62 research outputs found
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
Preâsymptomatic transmission of novel coronavirus in community settings
We used contact tracing to document how COVIDâ19 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 6.0Â days (interquartile range, 3.5â9.5Â days). The last two generations were infected in public places, 3 and 4Â 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 (â„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
Extractive oxidative desulfurization of model oil/crude oil using KSF montmorillonite-supported 12-tungstophosphoric acid
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 âpeer-to-peerâ 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 seaportâAES 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 seaportâAES 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
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