dataesr/scientific-paragraphs-categorization huggingface dataset:
Another source of limitation lies in the health databases analyzed, which led the authors to collect different newborn and maternal characteristics. For instance, several studies investigated air pollution effects on infant death by cause [10,  21, 23, 24] , whereas others restricted their analysis to specific cause of death  [12]  or indeed considered all-causes of death due to lack of precise information  [32]. Also, many studies ignored well-known risk factors for infant death such as gestational age, birth weight, and maternal age  [9, 10, 12, 18, 20, [22] [23] [24] [25] [26] 32]. Furthermore, several other studies considered birth weight and/or gestational age as potential modifiers of the association between infant death and air pollution exposure. In order to do this, these studies stratified their analysis on birth weight by considering the infant death effect of air pollution exposure separately among the low and normal birth weight  [21, 27] , or among preterm and normal term birth  [25].
Exposure Assessment Different approaches for exposure assessment were implemented, and this may induce misclassification of exposure. Most of the studies used air pollution data from monitoring stations as a proxy for individual exposure. The main advantages of these databases relate to their easy accessibility and availability. However, their use presents several limitations, particularly when the objective is to quantify individual levels of exposure and investigate the health consequences of exposure. The first of these limitations is related to the method used to convert concentration measures from monitoring stations to individual exposure: most of the studies either averaged air pollution concentrations from all monitoring stations covering the study area or selected a sub-sample of monitoring stations-just one, perhaps the one the closest to the dwelling  [9, 18, 21, 22, [27] [28] [29] [30] -while in others, no detail was given at all  [8, [12] [13] [14] 19, 20].
In both cases, digital images are collected at regular intervals and at different focal planes throughout embryo development and subsequently processed into videos. With this information, TLT enables embryologists to record preimplantation embryo development in a dynamic, real-time manner and permits the interpretation of morphokinetic events more precisely. Data from these observations can be annotated and analysed using integrated TLT software, facilitating the development of more complex embryo selection/deselection algorithms  (Ciray et al. , 2014 , Rubio et al. , 2014). This paper will describe different types of TLT systems, discuss the potential benefits and uses of TLT and evaluate the impact on laboratory workflow, in order to inform IVF clinics as they choose a system appropriate for their own needs.
List of recommendations for clinics before getting started with time-lapse technology in human IVF • Clearly identify the reasons to introduce a TLT system • Assess pros and cons of acquiring a TLT system, both financially and operatively • Identify whether morphokineticparameters will be used in selection/deselection /ranking of embryos • Identify (from scientific literature) the morphokineticparameters of interest and assess how to monitor and use them • Find the suitable system based on considerations of culture conditions/systems and other costs, including hardware maintenance and software upgrades • Evaluate technical/customer support available, including accessibility, the level of embryologist support and the expertise the manufacturer will provide to your team • Seek appropriate installation and training from the manufacturer/distributor • Develop an internal checklist, based on a user requirement specification for the system, identifying and matching what the clinic/laboratory wants in a system e.
g. type of gas, humidity, footprint, capacity, type of dish, software, cost, supply chain and manufacturer support. • Once introduced in the lab, find the appropriate system settings • Identify and train one or more embryologists (depending on the size of the laboratory) who will develop the role of "TLT referent"; the designated person(s) will be responsible for the annotation of morphokineticvariables (to avoid initially interoperator variations with other members of staff ) and for the implementation of quality control programs • Educate clinic staff on the current evidence behind TLT in order to counsel patients alongside offering the technology. TLT: time-lapse technology.
