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For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Time-course transcriptomic information unravels the mechanisms of improved drought tolerance by drought-priming in wheat Qing Li, Zhuangzhuang Sun, Zihan Jing, Xiao Wang, Chuan Zhong, Wenliang Wan, Maguje Masa Malko, Linfeng Xu, Zhaofeng Li, Qin Zhou, Jian Cai, Yingxin Zhong, Mei Huang, Dong Jiang DOI: 10.1016/j.jia.2024.03.081 Online: 26 April 2024 Abstract （3）      PDF in ScienceDirect       Frequent drought events especially those occur in the reproductive stages severely restrict global crop productivity.  Moderate drought priming during the earlier growth stages is a promising strategy for plants to resist to recurrent severe drought stress.  However, the underlying mechanisms remain unclear.  Here, we subjected wheat plants to drought priming during the vegetative growth stage and to severe drought stress at 10 days after anthesis.  We then collected leaf samples at the ends of the drought priming, recovery periods, and at the ends of drought stress for transcriptome sequencing in combination with phenotypic and physiological determination.  The drought-primed wheat plant maintained a lower plant temperature, with higher stomatal openness and photosynthesis, thereby resulting in much less 1,000-grain weight and grain yield losses under the later drought stress than the non-primed plants.  Interestingly, 416 genes of which 27 transcription factors (e.g., MYB, NAC, HSF) seemed to be closely related to the improved drought tolerance as indicated by the dynamic transcriptome analysis.  Moreover, the candidate genes showed six temporal expression patterns and significantly enriched in several stress response related pathways such as plant hormone signal transduction, starch and sucrose metabolism, arginine and proline metabolism, inositol phosphate metabolism, and wax synthesis.  These findings illustrate new insights into physiological and molecular mechanisms of the long-term effects of early drought priming to effectively improve drought tolerance in wheat, which proved potential approaches to challenge the increasing abiotic stresses and secure food safety under global warming scenarios. Reference | Related Articles | Metrics Select Investigating the impact of hyperspectral reconstruction techniques on the quantitative inversion of rice physiological parameters: A case study using the MST++ model Weiguang Yang, Bin Zhang, Weicheng Xu, Shiyuan Liu, Yubin Lan, Lei Zhang DOI: 10.1016/j.jia.2024.03.082 Online: 26 April 2024 Abstract （2）      PDF in ScienceDirect       Quantitative inversion is a significant topic in remote sensing science.  The development of visible light-based hyperspectral reconstruction techniques has opened up novel prospects for low-cost, high-precision remote sensing inversion in agriculture.  The aim of this study was to assesses the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications.  Hyperspectral images were reconstructed using the MST++ hyperspectral reconstruction model and compared with the original visible light images in terms of their correlation with physiological parameters, the accuracy of single-feature modeling, and the accuracy of combined feature modeling.  The results showed that compared to the visible light image, the reconstructed data exhibited a stronger correlation with physiological parameters, and the accuracy was improved in both the single-feature and the combined feature inversion mode.  However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement on the inversion model accuracy.  It was concluded that for physiological parameters that are not easy to be directly observed, deep mining of features in visible light.   data through hyperspectral reconstruction technology can improve the accuracy of the inversion model.  Appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments. To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing, further development is necessary with broader wavelength ranges and more diverse agricultural scenes. Reference | Related Articles | Metrics Select Using mixed kernel support vector machine to improve the predictive accuracy of genome selection Jinbu Wang, Wencheng Zong, Liangyu Shi, Mianyan Li, Jia Li, Deming Ren, Fuping Zhao, Lixian Wang, Ligang Wang DOI: 10.1016/j.jia.2024.03.083 Online: 26 April 2024 Abstract （2）      PDF in ScienceDirect       The advantages of genome selection (GS) in animal and plant breeding are self-evident. Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately. Machine learning models have demonstrated remarkable potential in addressing these challenges. In this study, we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression (SVR) in GS. Six single kernel functions (SVR_L, SVR_C, SVR_G, SVR_P, SVR_S, SVR_L) and four mixed kernel functions (SVR_GS, SVR_GP, SVR_LS, SVR_LP) were used to predict genome breeding values. The prediction accuracy, mean squared error (MSE) and mean absolute error (MAE) were used as evaluation indicators to compare with two traditional parametric models (GBLUP, BayesB) and two popular machine learning models (RF, KcRR). The results indicate that in most cases, the performance of the mixed kernel function model significantly outperforms that of GBLUP, BayesB and single kernel function. For instance, for T1 in the pig dataset, the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP, and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively. For E1 in the wheat dataset, SVR_GS achieves 13.3% higher prediction accuracy than GBLUP. Among single kernel functions, the Laplacian and Gaussian kernel functions yield similar results, with the Gaussian kernel function performing better. The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions. Furthermore, regarding runtime, SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset, with only a slight increase in runtime compared to the single kernel function model. In summary, the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness, and the model such as SVR_GS has important application potential for GS. Reference | Related Articles | Metrics Select Changes of bone remodeling, cartilage damage and apoptosis-related pathways in broilers with femoral head necrosis Yaling Yu, Hongfan Ge, Hang Gao, Yanyan Zhang, Kangping Liu, Zhenlei Zhou DOI: 10.1016/j.jia.2024.03.084 Online: 26 April 2024 Abstract （8）      PDF in ScienceDirect       Femoral head necrosis (FHN) is a common leg disorder in the poultry industry often leads to significant cartilage damage. The mechanism behind abnormal apoptosis in FHN broilers, leading to cartilage damage, remains unclear; although endoplasmic reticulum stress (ERS) has been found to play a role in glucocorticoid-induced FHN broilers. In this study, we collected samples from broilers with femoral head separation (FHS) and femoral head separation accompanied with growth plate lacerations (FHSL) in a broiler farm. The aim was to investigate the potential association between the severity of FHN, bone remodeling and cartilage damage. Additionally, primary chondrocytes were treated with methylprednisolone (MP) to construct an in vitro FHN model, followed by inhibition or activation of ERS or hypoxia inducible factor-1α (HIF-1α) to further investigate the mechanism of apoptosis in cartilage. The results suggested that cartilage appeared to be the appropriate tissue to investigate the potential mechanisms of FHN, as the degree of cartilage damage was found to be closely related to the severity of the disease. Bone quality was only affected in FHSL broilers, although factors related to bone metabolism were significantly altered among FHN-affected broilers. In addition, cartilage in FHN-affected broilers exhibited high levels of apoptosis and upregulated expression of ERS-related and HIF-1α, which was consistent with both in vivo and in vitro findings after MP treatment. The results were further supported by treatment with HIF-1α or ERS inhibition or activation. In conclusion, bone remodeling and cartilage homeostasis were affected in FHN broilers, but only cartilage damage was significantly exacerbated with FHN development. Moreover, activation of ERS or HIF-1α resulted in apoptosis in cartilage, thus exhibiting a significant correlation with FHN severity. Reference | Related Articles | Metrics Select Fresh maize yield in response to nitrogen application rates and characteristics of nitrogen-efficient varieties Fei Bao, Ping Zhang, Qiying Yu, Yunfei Cai, Bin Chen, Heping Tan, Hailiang Han, Junfeng Hou, Fucheng Zhao DOI: 10.1016/j.jia.2024.03.085 Online: 26 April 2024 Abstract （1）      PDF in ScienceDirect       Efficient nitrogen management is crucial in developing sustainable strategies aimed at enhancing yield while mitigating negative environmental impacts.  However, limited research has focused on this aspect in the production of fresh maize.  Therefore, this study analyzed nitrogen application rates and yield for 40 sweet and 44 waxy maize varieties in Zhejiang Province, China from 2015 to 2019 across five sites.  Nitrogen application rates were categorized as relatively high (RHN: >300 kg ha-1 for sweet maize, >320 kg ha-1 for waxy maize) or relatively low (RLN).  