Optimizing rice yield, quality and nutrient use efficiency through combined application of nitrogen and potassium.

Reasonable nitrogen (N) and potassium (K) application rates can effectively improve fertilizer use efficiency, rice yield and quality. A two-year field experiment was conducted with combined application of three N rates (135, 180, and 225 kg ha-1, denoted as N1-N3) and four K rates (0, 90, 135, and 180 kg ha-1, denoted as K0-K3) using super indica hybrid rice cultivar Yixiangyou (YXY) 2115 to explore the effects of co-application of N and K on rice growth and development. The results indicated that the combined application of N and K had significantly interactive effects on dry matter (DM) accumulation, nutrients absorption, N harvest index (NHI), K harvest index (KHI), spikelets per panicle and most rice quality indexes. The highest total DM accumulation (17998.17-19432.47 kg ha-1) at maturity stage was obtained under N3K2. The effect of co-application of N and K on nutrients absorption and utilization varied between the two years and within each year. The highest total N and K accumulations at maturity stage were observed under N3K1 and N3K2, respectively, while the highest N recovery efficiency (NRE) and K recovery efficiency (KRE) were observed under N1K3. High expression levels of N and K metabolism-related genes in rice grains were observed under N3K2 or N3K3, consistent with N and K uptake. Co-application of N and K increased rice yield significantly and the highest yield (6745.02-7010.27 kg ha-1) was obtained under N2K2. As more N was gradually applied, rice appearance quality improved but milling, cooking and eating quality decreased. Although appropriate application of K could improve rice milling, cooking and eating quality, it reduced appearance quality. The optimal milling, cooking and eating quality were obtained under N1K2, while the best appearance quality was obtained under N3K0. Overall, a combination of 135-210 kg ha-1 N and 115-137 kg ha-1 K application rates was recommended for achieving relatively higher yield and better quality in rice production.

Responses of Yield and Photosynthetic Characteristics of Rice to Climate Resources under Different Crop Rotation Patterns and Planting Methods.

Climate is the most important environmental factor influencing yield during rice growth and development. To investigate the relationships between climate and yield under different crop rotation patterns and planting methods, three typical rotation patterns (vegetable-rice (V), rape-rice (R), and wheat-rice (W)) and two mechanical planting methods (mechanical transplanting (T1) and mechanical direct seeding (T2)) were established. The results showed that compared to the V rotation pattern, the average daily temperature (ADT) during the sowing to heading stage increased under both R and W rotation patterns, which significantly shortened the growth period. Thus, the effective accumulated temperature (EAT), photosynthetic capacity, effective panicle (EP), and spikelet per panicle (SP) under R and W rotation patterns significantly decreased, leading to reductions in grain yield (GY). VT2 had a higher ratio of productive tillers (RPT), relative chlorophyll content (SPAD), leaf area index (LAI), and net photosynthetic rate (Pn) than those of VT1, which significantly increased panicle dry matter accumulation (DMA), resulting in an increase in GY. Although RT2 and WT2 had a higher RPT than those of RT1 and WT1, the GY of RT1 and WT1 decreased due to the significant reductions in EAT and photosynthetic capacity. Principal component analysis (PCA) showed that the comprehensive score for different rotation patterns followed the order of V > R > T with VT2 ranking first. The structural equation model (SEM) showed that EAT and ADT were the most important climate factors affecting yield, with total effects of 0.520 and -0.446, respectively. In conclusion, mechanical direct seeding under vegetable-rice rotation pattern and mechanical transplanting under rape-rice or wheat-rice rotation pattern were the rice-planting methods that optimized the climate resources in southwest China.

Giant electrically tunable magnon transport anisotropy in a van der Waals antiferromagnetic insulator.

