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Abstract In agricultural production activities, the growth of crops always accompanies the competition of weeds for nutrients and sunlight. In order to mitigate the adverse effects of weeds on yield, we apply semantic segmentation techniques to differentiate between seedlings and weeds, leading to precision weeding. The proposed EPAnet employs a loss function coupled with Cross-entropy loss and Dice loss to enhance attention to feature information. A multi-Decoder cooperative module based on ERFnet is designed to enhance information transfer during feature mapping. The SimAM is introduced to enhance position recognition. DO-CONV is used to replace the traditional convolution Feature Pyramid Networks (FPN) connection layer to integrate feature information, improving the model’s performance on leaf edge processing, and is named FDPN. Moreover, the Overall Accuracy has been improved by 0.65%, the mean Intersection over Union (mIoU) by 1.91%, and the Frequency-Weighted Intersection over Union (FWIoU) by 1.19%. Compared to other advanced methods, EPAnet demonstrates superior image segmentation results in complex natural environments with uneven lighting, leaf interference, and shadows.

Currently, foliar diseases of chili have significantly impacted both yield and quality. Despite effective advancements in deep learning techniques for the classification of chili leaf diseases, most existing classification models still face challenges in terms of accuracy and practical application in disease identification. Therefore, in this study, an optimized and enhanced convolutional neural network model named MCCM (MCSAM-ConvNeXt-MSFFM) is proposed by introducing ConvNeXt. The model incorporates a Multi-Scale Feature Fusion Module (MSFFM) aimed at better capturing disease features of various sizes and positions within the images. Moreover, adjustments are made to the positioning, activation functions, and normalization operations of the MSFFM module to further optimize the overall model. Additionally, a proposed Mixed Channel Spatial Attention Mechanism (MCSAM) strengthens the correlation between non-local channels and spatial features, enhancing the model’s extraction of fundamental characteristics of chili leaf diseases. During the training process, pre-trained weights are obtained from the Plant Village dataset using transfer learning to accelerate the model’s convergence. Regarding model evaluation, the MCCM model is compared with existing CNN models (Vgg16, ResNet34, GoogLeNet, MobileNetV2, ShuffleNet, EfficientNetV2, ConvNeXt), and Swin-Transformer. The results demonstrate that the MCCM model achieves average improvements of 3.38%, 2.62%, 2.48%, and 2.53% in accuracy, precision, recall, and F1 score, respectively. Particularly noteworthy is that compared to the original ConvNeXt model, the MCCM model exhibits significant enhancements across all performance metrics. Furthermore, classification experiments conducted on rice and maize disease datasets showcase the MCCM model’s strong generalization performance. Finally, in terms of application, a chili leaf disease classification website is successfully developed using the Flask framework. This website accurately identifies uploaded chili leaf disease images, demonstrating the practical utility of the model.

Abstract Deep learning technologies have enabled the development of a variety of deep learning models that can be used to detect plant leaf diseases. However, their use in the identification of soybean leaf diseases is currently limited and mostly based on machine learning methods. In this investigation an enhanced deep learning network model was developed to recognize soybean leaf diseases more accurately. The improved network model consists of three parts: feature extraction, attention calculation, and classification. The dataset used was first diversified through data augmentation operations such as random masking to enhance network robustness. An attention module was then used to generate feature maps at various depths. This increased the network’s focus on discriminative features, reduced background noise, and enabled the use of the LeakyReLu activation function in the attention module to prevent situations in which neurons fail to learn when the input is negative. Finally, the extracted features were then integrated using a fully connected layer, and the predicted disease category inferred to improve the classification accuracy of soybean leaf diseases. The average recognition accuracy of the improved network model for soybean leaf diseases was 85.42% both higher than the six deep learning comparison models (ConvNeXt (66.41%), ResNet50 (72.22%), Swin Transformer (77.00%), MobileNetV3 (67.27%), ShuffleNetV2 (59.89%), and SqueezeNet (72.92%)), thus proving the effectiveness of the improved method.The model proposed in this paper was also tested on the grapevine leaf dataset, and the performance ability of the improved network model remained due to other common network models, and overall the proposed network model was very effective in leaf disease identification.

