Stratified Learning: a general-purpose statistical method for improved learning under Covariate Shift

Covariate shift arises when the labelled training (source) data is not representative of the unlabelled (target) data due to systematic differences in the covariate distributions. A supervised model trained on the source data subject to covariate shift may suffer from poor generalization on the target data. We propose a novel, statistically principled and theoretically justified method to improve learning under covariate shift conditions, based on propensity score stratification, a well-established methodology in causal inference. We show that the effects of covariate shift can be reduced or altogether eliminated by conditioning on propensity scores. In practice, this is achieved by fitting learners on subgroups ("strata") constructed by partitioning the data based on the estimated propensity scores, leading to balanced covariates and much-improved target prediction. We demonstrate the effectiveness of our general-purpose method on contemporary research questions in observational cosmology, and on additional benchmark examples, matching or outperforming state-of-the-art importance weighting methods, widely studied in the covariate shift literature. We obtain the best reported AUC (0.958) on the updated "Supernovae photometric classification challenge" and improve upon existing conditional density estimation of galaxy redshift from Sloan Data Sky Survey (SDSS) data

Grubbs-Hoveyda type catalysts bearing a dicationic N-heterocyclic carbene for biphasic olefin metathesis reactions in ionic liquids

Abstract The novel dicationic metathesis catalyst [(RuCl 2 (H 2 ITapMe 2 )(=CH-2-(2-PrO)- N-heterocyclic carbene (NHC) ligand was prepared. The reactivity was tested in ring opening metathesis polymerization (ROMP) under biphasic conditions using a nonpolar organic solvent (toluene) and the ionic liquid (IL) 1-butyl-2,3-dimethylimidazolium The structure of Ru-2 was confirmed by single crystal X-ray analysis. 163

PuraStat in gastrointestinal bleeding: results of a prospective multicentre observational pilot study

Background: A recently developed haemostatic peptide gel for endoscopic application has been introduced to improve the management of gastrointestinal bleeding. The aim of this pilot study was to evaluate the feasibility, safety, efficacy and indication profiles of PuraStat in a clinical setting. Methods: In this prospective observational multicentre pilot study, patients with acute non-variceal gastrointestinal bleeding (upper and lower) were included. Primary and secondary application of PuraStat was evaluated. Haemoglobin, prothrombin time, platelets and transfusion behaviour were documented before and after haemostasis. The efficacy of PuraStat was assessed during the procedure, at 3 days and 1 week after application. Results: 111 patients with acute gastrointestinal bleeding were recruited into the study. 70 percent (78/111) of the patients had upper gastrointestinal bleeding and 30% (33/111) had lower gastrointestinal bleeding. After primary application of PuraStat, initial haemostatic success was achieved in 94% of patients (74/79, 95% CI 88-99%), and in 75% of the patients when used as a secondary haemostatic product, following failure of established techniques (24/32, 95% CI 59-91%). The therapeutic success rates (absence of rebleeding) after 3 and 7 days were 91% and 87% after primary use, and 87% and 81% in all study patients. Overall rebleeding rate at 30 day follow-up was 16% (18/111). In the 5 patients who finally required surgery (4.5%), PuraStat allowed temporary haemostasis and stabilisation. Conclusions: PuraStat expanded the therapeutic toolbox available for an effective treatment of gastrointestinal bleeding sources. It could be safely applied and administered without complications as a primary or secondary therapy. PuraStat may additionally serve as a bridge to surgery in order to achieve temporary haemostasis in case of refractory severe bleeding, possibly playing a role in preventing immediate emergency surgery

Principled bayesian modeling and statistical learning with non-representative data in astrophysics

This thesis tackles the fundamental issue of non-representative data in astrophysics via the development and application of methodology within the areas of statistical machine learning, Bayesian statistics and causal inference; to efficiently handle big data, to allow for probabilistic and principled parameter estimation with proper uncertainty quantification, and to deal with systematic uncertainties and biases in the data collection process. To enable (a) statistically principled, (b) scientifically justified, and (c) computationally efficient analysis of non-representative, complex astrophysical data, this thesis provides novel general-purpose statistical methodology, and statistical methodology tailored to topical scientific problems, in cosmology and high-energy astrophysics, as grouped into three related projects hereafter: (i) We propose a simple, statistically principled, and theoretically justified general-purpose method, StratLearn, to improve supervised learning when the training set is not representative, a situation known as covariate shift. Building upon a well-established methodology in causal inference, we show that the effects of covariate shift can be reduced or eliminated by conditioning on propensity scores. We demonstrate that fitting learners within strata constructed on the estimated propensity scores improves upon state-of-the-art importance weighting methods on two topical scientific tasks – conditional density estimation of galaxy redshift (photo-z), and photometric supernovae type Ia (SNIa) classification. (ii) We improve weak lensing photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional density estimates obtained within StratLearn. We substantially improve the galaxy tomographic bin assignment, and obtain almost unbiased estimates of target population means within tomographic bins. (iii) We propose a science-driven hierarchical Bayesian framework to estimate the galaxy luminosity distribution in X-rays, combining non-representative X-ray and optical surveys. Our proposed framework accounts for incompleteness bias by incorporating an X-ray incompleteness function (estimated from simulations) and an optical incompleteness function (with parameters learned from the observed data) into the model. This allows for improved recovery of the luminosity function even with high proportions of systematic incompleteness, evaluated on simulations, and applied to data from the Chandra Deep Field South (CDFS).Open Acces

Grubbs–Hoveyda type catalysts bearing a dicationic N-heterocyclic carbene for biphasic olefin metathesis reactions in ionic liquids

The novel dicationic metathesis catalyst [(RuCl2(H2ITapMe2)(=CH–2-(2-PrO)-C6H4))2+ (OTf−)2] (Ru-2, H2ITapMe2 = 1,3-bis(2’,6’-dimethyl-4’-trimethylammoniumphenyl)-4,5-dihydroimidazol-2-ylidene, OTf− = CF3SO3−) based on a dicationic N-heterocyclic carbene (NHC) ligand was prepared. The reactivity was tested in ring opening metathesis polymerization (ROMP) under biphasic conditions using a nonpolar organic solvent (toluene) and the ionic liquid (IL) 1-butyl-2,3-dimethylimidazolium tetrafluoroborate [BDMIM+][BF4−]. The structure of Ru-2 was confirmed by single crystal X-ray analysis

Rapid and sensitive identification of omicron by variant-specific PCR and nanopore sequencing: paradigm for diagnostics of emerging SARS-CoV-2 variants

On November 26, 2021, the World Health Organization classified B.1.1.529 as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant of concern (VoC), named omicron. Spike-gene dropouts in conventional SARS-CoV-2 PCR systems have been reported over the last weeks as indirect diagnostic evidence for the identification of omicron. Here, we report the combination of PCRs specific for heavily mutated sites in the spike gene and nanopore-based full-length genome sequencing for the rapid and sensitive identification of the first four COVID-19 patients diagnosed in Germany to be infected with omicron on November 28, 2021. This study will assist the unambiguous laboratory-based diagnosis and global surveillance for this highly contagious VoC with an unprecedented degree of humoral immune escape. Moreover, we propose that specialized diagnostic laboratories should continuously update their assays for variant-specific PCRs in the spike gene of SARS-CoV-2 to readily detect and diagnose emerging variants of interest and VoCs. The combination with established nanopore sequencing procedures allows both the rapid confirmation by whole genome sequencing as well as the sensitive identification of newly emerging variants of this pandemic beta-coronavirus in years to come

Variable detection of Omicron-BA.1 and -BA.2 by SARS-CoV-2 rapid antigen tests

During 2022, the COVID-19 pandemic has been dominated by the variant of concern (VoC) Omicron (B.1.1.529) and its rapidly emerging subvariants, including Omicron-BA.1 and -BA.2. Rapid antigen tests (RATs) are part of national testing strategies to identify SARS-CoV-2 infections on site in a community setting or to support layman's diagnostics at home. We and others have recently demonstrated an impaired RAT detection of infections caused by Omicron-BA.1 compared to Delta. Here, we evaluated the performance of five SARS-CoV-2 RATs in a single-centre laboratory study examining a total of 140 SARS-CoV-2 PCR-positive respiratory swab samples, 70 Omicron-BA.1 and 70 Omicron-BA.2, as well as 52 SARS-CoV-2 PCR-negative swabs collected from March 8th until April 10th, 2022. One test did not meet minimal criteria for specificity. In an assessment of the analytical sensitivity in clinical specimen, the 50% limit of detection (LoD50) ranged from 4.2 x 10(4) to 9.2 x 10(5) RNA copies subjected to the RAT for Omicron-BA.1 compared to 1.3 x 10(5) to 1.5 x 10(6) for Omicron-BA.2. Overall, intra-assay differences for the detection of Omicron-BA.1-containing and Omicron-BA.2-containing samples were non-significant, while a marked overall heterogeneity among the five RATs was observed. To score positive in these point-of-care tests, up to 22-fold (LoD50) or 68-fold (LoD95) higher viral loads were required for the worst performing compared to the best performing RAT. The rates of true-positive test results for these Omicron subvariant-containing samples in the highest viral load category (Ct values < 25) ranged between 44.7 and 91.1%, while they dropped to 8.7 to 22.7% for samples with intermediate Ct values (25-30). In light of recent reports on the emergence of two novel Omicron-BA.2 subvariants, Omicron-BA.2.75 and BJ.1, awareness must be increased for the overall reduced detection rate and marked differences in RAT performance for these Omicron subvariants