dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, to combine Scrum and MDA to develop epidemiological surveillance software, Scrum practices (Involvement of stakeholders, Product Backlog definition, Sprint definition, Scrum iteration, Scrum meetings) must be used to build models and generate related software. The primary novelty of this work is the ability to use Scrum Process in the modeling and the generation of software. To our knowledge, the combination of Scrum and MDA for the complete development of health software has not been attempted previously. The direct perspectives to this work are: (1) the application of the approach presented in Section 5. 4 into many other settings in order to come up with a generic approach that can be applied in any epidemiological surveillance and other industrial settings. To this end, we are currently working with nutritionists on a system for nutrition (overnutrition and undernutrition) surveillance; (2) generally, many people whose domain experts and computer scientists intervene in the development of epidemiological surveillance systems.
The paper is structured as follows. Section 2 outlines the model setup and introduces definitions and details of the MYI tracers implemented in the model. Section 3 provides a thorough evaluation of the modelled MYI extent against a MYI type dataset derived from satellite observations. Section 4 then provides an analysis of the MYI volume and a partitioning of the processes contributing to its interannual variability, and it additionally focuses on two extreme years and how these contribute to the interannual decline. Section 5 discusses anomalous conditions in the context of the available literature and how the definition of MYI volume can affect its evolution. Section 6 summarises and concludes.
Observations of ice type We use version 1 of the sea ice type Climate Data Record from the Copernicus Climate Change Service Climate Data The modelled MYI concentration is converted to a binary type classification for comparison with the CDR dataset using a threshold T : elements with MYI concentration exceeding T are considered as MYI. The extent of modelled MYI (area of the elements detected as MYI) is sensitive to the value of T. Figure  2a  shows the evolution of MYI extent within the Arctic (Fig. 2c ) computed with values of T ranging from 0. 30 (blue) to 0. 70 (red). The optimal value of T = 0. 40 was found by minimisation of the root mean square difference (RMSD) between the MYI extent from CDR and from the model (Fig. 2b ). RMSD was computed from November to March due to large uncertainties in other periods; this affected the magnitude of the RMSD but not the value of T.
Observations of ice age For additional evaluation of our model, we use the dataset of Arctic ice age from the National Snow and Ice Data Center (NSIDC)  (Tschudi et al. , 2019) , hereafter NSIDC_age. https://doi. org/10. 5194/tc-17-1873-2023 The Cryosphere, 17, 1873-1893, 2023 The dataset is constructed by creating a 12. 5 × 12. 5 km grid of ice parcels for each age category, which are then advected and tracked as Lagrangian parcels according to weekly sea ice motion vectors estimated from satellite observations  (Maslanik et al. , 2011; Tschudi et al. , 2020)  and then interpolated back onto the original grid. If two parcels of different ages are advected into the same grid cell, the oldest age is taken for that grid cell, under the assumption that younger ice is easier to deform than older ice.
At the summer minimum ice extent, if parcels remain, they are put into the next age category. More information can be found in  Tschudi et al. (2016 Tschudi et al. ( , 2019 Tschudi et al. ( , 2020)). The approach taken to produce this dataset is rather different to that of the CDR ice type: it is based on the age of ice rather than type, which makes a direct comparison of the two datasets difficult. Further, by choosing the oldest ice in the case formulated above, it may overestimate the age of the ice by prioritising small concentrations of old ice  (Korosov et al. , 2018). Despite differences between these methods, the dataset can still provide some information on how well the model is advecting the ice, particularly at lower MYI concentrations which the CDR data do not provide. Additionally, it provides weekly data during the summer season when satellites that differentiate between ice types based on surface properties fail.
To compare with ice types from our model, we assign ice types to the NSIDC_age product as FYI (ice age less than 1 year) and MYI (ice age greater than 1 year). A similar approach was used for a comparison of this dataset with MYI in Community Earth System Model (CESM) previously  (Jahn et al. , 2012). We show the total extent of these age-based types in Fig. 3a.
