Torben Lange

Abstract Background Accurate risk stratification is important to improve patient selection and outcome of patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). As epicardial adipose tissue (EAT) is discussed to be involved in cardiovascular disease, it could be useful as a marker of poor prognosis in patients with severe AS undergoing TAVR. Methods A total of 416 patients diagnosed with severe AS by transthoracic echocardiography were assigned for TAVR and enrolled for systematic assessment. Patients underwent clinical surveys and 5-year long-term follow-up, with all-cause mortality as the primary endpoint. EAT volume was quantified on pre-TAVR planning CTs. Patients were retrospectively dichotomized at the median of 74 cm 3 of EAT into groups with low EAT and high EAT volumes. Mortality rates were compared using Kaplan-Meyer plots and uni- and multivariable cox regression analyses. Results A total number of 341 of 416 patients (median age 80.9 years, 45% female) were included in the final analysis. Patients with high EAT volumes had similar short-term outcome ( p = 0.794) but significantly worse long-term prognosis ( p = 0.023) compared to patients with low EAT volumes. Increased EAT volumes were associated with worse long-term outcome (HR1.59; p = 0.031) independently from concomitant cardiovascular risk factors, general type of AS, and functional echocardiography parameters of AS severity (HR1.69; p = 0.013). Conclusion Increased EAT volume is an independent predictor of all-cause mortality in patients with severe AS undergoing TAVR. It can be easily obtained from pre-TAVR planning CTs and may thus qualify as a novel marker to improve prognostication and management of patient with severe AS. Trial registration DRKS, DRKS00024479. Graphical abstract AS, aortic stenosis; TAVR, transcatheter aortic valve replacement; EAT, epicardial adipose tissue

Identifying and reconstructing hadronic τ decays (τh) is an important task at current and future high-energy physics experiments, as τh represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of τh can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful τh identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating τh from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art τh reconstruction and identification algorithms currently in use at the Large Hadron Collider.

Abstract This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors ( P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771—0.777) vs. 0.683 (0.681—0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment.

Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $\tau_{\textrm{h}}$ represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of $\tau_{\textrm{h}}$ can be viewed as a generalization and extension of jet-flavour tagging, which has in the recent years undergone significant progress due to the use of deep learning. Based on a granular simulation with realistic detector effects and a particle flow-based event reconstruction, we show in this paper that deep learning-based jet-flavour-tagging algorithms are powerful $\tau_{\textrm{h}}$ identifiers. Specifically, we show that jet-flavour-tagging algorithms such as LorentzNet and ParticleTransformer can be adapted in an end-to-end fashion for discriminating $\tau_{\textrm{h}}$ from quark and gluon jets. We find that the end-to-end transformer-based approach significantly outperforms contemporary state-of-the-art $\tau_{\textrm{h}}$ reconstruction and identification algorithms currently in use at the Large Hadron Collider.

Introduction: Precise risk assessment is essential for accurate management of patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). This study aimed to assess the prognostic implications of cardiovascular magnetic resonance (CMR)-derived imaging biomarkers in a large cohort of AS patients. Methods: 145 patients with severe AS underwent CMR imaging before TAVR. Image analyses included myocardial volumes, CMR-feature-tracking derived left and right atrial (LA & RA) as well as left and right ventricular (LV & RV) strain, myocardial T1 mapping as well as late gadolinium enhancement analyses. Cardiovascular (CV) mortality was defined as primary clinical endpoint. Results: Patients with CV death during follow-up had significantly enlarged RV enddiastolic volumes (82.9ml/ml 2 [70.8-96.0] vs. 62.8ml/ml 2 [54.7-76.0], p<0.001) and impaired strain values of all cardiac chambers compared to patients that survived (LV GLS: -18.1% [-13.1- -20.4] vs. -22.5% [-16.1- -27.3], p=0.019; RV GLS: -22.9% [-18.6 - -25.4] vs. -27.9% [-22.9- -32.0], p=0.002; LA Es: 9.5% [7.2 - 15.4] vs. 14.3% [9.0-18.1], p=0.036; RA Es: 12.4% [6.8-14.4] vs. 16.2% [11.2-22.1], p<0.001). RA reservoir strain independently predicted CV mortality after adjusting for other CMR imaging biomarkers and clinical parameters of heart failure. Within patients with high ECV values especially RA strain further identified AS patients at high-risk for CV mortality (p=0.001 on log-rank testing). Conclusion: Comprehensive CMR-imaging allows accurate outcome assessment and additional identification of high-risk groups in AS patients. Especially RA strain emerged as independent predictor for cardiovascular mortality and might serve for future optimized patient management.