Extensive application of lead (Pb) brought about environmental pollution and toxic reactions of organisms. Selenium (Se) has the effect of antagonizing Pb poisoning in humans and animals. However, it is still unclear how Pb causes brainstem toxicity. In the present study, we wanted to investigate whether Se can alleviate Pb toxicity in chicken brainstems by reducing apoptosis. One hundred and eighty chickens were randomly divided into four groups, namely the control group, the Se group, the Pb group, and the Se/Pb group. Morphological examination, ultrastructural observation, relative mRNA expressions of genes on heat shock proteins (HSPs); selenoproteins; inflammatory cytokines; and apoptosis-related factors were investigated. The results showed that Pb exposure led to tissue damage and apoptosis in chicken brainstems. Furthermore, an atypical expression of HSPs (HSP27, HSP40, HSP60, HSP70, and HSP90); selenoprotein family glutathione peroxidase (GPx) 1, GPx2, GPx3, and GPx4), thioredoxin reductases (Txnrd) (Txnrd1, Txnrd2, and Txnrd3), dio selenoprotein famliy (diodothyronine deiodinases (Dio)1, Dio2, and Dio3), as well as other selenoproteins (selenoprotein (Sel)T, SelK, SelS, SelH, SelM, SelU, SelI, SelO, Selpb, selenoprotein n1 (Sepn1), Sepp1, Sepx1, Sepw1, 15-kDa selenoprotein (Sep15), and selenophosphate synthetases 2 (SPS2)); inflammatory cytokines (Interleukin 2 (IL-2), IL-4, IL-6, IL-12β, IL-17, and Interferon-γ (IFN-γ)); and apoptosis-related genes (B-cell lymphoma-2 (Bcl-2), tumor protein 53 (p53), Bcl-2 Associated X (Bax), Cytochrome c (Cyt c), and Caspase-3) were identified. An inflammatory reaction and apoptosis were induced in chicken brainstems after exposure to Pb. Se alleviated the abnormal expression of HSPs, selenoproteins, inflammatory cytokines, and apoptosis in brainstem tissues of chickens treated with Pb. The results indicated that HSPs, selenoproteins, inflammatory, and apoptosis were involved in Se-resisted Pb poisoning. Overall, Se had resistance effect against Pb poisoning, and can be act as an antidote for Pb poisoning in animals.

Lead (Pb), a heavy metal environmental pollutant, poses a threat to the health of humans and birds. Inflammation is one of the most common pathological phenomena in the case of illness and poisoning. However, the underlying mechanisms of inflammation remain unclear. The cerebellum and the thalamus are important parts of the nervous system. To date, there have been no reports of Pb inducing inflammation in animal cerebellums or thalami. Selenium (Se) can relieve Pb poisoning. Therefore, we aimed to explore the mechanism by which Se alleviates Pb toxicity to the cerebellums and thalami of chickens by establishing a chicken Pb or/and Se treatment model. Our results demonstrated that exposure to Pb caused inflammatory damage in cerebellums and thalami, evidenced by the characteristics of inflammation, the decrease in anti-inflammatory factors (interleukin (IL)-2 and interferon-γ (INF-γ)), and the increase in pro-inflammatory factors (IL-4, IL-6, IL-12β, IL-17, and nitric oxide (NO)). Moreover, we found that the IL-2/IL-17–NO pathway took part in Pb-caused inflammatory injury. The above findings were reversed by the supplementation of dietary Se, meaning that Se relieved inflammatory damage caused by Pb via the IL-2/IL-17–NO pathway. In addition, an up-regulated oxidative index malondialdehyde (MDA) and two down-regulated antioxidant indices (glutathione (GSH) and total antioxidant capacity (TAC)) were recorded after the chickens received Pb stimulation, indicating that excess Pb caused an oxidant/antioxidant imbalance and oxidative stress, and the oxidative stress mediated inflammatory damage via the GSH–IL-2 axis. Interestingly, exposure to Pb inhibited four glutathione peroxidase (GPx) family members (GPx1, GPx2, GPx3, and GPx4), three deiodinase (Dio) family members (Dio1, Dio2, and Dio3), and fifteen other selenoproteins (selenophosphate synthetase 2 (SPS2), selenoprotein (Sel)H, SelI, SelK, SelM, SelO, SelP1, SelPb, SelS, SelT, SelU, and selenoprotein (Sep)n1, Sepw1, Sepx1, and Sep15), suggesting that Pb reduced antioxidant capacity and resulted in oxidative stress involving the SPS2–GPx1–GSH pathway. Se supplementation, as expected, reversed the changes mentioned above, indicating that Se supplementation improved antioxidant capacity and mitigated oxidative stress in chickens. For the first time, we discovered that the SPS2–GPx1–GSH–IL-2/IL-17–NO pathway is involved in the complex inflammatory damage mechanism caused by Pb in chickens. In conclusion, this study demonstrated that Se relieved Pb-induced oxidative stress and inflammatory damage via the SPS2–GPx1–GSH–IL-2/IL-17–NO pathway in the chicken nervous system. This study offers novel insights into environmental pollutant-caused animal poisoning and provides a novel theoretical basis for the detoxification effect of Se against oxidative stress and inflammation caused by toxic pollutants.

