VIPurPCA: Visualizing and Propagating Uncertainty in Principal Component Analysis.

Variables obtained by experimental measurements or statistical inference typically carry uncertainties. When an algorithm uses such quantities as input variables, this uncertainty should propagate to the algorithm's output. Concretely, we consider the classic notion of principal component analysis (PCA): If it is applied to a finite data matrix containing imperfect (i.e., uncertain) multidimensional measurements, its output-a lower-dimensional representation-is itself subject to uncertainty. We demonstrate that this uncertainty can be approximated by appropriate linearization of the algorithm's nonlinear functionality, using automatic differentiation. By itself, however, this structured, uncertain output is difficult to interpret for users. We provide an animation method that effectively visualizes the uncertainty of the lower dimensional map. Implemented as an open-source software package, it allows researchers to assess the reliability of PCA embeddings.

Microbial communities living inside plant leaves or on the leaf surface are differently shaped by environmental cues.

Leaf-associated microbial communities can promote plant health and resistance to biotic and abiotic stresses. However, the importance of environmental cues in the assembly of the leaf endo- and epi-microbiota remains elusive. Here, we aimed to investigate the impact of seasonal environmental variations, on the establishment of the leaf microbiome, focusing on long-term changes (five years) in bacterial, fungal, and nonfungal eukaryotic communities colonizing the surface and endosphere of six wild Arabidopsis thaliana populations. While leaf-microbial communities were found to be highly stochastic, the leaf niche had a predominant importance with endophytic microbial communities consistently exhibiting a lower diversity and variability. Among environmental factors, radiation- and humidity-related factors are the most important drivers of diversity patterns in the leaf, with stronger effects on epiphytic communities. Using linear models, we identified 30 important genera whose relative abundance in leaf compartments could be modeled from environmental variables, suggesting specific niche preferences for these taxa. With the hypothesis that environmental factors could impact interactions within microbial communities, we analyzed the seasonal patterns of microbial interaction networks across leaf compartments. We showed that epiphytic networks are more complex than endophytic and that the complexity and connectivity of these networks are partially correlated with the mentioned environmental cues. Our results indicate that humidity and solar radiation function as major environmental cues shaping the phyllosphere microbiome at both micro (leaf compartment) and macro (site) scales. These findings could have practical implications for predicting and developing field-adapted microbes in the face of global change.

The unusual mode of action of the polyketide glycoside antibiotic cervimycin C.

Cervimycins A-D are bis-glycosylated polyketide antibiotics produced by Streptomyces tendae HKI 0179 with bactericidal activity against Gram-positive bacteria. In this study, cervimycin C (CmC) treatment caused a spaghetti-like phenotype in Bacillus subtilis 168, with elongated curved cells, which stayed joined after cell division, and exhibited a chromosome segregation defect, resulting in ghost cells without DNA. Electron microscopy of CmC-treated Staphylococcus aureus (3 × MIC) revealed swollen cells, misshapen septa, cell wall thickening, and a rough cell wall surface. Incorporation tests in B. subtilis indicated an effect on DNA biosynthesis at high cervimycin concentrations. Indeed, artificial downregulation of the DNA gyrase subunit B gene (gyrB) increased the activity of cervimycin in agar diffusion tests, and, in high concentrations (starting at 62.5 × MIC), the antibiotic inhibited S. aureus DNA gyrase supercoiling activity in vitro. To obtain a more global view on the mode of action of CmC, transcriptomics and proteomics of cervimycin treated versus untreated S. aureus cells were performed. Interestingly, 3 × MIC of cervimycin did not induce characteristic responses, which would indicate disturbance of the DNA gyrase activity in vivo. Instead, cervimycin induced the expression of the CtsR/HrcA heat shock operon and the expression of autolysins, exhibiting similarity to the ribosome-targeting antibiotic gentamicin. In summary, we identified the DNA gyrase as a target, but at low concentrations, electron microscopy and omics data revealed a more complex mode of action of cervimycin, which comprised induction of the heat shock response, indicating protein stress in the cell.IMPORTANCEAntibiotic resistance of Gram-positive bacteria is an emerging problem in modern medicine, and new antibiotics with novel modes of action are urgently needed. Secondary metabolites from Streptomyces species are an important source of antibiotics, like the cervimycin complex produced by Streptomyces tendae HKI 0179. The phenotypic response of Bacillus subtilis and Staphylococcus aureus toward cervimycin C indicated a chromosome segregation and septum formation defect. This effect was at first attributed to an interaction between cervimycin C and the DNA gyrase. However, omics data of cervimycin treated versus untreated S. aureus cells indicated a different mode of action, because the stress response did not include the SOS response but resembled the response toward antibiotics that induce mistranslation or premature chain termination and cause protein stress. In summary, these results point toward a possibly novel mechanism that generates protein stress in the cells and subsequently leads to defects in cell and chromosome segregation.

Fosfomycin Uptake in Escherichia coli Is Mediated by the Outer-Membrane Porins OmpF, OmpC, and LamB.

The antibiotic fosfomycin (FOS) is widely recognized for the treatment of lower urinary tract infections with Escherichia coli and has lately gained importance as a therapeutic option to combat multidrug-resistant bacteria. However, resistance to FOS frequently develops through mutations reducing its uptake. Although the inner-membrane transport of FOS has been extensively studied in E. coli, its outer-membrane (OM) transport remains insufficiently understood. While evaluating minimal inhibitory concentrations in OM porin-deficient mutants, we observed that the E. coli ΔompFΔompC strain is four times more resistant to FOS than the wild type and the respective single mutants. Continuous monitoring of FOS-induced lysis of porin-deficient strains additionally highlighted the importance of LamB. The relevance of OmpF, OmpC, and LamB to FOS uptake was confirmed by electrophysiological and transcriptional analysis. Our study gives for the first time in-depth insight into the transport of FOS through the OM in E. coli.

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education.

Background: The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods: The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions: The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.