Anisotropy is a manifestation of lowered symmetry in material systems that have profound fundamental and technological implications. For van der Waals magnets, the two-dimensional (2D) nature greatly enhances the effect of in-plane anisotropy. However, electrical manipulation of such anisotropy as well as demonstration of possible applications remains elusive. In particular, in-situ electrical modulation of anisotropy in spin transport, vital for spintronics applications, has yet to be achieved. Here, we realized giant electrically tunable anisotropy in the transport of second harmonic thermal magnons (SHM) in van der Waals anti-ferromagnetic insulator CrPS4 with the application of modest gate current. Theoretical modeling found that 2D anisotropic spin Seebeck effect is the key to the electrical tunability. Making use of such large and tunable anisotropy, we demonstrated multi-bit read-only memories (ROMs) where information is inscribed by the anisotropy of magnon transport in CrPS4. Our result unveils the potential of anisotropic van der Waals magnons for information storage and processing.

Effects of water stress on starch synthesis and accumulation of two rice cultivars at different growth stages.

Rice is a water intensive crop and soil water conditions affect rice yield and quality. However, there is limited research on the starch synthesis and accumulation of rice under different soil water conditions at different growth stages. Thus, a pot experiment was conducted to explore the effects of IR72 (indica) and Nanjing (NJ) 9108 (japonica) rice cultivars under flood-irrigated treatment (CK, 0 kPa), light water stress treatment (L, -20 ± 5 kPa), moderate water stress treatment (M, -40 ± 5 kPa) and severe water stress treatment (S, -60 ± 5 kPa) on the starch synthesis and accumulation and rice yield at booting stage (T1), flowering stage (T2) and filling stage (T3), respectively. Under LT treatment, the total soluble sugar and sucrose contents of both cultivars decreased while the amylose and total starch contents increased. Starch synthesis-related enzyme activities and their peak activities at mid-late growth stage increased as well. However, applying MT and ST treatments produced the opposite effects. The 1000-grain weight of both cultivars increased under LT treatment while the seed setting rate increased only under LT3 treatment. Compared with CK, water stress at booting stage decreased grain yield. The principal component analysis (PCA) showed that LT3 got the highest comprehensive score while ST1 got lowest for both cultivars. Furthermore, the comprehensive score of both cultivars under the same water stress treatment followed the trend of T3 > T2 > T1, and NJ 9108 had a better drought-resistant ability than IR72. Compared with CK, the grain yield under LT3 increased by 11.59% for IR72 and 16.01% for NJ 9108, respectively. Overall, these results suggested that light water stress at filling stage could be an effective method to enhance starch synthesis-related enzyme activities, promote starch synthesis and accumulation and increase grain yield.

LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.

MOTIVATION: From a systematic perspective, it is crucial to infer and analyze gene regulatory network (GRN) from high-throughput single-cell RNA sequencing data. However, most existing GRN inference methods mainly focus on the network topology, only few of them consider how to explicitly describe the updated logic rules of regulation in GRNs to obtain their dynamics. Moreover, some inference methods also fail to deal with the over-fitting problem caused by the noise in time series data. RESULTS: In this article, we propose a novel embedded Boolean threshold network method called LogBTF, which effectively infers GRN by integrating regularized logistic regression and Boolean threshold function. First, the continuous gene expression values are converted into Boolean values and the elastic net regression model is adopted to fit the binarized time series data. Then, the estimated regression coefficients are applied to represent the unknown Boolean threshold function of the candidate Boolean threshold network as the dynamical equations. To overcome the multi-collinearity and over-fitting problems, a new and effective approach is designed to optimize the network topology by adding a perturbation design matrix to the input data and thereafter setting sufficiently small elements of the output coefficient vector to zeros. In addition, the cross-validation procedure is implemented into the Boolean threshold network model framework to strengthen the inference capability. Finally, extensive experiments on one simulated Boolean value dataset, dozens of simulation datasets, and three real single-cell RNA sequencing datasets demonstrate that the LogBTF method can infer GRNs from time series data more accurately than some other alternative methods for GRN inference. AVAILABILITY AND IMPLEMENTATION: The source data and code are available at https://github.com/zpliulab/LogBTF.

Invariant Pattern Recognition with Log-Polar Transform and Dual-Tree Complex Wavelet-Fourier Features.

