The Communication Factor EDF and the Toxin–Antitoxin Module mazEF Determine the Mode of Action of Antibiotics

It was recently reported that the production of Reactive Oxygen Species (ROS) is a common mechanism of cell death induced by bactericidal antibiotics. Here we show that triggering the Escherichia coli chromosomal toxin–antitoxin system mazEF is an additional determinant in the mode of action of some antibiotics. We treated E. coli cultures by antibiotics belonging to one of two groups: (i) Inhibitors of transcription and/or translation, and (ii) DNA damaging. We found that antibiotics of both groups caused: (i) mazEF-mediated cell death, and (ii) the production of ROS through MazF action. However, only antibiotics of the first group caused mazEF-mediated cell death that is ROS-dependent, whereas those of the second group caused mazEF-mediated cell death by an ROS-independent pathway. Furthermore, our results showed that the mode of action of antibiotics was determined by the ability of E. coli cells to communicate through the signaling molecule Extracellular Death Factor (EDF) participating in mazEF induction

Bacterial Programmed Cell Death and Multicellular Behavior in Bacteria

Traditionally, programmed cell death (PCD) is associated with eukaryotic multicellular organisms. However, recently, PCD systems have also been observed in bacteria. Here we review recent research on two kinds of genetic programs that promote bacterial cell death. The first is mediated by mazEF, a toxin–antitoxin module found in the chromosomes of many kinds of bacteria, and mainly studied in Escherichia coli. The second program is found in Bacillus subtilis, in which the skf and sdp operons mediate the death of a subpopulation of sporulating bacterial cells. We relate these two bacterial PCD systems to the ways in which bacterial populations resemble multicellular organisms

Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment

Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance

Escherichia coli MazF Leads to the Simultaneous Selective Synthesis of Both “Death Proteins” and “Survival Proteins”

The Escherichia coli mazEF module is one of the most thoroughly studied toxin–antitoxin systems. mazF encodes a stable toxin, MazF, and mazE encodes a labile antitoxin, MazE, which prevents the lethal effect of MazF. MazF is an endoribonuclease that leads to the inhibition of protein synthesis by cleaving mRNAs at ACA sequences. Here, using 2D-gels, we show that in E. coli, although MazF induction leads to the inhibition of the synthesis of most proteins, the synthesis of an exclusive group of proteins, mostly smaller than about 20 kDa, is still permitted. We identified some of those small proteins by mass spectrometry. By deleting the genes encoding those proteins from the E. coli chromosome, we showed that they were required for the death of most of the cellular population. Under the same experimental conditions, which induce mazEF-mediated cell death, other such proteins were found to be required for the survival of a small sub-population of cells. Thus, MazF appears to be a regulator that induces downstream pathways leading to death of most of the population and the continued survival of a small sub-population, which will likely become the nucleus of a new population when growth conditions become less stressful

Inferring Ontological Categories of OWL Classes Using Foundational Rules

Several efforts that leverage the tools of formal ontology (such as OntoClean, OntoUML, and UFO) have demonstrated the fruitfulness of considering key metaproperties of classes in ontology engineering. These metaproperties include sortality, rigidity, and external dependence, and give rise to many fine-grained ontological categories for classes, including, among others, kinds, phases, roles, mixins, etc. Despite that, it is still common practice to apply representation schemes and approaches - such as OWL - that do not benefit from identifying these ontological categories, and simplistically treat all classes in the same manner. In this paper, we propose an approach to support the automated classification of classes into the ontological categories underlying the (g)UFO foundational ontology. We propose a set of inference rules derived from (g)UFO's axiomatization that, given an initial classification of the classes in an OWL ontology, can support the inference of the classification for the remaining classes in the ontology. We formalize these rules, implement them in a computational tool and assess them against a catalog of ontologies designed by a variety of users for a number of domains.</p

Boosting D3FEND: Ontological Analysis and Recommendations

Formal Ontology is a discipline whose business is to develop formal theories about general aspects of reality such as identity, dependence, parthood, truthmaking, causality, etc. A foundational ontology is a specific consistent set of these ontological theories that support activities such as domain analysis, conceptual clarification, and meaning negotiation. A (well-founded) core ontology specifies, under a foundational ontology, the central concepts and relations of a given domain. Foundational and core ontologies can be seen as ontology engineering frameworks to systematically address the laborious task of building large (more specific) domain ontologies. However, both in research and industry, it is common that ontologies as computational artifacts are built without the aid of any framework of this kind, favoring the occurrence of numerous modeling mistakes and gaps. Through a case study, here we show an exemplar of such a case in the domain of cybersecurity by providing an ontological analysis of D3FEND, an OWL knowledge graph of cybersecurity countermeasure techniques proposed by the MITRE Corporation. Based on the Reference Ontology for Security Engineering (ROSE), a core ontology of the security domain founded in the Unified Foundational Ontology (UFO), our investigation reveals a number of semantic deficiencies in D3FEND, including missing concepts, semantic overload of terms, and a systematic lack of constraints that renders that model under-specified. As a result of our ontological analysis, we propose several suggestions for the appropriate redesign of D3FEND to overcome those issues.</p

A Differential Effect of E. coli Toxin-Antitoxin Systems on Cell Death in Liquid Media and Biofilm Formation

Toxin-antitoxin (TA) modules are gene pairs specifying for a toxin and its antitoxin and are found on the chromosomes of many bacteria including pathogens. Here we report how each of five such TA systems in E. coli affect bacterial cell death differently in liquid media and during biofilm formation. Of all these systems, only the TA system mazEF mediated cell death both in liquid media and during biofilm formation. At the other extreme, as our results have revealed here, the TA system dinJ-YafQ is unique in that it is involved only in the death process during biofilm formation. Cell death governed by mazEF and dinJ-YafQ seems to participate in biofilm formation through a novel mechanism

Reading Comprehension Assessment Using LLM-based Chatbot

Users who are learning to read or learning about a topic by viewing content on a device can benefit from conversational activities, such as question-answer turns for the viewed content. This disclosure describes techniques to perform natural language assessments of content that is being consumed on a user device. A chatbot is implemented using suitable technology, such as a large language model. With user permission, the model is used to generate questions that evaluate the user’s understanding of the content viewed. User provided answers are evaluated and suitable responses are provided to the user. The techniques enable automated assessment and feedback. The described features for assessment via chatbot (or virtual assistant) can be built into any application. Assessment is performed on-device and in a confidential manner

On the Semantics of Risk Propagation

Risk propagation encompasses a plethora of techniques for analyzing how risk “spreads” in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the lit- erature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated is fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpre- tation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios