show that the best hyper-parameters from the implementation of the proposed approach using one dataset can be applied for the reconstruction for different datasets and the images obtained via this approach are closer to the exact phantom image than the reconstruction obtained with random hyper-parameters. Depending on the task, the reconstructed images using the best hyper-parameters obtained from the training dataset may prove useful for a particular imaging application. The quality of the reconstructed image can be improved further by fine-tuning the set of hyper-parameters. It is very promising from the results that the set of hyper-parameters obtained from.
Acknowledgments: We thank all subjects who participated in the study and the study interviewers for their help and collaboration. We also thank all MCC-Spain collaborators. ISGlobal acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program.
Rationale and objectives: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that leads to an increased risk of emphysema and liver disease. Despite extensive investigation, there remain unanswered questions concerning the natural history, pathophysiology, genetics and the prognosis of the lung disease in association with AATD. The European Alpha-1 Clinical Research Collaboration (EARCO) is designed to bring together researchers from European countries and to create a standardised database for the follow-up of patients with AATD.
Study design and population: The EARCO Registry is a non-interventional, multicentre, pan-European, longitudinal observational cohort study enrolling patients with AATD. Data will be collected prospectively without interference/modification of patient's management by the study team. The major inclusion criterion is diagnosed severe AATD, defined by an AAT serum level <11 µM (50 mg•dL -1 ) and/or a proteinase inhibitor genotype ZZ, SZ or compound heterozygotes or homozygotes of other rare deficient variants. Assessments at baseline and during the yearly follow-up visits include lung function testing (spirometry, body plethysmography and diffusing capacity of the lung), exercise capacity, blood tests and questionnaires (symptoms, quality of life and physical activity). To ensure correct data collection, there will be designated investigator staff to document the data in the case report form.
EARCO takes advantage of existing AATD registries that have been developed at the national and international level. Several countries have established registries in which AATD patients are included and followed-up with clinical and biological data collected  [14, [17] [18] [19] [20] [21] [22] [23]. However, these registries differ in terms of inclusion criteria, data collected and frequency and extent of follow-up. Within EARCO, we will harmonise the data collection process and assess the quality of the data entered into the database. This article describes the EARCO registry protocol: a pan-European multicentre observational study in patients with alpha-1 antitrypsin deficiency.
All patients will provide written informed consent to participate, and participating investigators will collect data prospectively. The following domains are covered: demographics, proteinase inhibitor genotype and other laboratory analyses, comorbidities, lung function, respiratory symptoms, ultrasound-based elastography of the liver, exacerbations of respiratory disease, quality of life, physical activity, chest CT (as applicable) and treatment. The study protocol has received central ethics approval by the research ethics committee of the Vall d'Hebron University Hospital of Barcelona, Spain (PR(AG)480/2018), and the study database will be hosted in the Vall d'Hebron Research Institute (Barcelona). The study website is located at www. EARCO. eu. The registry was developed according to recommendations on the design, implementation, governance and long-term sustainability of disease registries in the European Union (EU)  [24, 25].
Visit schedule and assessments Written informed consent form will be obtained at the baseline visit. All available information will be collected according to the CRF. Follow-up visits will be scheduled on a yearly basis (±3 months) and assessments, as described in the following sections, will be performed and recorded in the CRF ((b) represents baseline only) (figure  2 ). Because the registry reflects standard assessments in many different countries and healthy individuals (fulfilling the inclusion criteria) will be included, only the participant's sociodemographics and basic information about the presence/type of lung or liver disease will be absolutely mandatory. All other assessments are not mandatory, but the data manager will request a minimal set of clinical data through queries to the investigators.
