moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets-gene expression, DNA methylation, and DNA accessibility-while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

Omnidirectional Energy Harvesting Fleeces.

Underwater mechanical energy harvesters are of rising interest due to their potential for various applications, such as self-powered ocean energy harvesters, monitoring devices, and wave sensors. Pressure-responsive films and stretch-responsive fibers, which provide high electrical power in electrolytes and have simple structures that do not require packing systems, are promising as harvesters in the ocean environment. One drawback of underwater mechanical energy harvesters is that they are highly dependent on the direction of receiving external forces, which is unfavorable in environments where the direction of the supplied force is constantly changing. Here, we report spherical fleece, consisting of wool fibers and single-walled carbon nanotubes (SWCNTs), which exhibit repetitive electrical currents in all directions. No matter which direction the fleece is deformed, it changes the surface area available for ions to access SWCNTs electrochemically, causing a piezoionic phenomenon. The current per input mechanical stress of the fabricated SWCNT/wool energy harvester is up to 33.476 mA/MPa, which is the highest among underwater mechanical energy harvesters reported to date. In particular, it is suitable for low-frequency (<1 Hz) environments, making it ideal for utilizing natural forces such as wind and waves as harvesting sources. The operating mechanism in the nanoscale region of the proposed fleece harvester has been theoretically elucidated through all-atom molecular dynamics simulations.

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles.

BACKGROUND: Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed. RESULTS: In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets. CONCLUSIONS: From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

Chemo-Mechanical Energy Harvesters with Enhanced Intrinsic Electrochemical Capacitance in Carbon Nanotube Yarns.

Predicting and preventing disasters in difficult-to-access environments, such as oceans, requires self-powered monitoring devices. Since the need to periodically charge and replace batteries is an economic and environmental concern, energy harvesting from external stimuli to supply electricity to batteries is increasingly being considered. Especially, in aqueous environments including electrolytes, coiled carbon nanotube (CNT) yarn harvesters have been reported as an emerging approach for converting mechanical energy into electrical energy driven by large and reversible capacitance changes under stretching and releasing. To realize enhanced harvesting performance, experimental and computational approaches to optimize structural homogeneity and electrochemical accessible area in CNT yarns to maximize intrinsic electrochemical capacitance (IEC) and stretch-induced changes are presented here. Enhanced IEC further enables to decrease matching impedance for more energy efficient circuits with harvesters. In an ocean-like environment with a frequency from 0.1 to 1 Hz, the proposed harvester demonstrates the highest volumetric power (1.6-10.45 mW cm-3 ) of all mechanical harvesters reported in the literature to the knowledge of the authors. Additionally, a high electrical peak power of 540 W kg-1 and energy conversion efficiency of 2.15% are obtained from torsional and tensile mechanical energy.