Diseased Area Research Articles

Harnessing X-Ray Energy-Dependent Attenuation of Bismuth-Based Nanoprobes for Accurate Diagnosis of Liver Fibrosis.

Timely detection of liver fibrosis by X‐ray computed tomography (CT) can prevent its progression to fatal liver diseases. However, it remains quite challenging because conventional CT can only identify the difference in density instead of X‐ray attenuation characteristics. Spectral CT can generate monochromatic imaging to specify X‐ray attenuation characteristics of the scanned matter. Herein, an X‐ray energy‐dependent attenuation strategy originated from bismuth (Bi)‐based nanoprobes (BiF3@PDA@HA) is proposed for the accurate diagnosis of liver fibrosis. Bi element in BiF3@PDA@HA can exhibit characteristic attenuation depending on different levels of X‐ray energy via spectral CT, and that is challenging for conventional CT. In this study, selectively accumulating BiF3@PDA@HA nanoprobes in the hepatic fibrosis areas can significantly elevate CT value for 40 Hounsfield units on 70 keV monochromatic images, successfully differentiating from healthy livers and achieving the diagnosis of liver fibrosis. Furthermore, the enhancement produced by the BiF3@PDA@HA nanoprobes in vivo increases as the monochromatic energy decreases from 70 to 40 keV, optimizing the conspicuity of the diseased areas. As a proof of concept, the strategically designed nanoprobes with energy‐dependent attenuation characteristics not only expand the scope of CT application, but also hold excellent potential for precise imaging‐based disease diagnosis.

First Report of Leaf Spot on White Chrysanthemum (Chrysanthemum morifolium) Caused by Epicoccum sorghinum in Hubei province, China.

White Chrysanthemum (Chrysanthemum morifolium), a perennial herb of the Compositae family, is used for traditional medicine. The planting area of white chrysanthemum in Macheng city, Hubei Province is about 3333 ha and the annual output can reach more than 5000 tons. In 2019, leaf spot disease appeared on almost all middle and lower leaves of white chrysanthemum in most fields of Fengshumiao county, Macheng city (N31°29'57″, E115°05'49″). This county has 33 acres white chrysanthemum planting area, and most of the plants in the county were infected with the leaf spot disease. The average incidence of leaf spot disease was 65%, and incidence in some areas was 100%. In our observations, leaf spot disease can occur throughout the whole growth period of white chrysanthemum, and it will become more serious under the high temperature and humidity condition. Usually, the diseased leaves account for 30 to 80% of the total leaves on the plant. Leaf spot initially manifests as necrotic lesions on the edge and tip of the leaf, and then the lesions coalesce and gradually expand to form irregular light-brown to brown-black spots, eventually leading to necrosis and curling of the entire leaf. This disease seriously affects the growth and development of plants, resulting in the decline of yield and quality of white chrysanthemum. Ten symptomatic leaf samples were collected, the surfaces were disinfected with 0.1% mercuric chloride (HgCl2) for 3 min, and washed with sterile distilled water three times. Ten tissue samples at the junction of diseased and healthy areas (0.5 × 0.5 cm2) were cut and placed on potato dextrose agar (PDA) medium containing 100 µg/ml cefotaxime sodium and incubated in a dark chamber at 28°C. After 2 days, the hyphal tips from the edges of growing colonies were transferred to fresh PDA plates for further purification. Finally, eight isolates were obtained and these isolates were similar in morphology. The color of purified isolates was initially white to pale yellow. After six days of incubation, colonies had a diameter of 8 cm and the cultures were pale gray and starting to secrete scarlet pigment. After 15 days incubation, the colonies were grayish brown, while the backside was reddish-brown. Gray to tan chlamydospores were observed, nearly spherical, with a wart-like surface. Unicellular chlamydospores were 7.91 to 32.23 × 12.03 to 38.42 µm (n=30) and multicellular chlamydospores were 6.32 to 25.10 × 21.75 to 100.05 µm (n=30). The morphological characteristics were similar to Epicoccum sorghinum (Kang et al. 2019). The isolate FDY-5 was chosen for molecular identification. The sequence of rDNA-ITS, TUB, and LSU of the FDY-5 were amplified (GenBank MT800929, MT799852, and MT800935, respectively) (White et al. 1990; Carbone and Kohn 1999; Lumbsch et al. 2000). BLAST results showed that the rDNA-ITS sequences, the TUB gene sequences, and LSU gene sequences of strain FDY-5 shared 99% identity with the sequences of E. sorghinum (syn. Phoma sorghina) in GenBank (MN555348.1, MF987525.1, MK516207.1, respectively). Moreover, a phylogenetic tree of the LSU gene sequence of FDY-5 was constructed based on the Neighbor-Joining (NJ) method in MEGA6 software (Tamura et al. 2013) and revealed that strain FDY-5 was closest to E. sorghinum. Based on morphological and molecular characteristics, the fungus was identified as E. sorghinum. Pathogenicity tests were conducted on two-month-old white chrysanthemum plants. The upper three leaves of three plants were randomly selected for stab treatment and were inoculated with 5 × 5 mm mycelial discs produced from a fifteen-day-old colony on PDA. The inoculated and control (treated with sterile PDA disks) plants were incubated in a moist chamber (25 ± 2 °C, RH 85%). The first lesions appeared 1 day after inoculation on leaves, and the necrotic lesion area expanded outward and showed typical symptoms 3 days later. To fulfill Koch's postulates, the pathogen was reisolated from nine inoculated leaves by repeating the above isolating operation, and confirmed as E. sorghinum by morphology. To the best of our knowledge, this is the first report of E. sorghinum causing leaf spot on white chrysanthemum in China. E. sorghinum has a wide host range worldwide and often causes crop yield reduction. This report will facilitate the diagnosis of white chrysanthemum leaf spot of white chrysanthemum allowing control measures to be adopted to manage this disease in a timely manner. References Carbone, I., and Kohn, L. M. 1999. Mycologia 91:553. Kang, Y., et al. 2019. Plant Dis. 103 (7):1787. Lumbsch, H., et al. 2000. Plant Biol. 2:525. Tamura, K., et al. 2013. Mol. Biol. Evol. 30:2725-2729. White, T. J., et al. 1990. Page 315 in:PCR protocols:a guide to methods and applications. Academic Press, San Diego, CA. Funding Funding was supported by Major Increase and Decrease Projects at the Central Level of China (2060302) and the National Key Research and Development Program (2017FYC1700704).