An increase in nitrogen application rates for both sweet and waxy maize significantly reduced nitrogen fertilizer partial productivity (R2=0.616, P<0.01; R2=0.643, P<0.01), indicating that the optimum nitrogen application rate in this study might be the lowest values (160 kg ha-1 for sweet maize and 180 kg ha-1 for waxy maize).  The kernel number per ear of sweet maize had a potentially more significant impact on fresh grain yield compared to the 1,000-fresh kernel weight both under RLN and RHN.  In waxy maize, 1,000-kernel weight contributed more to fresh grain yield under RLN, and kernel number ear-1 and 1,000-kernel weight cooperatively affected yield under RHN.  In this study, it was observed that sweet maize required taller plant and ear height, along with an optimal ear-plant height ratio, to enhance dry matter accumulation and increase source size, particularly under RLN, to achieve a higher fresh grain yield.  In contrast, a lower ear height and ear-plant height ratio of waxy maize probably contributed more to increased kernel number and weight under RLN, likely due to a lower ear height can reduce the distance between sink and source, enabling more efficient photoassimilate allocation to the ear. Reference | Related Articles | Metrics Select Assessment of CH4 flux and its influencing drivers in the rice-wheat agroecosystem of the Huai River Basin, China Xiaolan Yu, Fangmin Zhang, Yanqiu Fang, Xiaohan Zhao, Kaidi Zhang, Yanyu Lu DOI: 10.1016/j.jia.2024.03.076 Online: 25 April 2024 Abstract （6）      PDF in ScienceDirect       To understand the CH4 flux variations and their climatic drivers in the rice-wheat agroecosystem in the Huai River Basin of China, the CH4 flux was observed by using open-path eddy covariance at a typical rice-wheat rotation system in Anhui Province from November 2019 to October 2021. The variations and their drivers were then analyzed with the Akaike information criterion method. CH4 flux showed distinct diurnal variations with single peaks during 9:00~13:00 local time. The highest peak was 2.15 µg m-2 s-1 which occurred at 11:00 in the vegetative growth stage in the rice growing season (RGS). CH4 flux also showed significant seasonal variations. The average CH4 flux in the vegetative growth stage in the RGS (193.8±74.2 mg m-2 d-1) was the highest among all growth stages. The annual total CH4 flux in the non-rice growing season (3.2 g m-2, 11.8%) was relatively small compared to that in the RGS (23.9 g m-2, 88.2%). CH4 flux increased significantly with increase in air temperature, soil temperature, and soil water content in both the RGS and the non-RGS, while it decreased significantly with increase in vapor pressure deficit in the RGS. This study provided a comprehensive understanding of the CH4 flux and its drivers in the rice-wheat rotation agroecosystem in the Huai River Basin of China. In addition, our findings will be helpful for the validation and adjustment of the CH4 models in this region. Reference | Related Articles | Metrics Select toGC: a pipeline to correct gene model for functional excavation of dark GPCRs in Phytophthora sojae Min Qiu, Chun Yan, Huaibo Li, Haiyang Zhao, Siqun Tu, Yaru Sun, Saijiang Yong, Ming Wang, Yuanchao Wang DOI: 10.1016/j.jia.2024.03.077 Online: 25 April 2024 Abstract （5）      PDF in ScienceDirect       The accuracy of genomic annotation is crucial for subsequent functional investigations; however, computational protocols used in high-throughput annotation of open reading frames (ORFs) can introduce inconsistencies. These inconsistencies, which lead to non-uniform extension or truncation of sequence ends, pose challenges for downstream analyses. Existing strategies to rectify these inconsistencies are time-consuming and labor-intensive, lacking specific approaches. To address this gap, we developed toGC, a tool that integrates genomic annotation with RNA-seq datasets to rectify annotation inconsistencies. Using toGC, we achieved an accuracy of nearly 100% accuracy in correcting inconsistencies in published P. sojae ORFs. We applied this innovative pipeline to the GPCR-bigrams gene family, which was predicted to have 42 members in the P. sojae genome but lacked experimental validation. By employing toGC, we identified 32 GPCR-bigram ORFs with inconsistencies between previous annotations and toGC-corrected sequences. Notably, among these were 5 genes (GPCR-TKL9, GPCR-TKL15, GPCR-PDE3, GPCR-AC3, and GPCR-AC4) showed substantial inconsistencies. Experimental gene annotation confirmed the