Actual Breeding Research Articles

GEFormer: a Genomic Prediction Method of Genotype-Environment Interaction in Maize by Integrating Gating Mechanism MLP and Linear Attention Mechanism.

The integration of genotypic and environmental data can enhance the prediction accuracy of field traits of crops. The existing genomic prediction methods fail to consider the environmental factors and do not consider the real growing environment of crops, resulting in low genomic prediction accuracy. In this work, we propose a genotype-environment interaction genomic prediction method in maize, called GEFormer, based on integrating the gating mechanism MLP and linear attention mechanism. Firstly, it uses gated multilayer perceptron (gMLP) to extract the local and global features among SNPs. Then, the Omni-dimensional Dynamic Convolution is used to extract the dynamic and comprehensive features of multiple environmental factors within each day in the consideration of the real growth pattern of crops. The linear attention mechanism is used to capture the temporal features of environmental changes. Finally, it uses the gating mechanism to fuse the genomic and environmental features effectively. We validate the accuracy of GEFormer in predicting important agronomic traits of maize, rice and wheat in three experimental scenarios: untested genotypes in tested environments, tested genotypes in untested environments, untested genotypes in untested environments. Experimental results show that GEFormer outperforms six cutting-edge statistical learning methods and four machine learning methods. Furthermore, it shows great advantages in the experimental scenario of untested genotypes in untested environments. In addition, we used GEFormer into three real-world breeding applications: phenotype prediction in unknown environments, hybrid phenotype prediction using inbred population, and cross-population phenotype prediction. The results illustrate that GEFormer exhibiting better prediction performance in actual breeding scenarios, and it can be utilized to assist crop breeding.

Hybrid Prediction in Horticulture Crop Breeding: Progress and Challenges.

In the context of rapidly increasing population and diversified market demands, the steady improvement of yield and quality in horticultural crops has become an urgent challenge that modern breeding efforts must tackle. Heterosis, a pivotal theoretical foundation for plant breeding, facilitates the creation of superior hybrids through crossbreeding and selection among a variety of parents. However, the vast number of potential hybrids presents a significant challenge for breeders in efficiently predicting and selecting the most promising candidates. The development and refinement of effective hybrid prediction methods have long been central to research in this field. This article systematically reviews the advancements in hybrid prediction for horticultural crops, including the roles of marker-assisted breeding and genomic prediction in phenotypic forecasting. It also underscores the limitations of some predictors, like genetic distance, which do not consistently offer reliable hybrid predictions. Looking ahead, it explores the integration of phenomics with genomic prediction technologies as a means to elevate prediction accuracy within actual breeding programs.

Pedigree-Based Description of Danubia Alba Rabbit Breed Lines.

The diversity of livestock animal breeds is an integral part of global biodiversity and requires careful management for sustainability and future availability. Avoiding inbreeding is a crucial aspect of mating of breeding animals. Our aims were to describe the quality of the pedigree, generation interval, gene origin, inbreeding, and effective population size of Danubia Alba rabbit lines. Line "D" is the maternal, whereas lines "C" and "X" are used as the paternal lines. The pedigree information was followed back from the actual breeding rabbits up to the founder animals. The rabbits having offspring in 2023 were chosen as reference populations for each line. The complete generation equivalent (GenCom) was 17.68 for line "C", 18.32 for line "D", and 17.49 for line "X", respectively. The maximum number of generations (GenMax) was above 30 for each line. The estimated bottleneck effect is mostly the result of selection and not a real genetic loss. The Wright inbreeding coefficient (F_Wright) was the highest for the "X" line rabbits, whereas it was the lowest for the line "D". Kalinowski's decomposition of inbreeding showed that it originated mostly from the past; the current fixation of alleles was quite similar for the line "C" and "D". Based on the predicted effective population sizes, it seems that there is no problem in maintaining of Danubia Alba lines.

Dissection of a novel major stable QTL on chromosome 7D for grain hardness and its breeding value estimation in bread wheat.

