Peripheral blood dynamics in the 100 days before delivery reveal a transition from pregnancy maintenance to prelabor biology.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Head and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of tumors. While significant progress has been made using multimodal treatment, the 5‐year survival remains at 50%. Developing effective therapies, such as immunotherapy, will likely lead to better treatment of primary and metastatic disease. However, not all HNSCC tumors respond to immune checkpoint blockade therapy. Understanding the complex cellular composition and interactions of the tumor microenvironment is likely to lead to new knowledge for effective therapies and treatment resistance. In this review, we discuss HNSCC characteristics, predictive biomarkers, factors influencing immunotherapy response, with a focus on the tumor microenvironment.

Xanthomonas species colonize many host plants and cause huge losses worldwide. Transcription activator-like effectors (TALEs) are secreted by Xanthomonas and translocated into host cells to manipulate the expression of target genes, especially by Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola, which cause bacterial blight and bacterial leaf streak, respectively, in rice. In this review, we summarize the progress of studies on the interaction between Xanthomonas and hosts, covering both rice and other plants. TALEs are not only key factors that make plants susceptible but are also essential components of plant resistance. Characterization of TALEs and TALE-like proteins has improved our understanding of TALE evolution and promoted the development of gene editing tools. In addition, the interactions between TALEs and hosts have also provided strategies and possibilities for genetic engineering in crop improvement.

Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative potential of artificial intelligence (AI) in oral pathology, this review highlights key studies demonstrating current AI’s improvement in oral pathology, such as detecting oral diseases accurately and streamlining diagnostic processes. However, several limitations, such as data quality, generalizability, legal and ethical considerations, financial constraints, and the need for paradigm shifts in practice, are critically examined. Addressing these challenges through collaborative efforts, robust validation, and strategic integration can pave the way for AI to revolutionize oral pathology, ultimately improving patient outcomes and advancing the field.