Bean crop and weed detection is a key component of precision agriculture, which can distinguish between bean crop and weeds. Accurate identification of weeds is essential for precision weed management. This study introduces PF-UperNet, a semantic segmentation approach rooted in an encoder-decoder architecture, designed to autonomously distinguish between bean crop and weeds using advanced computer vision techniques. Our methodology refines the foundational UperNet in several significant ways: Firstly, we adopt the PoolFormer-S12 as a substitute for UperNet’s backbone structure, aiming to reduce the model parameters and boost its performance metrics. Secondly, the Efficient Channel Attention (ECA) mechanism is integrated into both the PoolFormer-S12 and the Decoder, sharpening the network’s focus on extracting salient channel features. Then, within the Decoder, the Feature Alignment Pyramid Network (FaPN) supplants the conventional Feature Pyramid Network (FPN) module, remedying the misalignment issues observed in UperNet’s FPN feature maps. Lastly, we replace the Cross-Entropy loss with a combination of Cross-Entropy loss and Dice coefficient loss to increase the model’s attention on regions to be detected. Empirical evidence underlines the efficacy of our technique, with a Mean Intersection over Union (MIoU) of 87.45%, a Mean Pixel Accuracy (MPA) of 96.82%, and a total of 46.16M parameters encapsulated. Relative to the benchmark UperNet, our model demonstrates enhancements of 1.08% and 0.25% in MIoU and MPA, respectively, and accomplishes a parameter reduction of 27.92%. Experimental results demonstrate that the proposed model achieves remarkable detection performance in terms of MIoU, MPA, and model parameters. It can provide an effective detection method for weed management.

Lead (Pb), a hazardous heavy metal, can damage the health of organisms. However, it is not clear whether Pb can damage chicken cerebellums and thalami. Selenium (Se), an essential nutrient for organisms, has a palliative effect on Pb poisoning in chickens. In our experiment, a model of chickens treated with Pb and Se alone and in combination was established to investigate the molecular mechanism of Se alleviating Pb-caused damage in both chicken cerebellums and thalami. Our morphological results indicated that Pb caused apoptotic lesions, such as mitochondrial and nuclear damage. Further, the anti-apoptotic gene Bcl-2 decreased; on the contrary, four pro-apoptotic genes (p53, Bax, Cyt c, and Caspase-3) increased under Pb treatment, meaning that Pb caused apoptosis via the p53-Cyt c-Caspase-3 pathway. Furthermore, we further demonstrated that Pb elevated four HSPs (HSP27, HSP40, HSP70, and HSP90), as well as HSP70 took part in the molecular mechanism of Pb-caused apoptosis. In addition, we found that Pb exposure led to oxidative stress via up-regulating the oxidant H2O2 and down-regulating four antioxidants (CAT, SOD, GST, and GPx). Moreover, Pb decreased three Se-containing factors (Txnrd1, Txnrd2, and Txnrd3), further confirming that Pb caused oxidative stress. Interestingly, Se supplementation reversed the above changes caused by Pb and alleviated Pb-induced oxidative stress and apoptosis. A time dependency was demonstrated for Bcl-2, Bax, and Cyt c in the cerebellums, as well as CAT, GPx, and p53 in the thalami of Pb-exposed chickens. HSP70 in cerebellums and HSP27 in thalami were more sensitive than those in thalami and cerebellums, respectively, under Pb exposure. Pb-induced apoptosis of thalami was more severe than cerebellums. In conclusion, after Pb treatment, Txnrds mediated oxidative stress, oxidative stress up-regulated HSPs, and finally, HSP70 triggered apoptosis. Se supplementation antagonized Pb-induced oxidative stress and apoptosis via the mitochondrial pathway and selenoproteins in chicken cerebellums and thalami. This study provides new information for the mechanism of environmental pollutant poisoning and the detoxification of Se on abiotic stress.