Evaluation of modelled MYI Our main evaluation of the modelled MYI concentration is against the CDR data. We use the NSIDC_age data when no CDR data are available and to provide an indication of how the results of different observational products may vary. We first consider the evolution of total MYI extent, comparing the model results against the two datasets in Fig. 3a. The CDR data exhibit a substantial amount of noise in the daily values, but this is generally within the uncertainty estimate of the product. We also note that the MYI extent in the CDR data increases sharply during the autumn in some years and may continue increasing throughout winter in others. While such an increase in MYI extent can occur mid-winter due to diverging ice drift, neither the NSIDC_age data nor the model show such an extensive increase.
Note that in this study we use version 1 of the dataset, which is now deprecated and fully replaced by an upgraded version. The recently released version 3 of the dataset has major improvements relative to version 1, including both gap filling (bringing it to a level 4 product) and correction schemes based on temperature and ice drift information to correct for mis-classifications and to improve the early autumn classification (Signe  Aaboe, personal communication, 2023). In general, the correspondence between the three datasets is reasonable. There are no obvious biases between them, beyond the fact that MYI extent derived from the NSIDC_age data is generally high compared to the others, which is to be expected given the assumptions used to create the product (e. g. Korosov et al. , 2018).
These shortcomings generally do not affect the MYI extent and evolution after the summer melt (with the exception of 2016), showing that poor model performance during one winter has limited or no knock-on effect on the MYI extent in the following years. We note that the two observational datasets do not have the exact same variability, suggesting some uncertainty in the observed sub-annual behaviour. To demonstrate the evolution of the MYI over time, we show maps of MYI concentration distributions for mid-October, January, and April (Fig. 4 ). The years chosen aim to represent different model behaviour compared to the observations by showing an underestimate by the model  (2008-2009, panels a-c) , an overestimate by the model (2011-2012, panels d-f), and a good agreement between model and observations (2015-2016, panels g-i).
Summary and conclusions We have implemented a novel way of tracing MYI area and volume in a coupled ice-ocean model. By utilising the Lagrangian framework of the sea ice model, we can track when each grid element experiences the end of summer and therefore upgrade the MYI based on physical conditions. This gives a more accurate estimate of the amount of FYI that replenishes the MYI in autumn. The model also allows us to track how the source and sink terms for MYI impact its evolution. The model generally agrees well with multiple datasets, both in integrated amount of MYI extent and its spatial distribution, and the spatial distribution of MYI is good as long as the minimum ice extent in the autumn is reasonably well captured. The main drawback of our approach is that as MYI is treated as a tracer, we must make simple assumptions about how MYI and FYI behave when melting and ridging.
Acknowledgements. This research has been funded by the  Norwegian Research Council  ( FRASIL : grant no. 263044  and  Nansen Legacy : grant no. 27673 ) and  JPI Climate  and  JPI Oceans  ( MED-LEY  project, under agreement with the  Norwegian Research Council , grant no. 316730 ). The computations were performed on resources provided by Sigma2 -the  National Infrastructure for High Performance Computing  and Data Storage in Norway (project nos. NN9878K  and  NS9829K ). We are very grateful to two anonymous reviewers for their constructive feedback which helped us to improve the manuscript. Financial support. This research has been supported by the  Norges Forskningsråd  (grant nos. 263044  and  27673 ) and  JPI Climate  and  JPI Oceans  ( MEDLEY  project, under agreement with the  Norwegian Research Council , grant no.
Data availability. The ice type product Climate Data Record (version 1) from the Copernicus Climate Change Service Climate Data Store (  2020 ) is available at https://thredds. met. no/ thredds/c3s/c3s. html (last access: June 2020). The ice age product from the National Snow and Ice Data Center  (Tschudi et al. , 2019 ) is available at https://nsidc. org/data/nsidc-0611/ versions/4 (last access: September 2021). Monthly model outputs for variables discussed in this study are available on Zenodo (https://doi. org/10. 5281/zenodo. 7785918,  Regan et al. , 2023). Author contributions. PR and EO obtained the funding. PR, EO, and HR formulated the study. HR, EO, and GB developed the new code for tracking MYI. HR carried out the analysis. GB produced the simulation and helped with analysis. AK assisted with evaluation against observations.