Grain hardness (Gh) is important for wheat processing and end-product quality. Puroindolines polymorphism explains over 60% of Gh variation and the novel genetic factors remain to be exploited. In this study, a total of 153 quantitative trait loci (QTLs), clustered into 12 genomic intervals (C1-C12), for 13 quality-related traits were identified using a recombinant inbred line population derived from the cross of Zhongkemai138 (ZKM138) and Chuanmai44 (CM44). Among them, C7 (harboring eight QTLs for different quality-related traits) and C8 (mainly harboring QGh.cib-5D.1 for Gh) were attributed to the famous genes, Rht-D1 and Pina, respectively, indicating that the correlation of involved traits was supported by the pleotropic or linked genes. Notably, a novel major stable QTL for Gh was detected in the C12, QGh.cib-7D, with ZKM138-derived allele increasing grain hardness, which was simultaneously mapped by the BSE-Seq method. The geographic pattern and transmissibility of this locus revealed that the increasing-Gh allele is highly frequently present in 85.79% of 373 worldwide wheat varieties and presented 99.31% transmissibility in 144 ZKM138-derivatives, indicating the non-negative effect on yield performance and that its indirect passive selection has happened during the actual breeding process. Thus, the contribution of this new Gh-related locus was highlighted in consideration of improving the efficiency and accuracy of the soft/hard material selection in the molecular marker-assisted process. Further, TraesCS7D02G099400, TraesCS7D02G098000, and TraesCS7D02G099500 were initially deduced to be the most potential candidate genes of QGh.cib-7D. Collectively, this study provided valuable information of elucidating the genetic architecture of Gh for wheat quality improvement.

High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influences of epistasis and gene pleiotropy, ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits. The original traits and general combining ability of the parents, as well as the original traits and mid-parent heterosis of TC, were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected. Among these loci, 322 candidate genes located within or nearby the SALs were screened, 204 of which were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracies of the four models were generally not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracies of 5 (26%), 3 (16%), and 11 (58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines. They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Economically useful traits of cows received from different sires

When breeding Black-and-White holstinized cattle, the sperm of highly productive sires with high productivity potential of maternal ancestors, including Holstein breed, is used. Evaluation of sires by the quality of off spring is the most accurate method of determining the actual breeding value. The purpose of the work was to evaluate the economically useful traits of cows obtained from diff erent sires. The research was carried out in one of the breeding farms typical for the Sverdlovsk region for the breeding of Black-and-White cattle with a high proportion of blood in Holstein breed. The evaluation of three Holstein sires Zhadon 60044914, Onward 4512 and Plateau 62297905 on the quality of off spring was carried out by comparing the productivity of cows-daughters with their herdmates. It is established that when selecting sires for work in the herd, group selection of sires of the same type in terms of productive traits of maternal ancestors is used. This leads to almost equal milk yields for their daughters. There was also no signifi cant diff erence in milk quality indicators. When comparing the productivity of the cows-daughters of sires for the fi rst lactation with their herdmates, a certain diff erence was established, both in milk yield for 305 days of lactation, and in the mass fraction of fat and protein in milk. The cows-daughters of sire Onward 4512 turned out to be the best relative to herdmates in milk yield and mass fraction of fat in milk. The daughters of the sires Zhadon 60044914 and Plateau 62297905 had productivity lower than their herdmates by 315 and 127 kg, respectively. The daughters of the sire Plateau 62297905 surpassed their herdmates in terms of the mass fraction of fat and protein in milk. The level of profi tability of milk production for the highest lactation was 58,9–66,6 % and was higher in the group of cows–daughters of the sire Zhadon 60044914.

Recognition of fine-grained sow nursing behavior based on the SlowFast and hidden Markov models

Information on sow nursing behavior plays an important role in improving the survival rate of weaned piglets. To obtain more information on sow nursing processing, it is necessary to introduce beginning- and end-stage nursing behavior recognition. This is defined as fine-grained nursing behavior recognition. In this paper, we attempt to automate fine-grained nursing behavior recognition with surveillance video. First, nursing behavior is divided into three subbehaviors: before piglet sucking, piglet sucking and end piglet sucking. Then, a deep neural network model with a dual-stream structure, named SlowFast, is introduced to realize preliminary sow nursing behavior recognition by extracting spatiotemporal features. Finally, for some erroneous results in behavior recognition and the recognition ambiguity in behavior transition, a hidden Markov model (HMM) is introduced to modify the behavioral label sequence by using the Viterbi algorithm. To verify the effectiveness of our method, all experiments are carried out on sow nursing videos collected in an actual breeding environment. The results show that our method achieves a 90.50% sequence coincidence degree for the recognition results on long videos containing the whole nursing behavior, and the accuracy of behavior transition times reaches 87.05%. In summary, this study refines the automatic sow nursing behavior monitoring content to provide more information for precision livestock farming.

