A quasinonlocal coupling method for nonlocal and local diffusion models

In this paper, we extend the idea of "geometric reconstruction" to couple a nonlocal diffusion model directly with the classical local diffusion in one dimensional space. This new coupling framework removes interfacial inconsistency, ensures the flux balance, and satisfies energy conservation as well as the maximum principle, whereas none of existing coupling methods for nonlocal-to-local coupling satisfies all of these properties. We establish the well-posedness and provide the stability analysis of the coupling method. We investigate the difference to the local limiting problem in terms of the nonlocal interaction range. Furthermore, we propose a first order finite difference numerical discretization and perform several numerical tests to confirm the theoretical findings. In particular, we show that the resulting numerical result is free of artifacts near the boundary of the domain where a classical local boundary condition is used, together with a coupled fully nonlocal model in the interior of the domain

Designing Authentic Learning Activities to Train Pre-Service Teachers About Teaching Online

Online learning is increasingly being used in K-12 learning environments. A concomitant trend is found towards learning becoming authentic as students learn with tasks that are connected to real-world occupations. In this study, 48 pre-service teachers use an online environment to engage in authentic practice as they developed online learning experiences for their future students. Using a design-based research methodology, the researchers were involved in planning, designing, implementing, and evaluating the higher education class across two macro cycles. An authentic learning framework was utilized in the development of the class. Findings explicate the design of the course and how it aligned to the authentic learning framework. It appears that web-based tools were beneficial as the pre-service teachers designed their own K-12 online classes. Findings show that the pre-service teachers\u27 comfort increased when using the using online web building applications in the authentic environment. Furthermore, a high level of engagement in reflective and collaborative learning was uncovered during the activities. This research acts as a springboard for educators who are interested in designing online higher education courses incorporating authentic learning experiences

Localized JNK signaling regulates organ size during development.

A fundamental question of biology is what determines organ size. Despite demonstrations that factors within organs determine their sizes, intrinsic size control mechanisms remain elusive. Here we show that Drosophila wing size is regulated by JNK signaling during development. JNK is active in a stripe along the center of developing wings, and modulating JNK signaling within this stripe changes organ size. This JNK stripe influences proliferation in a non-canonical, Jun-independent manner by inhibiting the Hippo pathway. Localized JNK activity is established by Hedgehog signaling, where Ci elevates dTRAF1 expression. As the dTRAF1 homolog, TRAF4, is amplified in numerous cancers, these findings provide a new mechanism for how the Hedgehog pathway could contribute to tumorigenesis, and, more importantly, provides a new strategy for cancer therapies. Finally, modulation of JNK signaling centers in developing antennae and legs changes their sizes, suggesting a more generalizable role for JNK signaling in developmental organ size control

Using Twitter to Support Reflective Learning in an Asynchronous Online Course

The purpose of this study was to further our understanding of the use of Twitter for promoting reflective learning. Specifically, this study investigated how students participate in Twitter-supported activities, what type of knowledge are manifested when Twitter is used to reflect on the course readings, and how students perceive the Twitter-supported activities. The data showed that Twitter was successful in keeping the learners engaged in the reflective discussion activities for a prolonged period compared to Blackboard. Students overall had a positive perception towards the integration of Twitter to support reflection and discussion along with active participation. Twitter was effective in increasing perceived learner-content and learner-learner interactivity along with engagement. We also provide recommendations for educational practitioners and direction for future research

Applying a Modified Technology Acceptance Model to Qualitatively Analyse the Factors Affecting Microblogging Integration

The purpose of this research is to examine factors affecting students’ perception and engagement of microblogging integration using a qualitative approach. We employed a qualitative case study design to explore potential factors affecting microblogging integration in a hybrid course. Using the technology acceptance model (TAM) model as an umbrella framework, we examined through in-depth interviews with 18 participants the impact of microblogging integration into instruction that affected students’ reported use and perceptions of their microblogging-supported learning experiences. We found that individual differences, system characteristics, social influence and facilitating conditions all have impact on student participation and engagement in microblogging integration to varying degrees. We identified more granular factors within each of the four dimensions. Additionally, we proposed a Twitter user taxonomy based on perceived usefulness and usage behaviour and discussed its broad implications in higher education learning environments. Finally, we identified several pedagogical implications pertaining to strategies of microblogging integration under the context of a hybrid course and offered pertinent recommendations for future research

MAVS Is essential for primary CD4 + T cell immunity but not for recall T cell responses following an attenuated West Nile virus infection

