Massey Research Online - Browsing by Author "Li J"

Browsing by Author "Li J"

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A multi-label classification model for full slice brain computerised tomography image
(BioMed Central Ltd, 2020-11-18) Li J; Fu G; Chen Y; Li P; Liu B; Pei Y; Feng H
BACKGROUND: Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. RESULTS: In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. CONCLUSION: The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.
An Investigation about Gene Modules Associated with hDPSC Differentiation for Adolescents
(Hindawi Limited, 2019-04-04) Xu W; Li J; Li J; Yang J-J; Wang Q; Liu B; Qiu W; Ballini A
Dental pulp stem cells (DPSCs) have the property of self-renewal and multidirectional differentiation so that they have the potential for future regenerative therapy of various diseases. The latest breakthrough in the biology of stem cells and the development of regenerative biology provides an effective strategy for regenerative therapy. However, in the medium promoting differentiation during long-term passage, DPSCs would lose their differentiation capability. Some efforts have been made to find genes influencing human DPSC (hDPSC) differentiation based on hDPSCs isolated from adults. However, hDPSC differentiation is a very complex process, which involves multiple genes and multielement interactions. The purpose of this study is to detect sets of correlated genes (i.e., gene modules) that are associated to hDPSC differentiation at the crown-completed stage of the third molars, by using weighted gene coexpression network analysis (WGCNA). Based on the gene expression dataset GSE10444 from Gene Expression Omnibus (GEO), we identified two significant gene modules: yellow module (742 genes) and salmon module (9 genes). The WEB-based Gene SeT AnaLysis Toolkit showed that the 742 genes in the yellow module were enriched in 59 KEGG pathways (including Wnt signaling pathway), while the 9 genes in the salmon module were enriched in one KEGG pathway (neurotrophin signaling pathway). There were 660 (7) genes upregulated at P10 and 82 (2) genes downregulated at P10 in the yellow (salmon) module. Our results provide new insights into the differentiation capability of hDPSCs.
Caring for the last 3%: Telehealth potential and broadband implications for remote Australia
(CSIRO, 2012-11-20) Dods S; Freyne J; Alem L; Nepal S; Li J; Jang-Jaccard J
Australians living in remote regions of our nation live with far poorer health outcomes than those in our regional and urban areas. The gaps in health service availability and outcomes between people in urban areas and those in remote parts of our country are well known. Telehealth, the provision of health related services at a distance using technology assisted communications, offers a means to narrow this gap by improving the level and diversity of services in remote areas.
Comparison of algorithms for road surface temperature prediction
(Emerald Publishing Limited, 2018-12-13) Liu B; Shen L; You H; Dong Y; Li J; Li Y
Purpose: The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately. Design/methodology/approach: Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors. Findings: The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms. Originality/value: This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.
DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network
(BioMed Central Ltd, 2023-09-18) Zhang J; Liu B; Wu J; Wang Z; Li J
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations such as time consumption and high costs. To address these challenges, the field of bioinformatics has increasingly turned to deep learning technologies for analyzing gene sequences. Nevertheless, the pursuit of improved experimental results has led to the inclusion of numerous complex analysis function modules, resulting in models with a growing number of parameters. To overcome these limitations, it is proposed a novel approach for analyzing DNA transcription factor sequences, which is named as DeepCAC. This method leverages deep convolutional neural networks with a multi-head self-attention mechanism. By employing convolutional neural networks, it can effectively capture local hidden features in the sequences. Simultaneously, the multi-head self-attention mechanism enhances the identification of hidden features with long-distant dependencies. This approach reduces the overall number of parameters in the model while harnessing the computational power of sequence data from multi-head self-attention. Through training with labeled data, experiments demonstrate that this approach significantly improves performance while requiring fewer parameters compared to existing methods. Additionally, the effectiveness of our approach is validated in accurately predicting DNA transcription factor sequences.
