Learning Joint Representation of Human Motion and Language

In this work, we present MoLang (a Motion-Language connecting model) for learning joint representation of human motion and language, leveraging both unpaired and paired datasets of motion and language modalities. To this end, we propose a motion-language model with contrastive learning, empowering our model to learn better generalizable representations of the human motion domain. Empirical results show that our model learns strong representations of human motion data through navigating language modality. Our proposed method is able to perform both action recognition and motion retrieval tasks with a single model where it outperforms state-of-the-art approaches on a number of action recognition benchmarks

Gene Expression Profiles Identify Biomarkers of Resistance to Decitabine in Myelodysplastic Syndromes

Myelodysplastic syndrome (MDS) is a clonal hematopoietic stem cell disease characterized by inefficient hematopoiesis and the potential development of acute leukemia. Among the most notable advances in the treatment of MDS is the hypomethylating agent, decitabine (5-aza-2′deoxycytidine). Although decitabine is well known as an effective method for treating MDS patients, only a subset of patients respond and a tolerance often develops, leading to treatment failure. Moreover, decitabine treatment is costly and causes unnecessary toxicity. Therefore, clarifying the mechanism of decitabine resistance is important for improving its therapeutic efficacy. To this end, we established a decitabine-resistant F-36P cell line from the parental F-36P leukemia cell line, and applied a genetic approach employing next-generation sequencing, various experimental techniques, and bioinformatics tools to determine differences in gene expression and relationships among genes. Thirty-eight candidate genes encoding proteins involved in decitabine-resistant-related pathways, including immune checkpoints, the regulation of myeloid cell differentiation, and PI3K-Akt signaling, were identified. Interestingly, two of the candidate genes, AKT3 and FOS, were overexpressed in MDS patients with poor prognoses. On the basis of these results, we are pursuing development of a gene chip for diagnosing decitabine resistance in MDS patients, with the goal of ultimately improving the power to predict treatment strategies and the prognosis of MDS patients