Search

Abstract Triazoles are widely used to control pathogenic fungi. They inhibit the ergosterol biosynthetic pathway, but the precise mechanisms leading to fungicidal activities in many fungal pathogens are poorly understood. Here, we elucidate the mode of action of epoxiconazole and metconazole in the wheat pathogen Zymoseptoria tritici and the rice blast fungus Magnaporthe oryzae. We show that both azoles have fungicidal activity and reduce fluidity, but not integrity, of the plasma membrane. This impairs localisation of Cdc15-like F-BAR proteins, resulting in defective actin ring assembly and incomplete septation. However, mutant studies and pharmacological experiments in vitro and in planta show that azole lethality is due to a combination of reactive oxygen species-induced apoptosis and macroautophagy. Simultaneous inhibition of both programmed cell death pathways abolishes azole-induced cell death. Other classes of ergosterol biosynthesis inhibitors also induce apoptosis and macroautophagy, suggesting that activation of these two cell death pathways is a hallmark of ergosterol synthesis-targeting fungicides. This knowledge will inform future crop protection strategies.

Abstract Background and aim The prevalence of tooth and jaw malocclusions in 8- to 9-year-olds was surveyed in a nationwide setting as part of the orthodontic module of the Sixth German Study on Oral Health (DMS•6), using the orthodontic indication groups (KIG) as index. Aim of this study was the detection of the prevalence of malocclusions requiring treatment according to the KIG index in statutorily insured patients of an orthodontic practice in North Rhine Westphalia, Germany, and to compare results with corresponding DMS•6 and KZBV data. Patients and methods Between 2017–2021, n = 953 statutorily insured patients called for an initial consultation and subsequent determination of the KIG-classification and -grades. The malocclusions were classified and graded in the highest possible KIG-grade according to valid SHI guidelines. Multiple classifications were not recorded. KIG-grade > 3 according to the valid guidelines was detected in n = 815 patients. Since the DMS•6 does not contain information on KIG classifications "U" and "S", their inclusion was waived despite evaluation, leaving data from n = 683 patients for analysis and comparison. Results During the study period, n = 235 patients (34.4%) had KIG-classification "D". More than 10% were classified as "K" (120 patients, 17.6%), "P" (98 patients, 14.2%), "M" (89 patients, 13.0%), and "E" (81 patients, 11.9%). Of 16 possible classifications with KIG-grade > 3, "D4" was the most common with 26.6% (182 patients). The results confirm the findings from the multicentric DMS•6 from2021 and corresponding KZBV data from 2020. Conclusions Sagittal deviations described by classifications "D" and "M" represent with 47.4% almost half of the malocclusions with treatment need. KIG-grade D4 is the most frequent classification. There were no regional deviations of the prevalence of KIG-grades 3–5 in the district of Viersen / North Rhine compared with the national average, not even when scrutinizing a five-year-period.

This paper presents a modified approach to subject-matter didactics, in which the focus is not on the content itself, but on the students' view of the content. The introduction deals with an overview of subject-matter didactics and the notion of beliefs used in this paper. The main portion of the paper deals with presenting the concepts of a book and a seminar based on the student-centered subject-matter didactics approach. For the first qualitative evaluation, selected reflections of students are analyzed. Finally, initial findings are summarized and an outlook is provided.

This paper addresses the desideratum identified by Törner (2018), that researchers' beliefs are rarely addressed in the research literature dealing with beliefs. For this purpose, firstly a suitable theoretical framework is outlined that links the concept of belief with the research perspectives of researchers. Secondly, examples are given of how beliefs were, can and should be addressed in corresponding research on beliefs. Finally, it is shown in which ways explicating beliefs of mathematics education researchers might made their research, as well as their teaching more effective.

