Variations in total phenolics and antioxidant enzymes cause phenotypic variability and differential resistant response in tomato genotypes against early blight disease

Awan, Zoia Arshad; Shoaib, Amna; Khan, Kashif Ali

doi:10.1016/j.scienta.2018.05.044

Cited by 39 publications

(31 citation statements)

References 31 publications

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“…Results revealed that EB infected plants (T 2 ) showed the substantial increase in disease intensity (DI: 100% and PSI: 70%) with a significant reduction in growth attributes by 22–37% when compared with healthy plants (T 1 ). The infected plants showed typical early blight symptoms (dark brown to black spots or irregular lesions surrounded by a chlorotic halo with concentric rings leading to blighting of leaves) under pathogenic stress ( Awan et al, 2018 ). Application of macro- and micro- nutrients (NPK, Zn, Mg & B) improved the plant resistance/tolerance against pathogen to a variable degree by reducing the disease severity ( Ghorbani et al, 2008 ; Potarzycki 2011 ; Sten et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Tomato seeds of susceptible germline (Miracle) were surface sterilized with 1% sodium hypochlorite solution and used for nursery preparation ( Awan et al, 2018 ). The pot experiment consisted of 15 treatments [T 1 –T 5 (without Zn 0.0 mg/kg), T 6 –T 10 (with Zn 2.5 mg/kg) and T 11 –T 15 (with Zn 5.0 mg/kg)] with three replicates and was laid out in a completely randomized design ( Table 1 ).…”

Section: Methodsmentioning

confidence: 99%

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Crosstalk of Zn in Combination with Other Fertilizers Underpins Interactive Effects and Induces Resistance in Tomato Plant against Early Blight Disease

2019

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Crosstalk of Zn in Combination with Other Fertilizers Underpins Interactive Effects and Induces Resistance in Tomato Plant against Early Blight Disease

2019

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“…We employed 6 filter methods commonly used in the medical field, including those based on standard deviation [57], Pearson correlation coefficient [58], Fisher score [59], information gain [60], Relief [61], and chi-squared test [62]. We used a 10-fold cross-validation to train and test classification models.…”

Section: Methodsmentioning

confidence: 99%

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

et al. 2019

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Background Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging because of a large number of risk factors for stroke. Objective This study aimed to address the limitation of ineffective feature selection in existing research on stroke risk detection. We have proposed a new feature selection method called weighting- and ranking-based hybrid feature selection (WRHFS) to select important risk factors for detecting ischemic stroke. Methods WRHFS integrates the strengths of various filter algorithms by following the principle of a wrapper approach. We employed a variety of filter-based feature selection models as the candidate set, including standard deviation, Pearson correlation coefficient, Fisher score, information gain, Relief algorithm, and chi-square test and used sensitivity, specificity, accuracy, and Youden index as performance metrics to evaluate the proposed method. Results This study chose 792 samples from the electronic records of 13,421 patients in a community hospital. Each sample included 28 features (24 blood test features and 4 demographic features). The results of evaluation showed that the proposed method selected 9 important features out of the original 28 features and significantly outperformed baseline methods. Their cumulative contribution was 0.51. The WRHFS method achieved a sensitivity of 82.7% (329/398), specificity of 80.4% (317/394), classification accuracy of 81.5% (645/792), and Youden index of 0.63 using only the top 9 features. We have also presented a chart for visualizing the risk of having ischemic strokes. Conclusions This study has proposed, developed, and evaluated a new feature selection method for identifying the most important features for building effective and parsimonious models for stroke risk detection. The findings of this research provide several novel research contributions and practical implications.

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“…Our findings are supported by the results of Sharma et al [6], who reported that PPO activity was increased in tomato PT3 after dual inoculation of plant growth promoter rhizobacteria (PGPR) and mycorrhiza against Mi. Awan et al [82] reported that groups of tomatoes resistant against Alternaria solani had higher concentrations of PPO, CAT, and POD than susceptible groups. According to Arshad et al [83], ascorbic acid contents of plants are directly related to resistance against pathogens.…”

Section: Discussionmentioning

confidence: 99%

Variations in Growth, Physiology, and Antioxidative Defense Responses of Two Tomato (Solanum lycopersicum L.) Cultivars after Co-Infection of Fusarium oxysporum and Meloidogyne incognita

Maqsood

Kamran

et al. 2020

Agronomy

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The soil-borne fungus Fusarium oxysporum (Fo) and the nematode Meloidogyne incognita (Mi) are destructive pathogens that cause substantial yield losses to tomato (Solanum lycopersicum L.) crops worldwide. The present study sought to elucidate the physiological, biochemical, and cytological responses of tomato cultivars (Gailing maofen 802 and Zhongza 09) by root invasion of Fo (1 × 105 CFUmL−1) and Mi (1500 second-stage juveniles (J2) alone and in combination after 14 days. Results revealed that combined inoculation of Fo and Mi significantly increased disease intensity, electrolyte leakage, and hydrogen peroxide and malondialdehyde contents; and decreased photosynthetic capacity and enzyme activity in both cultivars as compared to their solo inoculation. Increasing the disease intensity reduced the maximum morphological traits, such as shoot length, total dry weight, and total chlorophyll contents, in G. maofen 802 (by 32%, 54.2%, and 52.3%, respectively) and Zhongza 09 (by 18%, 32%, and 21%, respectively) as compared to the control. Others factors were also reduced in G. maofen 802 and Zhongza 09, such as photosynthetic capacity (by 70% and 57%, respectively), stomatal conductance (by 86% and 70%, respectively), photochemical quantum yield of photosystem II (YII) (by 36.6% and 29%, respectively), and electron transport rate (by 17.7% and 10%, respectively), after combined inoculation of Fo and Mi. Furthermore, the combined infestation of Fo and Mi resulted in reduced activity of plant-defense-related antioxidants in G. maofen 802 compared with their single application or control. However, these antioxidants were highly up-regulated in Zhongza 09 (by 59%–93%), revealing the induction of tolerance against studied pathogens. The transmission electron microscopy (TEM) results further demonstrated that root cells of Zhongza 09 had unique tetrahedral crystal-like structures in the membrane close to mitochondria under all treatments except control. Therefore, it is concluded that Mi caused severe root damage, suppressed plant growth, depleted antioxidants, and caused high generation of ROS in the presence of Fo as compared to its solo inoculation. Tolerant cultivars adopted different mechanistic strategies at the structural and cellular levels to tolerate the Mi and Fo stresses.

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Variations in total phenolics and antioxidant enzymes cause phenotypic variability and differential resistant response in tomato genotypes against early blight disease

Cited by 39 publications

References 31 publications

Crosstalk of Zn in Combination with Other Fertilizers Underpins Interactive Effects and Induces Resistance in Tomato Plant against Early Blight Disease

Crosstalk of Zn in Combination with Other Fertilizers Underpins Interactive Effects and Induces Resistance in Tomato Plant against Early Blight Disease

A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Variations in Growth, Physiology, and Antioxidative Defense Responses of Two Tomato (Solanum lycopersicum L.) Cultivars after Co-Infection of Fusarium oxysporum and Meloidogyne incognita

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