Dataset Research Articles

Nocturnal heat exposure and stroke risk.

In recent decades, nighttime temperatures have increased faster than daytime temperatures. The increasing prevalence of nocturnal heat exposure may pose a significant risk to cardiovascular health. This study investigated the association between nighttime heat exposure and stroke risk in the region of Augsburg, Germany, and examined its temporal variations over 15 years. Hourly meteorological parameters, including mean temperature, relative humidity, and barometric pressure, were acquired from a local meteorological station. A data set was obtained consisting of 11 037 clinical stroke cases diagnosed during warmer months (May to October) between the years 2006 and 2020. The average age of cases was 71.3 years. Among these cases, 642 were identified as haemorrhagic strokes, 7430 were classified as ischaemic strokes, and 2947 were transient ischaemic attacks. A time-stratified case-crossover analysis with a distributed lag non-linear model was used to estimate the stroke risk associated with extreme nighttime heat, as measured by the hot night excess (HNE) index after controlling for the potential confounding effects of daily maximum temperature and other climatic variables. Subgroup analyses by age group, sex, stroke subtype, and stroke severity were performed to identify variations in susceptibility to nighttime heat. Results suggested a significant increase in stroke risk on days with extreme nighttime heat (97.5% percentile of HNE) (odds ratio 1.07, 95% confidence interval 1.01-1.15) during the full study period. When comparing the results for 2013-20 with the results for 2006-12, there was a significant increase (P < .05) in HNE-related risk for all strokes and specifically for ischaemic strokes during the more recent period. Furthermore, older individuals, females, and patients with mild stroke symptoms exhibited a significantly increased vulnerability to nighttime heat. This study found nocturnal heat exposure to be related to elevated stroke risk after controlling for maximum daytime temperature, with increasing susceptibility between 2006 and 2020. These results underscore the importance of considering nocturnal heat as a critical trigger of stroke events in a warming climate.

POSTN Regulates Fibroblast Proliferation and Migration in Laryngotracheal Stenosis Through the TGF-β/RHOA Pathway.

To investigate the role of periostin (POSTN) and the transforming growth factor β (TGF-β) pathway in the formation of laryngotracheal stenosis (LTS) scar fibrosis and to explore the specific signaling mechanism of POSTN-regulated TGF-β pathway in tracheal fibroblasts. Bioinformatics analysis was performed on scar data sets from the GEO database to preliminarily analyze the involvement of POSTN and TGF-β pathways in fibrosis diseases. Expression of POSTN and TGF-β pathway-related molecules was analyzed in LTS scar tissue at the mRNA and protein levels. The effect of POSTN on the biological behavior of tracheal fibroblasts was studied using plasmid DNA overexpression and siRNA silencing techniques to regulate POSTN expression and observe the activation of TGF-β1 and the regulation of cell proliferation and migration via the TGF-β/RHOA pathway. The bioinformatics analysis revealed that POSTN and the TGF-β pathway are significantly involved in fibrosis diseases. High expression of POSTN and TGF-β/RHOA pathway-related molecules (TGFβ1, RHOA, CTGF, and COL1) was observed in LTS tissue at both mRNA and protein levels. In tracheal fibroblasts, overexpression or silencing of POSTN led to the activation of TGF-β1 and regulation of cell proliferation and migration through the TGF-β/RHOA pathway. POSTN is a key molecule in scar formation in LTS, and it regulates the TGF-β/RHOA pathway to mediate the formation of cicatricial LTS by acting on TGF-β1. This study provides insights into the molecular mechanisms underlying LTS and suggests potential therapeutic targets for the treatment of this condition. NA Laryngoscope, 2024.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Group B Streptococcus transcriptome when interacting with brain endothelial cells.

Group B Streptococcus (GBS) meningitis remains the leading cause of neonatal meningitis. Research work has identified surface factors and two-component systems that contribute to GBS disruption of the blood-brain barrier (BBB). These discoveries often relied on genetic screening approaches. Here, we provide transcriptomic data describing how GBS changes its transcriptome when interacting with brain endothelial cells. Additionally, we have phenotypically validated these data by obtaining mutants of a select regulator that is highly down-regulated during infection and testing on our BBB model. This work provides the research field with a validated data set that can provide an insight into potential pathways that GBS requires to interact with the BBB and open the door to new discoveries.

