University of Applied Sciences Leiden

To investigate how task delegation for perioperative anticoagulation management is reported in protocols and daily practice, and how this translates to additional information in work-as-imagined and work-as-done using FRAM to identify improvement possibilities. Existing FRAM visualisations of perioperative anticoagulant management in a Dutch academic hospital were extended to include task delegation in protocols using document analysis and in daily practice using a focus group of eight healthcare professionals, analysed using content analysis. Distinguishing between formal responsibility and task execution revealed additional functions and roles in both work-as-done and work-as-imagined, resulting in more discrepancies between work-as-done and work-as-imagined. Professionals experienced responsibility for delegated tasks, even without formal accountability, and needed to feel qualified and capable as well as complete clear information to accept these tasks. Including task delegation in FRAM models improves the understanding of healthcare processes, communication about distribution of responsibilities and thereby development of more effective improvement strategies.

Abstract Atmospheric chemistry encapsulates the emission of various pollutants, the complex chemistry reactions, and the meteorology dominant transport, which form a dynamic system that governs air quality. While deep learning (DL) models have shown promise in capturing intricate patterns for forecasting individual atmospheric components—such as and ozone—the critical interactions among multiple pollutants and the combined influence of emissions and meteorology are often overlook. This study introduces a DL‐based framework–Zeeman for atmospheric chemistry forecasting. Our model effectively captures the nuanced relationships among these constituents and while achieving a 68.5‐fold increase in computational speed compared to traditional numerical model. Evaluations demonstrate that our approach rivals numerical model, offering an efficient solution for atmospheric chemistry forecasting. In the future, this model could be further integrated with data assimilation techniques to facilitate efficient and accurate atmospheric emission estimation and concentration forecast.

Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential‑outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad‑hoc 'causal' heuristics, typically without rigorous justification for why a given heuristic may be used or whether such heuristics are necessary at all. We address these issues by studying the problem directly under the SCM framework. Under the assumption of a partition-based model, we show that optimal subgroup discovery reduces to recovering the data-generating models and hence a standard supervised learning problem (regression or classification). This allows us to adopt any partition-based methods to learn the subgroup from data. We instantiate the approach with CART, arguably one of the most widely used tree-based method, to learn the subgroup with maximum treatment effect. Finally, on a large collection of synthetic and semi‑synthetic datasets, we compare our method against a wide range of baselines and find that our approach, which avoids such causal heuristics, more accurately identifies subgroups with maximum treatment effect.

Highly pathogenic avian influenza viruses (HPAIVs) derive from H5 and H7 low pathogenic avian influenza viruses (LPAIVs). Although insertion of a furin-cleavable multibasic cleavage site (MBCS) in the hemagglutinin gene was identified decades ago as the genetic basis for the LPAIV-to-HPAIV transition, the mechanisms underlying the occurrence of insertion are unknown. Here, we show that transient H5 RNA structures, predicted to trap the influenza virus polymerase on purine-rich sequences, drive nucleotide insertions, providing empirical evidence of RNA structure involvement in MBCS acquisition. Introduction of H5-like sequences and structures into an H6 hemagglutinin resulted in MBCS-yielding insertions. Our results show that nucleotide insertions that underlie H5 HPAIV emergence result from an RNA structure-driven diversity-generating mechanism, which could also occur in other RNA viruses.

Cyclooxygenase (COX) inhibitors (COXIBs) have shown preventive and therapeutic potential for colorectal cancer (CRC). In addition to inhibiting COX, approved COXIBs also target COX-independent pathways, including NF-κB and AKT. We evaluated how inhibition of various COXIB targets affects cell proliferation and apoptosis using a Boolean model incorporating the effect of several genetic mutations linked to CRC. The mutations activate two positive feedback loops and cause increased cell proliferation and survival. When simulating loss of function of APC, proliferation and apoptosis can be restored to healthy rates by inhibiting COX2. In simulations reflecting other genetic mutations, none of the investigated targets restore apoptosis and proliferation is reduced by inhibiting AKT or NF-κB, but not by any of the other COX targets tested. These results show that the off-target effects of COXIBs are crucial for their efficacy in the treatment of sporadic CRC.

The aim of this study was to identify the characteristics of adolescents and young adults with selective mutism (SM) and their specific (treatment) needs, as well as those of their environment. We conducted a mixed methods systematic review of scientific databases APA PsycINFO and MEDLINE until February 2025. Descriptive studies were included if they incorporated information on individuals between 10-24years old with a current diagnosis of SM. Two independent reviewers extracted data using combined deductive and inductive coding, with categories finalized by consensus. Data were synthesized using a convergent segregated approach. A total of 749 records were identified, of which 40 studies (n = 54 participants) met inclusion criteria. Methodological quality varied, with 27.5% studies rated as high quality, 65% as moderate, and 7.5% as low. Key findings showed that SM manifests differently in adolescents and young adults compared to children, and therefore requires a different approach to treatment. The reviewed literature shows that adolescents and young adults with SM have a later onset, longer diagnostic delays, and a prolonged history of negative reinforcement of symptoms from accommodations by others. Consequently, SM tends to be more therapy-resistant, requiring greater patience and diligence from therapists.

While both segmental and suprasegmental aspects of words have been recognised as potential factors influencing their iconic interpretations, how these components collectively drive the associations of form and affective meaning remains elusive. The current study addressed this issue in a lexical tonal language, Standard Chinese, where suprasegmental pitch information distinguishes word meanings. Specifically, we investigated how phonemic information at both the segmental level (i.e., vowels and consonants) and suprasegmental level (i.e., lexical tones) may influence native Standard Chinese listeners' rating of auditory stimuli's emotional arousal and valence in two-alternative forced-choice tasks. The results indicated a consistent correlation between tones and the perceived arousal and valence ratings of the tone-carrying nonce words. At the segmental level, consonants were more consistently associated with arousal, while vowels correlated with valence. Furthermore, lexical tones were more influential than segmental phonemes in biasing listeners' rating of affective meanings. Regarding arousal ratings, nonce words with falling and rising tones tended to be rated with higher arousal than those with high- and low-dipping tones. Additionally, those with an onset /t/ were rated higher in arousal than those with /n/. Regarding valence ratings, nonce words with falling and low-dipping tones were more likely to receive negative ratings than those with high and rising tones. Moreover, stimuli with /u/ were more inclined to be perceived negatively than those with /i/. Though subtle and sporadic, these findings support the universal tendency of affective iconicity across segments and suprasegmental tones.

Abstract In this study, we examine differences in the net fiscal positions of migrants and native-born individuals across 15 EU countries between 2007 and 2018. To do so, we combine EU-SILC cross-sectional data on individual characteristics, income, and welfare benefits with OECD information on personal income taxes and social security contributions. Using OLS regressions, we find that an intra-EU migration background always has a statistically significant positive effect on the individual net fiscal position, whereas an extra-EU migration background has a positive effect when background characteristics are excluded but turns negative and significant once they are included. To explore the sources of these differences, we apply the Oaxaca-Blinder decomposition for the first time in this context. The findings reveal that, to varying degrees, age, education, and country effects are the largest contributors to the observed differential between the net fiscal positions of natives (more negative) and migrants (more positive). Remarkably, education has a significant positive effect on the net fiscal position of intra-EU migrants but is nearly irrelevant for extra-EU migrants. The findings offer valuable insights for policymakers seeking to design integration and fiscal policies that address the specific factors underlying fiscal disparities between migrants and natives in Europe.