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pancies in the findings. In the VerICiguaT Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) trial, vericiguat reduced the risk of mortality due to cardiovascular problems and of hospitalization due to heart failure (HF) among patients with HF with reduced ejection fraction (HFrEF) and recent worsening HF events (WHFEs). The representativeness of the VICTORIA population of patients with WHFE in clinical practice is unknown. Patients with HF and ejection fraction <45% were identified in the Practice Innovation And Clinical Excellence (PINNACLE) registry and were stratified by the occurrence of WHFEs. Characteristics and outcomes of patients in the PINNACLE registry with and without WHFEs were compared to the VICTORIA population. Of the 14,180 PINNACLE patients identified with HFrEF, 26.5% had had a WHFE. The VICTORIA population was similar to PINNACLE patients with WHFEs in mean age (67.3 vs 66.7), ejection fraction (28.9% vs 28.3%), body mass index (26.8 vs 27.6), and comorbidity burden. The rate of hospitalization because of HF at 1 year was 29.6% in the placebo group of VICTORIA, compared to 35.8% in PINNACLE patients with WHFEs and 13.3% in patients without WHFEs. The PINNACLE patients with WHFEs meeting the VICTORIA definition resembled the VICTORIA population in characteristics and outcomes, suggesting that VICTORIA's population may be generalizable to patients with WHFEs in clinical practice. The PINNACLE patients with WHFEs meeting the VICTORIA definition resembled the VICTORIA population in characteristics and outcomes, suggesting that VICTORIA's population may be generalizable to patients with WHFEs in clinical practice.Circular RNAs (circRNAs) are a new class of covalently circularized noncoding RNAs widely expressed in the human heart. Emerging evidence suggests they have a regulatory role in a variety of cardiovascular diseases (CVDs). This review's current focus includes our understanding of circRNA classification, biogenesis, function, stability, degradation mechanisms, and their roles in various cardiovascular disease conditions. Our knowledge of circRNA, the relatively recent member of the noncoding RNA family, is still in its infancy; however, recent literature proposes circRNAs may be promising targets for the understanding and treatment of CVD.We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.Spontaneous fluctuations of Blood Oxygenation-Level Dependent (BOLD) MRI signal in a resting state have previously been detected and analyzed to describe intrinsic functional networks in the spinal cord of rodents, non-human primates and human subjects. In this study we combined high resolution imaging at high field with data-driven Independent Component Analysis (ICA) to i) delineate fine-scale functional networks within and between segments of the cervical spinal cord of monkeys, and also to ii) characterize the longitudinal effects of a unilateral dorsal column injury on these networks. Seven distinct functional hubs were revealed within each spinal segment, with new hubs detected at bilateral intermediate and gray commissure regions in addition to the bilateral dorsal and ventral horns previously reported. Pair-wise correlations revealed significantly stronger connections between hubs on the dominant hand side. Unilateral dorsal-column injuries disrupted predominantly inter-segmental rather than intra-segmental functional connectivities as revealed by correlation strengths and graph-theory based community structures. The effects of injury on inter-segmental connectivity were evident along the length of the cord both below and above the lesion region. Connectivity strengths recovered over time and there was revival of inter-segmental communities as animals recovered function. BOLD signals of frequency 0.01-0.033 Hz were found to be most affected by injury. Cisplatin nmr The results in this study provide new insights into the intrinsic functional architecture of spinal cord and underscore the potential of functional connectivity measures to characterize changes in networks after an injury and during recovery.Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a “bottleneck” at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the “neural locus” of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM).