University of Kaposvár

In the context of environmental sustainability, food waste is a major challenge. Digitalization and especially the proliferation of smartphones suggest the use of mobile apps to address food waste. One of the most important apps in Europe and the US is Too Good To Go. However, consumer intentions to use apps like Too Good To Go are largely unexplored. Based on a survey of 380 respondents in Germany, this paper uses a structural equation model to investigate the influence of sustainable consumption consciousness, price consciousness, and hedonic benefits on the intention to use apps against food waste. The results show that neither sustainable consumption consciousness nor price consciousness significantly influence the intention to use apps against food waste. Users use apps against food waste mainly for hedonic reasons. The results indicate that not only users with sustainable consumption consciousness are willing to use apps against food waste in the interest of sustainability. This research paper therefore makes an important contribution for companies in the food sector to better understand the intentions of consumers who use mobile apps to combat food waste. Furthermore, the paper provides important insights for developers of such apps for future development.

Introduction: Faith and systems of beliefs are known to impact not only the emotional, but also the immunological state of believers in ways that we are just starting to understand. Moreover, clinical implications of previous studies are limited. The aim of the “HEALING” (Hospital-based Ecumenical and Linguistic Immuno-NeuroloGic) Study was to examine immunological and neurological changes in hospitalized patients after meeting a chaplain coupled with biblical readings. Methods: Hospitalized patients were pre-screened to find those who were the most in need of an intervention. A passage from the Bible was read to them during a meeting with the chaplain at the bedside (n= 20) or in the chapel (n= 18). No meeting occurred in the randomized control group (n=19). Blood samples were taken 30 minutes prior, and 60 minutes after the meeting to measure white blood cells (WBC), interferon gamma (IFN-γ), immunoglobulin M (IgM), IgA, IgG, and complement 3 (C3). A subgroup of the visited patients was subjected to functional magnetic resonance imaging (fMRI), where they were played an audiotape of readings of the same passage from the Bible (n=21). Results: Lymphocyte counts increased more often after the more successful visits, but the immunological changes were not significant. Conversely, a significant (pfwe=0.003) correlation was revealed between changes in lymphocytes and activation of the angular gyrus (left BA39) during fMRI, a brain area involved in word recognition. Conclusions: Although limited by the sample size and cohort study design, the findings suggest the depth of psycho-immunological changes could depend on the degree to which the chaplains’ main message is understood.

Pine wood nematode (Bursaphelenchus xylophilus) (Aphelenchida: Parasitaphelencidae) is one of the most harmful agents in coniferous forests. The most important vectors of pine wood nematode are considered to be some Monochamus species (Col.: Cerambycidae), which had been forest insects with secondary importance before the appearance of B. xylophilus. However, the continuous spreading of the nematode has changed this status and necessitated detailed biological and climatological investigation of the main European vector, Monochamus galloprovincialis. The potential distribution area of M. galloprovincialis involves those areas where the risk of the appearance of pine wood nematode B. xylophilus is significant. The main objective of our analysis was to obtain information about the influencing effects of North Atlantic Oscillation (NAO) on the potential European range of B. xylophilus and its vector species M. galloprovincialis based on the connection between the mean temperature of July in Europe, the distribution of day-degrees of the vector and the NAO index. Our assessment was based on fundamental biological constants of the nematode and the cerambycid pest as well as the ECMWF ERA5 Global Atmospheric Reanalysis dataset. Our hypothesis was built on the fact that the monthly mean temperature had to exceed 20°C in the interest of an efficient expansion of the nematode. In addition, the threshold temperature of the vector involved in the calculations was 12.17°C, while the accumulated day-degree (DD) had to exceed the annual and biennial 370.57°DD for univoltine and semivoltine development, respectively. Our finding that a connection could be found between a mean temperature in July above 20°C and NAO as well as between the accumulated day-degrees and NAO can be the basis for further investigations for a reliable method to forecast the expansion of pine wood nematode and its vector species in a given year.

Purpose:For the identification of high-risk patients in diffuse large B-cell lymphoma (DLBCL), we investigated the prognostic significance of in vivo radiomics derived from baseline [18F]FDG PET/CT and clinical parameters.Methods:Pre-treatment [18F]FDG PET/CT scans of 85 patients diagnosed with DLBCL were assessed. The scans were carried out in two clinical centers. Two-year event-free survival (EFS) was defined. After delineation of lymphoma lesions, conventional PET parameters and in vivo radiomics were extracted. For 2-year EFS prognosis assessment, the Center 1 dataset was utilized as the training set and underwent automated machine learning analysis. The dataset of Center 2 was utilized as an independent test set to validate the established predictive model built by the dataset of Center 1.Results:The automated machine learning analysis of the Center 1 dataset revealed that the most important features for building 2-year EFS are as follows: max diameter, neighbor gray tone difference matrix (NGTDM) busyness, total lesion glycolysis, total metabolic tumor volume, and NGTDM coarseness. The predictive model built on the Center 1 dataset yielded 79% sensitivity, 83% specificity, 69% positive predictive value, 89% negative predictive value, and 0.85 AUC by evaluating the Center 2 dataset.Conclusion:Based on our dual-center retrospective analysis, predicting 2-year EFS built on imaging features is feasible by utilizing high-performance automated machine learning.