DynaConF: Dynamic Forecasting of Non-Stationary Time-Series

Deep learning models have shown impressive results in a variety of time series forecasting tasks, where modeling the conditional distribution of the future given the past is the essence. However, when this conditional distribution is non-stationary, it poses challenges for these models to learn consistently and to predict accurately. In this work, we propose a new method to model non-stationary conditional distributions over time by clearly decoupling stationary conditional distribution modeling from non-stationary dynamics modeling. Our method is based on a Bayesian dynamic model that can adapt to conditional distribution changes and a deep conditional distribution model that can handle large multivariate time series using a factorized output space. Our experimental results on synthetic and popular public datasets show that our model can adapt to non-stationary time series better than state-of-the-art deep learning solutions

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system and which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21

Viral etnografi

I denne artikel beskriver vi, hvordan den antropologiske komponent af „HOPE – How Democracies Cope with COVID-19: A Data-Driven Approach“ har arbejdet etnografisk under covid-19-epidemien i Danmark. Artiklen illustrerer, hvordan coronavirus’ ustabilitet fordrede en kontinuerlig gentænkning af det etnografiske feltarbejde. Coronakrisen skabte en akut situation, hvor forudgående viden og forbindelser til en divers række felter blev centrale for indsamlingen af data om befolkningen i Danmarks oplevelser med Sars-CoV-2. Samtidig med at være objekt for den antropologiske undersøgelse var coronavirus definerende for de metodiske muligheder, idet risikoen for smitte samt restriktioner ansporede til brugen af virtuelle interviews, hurtige graf-elicitationer og indsamling af data fra Facebook. Vi argumenterer for, at mens den etnografiske natur af den enkelte metode varierer, er de samlet og i kombination med teamets personlige erfaringer med livet under coronakrisen og den antropologiske refleksion og teoretisering med til at frembringe en kvasietnografi, der åbner for centrale etnografiske indsigter om coronakrisens sociale konsekvenser. Endvidere argumenterer vi for vigtigheden af metodisk fleksibilitet, hvis etnografisk viden skal spille en rolle i håndteringen af situationer som coronakrisen

Multi-national survey on the methods, efficacy, and safety on the post-approval clinical use of pulsed field ablation (MANIFEST-PF).

AIMS Pulsed field ablation (PFA) is a novel atrial fibrillation (AF) ablation modality that has demonstrated preferential tissue ablation, including no oesophageal damage, in first-in-human clinical trials. In the MANIFEST-PF survey, we investigated the 'real world' performance of the only approved PFA catheter, including acute effectiveness and safety-in particular, rare oesophageal effects and other unforeseen PFA-related complications. METHODS AND RESULTS This retrospective survey included all 24 clinical centres using the pentaspline PFA catheter after regulatory approval. Institution-level data were obtained on patient characteristics, procedure parameters, acute efficacy, and adverse events. With an average of 73 patients treated per centre (range 7-291), full cohort included 1758 patients: mean age 61.6 years (range 19-92), female 34%, first-time ablation 94%, paroxysmal/persistent AF 58/35%. Most procedures employed deep sedation without intubation (82.1%), and 15.1% were discharged same day. Pulmonary vein isolation (PVI) was successful in 99.9% (range 98.9-100%). Procedure time was 65 min (38-215). There were no oesophageal complications or phrenic nerve injuries persisting past hospital discharge. Major complications (1.6%) were pericardial tamponade (0.97%) and stroke (0.4%); one stroke resulted in death (0.06%). Minor complications (3.9%) were primarily vascular (3.3%), but also included transient phrenic nerve paresis (0.46%), and TIA (0.11%). Rare complications included coronary artery spasm, haemoptysis, and dry cough persistent for 6 weeks (0.06% each). CONCLUSION In a large cohort of unselected patients, PFA was efficacious for PVI, and expressed a safety profile consistent with preferential tissue ablation. However, the frequency of 'generic' catheter complications (tamponade, stroke) underscores the need for improvement