RETRACTED ARTICLE: Application of data mining based on swarm intelligence algorithm in financial support of livestock and poultry breeding insurance

Among the components of China’s actual economic structure, aquaculture has always been a key project, and as a part of agriculture, it has been greatly developed. However, there are still many problems in the actual breeding process, such as low financial support, low information transparency, high financing difficulty and lack of channels, high claims costs and serious moral hazard in the livestock and poultry breeding insurance field, which will affect the development and progress of the industry. Therefore, in this context, this paper improves the efficiency analysis of financial support for livestock and poultry breeding insurance by introducing data mining and swarm intelligence algorithms. Then use the analysis data to build a model to further evaluate the efficiency of financial support. The construction of the model is roughly divided into four parts: insurance density, insurance depth, insurance amount and income. The weight of different indicators is calculated through the analytic hierarchy process, and the overall ranking results are output through consistency test. Some data of the above models are used as the output indicators of DEA decision-making units for top-down ranking. The result analysis shows that the development of technology in this industry can effectively improve the actual productivity of agricultural crops, thus effectively promoting the development of local agriculture. In this paper, data mining and swarm intelligence algorithms are introduced into livestock and poultry breeding insurance to complete the analysis and evaluation of financial support.

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds

BackgroundIn genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors.ResultsFor body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV.ConclusionsBased on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

Современный статус степного орла в Казахстане

Современный статус степного орла в Казахстане

Identification of volatile organic compounds related to the eating quality of cooked japonica rice

Eating quality (EQ) of rice has a complex nature composed of physicochemical properties. Nevertheless, breeding programs evaluating EQ through sensory test or taste-evaluation instruments have been laborious, time-consuming and inefficient. EQ is affected by both taste and aroma. However, in actual breeding programs, aroma of cooked rice has been considered the least due to lack of information. Here we identified a total of 41 volatile compounds potentially affecting the EQ of non-aromatic, cooked japonica rice, identified by GC–MS, sensory panel test, and Toyo taste-meter analyses. Partial least squares discriminant analysis demonstrated an outstanding classification effect of the identified volatile compounds on eating-quality discrimination. Several volatile compounds related to lipid oxidation and fatty acid degradation were identified to affect the EQ in japonica rice. Of them, 1-octen-3-ol, 1-ethyl-3,5-dimethylbenzene, 2,6,11-trimethyldodecane, 3-ethyloctane, 2,7,10-trimethyldodecane, methyl salicylate, 2-octanone, and heptanal were selected as important compounds. The discriminant model for the classification of the quality of cultivars was robust and accurate, an r-squared value was 0.91, a q squared value was 0.85, and an accuracy was 1.0. Overall, the results of this study characterize EQ of rice cultivars based on volatile compounds, suggesting the application of metabolite profiling data for rice breeding of high eating quality.

Pedigree-Based Description of Three Traditional Hungarian Horse Breeds.

Simple SummaryThe most important purpose of animal conservation programs is to maintain genetic variability. The Furioso-North Star, the Gidran, and the Nonius are indigenous Hungarian horse breeds from the Mezőhegyes Stud. In the last century, the role of the horses was changed, the technical innovations and motorization replaced them, so the population size and the genetic variability of these breeds were reduced. Nowadays these breeds are endangered. The aims of this study were to give information about the current breeding population and support breeder associations during their gene conservation work. The pedigree quality, generation intervals, probability of gene origin, and inbreeding were evaluated. We found that breeds had a large bottleneck effect during breeding history. The level of inbreeding was measured with different methods, such as Ballou’s, Wright’s, and Kalinowski’s coefficient. Most of the current inbreeding coefficient was the result of previously fixed alleles for each breed. Effective population size was also estimated, and the status of the breeds was found to be not critical according to FAO criteria.The Mezőhegyes Stud was founded in 1784 where three different horse breeds were developed: the Furioso-North Star, the Gidran, and the Nonius. These breeds were based on the same mare population, but each breed had different utilization purposes. Our aim was to analyze the pedigree information of these three indigenous breeds. The genealogical information was traced back from the actual breeding population back to the founder animals, and the final database contained more than 47,000 horses. The reference populations were defined as the registered breeding animals in 2019. The complete generation equivalent was 16.45 for the Gidran breed, 15.18 for Furioso-North Star, and 12.64 for Nonius, respectively. Due to the utilization of English Thoroughbred during the breeding history, the average maximum generations were close to 36 generations for each breed. The average relatedness was approximately 4%. The average Wright’s inbreeding coefficient was the highest for the Nonius breed (5.59%). Kalinowski’s decomposition of inbreeding showed that inbreeding is originated mainly from the past; the current fixation of alleles was higher for the Nonius horse breed. There was a reasonable bottleneck effect for each breed. The estimated effective population sizes suggest that there is no problem with the maintaining of Mezőhegyes horse breeds.

