Our findings suggest that the HMGB-1 is a mediator of neutrophilic airway inflammation in asthma and that imbalance between HMGB-1 and esRAGE is related to the severity of asthma. Combined measurement of HMGB-1 and esRAGE may be novel biomarkers in asthma with severe airflow limitation.

The neuroendocrine carcinoma of the gastrointestinal system (GIS-NEC) is a rare but highly malignant neoplasm. We analyzed 115 cases using whole-genome/exome sequencing, transcriptome sequencing, DNA methylation assays, and/or ATAC-seq and found GIS-NECs to be genetically distinct from neuroendocrine tumors (GIS-NETs) in the same location. Clear genomic differences were also evident between pancreatic NECs (Panc-NECs) and non-pancreatic GIS-NECs (Nonpanc-NECs). Panc-NECs could be classified into two subgroups (i.e., 'Ductal-type' and 'Acinar-type') based on genomic features. Alterations in TP53 and RB1 proved common in GIS-NECs and most Nonpanc-NECs with intact Rb demonstrated mutually exclusive amplification of CCNE1 or MYC. Alterations of the Notch gene family were characteristic of Nonpanc-NECs. Transcription factors for neuroendocrine differentiation, especially the SOX2 gene, appeared overexpressed in most GIS-NECs due to hypermethylation of the promoter region. This first comprehensive study of genomic alterations in GIS-NECs uncovered several key biological processes underlying genesis of this very lethal form of cancer. SIGNIFICANCE: GIS-NECs are genetically distinct from GIS-NETs. GIS-NECs arising in different organs show similar histopathological features and share some genomic features, but considerable differences exist between Panc-NECs and Nonpanc-NECs. In addition, Panc-NECs could be classified into two subgroups (i.e., 'Ductal-type' and 'Acinar-type') based on genomic and epigenomic features.

High-level HGF expression was detected more frequently than EGFR T790M secondary mutation or MET amplification in tumors with intrinsic and acquired EGFR-TKI resistance in EGFR mutant lung cancer in Japanese patients. These observations provide a rationale for targeting HGF in EGFR-TKI resistance in EGFR mutant lung cancer.

The typical HIP case was associated with ultrasound attenuation, positive remodeling, remarkably low computed tomography density, and a high incidence of slow-flow phenomena. Noncontrast T1WI in cardiac magnetic resonance imaging may be useful for the assessment of coronary plaque characterization in patients with coronary artery disease.

Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time. Recent works have identified, through an expensive sequence of training and pruning cycles, the existence of winning lottery tickets or sparse trainable subnetworks at initialization. This raises a foundational question: can we identify highly sparse trainable subnetworks at initialization, without ever training, or indeed without ever looking at the data? We provide an affirmative answer to this question through theory driven algorithm design. We first mathematically formulate and experimentally verify a conservation law that explains why existing gradientbased pruning algorithms at initialization suffer from layer-collapse, the premature pruning of an entire layer rendering a network untrainable. This theory also elucidates how layer-collapse can be entirely avoided, motivating a novel pruning algorithm Iterative Synaptic Flow Pruning (SynFlow). This algorithm can be interpreted as preserving the total flow of synaptic strengths through the network at initialization subject to a sparsity constraint. Notably, this algorithm makes no reference to the training data and consistently outperforms existing state-of-the-art pruning algorithms at initialization over a range of models (VGG and ResNet), datasets (CIFAR-10/100 and Tiny ImageNet), and sparsity constraints (up to 99.9 percent). Thus our data-agnostic pruning algorithm challenges the existing paradigm that data must be used to quantify which synapses are important.

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Adequate broad-spectrum antimicrobial therapy should be administered perioperatively even for Mild patients classified according to the current Tokyo guidelines. These results suggest that more precise severity grades may need to be established, including age and CRP as additional parameters.

The normal function of the retina is to convey information about natural visual images. It is this visual environment that has driven evolution, and that is clinically relevant. Yet nearly all of our understanding of the neural computations, biological function, and circuit mechanisms of the retina comes in the context of artificially structured stimuli such as flashing spots, moving bars and white noise. It is fundamentally unclear how these artificial stimuli are related to circuit processes engaged under natural stimuli. A key barrier is the lack of methods for analyzing retinal responses to natural images. We addressed both these issues by applying convolutional neural network models (CNNs) to capture retinal responses to natural scenes. We find that CNN models predict natural scene responses with high accuracy, achieving performance close to the fundamental limits of predictability set by intrinsic cellular variability. Furthermore, individual internal units of the model are highly correlated with actual retinal interneuron responses that were recorded separately and never presented to the model during training. Finally, we find that models fit only to natural scenes, but not white noise, reproduce a range of phenomena previously described using distinct artificial stimuli, including frequency doubling, latency encoding, motion anticipation, fast contrast adaptation, synchronized responses to motion reversal and object motion sensitivity. Further examination of the model revealed extremely rapid context dependence of retinal feature sensitivity under natural scenes using an analysis not feasible from direct examination of retinal responses. Overall, these results show that nonlinear retinal processes engaged by artificial stimuli are also engaged in and relevant to natural visual processing, and that CNN models form a powerful and unifying tool to study how sensory circuitry produces computations in a natural context.