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To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

The current study draws on work in the areas of team adaptation, team compilation, and small groups as complex systems to predict and test relationships between time, taskwork team mental models, team action patterns, and team effectiveness. Three-person teams performed 9 scenarios of a firefighting simulation distributed over 3 days with discontinuous task changes introduced in the fourth and seventh scenarios (N = 41 teams; 123 individuals). We applied pattern detection algorithm software to the behavioral data to identify emergent performative patterns in the team members’ task-oriented actions. We also used discontinuous growth modeling to track the development of these team action patterns and their dynamic relation to team effectiveness. The results indicate that pattern emergence increased over time. This was particularly true for teams with similar taskwork mental models, and these teams also showed a more acute decrease in action patterns after a task change. In addition, team action patterns became increasingly positively related to team effectiveness over time, but this effect was reset after the occurrence of a task change. Overall, our research provides practical guidance to managers by illustrating the value of teams having highly shared taskwork team mental models and of enhancing the effects of teams’ action patterns on team adaptive outcomes.

This study aimed to develop a deep learning model to recognize cell interaction patterns in pathological slides of malignant bowel obstruction. The model classifies lesions into four categories—normal mucosa, serrated lesions, adenomas, and adenocarcinomas—and evaluates its diagnostic utility in tumor-associated obstruction. Pathological slides from patients with tumor-induced intestinal obstruction (TICO) were retrospectively collected from First Affiliated Hospital of Bengbu Medical University and annotated into four histological categories: normal, serrated lesions, adenomas, and adenocarcinomas. The proposed deep learning framework combines a residual convolutional network with a bidirectional state-space module (SSM), enabling multiscale feature extraction through convolution and down-sampling, while modeling the spatiotemporal dynamics of cellular interactions. The model was designed to learn spatial and structural characteristics of cell interactions—such as glandular organization, intercellular spacing, and nuclear density—across different lesion types. Grad-CAM was used to visualize attention regions and assess consistency between model focus and pathological features. However, Grad-CAM was used solely for interpretability and not clinical validation; no expert verification of the visualizations has been performed. On an independent Chaoyang test set, the model achieved a validation accuracy of 85% and a macro-F1 score of 0.843 (95% CI: 0.829–0.857), showing only a 3% decline from training accuracy (88%), thus demonstrating strong generalizability. In addition, we calculated 95% confidence intervals using 1,000 bootstrap resamples and applied both the DeLong test and McNemar test to compare the performance of our model with baseline methods. The results demonstrated statistically significant improvements ( P  < 0.05) in Accuracy, Macro-F1, and ROC-AUC, thereby further strengthening the reliability of our conclusions. The recall for adenocarcinoma (Class 3) reached 88%, while Classes 0–2 (normal, serrated lesions, and adenomas) ranged from 78% to 83%. These results highlight the impact of sample imbalance and morphological similarity, which will be addressed in future work through Focal Loss reweighting and detailed error analysis. Grad-CAM visualizations identified regions of glandular disruption and abnormal nuclear density, aligning with WHO-2022 diagnostic criteria and enhancing model interpretability. Overall performance is comparable to state-of-the-art gastrointestinal pathology AI systems from recent years, offering rapid and quantitative diagnostic support in emergency pathology settings. The proposed deep learning model effectively distinguishes four categories of tumor-associated colorectal lesions, demonstrating strong diagnostic potential. Limitations include: (i) all data were retrospectively collected from a single center, without external multicenter validation. Differences in population composition, scanning platforms, and staining batches may affect the model’s external generalizability; future studies will prioritize the inclusion of multicenter datasets to systematically evaluate the robustness and applicability of the model under diverse clinical conditions; (ii) the model has so far been assessed only in an offline environment, lacking prospective clinical validation within real-world workflows. Nonetheless, this model provides an important foundation for the early diagnosis of TICO, the formulation of personalized treatment strategies, and the advancement of pathological image analysis technologies.

In this study, students’ interactions with different learning activities are examined and the relation among learning performance with different interaction patterns, learning performance, self-regulated learning (SRL) strategies and motivation is presented. Learning materials including different kinds of activities are prepared and presented to the use of 122 university students. As a result of the study that students spent longer time in the tutorial and video activities and they visit these activities more frequently. As a result of cluster analysis, students with the least interaction with learning activities take place in the first cluster, students who use video, example and forum activities to an intense take place in the second cluster, and students who spend more time in tutorial, exercise and concept map activities take place in the third cluster. The academic performances of students, who spend longer time in learning activities, are higher. Students in the third cluster have higher points in terms of intrinsic goal orientations, task value, control beliefs and self-efficacy for learning and performance. Finally, the results of this study show that SRL strategies differ from its sub-dimensions in terms of rehearsal, organization, elaboration, metacognitive self-regulation, time and study environment.

