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The defect rate of initially produced block bamboo (Bambusoideae) parts is >20 percent. Sorting out these defective parts manually is a highly time-consuming and tedious process. An intelligent sorting system was developed based on machine vision using a Radial Basis Function (RBF) neural network learning algorithm in this study. First, a high-speed charge-coupled device camera was used to obtain a series of images of perfect and defective block bamboo parts. Next, the RBF neural-network learning algorithm was applied to obtain defect characteristics and to locate defective parts moving forward on a conveyor belt. An array of air jets was designed to force defective parts off the belt. Experimental results showed that the average defective part removal rate of the proposed system was 91.7 percent.

Smart and sustainable food systems have been discussed based on the international consensus on “sustainable food system.” Indeed, learning and designing such systems becomes a critical task for practitioners, educators, and students, especially when designing and implementing the next generation of smart agriculture, smart forestry, and smart fishery projects. In order to gain a more systematic understanding, the research has conducted an exploratory scientometric analysis, based on 825 articles collected from the Web of Science (WoS). The findings reveal the emerging knowledge demand for the value-chains and supply-chains across agricultural, food, and Internet industries. More cross-disciplinary work is needed among engineering, science, and social science disciplines, for advancing digital agrifood chains.

Wen-Kang Wei and Qin-Ling Chen are also at the Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences Since the discovery of SARS-CoV-2 in Wuhan city, China, it has been reported in over 200 other countries and caused a global pandemic. Although some studies have suggested that bats and pangolins were potential hosts, the origin of SARS-CoV-2 remains unknown and the infection evidence of transmission from animals to people is not well established.1 On 28 February and 19 March …

To characterize ticks in cattle from Guangdong Province and Guangxi Zhuang Nationality Autonomous Region, Southern China, 783 cattle in four localities were examined. Among them, 232 (29.63%) cattle were positive for tick infection. A total of 503 ticks collected in these cattle were further investigated. Two Rhipicephalus species, namely R. microplus and R. sanguineus, were firstly identified by morphological features. Thereinto, R. microplus is the prevalent species in cattle in southern China, with high prevalent in summer and autumn annually. Mixed infection of R. microplus and R. sanguineus was just found in yellow cattle. To further confirm the morphological identification of these cattle-origin ticks, a phylogeographic analysis inferred from the sequences of the ribosomal internal transcribed spacer-2 (ITS-2) was performed, and R. microplus and R. sanguineus were identified. However, the morphological taxonomy of R. microplus has been challenged in recent years. The mitochondrial cytochrome c oxidase subunit 1 (cox1) marker was then used to provide higher resolution of R. mircoplus complex. The re-constructed cox1 phylogenetic tree further identified these R. mircoplus tick samples as R. microplus Clade A. These findings illustrated the prevalence and characterization of cattle-origin ticks in Southern China for the first time, and provided base-line information for further control of tick and tick-borne disease in these areas.

Porcine reproductive and respiratory syndrome virus (PRRSV) is considered an important economic pathogen for the international swine industry. At present, both PRRSV-1 and PRRSV-2 have been confirmed to be co-circulating in China. However, there is little available information about the prevalence or distribution of PRRSV-1 in Guangdong province, southern China. In this study, we performed molecular detection of PRRSV-1 in 750 samples collected from 50 farms in 15 major pig farming regions in this province. After RT-PCR testing, 64% (32/50) of farms were confirmed as PRRSV-1-positive. Surprisingly, PRRSV-1 was circulating on at least one pig farm in all 15 regions; of the 750 samples, 186 samples (24.8%) were positive for PRRSV-1. Furthermore, 15 representative PRRSV-1 ORF5 sequences (606 bp) (n = 1 per region) were obtained from those PRRSV-1-positive regions. Sequence alignment analysis indicated that they shared 81.8% ~ 100% nucleotide and 81.2% ~ 100% amino acid similarity with each other. Although all current PRRSV-1 sequences were divided into pandemic subtype 1, most of them had unique glycoprotein-5 amino acid sequences that are significantly different from other known PRRSV-1 isolates. To conclude, the present findings revealed wide geographical distribution of PRRSV-1 in Guangdong province, southern China. This study further extends the epidemiological significance of PRRSV-1 in China.

