On-Device and System-Wide Audio Live Captioning with Language Translation

This publication describes techniques and apparatuses that enable an electronic device (e.g., a smartphone) to provide on-device (e.g., offline), system-level (e.g., operating system), live captioning with language translation in a language that a user can choose (select). Therefore, the smartphone enables the user to read live captioning in the language of their choice without relying on an internet connection, cellular data, or any wired and/or wireless communication with a remote server. Also, the smartphone enables the user to read live captioning in the language of their choice on any medium with audio content supported by the smartphone

Dimensionality Reduction Reveals Distinct Shapes of Normal and Malignant Hematopoietic Cell Populations

Abstract Abstract 1451 Simultaneously examining multiple epitopes in single cells has become increasingly useful as improvements are made to multi-parametric flow techniques. Increased parameterization has enabled subdivision of functionally distinct cell populations based on an increasing variety of physiological attributes. It has not only helped better define the landscape of “normal” hematopoiesis, but also has been clinically applied in detection of minimal residual disease (MRD) in hematopoietic malignancies. Detection of rare “abnormal” cells is the crux of MRD-based risk stratification where sporadic residual cancer cells are identified through multi-parameter flow cytometry. Current methods for the detection and characterization of cellular populations are generally reliant on manual examination and targeted gating techniques. This approach relies almost entirely on prior knowledge and leaves little room for discovery of novel populations. As the number of parameter per cell increases there is a rising need for dimensionality reduction (DR) methods to resolve high dimensional data “down” into a human-interpretable space. Classical DR, such as principle component analysis (PCA), fail to address the non-linear relationships in cellular phenotypes while newer approaches, such as spanning-tree progression of density normalized events (SPADE), have an inherent level of stochasticity that might adversely affect the robust separation of cellular phenotypes (i.e. discriminating healthy vs. diseased cells). Here, we present a novel algorithm that identifies and characterizes distinct cell populations, preserving the high dimensional information, but providing an interpretable visualization of their phenotypic relationships. This approach was applied to a cohort of normal human bone marrow (BM) specimens to discern a landscape of normal hematopoietic phenotypes. We then contrasted this to overlays of human leukemic bone marrow aspirates (AML and ALL) to understand the extent to which cancer corrupts the shape and form of the landscape. We illustrate the application for automated MRD detection in human leukemia (Figure 1). Method: Our approach, CellSNE, is an adaptation of t-Distributed Stochastic Neighbor Embedding (t-SNE), a non-linear dimensionality reduction algorithm. CellSNE finds a low dimensional mapping of cells that preserves their pairwise distances in a high dimensional space. A distance between each cell to every other cell in the dataset is calculated, based on a vector defined by the combined values of cellular parameters measure. An optimization algorithm then searches for a projection of the points into 2D, in such a way that maximizes the similarity in pairwise distances between the high-dimensional and two dimensional spaces. The resulting 2D projection organizes the sample into subpopulations that conserve the shape and relative distances between each cell. Results/Conclusion: Application of CellSNE to healthy BM clearly separated cells based on their known immune subtype and was confirmed by manual analysis (Figure 1A). The results are robust across data collected from different individuals on different days as well as in analyses conducted using low numbers of single cell parameters, suggesting that healthy BM generally maintains the same cellular population characteristics (or “shape”) across samples. When applied to leukemic BM from patients with AML and ALL CellSNE demonstrates a unique cancer landscape (“shape”) for each patient that is dramatically different from normal (Figure 1B). It is critical to note that despite the overwhelming infiltration by cancer cells, rare “normal” cell populations can still be discerned in the ALL BM. CellSNE succeeded in automatically identifying rare (&lt;1%) abnormal ALL cells (tracked using a CellSNE independent parameter) in an otherwise normal BM (Figure 1C). As such, CellSNE achieves in identifying and characterizing rare cellular populations that can be applied in both normal and malignant hematopoiesis. Thus, it provides opportunities for the automated analysis of both large cytometry datasets and clinical MRD detection. Disclosures: No relevant conflicts of interest to declare. </jats:sec

Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

SummaryTissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and “corrupted” developmental processes including cancer

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

High-dimensional single-cell technologies are revolutionizing the way we understand biological systems. Technologies such as mass cytometry measure dozens of parameters simultaneously in individual cells, making interpretation daunting. We developed viSNE, a tool to map high-dimensional cytometry data onto 2D while conserving high-dimensional structure. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow maps into a canonical shape that separates between immune subtypes. In leukemia, however, the shape is malformed: the maps of cancer samples are distinct from the healthy map and from each other. viSNE highlights structure in the heterogeneity of surface phenotype expression in cancer, traverses the progression from diagnosis to relapse, and identifies a rare leukemia population in minimal residual disease settings. As several new technologies raise the number of simultaneously measured parameters in each cell to the hundreds, viSNE will become a mainstay in analyzing and interpreting such experiments

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis

SummaryAcute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology

Trajectories of cell-cycle progression from fixed cell populations

An accurate dissection of sources of cell-to-cell variability is crucial for quantitative biology at the single-cell level but has been challenging for the cell cycle. We present Cycler, a robust method that constructs a continuous trajectory of cell-cycle progression from images of fixed cells. Cycler handles heterogeneous microenvironments and does not require perturbations or genetic markers, making it generally applicable to quantifying multiple sources of cell-to-cell variability in mammalian cells

Toward a Mechanistic Understanding of Linguistic Diversity

Our species displays remarkable linguistic diversity. While the uneven distribution of this diversity demands explanation, the drivers of these patterns have not been conclusively determined. We address this issue in two steps. First, we review previous empirical studies that have suggested environmental, geographical, and socio-cultural drivers of linguistic diversification. However, contradictory results and methodological variation make it difficult to draw general conclusions. Second, we outline a program for future research. We suggest that future analyses should account for interactions among causal factors, lack of spatial and phylogenetic independence of data, and transitory patterns. Recent analytical advances in biogeography and evolutionary biology, such as simulation modeling of diversity patterns, hold promise for testing four key mechanisms of language diversification proposed here: neutral change, population movement, contact, and selection. Future modeling approaches should also evaluate how the outcomes of these processes are influenced by demography, environmental heterogeneity, and time