Transitioning from spores to hyphae is crucial for host invasion by the plant pathogenic fungus Zymoseptoria tritici. Here, the authors show that the spore-to-hypha transition is enhanced by wheat leaf surface compounds and is regulated by the white-collar complex, which integrates light with biotic and abiotic cues to allow host invasion through open stomata.

mlr3shiny provides a simple accessible and user-friendly web-application, combining the graphical user interface with state-of-the-art ML functionalities in order to enable researchers less familiar with machine learning and programming to apply this methodology in their field and build experience with machine learning practice.

Global banana production is currently challenged by Panama disease, caused by Fusarium oxysporum f.sp. cubense Tropical Race 4 (FocTR4). There are no effective fungicide-based strategies to control this soil-borne pathogen. This could be due to insensitivity of the pathogen to fungicides and/or soil application per se. Here, we test the effect of 12 single-site and 9 multi-site fungicides against FocTR4 and Foc Race1 (FocR1) in quantitative colony growth, and cell survival assays in purified FocTR4 macroconidia, microconidia and chlamydospores. We demonstrate that these FocTR4 morphotypes all cause Panama disease in bananas. These experiments reveal innate resistance of FocTR4 to all single-site fungicides, with neither azoles, nor succinate dehydrogenase inhibitors (SDHIs), strobilurins or benzimidazoles killing these spore forms. We show in fungicide-treated hyphae that this innate resistance occurs in a subpopulation of "persister" cells and is not genetically inherited. FocTR4 persisters respond to 3 μg ml-1 azoles or 1000 μg ml-1 strobilurins or SDHIs by strong up-regulation of genes encoding target enzymes (up to 660-fold), genes for putative efflux pumps and transporters (up to 230-fold) and xenobiotic detoxification enzymes (up to 200-fold). Comparison of gene expression in FocTR4 and Zymoseptoria tritici, grown under identical conditions, reveals that this response is only observed in FocTR4. In contrast, FocTR4 shows little innate resistance to most multi-site fungicides. However, quantitative virulence assays, in soil-grown bananas, reveals that only captan (20 μg ml-1) and all lipophilic cations (200 μg ml-1) suppress Panama disease effectively. These fungicides could help protect bananas from future yield losses by FocTR4.

Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.

New fungicides are needed due to emerging resistance shown by crop pathogens. Here, the authors show that a mono-alkyl lipophilic cation protects plants from fungal pathogens by inhibiting fungal mitochondrial respiration, inducing production of reactive oxygen species, triggering fungal apoptosis, and activating innate plant defense.

ObjectiveTo develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission.MethodsHaematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent.ResultsThe final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210).ConclusionsThe presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system.Clinical Trial RegistrationAustrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.

Algorithmic scoring methods are widely used in the finance industry for several decades in order to prevent risk and to automate and optimize decisions. Regulatory requirements as given by the Basel Committee on Banking Supervision (BCBS) or the EU data protection regulations have led to an increasing interest and research activity on understanding black box machine learning models by means of explainable machine learning. Even though this is a step into a right direction, such methods are not able to guarantee for a fair scoring as machine learning models are not necessarily unbiased and may discriminate with respect to certain subpopulations such as a particular race, gender, or sexual orientation—even if the variable itself is not used for modeling. This is also true for white box methods like logistic regression. In this study, a framework is presented that allows analyzing and developing models with regard to fairness. The proposed methodology is based on techniques of causal inference and some of the methods can be linked to methods from explainable machine learning. A definition of counterfactual fairness is given together with an algorithm that results in a fair scoring model. The concepts are illustrated by means of a transparent simulation and a popular real-world example, the German Credit data using traditional scorecard models based on logistic regression and weight of evidence variable pre-transform. In contrast to previous studies in the field for our study, a corrected version of the data is presented and used. With the help of the simulation, the trade-off between fairness and predictive accuracy is analyzed. The results indicate that it is possible to remove unfairness without a strong performance decrease unless the correlation of the discriminative attributes on the other predictor variables in the model is not too strong. In addition, the challenge in explaining the resulting scoring model and the associated fairness implications to users is discussed.