In this paper, we propose a novel method for 2D pattern recognition by extracting features with the log-polar transform, the dual-tree complex wavelet transform (DTCWT), and the 2D fast Fourier transform (FFT2). Our new method is invariant to translation, rotation, and scaling of the input 2D pattern images in a multiresolution way, which is very important for invariant pattern recognition. We know that very low-resolution sub-bands lose important features in the pattern images, and very high-resolution sub-bands contain significant amounts of noise. Therefore, intermediate-resolution sub-bands are good for invariant pattern recognition. Experiments on one printed Chinese character dataset and one 2D aircraft dataset show that our new method is better than two existing methods for a combination of rotation angles, scaling factors, and different noise levels in the input pattern images in most testing cases.

Inferring causal gene regulatory network via GreyNet: From dynamic grey association to causation.

Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks. Nevertheless, machine learning, especially deep learning, is generally a "black box," which lacks interpretability. The causality has not been well recognized in GRN inference procedures. In this article, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene-gene interactions in the uncertain regulatory system. Then, we incorporate generalized multivariate Granger causality regression methods to transform the dynamic grey association into causation to generate directional regulatory links. We evaluate our model on the DREAM4 in silico benchmark dataset and real-world hepatocellular carcinoma (HCC) time-series data. We achieved competitive results on the DREAM4 compared with other state-of-the-art algorithms and gained meaningful GRN structure on HCC data respectively.

Response of yield and quality of giant embryo rice to nitrogen application and analysis of lipid-lowering effect.

Giant embryo rice is known as a highly nutritious functional rice because it is rich in gamma-aminobutyric acid (GABA), which has various regulatory functions in the human body. To study the response of giant embryo rice yield and quality to nitrogen (N) application, and to verify the effect of giant embryo brown rice on alleviating hyperlipidemia in rats. In this study, field experiments were conducted in 2020 and 2021 using the giant embryo rice varietiers J20 (japonica) and Koshihikari (japonica) rice as experimental materials and five N levels, 0 (N0), 90 (N1), 135 (N2), 180 (N3) and 225 (N4) kg ha-1. The results showed that the yield of both varieties increased with increasing N and the maximum values were observed under the N2 treatment. As more N was gradually applied, the brown rice rate, milled rice rate, head rice rate and GABA content of both varieties first increased and then decreased, while the chalky grain rate and chalkiness showed the opposite trend. The optimal values of these indexes were observed under the N2 treatment. The peak viscosity and breakdown value of J20 decreased, while its setback value and pasting temperature increased with increasing N. In contrast, Koshihikari showed the opposite trend. The protein content and protein component contents of both varieties showed an increasing trend with increasing N, among which gliadin was the most sensitive protein component to N fertilizer. Animal experiments results showed that J20 brown rice could significantly slow the rate of weight gain of rats, reduce serum total cholesterol and triglyceride levels and increase high-density lipoprotein cholesterol levels. Therefore, increasing N could effectively enhance J20 yield and improve processing, appearance and nutritional quality but decrease cooking and eating quality. The brown rice J20 had the effect of slowing the rate of weight gain and reducing the hyperlipidemia level of rats, the optimal N application rate for achieving high yield, high quality and good functional characteristics in the giant embryo rice J20 was 135 kg ha-1. These findings will provide a theoretical and technical foundation for the popularization and application of giant embryo rice in the future.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms.

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) data provides unprecedented opportunities to reconstruct gene regulatory networks (GRNs) at fine-grained resolution. Numerous unsupervised or self-supervised models have been proposed to infer GRN from bulk RNA-seq data, but few of them are appropriate for scRNA-seq data under the circumstance of low signal-to-noise ratio and dropout. Fortunately, the surging of TF-DNA binding data (e.g. ChIP-seq) makes supervised GRN inference possible. We regard supervised GRN inference as a graph-based link prediction problem that expects to learn gene low-dimensional vectorized representations to predict potential regulatory interactions. RESULTS: In this paper, we present GENELink to infer latent interactions between transcription factors (TFs) and target genes in GRN using graph attention network. GENELink projects the single-cell gene expression with observed TF-gene pairs to a low-dimensional space. Then, the specific gene representations are learned to serve for downstream similarity measurement or causal inference of pairwise genes by optimizing the embedding space. Compared to eight existing GRN reconstruction methods, GENELink achieves comparable or better performance on seven scRNA-seq datasets with four types of ground-truth networks. We further apply GENELink on scRNA-seq of human breast cancer metastasis and reveal regulatory heterogeneity of Notch and Wnt signalling pathways between primary tumour and lung metastasis. Moreover, the ontology enrichment results of unique lung metastasis GRN indicate that mitochondrial oxidative phosphorylation (OXPHOS) is functionally important during the seeding step of the cancer metastatic cascade, which is validated by pharmacological assays. AVAILABILITY AND IMPLEMENTATION: The code and data are available at https://github.com/zpliulab/GENELink. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unintentional Action Localization via Counterfactual Examples.