effectiveness of toGC, as sequences obtained through cloning matched those annotated by toGC. Importantly, we discovered two novel GPCRs (GPCR-AC3 and GPCR-AC4), which were previously mispredicted as a single gene. CRISPR/Cas9-mediated knockout experiments revealed the involvement of GPCR-AC4 but not GPCR-AC3 in oospore production, further confirming their status as two separate genes. In addition to P. sojae, the reliability of the toGC pipeline in Phytophthora capsici and Pythium ultimum further emphasizes the robustness of this pipeline. Our findings highlight the utility of toGC for reliable gene model correction, facilitating investigations into biological functions and offering potential applications in diverse species analyses. Reference | Related Articles | Metrics Select Optimizing sowing dates increase solar radiation to mitigate maize lodging and yield variability: A five-year field study Xinglong Wang, Fan Liu, Nan Zhao, Xia Du, Pijiang Yin, Tongliang Li, Tianqiong Lan, Dongju Feng, Fanlei Kong, Jichao Yuan DOI: 10.1016/j.jia.2024.03.078 Online: 25 April 2024 Abstract （4）      PDF in ScienceDirect       Optimizing sowing dates (SDs) is a potential strategy for adjusting maize production to climate change and increasing yield.  However, there is still a lack of research on the combined effects of lodging and yield in relation to climatic variables across various SDs.  This study aims to investigate the changing patterns and distribution of important climatic variables during the maize growth season, their impact on yield and lodging, and the critical factors affecting lodging at crucial growth stages under different SD scenarios.  In this study, we assessed the impact of climate change on yield and lodging by conducting field experiments over 5 years (2015, 2016, 2019–2021) encompassing 25 SDs in the Sichuan basin, China.  Results demonstrated that the lodging rate had a significant effect on the coefficient of variation (CV, 3.31–10.50%) of maize yield.  A 1% increase in lodging rate, led to a decrease of 58.05 kg ha-1 in yield.  Changes in SDs notably affected solar radiation (Sr) from emergence to silking (E-R1).  Additionally, the study found that Sr explained 34.7% of the lodging rate variation in E-R1.  Analysis of historical meteorological data showed notable inter-annual variations in Sr trends, with a decline of -8.7763 MJ m-2 yr-1 from 1990 to 2021, especially noticeable from late May to early July.  Variation Partitioning Analysis (VPA) revealed that climatic variables during the period from emergence to physiological maturity (E-R6) and E-R1 explained 43.9 and 53.2% of yield, respectively, across different SDs.  These variables also contributed 56.0 and 45.4% to lodging.  Using Random Forest (RF) determined that changes in SDs significantly impacted lodging rates mainly through modifications in basal internode morphology, which explained 69.79% of the variation.  The study identified optimal sowing dates for achieving high and consistent yields, primarily occurring between late March and mid-April, attributed to increased Sr during E-R1.  Overall, this research provides valuable insights into the effects of climate change on stalk lodging and offers guidance on adjusting sowing dates to mitigate maize lodging rates. Reference | Related Articles | Metrics Select Genome-wide association study dissecting drought resistance-associated loci based on physiological traits in common bean Lei Wu, Yujie Chang, Lanfen Wang, Shumin Wang, Jing Wu DOI: 10.1016/j.jia.2024.03.079 Online: 25 April 2024 Abstract （3）      PDF in ScienceDirect       Genetic improvement of drought resistance is one of the main breeding goals for common bean; therefore, molecular markers need to be identified to facilitate drought resistance breeding.  In this study, we evaluated the proline, trehalose, raffinose and stachyose contents of 210 common bean accessions under two watering conditions and found high variation.  The coefficient of variation ranged from 21.21% for proline content to 78.69% for stachyose content under well-watered conditions and from 20.11% for proline content to 50.08% for trehalose content under drought stress.  According to our genome-wide association analysis, 32 quantitative trait loci were associated with drought resistance, and seven overlapped with known loci.  Four hotspot regions were identified at Pv01, Pv07 and Pv11.  A set of candidate genes was identified, including genes encoding MYB, bZIP, bHLH, ERF and protein kinases. Among these genes, Phvul.001G189400, Phvul.007G273000 and Phvul.008G270500 were annotated as bZIP, ERF and WRKY, respectively.  