Search

The protection of non-combatants in times of autonomous warfare raises the question of the timeliness of the international protective emblem. (Fully) Autonomous weapon systems are often launched from a great distance, and there may be no possibility for the operators to notice protective emblems at the point of impact; therefore, such weapon systems will need to have a way to detect protective emblems and react accordingly. In this regard, the present contribution suggests a cross-frequency protective emblem. Technical deployment is considered, as well as interpretation by methods of machine learning. Approaches are explored as to how software can recognize protective emblems under the influence of various boundary conditions. Since a new protective emblem could also be misused, methods of distribution are considered, including encryption and authentication of the received signal. Finally, ethical aspects are examined. [ABSTRACT FROM AUTHOR]

Reaching population immunity against COVID‐19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community‐specific agent‐based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID‐19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January–September 2021. Bimodality emerges from the heterogeneity and stochasticity of community‐specific human–human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID‐19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination. [ABSTRACT FROM AUTHOR]

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions. [ABSTRACT FROM AUTHOR]

SUMMARY: Chloroplast perturbations activate retrograde signalling pathways, causing dynamic changes of gene expression. Besides transcriptional control of gene expression, different classes of small non‐coding RNAs (sRNAs) act in gene expression control, but comprehensive analyses regarding their role in retrograde signalling are lacking. We performed sRNA profiling in response to norflurazon (NF), which provokes retrograde signals, in Arabidopsis thaliana wild type (WT) and the two retrograde signalling mutants gun1 and gun5. The RNA samples were also used for mRNA and long non‐coding RNA profiling to link altered sRNA levels to changes in the expression of their cognate target RNAs. We identified 122 sRNAs from all known sRNA classes that were responsive to NF in the WT. Strikingly, 142 and 213 sRNAs were found to be differentially regulated in both mutants, indicating a retrograde control of these sRNAs. Concomitant with the changes in sRNA expression, we detected about 1500 differentially expressed mRNAs in the NF‐treated WT and around 900 and 1400 mRNAs that were differentially regulated in the gun1 and gun5 mutants, with a high proportion (~30%) of genes encoding plastid proteins. Furthermore, around 20% of predicted miRNA targets code for plastid‐localised proteins. Among the sRNA–target pairs, we identified pairs with an anticorrelated expression as well pairs showing other expressional relations, pointing to a role of sRNAs in balancing transcriptional changes upon retrograde signals. Based on the comprehensive changes in sRNA expression, we assume a considerable impact of sRNAs in retrograde‐dependent transcriptional changes to adjust plastidic and nuclear gene expression. Significance Statement: Perturbations of plastid functions trigger retrograde signalling to adjust plastidic and nuclear gene expression; however, the role of small non‐coding RNAs acting as regulators in these pathways is not well understood. We analysed small non‐coding RNA expression in response to retrograde signals in Arabidopsis thaliana wild type and two retrograde signalling mutants and identified members of all known small non‐coding RNA classes, pointing to a functional role of these RNA classes in retrograde pathways. [ABSTRACT FROM AUTHOR]

Moonlighting refers to a protein with at least two unrelated, mechanistically different functions. As a concept, moonlighting describes a large and diverse group of proteins which have been discovered in a multitude of organisms. As of today, a systematized view on these proteins is missing. Here, we propose a classification of moonlighting proteins by two classifiers. We use the function of the protein as a first classifier: activating - activating (Type I), activating - inhibiting (Type II), inhibiting - activating (Type III) and inhibiting - inhibiting (Type IV). To further specify the type of moonlighting protein, we used a second classifier based on the character of the factor that switches the function of the protein: external factor affecting the protein (Type A), change in the first pathway (Type B), change in the second pathway (Type C), equal competition between both pathways (Type D). Using a small two-pathway model we simulated these types of moonlighting proteins to elucidate possible behaviors of the types of moonlighting proteins. We find that, using the results of our simulations, we can classify the behavior of the moonlighting types into Blinker, Splitter andSwitch. [ABSTRACT FROM AUTHOR]

Moonlighting refers to a protein with at least two unrelated, mechanistically different functions. As a concept, moonlighting describes a large and diverse group of proteins which have been discovered in a multitude of organisms. As of today, a systematized view on these proteins is missing. Here, we propose a classification of moonlighting proteins by two classifiers. We use the function of the protein as a first classifier: activating - activating (Type I), activating - inhibiting (Type II), inhibiting - activating (Type III) and inhibiting - inhibiting (Type IV). To further specify the type of moonlighting protein, we used a second classifier based on the character of the factor that switches the function of the protein: external factor affecting the protein (Type A), change in the first pathway (Type B), change in the second pathway (Type C), equal competition between both pathways (Type D). Using a small two-pathway model we simulated these types of moonlighting proteins to elucidate possible behaviors of the types of moonlighting proteins. We find that, using the results of our simulations, we can classify the behavior of the moonlighting types into Blinker, Splitter andSwitch. [ABSTRACT FROM AUTHOR]