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.

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.

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.