Treatment of psychiatric comorbidities and interaction patterns in Coffin-Siris syndrome: A case report of a 4-year-old girl

Coffin-Siris syndrome (CSS) is a rare genetic disorder and often co-occurs with attention-deficit hyperactivity disorder (ADHD) and autism spectrum (ASD). The present case study illustrates possible therapeutic interventions of these common psychiatric comorbidities taking into account the family interaction patterns. This can contribute to improve holistic management and overall level of functionality.

Favipiravir Analogues as Inhibitors of SARS-CoV-2 RNA-Dependent RNA Polymerase, Combined Quantum Chemical Modeling, Quantitative Structure-Property Relationship, and Molecular Docking Study

Our study was motivated by the urgent need to develop or improve antivirals for effective therapy targeting RNA viruses. We hypothesized that analogues of favipiravir (FVP), an inhibitor of RNA-dependent RNA polymerase (RdRp), could provide more effective nucleic acid recognition and binding processes while reducing side effects such as cardiotoxicity, hepatotoxicity, teratogenicity, and embryotoxicity. We proposed a set of FVP analogues together with their forms of triphosphate as new SARS-CoV-2 RdRp inhibitors. The main aim of our study was to investigate changes in the mechanism and binding capacity resulting from these modifications. Using three different approaches, QTAIM, QSPR, and MD, the differences in the reactivity, toxicity, binding efficiency, and ability to be incorporated by RdRp were assessed. Two new quantum chemical reactivity descriptors, the relative electro-donating and electro-accepting power, were defined and successfully applied. Moreover, a new quantitative method for comparing binding modes was developed based on mathematical metrics and an atypical radar plot. These methods provide deep insight into the set of desirable properties responsible for inhibiting RdRp, allowing ligands to be conveniently screened. The proposed modification of the FVP structure seems to improve its binding ability and enhance the productive mode of binding. In particular, two of the FVP analogues (the trifluoro- and cyano-) bind very strongly to the RNA template, RNA primer, cofactors, and RdRp, and thus may constitute a very good alternative to FVP.

Ubiquitous conservative interaction patterns between post-spliced introns and their mRNAs revealed by genome-wide interspecies comparison

Introns, as important vectors of biological functions, can influence many stages of mRNA metabolism. However, in recent research, post-spliced introns are rarely considered. In this study, the optimal matched regions between introns and their mRNAs in nine model organism genomes were investigated with improved Smith-Waterman local alignment software. Our results showed that the distributions of mRNA optimal matched frequencies were highly consistent or universal. There are optimal matched frequency peaks in the UTR regions, which are obvious, especially in the 3'-UTR. The matched frequencies are relatively low in the CDS regions of the mRNA. The distributions of the optimal matched frequencies around the functional sites are also remarkably changed. The centers of the GC content distributions for different sequences are different. The matched rate distributions are highly consistent and are located mainly between 60% and 80%. The most probable value of the optimal matched segments is about 20 bp for lower eukaryotes and 30 bp for higher eukaryotes. These results show that there are abundant functional units in the introns, and these functional units are correlated structurally with all kinds of sequences of mRNA. The interaction between the post-spliced introns and their corresponding mRNAs may play a key role in gene expression.

Three Different Interaction Patterns between MCM-41 and Proteins

As one of the most studied mesoporous silica nanoparticles (MSNs) in drug delivery systems, Mobil Composition of Matter No. 41 (MCM-41) possesses unique properties including perfect channel architecture, excellent load capacity, and good biocompatibility. However, the applications of MCM-41 nanoparticles in drug delivery have not yet been industrialized, due to the interaction between MCM-41 and biomolecules (especially proteins) that affect their in vivo behaviors after dosing. To investigate the interactions between MCM-41 and proteins, this study selected bovine serum albumin (BSA), lysozyme (Lyso), and bovine hemoglobin (BHb) as model proteins and characterized the ultraviolet-visible, fluorescence, circular dichroism spectra and the protein adsorption of MCM-41-protein complex. The UV-Vis spectra exhibited the different absorption increment degrees of three proteins. The fluorescence spectra showed that the fluorescence intensity of proteins changed by different trends. The CD spectra indicated that the secondary structure changes were ranked as BSA > Lyso > BHb, which is consistent with the protein’s adsorption capability on MCM-41. It was shown that there were three different patterns of MCM-41-proteins interactions. The hydrophilic and low-charged BSA followed the strong interaction pattern, the hydrophilic but heavily charged Lyso followed the moderate interaction pattern, and the hydrophobic BHb followed the weak interaction pattern. Different interaction patterns would lead to different effects on the structural properties of proteins, the surface chemistry of MCM-41, and the absorption capability of proteins on MCM-41. We believe our study will provide a better insight into the application of MCM-41 nanoparticles in drug delivery systems.

