Exploiting cell cycle-dependent dephosphorylation for mitosis-specific protein recruitment
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AMPK phosphosite profiling by label-free mass spectrometry reveals a multitude of mTORC1-regulated substrates
The nutrient-sensitive protein kinases AMPK and mTORC1 form a fundamental negative feedback loop that governs cell growth and proliferation. mTORC1 phosphorylates α2-S345 in the AMPK αβγ heterotrimer to suppress its activity and promote cell proliferation under nutrient stress conditions. Whether AMPK contains other functional mTORC1 substrates is unknown. Using mass spectrometry, we generated precise stoichiometry profiles of phosphorylation sites across all twelve AMPK complexes expressed in proliferating human cells and identified seven sites displaying sensitivity to pharmacological mTORC1 inhibition. These included the abundantly phosphorylated residues β1-S182 and β2-S184, which were confirmed as mTORC1 substrates on purified AMPK, and four residues in the unique γ2 N-terminal extension. β-S182/184 phosphorylation was elevated in α1-containing complexes relative to α2, an effect attributed to the α-subunit serine/threonine-rich loop. Mutation of β1-S182 to non-phosphorylatable Ala had no effect on basal and ligand-stimulated AMPK activity; however, β2-S184A mutation increased nuclear AMPK activity, enhanced cell proliferation under nutrient stress and altered expression of genes implicated in glucose metabolism and Akt signalling. Our results indicate that mTORC1 directly or indirectly phosphorylates multiple AMPK residues that may contribute to metabolic rewiring in cancerous cells.
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.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
Cytoplasmic flow is a cell size sensor that scales anaphase
During early embryogenesis, fast mitotic cycles without interphase lead to a decrease in cell size, while scaling mechanisms must keep cellular structures proportional to cell size. For instance, as cells become smaller, if the position of nuclear envelope reformation (NER) did not adapt, NER would have to occur beyond the cell boundary. Here we found that NER position in anaphase scales with cell size via changes in chromosome motility, mediated by cytoplasmic flows that themselves scale with cell size. Flows are a consequence of friction between viscous cytoplasm and bulky cargo transported by dynein on astral microtubules. As an emerging property, confinement in cells of different sizes yields scaling of cytoplasmic flows. Thus, flows behave like a cell geometry sensor: astral microtubules approach the boundary causing flow velocity changes, which then affect the velocity of chromosome separation, thus scaling NER.
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.
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