How do humans localize unintentional action like " A boy falls down while playing skateboard "? Cognitive science shows that an 18-month-old baby understands the intention by observing the actions and comparing the feedback. Motivated by this evidence, we propose a causal inference approach that constructs a video pool containing intentional knowledge, conducts the counterfactual intervention to observe intentional action, and compares the unintentional action with intentional action to achieve localization. Specifically, we first build a video pool, where each video contains the same action content as an original unintentional action video. Then we conduct the counterfactual intervention to generate counterfactual examples. We further maximize the difference between the predictions of factual unintentional action and counterfactual intentional action to train the model. By disentangling the effects of different clues on the model prediction, we encourage the model to highlight the intention clue and alleviate the negative effect brought by the training bias of the action content clue. We evaluate our approach on a public unintentional action dataset and achieve consistent improvements on both unintentional action recognition and localization tasks.

Probabilistic Temporal Modeling for Unintentional Action Localization.

Humans have the inherent advantage of understanding action intention, while it is an enormous challenge to train the machine to localize unintentional action in videos due to the lack of reliable annotations for stable training. The annotations of unintentional action are unreliable since different annotators are affected by their subjective appraisals and intrinsic ambiguity, which brings heavy difficulties for the training. To address this issue, we propose a probabilistic framework for unintentional action localization by modeling the uncertainty of annotations. Our framework consists of two main components, including Temporal Label Aggregation (TLA) and Dense Probabilistic Localization (DPL). We first formulate each annotated failure moment as a temporal label distribution. Then we propose a TLA component to aggregate temporal label distributions of different failure moments in an online manner and generate dense probabilistic supervision. Based on TLA, We further develop a DPL component to jointly train three heads (i.e., probabilistic dense classification, probabilistic temporal detection, and probabilistic regression) with different supervision granularities and make them highly collaborative. We evaluate our approach on the largest unintentional action dataset OOPS and demonstrate that our approach can achieve significant improvement over the baseline and state-of-the-art methods.

Remaining Useful Life prediction of rolling bearings based on risk assessment and degradation state coefficient.

Prediction of Remaining Useful Life (RUL) of bearings is very important for the condition-based maintenance of the rotating machinery. In order to predict the RUL more accurately, an approach of RUL prediction based on risk assessment and degradation state coefficient is proposed. The Mahalanobis Distance (MD) is calculated depending on the characteristics of vibration signals in the time domain and time-frequency domain. The features in the time-frequency domain are extracted by using Variational Mode Decomposition-Singular Value Decomposition (VMD-SVD). The monotonously increasing Health Indicator (HI) is obtained by using MD1-CUMSUM. The risk assessment is proposed to adaptively determine the thresholds of initial fault and failure, which is a trade-off between the false alarm rate and sensitivity. The RUL prediction of the testing bearing is completed based on the Genetic Algorithm-Support Vector Regression (GA-SVR) and Modified Health Indicator (MHI) with the degradation state coefficient. The proposed approach is verified by using two experimental vibration signal datasets. The results show that the proposed method has good capability to predict the RUL of rolling bearings.

Electrically switchable van der Waals magnon valves.

Van der Waals magnets have emerged as a fertile ground for the exploration of highly tunable spin physics and spin-related technology. Two-dimensional (2D) magnons in van der Waals magnets are collective excitation of spins under strong confinement. Although considerable progress has been made in understanding 2D magnons, a crucial magnon device called the van der Waals magnon valve, in which the magnon signal can be completely and repeatedly turned on and off electrically, has yet to be realized. Here we demonstrate such magnon valves based on van der Waals antiferromagnetic insulator MnPS3. By applying DC electric current through the gate electrode, we show that the second harmonic thermal magnon (SHM) signal can be tuned from positive to negative. The guaranteed zero crossing during this tuning demonstrates a complete blocking of SHM transmission, arising from the nonlinear gate dependence of the non-equilibrium magnon density in the 2D spin channel. Using the switchable magnon valves we demonstrate a magnon-based inverter. These results illustrate the potential of van der Waals anti-ferromagnets for studying highly tunable spin-wave physics and for application in magnon-base circuitry in future information technology.

Person Re-Identification via Attention Pyramid.

In this paper, we propose an attention pyramid method for person re-identification. Unlike conventional attention-based methods which only learn a global attention map, our attention pyramid exploits the attention regions in a multi-scale manner because human attention varies with different scales. Our attention pyramid imitates the process of human visual perception which tends to notice the foreground person over the cluttered background, and further focus on the specific color of the shirt with close observation. Specifically, we describe our attention pyramid by a "split-attend-merge-stack" principle. We first split the features into multiple local parts and learn the corresponding attentions. Then, we merge local attentions and stack these merged attentions with the residual connection as an attention pyramid. The proposed attention pyramid is a lightweight plug-and-play module that can be applied to off-the-shelf models. We implement our attention pyramid method in two different attention mechanisms including: channel-wise attention and spatial attention. We evaluate our method on four large-scale person re-identification benchmarks including Market-1501, DukeMTMC, CUHK03, and MSMT17. Experimental results demonstrate the superiority of our method, which outperforms the state-of-the-art methods by a large margin with limited computationa cost. Code is available at https://github.com/CHENGY12/APNet.

Vagus nerve plays a pivotal role in CD4+ T cell differentiation during CVB3-induced murine acute myocarditis.

Abnormalities in CD4+ T cell (Th cell) differentiation play an important role in the pathogenesis of viral myocarditis (VMC). Our previous studies demonstrated that activation of the cholinergic anti-inflammatory pathway (CAP) alleviated the inflammatory response. In addition, we observed that right cervical vagotomy aggravates VMC by inhibiting CAP. However, the vagus nerve's effect on differentiation of CD4+ T cells has not been studied in VMC mice to date. In this study, we investigated the effects of cervical vagotomy and the α7nAChR agonist pnu282987 on CD4+ T cell differentiation in a murine myocarditis model (BALB/c) infected with coxsackievirus B3 (CVB3). Splenic CD4+ T cells from CVB3-induced mice obtained and cultured to investigate the potential mechanism of CD4+ T cell differentiation. Each Th cell subset was analyzed by flow cytometry. Our results showed that right cervical vagotomy increased proportions of Th1 and Th17 cells and decreased proportions of Th2 and Treg cells in the spleen. Vagotomy-induced upregulation of T-bet, Ror-γ, IFN-γ, and IL-17 expression while downregulating the expression of Gata3, Foxp3, and IL-4 in the heart. In addition, we observed upregulated levels of proinflammatory cytokines, aggravated myocardial lesions and cellular infiltration, and worsened cardiac function in VMC mice. Pnu282987 administration reversed these outcomes. Furthermore, vagotomy inhibited JAK2-STAT3 activation and enhanced NF-κB activation in splenic CD4+ T cells. The CD4+ T cell differentiation was related to JAK2-STAT3 and NF-κB signal pathways. In conclusion, vagus nerve modulates the inflammatory response by regulating CD4+ T cell differentiation in response to VMC.

Self-growth Ni2P nanosheet arrays with cationic vacancy defects as a highly efficient bifunctional electrocatalyst for overall water splitting.

