Rotating binary Bose-Einstein condensates and vortex clusters in quantum droplets

Quantum droplets may form out of a gaseous Bose-Einstein condensate, stabilized by quantum fluctuations beyond mean field. We show that multiple singly-quantized vortices may form in these droplets at moderate angular momenta in two dimensions. Droplets carrying these precursors of an Abrikosov lattice remain self-bound for certain timescales after switching off an initial harmonic confinement. Furthermore, we examine how these vortex-carrying droplets can be formed in a more pertubation-resistant setting, by starting from a rotating binary Bose-Einstein condensate and inducing a metastable persistent current via a non-monotonic trapping potential.Comment: 5 page, 4 figure

How mycorrhizal associations and plant density influence intra- and inter-specific competition in two tropical tree species: Cabralea canjerana (Vell.) Mart. and Lafoensia pacari A.St.-Hil.

Arbuscular mycorrhizal fungi (AMF) associations benefit host plants due to increased ability to obtain resources and hence may influence competitive interactions. Here we experimentally examine growth in Cabralea canjerana and Lafoensia pacari at different densities and with and without AMF. In the density treatment pots had either six or 12 individuals. Half of each treatment was innoculated with AMF and the other half was not. The proportion of each species in each pot was also varied. The AMF did not apparently influence interspecific competitive interactions because growth was similar in both treatments. However, intra-specific competition was very strong in C. canjerana while more moderate in L. pacari and both were influenced by the presence of the AMF. The AMF?Cabralea canjerana interaction was parasitic, while AMF?L. pacari interactions were mutualistic. Thus, dependence upon AMF and intraspecific interactions that result as a consequence of that dependence varies among species and may be an important influence in community structure.Publicação somente on-line

Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Dataset

The assessment of explainability in Legal Judgement Prediction (LJP) systems is of paramount importance in building trustworthy and transparent systems, particularly considering the reliance of these systems on factors that may lack legal relevance or involve sensitive attributes. This study delves into the realm of explainability and fairness in LJP models, utilizing Swiss Judgement Prediction (SJP), the only available multilingual LJP dataset. We curate a comprehensive collection of rationales that `support' and `oppose' judgement from legal experts for 108 cases in German, French, and Italian. By employing an occlusion-based explainability approach, we evaluate the explainability performance of state-of-the-art monolingual and multilingual BERT-based LJP models, as well as models developed with techniques such as data augmentation and cross-lingual transfer, which demonstrated prediction performance improvement. Notably, our findings reveal that improved prediction performance does not necessarily correspond to enhanced explainability performance, underscoring the significance of evaluating models from an explainability perspective. Additionally, we introduce a novel evaluation framework, Lower Court Insertion (LCI), which allows us to quantify the influence of lower court information on model predictions, exposing current models' biases.Comment: Accepted at LREC-COLING 202

Mutations in the C-terminal region of the HIV-1 reverse transcriptase and their correlation with drug resistance associated mutations and antiviral treatment

<p>Abstract</p> <p>Objective</p> <p>Replication of HIV-1 after cell entry is essentially dependent on the reverse transcriptase (RT). Antiretroviral drugs impairing the function of the RT currently aim at the polymerase subunit. One reason for failure of antiretroviral treatment is the evolvement of resistance-associated mutations in the viral genome. For RT inhibitors, almost all identified mutations are located within the polymerase; therefore, general genotyping confines to investigate this subunit. Recently several studies have shown that substitutions within the RNase H and the connection domain increase antiviral drug-resistance in vitro, and some of them are present in patient isolates.</p> <p>Aim</p> <p>The aim of the present study was to investigate the prevalence of these substitutions and their association with mutations in the polymerase domain arising during antiretroviral treatment.</p> <p>Materials and methods</p> <p>We performed genotypic analyzes on seventy-four virus isolates derived from treated and untreated patients, followed at the HIV Centre of the Johann Wolfgang Goethe University Hospital (Frankfurt/Main, Germany). We subsequently analysed the different substitutions in the c-terminal region to evaluate whether there were associations with each other, n-terminal substitutions or with antiretroviral treatment.</p> <p>Results</p> <p>We identified several primer grip substitutions, but almost all of them were located in the connection domain. This is consistent with other in-vivo studies, in which especially the primer grip residues located in the RNase H were unvaried. Furthermore, we identified other substitutions in the connection domain and in the RNase H. Especially E399D seemed to be associated with an antiretroviral treatment and N-terminal resistance-delivering mutations.</p> <p>Conclusion</p> <p>Some of the identified substitutions were associated with antiviral treatment and drug resistance-associated mutations. Due to the low prevalence of C-terminal mutations and as only a few of them could be associated with antiviral treatment and N-terminal resistance-delivering mutations, we would not recommend routinely testing of the C-terminal RT region.</p

Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration

Often, data on important confounders are not available in cohort studies. Sensitivity analyses based on the relation of single, but not multiple, unmeasured confounders with an exposure of interest in a separate validation study have been proposed. In this paper, the authors controlled for measured confounding in the main cohort using propensity scores (PS's) and addressed unmeasured confounding by estimating two additional PS's in a validation study. The "error-prone" PS exclusively used information available in the main cohort. The "gold standard" PS additionally included data on covariates available only in the validation study. Based on these two PS's in the validation study, regression calibration was applied to adjust regression coefficients. This propensity score calibration (PSC) adjusts for unmeasured confounding in cohort studies with validation data under certain, usually untestable, assumptions. The authors used PSC to assess the relation between nonsteroidal antiinflammatory drugs (NSAIDs) and 1-year mortality in a large cohort of elderly persons. "Traditional" adjustment resulted in a hazard ratio for NSAID users of 0.80 (95% confidence interval (CI): 0.77, 0.83) as compared with an unadjusted hazard ratio of 0.68 (95% CI: 0.66, 0.71). Application of PSC resulted in a more plausible hazard ratio of 1.06 (95% CI: 1.00, 1.12). Until the validity and limitations of PSC have been assessed in different settings, the method should be seen as a sensitivity analysis

Tamoxifen Initiation After Ductal Carcinoma In Situ

Endocrine therapy initiation after ductal carcinoma in situ (DCIS) is highly variable and largely unexplained. National guidelines recommend considering tamoxifen for women with estrogen receptor-positive (ER+) DCIS or who undergo excision alone. We evaluated endocrine therapy use after DCIS over a 15-year period in an integrated health care setting to identify factors related to initiation

Performance of propensity score calibration - A simulation study

Confounding can be a major source of bias in nonexperimental research. The authors recently introduced propensity score calibration (PSC), which combines propensity scores and regression calibration to address confounding by variables unobserved in the main study by using variables observed in a validation study. Here, the authors assess the performance of PSC using simulations in settings with and without violation of the key assumption of PSC: that the error-prone propensity score estimated in the main study is a surrogate for the gold-standard propensity score (i.e., it contains no additional information on the outcome). The assumption can be assessed if data on the outcome are available in the validation study. If data are simulated allowing for surrogacy to be violated, results depend largely on the extent of violation. If surrogacy holds, PSC leads to bias reduction between 32% and 106% (>100% representing overcorrection). If surrogacy is violated, PSC can lead to an increase in bias. Surrogacy is violated when the direction of confounding of the exposure-disease association caused by the unobserved variable(s) differs from that of the confounding due to observed variables. When surrogacy holds, PSC is a useful approach to adjust for unmeasured confounding using validation data