Chr13 locus controls mitochondrial complex-I. First, we analyzed the chr13 locus (26 proteins) that was highly enriched for mitochondrial complex-I (Figure  2A ). Mapping the eigengene of the chr13 trans-regulated proteins identified the peak SNP (rs48592660). NDUFS4, a protein critical for complex-I assembly and loss of which leads to cardiac hypertrophy (2) mapped near the locus but outside the region of linkage disequilibrium. However, within the locus was the microRNA(miR)-23b/27b/24-1 cluster, among which miR-27b, a conserved regulator of NDUFS4, was identified via nine miRNA target prediction algorithms and one dataset of experimentally validated miRNA targets (Figure  2B  and Table  S5 ). Cardiac overexpression of miR-27b has previously been shown to promote cardiac hypertrophy  (31)  but attenuate angiotensin II-induced atrial fibrosis  (32).
Mice All mice were purchased from The Jackson Laboratory and bred at UCLA according to approved institutional animal care and use committee (IACUC) protocols with daily monitoring. Both the ISO-induced HF  (15, 17, 19)  and DIO  (13, 14)   (73). The precursor MS tolerance were set to 20 ppm and the MS/MS tolerance was set to 0. 8 Da with a maximum of 2 miss-cleavage. The peptides were searched with oxidation of methionine set as variable modification, and TMT tags on peptide Nterminus / lysine and carbamidomethylation of cysteine set as a fixed modification. All data was searched as a single batch and the peptide spectral matches (PSMs) of each database search filtered to 1% FDR using a target/decoy approach with Percolator (74). The filtered PSMs from each database search were grouped and q-values generated at the peptide level with the Qvality algorithm  (75).
Finally, the grouped peptide data was further filtered to 1% protein FDR using Protein Validator. Quantification was performed with the reporter ion quantification node for TMT quantification based on MS3 scans in Proteome Discoverer. TMT precision was set to 20 ppm and corrected for isotopic impurities. Only spectra with <50% co-isolation interference were used for quantification with an average signal-to-noise filter of >10. The data was filtered to retain Master proteins that were measured in at least 50 mice.
Data were in 100 mM HEPEs pH 8. 5). Protein was quantified with BCA (ThermoFisher Scientific) and processed with Proteome Discoverer v2. 3 and searched against the Mouse UniProt database normalized in Digestion Buffer to a final concentration of 2 µg/µl. Protein was digested with (November 2018) using SEQUEST sequencing grade trypsin (Sigma; #T6567) and sequencing grade LysC (Wako; #129-02541) at a ) and 3 to 5 months old control and Ndufs4-cKO mice of both sex (n=5 each group) fed with chow diet ad libitum (Purina 5053, LabDiet) were used. All mice were maintained on a 14 h light/10 h dark cycle (light is on between 6 a. m. and 8 p. m. ) at a temperature of 25 degrees and 30-70% humidity. On the day of the experiment, the mice were sacrificed after 4-hour fasting. 1:50 enzyme:substrate ratio overnight at 37°C with shaking at 2000 x rpm.
Data synthesis SMO efficacy was investigated using a NMRA with population severity and treatment duration as covariates and was then explored in each population severity group separately using a network meta-analysis. Network meta-analysis is a technique for comparing three or more interventions simultaneously in a single analysis by combining both direct and indirect evidence across a network of studies. NMRA is a technique to investigate whether certain study characteristics are associated with the intervention effects in the network meta-analysis  (Higgins et al. , 2022). To address the risk of overfitting, a sensitivity analysis using a NMRA with only population severity as a covariate was conducted for the primary endpoint on the overall sample of retained studies. A second sensitivity analysis using NMRA was performed with only double-blind RCTs.