Evidence of a clinical benefit of TLT Like any new intervention, TLT should be implemented in routine clinical practice only after stringent tests demonstrating a benefit for patients  (Brison et al. , 2013; Harper et al. , 2017). However, a clear increase in IVF success rates with the use of TLT remains to be proven. The latest Cochrane review (nine RCTs, 2955 women)  (Armstrong et al. , 2019)  reported insufficient evidence for differences in live birth rate (odds ratio (OR0 1. 12, 95% CI 0. 92-1. 36), miscarriage rate (OR 0. 63, 95% CI 0. 45-0. 89) or clinical pregnancy rate (OR 0. 95, 95% CI 0. 78-1. 16) for TLT combined with embryo selection software versus conventional incubation and assessment. Likewise, a putative benefit of TLT was not demonstrated by meta-analyses  (Polanski et al.
7 versus 51. 2%; OR 1. 02, 95% CI: 0. 85-1. 22), although fresh embryo transfer live birth rates were higher for TLT cycles (36. 8 versus 33. 9%, adjusted OR 1. 28, 95% CI: 1. 05-1. 57). The main reason for the controversy over TLT efficacy is the fact that it entails two distinct components, i. e. an undisturbed incubation environment and embryo selection through imaging software. In this respect, these two components have not been effectively distinguished in the majority of studies, possibly masking the weight of the effect of better culture conditions or improved embryo selection on the reported outcomes  (Armstrong et al. , 2015b). Additional confounders that may explain the heterogeneity amongst studies include different days of assessment, different endpoints, the wide array of morphokinetic timings assessed, inter-and intra-operator variation in annotating and the various other confounding factors listed in Table  V.
2011 ). Important technical difficulties appear when patients present high numbers of oocytes to be fertilized, more than those that can be placed in a single slide; this may necessitate the need for several TLT slides per patient, each being considered as a 'new' or 'different' patient for the TLT software. This could result in reports being not immediately compatible and cause confusion for doctors and even patients. In the report, together with the images of the embryos transferred (at different times) thought needs to be given as to whether to include images (at least one per embryo) of those to be vitrified, or those to be discarded. In such images, the time reference (after ICSI) or the stage of embryo development may be included as headings. The presentation of the embryos, to distinguish those embryos to be transferred from those frozen or discarded, could be potentially useful.
Regardless of a possible direct impact on clinical outcome, TLT does confer several advantages that justify its use. Its introduction in the workflow of the IVF laboratory, however, has a multiplicity of implications requiring technical and managerial expertise, as well as a strategic vision for this technology. This manuscript has attempted to collate recommendations to assist with the choice, introduction, management and harnessing of the TLT in the IVF laboratory. Based on current technology, TLT probably offers the safest and most stable embryo culture environment. Continued embryo monitoring has allowed us to identify previously unknown or undetectable aspects of development, some of which, such as direct cleavage of the fertilized egg into three blastomeres, have significant clinical impact.
Based on the result of multiple sequence alignments performed in ClustalW  [19]  CP region was chosen as a target for amplification in LAMP assay. The set of diagnostic primers (Table  2 ) were designed using LAMP Designer software (OptiGene, Horsham, UK). The specificity of LAMP primers was examined using 50 ng/µl of total DNA extracted from WDV-infected plants and from healthy wheat cv. Muszelka and barley cv. Bażant plants as the templates for reactions. LAMP assay was performed in total volume of 25 μl. The reaction mixture consisted of 15 μl Isothermal Mastermix ISO-001nd (Novazym, Poznan, Poland), 2 μl of 10 µM WDV FIP and WDV BIP, 1 µl of 10 µM WDV Loop-F and WDV Loop-B, 0. 5 μl of 10 µM WDV F3 and WDV B3 primers, 1 µl of template DNA and 2 μl of sterile water. DNA samples from healthy plants and the reaction mix without a template were included as the negative controls.