Bird Use of Fruit Orchards and Vineyards in Japan: Mitigating a Knowledge Gap with a Systematic Review of Published and Grey Literature

Perennial farmlands such as fruit orchards and vineyards often substitute for natural habitats and provide nesting and foraging habitats for birds. Bird use of perennial farmlands has been investigated mainly in Europe and North America, but far less in Asia, which hosts one-third of identified biodiversity hotspots. We aimed to fill this knowledge gap by performing a systematic review of bird studies in fruit orchards and vineyards in Japan. We screened literature written in English or Japanese including both published and non-peer-reviewed (grey material, e.g., books, abstracts of annual meetings, blogs, and unpublished surveys). The review identified 31 bird species, including four Red List species, that nest in orchards and vineyards in Japan. Several studies have provided insights into the habitat quality of fruit orchards, which appears to vary among bird species. Other studies have reported: (1) the effects of orchard management on bird diversity (e.g., positive effects of organic farming and grass cover on bird richness and abundance); and (2) the ecosystem services by birds (e.g., reduction of pest rodents by predatory Ural Owls Strix uralensis). Our review demonstrates the potential importance of fruit orchards and vineyards to bird species that have adapted to human-modified landscapes, although more quantitative studies are needed to investigate the actual breeding performance of birds. We also illustrate the importance of non-English-language literature, including grey literature, to mitigate ecological knowledge gaps in countries where English is not the first language.

Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology

The quality of wheat kernels is critical to ensure crop yields. However, in actual breeding work, unsound kernels are scarce compared to healthy kernels. Limited data sets or unbalanced data sets make it difficult for many algorithms to accurately identify kernels in different states. A novel method based on deep convolutional generative adversarial network (DCGAN) and near-infrared hyperspectral imaging technology was proposed to identify unsound wheat kernels in this paper. Three classifiers, convolutional neural network (CNN), support vector machine (SVM) and decision tree (DT) were used. After expanding the samples, the results showed that the accuracy of the test set of the DT model increased from 51.67% to 80.83%, a total increase of 29.16%. And the CNN and SVM models increased by 8.34% and 14.17% respectively. This demonstrated that the DCGAN method had the ability to generate reliable data samples for unbalanced data sets for improving the performance of the classifier. On this basis, the training samples are further expanded for improving the performance of the classifier. The results showed that CNN model gained the most from incremental data, and its accuracy rate had been continuously improved from 79.17% to 96.67%, a total increase of 17.50%. This also demonstrated that the DCGAN method had the ability to expand a limited data set. In general, the joint model based on DCGAN and CNN combined with hyperspectral imaging technology had a good prospect in the identification of unsound kernels.

Swine manure facilitates the spread of antibiotic resistome including tigecycline-resistant tet(X) variants to farm workers and receiving environment

The prevalence of antibiotic resistance genes (ARGs) in livestock and poultry manure is a severe threat to human health. However, the comprehensive characterization of antibiotic resistance in swine, workers, and the receiving environment is still lacking in the actual breeding environment. Hence, the ARG profile and the potential bacterial hosts producing among swine manure (including sows, piglets, finishing pigs, and nursery pigs), worker feces, and the receiving environment (including sediment and vegetable soil) were comprehensively analyzed based on the metagenomic method. The results showed that swine manure exhibited the high levels of richness and diversity of ARGs. Inactivating tetracycline resistance genes such as tet(X), tet(X1), and tet(X10) were prevalent on swine farms. Workers and the environment were the primary recipients of ARGs, and shared ARGs accounted for at least 90% of their ARG abundances. Network analysis revealed that Escherichia, Acinetobacter, and Erysipelothrix were the most dominant genera co-occurring with specific shared ARGs. The abundance of coexisting ARGs in swine at different developmental stages accounted for 76.4% to 90.8% of the shared ARGs in swine, workers, and environmental samples. The Mantel test revealed that Firmicutes and Proteobacteria had a significant correlation with the ARG profiles. In addition, variation partitioning analysis (VPA) showed that the joint effects of mobile genetic elements (MGEs) and bacterial communities accounted for 24.7% of the resistome variation and played a significant role in the ARG profiles. These results improve our understanding of the transmission and persistence of ARGs in the actual breeding environment.

