Aspirin "Allergy"-Induced Thrombocytopenia: A Case Report

Aspirin is clinically widely used to inhibit platelet aggregation after coronary intervention. Herein we describe a case of aspirin-induced thrombocytopenia that may be related to allergy to aspirin. A 47-year-old man developed a delayed hypersensitivity reaction to aspirin, with pruritus, purpura and thrombocytopenia, increased peripheral blood eosinophils and enlarged inguinal lymph node. All the symptoms disappeared in 2 years after stopping aspirin. Aspirin-induced thrombocytopenia related to allergy is rarely reported. Aspirin hypersensitivity should be taken into consideration in case of unexplained thrombocytopenia in patients taking aspirin. Aspirin "allergy"-induced thrombocytopenia may involve both aspirin related IgG and IgE antibodies

Plant Density and Nitrogen Supply Affect the Grain-Filling Parameters of Maize Kernels Located in Different Ear Positions

Although yield output of maize (Zea mays L.) has improved markedly over the last century, procedures for improving the grain-filling process remain elusive. Our aim in this study was to relate grain-filling variation in maize (including kernels in apical and middle positions in the ears) to plant density and nitrogen (N) application rate using a crossed experimental design. We also investigated changes in zeatin riboside (ZR), indole-3-acetic acid (IAA), abscisic acid (ABA), and gibberellic acid (GA) in the kernels during the grain-filling period. Two high-yield maize varieties cultivated extensively in China were field grown under normal (67,500 pl ha-1) and high (97,500 pl ha-1) densities, and supplied with low, normal and high (0, 180, and 360 kg N ha-1) concentrations of N. Kernel weight (KW), the maximum grain-filling rate (Gmax), the average grain-filling rate (Gave), and the kernel weight increment achieving Gmax (Wmax) were all significantly depressed under high density (HD) conditions, but increased N supply partially offset the losses. The apical kernels were more sensitive to density and N application rate than middle kernels. Correlation analysis indicated that plant density and N rate affected KW mainly by influencing the grain-filling rate. Variation in ZR, IAA, and ABA content tracked the variation in KW, but variation in GA content did not. Furthermore, the grain-filling parameters (closely related to TKW) had strong canonical correlation with the content of all hormones across the filling period and ZR content had the strongest relationship. Based on our study, high N supply is beneficial to optimize grain-filling parameters and improve KW of maize kernels under crowded condition

A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.Comment: Accepted by Engineering Applications of Artificial Intelligence journal on Oct 31th. Upload the accepted clean versio