HYPOTHESIS: The transition metal phosphide is one of the promising bifunctional electrocatalysts for overall water splitting. Moreover, the activity of phosphide catalysts can be further enhanced by the cationic vacancy engineering. EXPERIMENTS: The self-growth Ni2P nanosheet arrays with abundant cationic vacancy defects (V-Ni2P/NF) has been synthesized via a facile multi-step reaction process involving hydrothermal, phosphorization and acid-etching of Mn which was doped in Ni2P nanosheets as a sacrificial dopant. Furthermore, the experimental studies and density functional theory (DFT) calculations were carried out to evaluate its electrochemical performance. FINDINGS: The chemical and electrocatalytic property of Ni2P were successfully optimized by cationic vacancy engineering and the obtained V-Ni2P/NF catalyst exhibited superior bifunctional catalytic performance for both hydrogen evolution (HER) and oxygen evolution reaction (OER) compared to pristine Ni2P and Mn-doped Ni2P in alkaline electrolyte. The V-Ni2P/NF can afford the current density of 10 mA cm-2 at a small overpotential of 55 mV for HER and 250 mV for OER. Additionally, the water electrolysis device assembled by the V-Ni2P/NF electrode as both the anode and cathode just requires a small voltage of 1.59 V to achieve 10 mA cm-2 and shows no obvious attenuation for 50 h.

Spatial-Temporal Attention-Aware Learning for Video-Based Person Re-Identification.

In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames. However, the image quality may be varying for different spatial regions and changing over time, which shall contribute to person representation and matching adaptively. Our STAL method aims to attend to the salient parts of persons in videos jointly in both spatial and temporal domains. To achieve this, we slice the video into multiple spatial-temporal units which preserve the body structure of a person and develop a joint spatial-temporal attention model to learn the quality scores of these units. We evaluate the proposed method on three challenging datasets including iLIDS-VID, PRID-2011, and the large-scale MARS dataset, and consistently improve the rank-1 accuracy by a large margin of 5.7%, 0.9%, and 6.6% respectively, in comparison with the state-of-the-art methods.

Diagnostic value of long noncoding RNAs for hepatocellular carcinoma: A PRISMA-compliant meta-analysis.

BACKGROUND: Increasing evidences have shown that long noncoding RNAs (lncRNAs) are involved in cancer diagnosis and prognosis. However, the overall diagnostic accuracy of lncRNAs for hepatocellular carcinoma (HCC) remains unclear. Herein, we perform a meta-analysis to assess diagnostic value of lncRNAs for HCC. METHODS: The online PubMed, Cochrane, Web of Science, and Embase database were searched for eligible studies published until October 5, 2016. Study quality was evaluated with the Quality Assessment for Studies of Diagnostic Accuracy (QUADAS). All statistical analyses were conducted with Stata 12.0 and Meta-Disc 1.4. RESULTS: We included 19 studies from 10 articles with 1454 patients with HCC and 1300 controls. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and AUC for lncRNAs in the diagnosis of HCC were 0.83 (95% confidence interval [CI]: 0.76-0.88), 0.80 (95% CI: 0.73-0.86), 4.2 (95% CI: 3.00-5.80), 0.21 (95% CI: 0.15-0.31), 20 (95% CI: 11-34), and 0.88 (95% CI: 0.85-0.91), respectively. Additionally, the diagnostic value of lncRNAs varied based on sex ratio of cases and characteristics of methods (specimen type and reference gen). CONCLUSION: This meta-analysis suggests lncRNAs show a moderate diagnostic accuracy for HCC. However, prospective studies are required to confirm its diagnostic value.

Bacterial microbiota in small brown planthopper populations with different rice viruses.

The small brown planthopper (SBPH) is an important virus vector, transmitting Rice stripe virus (RSV), and Rice black-streaked dwarf virus (RBSDV). Insect symbionts play an essential role in the insect fitness, however, it is still unclear about their contributions to viral transmission by SBPH. Here, we investigated endosymbiont communities in non-viruliferous, RSV-infected, and RBSDV-infected SBPH populations using Illumina 16S rRNA gene MiSeq sequencing. In total, 281,803 effective sequences of the 16S rRNA gene were generated from different samples. Sequence analysis revealed the percentages of these bacterial groups in different SBPH populations on several taxonomic levels ranging from phyla to genera. The extremely consistent bacterial diversity and abundance indicated that RSV or RBSDV infection did not affect the composition and abundance of symbionts in SBPH. It was notable that Wolbachia was dominant in all populations. The symbiosis between Wolbachia and SBPH might be potentially studied and utilized to control pest SBPH in the future.