ActiveSelfHAR: Incorporating Self Training into Active Learning to Improve Cross-Subject Human Activity Recognition

Deep learning-based human activity recognition (HAR) methods have shown great promise in the applications of smart healthcare systems and wireless body sensor network (BSN). Despite their demonstrated performance in laboratory settings, the real-world implementation of such methods is still hindered by the cross-subject issue when adapting to new users. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning's benefit of sparsely acquiring data with actual labels and self- training's benefit of effectively utilizing unlabeled data to enable the deep model to adapt to the target domain, i.e., the new users. In this framework, the model trained in the last iteration or the source domain is first utilized to generate pseudo labels of the target-domain samples and construct a self-training set based on the confidence score. Second, we propose to use the spatio-temporal relationships among the samples in the non-self-training set to augment the core set selected by active learning. Finally, we combine the self-training set and the augmented core set to fine-tune the model. We demonstrate our method by comparing it with state-of-the-art methods on two IMU-based datasets and an EMG-based dataset. Our method presents similar HAR accuracies with the upper bound, i.e. fully supervised fine-tuning with less than 1\% labeled data of the target dataset and significantly improves data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into smart healthcare systems and BSN

Sp1 and KLF15 regulate basal transcription of the human LRP5 gene

<p>Abstract</p> <p>Background</p> <p>LRP5, a member of the low density lipoprotein receptor superfamily, regulates diverse developmental processes in embryogenesis and maintains physiological homeostasis in adult organisms. However, how the expression of human <it>LRP5 </it>gene is regulated remains unclear.</p> <p>Results</p> <p>In order to characterize the transcriptional regulation of human <it>LRP5 </it>gene, we cloned the 5' flanking region and evaluated its transcriptional activity in a luciferase reporter system. We demonstrated that both KLF15 and Sp1 binding sites between -72 bp and -53 bp contribute to the transcriptional activation of human <it>LRP5 </it>promoter. Chromatin immunoprecipitation assay demonstrated that the ubiquitous transcription factors KLF15 and Sp1 bind to this region. Using <it>Drosophila </it>SL2 cells, we showed that KLF15 and Sp1 trans-activated the <it>LRP5 </it>promoter in a manner dependent on the presence of Sp1-binding and KLF15-binding motifs.</p> <p>Conclusions</p> <p>Both KLF15 and Sp1 binding sites contribute to the basal activity of human <it>LRP5 </it>promoter. This study provides the first insight into the mechanisms by which transcription of human <it>LRP5 </it>gene is regulated.</p

Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study

Assessing the progression of muscle fatigue for daily exercises provides vital indicators for precise rehabilitation, personalized training dose, especially under the context of Metaverse. Assessing fatigue of multi-muscle coordination-involved daily exercises requires the neuromuscular features that represent the fatigue-induced characteristics of spatiotemporal adaptions of multiple muscles and the estimator that captures the time-evolving progression of fatigue. In this paper, we propose to depict fatigue by the features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model. First, we extract muscle synergy fractionation and the variance of spinal module spikings as features inspired by the prior of fatigue-induced neuromuscular adaptations. Second, we treat the features as observations and develop a Bayesian Gaussian process to capture the time-evolving progression. Third, we solve the issue of lacking supervision information by mathematically formulating the time-evolving characteristics of fatigue as the loss function. Finally, we adapt the metrics that follow the physiological principles of fatigue to quantitatively evaluate the performance. Our extensive experiments present a 0.99 similarity between days, a over 0.7 similarity with other views of fatigue and a nearly 1 weak monotonicity, which outperform other methods. This study would aim the objective assessment of muscle fatigue.Comment: submitted to IEEE JBH

Lack of Cul4b, an E3 Ubiquitin Ligase Component, Leads to Embryonic Lethality and Abnormal Placental Development