Dietary Supplementation of Yeast Culture Into Pelleted Total Mixed Rations Improves the Growth Performance of Fattening Lambs
(Frontiers Media S.A., 2021-05) Song B; Wu T; You P; Wang H; Burke JL; Kang K; Yu W; Wang M; Li B; He Y; Huo Q; Li C; Tian W; Li R; Li J; Wang C; Sun X
There is a growing interest in the use of yeast (Saccharomyces cerevisiae) culture (YC) for the enhancement of growth performance and general animal health. Grain-based pelleted total mixed rations (TMR) are emerging in intensive sheep farming systems, but it is uncertain if the process of pelleting results in YC becoming ineffective. This study aimed to examine the effects of YC supplemented to pelleted TMR at two proportions of corn in the diet on animal performance, feed digestion, blood parameters, rumen fermentation, and microbial community in fattening lambs. A 2 × 2 factorial design was adopted with two experimental factors and two levels in each factor, resulting in four treatments: (1) low proportion of corn in the diet (LC; 350 g corn/kg diet) without YC, (2) LC with YC (5 g/kg diet), (3) high proportion of corn in the diet (HC; 600 g corn/kg diet) without YC, and (4) HC with YC. Fifty-six 3-month-old male F2 hybrids of thin-tailed sheep and Northeast fine-wool sheep with a liveweight of 19.9 ± 2.7 kg were randomly assigned to the four treatment groups with an equal number of animals in each group. The results showed that live yeast cells could not survive during pelleting, and thus, any biological effects of the YC were the result of feeding dead yeast and the metabolites of yeast fermentation rather than live yeast cells. The supplementation of YC resulted in 31.1 g/day more average daily gain regardless of the proportion of corn in the diet with unchanged feed intake during the 56-day growth measurement period. The digestibility of neutral detergent fibre and acid detergent fibre was increased, but the digestibility of dry matter, organic matter, and crude protein was not affected by YC. The supplementation of YC altered the rumen bacterial population and species, but the most abundant phyla Bacteroidetes, Firmicutes, and Proteobacteria remained unchanged. This study indicates that YC products can be supplemented to pelleted TMR for improved lamb growth performance, although live yeast cells are inactive after pelleting. The improved performance could be attributed to improved fibre digestibility.
DiffusionDCI: A Novel Diffusion-Based Unified Framework for Dynamic Full-Field OCT Image Generation and Segmentation
(IEEE Access, 2024) Yang B; Li J; Wang J; Li R; Gu K; Liu B; Militello C
Rapid and accurate identification of cancerous areas during surgery is crucial for guiding surgical procedures and reducing postoperative recurrence rates. Dynamic Cell Imaging (DCI) has emerged as a promising alternative to traditional frozen section pathology, offering high-resolution displays of tissue structures and cellular characteristics. However, challenges persist in segmenting DCI images using deep learning methods, such as color variation and artifacts between patches in whole slide DCI images, and the difficulty in obtaining precise annotated data. In this paper, we introduce a novel two-stage framework for DCI image generation and segmentation. Initially, the Dual Semantic Diffusion Model (DSDM) is specifically designed to generate high-quality and semantically relevant DCI images. These images not only serve as an effective means of data augmentation to assist downstream segmentation tasks but also help in reducing the reliance on expensive and hard-to-obtain large annotated medical image datasets. Furthermore, we reuse the pretrained DSDM to extract diffusion features, which are then infused into the segmentation network via a cross-attention alignment module. This approach enables our network to capture and utilize the characteristics of DCI images more effectively, thereby significantly enhancing segmentation results. Our method was validated on the DCI dataset and compared with other methods for image generation and segmentation. Experimental results demonstrate that our method achieves superior performance in both tasks, proving the effectiveness of the proposed model.
DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning
(BioMed Central Ltd, 2023-12) Wu J; Liu B; Zhang J; Wang Z; Li J
PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS: In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
Enhanced denitrification driven by a novel iron-carbon coupled primary cell: chemical and mixotrophic denitrification
(Springer, 2024-01-10) Wu R; Jeyakumar P; Nanthi B; Zhai X; Wang H; Pan M; Lian J; Cheng L; Li J; Hou M; Cui Y; Yang X; Dai K
Iron-carbon micro-electrolysis system is a promising method for promoting electron transfer in nitrate removal. However, many traditional approaches involving simple physical mixing inevitably suffered from the confined iron-carbon contact area and short validity period, leading to the overuse of iron. Here, a ceramsite-loaded microscale zero-valent iron (mZVI) and acidified carbon (AC) coupled-galvanic cell (CMC) was designed to support chemical, autotrophic and heterotrophic denitrification. Long-term experiments were conducted to monitor the nitrogen removal performance of denitrification reactors filled with CMC and thus optimized the denitrification performance by improving fabrication parameters and various operating conditions. The denitrification contributions test showed that the chemical denitrification pathway contributed most to nitrate removal (57.3%), followed by autotrophic (24.6%) and heterotrophic denitrification pathways (18.1%). The microbial analysis confirmed the significant aggregation of related denitrifying bacteria in the reactors, while AC promoted the expression of relevant nitrogen metabolism genes because of accelerated uptake and utilization of iron complexes. Meanwhile, the electrochemical analysis revealed a significantly improved electron transfer capacity of AC compared to pristine carbon. Overall, our study demonstrated the application of a novel mZVI-AC coupled material for effective nitrate removal and revealed the potential impact of CMC in the multipathway denitrification process. Graphical Abstract: [Figure not available: see fulltext.]
Increased precipitation enhances soil respiration in a semi-arid grassland on the Loess Plateau, China
(PeerJ Inc., 2021-02-02) Wang Y; Xie Y; Rapson G; Ma H; Jing L; Zhang Y; Zhang J; Li J; Zhu B
BACKGROUND: Precipitation influences the vulnerability of grassland ecosystems, especially upland grasslands, and soil respiration is critical for carbon cycling in arid grassland ecosystems which typically experience more droughty conditions. METHODS: We used three precipitation treatments to understand the effect of precipitation on soil respiration of a typical arid steppe in the Loess Plateau in north-western China. Precipitation was captured and relocated to simulate precipitation rates of 50%, 100%, and 150% of ambient precipitation. RESULTS AND DISCUSSION: Soil moisture was influenced by all precipitation treatments. Shoot biomass was greater, though non-significantly, as precipitation increased. However, both increase and decrease of precipitation significantly reduced root biomass. There was a positive linear relationship between soil moisture and soil respiration in the study area during the summer (July and August), when most precipitation fell. Soil moisture, soil root biomass, pH, and fungal diversity were predictors of soil respiration based on partial least squares regression, and soil moisture was the best of these. CONCLUSION: Our study highlights the importance of increased precipitation on soil respiration in drylands. Precipitation changes can cause significant alterations in soil properties, microbial fungi, and root biomass, and any surplus or transpired moisture is fed back into the climate, thereby affecting the rate of soil respiration in the future.
Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer
(Elsevier Inc, 2023-09-15) Wang H; Liu B; Long J; Yu J; Ji X; Li J; Zhu N; Zhuang X; Li L; Chen Y; Liu Z; Wang S; Zhao S
Comprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.
Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam
(Association for Information Systems, 2022-06-28) Yu J; Taskin N; Nguyen CP; Li J; Pauleen DJ
Background: As a breakthrough technology, big data provides an opportunity for organizations to acquire business value and enhance competitiveness. Many companies have listed big data analytics (BDA) as one of their top priorities. However, research shows that managers are still reluctant to change their work patterns to utilize this new technology. In addition, the empirical evidence on what determines their adoption of BDA in management decision making is still rare. Method: To more broadly understand the determinants affecting managers’ actual use of BDA in decision making, a survey was conducted on a sample of 363 respondents from New Zealand, China, and Vietnam who work in different managerial roles. The dual process theory, the technology–organization–environment framework, and the key associated demographic characteristics are integrated to form the theoretical foundation to study the internal and external factors influencing the adoption. Results: The findings illustrate that the common essential factors across countries linking BDA in decision making are technology readiness, data quality, managers’ and organizational knowledge related to BDA, and organizational expectations. The factors that are more situation-dependent and evident in one or two countries’ results are managers’ predilection toward valuing intuition and experience over analytics and organizational size. Conclusion: The findings enrich the current literature and provide implications for practitioners on how they can improve the adoption process of this new technology.
