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The profound complexity of the intestinal mucosa demands a spatial approach to the study of gut transcriptomics. Although single-cell RNA sequencing has revolutionized our ability to survey the diverse cell types of the intestine, knowledge of cell type alone cannot fully describe the cells that make up the intestinal mucosa. During homeostasis and disease, dramatic gradients of oxygen, nutrients, extracellular matrix proteins, morphogens, and microbiota collectively dictate intestinal cell state, and only spatial techniques can articulate differences in cellular transcriptomes at this level. Spatial transcriptomic techniques assign transcriptomic data to precise regions in a tissue of interest. In recent years, these protocols have become increasingly accessible, and their application in the intestinal mucosa has exploded in popularity. In the gut, spatial transcriptomics typically involve the application of tissue sections to spatially barcoded RNA sequencing or laser capture microdissection followed by RNA sequencing. In combination with single-cell RNA sequencing, these spatial sequencing approaches allow for the construction of spatial transcriptional maps at pseudosingle-cell resolution. In this review, we describe the spatial transcriptomic technologies recently applied in the gut and the previously unattainable discoveries that they have produced.

The 'Rotor Hub Flow Prediction Workshops' have been productive collaborations between experimental and computational efforts in the important area of high-Reynolds number model testing of rotor hubs and associated complex interactional aerodynamics in the long-age wake as relevant to current and future rotorcraft. As such the hub flow workshops have joined the ranks of past successful collaborations such as the UH-60 Airloads and HART-II workshops. This paper begins by describing the basic physics of rotor hub flows and gives a brief summary of recent water-tunnel test campaigns. Following, the evolution of the hub flow workshops is summarized, with emphasis on the productive interactions between experimentalists and computational participants. A compilation of computational blind comparison results against measured data for all three workshops thus far is presented. Challenges associated with uncertainties in both experiments and computations and their effect on quantitative comparisons are discussed. In particular, emphasis is given to the "lessons learned" on both sides and an outlook on remaining challenges and next research steps in the area of rotor hub flows is provided.

Colorectal cancer (CRC) is a leading cause of cancer-related death. There is an urgent need for new methods of early CRC detection and monitoring to improve patient outcomes. Extracellular vesicles (EVs) are secreted, lipid-bilayer bound, nanoparticles that carry biological cargo throughout the body and in turn exhibit cancer-related biomarker potential. RNA binding proteins (RBPs) are posttranscriptional regulators of gene expression that may provide a link between host cell gene expression and EV phenotypes. Insulin-like growth factor 2 RNA binding protein 1 (IGF2BP1/IMP1) is an RBP that is highly expressed in CRC with higher levels of expression correlating with poor prognosis. IMP1 binds and potently regulates tumor-associated transcripts that may impact CRC EV phenotypes. Our objective was to test whether IMP1 expression levels impact EV secretion and/or cargo. We used RNA sequencing, in vitro CRC cell lines, ex vivo colonoid models, and xenograft mice to test the hypothesis that IMP1 influences EV secretion and/or cargo in human CRC. Our data demonstrate that IMP1 modulates the RNA expression of transcripts associated with extracellular vesicle pathway regulation, but it has no effect on EV secretion levels in vitro or in vivo. Rather, IMP1 appears to affect EV regulation by directly entering EVs in a transformation-dependent manner. These findings suggest that IMP1 has the ability to shape EV cargo in human CRC, which could serve as a diagnostic/prognostic circulating tumor biomarker.

To identify patterns of visual field (VF) loss based on unsupervised machine learning and to identify patterns that are associated with rapid progression.Cross-sectional and longitudinal study.A total of 2231 abnormal VFs from 205 eyes of 176 Ocular Hypertension Treatment Study (OHTS) participants followed over approximately 16 years.Visual fields were assessed by an unsupervised deep archetypal analysis algorithm and an OHTS-certified VF reader to identify prevalent patterns of VF loss. Machine-identified patterns of glaucoma damage were compared against those patterns previously identified (expert-identified) in the OHTS in 2003. Based on the longitudinal VFs of each eye, VF loss patterns that were strongly associated with rapid glaucoma progression were identified.Machine-expert correspondence and type of patterns of VF loss associated with rapid progression.The average VF mean deviation (MD) at conversion to glaucoma was -2.7 decibels (dB) (standard deviation [SD] = 2.4 dB), whereas the average MD of the eyes at the last visit was -5.2 dB (SD = 5.5 dB). Fifty out of 205 eyes had MD rate of -1 dB/year or worse and were considered rapid progressors. Eighteen machine-identified patterns of VF loss were compared with expert-identified patterns, in which 13 patterns of VF loss were similar. The most prevalent expert-identified patterns included partial arcuate, paracentral, and nasal step defects, and the most prevalent machine-identified patterns included temporal wedge, partial arcuate, nasal step, and paracentral VF defects. One of the machine-identified patterns of VF loss predicted future rapid VF progression after adjustment for age, sex, and initial MD.An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.