The effect of spatially correlated noise on coherence resonance in a network of excitable cells

We study the effect of spatially correlated noise on coherence resonance (CR) in a Watts-Strogatz small-world network of Fitz Hugh-Nagumo neurons, where the noise correlation decays exponentially with distance between neurons. It is found that CR is considerably improved just by a small fraction of long-range connections for an intermediate coupling strength. For other coupling strengths, an abrupt change in CR occurs following the drastic fracture of the clustered structures in the network. Our study shows that spatially correlated noise plays a significant role in the phenomenon of CR through enforcing the clustering of the network.Comment: 11 pages, 4 figur

One-Sided Competition in Two-Sided Social Platform Markets? An Organizational Ecology Perspective

Similar to love, competition can often be unrequited. This study explores the asymmetric pattern of competition driven by membership overlap in two-sided mobile social apps (MSAs) markets. Building on the niche-width dynamics framework, we theorize and validate the relative prevalence and survival capabilities of messaging apps and SNS apps, especially when membership overlap fosters current or potential competition between the two app categories. The analyses—based on panel dataset consisting of information on 8,483 panel members’ exact amount of time used for 21 mobile social apps—show that competition between SNS and messaging apps can be asymmetric in favor of messaging apps. This asymmetric pattern is more pronounced for membership-based competition compared to usage-based competition. In addition, different MSAs developed by same platform providers exhibit synergistic effects, rather than destructive consequences, on each other’s growth. The findings identify the complex nature of competition within-category and between-category competition in MSAs markets

The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation

Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of obtaining large scale real-world data, computer graphics are typically leveraged for constructing datasets. However, there is a common belief that synthetic-to-real domain gaps limit generalization to real scenes. In this paper, we show that the required characteristics in an optical flow dataset are rather simple and present a simpler synthetic data generation method that achieves a certain level of realism with compositions of elementary operations. With 2D motion-based datasets, we systematically analyze the simplest yet critical factors for generating synthetic datasets. Furthermore, we propose a novel method of utilizing occlusion masks in a supervised method and observe that suppressing gradients on occluded regions serves as a powerful initial state in the curriculum learning sense. The RAFT network initially trained on our dataset outperforms the original RAFT on the two most challenging online benchmarks, MPI Sintel and KITTI 2015

Schwannoma Mimicking Laryngocele

A schwannoma of the larynx is a rare benign tumor that usually presents as a submucosal mass in the pyriform sinus and the aryepiglottic space, and this type of schwannoma constitutes a diagnostic and therapeutic challenge for otolaryngologists. We present here two cases of supraglottic schwannomas that were misdiagnosed as laryngoceles. Both were excised through a lateral thyrotomy approach without a tracheostomy, and the laryngeal function was successfully maintained. We discuss the clinical and imaging findings and the management of this rare neoplasm with focusing on the differential diagnosis of laryngeal schwannoma and laryngocele. We also review the relevant medical literature

Application of Copula-Based Markov Model to Generate Monthly Precipitation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Xenopus: An alternative model system for identifying muco-active agents

The airway epithelium in human plays a central role as the first line of defense against environmental contaminants. Most respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and respiratory infections, disturb normal muco-ciliary functions by stimulating the hypersecretion of mucus. Several muco-active agents have been used to treat hypersecretion symptoms in patients. Current muco-active reagents control mucus secretion by modulating either airway inflammation, cholinergic parasympathetic nerve activities or by reducing the viscosity by cleaving crosslinking in mucin and digesting DNAs in mucus. However, none of the current medication regulates mucus secretion by directly targeting airway goblet cells. The major hurdle for screening potential muco-active agents that directly affect the goblet cells, is the unavailability of in vivo model systems suitable for high-throughput screening. In this study, we developed a high-throughput in vivo model system for identifying muco-active reagents using Xenopus laevis embryos. We tested mucus secretion under various conditions and developed a screening strategy to identify potential muco-regulators. Using this novel screening technique, we identified narasin as a potential muco-regulator. Narasin treatment of developing Xenopus embryos significantly reduced mucus secretion. Furthermore, the human lung epithelial cell line, Calu-3, responded similarly to narasin treatment, validating our technique for discovering muco-active reagent

ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation

We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively. Since it is an under-explored problem, we first investigate the difficulty of the problem and identify the performance bottleneck by conducting systematic analyses of model components and individual sub-tasks with a simple baseline model. Based on the analyses, we propose ENInst with sub-task enhancement methods: instance-wise mask refinement for enhancing pixel localization quality and novel classifier composition for improving classification accuracy. Our proposed method lifts the overall performance by enhancing the performance of each sub-task. We demonstrate that our ENInst is 7.5 times more efficient in achieving comparable performance to the existing fully-supervised few-shot models and even outperforms them at times.Comment: Accepted at Pattern Recognition (PR

Identification of C16orf74 as a Marker of Progression in Primary Non-Muscle Invasive Bladder Cancer

PURPOSE: Methylation-induced silencing of PRSS3 has been shown to be significantly associated with invasive bladder cancer, and expression of the C16orf74 gene locus has been shown to correlate positively with PRSS3. The aim of the current study was to evaluate the relationship between C16orf74 expression level and progression in non-muscle invasive bladder cancer (NMIBC). MATERIALS AND METHODS: C16orf74 mRNA levels were examined by real-time reverse transcriptase polymerase chain reaction (RT-PCR) analysis of 193 tumor specimens from patients with primary NMIBC. Expression data were analyzed in terms of clinical and experimental parameters. Kaplan-Meier curves and multivariate Cox regression models, respectively, were used to determine progression-free survival and to identify independent predictive parameters of progression. RESULTS: Analysis using Kaplan-Meier curves revealed prolonged progression-free survival of high-C16orf74-expressors as compared to low-expressors (p<0.001). Multivariate Cox regression analysis revealed that low C16orf74 mRNA expression levels are a significant risk factor for disease progression in patients with primary NMIBC (HR: 10.042, CI:2.699-37.360, p = 0.001). CONCLUSIONS: Decreased expression of C16orf74 correlates significantly with progression in primary NMIBC. C16orf74 expression level represents a potentially useful marker for predicting progression in primary NMIBC patients