Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification.

Objective.Upper limb loss can profoundly impact an individual's quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations.Approach.By leveraging deep prototype learning, we introduce a method that goes beyond direct output prediction. Instead, it matches new EMG inputs to a set of learned prototypes and predicts the corresponding labels.Main results.This novel methodology significantly enhances the model's classification performance and generalizability by discriminating subtle differences between gestures, making it more reliable and precise in real-world applications. Our experiments on four Ninapro datasets suggest that our deep prototype learning classifier outperforms state-of-the-art methods in terms of intra-subject and inter-subject classification accuracy in gesture prediction.Significance.The results from our experiments validate the effectiveness of the proposed method and pave the way for future advancements in the field of EMG gesture classification for upper limb prosthetics.

drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.

Drug development is a lengthy process with a high failure rate. Increasingly, machine learning is utilized to facilitate the drug development processes. These models aim to enhance our understanding of drug characteristics, including their activity in biological contexts. However, a major challenge in drug response (DR) prediction is model interpretability as it aids in the validation of findings. This is important in biomedicine, where models need to be understandable in comparison with established knowledge of drug interactions with proteins. drGAT, a graph deep learning model, leverages a heterogeneous graph composed of relationships between proteins, cell lines, and drugs. drGAT is designed with two objectives: DR prediction as a binary sensitivity prediction and elucidation of drug mechanism from attention coefficients. drGAT has demonstrated superior performance over existing models, achieving 78% accuracy (and precision), and 76% F1 score for 269 DNA-damaging compounds of the NCI60 drug response dataset. To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug. We also examined whether known relationships were retained in the model by inspecting the neighborhoods of topoisomerase-related drugs. For example, our model retained TOP1 as a highly weighted predictive feature for irinotecan and topotecan, in addition to other genes that could potentially be regulators of the drugs. Our method can be used to accurately predict sensitivity to drugs and may be useful in the identification of biomarkers relating to the treatment of cancer patients.

Antimetastatic activity of seongsanamide B in γ-irradiated human lung cancer.

Lung cancer, which has a high incidence and mortality rates, often metastasizes and exhibits resistance to radiation therapy. Seongsanamide B has conformational features that suggest it has therapeutic potential; however, its antitumor activity has not yet been reported. We evaluated the possibility of seongsanamide B as a radiation therapy efficiency enhancer to suppress γ-irradiation-induced metastasis in non-small cell lung cancer. Seongsanamide B suppressed non-small cell lung cancer cell migration and invasion caused by γ-irradiation. Furthermore, it suppressed γ-irradiation-induced upregulation of Bcl-XL and its downstream signaling molecules, such as superoxide dismutase 2 (SOD2) and phosphorylated Src, by blocking the nuclear translocation of phosphorylated STAT3. Additionally, seongsanamide B markedly modulated the γ-irradiation-induced upregulation of E-cadherin and vimentin. Consistent with the results obtained in vitro, while seongsanamide B did not affect xenograft tumor growth, it significantly suppressed γ-irradiation-induced metastasis by inhibiting Bcl-XL/SOD2/phosphorylated-Src expression and modulating E-cadherin and vimentin expression in a mouse model. Thus, seongsanamide B may demonstrate potential applicability as a radiation therapy efficiency enhancer for lung cancer treatment.

Epidemic Vulnerability Index for Effective Vaccine Distribution against Pandemic.

COVID-19 vaccine distribution route directly impacts the community's mortality and infection rate. Therefore, optimal vaccination dissemination would appreciably lower the death and infection rates. This paper proposes the Epidemic Vulnerability Index (EVI) that quantitatively evaluates the subject's potential risk. Our primary aim for the suggested index is to diminish both infection rate and death rate efficiently. EVI was accordingly designed with clinical factors determining the mortality and social factors incorporating the infection rate. Through statistical COVID-19 patient dataset analysis and social network analysis with an agent-based model that is analogous to a real-world system, we define and experimentally validate the capability of EVI. Our experiments consist of nine vaccination distribution scenarios, including existing indexes which estimate the risk and stochastically proliferate the contagion and vaccine in a 300,000 agent-based graph network. We compared the outcome and variation of the three metrics in the experiments: infection case, death case, and death rate. Through this assessment, vaccination by the descending order of EVI has shown to have a significant outcome with an average of 5.0% lower infection cases, 9.4% lower death cases, and 3.5% lower death rate than other vaccine distribution routes.