ABSTRACT Although the phosphorothioate (PT) modification, in which the nonbridging oxygen in the DNA sugar-phosphate backbone is replaced by sulfur, has been reported to play versatile roles in multiple cellular processes, very little data have been obtained to define the role of PT in epigenetic regulation. In this study, we report that the PT system in Salmonella enterica serovar Cerro 87 is involved in the transcriptional regulation of the torCAD operon encoding the trimethylamine N-oxide (TMAO) respiration machinery that enables the use of TMAO as a terminal electron acceptor for respiration when oxygen is not available. In vitro, PT enhanced the binding of the transcriptional activator of the torCAD operon, namely, TorR, to its DNA substrate (tor boxes). However, in vivo, the PT modification protein complex DndCDE downregulated torCAD transcription through competing with the binding of TorR to the tor boxes. The altered expression of torCAD caused by PT modification proteins affected cell growth that relied on TMAO respiration. To our knowledge, this is the first report supporting that PT proteins participate in transcriptional regulation, showing a new function of PT systems. IMPORTANCE Since the initial discovery of DNA phosphorothioate (PT) modification systems in Streptomyces lividans in the 1980s, explorations of the biological functions of DNA PT systems have advanced and yielded a number of important findings. However, the functions of PT systems, especially in genetic regulation, remain largely unknown. In this study, we report a case in which the PT system participates in the transcriptional regulation of the torCAD operon in Salmonella enterica serovar Cerro 87. While the PT modification enhanced the binding of TorR, the torCAD operon transcriptional activator, to its DNA substrate in vitro, we found that the PT modification protein complex DndCDE directly competed with TorR binding in vivo and subsequently repressed the expression of torCAD and attenuated cell growth that relied on TMAO respiration. These findings provide a deeper understanding of the characteristics of the PT chemical structure and broaden our understanding of the mechanisms by which PT regulates gene expression.

Ammonia gas, a toxic environmental pollutant, is a vital component of PM2.5 aerosols, and can decrease human and animal immunity. Peripheral blood lymphocytes (PBLs) are main immune cells. Nevertheless, poisoning mechanism of PBLs under ammonia exposure remains unclear. Here, we established an ammonia poisoning model of chicken PBLs to explore poisoning mechanism of ammonia-caused apoptosis in chicken PBLs. Cell viability and apoptosis rate were detected using CCK8 assay and flow cytometry, respectively. Mitochondrial membrane potential (MMP) was observed using fluorescent staining. In addition, qRT-PCR was performed to measure mRNA levels of apoptosis-related genes (tumor necrosis factor-α (TNF-α), tumor necrosis factor receptor 1 (TNFR1), TNF receptor-associated death domain (TRADD), Fas-associated death domain (FADD), Caspase-8, BH3-interacting domain death agonist (Bid), Bcl-2-associated X protein (Bax), Bcl-2 homologous antagonist/killer (Bak), B-cell lymphoma-2 (Bcl-2), Cytochrome-c (Cytc), apoptotic protease activating factor-1 (APAF1), Caspase-9, and Caspase-3), immune-related genes (interferon-γ (IFN-γ), interleukin-2 (IL-2), IL-4, IL-6, IL-1β, IL-10, transforming growth factor-β1 (TGF-β1), IL-17, IL-21, and IL-22), heat shock protein (HSP) genes (HSP25, HSP40, HSP60, HSP70, HSP90, and HSP110), as well as miR-27b-3p. Western blot was used to determine protein levels of apoptosis-related factors (TNF-α, Caspase-8, Bcl-2, Caspase-9, and Caspase-3), as well as HSPs (HSP40, HSP60, HSP70, and HSP90). The results indicated that TRADD, FADD, and APAF1 were target genes of miR-27b-3p, as well as miR-27b-3p participated in molecular mechanism of apoptosis through targeting TNF-α/TNFR1/Caspase-8 death receptor pathway-triggered Bid/Cytc/Caspase-9 mitochondrial pathway in ammonia-treated chicken PBLs. In addition, our findings demonstrated that excess ammonia led to immunosuppression via Th1/Th2 imbalance and Treg/Th17 imbalance. Simultaneously, ammonia stress activated HSPs. In summary, for the first time, our data demonstrated that HSPs-triggered immunosuppression led to apoptosis under ammonia exposure. Our findings provided a new insight into molecular mechanism of ammonia poisoning and an important reference for environmental risk assessment related to ammonia.

