General Fabrication of Robust Alloyed Metal Anodes for High-Performance Metal Batteries.

Revitalizing metal anodes for rechargeable batteries confronts challenges such as dendrite formation, limited cyclicity, and suboptimal energy density. Despite various efforts, a practical fabrication method for dendrite-free metal anodes remains unavailable. Herein, focusing on Li as exemplar, a general strategy is reported to enhance reversibility of the metal anodes by forming alloyed metals, which is achieved by induction heating of 3D substrate, lithiophilic metals, and Li within tens of seconds. It is demonstrated that preferred alloying interactions between substrates and lithiophilic metals created a lithiophilic metal-rich region adjacent to the substrate, serving as ultrastable lithiophilic host to guide dendrite-free deposition, particularly during prolonged high-capacity cycling. Simultaneously, an alloying between lithiophilic metals and Li creates a Li-rich region adjacent to electrolyte that reduces nucleation overpotential and constitutes favorable electrolyte-Li interface. The resultant composite Li anodes paired with high areal loading LiNi0.8Co0.1Mn0.1O2 cathodes achieve superior cycling stability and remarkable energy density above 1200 Wh L-1 (excluding packaging). Furthermore, this approach shows broader applicability to other metal anodes plagued by dendrite-related challenges, such as Na and Zn. Overall, this work paves the way for development of commercially viable metal-based batteries that offer a combination of safety, high energy density, and durability.

CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data.

Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.

Spheres of Graphene and Carbon Nanotubes Embedding Silicon as Mechanically Resilient Anodes for Lithium-Ion Batteries.

Novel anode materials for lithium-ion batteries were synthesized by in situ growth of spheres of graphene and carbon nanotubes (CNTs) around silicon particles. These composites possess high electrical conductivity and mechanical resiliency, which can sustain the high-pressure calendering process in industrial electrode fabrication, as well as the stress induced during charging and discharging of the electrodes. The resultant electrodes exhibit outstanding cycling durability (∼90% capacity retention at 2 A g-1 after 700 cycles or a capacity fading rate of 0.014% per cycle), calendering compatibility (sustain pressure over 100 MPa), and adequate volumetric capacity (1006 mAh cm-3), providing a novel design strategy toward better silicon anode materials.

scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) allows quantitative analysis of gene expression at the level of single cells, beneficial to study cell heterogeneity. The recognition of cell types facilitates the construction of cell atlas in complex tissues or organisms, which is the basis of almost all downstream scRNA-seq data analyses. Using disease-related scRNA-seq data to perform the prediction of disease status can facilitate the specific diagnosis and personalized treatment of disease. Since single-cell gene expression data are high-dimensional and sparse with dropouts, we propose scIAE, an integrative autoencoder-based ensemble classification framework, to firstly perform multiple random projections and apply integrative and devisable autoencoders (integrating stacked, denoising and sparse autoencoders) to obtain compressed representations. Then base classifiers are built on the lower-dimensional representations and the predictions from all base models are integrated. The comparison of scIAE and common feature extraction methods shows that scIAE is effective and robust, independent of the choice of dimension, which is beneficial to subsequent cell classification. By testing scIAE on different types of data and comparing it with existing general and single-cell-specific classification methods, it is proven that scIAE has a great classification power in cell type annotation intradataset, across batches, across platforms and across species, and also disease status prediction. The architecture of scIAE is flexible and devisable, and it is available at https://github.com/JGuan-lab/scIAE.

Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism.

Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.