Wear particle image analysis: feature extraction, selection and classification by deep and machine learning

Purpose This study aims to explore the integration of machine learning (ML) in tribology to optimize lubrication interval decisions, aiming to enhance equipment lifespan and operational efficiency through wear image analysis. Design/methodology/approach Using a data set of scanning electron microscopy images from an internal combustion engine, the authors used AlexNet as the feature extraction algorithm and the J48 decision tree algorithm for feature selection and compared 15 ML classifiers from the lazy-, Bayes and tree-based families. Findings From the analyzed ML classifiers, instance-based k-nearest neighbor emerged as the optimal algorithm with a 95% classification accuracy against testing data. This surpassed individually trained convolutional neural networks’ (CNNs) and closely approached ensemble deep learning (DL) techniques’ accuracy. Originality/value The proposed approach simplifies the process, enhances efficiency and improves interpretability compared to more complex CNNs and ensemble DL techniques.

Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling

Background Passive mobile sensing provides opportunities for measuring and monitoring health status in the wild and outside of clinics. However, longitudinal, multimodal mobile sensor data can be small, noisy, and incomplete. This makes processing, modeling, and prediction of these data challenging. The small size of the data set restricts it from being modeled using complex deep learning networks. The current state of the art (SOTA) tackles small sensor data sets following a singular modeling paradigm based on traditional machine learning (ML) algorithms. These opt for either a user-agnostic modeling approach, making the model susceptible to a larger degree of noise, or a personalized approach, where training on individual data alludes to a more limited data set, giving rise to overfitting, therefore, ultimately, having to seek a trade-off by choosing 1 of the 2 modeling approaches to reach predictions. Objective The objective of this study was to filter, rank, and output the best predictions for small, multimodal, longitudinal sensor data using a framework that is designed to tackle data sets that are limited in size (particularly targeting health studies that use passive multimodal sensors) and that combines both user agnostic and personalized approaches, along with a combination of ranking strategies to filter predictions. Methods In this paper, we introduced a novel ranking framework for longitudinal multimodal sensors (FLMS) to address challenges encountered in health studies involving passive multimodal sensors. Using the FLMS, we (1) built a tensor-based aggregation and ranking strategy for final interpretation, (2) processed various combinations of sensor fusions, and (3) balanced user-agnostic and personalized modeling approaches with appropriate cross-validation strategies. The performance of the FLMS was validated with the help of a real data set of adolescents diagnosed with major depressive disorder for the prediction of change in depression in the adolescent participants. Results Predictions output by the proposed FLMS achieved a 7% increase in accuracy and a 13% increase in recall for the real data set. Experiments with existing SOTA ML algorithms showed an 11% increase in accuracy for the depression data set and how overfitting and sparsity were handled. Conclusions The FLMS aims to fill the gap that currently exists when modeling passive sensor data with a small number of data points. It achieves this through leveraging both user-agnostic and personalized modeling techniques in tandem with an effective ranking strategy to filter predictions.

Water function zone: A method to improve the accuracy of remote sensing retrieval of water bodies

In water monitoring, it is always a hot issue to exploring a method to improve the accuracy of remote sensing inversion. To this end, based on the actual investigation of the surrounding environment and water quality characteristics of the Fuyang river, this study has established five distinct water functional zones: reservoir (RS), village river (VR), industrial river (IR), artificial lake (AL), and urban river (UR), which are considered as subdivided datasets. Meanwhile, considering that lakes and reservoirs typically contain relatively stationary or slow-moving water bodies, while river channels are characterized by moving water bodies, three multi-type datasets have been constructed for comparison. These are based on the morphological and flow characteristics of different regions within the Fuyang river and include river channel (R), lake (L), and whole basin (W). Based on multi-spectral images of unmanned aerial vehicle (UAV) and measured water quality data, ten types of inversion models were to invert six kinds of water quality parameters, and compare and analyze the performance of the optimal inversion model of the corresponding data sets. The results show that the multivariate regression model is superior to the univariate regression model, and the mean coefficient of determination (R2) of the subdivided data set is 0.936, and the root mean square error (RMSE) and mean absolute error (MAE) appear decrease in different degrees compared with the W with 3.573 and 2.662, respectively. The mean ratio performance to interquartile (RPIQ) of the corresponding model in UR and VR are 3.963 and 2.748, respectively. Extremely Randomized Trees (ERT) model is more suitable for the inversion of multi-type data sets lacking obvious water quality characteristics and with complex components, while Categorical Boosting (CatBoost) model is more suitable for the inversion of subdivided data sets with obvious water quality characteristics. The current method has practical guiding significance for improving the application level of water monitoring technology in ecological environment protection and urban water resources protection.