Results for all comparisons were synthetized using risk ratio (RR) for abstinence rate and mean difference for PDA, including the 95% confidence interval. For evaluations of consistency in the network, we used the design-by-treatment interaction model  (Higgins et al. , 2012). Analyses assumed random effects for intervention effects within a frequentist framework. The statistical software that we used was STATA ® SE 14. 2. NETWORK META was used for performing NMRA and network meta-analysis models.
Similarly, an analysis of the France-2 TAVI registry, which encompassed 4201 patients without a history of bypass surgery, indicated a 30% prevalence of coronary artery disease, with multivessel disease observed in half of these cases. Additionally, the extent of coronary artery disease was found to correlate with an increased cardiovascular risk profile and EuroSCORE logistic operative risk score  3. These findings emphasize the high occurrence of CAD in patients with severe AS and highlight the need for a thorough evaluation of coronary lesions in this population. The data from the Swedish registry and the France-2 TAVI registry provide valuable insights into the prevalence and clinical implications of CAD in AS patients, supporting the importance of considering both pathologies when determining the optimal treatment strategy.
Data collection In this retrospective study, a population of 432 patients who underwent pre-TAVI coronary angiography between January 2020 and December 2022 was initially considered. After applying rigorous inclusion and exclusion criteria, a total of 26 patients were included in the final analysis. The inclusion criteria involved adult patients (age > 18 years) with symptomatic severe aortic stenosis and a confirmed need for intervention. Patients with no history of coronary bypass surgery and those with documented coronary lesions showing stenosis (40-70%) in at least one of the proximal trunks were included. We note that patients with low-flow, low-gradient aortic stenosis and severe left ventricular dysfunction (left ventricular ejection fraction < 35%) were excluded from the study due to potential limitations in the accuracy of flow modeling (Figure  2 ).
Data preparation and 3D model creation The process of obtaining the 3D model involved meticulous manual segmentation based on both the aortic CT scan images and the coronary angiography data. Using the SIMVASCULAR workflow, the initial aorta was segmented by tracing its boundaries on each CT slice. Simultaneously, the major vessels, including the right coronary, left main, LAD, and LCx, were designated by carefully identifying their positions. To ensure precision, the vessel diameters with stenosis observed in the coronary angiography data were accurately projected onto the segmented aorta. The resulting model underwent visual verification and necessary geometric filtering to obtain natural curves and enhance overall quality. Subsequently, the segmented and filtered model, consisting of the initial aorta and proximal segments, was exported to the SLICER 3D software where a solid 3D model was generated, capturing the geometric details of the segmented structures.
Boundary Conditions and Physiology Models To ensure accurate physiological modeling, the data preparation process involved several steps, particularly in relation to boundary conditions and parameter settings. The modeling of the coronary circuit was based on the work of Kim et al. 11  which required prescribing conditions at the outlets of the coronary vessels. In addition to incorporating resistances and capacitances from the classical Windkessel model, our modified model also considered venous circulation and accounted for the effects of left ventricular pressure, which plays a critical role in coronary flow. The total resistance at each outlet (R total ) was calculated based on the average arterial pressure of each patient and the flow through each outlet, which was determined using invasive pressure measurements taken during coronary angiography and Doppler-derived cardiac output measured before and after aortic valve replacement.
However, all these findings are based on small cohort studies, awaiting randomized controlled trials for formal conclusions. Our method uses patient specific hemodynamic and imaging data unlike commercial solutions which use only the anatomy. Our analysis reveals excellent agreement between measured FFR values and those calculated by computational fluid dynamics (CFD). Several studies have investigated this agreement between measured FFR and FFR calculated by CFD-based techniques (such as FFRct, vFFR, and QFR), with excellent results. The feasibility and diagnostic performance of FFRct were evaluated in the DISCOVER FLOW trial  32. This prospective study analyzed 103 patients (159 lesions) who underwent coronary computed tomography angiography (CTA) and invasive angiography with FFR measurements.