The interaction patterns of pandemic-initiated online teaching: How teachers adapted

This study investigated how teachers adjusted their teaching practices for online teaching during the COVID-19 pandemic in Vietnam. In the study, we collected data from ten teachers within the Department of English at a university in Vietnam through semi-structured interviews via Zoom. During the interviews, the participants shared how they organised their teaching activities and addressed challenges in engaging students. To analyse the data, we focused on the variety of interaction patterns (teacher-student, student-student and student-content) in online teaching. The results showed that most of the teachers deployed activities for two main types of interaction: teacher-student, and student-content, but not for student-student interaction. Teachers also reported that they received limited online teacher training and had to learn by themselves how to engage students remotely. While the university tried to support teachers, no consistent online teaching guidelines were provided. The study suggests that higher education institutions should offer training opportunities and provide teachers with clear guidelines for online teaching.

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

Modeling Child-Nature Interaction in a Nature Preschool: A Proof of Concept

This article provides a proof of concept for an approach to modeling child-nature interaction based on the idea of interaction patterns: characterizations of essential features of interaction between humans and nature, specified abstractly enough such that countless different instantiations of each one can occur - in more domestic or wild forms - given different types of nature, people, and purposes. The model draws from constructivist psychology, ecological psychology, and evolutionary psychology, and is grounded in observational data collected through a time-sampling methodology at a nature preschool. Through using a nature language that emphasizes ontogenetic and phylogenetic significance, seven keystone interaction patterns are described for this nature preschool: using one's body vigorously in nature, striking wood on wood, constructing shelter, being in solitude in nature, lying on earth, cohabiting with a wild animal, and being outside in weather. These 7 interactions patterns are then brought together with 13 other patterns published elsewhere to provide a total of 20 keystone interaction patterns that begin to fill out the model, and to show its promise. Discussion focuses on what the model aims to be in terms of both product and process, on what work the model can currently do, and how to further develop the model.

A method for detecting characteristic patterns in social interactions with an application to handover interactions

Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.

A method for detecting characteristic patterns in social interactions with an application to handover interactions

Social interactions are a defining behavioural trait of social animals. Discovering characteristic patterns in the display of such behaviour is one of the fundamental endeavours in behavioural biology and psychology, as this promises to facilitate the general understanding, classification, prediction and even automation of social interactions. We present a novel approach to study characteristic patterns, including both sequential and synchronous actions in social interactions. The key concept in our analysis is to represent social interactions as sequences of behavioural states and to focus on changes in behavioural states shown by individuals rather than on the duration for which they are displayed. We extend techniques from data mining and bioinformatics to detect frequent patterns in these sequences and to assess how these patterns vary across individuals or changes in interaction tasks. To illustrate our approach and to demonstrate its potential, we apply it to novel data on a simple physical interaction, where one person hands a cup to another person. Our findings advance the understanding of handover interactions, a benchmark scenario for social interactions. More generally, we suggest that our approach permits a general perspective for studying social interactions.

Exploring the basis for gender differences in the demand-withdraw pattern

During marital conflict, wives tend to demand and husbands tend to withdraw. These behaviors were historically thought to stem from essential differences between men and women. An alternative explanation implicates one form of power differences-wives desire more change and, therefore, demand; husbands desire less change and withdraw to maintain status quo. Studying same-sex as well as cross-sex couples enables an evaluation of both explanations. We examined demand-withdraw behaviors in 63 heterosexual, gay, and lesbian couples. The demand-withdraw pattern was seen regardless of type of couple. Further, for all couples, differences in the amount of change desired in partners during a conflict interaction predicted differences in demand and withdraw behaviors. These results offer further evidence that an often-observed difference in heterosexual relationships may result from social conventions that afford men greater power and women less power.