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

To develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets. Cross-sectional study. A total of 325 CT scans from a clinical database. A state-of-the-art deep learning (DL) algorithm (SwinUNETR) was used to train a prediction model for rapid segmentation of 9 key temporal bone structures in a data set of 325 clinical CTs. The data set was manually annotated by a specialist to serve as the ground truth. The data set was randomly split into training (n = 260) and testing (n = 65) sets. The model's performance was objectively assessed through external validation on the test set using metrics including Dice, Balanced accuracy, Hausdorff distances, and processing time. The model achieved an average Dice coefficient of 0.87 for all structures, an average balanced accuracy of 0.94, an average Hausdorff distance of 0.79 mm, and an average processing time of 9.1 seconds per CT. The present DL model for the automated simultaneous segmentation of multiple structures within the temporal bone from CTs achieved high accuracy according to currently commonly employed objective analysis. The results demonstrate the potential of the method to improve preoperative evaluation and intraoperative guidance in otologic surgery.

The Crane bag: Braiding Irish mythology, indigenous knowledge and posthuman concepts in early childhood education and care

Initially, this article aimed to present findings from an empirical set of data, where postgraduates explored their pedagogical practice within early childhood education and care (ECEC). However, predictable modes of knowing were disrupted while braiding an artefact from Irish mythology known as the Crane bag, together with indigenous knowledge and posthuman concepts. In the generation of knowledge, the teachings of the Crane bag transformed the methodological approach to go beyond the fabricated split of matter and spirit. This transformation created the conditions for an onto-epistemological becoming with water. Hence, the intra-action of braiding together multiple theories became part of the empirical data. From the initial set of data, a photograph, capturing children rolling down a hill, was submitted by a research participant as an example of their approach to safety. In agreement with the research participant, I re-present the photograph in a different format, to tell the story of how an onto-epistemological becoming created an opening to decentre the image of the child. This opening changed the approach to the data analysis, which in turn allowed the more-than-human to come to the fore in the spirit of reciprocity.

Site Identification and Next Choice Protocol for Hit-to-Lead Optimization.

Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.

Global System Errors to Simultaneously Improve the Identification of Subsystems with Mixed Data Gaussian Process Regression

Abstract This paper explores the use of Gaussian Process Regression (GPR) for system iden-&amp;#xD;tification in control engineering. It introduces two novel approaches that utilize the&amp;#xD;data from a measured global system error. The paper demonstrates these approaches&amp;#xD;by identifying a simulated system with three subsystems, a one degree of freedom&amp;#xD;mass with two antagonist muscles. The first approach uses this whole-system error&amp;#xD;data alone, achieving accuracy on the same order of magnitude as subsystem-specific&amp;#xD;data (9.28 ± 0.87 N vs. 6.96 ± 0.32 N of total model errors). This is significant, as&amp;#xD;it shows that the same data set can be used to identify unique subsystems, as op-&amp;#xD;posed to requiring a set of data descriptive of only a single subsystem. The second&amp;#xD;approach demonstrated in this paper mixes traditional subsystem-specific data with&amp;#xD;the whole system error data, achieving up to 98.71% model improvement.

Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model

The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20, batch size 16, and neuron 32 with the lowest evaluation value on MSLE of 0.094, MSE of 9.067, MAE of 2.440, RMSE of 3.010, and MAPE of 10.507 (very good model because the value is less than 15) compared other variations. There is a negative correlation for INTRUSION-MALWARE, BLOCKED-IGNORED, IGNORED-LOGGED, and LOW-MEDIUM. The predicted results for the next 12 months will increase starting from the second month at the same time. The resulting predictions can be used as a basis for policy and strategy decisions by stakeholders in dealing with fluctuations in cyber attacks that occur.

