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A multiresolution approach with method-informed statistical analysis for quantifying lymphatic pumping dynamics

Despite significant strides in lymphatic system imaging, the timely diagnosis of lymphatic disorders remains elusive. This is driven by the absence of standardized, non-invasive, reliable, quantitative methods for real-time functional analysis of lymphatic contractility with adequate spatial and temporal resolution. Here, we address this unmet need by integrating near-infrared fluorescence lymphangiography imaging with an innovative analytical workflow that combines data acquisition, signal processing, and statistical analysis to integrate traditional peak-and-valley analysis with advanced wavelet time-frequency analyses. Variance component analysis was used to evaluate the drivers of variance attributable to each experimental variable for each lymphangiography measurement type. Generalizability studies were used to assess the reliability of measured parameters and how reliability improves as the number of repeat measurements per subject increases. This allowed us to determine the minimum number of repeat measurements needed per subject for acceptable measurement reliability. This approach not only offers detailed insights into lymphatic pumping behaviors across species, sex and age, but also significantly boosts the reliability of these measurements by incorporating multiple regions of interest and evaluating the lymphatic system under various gravitational loads. For example, the reliability of the peak-and-valley analysis of human lymphatic vessels was increased 3-fold using the described approach. By addressing the critical need for improved imaging and quantification methods, our study offers a new standard approach for the imaging and analysis of lymphatic function that can improve our understanding, diagnosis, and treatment of lymphatic diseases. The results highlight the importance of comprehensive data acquisition strategies to fully capture the dynamic behavior of the lymphatic system.

Label-free live cell recognition and tracking for biological discoveries and translational applications

Label-free, live cell recognition (i.e. instance segmentation) and tracking using computer vision-aided recognition can be a powerful tool that rapidly generates multi-modal readouts of cell populations at single cell resolution. However, this technology remains hindered by the lack of accurate, universal algorithms. This review presents related biological and computer vision concepts to bridge these disciplines, paving the way for broad applications in cell-based diagnostics, drug discovery, and biomanufacturing.

Direct specification of lymphatic endothelium from mesenchymal progenitors

During embryogenesis, endothelial cells (ECs) are generally described to arise from a common pool of progenitors termed angioblasts, which diversify through iterative steps of differentiation to form functionally distinct subtypes of ECs. A key example is the formation of lymphatic ECs (LECs), which are thought to arise largely through transdifferentiation from venous endothelium. Opposing this model, here we show that the initial expansion of mammalian LECs is primarily driven by the in situ differentiation of mesenchymal progenitors and does not require transition through an intermediate venous state. Single-cell genomics and lineage-tracing experiments revealed a population of paraxial mesoderm-derived Etv2+Prox1+ progenitors that directly give rise to LECs. Morphometric analyses of early LEC proliferation and migration, and mutants that disrupt lymphatic development supported these findings. Collectively, this work establishes a cellular blueprint for LEC specification and indicates that discrete pools of mesenchymal progenitors can give rise to specialized subtypes of ECs.

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

Emerging insights in senescence: pathways from preclinical models to therapeutic innovations

Senescence is a crucial hallmark of ageing and a significant contributor to the pathology of age-related disorders. As committee members of the young International Cell Senescence Association (yICSA), we aim to synthesise recent advancements in the identification, characterisation, and therapeutic targeting of senescence for clinical translation. We explore novel molecular techniques that have enhanced our understanding of senescent cell heterogeneity and their roles in tissue regeneration and pathology. Additionally, we delve into in vivo models of senescence, both non-mammalian and mammalian, to highlight tools available for advancing the contextual understanding of in vivo senescence. Furthermore, we discuss innovative diagnostic tools and senotherapeutic approaches, emphasising their potential for clinical application. Future directions of senescence research are explored, underscoring the need for precise, context-specific senescence classification and the integration of advanced technologies such as machine learning, long-read sequencing, and multifunctional senoprobes and senolytics. The dual role of senescence in promoting tissue homoeostasis and contributing to chronic diseases highlights the complexity of targeting these cells for improved clinical outcomes.

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