Proteolytic profiling of human plasma reveals an immunoactive complement C3 fragment

\ua9 The Author(s) 2025.Dysregulated proteolysis is central to autoimmune pathogenesis. The complement cascade, a major protease network, generates fragments that modulate immunity and tissue injury. We developed a scalable blood plasma N-terminomics workflow that markedly expands detection of proteolytic events in vitro and in vivo. Applied to 143 systemic lupus erythematosus (SLE) patients, Multi-Omics Factor Analysis (MOFA) linked N-terminal signatures to immunological and clinical heterogeneity. This revealed a previously unrecognized complement fragment, C3-LHF1, encompassing the C345C domain and rivaling, based on intensity detected by mass spectrometry, the abundance of canonical fragments like C3a and C3b. C3-LHF1 associated with renal function and remission in lupus nephritis, and exhibited dual functions: inhibiting classical and lectin complement pathways and acting as a partial IL6ST (gp130) agonist, independent of IL6Rα. In human kidney organoids, C3-LHF1 induced JAK/STAT3 signaling, amplified TNFα-driven CXCL10 secretion, and reduced podocyte marker expression, suggesting a role in tissue remodeling. These findings reveal unanticipated complexity in complement-mediated signaling and provide a comprehensive atlas of protein N-termini in human plasma, which enables discovery of novel immunoregulatory mechanisms and therapeutic targets in inflammatory disease

A Pedagogical framework for learning analytics in collaborative inquiry tasks: An Example from a teamwork competency awareness program

© 2016 ACM. Many pedagogical models in the field of learning analytics are implicit and do not overtly direct learner behavior. While this allows flexibility of use, this could also result in misaligned practice, and there are calls for more explicit pedagogical models in learning analytics. This paper presents an explicit pedagogical model, the Team and Self Diagnostic Learning (TSDL) framework, in the context of collaborative inquiry tasks. Key informing theories include experiential learning, collaborative learning, and the learning analytics process model. The framework was trialed through a teamwork competency awareness program for 14 year old students. A total of 272 students participated in the program. This paper foregrounds students' and teachers' evaluative accounts of the program. Findings reveal positive perceptions of the stages of the TSDL framework, despite identified challenges, which points to its potential usefulness for teaching and learning. The TSDL framework aims to provide theoretical clarity of the learning process, and foster alignment between learning analytics and the learning design. The current work provides trial outcomes of a teamwork competency awareness program that used dispositional analytics, and further efforts are underway to develop the discourse layer of the analytic engine. Future work will also be dedicated to application and refinement of the framework for other contexts and participants, both learners and teachers alike

Designing a web tool to support teamwork awareness and reflection: Evaluation of two trial cycles

Teamwork is an important 21st century competency to be nurtured in students. In this study, we describe the first two trial cycles of a web tool "My Groupwork Buddy" that is designed to support teamwork awareness and reflection in collaborative inquiry tasks. The tool is developed using a design-based approach and was based on the Team and Self Diagnostic Learning pedagogical framework. The system was trialled with 35 Secondary School students in a blended learning environment. Qualitative feedback from student focus group discussions and questionnaires were analysed from each trial. Design changes that focused students on specific reflection questions and goals helped to improve the quality of student responses of their teamwork. Further refinement of the tool and activity designs is in progress to better support teamwork awareness and reflection to build the teamwork competency of 21st century learners

Reconstruction of the state space figure of indian ocean dipole

State space reconstruction is an important index for describing nonlinear time series. However, reconstruction of state space figure is difficult if the data is noisy. Hence, noise reduction is an important step for reconstructing state space figure. In this study, we propose a method which can reconstruct state space picture from a noisy time series. This method is used for reconstructing state space figure from the data of Indian Ocean Dipole. Dimension of the reconstructed attractor is measured by computing correlation dimension. The dynamics of Indian Ocean Dipole is not well understood. The reconstruction of state space figure indicates that there is chaos in Indian Ocean Dipole. Positive Lyapunov exponent reconfirms that the dynamics of Indian Ocean Dipole is chaotic. © Springer Nature Singapore Pte Ltd. 2019