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 m) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth to embrace a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Dual generative adversarial networks for merging ocean transparency from satellite observations

ABSTRACT Satellite ocean transparency data have low spatial coverage due to cloud shading, sun glint, swath width, and temporal revisit. Merging multiple satellite ocean transparency data can improve spatial coverage and create a high-accuracy data set. This study proposed a new satellite ocean transparency merging model based on dual generative adversarial networks (ZSD-merging GAN), and the products of full-coverage and high-accuracy ocean transparency were produced. The ZSD-merging GAN comprises the guess GAN and the merging GAN. The guess GAN is used to generate the guess of the ocean transparency merged product, while the merging GAN combines the guess and satellite ocean transparency data to produce the merged product. The experiments show that the spatial coverage of the ZSD-merging GAN product is 100%. The root-mean-square error (RMSE) and average relative error (ARE) between the ZSD-merging GAN product and unmerged ocean transparency data from the Visible Infrared Imaging Radiometer Suite (VIIRS) on JPSS1 are 4.31 m and 11%, respectively, which are better than 5.59 m and 17% for historical average, 5.55 m and 19% for guess product, 5.08 m and 17% for Poisson blending product, and 5.12 m and 21% for Kriging interpolation product.

Robust integration of secondary outcomes information into primary outcome analysis in the presence of missing data.

In clinical and observational studies, secondary outcomes are frequently collected alongside the primary outcome for each subject, yet their potential to improve the analysis efficiency remains underutilized. Moreover, missing data, commonly encountered in practice, can introduce bias to estimates if not appropriately addressed. This article presents an innovative approach that enhances the empirical likelihood-based information borrowing method by integrating missing-data techniques, ensuring robust data integration. We introduce a plug-in inverse probability weighting estimator to handle missingness in the primary analysis, demonstrating its equivalence to the standard joint estimator under mild conditions. To address potential bias from missing secondary outcomes, we propose a uniform mapping strategy, imputing incomplete secondary outcomes into a unified space. Extensive simulations highlight the effectiveness of our method, showing consistent, efficient, and robust estimators under various scenarios involving missing data and/or misspecified secondary models. Finally, we apply our proposal to the Uniform Data Set from the National Alzheimer's Coordinating Center, exemplifying its practical application.

Enhancing citizen science impact in environmental monitoring: Targeted engagement strategies with stakeholder groups

Understanding the motivations and benefits of citizen science (CS) participants is critical to the success of environmental science projects that rely on data collection from engaged citizens. Tailored communication with citizen scientists is essential, leading to the need to target specific societal groups for extensive and high-quality data sets. The purpose of the study was to apply marketing concepts such as stakeholder analysis, value proposition canvas (VPC), and key performance indicators (KPIs) to CS projects. The study examined the extent to which these strategies can be applied to CS projects and add value, resulting in improved recruitment and retention of participants, as well as improved project management. The dynamic landscape of CS projects requires adapted business-oriented approaches supporting ongoing participation with high motivation, acknowledging community needs, and recognizing institutional scientists, akin to targeting potential customers. The case study focuses on the CityCLIM initiative, an EU-funded project collecting urban climate data, especially air temperature, using mobile weather stations. The CityCLIM VPC, analyzing stakeholder groups, facilitated tailored communication strategies by analyzing stakeholder groups and highlighting the effectiveness of the CS cycling initiative within the “Stadtradeln” program. Impact KPIs served as a roadmap for strategic refinement, while data quality KPIs identified deficiencies, guiding adjustments to the campaign. Applied marketing tools improved project planning, engagement, and evaluation, demonstrating the potential of this concept. Adapting marketing tools to recruitment and communication strategies benefits CS projects by targeting specific groups. The holistic integration of stakeholder analysis, VPC, and KPIs streamlines project management and creates a framework for sustainable success. This adaptation forms a robust toolkit for CS project coordinators, contributing to effective communication, engagement, and impact assessment. Applying marketing tools significantly increases CS projects’ reach and impact, ensuring long-term success and meaningful scientific contributions.

New and Efficient Estimators of Reliability Characteristics for a Family of Lifetime Distributions Under Progressive Censoring

Estimation of reliability and stress–strength parameters is important in the manufacturing industry. In this paper, we develop shrinkage-type estimators for the reliability and stress–strength parameters based on progressively censored data from a rich class of distributions. These new estimators improve the performance of the commonly used Maximum Likelihood Estimators (MLEs) by reducing their mean squared errors. We provide analytical asymptotic and bootstrap confidence intervals for the targeted parameters. Through a detailed simulation study, we demonstrate that the new estimators have better performance than the MLEs. Finally, we illustrate the application of the new methods to two industrial data sets, showcasing their practical relevance and effectiveness.