Pharmacokinetic Properties of Baitouweng Decoction in Bama Miniature Pigs: Implications for Clinical Application in Humans
(Hindawi, 2024-05-10) Xu Q; Gao H; Zhu F; Xu W; Wang Y; Xie J; Guo G; Yang L; Ma L; Shen Z; Li J; Regmi B
Traditional Chinese medicine (TCM) serves as a significant adjunct to chemical treatment for chronic diseases. For instance, the administration of Baitouweng decoction (BTWD) has proven effective in the treatment of ulcerative colitis. However, the limited understanding of its pharmacokinetics (PK) has impeded its widespread use. Chinese Bama miniature pigs possess anatomical and physiological similarities to the human body, making them a valuable model for investigating PK properties. Consequently, the identification of PK properties in Bama miniature pigs can provide valuable insights for guiding the clinical application of BTWD in humans. To facilitate this research, a rapid and sensitive UPLC-MS/MS method has been developed for the simultaneous quantification of eleven active ingredients of BTWD in plasma. Chromatographic separation was conducted using an Acquity UPLC HSS T3 C18 column and a gradient mobile phase comprising acetonitrile and water (containing 0.1% acetic acid). The methodology was validated in accordance with the FDA Bioanalytical Method Validation Guidance for Industry. The lower limit of quantitation fell within the range of 0.60-2.01 ng/mL. Pharmacokinetic studies indicated that coptisine chloride, berberine, columbamine, phellodendrine, and obacunone exhibited low Cmax, while fraxetin, esculin, fraxin, and pulchinenoside B4 were rapidly absorbed and eliminated from the plasma. These findings have implications for the development of effective components in BTWD and the adjustment of clinical dosage regimens.
Prediction of seasonal population dynamics of Grapholita molesta (Busck) and Adoxophyes orana (Fischer von Röslerstamm) in peach orchards using sex pheromone trap and degree-days and its implications in pest management
(2023-10-04) Ma A; Zhang H; Ran H; Yang X; J Hao J; Zhang J; Li H; Yu Z; Wang X; He X; Li J
The successful management of lepidopteran moths in orchards usually depends on the precise forecast of adult activity. However, the seasonal phenology of moths varies between crop cultivars and years, making it difficult to schedule the control measures. Here, we monitored male flight activity of oriental fruit moth Grapholita molesta and summer fruit tortrix moth Adoxophyes orana by using sex pheromone traps in peach orchards of three different cultivars for three successive years. We developed a logistic multiple-peaks model to fit data and then calculated degree-days (DD) required for male activity and neonate emergency. Results show that G. molesta and A. orana males had 4–5 and 3 flight peaks per year, respectively. The seasonal phenology of G. molesta or A. orana was quite stable with an identical timing of each flight peak between cultivars in a year. The flight activity was usually higher in the second and third peaks for both moths, with a higher cumulative number of G. molesta males captured than that of A. orana. Compared to A. orana, G. molesta emerged early in spring and required lower degree-days to reach the subsequent flight peaks and for neonate emergency. Our results suggest that to decline the possibility of outbreaks of moths during the growing seasons, pheromone traps should be scheduled in April with a cumulative DD between 49.6 and 207.1 for G. molesta and in mid-May–early June with a cumulative DD between 450.4 and 866.7 for A. orana, aiming to trap the newly emerged male adults or disrupting female mating success of overwintered moths in orchards. Based on the thermal requirement for egg hatching (i.e., 79.4 DD for G. molesta and 90.0 DD for A. orana), insecticide treatments would be applied in late-April–early May and late May–early June to reduce the field population density of neonates of G. molesta and A. orana, respectively, to reduce fruit damage in orchards. Furthermore, pheromone traps set up in late July–early August (573.8–1025.2 DD) for G. molesta and in mid-September (1539.7–1788.9 DD) for A. orana may suppress overwintering populations and thus decrease pest infestation in next year.
RADseq-based population genomic analysis and environmental adaptation of rare and endangered recretohalophyte Reaumuria trigyna.