Characterization As the polymers are reactive towards moisture and oxygen, the following sample preparations were performed within a glove box. The chemical structure of polymers was determined by transmission FTIR spectroscopy using a Nicolet Magna 550 Fourier transform-infrared spectrometer. 1  H NMR data of PSZ solutions in CDCl 3 were obtained using a Bruker AM 300 spectrometer operating at 300 MHz. Tetramethylsilane (TMS) was used as a reference for the NMR data. Solid-state  13  C CP MAS and  29  Si MAS NMR spectra of solid samples were recorded on a Bruker AVANCE 300 spectrometer (7. 0 T, n 0 ( 1 H) = 300. 29 MHz, n 0 ( 13 C) = 75. 51 MHz, n 0 ( 29 Si) = 59. 66 MHz) using a 4 mm Bruker probe at a spinning frequency of 10 kHz. 13  C CP MAS experiments were recorded with ramped-amplitude crosspolarization in the 1 H channel to transfer magnetization from 1 H to  13  C.
25 to 2. 5 mm, the near-normal (81) hemispherical reflection spectrum was acquired using a Shimadzu UV-3600 UV-vis-NIR spectrophotometer equipped with an integrating sphere BaSO 4 coating of 60 mm diameter. Over the wavelength range from 2. 5 mm to 16 mm, the hemispherical directional reflectance (R -, y) was recorded using a PerkinElmer Spectrum 100 spectrophotometer with an integrating sphere gold coating of 150 mm diameter. A mathematical treatment (Mathematica software) was used to get access to the final optical parameters such as the thermal emittance e (in the infrared region; also referred to as spectrally averaged emissivity) and the solar absorptance a (in the UV-visible-near infrared regions; also referred to as spectrally averaged absorptivity) from the spectroscopy data, by using eqn (  1 ) and (  2 ), respectively: e ¼ Ð 1:6 mm 2:5 mm 1 À R l ð Þ ð ÞÃP l ð Þdl Ð 1:6 mm 2:5 mm P l ð Þdl (1) a ¼ Ð 2:5 mm 0:25 mm 1 À R l ð Þ ð ÞÃG l ð Þdl Ð 2:5 mm 0:25 mm G l ð Þdl (2) With l: the wavelength [mm], R(l): the spectral reflectance of the sample measured at room temperature, G(l): the standard solar irradiance spectrum (ASTM-G173 AM1.
Acknowledgements Dr Samuel Bernard ,  Dr Maxime Balestrat , and  Dr Xavier Deschanels  acknowledge  Agence Nationale de la Recherche  ( ANR ) for supporting this work through the  Carapass  project (PhD thesis of  Maxime Balestrat , Project No. ANR-16-CE08-0026-01 ). Dr Samuel Bernard  thanks the  Nouvelle-Aquitaine region  and  CTTC  for the financial support of the PhD thesis of  Maxime Cheype.
Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data.
Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92. 1% and 96. 3%, 95% and 87. 8%, and 73. 9% and 70. 6%, respectively. At slide level, accuracy, recall, and precision were 97. 6% and 92. 0%, 90. 9% and 100%, and 100% and 70. 8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center.
With these challenges in mind, we aimed to design a fast and simple processing pipeline that can be generalized to any center using a limited amount of training data, sticking to light model architecture for performance purposes. Here we present a patch-based approach that detects and locates invasive carcinoma (IC) on WSI. The algorithm consists of a two-step pipeline. First, a filtering algorithm parses WSI regions that are relevant to IC detection, focusing the analysis on a small part of the slide and leading to faster processing. Filtered regions are partitioned into patches and fed into a convolutional neural network (CNN) based classifier that predicts whether those patches contain IC or not. This model was first trained with labeled data from a so-called "reference center", and performance was measured on a test dataset containing WSI from this same reference center.