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Human brucellosis caused by Brucella is a widespread zoonosis that is prevalent in many countries globally. The high homology between members of the Brucella genus and Ochrobactrum spp. often complicates the determination of disease etiology in patients. The efficient and reliable identification and distinction of Brucella are of primary interest for both medical surveillance and outbreak purposes. A large amount of genomic data for the Brucella genus was analyzed to uncover novel probes containing single-nucleotide polymorphisms (SNPs). GAMOSCE v1.0 software was developed based on the above novel eProbes. In conjunction with clinical requirements, an RPA-Cas12a detection method was developed for the on-site determination of B. abortus and B. melitensis by fluorescence and lateral flow dipsticks (LFDs). We demonstrated the potential of these probes for rapid and accurate detection of the Brucella genus and five significant Brucella species in silico using GAMOSCE. GAMOSCE was validated on different Brucella datasets and correctly identified all Brucella strains, demonstrating a strong discrimination ability. The RPA-Cas12a detection method showed good performance in detection in clinical blood samples and veterinary isolates. We provide both in silico and on-site methods that are convenient and reliable for use in local hospitals and public health programs for the detection of brucellosis. [ABSTRACT FROM AUTHOR]

Face recognition is one of the core and challenging issues in computer vision field. Compared to computer vision, human visual system can identify a target from complex backgrounds quickly and accurately. This paper proposes a new network model deriving from Where-What Networks (WWNs), which can approximately simulate the information processing pathways (i.e., dorsal pathway and ventral pathway) of human visual cortex and recognize different types of faces with different locations and sizes in complex background. To enhance the recognition performance, synapse maintenance mechanism and neuron regenesis mechanism are both introduced. Synapse maintenance is used to reduce the background interference while neuron regenesis mechanism is introduced to regulate the neuron resource dynamically to improve the network usage efficiency. Experiments have been conducted on human face images of 5 types, 11 sizes, and 225 locations in complex backgrounds. Experiment results demonstrate that the proposed WWN model can basically learn three concepts (type, location and size) simultaneously. The experiment results also show the advantages of the enhanced WWN-7 model for face recognition in comparison with several existing methods.