Sustainability transitions go hand in hand with learning. Theories in the realm of sustainability sciences mostly concentrate on diversity and learning outcomes, whereas theories from the educational sciences mostly focus on learning as an interactive process. In this contribution, we aim to benefit from an integration of these perspectives in order to better understand how different interaction patterns contribute to learning. We studied STAP, an innovation initiative of Dutch greenhouse growers. The Dutch greenhouse sector is predominantly focused on production and efficiency, which causes problems for its future viability. STAP aimed to make the sector more market-oriented while at the same time increasing its societal acceptability (societally responsible innovation). To that end, STAP focused on the development of integrated value chains (primary production, sales, trade) that can contribute to a transition towards a societally sensitive greenhouse sector. As action researchers, we collected extensive transcripts of meetings, interviews, and various other documents. We used an open coding strategy to identify different patterns of interaction and the learning outcomes produced by the initiative. We then linked the interaction patterns to the outcomes. Analysis suggests that seemingly negative attack-and-defend patterns of interaction certainly can result in substantial learning results, while seemingly positive synthetic interaction patterns, where participants strive to build on each other, can result in rather bland interaction without substantial outcomes. The results offer an empirical basis to our approach of linking learning interactions to learning outcomes, and it suggests that learning for sustainability can be enhanced by focusing on interaction patterns.

Prior studies have mainly focused on testing collaborative programming learning (CPL) patterns while neglecting the exploration of the dynamic evolution of social epistemic interaction patterns among different groups. Studying the social and epistemic network nature of learner interaction is crucial to understanding the CPL process. This study aims to explore the social epistemic interaction patterns and their evolutionary path among different groups. In this quasi-experimental design, 51 high school students were randomly allocated into 17 groups. Content analysis was used to analyze online collaborative conversations and interaction contents in the early, middle, and later periods of CPL. Social epistemic network and cluster analyses revealed three interaction patterns. The results showed that groups in cluster 1 were composed of core roles, which exhibited a multi-center balanced collaboration pattern (MBCP), and their social epistemic interaction levels showed a continuous upward trend; groups in cluster 2 included core, semi-core, and edge roles, respectively, and demonstrated a hierarchical center-led coordination pattern (HLCP) that initially gained but later declined in social epistemic interaction levels; groups in cluster 3 included core and edge roles, and displayed a single-center feedback cooperation pattern (SFCP), which remained consistently low in social epistemic interaction levels. Our findings emphasize the importance of CPL's social epistemic interactions. By recognizing these patterns, educators can better facilitate meaningful student interactions, fostering deeper learning and social development.

Recently, Artificial Intelligence (AI), seen as an engineering domain, has been introduced into school education, but its pedagogy remains unclear. In general, group learning has been applied as a primary form of instruction in hands-on engineering activities. This learning approach is more common in higher education. School students are less mature; therefore, the benefits of adopting group learning as a pedagogical approach remain unclear. Group learning quality can be reflected by student–student interactions and dialogue within a group, and is classified into four types: collective, cooperating-in-parallel, dominant/defensive, and expert/novice. Accordingly, this experimental study involved 37 middle school students, and explored how they interacted within groups when learning AI through hands-on activities in the four group learning types. The Teacher Scheme for Educational Dialogue Analysis (T-SEDA) was used to code student–student interactions and compute their frequencies, and Lag Sequential Analysis was used to analyze the behavioral interaction sequence characteristics of the four group interaction patterns. The results showed that the expert/novice group learning had higher frequency of interaction, and also produced the longest, richest, and most complex sequences. The results suggest that this is the optimal approach to learning for younger students in hands-on AI activities as it encourages group members to interact with each other and reach a consensus. The results contribute to the literature by suggesting effective practices and confirming the use of T-SEDA in a new engineering school subject.

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.

This study investigated student interaction patterns and their co-regulation practices in text-based and multimodal computer mediated collaborative (CMC) writing. To this end, 30 EFL (English as foreign language) participants’ online collaborative writing performances were analyzed. The analysis included conversation analysis on the transcription of stored conversations of multimodal Moodle and discussion logs of online text-based writing Forum. Data were coded according to Storch (Storch, Language Learning 52:119–158, 2002)’s collaboration patterns coding scheme in order to trace interactional styles (collaborative pattern, dominant/passive pattern, dominant/dominant, and expert/novice pattern). Besides, data were coded to trace co-regulation patterns (planning, executing, monitoring, evaluation, orientation and elaboration). The chi-square analysis indicated that there were significantly more collaborative patterns in multimodal and expert/novice pattern in text-based CMCs. Co-regulation practices of “Elaboration” and “evaluation” occurred more in text-based CMC whereas “monitoring” practice had the highest occurrence in multimodal CMC. “Executing”, “planning” and “orientation” indicated no significant difference of occurrence. The results also indicated that there were significant differences in interaction patterns in each co-regulation practice in text-based and multimodal CMCs. The results imply that both mediums showed benefits in preparing learners for learning process but facilitated learning in different ways and consequently, they prepared learners for distinct collaborative composing processes.