Diseased Area Research Articles

QTL for induced resistance against leaf rust in barley.

Leaf rust caused by Puccinia hordei is one of the major diseases of barley (Hordeum vulgare L.) leading to yield losses up to 60%. Even though, resistance genes Rph1 to Rph28 are known, most of these are already overcome. In this context, priming may promote enhanced resistance to P. hordei. Several bacterial communities such as the soil bacterium Ensifer (syn. Sinorhizobium) meliloti are reported to induce resistance by priming. During quorum sensing in populations of gram negative bacteria, they produce N-acyl homoserine-lactones (AHL), which induce resistance in plants in a species- and genotype-specific manner. Therefore, the present study aims to detect genotypic differences in the response of barley to AHL, followed by the identification of genomic regions involved in priming efficiency of barley. A diverse set of 198 spring barley accessions was treated with a repaired E. meliloti natural mutant strain expR+ch producing a substantial amount of AHL and a transformed E. meliloti strain carrying the lactonase gene attM from Agrobacterium tumefaciens. For P. hordei resistance the diseased leaf area and the infection type were scored 12 dpi (days post-inoculation), and the corresponding relative infection and priming efficiency were calculated. Results revealed significant effects (p<0.001) of the bacterial treatment indicating a positive effect of priming on resistance to P. hordei. In a genome-wide association study (GWAS), based on the observed phenotypic differences and 493,846 filtered SNPs derived from the Illumina 9k iSelect chip, genotyping by sequencing (GBS), and exome capture data, 11 quantitative trait loci (QTL) were identified with a hot spot on the short arm of the barley chromosome 6H, associated to improved resistance to P. hordei after priming with E. meliloti expR+ch. Genes in these QTL regions represent promising candidates for future research on the mechanisms of plant-microbe interactions.

Automatic blight disease detection in potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plants using deep learning

Early and late blight are two diseases which pose a huge risk to both potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) crops and make farmers run at a loss. The early and automatic detection of these diseases would save time as well as enable farmers to act quickly on crops which have been affected. Machine learning and deep learning technology provide many solutions for the detection of the blight diseases in affected crops, and are common in the literature. However, explanation methods for such solutions are not common, but are necessary, considering some machine learning models are seen as black boxes. This study proposes a ResNet-9 model which detects the blight disease state of potato and tomato leaf images, which farmers can leverage. With the data obtained from the popular “Plant Village Dataset”, there were 3,990 initial training data samples. After augmenting the training set and a rigorous hyperparameter optimization procedure, the model was trained with these hyperparameter values, and examined on the test set, which contained 1,331 images. A test accuracy of 99.25%, 99.67% overall precision, 99.33% overall recall and 99.33% overall F1-score values were achieved. To fully understand the model, explanations for the proposed model were provided through saliency maps, which showed the reasoning behind the predictions of the model. It was observed that the ResNet-9 model considered the shape of the leaf, diseased areas present and general green areas of the leaf for its predictions and this makes us understand the model predictions better and see that the model behaves as expected. Our results could contribute to the testing and deployment of Convolutional Neural Network (CNN) models for classification of proximal sensing images of potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum, L. 1753) plant leaves. Further studies would benefit from this modeling framework and would have the chance to test several other variables to determine the leaf infections in an earlier stage for crop protection.

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.

Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

Identification of a pathogen causing fruiting body rot of Sanghuangporus vaninii.

Sanghuangporus vaninii is a medicinal macrofungus that is increasingly cultivated in China. During cultivation, it was found that the fruiting body of S. vaninii was susceptible to pathogenic fungi, resulting in significant economic losses to the industry. The symptoms of the disease occur in the initial stage of fruiting body development. The isolate YZB-1 was obtained from the junction of the diseased and healthy areas of the fruiting body. In order to verify the pathogenicity of YZB-1, its purified spore suspension was inoculated into the exposed area nearby the developing fruiting body of S. vaninii. After 10 days, the same disease symptoms appeared in the inoculated area. Morphological identification and molecular analysis of rDNA ITS region confirmed that the isolate YZB-1 was identified as Trichoderma virens. The temperature stability assay revealed that the mycelia of YZB-1 grew the fastest at 25 °C, with growth slowing down gradually as the temperature increased or decreased. Dual-culture tests of T. virens and S. vaninii showed that the inhibition rate of T. virens on S. vaninii mycelium was the highest (79.01 ± 2.79%) at 25 °C, and more green spores were produced at the intersection of T. virens and S. vaninii.

Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves Interval Neutrosophic Set (INS) technique to distinguish the diseased areas in fundus image. In addition, three feature extraction techniques such as histogram features, texture features, and wavelet features are used in this study. Besides, Optimal Deep Belief Network (ODBN) model is utilized as a classification model for DR. ODBN model involves Shuffled Shepherd Optimization (SSO) algorithm to regulate the hyperparameters of DBN technique in an optimal manner. The utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model significantly. The presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation metrics. The resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

Highly sensitive H2O2-scavenging nano-bionic system for precise treatment of atherosclerosis.

In atherosclerosis, chronic inflammatory processes in local diseased areas may lead to the accumulation of reactive oxygen species (ROS). In this study, we devised a highly sensitive H2O2-scavenging nano-bionic system loaded with probucol (RPP-PU), to treat atherosclerosis more effectively. The RPP material had high sensitivity to H2O2, and the response sensitivity could be reduced from 40 to 10μmol/L which was close to the lowest concentration of H2O2 levels of the pathological environment. RPP-PU delayed the release and prolonged the duration of PU invivo. In Apolipoprotein E deficient (ApoE‒/‒) mice, RPP-PU effectively eliminated pathological ROS, reduced the level of lipids and related metabolic enzymes, and significantly decreased the area of vascular plaques and fibers. Our study demonstrated that the H2O2-scavenging nano-bionic system could scavenge the abundant ROS in the atherosclerosis lesion, thereby reducing the oxidative stress for treating atherosclerosis and thus achieve the therapeutic goals with atherosclerosis more desirably.

Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field. In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields. This helps to show better results especially for diagnosing diseases. Of late the early prediction of cancer is necessary to prevent disease-causing problems. This work is proposed to identify lung cancer using lung computed tomography (CT) scan images. It helps to identify cancer cells’ affected areas. In the present work, the original input image from Lung Image Database Consortium (LIDC) typically suffers from noise problems. To overcome this, the Gabor filter used for image processing is highly enhanced. In the next stage, the Spherical Iterative Refinement Clustering (SIRC) algorithm identifies cancer-suspected areas on the CT scan image. This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages. These new methods help to remove unwanted portions of the CT image and better utilization the image. The subspace extraction of features approach is beneficial for evaluating lung cancer. This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures (CCPNN). By using the feature values from the fourth step of the procedure, the proposed CCPNN tends to categorize the lesion in the lung nodular site. The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall (%), precision (%), F-measure (percent), and accuracy (%). Finally, the incorrect classification ratios are determined to compare the trained networks’ effectiveness, through these parameters of CCPNN, it obtains the outstanding performance of 98.06% and it has provided the lowest false ratio of 1.8%.

A novel combination for the treatment of small cell lung cancer: Active targeted irinotecan and stattic co-loaded PLGA nanoparticles

Polymeric nanoparticles are widely used drug delivery systems for cancer treatment due to their properties such as ease of passing through biological membranes, opportunity to modify drug release, specifically targeting drugs to diseased areas, and potential of reducing side effects. Here, we formulated irinotecan and Stattic co-loaded PLGA nanoparticles targeted to small cell lung cancer. Nanoparticles were successfully conjugated with CD56 antibody with a conjugation efficiency of 84.39 ± 1.01%, and characterization of formulated nanoparticles was conducted with in-vitro and in-vivo studies. Formulated particles had sizes in the range of 130–180 nm with PDI values smaller than 0.3. Encapsulation and active targeting of irinotecan and Stattic resulted in increased cytotoxicity and anti-cancer efficiency in-vitro. Furthermore, it was shown with ex-vivo biodistribution studies that conjugated nanoparticles were successfully targeted to CD56-expressing SCLC cells and distributed mainly to tumor tissue and lungs. Compliant with our hypothesis and literature, the STAT3 pathway was successfully inhibited with Stattic solution and Stattic loaded nanoparticles. Additionally, intravenous injection of conjugated co-loaded nanoparticles resulted in decreased side effects and better anti-tumor activity than individual solutions of drugs in SCLC tumor-bearing mice. These results may indicate a new treatment option for clinically aggressive small cell lung cancer.

