Popegleason9138

The brain-expressed ubiquilins, UBQLNs 1, 2 and 4, are highly homologous proteins that participate in multiple aspects of protein homeostasis and are implicated in neurodegenerative diseases. Studies have established that UBQLN2 forms liquid-like condensates and accumulates in pathogenic aggregates, much like other proteins linked to neurodegenerative diseases. However, the relative condensate and aggregate formation of the three brain-expressed ubiquilins is unknown. Here we report that the three ubiquilins differ in aggregation propensity, revealed by in-vitro experiments, cellular models, and analysis of human brain tissue. UBQLN4 displays heightened aggregation propensity over the other ubiquilins and, like amyloids, UBQLN4 forms ThioflavinT-positive fibrils in vitro. Measuring fluorescence recovery after photobleaching (FRAP) of puncta in cells, we report that all three ubiquilins undergo liquid-liquid phase transition. UBQLN2 and 4 exhibit slower recovery than UBQLN1, suggesting the condensates formed by these brain-expressed ubiquilins have different compositions and undergo distinct internal rearrangements. We conclude that while all brain-expressed ubiquilins exhibit self-association behavior manifesting as condensates, they follow distinct courses of phase-separation and aggregation. We suggest that this variability among ubiquilins along the continuum from liquid-like to solid informs both the normal ubiquitin-linked functions of ubiquilins and their accumulation and potential contribution to toxicity in neurodegenerative diseases.Fibrosis is a key pathological feature in muscle disorders, but its quantification mainly relies on histological and biochemical assays. Muscle fibrosis most frequently is entangled with other pathological processes, as cell membrane lesions, inflammation, necrosis, regeneration, or fatty infiltration, making in vivo assessment difficult. Here, we (1) describe a novel mouse model with variable levels of induced skeletal muscle fibrosis displaying minimal inflammation and no fat infiltration, and (2) report how fibrosis affects non-invasive metrics derived from nuclear magnetic resonance (NMR) and ultrasound shear-wave elastography (SWE) associated with a passive biomechanical assay. Our findings show that collagen fraction correlates with multiple non-invasive metrics. Among them, muscle stiffness as measured by SWE, T2, and extracellular volume (ECV) as measured by NMR have the strongest correlations with histology. We also report that combining metrics in a multi-modality index allowed better discrimination between fibrotic and normal skeletal muscles. This study demonstrates that skeletal muscle fibrosis leads to alterations that can be assessed in vivo with multiple imaging parameters. Furthermore, combining NMR and SWE passive biomechanical assay improves the non-invasive evaluation of skeletal muscle fibrosis and may allow disentangling it from co-occurring pathological alterations in more complex scenarios, such as muscular dystrophies.The experimental dataset presented was collected in an 18 m long and 1 m wide laboratory flume. Selleck Hexadimethrine Bromide Low to high flood flows through an urbanized floodplain were modelled. The floodplain bed is rough, modelled with dense artificial grass. A square cylinder array, representing house models, was set on the rough bed. The cylinder immersion rate was varied cylinders are emerged for three flow cases and slightly submerged for one case. The experimental dataset comprises water levels, measured using an ultrasonic transit time probe, velocities across the channel measured using an Acoustic Doppler Velocimetry with a side looking probe, and velocities in longitudinal-vertical planes measured using Particle Image Velocimetry. These data could help understanding the physical processes associated with high flood flows through urbanized floodplains, with a focus on the transition from emerged to submerged obstacles. They could also be used as benchmark data to assess the ability of numerical models from one to three-dimensions to estimate the flood hazard (water depth, velocity) over a wide range of flood event magnitudes.Hearing aid and cochlear implant (CI) users often struggle to locate and segregate sounds. The dominant sound-localisation cues are time and intensity differences across the ears. A recent study showed that CI users locate sounds substantially better when these cues are provided through haptic stimulation on each wrist. However, the sensitivity of the wrists to these cues and the robustness of this sensitivity to aging is unknown. The current study showed that time difference sensitivity is much poorer across the wrists than across the ears and declines with age. In contrast, high sensitivity to across-wrist intensity differences was found that was robust to aging. This high sensitivity was observed across a range of stimulation intensities for both amplitude modulated and unmodulated sinusoids and matched across-ear intensity difference sensitivity for normal-hearing individuals. Furthermore, the usable dynamic range for haptic stimulation on the wrists was found to be around four times larger than for CIs. These findings suggest that high-precision haptic sound-localisation can be achieved, which could aid many hearing-impaired listeners. Furthermore, the finding that high-fidelity across-wrist intensity information can be transferred could be exploited in human-machine interfaces to enhance virtual reality and improve remote control of military, medical, or research robots.Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious.