Andrew Shin

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

Recently there have been major advances in the electro-mechanical design of upper extremity prosthetics. However, the development of control strategies for such prosthetics has lagged significantly behind. Conventional noninvasive myoelectric control strategies rely on the amplitude of electromyography (EMG) signals from flexor and extensor muscles in the forearm. Surface EMG has limited specificity for deep contiguous muscles because of cross talk and cannot reliably differentiate between individual digit and joint motions. We present a novel ultrasound imaging based control strategy for upper arm prosthetics that can overcome many of the limitations of myoelectric control. Real time ultrasound images of the forearm muscles were obtained using a wearable mechanically scanned single element ultrasound system, and analyzed to create maps of muscle activity based on changes in the ultrasound echogenicity of the muscle during contraction. Individual digit movements were associated with unique maps of activity. These maps were correlated with previously acquired training data to classify individual digit movements. Preliminary results using ten healthy volunteers demonstrated this approach could provide robust classification of individual finger movements with 98% accuracy (precision 96%-100% and recall 97%-100% for individual finger flexions). The change in ultrasound echogenicity was found to be proportional to the digit flexion speed (R(2)=0.9), and thus our proposed strategy provided a proportional signal that can be used for fine control. We anticipate that ultrasound imaging based control strategies could be a significant improvement over conventional myoelectric control of prosthetics.

Abstract Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

Abstract The electrodeposition of low surface area lithium is critical to successful adoption of lithium metal batteries. Here, we discover the dependence of lithium metal morphology on electrical resistance of substrates, enabling us to design an alternative strategy for controlling lithium morphology and improving electrochemical performance. By modifying the current collector with atomic layer deposited conductive (ZnO, SnO 2 ) and resistive (Al 2 O 3 ) nanofilms, we show that conductive films promote the formation of high surface area lithium deposits, whereas highly resistive films promote the formation of lithium clusters of low surface area. We reveal an electrodeposition mechanism in which radial diffusion of electroactive species is promoted on resistive substrates, resulting in lateral growth of large (150 µm in diameter) planar lithium deposits. Using resistive substrates, similar lithium morphologies are formed in three distinct classes of electrolytes, resulting in up to ten-fold improvement in battery performance. Ultimately, we report anode-free pouch cells using the Al 2 O 3 -modified copper that maintain 60 % of their initial discharge capacity after 100 cycles, displaying the benefits of resistive substrates for controlling lithium electrodeposition.