Abstract Background Grape phylloxera (Daktulosphaira vitifoliae) is one of the world’s most important viticultural pests. However, the reproductive mode, genetic structure and host adaptation of phylloxera in various viticultural environments remains unclear. We examined reproductive mode and genetic structure of phylloxera by analyzing microsatellite makers across the samples from four vineyard-sites in California. Result The phylloxera populations in California are believed to have predominantly parthenogenetic reproduction. Therefore, genetic diversity of phylloxera is expected to be limited. However, this study showed relatively high levels of diversity in Napa and Yolo county populations with a large number of unique genotypes, average number of alleles (2.1 to 2.9) and observed heterozygosities (0.330 to 0.388) per vineyard-sites. Reproduction diversity index (G: N—unique genotypes versus number of samples) ranged from 0.500 to 0.656 among vineyard-sites. Both significant and non-significant P sex (probability of sexual reproduction) were observed among different repeated genotypes within each vineyard. Moreover, high variation of F IS was observed among different loci in each vineyard-site. Genetic structure analysis (UPGMA) and various measures of population differentiations (F ST, PCA, and gene flow estimates) consistently separated AXR#1 (Vitis vinifera x V. rupestris—widely planted in California during the 1960s and 1970s) associated populations from the populations associated with other different rootstocks. Conclusion Genetic diversity, G: N ratio, P sex and F IS consistently suggested the occurrence of both parthenogenetic and sexual reproduction in California populations. This study clearly identified two major groups of phylloxera obtained from various rootstocks, with one group exclusively associated with only AXR#1 rootstock, defined as “biotype B”, and another group associated with vinifera-based rootstocks, known as “biotype A”.

Abstract Background Plant cytochrome P450 monooxygenases (CYP) mediate synthesis and metabolism of many physiologically important primary and secondary compounds that are related to plant defense against a range of pathogenic microbes and insects. To determine if cytochrome P450 monooxygenases are involved in defense response to Xylella fastidiosa (Xf) infection, we investigated expression and regulatory mechanisms of the cytochrome P450 monooxygenase CYP736B gene in both disease resistant and susceptible grapevines. Results Cloning of genomic DNA and cDNA revealed that the CYP736B gene was composed of two exons and one intron with GT as a donor site and AG as an acceptor site. CYP736B transcript was up-regulated in PD-resistant plants and down-regulated in PD-susceptible plants 6 weeks after Xf inoculation. However, CYP736B expression was very low in stem tissues at all evaluated time points. 5'RACE and 3'RACE sequence analyses revealed that there were three candidate transcription start sites (TSS) in the upstream region and three candidate polyadenylation (PolyA) sites in the downstream region of CYP736B. Usage frequencies of each transcription initiation site and each polyadenylation site varied depending on plant genotype, developmental stage, tissue, and treatment. These results demonstrate that expression of CYP736B is regulated developmentally and in response to Xf infection at both transcriptional and post-transcriptional levels. Multiple transcription start and polyadenylation sites contribute to regulation of CYP736B expression. Conclusions This report provides evidence that the cytochrome P450 monooxygenase CYP736B gene is involved in defense response at a specific stage of Xf infection in grapevines; multiple transcription initiation and polyadenylation sites exist for CYP736B in grapevine; and coordinative and selective use of transcription initiation and polyadenylation sites play an important role in regulation of CYP736B expression during growth, development and response to Xf infection.

The spindle assembly checkpoint (SAC) ensures faithful chromosome segregation during cell division by monitoring kinetochore-microtubule attachment. Plants produce both sequence-conserved and diverged SAC components, and it has been largely unknown how SAC activation leads to the assembly of these proteins at unattached kinetochores to prevent cells from entering anaphase. In Arabidopsis thaliana, the noncanonical BUB3.3 protein was detected at kinetochores throughout mitosis, unlike MAD1 and the plant-specific BUB1/MAD3 family protein BMF3 that associated with unattached chromosomes only. When BUB3.3 was lost by a genetic mutation, mitotic cells often entered anaphase with misaligned chromosomes and presented lagging chromosomes after they were challenged by low doses of the microtubule depolymerizing agent oryzalin, resulting in the formation of micronuclei. Surprisingly, BUB3.3 was not required for the kinetochore localization of other SAC proteins or vice versa. Instead, BUB3.3 specifically bound to BMF3 through two internal repeat motifs that were not required for BMF3 kinetochore localization. This interaction enabled BMF3 to recruit CDC20, a downstream SAC target, to unattached kinetochores. Taken together, our findings demonstrate that plant SAC utilizes unconventional protein interactions for arresting mitosis, with BUB3.3 directing BMF3's role in CDC20 recruitment, rather than the recruitment of BUB1/MAD3 proteins observed in fungi and animals. This distinct mechanism highlights how plants adapted divergent versions of conserved cell cycle machinery to achieve specialized SAC control.

Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application.

Temperature is a characteristic often correlated with environmental and health issues. This paper presents an organic thin film transistor (OTFT) based temperature sensor having a detection range of 30–45 °C, which, therefore, encompasses the human body temperature. The OTFT sensor featured thermosensitive poly(N-isopropylacrylamide) (PNIPAM) and pentacene as the gate dielectric and semiconductor, respectively. The PNIPAM film possessed a dielectric constant of 4.2 with very low leakage current density. The OTFT exhibited high electrical performance, with a hole mobility (μ) of 0.90 ± 0.04 cm2 V−1 s−1, a threshold voltage (Vth) of −15.4 ± 1.16 V, and an on/off ratio of 104. Significant changes in the drain current and the values of Vth and μ occurred when the temperature of the device was varied within the range 30–45 °C at an interval of 0.5 °C. The operating principle for this temperature sensor was based on the structural transformation of the PNIPAM dielectric and the enhanced charge transport of the pentacene semiconductor upon varying the temperature. Flexible OTFTs fabricated on polyethylene terephthalate substrate displayed hole mobilities as high as 0.39 ± 0.01 cm2 V−1 s−1, values of Vth of −18.6 ± 0.45, and on/off ratios of 102, and were workable for over 100 bending cycles.

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in cross-domain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability.

Few-shot learning (FSL) is suitable for plant-disease recognition due to the shortage of data. However, the limitations of feature representation and the demanding generalization requirements are still pressing issues that need to be addressed. The recent studies reveal that the frequency representation contains rich patterns for image understanding. Given that most existing studies based on image classification have been conducted in the spatial domain, we introduce frequency representation into the FSL paradigm for plant-disease recognition. A discrete cosine transform module is designed for converting RGB color images to the frequency domain, and a learning-based frequency selection method is proposed to select informative frequencies. As a post-processing of feature vectors, a Gaussian-like calibration module is proposed to improve the generalization by aligning a skewed distribution with a Gaussian-like distribution. The two modules can be independent components ported to other networks. Extensive experiments are carried out to explore the configurations of the two modules. Our results show that the performance is much better in the frequency domain than in the spatial domain, and the Gaussian-like calibrator further improves the performance. The disease identification of the same plant and the cross-domain problem, which are critical to bring FSL to agricultural industry, are the research directions in the future.