These questionnaires will include the COPD assessment test  [33] , the modified Medical Research Council (mMRC) dyspnoea scale  [34] , the EuroQoL 5-dimension (EQ-5D) generic quality-of-life questionnaire  [35]  and the physical activity vital sign (PAVS)  [36]. In some countries, the St George's Respiratory Questionnaire will be collected  [37]. Patients will complete the questionnaires when rested and in a quiet area, and will be allowed to ask questions to obtain the truest or best response. The questionnaires will be collected and checked for completeness before the patient leaves the centre. Instrument scoring and handling of missing item data will be conducted in accordance with the user guidelines. The body mass index, obstruction, dyspnoea and exercise (BODE) and the Body mass index, Obstruction, Dyspnoea and Exacerbations (BODEx) indices will be calculated automatically from entered and quality-controlled data  [38, 39].
Data collection It is the investigator's responsibility to ensure adequate source documentation for all collected data. Designated investigator staff will enter the data required by the protocol into the CRFs. Spirometry reports (including the flow-volume curve) and CT reports may be uploaded after appropriate anonymisation. The investigator must certify that the data are complete and accurate by entering a password. After review for completeness, plausibility and correctness (flow-volume curve, assessed in uploaded curves) by the EARCO database monitor, the data will be locked.
Discussion In the last official statement about AATD from the ERS, the development of a European, follow-up registry (a longitudinal cohort) was identified by healthcare providers and patients as a research priority  [12]. Registries have the potential to provide important insights into the natural history of rare diseases where epidemiological data cannot be derived from small-scale prospective cohort studies  [41]. In developing the EARCO registry, we have followed guidance proposed in the EU and the extensive experience from other registries in the field of respiratory disease  [24, 25, 41, 42]. According to these recommendations, it is essential to define a minimum required dataset that allows the collection of relevant information and limits the burden on investigators entering the data.
Furthermore, the definitions across participating sites have to be harmonised to ensure that data are consistent and reliable. We will provide a shared data collection platform, and standardised definitions will be made available on the website (www. earco. eu) and upon request to all study sites. Many different national registries on AATD exist throughout Europe  [14, [17] [18] [19] [20] [21] [22] [23] , but inclusion criteria, variables collected and level of quality control are different. The EARCO registry will harmonise the collection of prospective data, and its design will allow the new prospectively recorded data to be linked with the historical data collected in the different national registries. In order to do that, the EARCO registry will include the sum of all variables included in all national European registries, but some of them will only be mandatory for patients in a given country where these variables were collected historically.
EARCO may therefore represent a platform for the development of future multicentre studies using CT densitometry to evaluate the evolution of emphysema in large cohorts of patients with AATD in usual clinical practice  [47]. In contrast, other lung function derived markers that have the potential to reflect the magnitude and evolution of emphysema are gas transfer and the hallmarks of hyperinflation  [48]. Therefore, diffusing capacity of the lung for carbon monoxide and body plethysmographic measurements are measured routinely in the majority of participating countries, according to the most recent international recommendations  [49] [50] [51]. We consider a biobank as an important objective for EARCO. However, in the first step we have to overcome the legal and organisational hurdles that are associated with an international database.
Composite scores that combine different physiological measures are used to assess the severity and prognosis of obstructive lung disease and liver disease, but the experience in AATD is very limited  [57]. We will use the BODE  [38]  and BODEx  [39]  for lung disease and the Fibrosis-4  [57]  for liver disease to assess their values in monitoring and determining long-term prognosis in AATD. The EARCO registry is not the first European registry in AATD. The Alpha One International Registry (AIR) group included representatives from 14 European and some non-European countries  [58]. AIR was very successful in collecting baseline data. However, due to limited funding, longitudinal data could not be collected systematically, and the quality of existing data (completeness and accuracy) may be regarded as suboptimal.
This can be partially explained by the fact that data were not processed centrally for data clearing and data control  [59]. The EARCO registry will collaborate closely with existing national AATD registries and will harmonise the process of data collection and assess the quality of the data included prospectively, thus overcoming the limitations present in AIR. The target sample size is based on experiences with similar projects. A short-term target of 1000 patients in the first year of the project after database setup has been set. To ascertain representativeness for the European population we aim to include >3000 patients from ⩾20 European countries by the end of the third year.