The paper describes an ongoing project, a sustainable design of an agricultural building, part of an ecological meat production farm on the island of Glaeno in Denmark. It is a low-cost design using local skills and materials. The building is resource saving on every level; it also utilizes excess heat and sun energy. The paper presents the overall sustainable design approaches and mainly concentrates on the design of the optimized timber roof truss which uses locally grown timber. The main design criteria for the timber roof structure are structural efficiency, buildability and aesthetic appearance of both the concept and detailing.

LV 3D shape and contraction atlases, as well as statistical shape models, <b>embargoed until 20th January 2022.</b><br>Files coming soon.<br><br>

3D Shape and Contraction Analysis Pipeline. Please, unzip the pipeline and check 'ReadMe' for details on how to run it.<br>Reference paper: https://doi.org/10.1016/j.jcmg.2021.11.027<br>

3D Shape and Contraction Analysis Pipeline. Please, unzip the pipeline and check 'ReadMe' for details on how to run it.<br>Reference paper: https://doi.org/10.1016/j.jcmg.2021.11.027<br>

This paper describes the development process of ScaLib, a scalable library system that utilizes a hybrid cloud infrastructure consisting of Google App Engine and a self-hosted component, which is responsible for managing sensitive user data. The paper discusses the design decisions and especially the tradeoffs that have come along with the development.

Type 2 diabetes mellitus predicts outcome following acute myocardial infarction (AMI). Since underlying mechanics are incompletely understood, we investigated left ventricular (LV) and atrial (LA) pathophysiological changes and their prognostic implications using cardiovascular magnetic resonance (CMR). Consecutive patients (n=1147, n=265 diabetic; n=882 non-diabetic) underwent CMR 3 days after AMI. Analyses included LV ejection fraction (LVEF), global longitudinal, circumferential and radial strains (GLS, GCS and GRS), LA reservoir, conduit and booster pump strains, as well as infarct size, edema and microvascular obstruction. Predefined endpoints were major adverse cardiovascular events (MACE) within 12 months. Diabetic&lt;b&gt; &lt;/b&gt;patients had impaired LA reservoir (19.8 vs. 21.2%, p&lt;0.01) and conduit strains (7.6 vs. 9.0%, p&lt;0.01) but not ventricular function or myocardial damage. They were at higher risk of MACE than non-diabetic patients (10.2% vs. 5.8%, p&lt;0.01) with most MACE occurring in patients with LVEF≥35%. Whilst LVEF (p=0.045) and atrial reservoir strain (p=0.024) were independent predictors of MACE in non-diabetic patients, GLS was in diabetic patients (p=0.010). Considering patients with diabetes and LVEF≥35% (n=237), GLS and LA reservoir strain below median were significantly associated with MACE. In conclusion, in patients with diabetes, LA and LV longitudinal strain permit optimized risk assessment early after reperfused AMI with incremental prognostic value over and above LVEF.

Type 2 diabetes mellitus predicts outcome following acute myocardial infarction (AMI). Since underlying mechanics are incompletely understood, we investigated left ventricular (LV) and atrial (LA) pathophysiological changes and their prognostic implications using cardiovascular magnetic resonance (CMR). Consecutive patients (n=1147, n=265 diabetic; n=882 non-diabetic) underwent CMR 3 days after AMI. Analyses included LV ejection fraction (LVEF), global longitudinal, circumferential and radial strains (GLS, GCS and GRS), LA reservoir, conduit and booster pump strains, as well as infarct size, edema and microvascular obstruction. Predefined endpoints were major adverse cardiovascular events (MACE) within 12 months. Diabetic&lt;b&gt; &lt;/b&gt;patients had impaired LA reservoir (19.8 vs. 21.2%, p&lt;0.01) and conduit strains (7.6 vs. 9.0%, p&lt;0.01) but not ventricular function or myocardial damage. They were at higher risk of MACE than non-diabetic patients (10.2% vs. 5.8%, p&lt;0.01) with most MACE occurring in patients with LVEF≥35%. Whilst LVEF (p=0.045) and atrial reservoir strain (p=0.024) were independent predictors of MACE in non-diabetic patients, GLS was in diabetic patients (p=0.010). Considering patients with diabetes and LVEF≥35% (n=237), GLS and LA reservoir strain below median were significantly associated with MACE. In conclusion, in patients with diabetes, LA and LV longitudinal strain permit optimized risk assessment early after reperfused AMI with incremental prognostic value over and above LVEF.

LV 3D shape and contraction atlases, as well as statistical shape models, embargoed until main manuscript publication.<br>

LV 3D shape and contraction atlases, as well as statistical shape models, <b>embargoed until 20th January 2022.</b><br>Files coming soon.<br><br>