These genes have been reported to be involved in drought stress responses in Arabidopsis thaliana and were induced by drought stress in common bean.  Significant SNPs in six candidate gene regions formed different haplotypes, and phenotypic analysis revealed significant differences among the haplotypes.  These results provide new insight into the genetic basis of drought resistance in common bean and reveal candidate genes and superior natural variations that will be useful for improving common bean. Reference | Related Articles | Metrics Select Changes in milk fat globule membrane proteins along lactation stage of Laoshan dairy goat Chuozi Liang, Zhongna Yu, Guangming Zhu, Yixuan Li, Xueheng Sun, Hongning Jiang, Qijing Du, Rongbo Fan, Jun Wang, Yongxin Yang, Rongwei Han DOI: 10.1016/j.jia.2024.03.080 Online: 25 April 2024 Abstract （2）      PDF in ScienceDirect       The milk fat globule membrane (MFGM) is a complex structure with numerous functions, and its composition is affected by a number of circumstances. There have been few systematic investigations on goat MFGM proteome profiling during lactation. Individual milk samples from 15 healthy dairy goats were obtained at six lactation time points for investigation of the MFGM proteome using both data-independent acquisition (DIA) and data-dependent acquisition (DDA) proteomics techniques combined with multivariate statistical analysis. Using the DIA method, 890 variably abundant MFGM proteins were discovered throughout the lactation cycle. From 1 to 240 d, butyrophilin subfamily 1 member A1, lipoprotein lipase, perilipin-2, and adipose triglyceride lipase were upregulated, while APOE, complement C3, clusterin, and IgG were downregulated. Furthermore, from 1 to 90 d, annexin A1, annexin A2, and antithrombin-III were downregulated, then upregulated by d 240. Albumin had a high degree of connectedness, indicating that it was a key protein, according to protein-protein interaction research. Overall, our findings gave new insights into the biological features of MFGM protein in goat milk throughout lactation, which may aid in the creation of specialized MFGM products and infant formula. Reference | Related Articles | Metrics Select Quantitative Trait Loci Identification Uncovers Zinc Finger Protein CONSTANS-LIKE 4 as the Key Candidate Gene of Stigma Color in Watermelon (Citrullus lanatus) Shuang Pei, Zexu Wu, Ziqiao Ji, Zheng Liu, Zicheng Zhu, Feishi Luan, Shi Liu DOI: 10.1016/j.jia.2024.03.070 Online: 24 April 2024 Abstract （4）      PDF in ScienceDirect       Stigma color is a critical agronomic trait in watermelon and plays an important role in pollination. However, there are few reports the regulation of this process to date. In this study, a genetic analysis of the F2 population derived from ZXG1553 (P1, with orange stigma) and W1-17 (P2, with yellow stigma) indicated that stigma color is a quantitative trait and orange stigma is recessive compared with yellow stigma. BSA-seq (Bulk segregant analysis sequencing) determined a chromosome segment related to stigma color in a 3.75 Mb on chromosome 6, and a major stable effective quantitative trait locus (QTL) Clqsc6.1 (QTL stigma color) was detected in two years between CAPS markers Chr06_8338913 and Chr06_9344593 spanning ~1.01 Mb interval harboring 51 annotated genes. Cla97C06G117020 (annotated as Zinc finger protein CONSTANS-LIKE 4) was identified as the best candidate gene for stigma color trait through RNA-seq, quantitative real-time (qRT)–polymerase chain reaction (PCR), and gene structure alignment analysis among the nature watermelon panel. The expression level of Cla97C06G117020 in orange stigma accession was lower than yellow stigma accessions with a significant difference. A nonsynonymous SNP site of Cla97C06G117020 coding region causing amino acid variation was related to stigma color variation among nine watermelon accessions according to their re-sequencing data. Stigma color formation is often related to carotenoid, and we also found that the expression trend of ClCHYB (annotated as Beta-carotene hydroxylase) in the carotenoid metabolic pathway was consistent with Cla97C06G117020, which was expressed in low amounts in the orange stigma accession. These data indicated that Cla97C06G117020 and ClCHYB may have interacted to form stigma color. This study provided a theoretical basis for gene fine mapping and mechanisms for the regulation of stigma color in watermelon.  