Emission of atmospheric ammonia (NH3) is an environmental challenge because of its harmful effects on humans and animals including birds. Among all organisms, NH3 is highly sensitive to birds. Autophagy plays a critical role in Bursa of fabricius (BF)-mediated immune responses against various hazardous substances. Therefore, we designed our work to demonstrate whether NH3 can induce autophagy in broiler chicken BF. In this study, the downregulated levels of mammalian target of rapamycin and light chain-3 (LC-Ⅰ), as well as the upregulated levels of phosphate and tensin homology (PTEN), protein kinase B (AKT), autophagy related-5, light chain-3 (LC3-Ⅱ), Becline-1, and Dynein, were found. Our results of transmission electron microscopy displayed signs of autophagosomes/autophagic lysosomes, and immunofluorescence assay displayed that NH3 exposure reduced the relative amount of CD8+ B-lymphocyte in chicken BF. Exposure of NH3 led to energy metabolism disturbance by decreasing mRNA levels of glucose metabolism factors aconitase-2, hexokinase-1, hexokinase-2, lactate dehydrogenase-A, lactate dehydrogenase-B, pyruvate kinase, phosphofructokinase and succinate dehydrogenase complex unit-B, and adenosine triphosphates (ATPase) activities (Na+/K+ ATPase, Ca2+ ATPase, Mg2+ ATPase, and Ca/Mg2+ ATPase). Moreover, phosphate and tensin homology was found as target gene of microRNA-99a-3p which confirmed that high concentration of NH3 caused autophagy in chicken BF. In summary, these findings suggested that ammonia induced autophagy via miR-99a-3p, the reduction of ATPase activity, and the alteration of autophagy-related factors, and energy metabolism mediation in BF. Our findings provide information to assess the harmful effects of NH3 on chicken and clues for human health pathophysiology.

Abstract Background Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either in a local or online way; however, there has been no such tool supporting both graphical user interface (GUI) and parallel computing. Results We developed a GUI software called LDkit for LD analysis, which supports parallel computing. The LDkit supports both variant call format (VCF) and PLINK ‘ped + map’ format. At the same time, users could also just analyze a subset of individuals from the whole population. The LDkit reads the data by block and then paralleled the computation process by monitoring the usage of processes. Assessment on the Human 1000 genome data showed that when paralleled with 32 threads, the running time was reduced to less than 6 minutes from ~77 minutes using the chromosome 22 dataset with 1,103,547 SNPs and 2504 individuals. Conclusions The software LDkit can be effectively used to calculate and plot LD decay, LD block, and linkage disequilibrium analysis between a site and a given region. Most importantly, both graphical user interface (GUI) and stand-alone packages are available for users’ convenience. LDkit was written in JAVA language under cross-platform support.

In the research on energy-efficient networking methods for precision agriculture, a hot topic is the energy issue of sensing nodes for individual wireless sensor networks. The sensing nodes of the wireless sensor network should be enabled to provide better services with limited energy to support wide-range and multi-scenario acquisition and transmission of three-dimensional crop information. Further, the life cycle of the sensing nodes should be maximized under limited energy. The transmission direction and node power consumption are considered, and the forward and high-energy nodes are selected as the preferred cluster heads or data-forwarding nodes. Taking the cropland cultivation of ginseng as the background, we put forward a particle swarm optimization-based networking algorithm for wireless sensor networks with excellent performance. This algorithm can be used for precision agriculture and achieve optimal equipment configuration in a network under limited energy, while ensuring reliable communication in the network. The node scale is configured as 50 to 300 nodes in the range of 500 × 500 m2, and simulated testing is conducted with the LEACH, BCDCP, and ECHERP routing protocols. Compared with the existing LEACH, BCDCP, and ECHERP routing protocols, the proposed networking method can achieve the network lifetime prolongation and mitigate the decreased degree and decreasing trend of the distance between the sensing nodes and center nodes of the sensor network, which results in a longer network life cycle and stronger environment suitability. It is an effective method that improves the sensing node lifetime for a wireless sensor network applied to cropland cultivation of ginseng.