Cullin-RING ligases (CRLs) complexes participate in the regulation of diverse cellular processes, including cell cycle progression, transcription, signal transduction and development. Serving as the scaffold protein, cullins are crucial for the assembly of ligase complexes, which recognize and target various substrates for proteosomal degradation. Mutations in human CUL4B, one of the eight members in cullin family, are one of the major causes of X-linked mental retardation. We here report the generation and characterization of Cul4b knockout mice, in which exons 3 to 5 were deleted. In contrast to the survival to adulthood of human hemizygous males with CUL4B null mutation, Cul4b null mouse embryos show severe developmental arrest and usually die before embryonic day 9.5 (E9.5). Accumulation of cyclin E, a CRL (CUL4B) substrate, was observed in Cul4b null embryos. Cul4b heterozygotes were recovered at a reduced ratio and exhibited a severe developmental delay. The placentas in Cul4b heterozygotes were disorganized and were impaired in vascularization, which may contribute to the developmental delay. As in human CUL4B heterozygotes, Cul4b null cells were selected against in Cul4b heterozygotes, leading to various degrees of skewed X-inactivation in different tissues. Together, our results showed that CUL4B is indispensable for embryonic development in the mouse

The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy

Surface Electromyography (sEMG) enables an intuitive control of wearable robots. The muscle fatigue-induced changes of sEMG signals might limit the long-term usage of the sEMG-based control algorithms. This paper presents the performance deterioration of sEMG-based gait phase classifiers, explains the deterioration by analyzing the time-varying changes of the extracted features, and proposes a training strategy that can improve the classifiers’ robustness against muscle fatigue. In particular, we first select some features that are commonly used in fatigue-related studies and use them to classify gait phases under muscle fatigue. Then, we analyze the time-varying characteristics of extracted features, with the aim of explaining the performance of the classifiers. Finally, we propose a training strategy that effectively improves the robustness against muscle fatigue, which contributes to an easy-to-use method. Ten subjects performing prolonged walking are recruited. Our study contributes to a novel perspective of designing gait phase classifiers under muscle fatigue

GEP, a Local Growth Factor, is Critical for Odontogenesis and Amelogenesis

Granulin epithelin precursor (GEP) is a new growth factor that functions in brain development, chondrogenesis, tissue regeneration, tumorigenesis, and inflammation. The goal of this study was to study whether GEP was critical for odontogenesis and amelogenesis both in vivo and in vitro. The in situ hybridization and immunohistochemistry data showed that GEP was expressed in both odontoblast and ameloblast cells postnatally. Knockdown of GEP by crossing U6-ploxPneo-GEP and Sox2-Cre transgenic mice led to a reduction of dentin thickness, an increase in predentin thickness, and a reduction in mineral content in enamel. The in vitro application of recombinant GEP up-regulated molecular markers important for odontogenesis (DMP1, DSPP, and ALP) and amelogenesis (ameloblastin, amelogenin and enamelin). In conclusion, both the in vivo and the in vivo data support an important role of GEP in tooth formation during postnatal development.</p

S113R mutation in SLC33A1 leads to neurodegeneration and augmented BMP signaling in a mouse model

The S113R mutation (c.339T>G) (MIM #603690.0001) in SLC33A1 (MIM #603690), an ER membrane acetyl-CoA transporter, has been previously identified in individuals with hereditary spastic paraplegia type 42 (SPG42; MIM #612539). SLC33A1 has also been shown to inhibit the bone morphogenetic protein (BMP) signaling pathway in zebrafish. To better understand the function of SLC33A1, we generated and characterized Slc33a1S113R knock-in mice. Homozygous Slc33a1S113R mutant mice were embryonic lethal, whereas heterozygous Slc33a1 mutant mice (Slc33a1wt/mut) exhibited behavioral abnormalities and central neurodegeneration, which is consistent with hereditary spastic paraplegia (HSP) phenotypes. Importantly, we found an upregulation of BMP signaling in the nervous system and mouse embryonic fibroblasts of Slc33a1wt/mut mice. Using a sciatic nerve crush injury model in vivo and dorsal root ganglion (DRG) culture in vitro we showed that injury-induced axonal regeneration in Slc33a1wt/mut mice was accelerated and mediated by upregulated BMP signaling. Exogenous addition of BMP signaling antagonist, noggin, could efficiently alleviate the accelerated injury-induced axonal regrowth. These results indicate that SLC33A1 can negatively regulate BMP signaling in mice, further supporting the notion that upregulation of BMP signaling is a common mechanism of a subset of hereditary spastic paraplegias