Reference | Related Articles | Metrics Select Enhancing the genomic prediction accuracy of swine agricultural economic traits using an expanded one-hot encoding in CNN models Zishuai Wang, Wangchang Li, Zhonglin Tang DOI: 10.1016/j.jia.2024.03.071 Online: 24 April 2024 Abstract （2）      PDF in ScienceDirect       Deep learning (DL) methods like Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. However, improving the genomic prediction accuracy still presents significant challenges. In this study, we applied CNNs to predict swine traits using previously published data. Specifically, we extensively evaluated the CNN model's performance by employing various sets of Single Nucleotide Polymorphisms (SNPs) and concluded that the CNN model achieved optimal performance when utilizing SNP sets comprising 1,000 SNPs. Furthermore, we adopted a novel approach using the one-hot encoding method that transforms the 16 different genotypes into sets of eight binary variables. This innovative encoding method significantly enhanced the CNN’s prediction accuracy for swine traits, outperforming the traditional one-hot encoding techniques. Our findings suggest that the expanded one-hot encode method can improve the accuracy of DL methods in the genomic prediction of swine agricultural economic traits. This discovery has significant implications for swine breeding programs, where genomic prediction is pivotal in improving breeding strategies. Furthermore, future research endeavors can explore additional enhancements to DL methods by incorporating advanced data pre-processing techniques.  Reference | Related Articles | Metrics Select Differences in N6-methyladenosine (m6A) methylation among the three major clonal lineages of Toxoplasma gondii tachyzoites Changning Wei, Hui Cao, Chenxu Li, Hongyu Song, Qing Liu, Xingquan Zhu, Wenbin Zheng DOI: 10.1016/j.jia.2024.03.072 Online: 24 April 2024 Abstract （4）      PDF in ScienceDirect       Toxoplasma gondii is an important zoonotic parasite which has over 200 genotypes worldwide. N6-methyladenosine (m6A) methylation is a common epigenetic modification in messenger RNAs (mRNAs), and has been implicated in many aspects of mRNA biology. However, little is known about the difference in m6A methylation among different genotypes of T. gondii. In the present study, we employed methylated RNA immunoprecipitation sequencing (MeRIP-seq) technology to identify key genes exhibiting m6A methylation in the three major clonal lineages (Types I, II and III) of T. gondii tachyzoites. A total of 7650, 8359 and 7264 m6A peaks were identified in 5211, 5607 and 4974 genes in tachyzoites of RH strain (Type I), ME49 strain (Type II) and VEG strain (Type III), respectively. By comparing RH vs. ME49, RH vs. VEG, and ME49 vs. VEG, 735, 192 and 615 differentially methylated peaks (DMPs) were identified in 676, 168 and 553 genes, respectively. A combined MeRIP-seq and RNA-seq analysis revealed 172, 41 and 153 differentially methylated genes (DMGs) at both the m6A methylation and transcriptional level. Gene ontology term enrichment analysis of the DMPs identified differences related to Golgi apparatus, plasma membrane, signal transduction, RNA processing and catalytic step 2 spliceosome. KEGG pathway enrichment analysis showed that the DMGs are mainly involved in endocytosis, systemic lupus erythematosus and mTOR signaling pathway. These findings reveal genotype-specific differences in m6A methylation, which provide new resources for further investigations of the role of m6A in the pathobiology of T. gondii. Reference | Related Articles | Metrics Select Biochar amendment modulates xylem ionic constituents and ABA signaling: its implications in enhancing water-use efficiency of maize (Zea mays L.) under reduced irrigation regimes Heng Wan, Zhenhua Wei, Chunshuo Liu, Xin Yang, Yaosheng Wang, Fulai Liu DOI: 10.1016/j.jia.2024.03.073 Online: 24 April 2024 Abstract （3）      PDF in ScienceDirect       While biochar amendment is known to enhance plant productivity and water-use efficiency (WUE), particularly under water-limited conditions, the specific mechanisms driving these benefits remain largely elusive.  Thus, the present study was conducted to elucidate the synergistic effects of biochar and reduced irrigation on maize (Zea mays L.) plants, focusing on xylem composition, root-to-shoot signaling, stomatal behavior, and WUE.  Maize plants were cultivated in split-root pots filled with clay loam soil, amended by either wheat-straw (WSB) or softwood (SWB) biochar at 2% (w/w).  Plants received full (FI), deficit (DI), or alternate partial root-zone drying (PRD) irrigation from the fourth leaf to the grain-filling stage.  