Linear building pattern recognition via spatial knowledge graph

Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific subgraph patterns can be efficiently obtained by using relevant reasoning tools. Thus, we try to apply the knowledge graph to recognize linear building patterns. First, we use the property graph to express the spatial relations in proximity, similar and linear arrangement between buildings; secondly, the rules of linear pattern recognition are expressed as the rules of knowledge graph reasoning; finally, the linear building patterns are recognized by using the rule-based reasoning in the built knowledge graph. The experimental results on a dataset containing 1289 buildings show that the method in this paper can achieve the same precision and recall as the existing methods; meanwhile, the recognition efficiency is improved by 5.98 times.Comment: in Chinese languag

Crypto-ransomware Detection through Quantitative API-based Behavioral Profiling

With crypto-ransomware's unprecedented scope of impact and evolving level of sophistication, there is an urgent need to pinpoint the security gap and improve the effectiveness of defenses by identifying new detection approaches. Based on our characterization results on dynamic API behaviors of ransomware, we present a new API profiling-based detection mechanism. Our method involves two operations, namely consistency analysis and refinement. We evaluate it against a set of real-world ransomware and also benign samples. We are able to detect all ransomware executions in consistency analysis and reduce the false positive case in refinement. We also conduct in-depth case studies on the most informative API for detection with context

Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition

Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may require decomposing shapes into parts or grouping them into clusters. Existing methods may not recognize all visually aware patterns, and the proximity graph model can be inefficient. To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns. First, we use a property graph to represent the relationships between buildings within and across different scales involved in C-shaped building pattern recognition. Next, we store this knowledge graph in a graph database and convert the rules for C-shaped pattern recognition and enrichment into query conditions. Finally, we recognize and enrich C-shaped building patterns using rule-based reasoning in the built knowledge graph. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from the Gaode Map. Our results show that our method achieves a higher recall rate of 26.4% for LOD1, 20.0% for LOD2, and 9.1% for LOD3 compared to existing approaches. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively

The inhibitory effect against collagen-induced arthritis by Schistosoma japonicum infection is infection stage-dependent

<p>Abstract</p> <p>Background</p> <p>A long-term existing schistosome infection can aid in maintaining immuno-homeostasis, thus providing protection against various types of autoimmune diseases to the infected host. Such benefits have often been associated with acute or egg stage infection and with the egg-induced Th2 response. However, since schistosome infection undergoes different stages, each associated with a specific induction of Th responses, the requirements for the ability of the different stages of schistosome infection to protect against autoimmune disease has not been elucidated. The present study was designed to study whether different stages of schistosome infection offer unique protection in collagen-induced arthritis and its mechanisms.</p> <p>Results</p> <p>Arthritis susceptible strain DBA/1 male mice were infected with <it>Schistosoma japonicum </it>for either 2 weeks resulting in early stage infection or for 7 weeks resulting in acute or egg stage infection. Following <it>Schistosoma japonicum </it>infection, collagen II was administered to induce collagen-induced arthritis, an animal model for human rheumatoid arthritis. Infection by <it>Schistosoma japonicum </it>significantly reduced the severity and the incidence of experimental autoimmune collagen-induced arthritis. However, this beneficial effect can only be provided by a pre-established acute stage of infection but not by a pre-established early stage of the infection. The protection against collagen-induced arthritis correlated with reduced levels of anti-collagen II IgG, especially the subclass of IgG2a. Moreover, in protected mice increased levels of IL-4 were present at the time of collagen II injection together with sustained higher IL-4 levels during the course of arthritis development. In contrast, in unprotected mice minimal levels of IL-4 were present at the initial stage of collagen II challenge together with lack of IL-4 induction following <it>Schistosoma japonicum </it>infection.</p> <p>Conclusion</p> <p>The protective effect against collagen-induced arthritis provided by <it>Schistosoma japonicum </it>infection is infection stage-dependent. Furthermore, the ability of schistosomiasis to negatively regulate the onset of collagen-induced arthritis is associated with a dominant as well as long-lasting Th2 response at the initiation and development of autoimmune joint and systemic inflammation.</p