ABSTRACT The use of pathogen recognition receptor (PRR) agonists and the molecular mechanisms involved have been the major focus of research in individual vaccine development. West Nile virus (WNV) nonstructural (NS) 4B-P38G mutant has several features for an ideal vaccine candidate, including significantly reduced neuroinvasiveness, induction of strong adaptive immunity, and protection of mice from wild-type (WT) WNV infection. Here, we determined the role of mitochondrial antiviral signaling protein (MAVS), the adaptor protein for RIG-I-like receptor in regulating host immunity against the NS4B-P38G vaccine. We found that Mavs −/− mice were more susceptible to NS4B-P38G priming than WT mice. Mavs −/− mice had a transiently reduced production of antiviral cytokines and an impaired CD4 + T cell response in peripheral organs. However, antibody and CD8 + T cell responses were minimally affected. NS4B-P38G induced lower type I interferon (IFN), IFN-stimulating gene, and proinflammatory cytokine responses in Mavs −/− dendritic cells and subsequently compromised the antigen-presenting capacity for CD4 + T cells. Interestingly, Mavs −/− mice surviving NS4B-P38G priming were all protected from a lethal WT WNV challenge. NS4B-P38G-primed Mavs −/− mice exhibited equivalent levels of protective CD4 + T cell recall response, a modestly reduced WNV-specific IgM production, but more robust CD8 + T cell recall response. Taken together, our results suggest that MAVS is essential for boosting optimal primary CD4 + T cell responses upon NS4B-P38G vaccination and yet is dispensable for host protection and recall T cell responses during secondary WT WNV infection. IMPORTANCE The production of innate cytokines induced by the recognition of pathogen recognition receptors (PRRs) via their cognate ligands are critical for enhancing antigen-presenting cell functions and influencing T cell responses during microbial infection. The use of PRR agonists and the underlying molecular mechanisms have been the major focus in individual vaccine development. Here, we determined the role of mitochondrial antiviral-signaling protein (MAVS), the adaptor protein for RIG-I like receptor in regulating host immunity against the live attenuated West Nile virus (WNV) vaccine strain, the nonstructural (NS) 4B-P38G mutant. We found that MAVS is important for boosting optimal primary CD4 + T cell response during NS4B-P38G vaccination. However, MAVS is dispensable for memory T cell development and host protection during secondary wild-type WNV infection. Overall, these results may be utilized as a paradigm to aid in the rational development of other efficacious live attenuated flavivirus vaccines

Learning Support and Trivial Prototypes for Interpretable Image Classification

Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space. Note that it is possible to make an analogy between ProtoPNet and support vector machine (SVM) given that the classification from both methods relies on computing similarity with a set of training points (i.e., trivial prototypes in ProtoPNet, and support vectors in SVM). However, while trivial prototypes are located far from the classification boundary, support vectors are located close to this boundary, and we argue that this discrepancy with the well-established SVM theory can result in ProtoPNet models with inferior classification accuracy. In this paper, we aim to improve the classification of ProtoPNet with a new method to learn support prototypes that lie near the classification boundary in the feature space, as suggested by the SVM theory. In addition, we target the improvement of classification results with a new model, named ST-ProtoPNet, which exploits our support prototypes and the trivial prototypes to provide more effective classification. Experimental results on CUB-200-2011, Stanford Cars, and Stanford Dogs datasets demonstrate that ST-ProtoPNet achieves state-of-the-art classification accuracy and interpretability results. We also show that the proposed support prototypes tend to be better localised in the object of interest rather than in the background region

Language Agents for Detecting Implicit Stereotypes in Text-to-image Models at Scale

The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products. While these exceptional AIGC products are gaining increasing recognition and sparking enthusiasm among consumers, the questions regarding whether, when, and how these models might unintentionally reinforce existing societal stereotypes remain largely unaddressed. Motivated by recent advancements in language agents, here we introduce a novel agent architecture tailored for stereotype detection in text-to-image models. This versatile agent architecture is capable of accommodating free-form detection tasks and can autonomously invoke various tools to facilitate the entire process, from generating corresponding instructions and images, to detecting stereotypes. We build the stereotype-relevant benchmark based on multiple open-text datasets, and apply this architecture to commercial products and popular open source text-to-image models. We find that these models often display serious stereotypes when it comes to certain prompts about personal characteristics, social cultural context and crime-related aspects. In summary, these empirical findings underscore the pervasive existence of stereotypes across social dimensions, including gender, race, and religion, which not only validate the effectiveness of our proposed approach, but also emphasize the critical necessity of addressing potential ethical risks in the burgeoning realm of AIGC. As AIGC continues its rapid expansion trajectory, with new models and plugins emerging daily in staggering numbers, the challenge lies in the timely detection and mitigation of potential biases within these models

Liquid crystal blue phases: stability, field effects and alignment

The blue phases are fascinating structures in liquid crystals, fluids that exhibit cubic structures that have true crystalline order. The blue phases were discovered in the 1970s and were the subject of extensive research in the 1980s, when a deep understanding of many of their properties was established. The discovery that the blue phases could be stabilised to exist over wide temperature ranges meant that they became more than scientific curiosities and led to a recent resurgence in research into them as they offer some promise in applications. This paper considers some important aspects of the blue phases that are recurrent topics in their research. It describes factors affecting blue phase stability, demonstrating on the role of the bend elastic constant; field effects, including the Kerr effect, electrostriction and relaxation phenomena; and alignment, in particular production and control of blue phase monodomains. The dependence of these phenomena on the physical properties of the liquid crystalline system, including the twist and bend elastic constants and the dielectric anisotropy, is emphasised wherever possible. The paper links work carried out in the 1980s with contemporary research, using a few key examples to show how there is still much to understand in this beautiful topic