Our results revealed that WSB amendment significantly enhanced plant water status, biomass accumulation, and WUE under reduced irrigation, particularly when combined with PRD.  Although reduced irrigation inhibited photosynthesis, it enhanced WUE by modulating stomatal morphology and conductance. Biochar amendment combined with reduced irrigation significantly increased xylem K+, Ca2+, Mg2+, NO3-, Cl-, PO43-, and SO42- while decreased Na+, which in turn lowered xylem pH.  Moreover, biochar amendment and especially WSB amendment further resulted in increased abscisic acid (ABA) contents in both leaf and xylem sap under reduced irrigation conditions due to changes in xylem ionic constituents and pH.  The synergistic interactions between xylem components and ABA led to refined adjustments in stomatal size and density, thereby affecting stomatal conductance and ultimately improving WUE of maize plants at different scales.  The combined application of WSB and PRD can therefore emerge as a promising approach for improving the overall plant performance of maize plants with increased stomatal adaptations and WUE especially under water-limited conditions. Reference | Related Articles | Metrics Select A CRISPR/Cas12a-based platform for rapid on-site bovine viral diarrhea virus diagnostics Meixi Wang, Jitao Chang, Yuxin Han, Chaonan Wang, Songkang Qin, Jun Wang, Lulu Zhang, Yuanmao Zhu, Fei Xue, Fang Wang, Hongliang Chai, Yulong Wang, Xinjie Wang, Xin Yin DOI: 10.1016/j.jia.2024.03.074 Online: 24 April 2024 Abstract （4）      PDF in ScienceDirect       Reference | Related Articles | Metrics Select E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis Md. Zasim Uddin, Md. Nadim Mahamood, Ausrukona Ray, Md. Ileas Pramanik, Fady Alnajjar, Md Atiqur Rahman Ahad DOI: 10.1016/j.jia.2024.03.075 Online: 24 April 2024 Abstract （4）      PDF in ScienceDirect       Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed  E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains. Reference | Related Articles | Metrics Select Low-fat microwaved peanut snacks production: effect of defatting treatment on structural characteristics, texture, color and nutrition Bo Jiao, Xin Guo, Yiying Chen, Shah Faisal, Wenchao Liu, Xiaojie Ma, Bicong Wu, Guangyue Ren, Qiang Wang DOI: 10.1016/j.jia.2024.03.069 Online: 23 April 2024 Abstract （6）      PDF in ScienceDirect       To meet people’s pursuit of healthy low-fat cuisine, this study developed low-fat microwaved peanut snacks (LMPS) made from partially defatted peanuts (PDP) with different defatting ratios. The effects of defatting treatment on the structural characteristics, texture, color, and nutrient composition of LMPS were comprehensively explored. The structural characteristics of LMPS were characterized using X-ray micro-computed tomography (Micro-CT) and scanning electron microscope (SEM). The results demonstrated that the porosity, pore number, pore volume, brightness, brittleness, protein content, and total sugar content of LMPS all significantly increased (P < 0.05) with the increase in the defatting ratio. At the micro level, porous structure, cell wall rupture, and loss of intracellular material could be observed in LMPS after defatting treatments. LMPS made from PDP with a defatting ratio of 64.44% had the highest internal pore structural parameters (porosity 59%, pore number 85.3×105, pore volume 68.23 mm3), the brightest color (L* 78.39±0.39), the best brittleness (3.64±0.21 mm-1), and the best nutrition (high protein content, 34.02%±0.38%; high total sugar content, 17.45%±0.59%; low fat content, 27.58%±0.85%). The study provided a theoretical basis for the quality improvement of LMPS. Reference | Related Articles | Metrics Select Physiological and transcriptome analyses of Chinese cabbage in response to drought stress Lin Chen, Chao Li, Jiahao Zhang , Zongrui Li, Qi Zeng, Qingguo Sun, Xiaowu Wang, Limin Zhao, Lugang Zhang, Baohua Li DOI: 10.1016/j.jia.2024.03.067 Online: 22 April 2024 Abstract （13）      PDF in ScienceDirect       Chinese cabbage is an important leafy vegetable crop with high water demand and susceptibility to drought stress. To explore the molecular mechanisms underlying the response to drought, we performed a transcriptome analysis of drought-tolerant and -sensitive Chinese cabbage genotypes under drought stress, and uncovered core drought-responsive genes and key signaling pathways. A co-expression network was constructed by a weighted gene co-expression network analysis (WGCNA) and candidate hub genes involved in drought tolerance were identified. Furthermore, ABA biosynthesis and signaling