MAC-ResNet: Knowledge Distillation Based Lightweight Multiscale-Attention-Crop-ResNet for Eyelid Tumors Detection and Classification.

Eyelid tumors are tumors that occur in the eye and its appendages, affecting vision and appearance, causing blindness and disability, and some having a high lethality rate. Pathological images of eyelid tumors are characterized by large pixels, multiple scales, and similar features. Solving the problem of difficult and time-consuming fine-grained classification of pathological images is important to improve the efficiency and quality of pathological diagnosis. The morphology of Basal Cell Carcinoma (BCC), Meibomian Gland Carcinoma (MGC), and Cutaneous Melanoma (CM) in eyelid tumors are very similar, and it is easy to be misdiagnosed among each category. In addition, the diseased area, which is decisive for the diagnosis of the disease, usually occupies only a relatively minor portion of the entire pathology section, and screening the area of interest is a tedious and time-consuming task. In this paper, deep learning techniques to investigate the pathological images of eyelid tumors. Inspired by the knowledge distillation process, we propose the Multiscale-Attention-Crop-ResNet (MAC-ResNet) network model to achieve the automatic classification of three malignant tumors and the automatic localization of whole slide imaging (WSI) lesion regions using U-Net. The final accuracy rates of the three classification problems of eyelid tumors on MAC-ResNet were 96.8%, 94.6%, and 90.8%, respectively.

An oral ratiometric NIR-II fluorescent probe for reliable monitoring of gastrointestinal diseases in vivo

Early monitoring of gastrointestinal diseases via orally delivered NIR-II ratiometric fluorescent probes represents a promising noninvasive diagnostic modality, but is challenging due to the limitation of harsh digestive environment. Here, we report a single-component NIR-II ratiometric molecular nanoprobe (LC-1250 NP) to monitor gastrointestinal disease with high specificity to its biomarker H2O2 via oral administration. LC-1250 NP displays stable fluorescence in the channel of 1250 long-pass (F1250LP) before and after the gastrointestinal disease detection as the reference, while it presents significantly enhanced fluorescence signal in the response channel of 1150 nm short-pass (F1150SP) in diseased gastrointestinal environment due to the intramolecular cyclization of LC-1250 molecules activated by H2O2. The fluorescence ratio (F1150SP/F1250LP) increases linearly with the concentration of H2O2 with a low detection limit of 20 nM. Therefore, when delivered orally, LC-1250 NP can accurately map the diseased areas and surmount the false-positive interference from biological heterogeneity by NIR-II ratiometric fluorescence imaging, providing sensitive and reliable evaluation for the progress of gastroenteritis.

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

Development of a RT-LAMP assay for real-time detection of criniviruses infecting tomato

Yellowing symptoms caused by tomato chlorosis virus (ToCV) and tomato infectious chlorosis virus (TICV), both assigned to the genus Crinivirus, resemble nutrient deficiencies. Therefore, early diagnosis of infections will prevent crop damage and the spread of the viruses. In this study, we established a rapid detection method for ToCV and TICV by reverse transcription-loop-mediated isothermal amplification (RT-LAMP). We first designed primer sets for RT-LAMP specific for ToCV and TICV. Next, by selecting the optimum primer set and determining the optimum conditions for the RT-LAMP reaction, each virus was detected within 50 min by piercing the diseased area of a tomato leaf with a toothpick, immersing the toothpick in the reaction solution, and conducting the RT-LAMP reaction. To verify the accuracy of the procedure, 61 tomato leaf samples showing disease symptoms were collected from five regions of Indonesia, and the RT-LAMP results for the samples were identical to those obtained with the commonly used reverse transcription-polymerase chain reaction.

Spatial distribution and computational modeling for mapping of tuberculosis in Pakistan.

