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This work adheres to the recommendations for publication in three steps (Marin-Garcia, 2021). We propose a protocol to develop research the objective of which is to analyze the needs and challenges, as perceived by university lecturers, related to teaching, learning, or assessing competencies related to the innovative capacity of students in an array of digital remote teaching contexts. In this sense, we want to consider situations where teachers watch their students interact with each other. The interaction between students can occur face-to-face or remotely and, in the latter case, either synchronous or asynchronous. In addition, teacher observation can be done synchronously (face-to-face or remotely) or by watching a previously recorded video.

Context Adapting the User Interface (UI) of software systems to users’ requirements and their context of use is a challenging task. It involves determining the right adaptation, at the right time and place, to make it valuable for end-users. We believe that recent progress in Machine Learning (ML) techniques could provide useful ways in which to support adaptation more effectively. In particular, Reinforcement Learning (RL) has proven to be effective in planning a sequence of UI adaptations over a long time horizon. However, RL requires either manually specifying a reward function or learning a reward model. Currently there is no empirical evidence supporting the usefulness of reward models for UI adaptation. Objective This paper presents a confirmatory empirical study aimed at investigating the effectiveness of two different approaches to generating reward models in the context of UI adaptation using reinforcement learning: (1) a reward model derived exclusively from predictive Human-Computer Interaction (HCI) models (AUI-HCI), and (2) a reward model derived from predictive HCI models augmented by human feedback (AUI-HCI-HF), compared to non-adaptive (NA) interfaces. Method A controlled experiment with an AB/BA crossover design was conducted to evaluate the impact of these reward models on user experience, measured through objective and subjective engagement, as well as user satisfaction. Our study contributes to the understanding of how reward modeling can facilitate UI adaptation through RL. Results The results showed a significant improvement in objective engagement for AUI-HCI-HF compared to non-adaptive interfaces. However, no significant differences were found between AUI-HCI and non-adaptive interfaces for any of the other measurements, across any conditions. Conclusion Integrating human feedback into RL reward models enhances objective engagement, but its impact on subjective engagement and user satisfaction remains limited. While AUI-HCI-HF shows promise for improving interaction metrics, further research is needed to better align reward models with broader user perceptions and preferences, particularly compared to non-adaptive interfaces.

One of the important issues in social networks is the social communities which are formed by interactions between its members. Three types of community including overlapping, non-overlapping, and hidden are detected by different approaches. Regarding the importance of community detection in social networks, this paper provides a systematic mapping of machine learning-based community detection approaches. The study aimed to show the type of communities in social networks along with the algorithms of machine learning that have been used for community detection. After carrying out the steps of mapping and removing useless references, 246 papers were selected to answer the questions of this research. The results of the research indicated that unsupervised machine learning-based algorithms with 41.46% (such as k means) are the most used categories to detect communities in social networks due to their low processing overheads. On the other hand, there has been a significant increase in the use of deep learning since 2020 which has sufficient performance for community detection in large-volume data. With regard to the ability of NMI to measure the correlation or similarity between communities, with 53.25%, it is the most frequently used metric to evaluate the performance of community identifications. Furthermore, considering availability, low in size, and lack of multiple edge and loops, dataset Zachary’s Karate Club with 26.42% is the most used dataset for community detection research in social networks.

There is still little research work focused on feature selection (FS) techniques including both categorical and continuous features in Software Development Effort Estimation (SDEE) literature. This paper addresses the problem of selecting the most relevant features from ISBSG (International Software Benchmarking Standards Group) dataset to be used in SDEE. The aim is to show the usefulness of splitting the ranked list of features provided by a mutual information-based sequential FS approach in two, regarding categorical and continuous features. These lists are later recombined according to the accuracy of a case-based reasoning model. Thus, four FS algorithms are compared using a complete dataset with 621 projects and 12 features from ISBSG. On the one hand, two algorithms just consider the relevance, while the remaining two follow the criterion of maximizing relevance and also minimizing redundancy between any independent feature and the already selected features. On the other hand, the algorithms that do not discriminate between continuous and categorical features consider just one list, whereas those that differentiate them use two lists that are later combined. As a result, the algorithms that use two lists present better performance than those algorithms that use one list. Thus, it is meaningful to consider two different lists of features so that the categorical features may be selected more frequently. We also suggest promoting the usage of Application Group, Project Elapsed Time, and First Data Base System features with preference over the more frequently used Development Type, Language Type, and Development Platform.

Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. The main challenge is to provide the appropriate adaptations at the appropriate time to offer value to end-users. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component to adapt user interfaces and ultimately improving the overall User Experience (UX). By using RL, the system is able to learn from past adaptations to improve the decision-making capabilities. Moreover, assessing the success of such adaptations remains a challenge. To overcome this issue, we propose to use predictive Human-Computer Interaction (HCI) models to evaluate the outcome of each action (ie adaptations) performed by the RL agent. In addition, we present an implementation of the instantiated framework, which is an extension of OpenAI Gym, that serves as a toolkit for developing and comparing RL algorithms. This Gym environment is highly configurable and extensible to other UI adaptation contexts. The evaluation results show that our RL-based framework can successfully train RL agents able to learn how to adapt UIs in a specific context to maximize the user engagement by using an HCI model as rewards predictor.

