Multilabel Video Classification Model of Navigation Mark's Lights Based on Deep Learning.

At night, buoys and other navigation marks disappear to be replaced by fixed or flashing lights. Navigation marks are seen as a set of lights in various colors rather than their familiar outline. Deciphering that the meaning of the lights is a burden to navigators, it is also a new challenging research direction of intelligent sensing of navigation environment. The study studied initiatively the intelligent recognition of lights on navigation marks at night based on multilabel video classification methods. To capture effectively the characteristics of navigation mark's lights, including both color and flashing phase, three different multilabel classification models based on binary relevance, label power set, and adapted algorithm were investigated and compared. According to the experiment's results performed on a data set with 8000 minutes video, the model based on binary relevance, named NMLNet, has highest accuracy about 99.23% to classify 9 types of navigation mark's lights. It also has the fastest computation speed with least network parameters. In the NMLNet, there are two branches for the classifications of color and flashing, respectively, and for the flashing classification, an improved MobileNet-v2 was used to capture the brightness characteristic of lights in each video frame, and an LSTM is used to capture the temporal dynamics of lights. Aiming to run on mobile devices on vessel, the MobileNet-v2 was used as backbone, and with the improvement of spatial attention mechanism, it achieved the accuracy near Resnet-50 while keeping its high speed.

Analysis of an Inherited Dysfibrinogenemia Pedigree Associated with a Heterozygous Mutation in the FGA Gene.

OBJECTIVE: This article aims to analyze the phenotype and genotype of an inherited dysfibrinogenemia pedigree associated with a heterozygous mutation in the FGA gene, and to investigate the pathogenesis of this disease. CLINICAL PRESENTATION: The proband of interest is a 29-year-old woman. She was in her 37 weeks of gestation. Routine coagulation tests showed low fibrinogen activity (0.91 g/L; normal range: 2.0-4.0 g/L) and normal fibrinogen antigen (FIB:Ag) level (2.09 g/L; normal range: 2.0-4.0 g/L). TECHNIQUES: The prothrombin time, activated partial thromboplastin time, thrombin time, and activity of plasma fibrinogen (FIB:C) were detected by the one-stage clotting method. The FIB:Ag, D-dimer, and fibrinogen degradation products were tested by the immunoturbidimetry method. To identify the novel missense mutation, fibrinogen gene sequencing and molecular modeling were performed. We used ClustalX-2.1-win and online bioinformatic software to analyze the conservation and possible effect of the amino acid substitution on fibrinogen. RESULTS: Phenotypic analysis revealed that the FIB:C of the proband was significantly reduced while the FIB:Ag was normal. Sequencing analysis detected a heterozygous C.2185G > A point mutation in the FGA gene (AαGlu710Lys). Bioinformatic and modeling analyses indicated that the mutation probably caused harmful effects on fibrinogen. CONCLUSION: The heterozygous mutation of Glu710Lys in the FGA gene was identified that could cause the reduction of the FIB structure stability and result in the dysfibrinogenemia.