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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. 

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. 

Abstract Small RNAs are non-coding RNAs that play important roles in the lives of both animals and plants. They are 21- to 24-nt in length and ∼10 nm in size. Their small size and high diversity have made it challenging to develop detection methods that have sufficient resolution and specificity to multiplex and quantify. We created a method, sRNA-PAINT, for the detection of small RNAs with 20 nm resolution by combining the super-resolution method, DNA-based points accumulation in nanoscale topography (DNA-PAINT), and the specificity of locked nucleic acid (LNA) probes for the in situ detection of multiple small RNAs. The method relies on designing probes to target small RNAs that combine DNA oligonucleotides (oligos) for PAINT with LNA-containing oligos for hybridization; therefore, we developed an online tool called ‘Vetting & Analysis of RNA for in situ Hybridization probes’ (VARNISH) for probe design. Our method utilizes advances in DNA-PAINT methodologies, including qPAINT for quantification, and Exchange-PAINT for multiplexing. We demonstrated these capabilities of sRNA-PAINT by detecting and quantifying small RNAs in different cell layers of early developmental stage maize anthers that are important for male sexual reproduction. 

Abstract Small RNAs play important roles during plant development by regulating transcript levels of target mRNAs, maintaining genome integrity, and reinforcing DNA methylation.Dicer-like 5(Dcl5) is proposed to be responsible for precise slicing in many monocots to generate diverse 24-nt phased, secondary small interfering RNAs (phasiRNAs), which are exceptionally abundant in meiotic anthers of diverse flowering plants. The importance and functions of these phasiRNAs remain unclear. Here, we characterized several mutants ofdcl5, including alleles generated by the clustered regularly interspaced short palindromic repeats (CRISPR)–Cas9system and a transposon-disrupted allele. We report thatdcl5mutants have few or no 24-nt phasiRNAs, develop short anthers with defective tapetal cells, and exhibit temperature-sensitive male fertility. We propose that DCL5 and 24-nt phasiRNAs are critical for fertility under growth regimes for optimal yield.