A dynamic path-planning algorithm based on a general constrained optimization problem (GCOP) model and a sequential quadratic programming (SQP) method with sensor input is proposed in this paper. In an unknown underwater space, the turtle-inspired amphibious spherical robot (ASR) can realise the path-planning control movement and achieve collision avoidance. Due to the special underwater environments, thrusters and diamond parallel legs (DPLs) are installed in the lower hemisphere to realise accurate motion control. A propulsion model for a novel water-jet thruster based on experimental analysis and a modified Denavit–Hartenberg (MDH) algorithm are developed for multiple degrees of freedom (MDOF) to realize high-precision and high-speed motion control. Simulations and experiments verify that the effectiveness of the GCOP and SQP algorithms can realize reasonable path planning and make it possible to improve the flexibility of underwater movement with a small estimation error.

Maize cultivation performance, including the efficiency of the input and output of maize, which reflect the allocation and utilization of resources in the process of maize cultivation, is crucial for evaluating and improving maize cultivation. This paper adopts the method of quadratic regression orthogonal rotation combination experimental design to explore the effects of four main cultivation measures (planting density, nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer) on maize yield at five levels (−2, −1, 0, 1; 2). The CCR (A. Charnes, W. Cooper and E. Rhodes) model, which is the basic model of data envelopment analysis (DEA), was used to evaluate the 36 groups of cultivation measures. The results show that 9 groups are CCR-effective cultivation measures, but the performance of these cultivation measures cannot be further evaluated. To improve the evaluation of cultivation performance, a novel method termed as the group decision method of DEA (GDM-DEA) is proposed to detect the improvement of evaluation performance and is tested using the measurements of maize cultivation. The results suggest that the GDM-DEA method can classify and sort the performance of all the cultivation measures, which is more sensitive and accurate than the CCR method. For the effective cultivation measures that meet the requirements of GDM-DEA, the optimal cultivation measures could be determined according to the ranking of yield. This method determined the most effective cultivation measure. Further independent validation showed that the final optimal cultivation measures fall in the range of the expected cultivation measures. The GDM-DEA model is capable of more effectively evaluating cultivation performance.

Canine hip dysplasia (CHD) and rupture of the cranial cruciate ligament (RCCL) are two complex inherited orthopedic traits of dogs. These two traits may occur concurrently in the same dog. Genomic prediction of these two diseases would benefit veterinary medicine, the dog’s owner, and dog breeders because of their high prevalence, and because both traits result in painful debilitating osteoarthritis in affected joints. In this study, 842 unique dogs from 6 breeds with hip and stifle phenotypes were genotyped on a customized Illumina high density 183 k single nucleotide polymorphism (SNP) array and also analyzed using an imputed dataset of 20,487,155 SNPs. To implement genomic prediction, two different statistical methods were employed: Genomic Best Linear Unbiased Prediction (GBLUP) and a Bayesian method called BayesC. The cross-validation results showed that the two methods gave similar prediction accuracy (r = 0.3–0.4) for CHD (measured as Norberg angle) and RCCL in the multi-breed population. For CHD, the average correlation of the AUC was 0.71 (BayesC) and 0.70 (GBLUP), which is a medium level of prediction accuracy and consistent with Pearson correlation results. For RCCL, the correlation of the AUC was slightly higher. The prediction accuracy of GBLUP from the imputed genotype data was similar to the accuracy from DNA array data. We demonstrated that the genomic prediction of CHD and RCCL with DNA array genotype data is feasible in a multiple breed population if there is a genetic connection, such as breed, between the reference population and the validation population. Albeit these traits have heritability of about one-third, higher accuracy is needed to implement in a natural population and predicting a complex phenotype will require much larger number of dogs within a breed and across breeds. It is possible that with higher accuracy, genomic prediction of these orthopedic traits could be implemented in a clinical setting for early diagnosis and treatment, and the selection of dogs for breeding. These results need continuous improvement in model prediction through ongoing genotyping and data sharing. When genomic prediction indicates that a dog is susceptible to one of these orthopedic traits, it should be accompanied by clinical and radiographic screening at an acceptable age with appropriate follow-up.

