Traveling Wave Solutions for Planar Lattice Differential Systems with Applications to Neural Networks

AbstractWe obtain some existence results for traveling wave fronts and slowly oscillatory spatially periodic traveling waves of planar lattice differential systems with delay. Our approach is via Schauder's fixed-point theorem for the existence of traveling wave fronts and via S1-degree and equivarant bifurcation theory for the existence of periodic traveling waves. As examples, the obtained abstract results will be applied to a model arising from neural networks and explicit conditions for traveling wave fronts and global continuation of periodic waves will be obtained

Prognosis for patients with apical hypertrophic cardiomyopathy: A multicenter cohort study based on propensity score matching

Background: Apical hypertrophic cardiomyopathy (AHCM) is a subtype of HCM, and few studies on the prognosis in AHCM are available.Aims: This study aimed to explore the clinical prognosis for AHCM and non-AHCM patients through clinical data based on propensity score matching (PSM) in a large cohort of Chinese HCM patients.Methods: The cohort study included 2268 HCM patients, 226 AHCM and 2042 non-AHCM patients from 13 tertiary hospitals, who were treated between 1996 and 2021. Fifteen demographic and clinical variables of 226 AHCM patients and 2042 non-AHCM patients were matched using 1:2 PSM. A Cox proportional hazard regression model was constructed to assess the effect of AHCM on mortality.Results: During a median follow-up of 5.1 (2.4–8.4) years, 353 (15.6%) of the 2268 HCM patients died, of whom 205 died due to cardiovascular mortality/cardiac transplantation and 94 experienced sudden cardiac death (SCD). In the matched cohort, the ACHM patients had lower rates of all-cause mortality (P = 0.003), cardiovascular mortality/cardiac transplantation (P = 0.03), and SCD (P = 0.02) than the non-AHCM patients. Furthermore, the Cox proportional hazard regression model showed that AHCM was an independent prognostic predictor of all-cause HCM mortality (P = 0.004) and a univariable prognostic predictor of cardiovascular mortality/cardiac transplantation (P = 0.03) and for SCD (P = 0.03). However, AHCM was not significant in multivariable Cox regression models in relation to cardiovascular mortality/cardiac transplantation and SCD.Conclusion: AHCM had a favorable prognosis both before and after matching, with lower all-cause mortality, cardiovascular mortality/cardiac transplantation, and SCD than non-AHCM

Investigation of nonlinear wave-induced seabed response around mono-pile foundation

YesStability and safety of offshore wind turbines with mono-pile foundations, affected by nonlinear wave effect and dynamic seabed response, are the primary concerns in offshore foundation design. In order to address these problems, the nonlinear wave effect on dynamic seabed response in the vicinity of mono-pile foundation is investigated using an integrated model, developed using OpenFOAM, which incorporates both wave model (waves2Foam) and Biot’s poro-elastic model. The present model was validated against several laboratory experiments and promising agreements were obtained. Special attention was paid to the systematic analysis of pore water pressure as well as the momentary liquefaction in the proximity of mono-pile induced by nonlinear wave effects. Various embedment depths of mono-pile relevant for practical engineering design were studied in order to attain the insights into nonlinear wave effect around and underneath the mono-pile foundation. By comparing time-series of water surface elevation, inline force, and wave-induced pore water pressure at the front, lateral, and lee side of mono-pile, the distinct nonlinear wave effect on pore water pressure was shown. Simulated results confirmed that the presence of mono-pile foundation in a porous seabed had evident blocking effect on the vertical and horizontal development of pore water pressure. Increasing embedment depth enhances the blockage of vertical pore pressure development and hence results in somewhat reduced momentary liquefaction depth of the soil around the mono-pile foundation.Energy Technology Partnership (ETP), Wood Group Kenny, and University of Aberdeen; the National Science Fund for Distinguished Young Scholars (51425901) and the 111 project (B12032)

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

CEO Self-Monitoring and Corporate Entrepreneurship: A Moderated Mediation Model of the CEO-TMT Interface

Focusing on the interface between CEOs and top management teams (TMTs), we argue that CEO self-monitoring positively impacts a firm's pursuit of corporate entrepreneurship through the intervening role of TMT behavioral integration. We additionally argue that the impact becomes stronger as the firm's discretionary slack decreases because decreased slack creates an organizational context more favorable to the influences of both CEO self-monitoring and TMT behavioral integration. Results based on multisource (CEOs and TMTs) and multiwave data from 110 firms support the model and associated hypotheses

The roles of epigenetic regulation in cholangiocarcinogenesis

Cholangiocarcinoma (CCA), a heterogeneous malignancy of bile duct epithelial cells, is characterized by aggressiveness, difficult diagnosis, and poor prognosis due to limited understanding and lack of effective therapeutic strategies. Genetic and epigenetic alterations accumulated in CCA cells can cause the aberrant regulation of oncogenes and tumor suppressors. Epigenetic alterations with histone modification, DNA methylation, and noncoding RNA modulation are associated with the carcinogenesis of CCA. Mutation or silencing of genes by various mechanisms can be a frequent event during CCA development. Alterations in histone acetylation/deacetylation at the posttranslational level, DNA methylation at promoters, and noncoding RNA regulation contribute to the heterogeneity of CCA and drive tumor development. In this review article, we mainly focus on the roles of epigenetic regulation in cholangiocarcinogenesis. Alterations in epigenetic modification can be potential targets for the therapeutic management of CCA, and epigenetic targets may become diagnostic biomarkers of CCA

Identification and Classification of Aluminum Scrap Grades Based on the Resnet18 Model

In order to reduce the elemental species produced in the recycling and melting of aluminum scrap and to improve the quality of pure aluminum and aluminum alloys, it is necessary to classify the different grades of aluminum scrap before melting. For the problem of classifying different grades of aluminum scrap, most existing studies are conducted using laser-induced breakdown spectroscopy for identification and classification, which requires a clean and flat metal surface and enormous equipment costs. In this study, we propose a new classification and identification method for different grades of aluminum scrap based on the ResNet18 network model, which improves the identification efficiency and reduces the equipment cost. The objects of this research are three grades of aluminum scrap: 1060, 5052, and 6061. The surface features of the three grades were compared using a machine vision algorithm; three different datasets, using RGB, HSV, and LBP, were built for comparison to find the best training dataset for subsequent datasets, and the hyperparameters of learning rate and batch size were tuned for the ResNet18 model. The results show that there was a differentiation threshold between different grades through the comparison of surface features; the ResNet18 network model trained the three datasets, and the results showed that RGB was the best dataset. With hyperparameter optimization of the ResNet18 model, the accuracy of final classification and recognition could reach 100% and effectively achieve the classification of different grades of aluminum scrap