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Abstract Extracellular vesicles (EVs) are released by all cell types into the extracellular environment. A subset of EVs, known as exosomes, range in size from 30 to 200 nm and are of biochemical interest due to their function as vehicles of intercellular communication. Their ability to transport proteinaceous species and genetic material at the cellular level makes them prime candidates as vectors in gene therapies. Focusing on biotherapeutics, bovine milk–derived extracellular vesicles (MDEVs) hold particular promise as an alternative to other exosome sources for therapeutics delivery. Bovine milk poses unique challenges due to the complex colloidal matrix, composed predominantly of fats and proteins like casein, which form micelles that exhibit exosome-like characteristics, specifically size and density. When faced with complex matrices like milk, conventional size/density-based isolation methods including ultracentrifugation and size exclusion chromatography struggle to provide high purity yields on practical time and cost scales. When paired with a stepwise hydrophobic interaction chromatography (HIC) gradient, polyester (PET) capillary-channeled polymer (C-CP) fibers in column and spin-down tips formats have been used effectively to isolate exosomes from highly diverse sources. Here, PET C-CP fiber columns are demonstrated to isolate MDEVs from pre-treated raw milk, yielding concentrations of 1.5 × 10¹⁰particles mL⁻¹with purities of ~2 × 10¹⁰EVs µg⁻¹protein in less than 20 min. The efficacy of the isolation process is verified by a suite of characterization methods. Implementing PET C-CP fiber columns for MDEV isolation addresses the challenges associated with conventional isolation methods, holding promise for scale-up towards therapeutic applications. 

Abstract This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies. 

In maize, 24-nt phased, secondary small interfering RNAs (phasiRNAs) are abundant in meiotic stage anthers, but their distribution and functions are not precisely known. Using laser capture microdissection, we analyzed tapetal cells, meiocytes, and other somatic cells at several stages of anther development to establish the timing of 24-PHAS precursor transcripts and the 24-nt phasiRNA products. By integrating RNA and small RNA profiling plus single-molecule and small RNA FISH (smFISH or sRNA-FISH) spatial detection, we demonstrate that the tapetum is the primary site of 24-PHAS precursor and Dcl5 transcripts and the resulting 24-nt phasiRNAs. Interestingly, 24-nt phasiRNAs accumulate in all cell types, with the highest levels in meiocytes, followed by tapetum. Our data support the conclusion that 24-nt phasiRNAs are mobile from tapetum to meiocytes and to other somatic cells. We discuss possible roles for 24-nt phasiRNAs in anther cell types. 

Abstract Plant small RNAs are important regulatory elements that fine-tune gene expression and maintain genome integrity by silencing transposons. Reproductive organs of monocots produce abundant phased, small interfering RNAs (phasiRNAs). The 21-nt reproductive phasiRNAs triggered by miR2118 are highly enriched in pre-meiotic anthers, and have been found in multiple eudicot species, in contrast with prior reports of monocot specificity. The 24-nt reproductive phasiRNAs are triggered by miR2275, and are highly enriched during meiosis in many angiosperms. Here, we report the widespread presence of the 21-nt reproductive phasiRNA pathway in eudicots including canonical and non-canonical microRNA (miRNA) triggers of this pathway. In eudicots, these 21-nt phasiRNAs are enriched in pre-meiotic stages, a spatiotemporal distribution consistent with that of monocots and suggesting a role in anther development. Although this pathway is apparently absent in well-studied eudicot families including the Brassicaceae, Solanaceae and Fabaceae, our work in eudicots supports an earlier singular finding in spruce, a gymnosperm, indicating that the pathway of 21-nt reproductive phasiRNAs emerged in seed plants and was lost in some lineages. 

State-of-the-art approaches to causal discovery usually assume a fixed underlying causal model. However, it is often the case that causal models vary across domains or subjects, due to possibly omitted factors that affect the quantitative causal effects. As a typical example, causal connectivity in the brain network has been reported to vary across individuals, with significant differences across groups of people, such as autistics and typical controls. In this paper, we develop a unified framework for causal discovery and mechanism-based group identification. In particular, we propose a specific and shared causal model (SSCM), which takes into account the variabilities of causal relations across individuals/groups and leverages their commonalities to achieve statistically reliable estimation. The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population. In addition, the estimated model directly provides the group information of each individual. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.