Tuberculosis (TB) like many other infectious diseases has a strong relationship with climatic parameters. The present study has been carried out on the newly diagnosed sputum smear-positive pulmonary TB cases reported to National TB Control Program across Pakistan from 2007 to 2020. In this study, spatial and temporal distribution of the disease was observed through detailed district wise mapping and clustered regions were also identified. Potential risk factors associated with this disease depending upon population and climatic variables, i.e. temperature and precipitation were also identified. Nationwide, the incidence rate of TB was observed to be rising from 7.03% to 11.91% in the years 2007-2018, which then started to decline. However, a declining trend was observed after 2018-2020. The most populous provinces, Punjab and Sindh, have reported maximum number of cases and showed a temporal association as the climatic temperature of these two provinces is higher with comparison to other provinces. Machine learning algorithms Maxent, Support Vector Machine (SVM), Environmental Distance (ED) and Climate Space Model (CSM) predict high risk of the disease with14.02%, 24.75%, 34.81% and 43.89% area, respectively. SVM has a higher significant probability of prediction in the diseased area with a 1.86 partial receiver-operating characteristics (ROC) value as compared with other models.

Plant disease symptom segmentation in chlorophyll fluorescence imaging with a synthetic dataset.

Despite the wide use of computer vision methods in plant health monitoring, little attention is paid to segmenting the diseased leaf area at its early stages. It can be explained by the lack of datasets of plant images with annotated disease lesions. We propose a novel methodology to generate fluorescent images of diseased plants with an automated lesion annotation. We demonstrate that a U-Net model aiming to segment disease lesions on fluorescent images of plant leaves can be efficiently trained purely by a synthetically generated dataset. The trained model showed 0.793% recall and 0.723% average precision against an empirical fluorescent test dataset. Creating and using such synthetic data can be a powerful technique to facilitate the application of deep learning methods in precision crop protection. Moreover, our method of generating synthetic fluorescent images is a way to improve the generalization ability of deep learning models.

Shell Disease Syndrome Is Associated with Reduced and Shifted Epibacterial Diversity on the Carapace of the Crustacean Cancer pagurus.

Cancer pagurus is highly susceptible to shell disease syndrome. However, little is known about concomitant changes in the epibacterial community. We compared the bacterial communities of black spot affected and nonaffected areas of the carapace by amplicon sequencing of 16S rRNA genes and 16S rRNA. Within each spot, bacterial communities of affected areas were less diverse compared to communities from nonaffected areas. Communities of different affected spots were, however, more divergent from each other, compared to those of different nonaffected areas. This indicates a reduced and shifted microbial community composition caused by the black spot disease. Different communities found in black spots likely indicate different stages of the disease. In affected areas, Flavobacteriaceae rose to one of the most abundant and active families due to the increase of Aquimarina spp., suggesting a significant role in shell disease syndrome. We isolated 75 bacterial strains from diseased and healthy areas, which are primarily affiliated with Proteobacteria and Bacteroidetes, reflecting the dominant phyla detected by amplicon sequencing. The ability to degrade chitin was mainly found for Gammaproteobacteria and Aquimarina spp. within the Flavobacteriia, while the ability to use N-acetylglucosamine, the monomer of the polysaccharide chitin, was observed for most isolates, including many Alphaproteobacteria. One-third of the isolates, including most Aquimarina spp., showed antagonistic properties, indicating a high potential for interactions between the bacterial populations. The combination of bacterial community analysis and the physiological properties of the isolates provided insights into a functional complex epibacterial community on the carapace of C. pagurus. IMPORTANCE In recent years, shell disease syndrome has been detected for several ecologically and economically important crustacean species. Large proportions of populations are affected, e.g., >60% of the widely distributed species Cancer pagurus in different North Sea areas. Bacteria play a significant role in the development of different forms of shell disease, all characterized by microbial chitinolytic degradation of the outer shell. By comparing the bacterial communities of healthy and diseased areas of the shell of C. pagurus, we demonstrated that the disease causes a reduced bacterial diversity within affected areas, a phenomenon co-occurring also with many other diseases. Furthermore, the community composition dramatically changed with some taxa rising to high relative abundances and showing increased activity, indicating strong participation in shell disease. Characterization of bacterial isolates obtained from affected and nonaffected spots provided deeper insights into their physiological properties and thus the possible role within the microbiome.