The tubes were incubated at 63 °C for 45 min in a thermoblock (Biometra, Göttingen, Germany). The LAMP products were analysed by electrophoretical separation in 1. 5% agarose gel containing Midori Green DNA Stain (NIPPON Genetics Europe GmbH, Düren, Germany) as well as by direct visual inspection of color solution under UV light after the addition of 2 μl of SYBR-Green Dye (Thermo Fisher Scientific, Waltham, MA, USA). The reaction was also carried out using Isothermal Mastermix ISO-001 (Novazym) in a LightCycler 96 Instrument (Roche, Basel, Switzerland) and the results were estimated by analysis of amplification curves. The fluorescence data were obtained on the FAM channel (excitation at 470 nm, detection at 510 nm) in 30 min. The sensitivity of LAMP assay and conventional PCR were estimated and compared.
Combining 4D flow MRI and CFD is then an excellent compromise as the CFD model can be set up using 4D flow MRI datasets to define the geometries and to tune the boundary conditions  [10]. The current challenge is now to apply these techniques for establishing possible universal effects relating hemodynamic descriptors and the wall mechanical and rupture properties derived from in vitro tests on real patient tissues. Relationships between WSS patterns and wall mechanical properties may stem from the regulatory role of vascular cells through mechanotransduction. It has been known for many years that endothelial cells sense the WSS and can stimulate relaxation in vascular smooth muscle cells by nitric oxide signaling for instance. This permits maintaining a rather uniform time-average WSS (TAWSS) of about 1 Pa across all arteries at vascular homeostasis.
Their WSS values were derived directly from 4D flow MRI data without using CFD. In the present study, based on our previous work,  [12]  we combine for the first time together in vivo 4D flow MRI and in silico CFD models to analyze the aortic flow patterns, the WSS and the TAWSS distribution of 10 aTAA patients for whom we subsequently characterized the mechanical and rupture properties of their aTAA after surgical resection. The goal is to look closer at the possible correlations between hemodynamic descriptors and wall properties.
C. Geometry and Mesh reconstruction Patient-specific tridimensional geometries of the aortic arch, the aortic branches (brachiocephalic artery, BCA, left common carotid artery, LCC, and left subclavian artery, LSUB) and the descending aorta (DescAo) were reconstructed from CT scans using a semi-automatic segmentation in the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software. Unstructured meshes, consisting of 1. 5-6. 2 millions of tetrahedral elements, were generated using ANSYS Mesh (v17. 2, ANSYS Inc. , Canonsburg, PA). A convergence analysis was carried out to obtain mesh independent analyses. The aorta was partitioned in three segments, including the aTA Segment of Interest (aTASoI), as: 1) segment A which nearly extends until the sinotubular junction, 2) segment B which approximately corresponds to the dilated aTA resected during surgery (aTASoI) and 3) segment C which includes the aortic arch and the descending aorta until the diaphragm.
F. Flow helicity, flow eccentricity and wall shear stress analysis The CFD-calculated velocity profiles were verified against the 4D MRI-measured velocities. For that, we extracted from the Fluent postprocessor the velocity maps averaged throughout the cardiac cycle in the PoI of the aTASoI whereas the averaged velocity maps at the same location were extracted from the 4D MRI datasets with Ensight. The flow eccentricity (Floweccentricity) was calculated in Matlab®, such as  [14] :.
G. In vitro bulge inflation test The 10 aTAA samples collected after surgical procedures were characterized mechanically using our in vitro bulge inflation bench test  [17]. A flat square piece of tissue (about 40x40mm²) was cut from the greater curvature of each aTAA specimen in the axial direction. Then, our bulge inflation test device  [17]  was used for clamping the sample during the inflation until the rupture occurred. During the test, the pressure was monitored using a digital manometer (WIKA, DG-10) and images of the sample, previously spayed with an appropriate dye, were recorded using a DIC system to the 3D deformations fields of the inflated aortic sample. The ARAMIS (GOM, v. 6. 2. 0) software was used to derive these deformation fields from the collected images and Matlab was used to derive the biomechanical properties of interest from the deformation fields.