pathways and their drought responses in Chinese cabbage leaves were systemically explored. We also found that drought treatment increased the antioxidant enzyme activities and glucosinolate contents significantly. These results substantially enhance our understanding of the molecular mechanisms underlying drought responses in Chinese cabbage. Reference | Related Articles | Metrics Select A novel histone methyltransferase gene CgSDG40 positively regulates carotenoid biosynthesis during citrus fruit ripening Jialing Fu, Qingjiang Wu, Xia Wang, Juan Sun, Li Liao, Li Li, Qiang Xu DOI: 10.1016/j.jia.2024.03.068 Online: 22 April 2024 Abstract （4）      PDF in ScienceDirect       The flesh color of pummelo (Citrus maxima) fruits is highly diverse and largely depends on the level of carotenoids, which are beneficial to human health. It is vital to investigate the regulatory network of carotenoid biosynthesis to improve the carotenoid content in pummelo. However, the molecular mechanism underlying carotenoid accumulation in pummelo is not fully understood. In this study, we identified a novel histone methyltransferase gene, CgSDG40, involved in carotenoid regulation by analyzing the flesh transcriptome of typical white-fleshed pummelo, red-fleshed pummelo and extreme-colored F1 hybrids from a segregated pummelo population. Expression of CgSDG40 corresponded to flesh color change and was highly coexpressed with CgPSY1. Interestingly, CgSDG40 and CgPSY1 are located physically adjacent to each other on the chromosome in opposite directions, sharing a partially overlapping promoter region. Subcellular localization analysis indicated that CgSDG40 localizes to the nucleus. Overexpression of CgSDG40 significantly increased the total carotenoid content in citrus calli relative to that in wild type. In addition, expression of CgPSY1 was significantly activated in overexpression lines relative to wild type. Taken together, our findings reveal a novel histone methyltransferase regulator, CgSDG40, involved in the regulation of carotenoid biosynthesis in citrus and provide new strategies for molecular design breeding and genetic improvement of fruit color and nutritional quality. Reference | Related Articles | Metrics Select Water and nitrogen footprint assessment of integrated agronomic practice management in a summer maize cropping system Ningning Yu, Bingshuo Wang, Baizhao Ren, Bin Zhao, Peng Liu, Jiwang Zhang DOI: 10.1016/j.jia.2024.03.061 Online: 19 April 2024 Abstract （5）      PDF in ScienceDirect       The footprints of water and nitrogen (WF and NF) provide a comprehensive overview of the type and quantity of water consumption and reactive nitrogen (Nr) loss in crop production. In this study, a field experiment over two years (2019 and 2020) compared three integrated agronomic practice management (IAPM) systems: An improved management system (T2), a high-yield production system (T3), and an integrated soil-crop management system (ISCM) using a local smallholder farmer's practice system (T1) as control, to investigate the responses of WF, Nr losses, water use efficiency (WUE), and nitrogen use efficiency (NUE) to IAPM. The results showed that IAPM optimized water distribution and promoted water use by summer maize. The evapotranspiration over the whole maize growth period of IAPM increased, but yield increased more, leading to a significant increase in WUE. The WUE of the T2, T3, and ISCM treatments was significantly greater than in the T1 treatment, in 2019 and 2020 respectively, by 19.8-21.5, 31.8-40.6, and 34.4-44.6%. The lowest WF was found in the ISCM treatment, which was 31.0% lower than that of the T1 treatment. In addition, the ISCM treatment optimized soil total nitrogen (TN) distribution and significantly increased TN in the cultivated layer. Excessive nitrogen fertilizer was applied in treatment T3, producing the highest maize yield, and resulting in the highest Nr losses. In contrast, the ISCM treatment used a reduced nitrogen fertilizer rate, sacrificing grain yield partly, which reduced Nr losses and eventually led to a significant increase in nitrogen use efficiency and nitrogen recovery. The Nr level in the ISCM treatment was 34.8% lower than in the T1 treatment while NUE was significantly higher than in the T1 treatment by 56.8-63.1% in 2019 and 2020, respectively. Considering yield, WUE, NUE, WF, and NF together, ISCM should be used as a more sustainable and clean system for sustainable production of summer maize. Reference | Related Articles | Metrics page Page 1 of 14 Total 267 records First page Prev page Next page Last page