Pre‐symptomatic transmission of novel coronavirus in community settings

We used contact tracing to document how COVID‐19 was transmitted across 5 generations involving 10 cases, starting with an individual who became ill on January 27. We calculated the incubation period of the cases as the interval between infection and development of symptoms. The median incubation period was 6.0 days (interquartile range, 3.5‐9.5 days). The last two generations were infected in public places, 3 and 4 days prior to the onset of illness in their infectors. Both had certain underlying conditions and comorbidity. Further identification of how individuals transmit prior to being symptomatic will have important consequences.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/2/irv12773.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163478/1/irv12773_am.pd

Phenotypic expression and outcomes in individuals with rare genetic variants of hypertrophic cardiomyopathy

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is caused by rare variants in sarcomere-encoding genes, but little is known about the clinical significance of these variants in the general population. OBJECTIVES: The goal of this study was to compare lifetime outcomes and cardiovascular phenotypes according to the presence of rare variants in sarcomere-encoding genes among middle-aged adults. METHODS: This study analyzed whole exome sequencing and cardiac magnetic resonance imaging in UK Biobank participants stratified according to sarcomere-encoding variant status. RESULTS: The prevalence of rare variants (allele frequency <0.00004) in HCM-associated sarcomere-encoding genes in 200,584 participants was 2.9% (n = 5,712; 1 in 35), and the prevalence of variants pathogenic or likely pathogenic for HCM (SARC-HCM-P/LP) was 0.25% (n = 493; 1 in 407). SARC-HCM-P/LP variants were associated with an increased risk of death or major adverse cardiac events compared with controls (hazard ratio: 1.69; 95% confidence interval [CI]: 1.38-2.07; P < 0.001), mainly due to heart failure endpoints (hazard ratio: 4.23; 95% CI: 3.07-5.83; P < 0.001). In 21,322 participants with both cardiac magnetic resonance imaging and whole exome sequencing, SARC-HCM-P/LP variants were associated with an asymmetric increase in left ventricular maximum wall thickness (10.9 ± 2.7 mm vs 9.4 ± 1.6 mm; P < 0.001), but hypertrophy (≥13 mm) was only present in 18.4% (n = 9 of 49; 95% CI: 9%-32%). SARC-HCM-P/LP variants were still associated with heart failure after adjustment for wall thickness (hazard ratio: 6.74; 95% CI: 2.43-18.7; P < 0.001). CONCLUSIONS: In this population of middle-aged adults, SARC-HCM-P/LP variants have low aggregate penetrance for overt HCM but are associated with an increased risk of adverse cardiovascular outcomes and an attenuated cardiomyopathic phenotype. Although absolute event rates are low, identification of these variants may enhance risk stratification beyond familial disease

Distributed Optimization of Joint Seaport-All-Electric-Ships System under Polymorphic Network

As a result of the trend towards auto intelligence and greening of vehicles and with the concept of polymorphic network being put forward, the power transmission mode between seaports and all-electric ships (AESs) is likely to be converted to “peer-to-peer” transmission. According to practical shore power systems and carbon trade mechanisms, an advanced peer-to-peer power dispatching model-joint seaport-AESs microgrid(MG) system has been proposed in the paper. The joint seaport–AES system model is proposed to minimize the total operational cost of power production and marketing, including distributed generation (DG) cost, electricity trading cost, and carbon emissions, and the boundary conditions are given as well. A parameter projection distributed optimization (PPDO) algorithm is utilized to solve the distributed optimization power operation planning of the proposed joint seaport–AES MG system under a polymorphic network and to guarantee the precision of power dispatching, which compensates for the insufficiency of the computing power. Finally, a case study of a five-node polymorphic joint seaport-AESs system is conducted. The feasibility of the parameter projection approach and the peer-to-peer power dispatching model are verified via the convergence of all the agents within the constraint sets