BackgroundAlong with the vigorous development of Internet technology, increasing the functions of the various types of equipment, network communication easy and diversity, at the same time, the amount of data is very huge, under the network bandwidth limitations, through long lead to a data need to be cut into more, one by one, transfer times, information delay problems.ResultsAiming at the problems of poor data integrity, low efficiency and poor serialization efficiency of traditional data storage information, this article introduces Protobuf technology to study the serialization of data storage information. The serpentine gap method is used to complete the allocation interval of the sequence nodes, so that the working state and the resting state always maintain a dynamic balance. According to the first-level rules, the storage data of the completed target node is obtained, and the grammatical structure and the semantics of the target data are analyzed, Meanwhile corresponding correspondences are established, and the data storage information is serialized. In order to verify the effectiveness of Protobuf's data storage information serialization method, a comparative experiment is designed. By using three methods of HDVM, Redis and Protobuf to serialize JSON data, the comparative analysis shows that HDVM has the longest processing time and Protobuf has the shortest processing time, and the data integrity is not affected. The simulation data shows that the Protobuf serialization method has short conversion time, high space utilization, and the Obvious advantages in correctness and integrity. It is vary suitable for serialization of JSON data in the case of limited bandwidth.

BACKGROUND:The massive quantities of genetic data generated by high-throughput sequencing pose challenges to data storage, transmission and analyses. These problems are effectively solved through data compression, in which the size of data storage is reduced and the speed of data transmission is improved. Several options are available for compressing and storing genetic data. However, most of these options either do not provide sufficient compression rates or require a considerable length of time for decompression and loading. RESULTS:Here, we propose TRCMGene, a lossless genetic data compression method that uses a referential compression scheme. The novel concept of two-step compression method, which builds an index structure using K-means and k-nearest neighbours, is introduced to TRCMGene. Evaluation with several real datasets revealed that the compression factor of TRCMGene ranges from 9 to 21. TRCMGene presents a good balance between compression factor and reading time. On average, the reading time of compressed data is 60% of that of uncompressed data. Thus, TRCMGene not only saves disc space but also saves file access time and speeds up data loading. These effects collectively improve genetic data storage and transmission in the current hardware environment and render system upgrades unnecessary. TRCMGene, user manual and demos could be accessed freely from https://github.com/tangyou79/TRCM. The data mentioned in this manuscript could be downloaded from: https://github.com/tangyou79/TRCM/wiki.

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

Deep learning methods for classifying maize leaf diseases often need a lot of labeled data for training during the model training phase, but labeling data is sometimes difficult and expensive. In addition, complicated environmental circumstances can readily impede the recognition effect. In order to solve this issue, SimCLR and the triplet attention mechanism were coupled to create a system for classifying maize leaf diseases. Firstly, to increase the robustness of the model, several data enhancement techniques were used to imitate the interference of complex components in the natural environment. Secondly, the use of labeled data was minimized by learning the similarity between related categories using a self-supervised SimCLR framework. The feature extraction network simultaneously employed a triplet attention mechanism to enhance the model's ability to extract information about the interactions between space and channel, improve attention to discriminative features, and lessen interference from complex factors. In order to verify the effects of incorporating SimCLR and triplet attention mechanisms, this work used three different types of maize leaf diseases and healthy leaves as experimental objects. The final enhanced model was compared and analyzed with seven widely used image classification models. The experimental results demonstrated that the maize leaf disease classification model suggested in this article effectively enhanced the classification performance of maize leaf diseases containing complex factors of interference with the test set, with the network model proposed in this article having an average classification accuracy of 91.87%, better than the seven comparative models and requiring no additional labeled data during the training process. [ABSTRACT FROM AUTHOR]