A Chinese SCA36 pedigree analysis of NOP56 expansion region based on long-read sequencing

Introduction: Spinocerebellar ataxias 36 (SCA36) is the neurodegenerative disease caused by the GGCCTG Hexanucleotide repeat expansions in NOP56, which is too long to sequence using short-read sequencing. Single molecule real time (SMRT) sequencing can sequence across disease-causing repeat expansion. We report the first long-read sequencing data across the expansion region in SCA36.Methods: We collected and described the clinical manifestations and imaging features of Han Chinese pedigree with three generations of SCA36. Also, we focused on structural variation analysis for intron 1 of the NOP56 gene by SMRT sequencing in the assembled genome.Results: The main clinical features of this pedigree are late-onset ataxia symptoms, with a presymptomatic presence of affective and sleep disorders. In addition, the results of SMRT sequencing showed the specific repeat expansion region and demonstrated that the region was not composed of single GGCCTG hexanucleotides and there were random interruptions.Discussion: We extended the phenotypic spectrum of SCA36. We applied SMRT sequencing to reveal the correlation between genotype and phenotype of SCA36. Our findings indicated that long-read sequencing is well suited to characterize known repeat expansion

Increased transgene expression mediated by recombinant adeno-associated virus in human neuroglia cells under microgravity conditions

The space environment has the special characteristics of radiation, noise particularity and weightlessness, all of which have adverse effects on astronauts’ muscles, bones, neurons and immune system. Some reports have shown that chemotherapy and radiotherapy can increase the activity of the recombinant adeno-associated virus (AAV) which is widely used in gene therapy. In this paper, recombinant AAV2 (rAAV2) was first packaged with the enhanced green fluorescence protein (eGFP) gene and used to infect neuroglia cells including the U87 and U251 cell lines, under microgravity conditions; it was then detected by fluorescence microscopy and flow cytometry. The results show that microgravity affects the adhesion ability of cells, promotes transgene expression induced by rAAV2 and causes changes of viral infection receptors at different time points. These findings broaden the current understanding of the microgravity effects on rAAV, with significant implications in gene therapy and the mechanisms of increased virus pathogenicity under space microgravity.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.Municipal Science & Technology Commission. Beijing Natural Science Foundation (Grants 3102028 and 3122034)General Logistics Science Foundation (Grant CWS11C108)National Institutes of Health (U.S.) (National Institute of General Medical Sciences (U.S.). Grant R01- EB001659)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Cooperative Agreement U01- EB-008577

Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works

Age-Related Effects on MSC Immunomodulation, Macrophage Polarization, Apoptosis, and Bone Regeneration Correlate with IL-38 Expression

Mesenchymal stem cells (MSCs) are known to promote tissue regeneration and suppress excessive inflammation caused by infection or trauma. Reported evidence indicates that various factors influence the expression of MSCs' endogenous immunomodulatory properties. However, the detailed interactions of MSCs with macrophages, which are key cells involved in tissue repair, and their regulatory mechanisms are not completely understood. We herein investigated how age-related immunomodulatory impairment of MSCs alters the interaction of MSCs with macrophages during bone healing using young (5-week old) and aged (50-week old) mice. To clarify the relationship between inflammatory macrophages (M1) and MSCs, their spatiotemporal localization at the bone healing site was investigated by immunostaining, and possible regulatory mechanisms were analyzed in vitro co-cultures. Histomorphometric analysis revealed an accumulation of M1 and a decrease in MSC number at the healing site in aged mice, which showed a delayed bone healing. In in vitro co-cultures, MSCs induced M1 apoptosis through cell-to-cell contact but suppressed the gene expression of pro-inflammatory cytokines by soluble factors secreted in the culture supernatant. Interestingly, interleukin 38 (Il-38) expression was up-regulated in M1 after co-culture with MSCs. IL-38 suppressed the gene expression of inflammatory cytokines in M1 and promoted the expression of genes associated with M1 polarization to anti-inflammatory macrophages (M2). IL-38 also had an inhibitory effect on M1 apoptosis. These results suggest that MSCs may induce M1 apoptosis, suppress inflammatory cytokine production by M1, and induce their polarization toward M2. Nevertheless, in aged conditions, the decreased number and immunomodulatory function of MSCs could be associated with a delayed M1 clearance (i.e., apoptosis and/or polarization) and consequent delayed resolution of the inflammatory phase. Furthermore, M1-derived IL-38 may be associated with immunoregulation in the tissue regeneration site

Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques

In this paper, we present our solution to the Multilingual Information Retrieval Across a Continuum of Languages (MIRACL) challenge of WSDM CUP 2023\footnote{https://project-miracl.github.io/}. Our solution focuses on enhancing the ranking stage, where we fine-tune pre-trained multilingual transformer-based models with MIRACL dataset. Our model improvement is mainly achieved through diverse data engineering techniques, including the collection of additional relevant training data, data augmentation, and negative sampling. Our fine-tuned model effectively determines the semantic relevance between queries and documents, resulting in a significant improvement in the efficiency of the multilingual information retrieval process. Finally, Our team is pleased to achieve remarkable results in this challenging competition, securing 2nd place in the Surprise-Languages track with a score of 0.835 and 3rd place in the Known-Languages track with an average nDCG@10 score of 0.716 across the 16 known languages on the final leaderboard