Rectifying a century of misidentifications: a taxonomic re-evaluation of sipo snakes (Colubridae: Chironius) on Trinidad

The island of Trinidad has several endemic reptile species that in some cases are morphologically indistinguishable, or almost so, from their mainland counterparts. In particular, many snakes from the island have not been examined thoroughly with modern scientific methods and may therefore be misidentified. At least two species of sipo snakes (Chironius carinatus, C. septentrionalis) are reported to inhabit Trinidad, both being considered species already inhabiting the mainland, though their identities are based solely on morphology. Here, we evaluate the molecular distinctiveness of these Trinidadian snakes and assess their relationships to other members of the genus. We constructed a multi-locus data set (12S rRNA, 16S rRNA, cyt b, ND4, cmos, NT3, Rag-1, Rag-2) including novel sequences and those available on GenBank to perform Bayesian and maximum likelihood phylogenetic analyses. Our phylogenetic reconstruction elucidates the identity of both Trinidad species, which have been misidentified since their discovery, and we provide a literature review of their taxonomic history. More specifically, our results suggest that the Trinidad population of Chironius carinatus is an undescribed species, that we describe herein as Chironius nigelnoriegai sp. nov., and is the sister to C. flavopictus. Our results also demonstrate that the Trinidad population previously identified as C. septentrionalis is instead the mainland species C. cochranae, a member of a clade with C. foveatus, C. laurenti, and C. multiventris. Finally, we generated a time tree and inferred that our new species separated from C. flavopictus approximately 4 million years ago in the Pliocene, a time when the island of Trinidad detached from Northern Venezuela. This work contributes to a better understanding of species diversity of Trinidad and we hope that it assists in conservation efforts towards this important endemic region. These findings support the prospect of rapid speciation on Trinidad and implies that more extensive surveys of island squamates will reveal additional cryptic diversity. http://zoobank.org/urn:lsid:zoobank.org:pub:500DB91B-62CD-4051-9831-0BEEAC64C8E2

Imaging Polarimetry of Comet 67 P/Churyumov–Gerasimenko: Homogeneous Distribution of Polarisation and its Implications

Abstract Comet 67P/Churyumov-Gerasimenko (67P) become observable for the first time in 2021 since the Rosetta rendezvous in 2014–16. Here, we present pre-perihelion polarimetric measurements of 67P from 2021 performed with the Very Large Telescope (VLT), as well as post-perihelion polarimetric measurements from 2015–16 obtained with the VLT and the William Herschel Telescope (WHT). This new data covers a phase angle range of ∼4 − 50○ and presents polarimetric measurements of unprecedentedly high S/N ratio. Complementing previous measurements, the polarimetric phase curve of 67P resembles that of other Jupiter family comets and high-polarisation, dusty comets. Comparing pre- and post-perihelion data sets, we find only a marginal difference between the polarimetric phase curves. In our imaging maps, we detect various linear structures produced by the dust in the inner coma of the comet. Despite this, we find a homogeneous spread of polarisation around the photocentre throughout the coma and tail, in contrast to previous studies. Finally, we explore the consequences of image misalignments on both polarimetric maps and aperture polarimetric measurements.

Procedural and clinical outcomes of patients undergoing a TAVI in TAVI procedure: Rationale and design of the multicentre, prospective, observational ReTAVI registry.

Transcatheter aortic valve implantation (TAVI) is increasingly being used in younger patients and those with lower peri-procedural risk, meaning more patients will live long enough to experience structural valve deterioration (SVD) of the bioprosthesis, indicating repeated TAVI. Experience of repeated TAVI-transcatheter heart valve (THV) implantation into an index THV is limited. This registry aims to assess the peri-procedural and short-term safety, efficacy and durability of repeated TAVI. The ReTAVI Prospective observational registry is an investigator-initiated, multicentre, international, prospective registry of patients undergoing repeated TAVI using balloon-expandable SAPIEN prosthesis to evaluate procedural and short-term safety, efficacy and durability as well as anatomical and procedural factors associated with optimal results. The registry will enrol at least 150 patients across 60 high-volume centres. Patients must be ≥18 years old, have had procedural success with their first TAVI, have index THV device failure, intend to undergo repeated TAVI and be considered suitable candidates by their local Heart Team. All patients will undergo a 30-day and 12-month follow-up. The estimated study completion is 2025. The registry will collect pre-, peri-, postoperative and 12-months data on patients undergoing repeated TAVI procedures with THVs for failure of the index THV and determine VARC-3-defined efficacy and safety at 30 days and functional outcome at 12 months. The registry will expand existing data sets and identify patient characteristics/indicators related to complications and clinical benefits for patients with symptomatic severe calcific degenerative aortic stenosis.