Modelling and controlling dissolved oxygen in recirculating aquaculture systems based on mechanism analysis and an adaptive PID controller

In aquaculture, dissolved oxygen (DO) content is critical to the growth and development of aquatic products, and it must be precisely controlled. Based on the theory of microporous aeration mass transfer and mass conservation equations, this paper analyzed the influence of the four main factors of recirculating water flow, mechanical aeration, surface reaeration and respiration of shrimp on the dynamic changes of DO, and established a DO system dynamics model in the recirculating aquaculture system. Based on the established model, this paper proposed a fuzzy rule-optimized single neuron adaptive PID controller (FL-SN-PID) for precise control of DO. In order to verify the reliability of the established model, several aeration experiments with different aeration flows were conducted in Simulink and actual breeding system. All fitting R2 between simulated data and measured data of the DO response are above 0.94, indicating that the established dynamics model can accurately approximate the actual breeding system and can provide a basis for the design of the controller. In the simulation experiment, the proposed controller was applied to the DO tracking control in three scenarios, and its tracking performance was also compared with the traditional PID controller and the SN-PID controller. In performance analysis, the integral of absolute error (IAE) and the integral of squared error (ISE) of the FL-SN-PID controller are obviously smaller than the other two controllers, indicating this controller has better control accuracy and robustness, and can be competent for precise control of DO in aquaculture.

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.).

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

Simultaneous determination of triazine herbicides and their metabolites in shellfish by HPLC-MS/MS combined with Q/E-Orbitrap HRMS.

Triazine herbicides are used extensively in agriculture and aquaculture worldwide because of their broad effectiveness in weed control. However, after they are discharged into the sea, they seriously contaminate aquatic ecosystems and threaten aquatic organisms, especially shellfish. Currently, there are no established methods for the detection and confirmation of triazine herbicides and their metabolites in biological matrixes. Hence, the food safety of aquatic products cannot be accurately evaluated, which creates a technical barrier against international aquatic product trade. In this study, for the first time, a method was developed for the analysis and confirmation of seven triazine herbicides and 13 metabolites in shellfish, based on alkaline acetonitrile extraction and neutral Al2O3 cartridge purification coupled with internal standard calibration. Specifically, quantitative and qualitative analysis was conducted using high-performance liquid chromatography-triple quadrupole mass spectrometry (HPLC-MS/MS), and accurate identification was carried out by quadrupole orbitrap high-resolution mass spectrometry (Q/E Orbitrap HRMS). The results showed that target analytes demonstrated good linearity within the corresponding range (R2 > 0.995). The limit of detection and limit of quantitation of the proposed method were 0.1 and 0.3 μg/kg, respectively. The average recoveries of analytes were between 70.0% and 120% when spiked at three levels with blank oyster (Crassostrea gigas) as the matrix, and the relative standard deviations (RSDs) were all less than 12% (n=6). The proposed method was successfully applied for the detection of triazine herbicide residues in oyster samples during actual breeding, and the presence of DIP, HP, DEHA, and other metabolites in positive samples was confirmed by Q/E Orbitrap HRMS. This method exhibits high accuracy, high sensitivity, and good reproducibility. It has promising application prospects in the field of hazard analysis and the positive identification of aquatic products.

Multi-target pig tracking algorithm based on joint probability data association and particle filter

In order to evaluate the health status of pigs in time, monitor accurately the disease dynamics of live pigs, and reduce the morbidity and mortality of pigs in the existing large-scale farming model, pig detection and tracking technology based on machine vision are used to monitor the behavior of pigs. However, it is challenging to efficiently detect and track pigs with noise caused by occlusion and interaction between targets. In view of the actual breeding conditions of pigs and the limitations of existing behavior monitoring technology of an individual pig, this study proposed a method that used color feature, target centroid and the minimum circumscribed rectangle length-width ratio as the features to build a multi-target tracking algorithm, which based on joint probability data association and particle filter. Experimental results show the proposed algorithm can quickly and accurately track pigs in the video, and it is able to cope with partial occlusions and recover the tracks after temporary loss. Keywords: joint probability data association, pig tracking, particle filter, centroid DOI: 10.25165/j.ijabe.20211404.6105 Citation: Sun L Q, Li Y Y. Multi-target pig tracking algorithm based on joint probability data association and particle filter. Int J Agric & Biol Eng, 2021; 14(4): 199–207.