Schmallenberg virus (SBV) is an emerging and rampant arbovirus in Europe, and even Africa and West Asia. Investigating whether SBV existed in new regions or countries, it was very helpful for the early warning and control of SBV. In this study, we collected 317 serum samples (n = 242 for dairy cattle, n = 13 for yellow cattle, n = 21 for buffalo, and n = 41 for goats) from Guangdong province of southern China, which is located in a subtropical region and is an important distribution area for arboviral diseases. A commercial competitive enzyme-linked immunosorbent assay (cELISA) kit and a previously established real-time PCR were used to detect SBV antibody and RNA in those serum samples. Via testing, serological evidence of SBV was confirmed, with total positive rates (57.4, 15.4, 19, and 9.8%) in dairy cattle, yellow cattle, buffalo, and goats, respectively, while no positive signal for SBV RNA was found. To summarize, this study for the first time provided preliminary serological evidence of SBV infection in China, East Asia. Further investigations on molecular evidence, origin, and pathogenesis of SBV in ruminants needed to be studied in China.

The detection results of qPCR for PCV3 in collected samples. (DOC 65Â kb)

Members of the family Anelloviridae are emerging circular DNA viruses infecting many species of vertebrates including pigs. To date, members of two distinct genera, Iotatorquevirus, including torque teno sus virus 1a and torque teno sus virus 1b (TTSuV1a and TTSuV1b), and Kappatorquevirus, including torque teno sus virus k2a and torque teno sus virus k2b (TTSuVk2a and TTSuVk2b), have been identified in domestic pigs and wild boars. The goal of this study was to evaluate the prevalence and genetic diversity of these viruses based on 5' non-coding genes in Chinese swine herds experiencing clinical symptoms. One hundred eighty-five clinical samples from 11 different regions, collected during 2008-2009, were analyzed using a PCR method, and the results revealed a high TTSuV-positive rate of 78.9 % (146/185) in pigs. Moreover, we detected co-infection with multiple TTSuV strains in the same pig. Nucleotide sequencing results revealed greater genetic diversity within the genus Kappatorquevirus than within the genus Iotatorquevirus. In addition, TTSuVk2b, a novel virus discovered in New Zealand in 2012, was also identified in this study. In summary, the present work helps us obtain more knowledge about the epidemiology and genetic diversity of TTSuVs.

The first Bocavirus (bovine parvovirus 1, BPV1) was discovered in calves with diarrhea in 1961. Since then, a series of bocaviruses have emerged in many animals including dogs, humans, swine, gorillas, California sea lions, and cats. These viruses are associated with digestive tract and respiratory tract diseases. Several emerging porcine bocaviruses (PBoVs) have been reported on pig farms in the past four years. However, the nomenclature used to describe these viruses has been confused and irregular. The goal of this study was to compare the sequences of these emerging PBoVs, perform phylogenetic analysis, and present a proposal regarding their nomenclature. Genome sequences of some PBoVs and other bocaviruses were downloaded from the GenBank database. Sequence similarity, sequence mutation, deletion, or insertion, and sequence recombination among reference bocaviruses were obtained using DNAStar software, and its genetic evolution was determined using MEGA 5.1 software. According to the differences in their sequences and genome structure, the nomenclature of PBoVs was proposed and they were divided into four different species (from PBoV1 to PBoV4) and many different genotypes. Among them, both PBoV1 and PBoV2 had two genotypes, PBoV1-a and PBoV1-b and PBoV2-a and PBoV2-a and PBoV2-b, respectively. PBoV3 included three genotypes, PBoV3-a, PBoV3-b and PBoV3-c, respectively. In addition, for PBoV4, there were at least eight genotypes (PBoV4-a to PBoV4-h) in intraspecies. In conclusion, PBoVs were highly genetically diversified. The proposed nomenclature of PBoVs was used in the present studies, it could help us to better understand and standardize the nomenclature of PBoVs.