Acknowledging the imminent limitations of a non-interventional cohort study, the EARCO registry itself will not provide an answer to all the research questions in the field of AATD, but it can be the backbone of different research initiatives, some of which have already been highlighted [15]. Acknowledgements: We want to acknowledge the support from  Elise Heuvelin ,  Céline Genton  and  Alessandra Marguerat  ( European Respiratory Society, Lausanne, Switzerland)  in the logistics and organisation of EARCO;  Barbara Johnson  ( European Lung Foundation ,  Sheffield, UK)  for her support connecting to different patient organisations;  Eduardo Loeb  ( Pneumology Dept, University Hospital Vall d'Hebron/VHIR, Barcelona, Spain ) for his contribution as data manager of EARCO; and  Joanna Shakespeare  ( Dept of Respiratory and Sleep Sciences, University Hospitals Coventry and Warwickshire NHS Trust, Walsgrave, UK ) for her help in designing the database.
In order to obtain high-resolution reconstructions of the full kinetoplastids mitoribosomes, we purified mitochondrial vesicles from both L. tarentolae and T. cruzi, and directly purified the mitoribosomes from sucrose gradient (see Methods). All of our collected fractions were analyzed by nano-LC MS/MS (Extended Data Table  1  and 2 ) in order to determine their proteomic composition. We collected micrographs from multiple vitrified samples corresponding to different sucrose-gradient density peaks for both species and following image processing we obtained cryo-EM reconstructions of the full mitoribosomes, but also the dissociated SSU. Most importantly, we also derived reconstructions of what appeared to be an assembly intermediate of L. tarentolae LSU. After extensive rounds of 2D and 3D classification and refinement we obtained the structure of L.
brucei  6  (PDB: 6SGB). In (d) mt-SAF32 and mt-LAF9 are respectively superimposed with NDUFB9 and NDUFAB1, here from the yeast mitochondrial complex I 32 (PDB: 6RFR), homolog proteins are annotated using the human nomenclature of complex I proteins. mt-LAF9, NDUFAB1, and mt-SAF31 are LYR proteins characterized by the LYR-protein fold (three antiparallel helices) shown in (c). IMM stands for inner mitochondrial membrane and IMS for inter membrane space. In (e), the high structural conservation of Leishmania mt-LAF8 is illustrated by structural superimposition and sequence alignment with the human mitochondrial Rsfs (PDB: 5OOL) as well as two bacterial Rsfs (PDB: 3UPS and 2O5A).
Single particle cryo-electron microscopy data collection For the two data-sets (Full and dissociated complexes), data collection was performed on a Talos Arctica instrument (Thermofisher) at 200 kV using the EPU software (Thermofisher) for automated data acquisition. Data were collected at a nominal underfocus of -0. 5 to -2. 7 µm at a magnification of 120,000 X yielding a pixel size of 1. 24 Å. Micrographs were recorded as movie stack on a Falcon III direct electron detector (Thermofisher), each movie stack were fractionated into 20 frames for a total exposure of 1 sec corresponding to an electron dose of 60 ē/Å2.
Electron microscopy image processing Drift and gain correction and dose weighting were performed using MotionCor2  44. A dose weighted average image of the whole stack was used to determine the contrast transfer function with the software Gctf  45. The following process has been achieved using RELION 3. 0  46. Particles were picked using a Laplacian of gaussian function (min diameter 280 Å, max diameter 480 Å). L. tarentolae full mitoribosome, after 2D classification, 303,140 particles were extracted with a box size of 400 pixels and binned four fold for 3D classification into six classes. Three classes depicting high-resolution features, consisting of 82,060 particles, were selected for refinement, yielding 3. 9 Å resolution. After focused refinement with a mask on the LSU, the body and the head of the SSU, 3.