ACKNOWLEDGMENT We thank  Dr A. J. Barker  and  Dr M. Markl  from  Northwestern University of Chicago (IL, USA ) who provided insight and expertise for the 4D MRI data analysis. We thank  SIEMENS Healthineers  for providing 4D flow acquisition sequences and collaboration support. We thank  Dr Morbiducci  and  Dr Gallo  from  Polytechnic of Turin (Italy ) who provided insight and expertise that greatly assisted the CFD modeling and data analysis. We are also grateful to  Ansys, Inc. for providing Ansys-Fluent ( ANSYS® Academic Research ,  Release 17. 2 ). This work was supported by the  ERC  through  ERC-2014-CoG BIOLOCHANICS grant.
A B S T R A C T Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting.
Study design The PULsE-AI trial is a prospective, randomised controlled trial conducted in the UK primary care setting. Eligible adult patients registered at participating general practices within the NIHR Clinical Research Network: West Midlands will be identified from medical records and then individually randomised to receive routine care plus the intervention/screening strategy (intervention arm) or routine care only (control arm). Diagnostic data will be extracted from medical records of both intervention and control arm participants at the beginning and end of the research window to determine the proportion of patients diagnosed with AF throughout the trial in the intervention and control arms. The research window is defined as the time between the first collection of patient medical records at the beginning of the trial (06 June 2019) and the last collection following the completion of the intervention at the end of the trial.
S1 ). The algorithm was calibrated  [19]  during development, using crossvalidation to prevent overfitting and random sampling to define training and holdout sets, ensuring the final set was broadly representative of what the model was trained on. The risk prediction algorithm will be run on data from all eligible patients with a valid index date following randomisation, and a risk score will be generated for each individual. A risk score of ≥7. 4% was chosen as the threshold needed for the AF risk prediction algorithm as this corresponded to a sensitivity of 50% (Supplementary Table  S1 ), as assumed in the sample size calculations.
Statistical analysis Demographics and clinical characteristics for all randomised participants, and for the primary analysis group of higher risk participants (risk score of ≥7. 4%), will be summarised using descriptive statistics. If the baseline characteristics of the intervention and control arms are well balanced, the proportion of patients diagnosed with AF during the research window will be compared between the intervention and control b Data on related events (e. g. stroke, mortality etc. ) may be collected alongside AF diagnoses. arms via an unadjusted chi-squared comparison. Alternately, if there are baseline differences between arms, these will be adjusted for within a penalised logistic regression. A sensitivity analysis will also be performed on all participants randomised into intervention and control groups irrespective of predicted AF risk score.
All statistical tests will be conducted at the 5% significance level. The primary outcome will be utilised to inform an economic analysis designed to evaluate the health economic impact of a screening strategy using the AF risk prediction algorithm (combined with a diagnostic test (s)) for the identification of patients with undiagnosed AF (secondary objective). The cost-effectiveness model will compare the use of the AF risk prediction algorithm intervention as an add-on to current practice against current practice alone. The model will use AF incidence rates determined from this trial alongside data from published literature (for variables such as AF-related event rates and cost estimates) to evaluate outcomes over a patient's lifetime. The economic value of the AF risk prediction algorithm will be captured through the estimation of total costs, life years gained as a result of earlier diagnosis of AF, and qualityadjusted life years gained as a result of avoiding AF-related events due to earlier diagnosis.
Data management In line with ethical approval requirements, pseudonymised patientlevel data from participating study sites will be extracted from the Egton Medical Information System (EMIS) medical records of all eligible patients via a search and report function. Eligible patients without a valid index date will be excluded prior to the randomisation of patients at each study site. Following randomisation, data will be used to generate AF risk scores for all participants in the intervention and control arms, and those at highest risk of AF (risk score ≥ 7. 4%) identified. Study sites will then de-anonymise records of the highest risk patients in the intervention arm for invitation. Once participants have signed informed consent, data collected during and after the research clinic appointment will be entered into the EMIS electronic medical records for each participant.
