Comparison of Biochemical Characteristics, Action Models, and Enzymatic Mechanisms of a Novel Exolytic and Two Endolytic Lyases with Mannuronate Preference

Recent explorations of tool-like alginate lyases have been focused on their oligosaccharide-yielding properties and corresponding mechanisms, whereas most were reported as endo-type with α-L-guluronate (G) preference. Less is known about the β-D-mannuronate (M) preference, whose commercial production and enzyme application is limited. In this study, we elucidated Aly6 of Flammeovirga sp. strain MY04 as a novel M-preferred exolytic bifunctional lyase and compared it with AlgLs of Pseudomonas aeruginosa (Pae-AlgL) and Azotobacter vinelandii (Avi-AlgL), two typical M-specific endolytic lyases. This study demonstrated that the AlgL and heparinase_II_III modules play indispensable roles in determining the characteristics of the recombinant exo-type enzyme rAly6, which is preferred to degrade M-enriched substrates by continuously cleaving various monosaccharide units from the nonreducing end, thus yielding various size-defined ΔG-terminated oligosaccharides as intermediate products. By contrast, the endolytic enzymes Pae-rAlgL and Avi-rAlgL varied their action modes specifically against M-enriched substrates and finally degraded associated substrate chains into various size-defined oligosaccharides with a succession rule, changing from ΔM to ΔG-terminus when the product size increased. Furthermore, site-directed mutations and further protein structure tests indicated that H195NHSTW is an active, half-conserved, and essential enzyme motif. This study provided new insights into M-preferring lyases for novel resource discoveries, oligosaccharide preparations, and sequence determinations.

Cartesian Difference Categories

Cartesian differential categories are categories equipped with a differential combinator which axiomatizes the directional derivative. Important models of Cartesian differential categories include classical differential calculus of smooth functions and categorical models of the differential $\lambda$-calculus. However, Cartesian differential categories cannot account for other interesting notions of differentiation of a more discrete nature such as the calculus of finite differences. On the other hand, change action models have been shown to capture these examples as well as more "exotic" examples of differentiation. But change action models are very general and do not share the nice properties of Cartesian differential categories. In this paper, we introduce Cartesian difference categories as a bridge between Cartesian differential categories and change action models. We show that every Cartesian differential category is a Cartesian difference category, and how certain well-behaved change action models are Cartesian difference categories. In particular, Cartesian difference categories model both the differential calculus of smooth functions and the calculus of finite differences. Furthermore, every Cartesian difference category comes equipped with a tangent bundle monad whose Kleisli category is again a Cartesian difference category.

Unsupervised Novelty Characterization in Physical Environments Using Qualitative Spatial Relations

Detecting, characterizing and adapting to novelty, whether in the form of previously unseen objects or phenomena, or unexpected changes in the behavior of known elements, is essential for Artificial Intelligence agents to operate reliably in unconstrained real-world environments. We propose an automatic, unsupervised approach to novelty characterization for dynamic domains, based on describing the behaviors and interactions of objects in terms of their possible actions. To abstract from the variety of realizations of an action that can occur in physical domains, we model states in terms of qualitative spatial relations (QSRs) between their entities. By first learning a model of actions in the non-novel environment from the state transitions observed as the agent interacts with the world, we can detect novelty by the persistent deviations from this model that it causes, and characterize the novelty by new or modified actions. We also present a new method of learning action models from observation, based on conceptual similarity and hierarchical clustering.

Goals, Plans, and Action Models

The messages spoken in everyday conversation are influenced by participants’ goals. Interpersonal scholars have distinguished two types of goals thought to influence the wording of a message: instrumental goals (primary goals) and secondary goals. An instrumental goal is related to a speaker’s primary reason for designing the message. Instrumental goals would include goals such as to ask for a favor, seek information, apologize, give advice, or change the other person’s opinion. Secondary goals pertain to more general concerns. They include goals such as to manage one’s impression, avoid offending the hearer, and act consistently with one’s values. The ability to design a message that pursues an instrumental goal effectively while also addressing (or at least not conflicting with) relevant secondary goals is associated with greater communication competence. Considerable research has sought to explain differences in the ability to design messages that effectively address multiple goals. One such factor appears to be the extent to which a speaker can adapt the language of a message to the communication-relevant features of a specific situation or hearer. If a speaker’s primary goal is to seek a favor, relevant situation features may include the speaker’s right to ask, expected resistance, and qualities of the speaker–hearer relationship. A second behavior associated with the ability to produce multiple-goal messages is suggested by research on cognitive editing. The latter research indicates that the likelihood of producing a message that addresses relevant secondary goals will sometimes depend upon whether a speaker becomes aware, prior to speaking, that a planned message could have an unwanted outcome (e.g., the message may offend the hearer). When such outcomes are anticipated in advance, the message may be left unspoken or edited prior to speaking. The ability to produce a message that achieves a speaker’s goals may also depend on the type of planning that precedes the design of a message. The plan-based theory of strategic communication views plans as hierarchical structures that specify goals and actions at different levels of specificity. The theory holds that a person pursuing a goal first tries to retrieve from memory a preexisting plan that could be modified for the current situation. When that is not possible, speakers must formulate a novel plan. Research employing indicants of fluency suggests that formulating a novel plan (which requires changes at a higher, more abstract level of a plan) makes heavier demands on limited capacity than does modifying an existing plan at a lower level of the hierarchy (e.g., speaking more slowly). Insight into how persons plan what to say has also come from research on imagined interactions, conflict management, anticipating obstacles to compliance, and verbal disagreement tasks. In an effort to better understand the design of messages in interpersonal settings, a number of scholars have proposed models of the cognitive processes and structures thought to be involved in designing, editing, and producing such messages. Action models of this sort, which generate testable hypotheses, draw from work in artificial intelligence, cognitive models of language production, and research on social cognition. Three such models are action assembly theory, the cognitive rules model, and the implicit rules model.

Online Learning of Action Models for PDDL Planning

The automated learning of action models is widely recognised as a key and compelling challenge to address the difficulties of the manual specification of planning domains. Most state-of-the-art methods perform this learning offline from an input set of plan traces generated by the execution of (successful) plans. However, how to generate informative plan traces for learning action models is still an open issue. Moreover, plan traces might not be available for a new environment. In this paper, we propose an algorithm for learning action models online, incrementally during the execution of plans. Such plans are generated to achieve goals that the algorithm decides online in order to obtain informative plan traces and reach states from which useful information can be learned. We show some fundamental theoretical properties of the algorithm, and we experimentally evaluate the online learning of the action models over a large set of IPC domains.

Hybrid Conflicts Actors’ Action Models Analysis

The present article highlights the need to understand the physiognomy of hybrid conflicts, the action methodology of the actors in these conflicts, and aims to identify the correlation between the characteristics of the hybrid operational environment and the typology of participating actors in order to model future military actions. The ever-changing nature of the operational environment emphasizes the importance of understanding the military phenomenon in terms of the process of conflict management and resolution, in general, and of the hybrid conflicts in particular.

Professional teachers’ lives in Hungary during the communist regime (1949-1990)

The study aims to introduce the memories of Hungarian high school teachers about their professional activities. The main aim is to investigate the experience of being a teacher during the communist era. Based on interviews with eleven retired secondary school teachers from a small town, the study attempts to depict personal life stories and identify altering pedagogical action models formed by history that are dependent on different social and political demands. With the help of the teachers’ memories, the study represents their educational paths and the way these educational professionals see the tasks, the roles, the prestige of their profession as well as the way they experienced their failures, victories, and their active and retired years.