Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

The V471A polymorphism in autophagy-related gene ATG7 modifies age at onset specifically in Italian Huntington disease patients

The cause of Huntington disease (HD) is a polyglutamine repeat expansion of more than 36 units in the huntingtin protein, which is inversely correlated with the age at onset of the disease. However, additional genetic factors are believed to modify the course and the age at onset of HD. Recently, we identified the V471A polymorphism in the autophagy-related gene ATG7, a key component of the autophagy pathway that plays an important role in HD pathogenesis, to be associated with the age at onset in a large group of European Huntington disease patients. To confirm this association in a second independent patient cohort, we analysed the ATG7 V471A polymorphism in additional 1,464 European HD patients of the “REGISTRY” cohort from the European Huntington Disease Network (EHDN). In the entire REGISTRY cohort we could not confirm a modifying effect of the ATG7 V471A polymorphism. However, analysing a modifying effect of ATG7 in these REGISTRY patients and in patients of our previous HD cohort according to their ethnic origin, we identified a significant effect of the ATG7 V471A polymorphism on the HD age at onset only in the Italian population (327 patients). In these Italian patients, the polymorphism is associated with a 6-years earlier disease onset and thus seems to have an aggravating effect. We could specify the role of ATG7 as a genetic modifier for HD particularly in the Italian population. This result affirms the modifying influence of the autophagic pathway on the course of HD, but also suggests population-specific modifying mechanisms in HD pathogenesis

Probabilistic rewrite strategies: Applications to elan

Abstract. Recently rule based languages focussed on the use of rewriting as a modeling tool which results in making specifications executable. To extend the modeling capabilities of rule based languages, we explore the possibility of making the rule applications subject to probabilistic choices. We propose an extension of the ELAN strategy language to deal with randomized systems. We argue trough several examples that we propose indeed a natural setting to model systems with randomized choices. This leads us to interesting new problems, and we address the generalization of the usual concepts in abstract reduction systems to randomized systems.

Self-organization of patrolling-ant algorithms

Abstract—We consider here multi-agent patrolling as the task for a group of agents to repeatedly visit all the cells of a discrete environment. Wagner et al. [1] have introduced patrolling ant algorithms, where each agent can only mark and move according to its local perception of the environment. Among various results, it has been experimentally observed that for some algorithms the agents often self-organize in stable cycles which are near optimal in terms of visit frequency. This property is particularly interesting as it guarantees the longterm performance of the patrol. The present paper focuses on the convergence behavior of a typical ant-based algorithm, EVAW [1; 2]. The main contribution of this paper is to theoretically prove that the group of agents self-organizes in cycles under certain hypotheses. These hypotheses rely on some implementation details that allow to control the predictability of the system. In addition to these qualitative results on the convergence behavior, we aim at experimentally evaluating its characteristics. This led us to a second contribution: an algorithm that detects steady states. Finally, we propose an improved behavior that dramatically speeds up the self-organization and allows us to experiment on larger problems (both in terms of size and number of agents). I