No abstract

No abstract

Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16 652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83 260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification. INDEX TERMS Convolutional neural network, fine-grained image classification, deep learning, convolutional kernel matrix, wheat leaf diseases.

Previous studies have demonstrated that the p.Lys232Ala substitution in the acylCoA: diacylglycerol acyltransferase (DGAT1) gene and the p.Phe279Tyr mutation in the growth hormone receptor (GHR) gene are the causative quantitative trait loci underlying milk yield and composition on BTA14 and BTA20 respectively. To examine their applications in the genetic improvement of Chinese dairy cattle productivity, we herein investigated the effects of the DGAT1 p.Lys232Ala and GHR p.Phe279Tyr mutations on milk, fat and protein yield, as well as fat and protein percentage in the milk of 1222 Holstein cows. Genotyping was performed using PCR-RFLP for DGAT1 or primer-introduced restriction analysis (PCR-PIRA) for GHR. With a mixed animal model, the significant associations of the DGAT1 p.Lys232Ala substitution with 305-day milk, fat and protein yield were identified (P = 0.0001). The DGAT1 allele that encode lysine at position 232 was associated with increased 305-day milk fat yield, but with decreased 305-day milk and protein yield, whereas the GHR p.Phe279Tyr mutation was found to be significantly associated with protein percentage (P = 0.0014). The allele substitution effect of p.279Phe by p.279Tyr may lead to a significant increase in protein percentage. Our findings indicate that DGAT1 p.232Ala and GHR p.279Phe could be used to increase milk yield and protein yield of Chinese Holstein cows.

Aims:  The aim of this study was to compare different primers for rapid and effective detection of Vibrio parahaemolyticus by polymerase chain reaction (PCR). Methods and Results:  A total of four pairs of primers, three previously published and one based on a newly developed V. parahaemolyticus metalloprotease (vpm) gene, have been assayed for PCR detection of V. parahaemolyticus. They have been tested for specificity and sensitivity on a total of 101 strains including reference and environment isolates belonging to V. parahaemolyticus and other species in Vibrio. Of the four sets of primers tested, the one designed on the basis of the metalloprotease gene (675 bp) gave optimal results with bacterial strains examined as they only amplified the specific fragment in strains that had been genetically and biochemically assessed as V. parahaemolyticus and the limit of detection was 4 pg of purified target DNA. Conclusions:  The primers designed on the metalloprotease gene gave optimal results for specific, sensitive and rapid detection of V. parahaemolyticus by PCR. Significance and Impact of the Study:  PCR amplification with the optimal primer set VPM1/VPM2 could facilitate the rapid diagnosis and surveillance of potentially pathogenic strains of V. parahaemolyticus and reduce food‐borne illness.

No abstract

This study aimed to adopt MPN-PCR (most probable number-polymerase chain reaction) for rapid detection of the quantity of Vibrio parahaemolyticus in seafood. V. parahaemolyticus in seafood could be quantitated by MPN statistics according to PCR products. The sensitivity of MPN-PCR was 100 times higher than that of direct PCR. Of 225 seafood samples from Qingdao, 165 were positive for the presence of V. parahaemolyticus, with an MPN value of >719 per gram, and about 41.5% of samples were positive for tdh gene-possessing cells. Eighty muscle tissues from the 225 seafood samples were investigated by direct PCR and MPN-PCR, but no V. parahaemolyticus was detected. The MPN-PCR test could be completed in less than 16 h from the time of sample preparation. It was rapid, sensitive, and reliable for comprehensive detection and quick quantitative determination of V. parahaemolyticus in seafood and it revealed the potential risk of illness associated with their consumption.

To address the problems of high computational complexity, inflexible frame length adjustment, and sub-optimal system efficiency of the RFID tag anti-collision algorithms in the Internet-of-Things systems, a low-complexity, and universal fast RFID tag anti-collision algorithm is proposed in this paper. A faster and less-complex tag number estimation method depends less on computing and storage resources, making it easier to integrate into the Internet of Things. Using the concepts of sub-frame and system efficiency priority, after each sub-frame is identified, the number of tags is estimated quickly and the frame length is adjusted dynamically to ensure the algorithm's efficiency. Moreover, the proposed algorithm is fully compatible with the EPC Class-1 Generation-2 standard, which ensures its universality and compatibility with the existing systems. The simulation results show that the proposed algorithm can achieve a system efficiency of 0.3554, a time efficiency of 0.7851, and an identification speed of 433 n/s. Compared with the standard Q algorithm, the performance is improved by 9.691%, 5.002%, and 8.250%, respectively. It is, hence, demonstrated that the proposed algorithm meets the requirements for the rapid identification of the RFID tags in the Internet-of-Things applications. INDEX TERMS Anti-collision algorithm, Internet of Things, low cost, RFID tag, system efficiency priority. I. INTRODUCTION Radio-frequency identification (RFID) technology has been widely used in industrial, agricultural, and commercial production systems such as warehouse management, logistics tracking and supply chains [1], [2], biological asset inventory, and ticketing systems. The integration of RFID and sensing technology makes it possible for RFID sensor tags to function as intelligent sensing nodes in the Internet of Things and has led to additional research results and commercial products [3]. Passive Ultra High Frequency (UHF) RFID technology is based on the EPC Global Class-1 Generation-2 (EPC C1G2) standard. UHF RFIDs have the advantages of fast identification, no need for a power supply, and a long communication distance. They have been widely studied and applied in Internet of Things systems [3], [4]. The associate editor coordinating the review of this manuscript and approving it for publication was Vyasa Sai.