Adaptive User Interfaces (AUI) play a crucial role in modern software applications by dynamically adjusting interface elements to accommodate users' diverse and evolving needs. However, existing adaptation strategies often lack real-time responsiveness. Reinforcement Learning (RL) has emerged as a promising approach for addressing complex, sequential adaptation challenges, enabling adaptive systems to learn optimal policies based on previous adaptation experiences. Although RL has been applied to AUIs,integrating RL agents effectively within user interactions remains a challenge. In this paper, we enhance a RL-based Adaptive User Interface adaption framework by incorporating personalized human feedback directly into the leaning process. Unlike prior approaches that rely on a single pre-trained RL model, our approach trains a unique RL agent for each user, allowing individuals to actively shape their personal RL agent's policy, potentially leading to more personalized and responsive UI adaptations. To evaluate this approach, we conducted an empirical study to assess the impact of integrating human feedback into the RL-based Adaptive User Interface adaption framework and its effect on User Experience (UX). The study involved 33 participants interacting with AUIs incorporating human feedback and non-adaptive user interfaces in two domains: an e-learning platform and a trip-planning application. The results suggest that incorporating human feedback into RL-driven adaptations significantly enhances UX, offering promising directions for advancing adaptive capabilities and user-centered design in AUIs.

This paper analyzes the interrelationships between creativity, innovation and entrepreneurship as key enablers of an entrepreneurial and innovation-oriented culture. Empirical results of exploring perceptions and opinions about these three concepts are presented, using a sample of 121 engineering students. The findings show that the majority of students perceive the traditional linear model of innovation and consider innovation strongly related to creativity but moderately related to entrepreneurship. There are contradictions between the students' self-perceptions as entrepreneurs, their high desirability to start a new firm and their work preferences after graduation, which are principally to get a job in a private company and become public servants. Their low willingness for mobility and the poor contribution of the education system in developing their innovation and entrepreneurial competences constitutes other relevant obstacles for improving an entrepreneurial and innovation-oriented culture.

NANOG is a pluripotency transcription factor in embryonic stem cells; however, its role in adult tissues remains largely unexplored. Here we show that mouse NANOG is selectively expressed in stratified epithelia, most notably in the oesophagus where the Nanog promoter is hypomethylated. Interestingly, inducible ubiquitous overexpression of NANOG in mice causes hyperplasia selectively in the oesophagus, in association with increased cell proliferation. NANOG transcriptionally activates the mitotic programme, including Aurora A kinase ( Aurka ), in stratified epithelia, and endogenous NANOG directly binds to the Aurka promoter in primary keratinocytes. Interestingly, overexpression of Nanog or Aurka in mice increased proliferation and aneuploidy in the oesophageal basal epithelium. Finally, inactivation of NANOG in cell lines from oesophageal or head and neck squamous cell carcinomas (ESCCs or HNSCCs, respectively) results in lower levels of AURKA and decreased proliferation, and NANOG and AURKA expression are positively correlated in HNSCCs. Together, these results indicate that NANOG has a lineage-restricted mitogenic function in stratified epithelia. The transcription factor Nanog regulates self-renewal in pluripotent stem cells and cancer stem cells. Here the authors show that Nanog is expressed in mouse adult stratified epithelia, and its overexpression increases proliferation and aneuploidy and activates pathways associated to mitosis.

The current CoVid-19 crisis is revealing the strengths and the weaknesses of the world’s capacity to respond to a global health crisis. A critical weakness has resulted from the excessive centralization of the current biomanufacturing capacities, a matter of great concern, if not a source of nationalistic tensions. On the positive side, scientific data and information have been shared at an unprecedented speed fuelled by the preprint phenomena, and this has considerably strengthened our ability to develop new technology-based solutions. In this work, we explore how, in a context of rapid exchange of scientific information, plant biofactories can serve as a rapid and easily adaptable solution for local manufacturing of bioreagents, more specifically recombinant antibodies. For this purpose, we tested our ability to produce, in the framework of an academic lab and in a matter of weeks, milligram amounts of six different recombinant monoclonal antibodies against SARS-CoV-2 in Nicotiana benthamiana . For the design of the antibodies, we took advantage, among other data sources, of the DNA sequence information made rapidly available by other groups in preprint publications. mAbs were engineered as single-chain fragments fused to a human gamma Fc and transiently expressed using a viral vector. In parallel, we also produced the recombinant SARS-CoV-2 N protein and the receptor binding domain (RBD) of the Spike protein in planta and used them to test the binding specificity of the recombinant mAbs. Finally, for two of the antibodies, we assayed a simple scale-up production protocol based on the extraction of apoplastic fluid. Our results indicate that gram amounts of anti-SARS-CoV-2 antibodies could be easily produced in little more than 6 weeks in repurposed greenhouses with little infrastructure requirements using N. benthamiana as production platform. Similar procedures could be easily deployed to produce diagnostic reagents and, eventually, could be adapted for rapid therapeutic responses.

Diabetic kidney disease is one of the fastest growing causes of death worldwide. Epigenetic regulators control gene expression and are potential therapeutic targets. There is functional interventional evidence for a role of DNA methylation and the histone post-translational modifications—histone methylation, acetylation and crotonylation—in the pathogenesis of kidney disease, including diabetic kidney disease. Readers of epigenetic marks, such as bromodomain and extra terminal (BET) proteins, are also therapeutic targets. Thus, the BD2 selective BET inhibitor apabetalone was the first epigenetic regulator to undergo phase-3 clinical trials in diabetic kidney disease with an endpoint of kidney function. The direct therapeutic modulation of epigenetic features is possible through pharmacological modulators of the specific enzymes involved and through the therapeutic use of the required substrates. Of further interest is the characterization of potential indirect effects of nephroprotective drugs on epigenetic regulation. Thus, SGLT2 inhibitors increase the circulating and tissue levels of β-hydroxybutyrate, a molecule that generates a specific histone modification, β-hydroxybutyrylation, which has been associated with the beneficial health effects of fasting. To what extent this impact on epigenetic regulation may underlie or contribute to the so-far unclear molecular mechanisms of cardio- and nephroprotection offered by SGLT2 inhibitors merits further in-depth studies.