Molecular Diversity and Evolutionary Relatedness of Paulownia Witches’-Broom Phytoplasma in Different Geographical Distributions in China

To reveal the distribution and transmission pathway of Paulownia witches'-broom (PaWB) disease, which is caused by phytoplasmas related to genetic variation, and the adaptability to the hosts and environments of the pathogenic population in different geographical regions in China, in this study, we used ten housekeeping gene fragments, including rp, fusA, secY, tuf, secA, dnaK, rpoB, pyrG, gyrB, and ipt, for multilocus sequence typing (MLST). A total of 142 PaWB phytoplasma strains were collected from 18 provinces or municipalities. The results showed that the genetic diversity was comparatively higher among the PaWB phytoplasma strains, and substantially different from that of the other 16SrI subgroup strains. The number of gene variation sites for different housekeeping genes in the PaWB phytoplasma strains ranged from 1 to 14 SNPs. Among them, rpoB (1.47%) and dnaK (1.12%) had higher genetic variation, and rp (0.20%) had the least genetic variation. The tuf and rpoB genes showed the fixation of positively selected beneficial mutations in the PaWB phytoplasma populations, and all housekeeping genes except tuf followed the neutral evolutionary model. We found an absence of recombination among PaWB phytoplasma sequence types (STs) for each housekeeping gene except dnaK, and no evidence for such recombination events for concatenated sequences of PaWB phytoplasma strains. The 22 sequence types were identified among the concatenated sequences of seven housekeeping genes (rp, fusA, secY, secA, tuf, dnaK, and rpoB) from 105 representative strains. We analyzed all 22 STs by goeBURST algorithm, forming two clonal complexes (CCs) and three singletons. Among them, ST1, as the primary founder of CC1, had the widest geographical distribution, accounting for 72.38% of all strains, with a high frequency of shared sequence type. The results of phylogenetic analysis of the concatenated sequences further revealed that the 105 strains were clustered into two representative lineages of PaWB phytoplasma, with obvious geographical differentiation. The ST1 strains of highly homogeneous lineage-1 were a widespread and predominant population in diseased areas. Lineage-2 contained strains from Jiangxi, Fujian, and Shaanxi provinces, highlighting the close genetic relatedness of the strains in these regions, which was also consistent with the results of most single-gene phylogenetic analysis of each gene. We also found that the variability in the northwest China population was higher than in other geographical populations; the range of genetic differentiation between the south of the Yangtze River population and the Huang-huai-hai Plain (or southwest China) population was relatively large. The achieved diversity and evolution data, as well as the MLST technique, are helpful for epidemiological studies and guiding PaWB disease control decisions.

Mesenchymal Stem Cell-Derived Extracellular Vesicles for Therapeutic Use and in Bioengineering Applications.

Extracellular vesicles (EVs) are small lipid bilayer-delimited particles that are naturally released from cells into body fluids, and therefore can travel and convey regulatory functions in the distal parts of the body. EVs can transmit paracrine signaling by carrying over cytokines, chemokines, growth factors, interleukins (ILs), transcription factors, and nucleic acids such as DNA, mRNAs, microRNAs, piRNAs, lncRNAs, sn/snoRNAs, mtRNAs and circRNAs; these EVs travel to predecided destinations to perform their functions. While mesenchymal stem cells (MSCs) have been shown to improve healing and facilitate treatments of various diseases, the allogenic use of these cells is often accompanied by serious adverse effects after transplantation. MSC-produced EVs are less immunogenic and can serve as an alternative to cellular therapies by transmitting signaling or delivering biomaterials to diseased areas of the body. This review article is focused on understanding the properties of EVs derived from different types of MSCs and MSC-EV-based therapeutic options. The potential of modern technologies such as 3D bioprinting to advance EV-based therapies is also discussed.

CRISPR/Cas9-Targeted Knockout of Rice Susceptibility Genes OsDjA2 and OsERF104 Reveals Alternative Sources of Resistance to Pyricularia oryzae

Rice genes OsDjA2 and OsERF104, encoding a chaperone protein and an APETELA2/ ethylene-responsive factor, respectively, are strongly induced in a compatible interaction with blast fungus, and also have function in plant susceptibility validated through gene silencing. Here, we reported the CRISPR/Cas9 knockout of OsDjA2 and OsERF104 genes resulting in considerable improvement of blast resistance. A total of 15 OsDjA2 (62.5%) and 17 OsERF104 (70.8%) T0 transformed lines were identified from 24 regenerated plants for each target and used in downstream experiments. Phenotyping of homozygous T1 mutant lines revealed not only a significant decrease in the number of blast lesions but also a reduction in the percentage of diseased leaf area, compared with the infected control plants. Our results supported CRISPR/Cas9-mediated target mutation in rice susceptibility genes as a potential and alternative breeding strategy for building resistance to blast disease.