(John Wiley and Sons, Inc., 2024-03-01) Dang Z; Li J; Liu Y; Song M; Lockhart PJ; Tian Y; Niu M; Wang Q; Varshney R
Genetic diversity reflects the survival potential, history, and population dynamics of an organism. It underlies the adaptive potential of populations and their response to environmental change. Reaumuria trigyna is an endemic species in the Eastern Alxa and West Ordos desert regions in China. The species has been considered a good candidate to explore the unique survival strategies of plants that inhabit this area. In this study, we performed population genomic analyses based on restriction-site associated DNA sequencing to understand the genetic diversity, population genetic structure, and differentiation of the species. Analyses of 92,719 high-quality single-nucleotide polymorphisms (SNPs) indicated that overall genetic diversity of R. trigyna was low (HO = 0.249 and HE = 0.208). No significant genetic differentiation was observed among the investigated populations. However, a subtle population genetic structure was detected. We suggest that this might be explained by adaptive diversification reinforced by the geographical isolation of populations. Overall, 3513 outlier SNPs were located in 243 gene-coding sequences in the R. trigyna transcriptome. Potential sites under diversifying selection occurred in genes (e.g., AP2/EREBP, E3 ubiquitin-protein ligase, FLS, and 4CL) related to phytohormone regulation and synthesis of secondary metabolites which have roles in adaptation of species. Our genetic analyses provide scientific criteria for evaluating the evolutionary capacity of R. trigyna and the discovery of unique adaptions. Our findings extend knowledge of refugia, environmental adaption, and evolution of germplasm resources that survive in the Ordos area.
The Complementarity of Amino Acids in Cooked Pulse/Cereal Blends and Effects on DIAAS
(MDPI (Basel, Switzerland), 2021-09-24) Han F; Moughan PJ; Li J; Stroebinger N; Pang S; Spina A; Pasqualone A
The aim was to study the complementary effect between cereals and pulses on protein quality. The values for the digestible indispensable amino acid score (DIAAS) in cooked cereals and pulses, given alone, and blends of cooked cereals and pulses, were determined. True ileal digestibility (TID) values of amino acids for adult humans were obtained. It is difficult to determine ileal amino acid digestibility in humans directly, and for this reason, the growing pig is often used to obtain such values, as a preferred animal model. Seven growing pigs fitted with a T-cannula at the terminal ileum were allotted to a 7 × 6 incomplete Latin square with seven semi-synthetic diets (cooked mung bean, adzuki bean, millet, adlay, mung bean + millet, adzuki bean + adlay, and an N-free diet) and six 7-day periods. The mean TID values for crude protein differed significantly (p < 0.05), with millet having the highest digestibility (89.4%) and the adzuki bean/adlay mixture having the lowest (79.5%). For lysine, adzuki bean had the highest TID (90%) and millet had the lowest (70%). For the mean of all the amino acids, there was a significant (p < 0.05) effect of diet, with the TID ranging from 72.4% for the adzuki bean/adlay mixture to 89.9% for the adzuki beans. For the older child, adolescent, and adult, the DIAAS (%) was 93 for mung beans, 78 for adzuki beans, 22 for millet, 16 for adlay, and 66 for mung beans + millet, and 51 for adzuki beans + adlay. For mung beans, valine was first-limiting, and the SAA for adzuki beans, while lysine was first-limiting for the other foods. Chinese traditional diets, containing both cereals and pulses, are complementary for most, but not all of the indispensable amino acids.
Transcriptome-Wide Gene Expression Plasticity in Stipa grandis in Response to Grazing Intensity Differences
(MDPI (Basel, Switzerland), 2021-11-02) Dang Z; Jia Y; Tian Y; Li J; Zhang Y; Huang L; Liang C; Lockhart PJ; Matthew C; Li FY; Hobza R
Organisms have evolved effective and distinct adaptive strategies to survive. Stipa grandis is a representative species for studying the grazing effect on typical steppe plants in the Inner Mongolia Plateau. Although phenotypic (morphological and physiological) variations in S. grandis in response to long-term grazing have been identified, the molecular mechanisms underlying adaptations and plastic responses remain largely unknown. Here, we performed a transcriptomic analysis to investigate changes in gene expression of S. grandis under four different grazing intensities. As a result, a total of 2357 differentially expressed genes (DEGs) were identified among the tested grazing intensities, suggesting long-term grazing resulted in gene expression plasticity that affected diverse biological processes and metabolic pathways in S. grandis. DEGs were identified in RNA-Seq and qRT-PCR analyses that indicated the modulation of the Calvin-Benson cycle and photorespiration metabolic pathways. The key gene expression profiles encoding various proteins (e.g., ribulose-1,5-bisphosphate carboxylase/oxygenase, fructose-1,6-bisphosphate aldolase, glycolate oxidase, etc.) involved in these pathways suggest that they may synergistically respond to grazing to increase the resilience and stress tolerance of S. grandis. Our findings provide scientific clues for improving grassland use and protection and identifying important questions to address in future transcriptome studies.

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