Menstruation is a highly subjective and situated embodied experience, yet these aspects are frequently overlooked in current menstrual tracking technologies and healthcare settings. This research proposes weaving as a feminist approach to material narrative that can illuminate subjectivity and situated embodiment. Through three woven pieces exhibited in the UK, the study explores how weaving can express menstrual experiences in tangible and sensory ways, specifically focusing on: (1) cyclical bodily perceptions of time within cycles; (2) trivialised endometriosis pain in medical contexts; and (3) daily lived experiences beyond datafication. By describing and reflecting on the making process, we found that the tactile, slow, and reflective qualities of weaving facilitate deeper and more introspective bodily awareness. It also allows menstrual experiences to be expressed in more fluid and flexible ways, supporting individuals in validating and appreciating their bodies. Those insights encourage designers to focus on materially oriented, sensory, and felt approaches, thereby expanding the design possibilities within menstrual technologies and healthcare.

Menstrual pain is an embodied, unpredictable, and diverse lived experience. However, current menstrual tracking technologies mainly adopt medicalised and quantitative approaches, reducing pain to numerical data, concealing its organic and messy nature. To uncover the felt, lived experience of pain, we explored soft robotics as a tactile, dynamic medium. Through a series of material workshops, we designed MenstaRay, a novel artefact that mimics the temporality and fluctuations of menstrual pain. Findings from sensory interactions with MenstaRay show that soft robotic materials sensitise and enhance menstruators’ bodily awareness, supporting them in contextually recalling, introspecting, and reflecting on their pain experiences, and encouraging a sense of self-care, self-acceptance, and companionship toward menstrual pain. We frame MenstaRay’s dynamic entanglements with fluid bodily experiences as a meaningful material practice through a feminist lens, highlighting the creative potential of novel programmable interactions of knitted soft robotics to express nuanced pain characteristics, extending to other somatic experience design beyond menstruation.

BACKGROUND: The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. RESULTS: We propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). CONCLUSIONS: Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding proteins.

Prediction of protein-protein interaction (PPI) is one of the most challenging problems in biology. Although great progress has been devoted to the development of methodology for predicting PPIs and PIN using machine learning methods, the problem is still far from being solved since the application of most existing methods is limited. In this study, we propose a method for PPI prediction based on amino acids differences between pairs of protein sequences. 10-fold cross-validation tests based on human PPI datasets with balanced positive-to-negative ratios indicate that it performs comparably well. Therefore, our finding suggests that amino acids differences of interacting protein pairs are relevant to the prediction of PPIs and hence provide important information on sequence-based encoding schemes.