Genetic association of agronomic traits with partial resistance to gray leaf spot in elite maize germplasm

AbstractStudies on gray leaf spot (GLS) of maize have reported inconsistencies in the relationship between partial disease resistance and agronomic traits. Understanding this variation could facilitate the use of agronomic traits as a basis for selection to improve partial resistance to GLS. Coinheritance of nine agronomic traits with partial resistance to GLS was examined among 48 maize (Zea mays L.) inbred lines artificially infected with Cercospora zeina in field evaluations across nine environments in western Kenya in 2013 and 2014. Five measures of disease severity and two disease resistance components were evaluated for their association with agronomic traits. Standardized area under disease progress curve (SAUDPC) was the most efficient in delineating differences in GLS severity between genotypes, whereas latent period (LP) was the least effective. Among maize genotypes, values of SAUDPC ranged from 29.3 to 97.9 across environments. Genotypic and phenotypic correlations were strongest between SAUDPC and the absolute rate of disease increase (ρ; r = .71), final percent diseased leaf area (r = .66) and International Maize and Wheat Improvement Center (CIMMYT) disease severity grade (r = .60), but weakest between SAUDPC and LP (r = −.19). Correlations of SAUDPC were significant (P = .05) with eight of the 11 agronomic traits examined, with the strongest being between SAUDPC and the stay‐green characteristic (SGR; r = −.87), days to maturity (DTM; r = −.60) and ear/plant height ratio (r = −.52). Genotypic and phenotypic coefficients of correlation of SAUDPC with agronomic traits were all negative. Overall, absolute genotypic correlations were numerically larger than the corresponding coefficients of phenotypic correlation with the magnitude and direction of coheritability estimates mimicking trends in genotypic and phenotypic correlations. Causal mediation analysis indicated that covariation of GLS resistance with agronomic traits was mainly due to direct effects of days to anthesis and DTM and indirect effects of SGR and silking–maturity interval.

Emission and transformation behaviors of trace elements during combustion of Cd-rich coals from coal combustion related endemic fluorosis areas of Southwest, China

Long-term combustion of low-quality coal may release hazardous elements into the environment causing serious environmental problems. This phenomenon is particularly prevalent in the Three Gorges Region of Southwest (SW), China. Cadmium (Cd), as well as other harmful elements are found to be highly enriched in coals and supergene environments in this area. In the existing literature, the behavioral issue of emission and transformation of the elevated trace elements during simulated household stove combustion from Cd-rich inferior coal remains unknown. This study investigated the emission of toxic elements, mineral assemblages, and provided technical guidance for reducing pollution by means of optimization combustion tests on inferior coals. The research may improve the understanding of geochemical characteristics from toxic elements emission in coal combustion endemic diseased areas. For this purpose, a series of simulated coal combustion experiments were conducted to reveal the release, mobility, and distribution of elevated elements in Cd-rich coal combustion products. The results showed that Cd, Mo, Cr, Cu, Zn, As, and Sb were significantly enriched in the inferior coals of the study area. Furthermore, large amounts of toxic elements were released as fly ash into the environment during the combustion process. In particular, combustion conditions played an important role in the emission and transformation of elevated elements. For example, higher temperatures promoted the release of Cd, Sb, Zn, and Tl into the environment. Oxygen-deficient combustion was found to liberate more Cd, Sb, and Tl to the atmosphere and generated complex mineral assemblages of lizardite, calcite, dolomite, forsterite, and enstatite. Moreover, toxic elements were found to be absorbed in the fine particle matter of fly ash from the endemic fluorosis area of SW, China. The findings of this work may aid to control the emission of toxic elements from inferior coals and mitigate the effect of toxic elements in the environment to protect human health.