All ECG data will be pseudonymised for cardiologist review. All KardiaMobile data will be uploaded to secure European Servers based on unique study identifiers. Pseudonymised data will be shared with the trial sponsor (and its delegates) using secure file transfer protocol (sFTP), which will ensure an end to end encryption of data extracted. Three years of additional follow-up data may be collected from the medical records of participants identified as higher risk by the AF risk prediction algorithm to evaluate the impact of the intervention on clinical outcomes, but will not involve direct patient contact. Consent for all pseudonymised data sharing outside of that collected at the research clinic has been provided by the general practice participating in the study.
Ethical approval and trial registration Ethical approval for this study was granted on 28 February 2019 (Wales Research Ethics Committee 5 -Bangor). Health Research Authority (HRA) and Health and Care Research Wales (HCRW) approval for the study were granted on 09 April 2019. The trial was registered with clinicaltrials. gov (NCT04045639) on 05 August 2019. This manuscript is aligned with version 3 of the protocol; substantial protocol amendments were approved on 12 December 2019 and 08 February 2020.
Second, all calculations were made based on the population distribution as per the CPRD GOLD dataset used to develop the risk prediction algorithm. Therefore, if local populations vary significantly from the CPRD GOLD dataset (e. g. they are younger), more practices may need to be recruited to reach a sample size of approximately 1000 higher risk participants per arm. Third, AF will be diagnosed during the study via 12-lead ECG or the KardiaMobile device. Whilst participants without a personal smartphone or tablet (or access to a device via a family member) will be offered a loan smartphone for the KardiaMobile monitoring period, some may lack confidence with technology and either decline the offer or are unable to use it correctly. Although these participants will be invited for subsequent 12-lead ECGs, the absence of home-based ECG monitoring may reduce the ability to confirm the actual presence or absence of AF.
Funding This trial is supported by  Bristol Myers Squibb UK. CDM is funded by a  National Institute for Health Research (NIHR)   Research Professorship  ( NIHR-RP-2014-04-026 ). CDM is also funded by the  NIHR Applied Research Collaboration (West Midlands ) and the  NIHR School for Primary Care Research. The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR , or the  Department of Health and Social Care.
Calculation of standard indices Due to the large amount of output produced by each simulation, it is impossible in practice to exhaustively compare all aspects of integrations A and B to one another. Therefore, the outputs from integrations A and B are first post-processed in an identical way. The code used to post-process the outputs is available (see "Code and data availability") and based on the list of standard metrics proposed by  Reichler and Kim (2008). We record for each integration standard ocean, atmosphere, and sea ice output variables: 3-D air temperature, humidity, and components of the wind; 2-D total precipitation, mean sea level pressure, air surface temperature, wind stress, and surface thermal radiation; 2-D sea surface temperature and salinity, as well as sea ice concentration.
A sensible option would then be to compare together the spatial averages of the aforementioned variables from integrations conducted on A and B. However, by definition, spatial averages hide regional differences and one simulation could be deemed replicable with respect to another despite non-replicable differences at the regional scale. To address this point, we rather first compare the fields from each integration at the grid point level to common reference datasets (those used in  Reichler and Kim, 2008)  and then compare the resulting metrics from A and B together in order to possibly detect an incompatibility between the two integrations. For each field, a grid cell area-weighted average of the model departure from the corresponding reference is evaluated and then normalized by the variance of that field in the reference dataset.
Thus, for each field, one metric (positive scalar number) is retained that describes the mismatch between that field in the integration and the reference field. Five such metrics are available for each integration for each field, since each integration uses five ensemble members. We stress that the goal of this approach is not to evaluate the quality of the model but rather to come up with a set of scalars characterizing the distance between model output and a reference. Therefore, the intrinsic quality of the reference datasets does not matter for our question. As a matter of fact, the datasets used in  Reichler and Kim (2008)  and in our protocol correspond to observations affected by historical climate forcings, whereas the model output is generated assuming constant preindustrial forcing.