Surveillance and monitoring of some major diseases of wheat in Bangladesh with special emphasis on wheat blast- a new disease in Bangladesh

An intensive survey was conducted on wheat diseases in 152 farmer’s fields in 36 districts of Bangladesh to assess plant responses of mature plants. During the survey, ten diseases including three majors diseases viz. Bipolaris leaf blight, leaf rust and wheat blast were identified. The maximum prevalence was recorded for Bipolaris leaf blight, with 100% incidence and 28–56% diseased leaf area (DLA). The disease was found on every cultivar in every field with varying levels of severity. Seventy-four percent of the fields surveyed had leaf rust. A significant variation in disease severity was observed among surveyed fields and cultivars. Most of the cultivars showed low to moderate disease severity with moderately susceptible to susceptible (MSS) reactions. Wheat blast, a new disease in Bangladesh, was found in 40% of fields but with low levels of infection. Among the surveyed districts, wheat fields in Bhola had higher levels of blast severity while Naogaon district had the lowest. The major wheat growing districts eg., north-western parts of the country were free from wheat blast. This survey provided fundamental information on the current status of wheat diseases which will allow for more effective management strategies.

Synthesis of Paeonol-Ozagrel Conjugate: Structure Characterization and In Vivo Anti-Ischemic Stroke potential

Ischemic stroke is a common neurological disease that can lead to mortality and disability. The current curative effect remains unsatisfactory because drug accumulation in the diseased areas is insufficient as a result of the unique blood–brain barrier. Therefore, much attention has been paid to develop a novel therapeutic compound, paeonol-ozagrel conjugate (POC), for ischemic stroke. Then, POC was successfully synthesized by conjugating of paeonol and ozagrel as mutual prodrug. A series of in vitro characterizations and evaluations, including high - resolution mass spectroscopy, nuclear magnetic resonance spectroscopy, partition coefficient, and assessment of cytotoxicity against PC12 cells, were performed. Pharmacokinetic study demonstrated POC is eliminated quickly (t1/2 = 53.46 ± 19.64 min), which supported a short dosing interval. The neurological score, infarct volume, histopathological changes, oxidative stress, inflammatory cytokines levels, and TXA2 levels also were evaluated in vivo in middle cerebral artery occlusion (MCAO) rats. All results showed that POC had a significant curative and therapeutic effect on ischemic stroke, as evaluated by the middle cerebral artery occlusion. Overall, POC can be expected to become a new drug candidate for the treatment of ischemic stroke.

Anthropogenic Disturbance Impacts Mycorrhizal Communities and Abiotic Soil Properties: Implications for an Endemic Forest Disease

In forest ecosystems, habitat fragmentation negatively impacts stand structure and biodiversity; the resulting fragmented patches of forest have distinct, disturbed edge habitats that experience different environmental conditions than the interiors of the fragments. In southwest Western Australia, there is a large-scale decline of the keystone tree species Corymbia calophylla following fragmentation and land use change. These changes have altered stand structure and increased their susceptibility to an endemic fungal pathogen, Quambalaria coyrecup, which causes chronic canker disease especially along disturbed forest habitats. However, the impacts of fragmentation on belowground processes in this system are not well-understood. We examined the effects of fragmentation on abiotic soil properties and ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) fungal communities, and whether these belowground changes were drivers of disease incidence. We collected soil from 17 sites across the distribution range of C. calophylla. Soils were collected across a gradient from disturbed, diseased areas to undisturbed, disease-free areas. We analysed soil nutrients and grew C. calophylla plants as a bioassay host. Plants were harvested and roots collected after 6 months of growth. DNA was extracted from the roots, amplified using fungal specific primers and sequenced using Illumina MiSeq. Concentrations of key soil nutrients such as nitrogen, phosphorus and potassium were much higher along the disturbed, diseased edges in comparison to undisturbed areas. Disturbance altered the community composition of ECM and AM fungi; however, only ECM fungal communities had lower rarefied richness and diversity along the disturbed, diseased areas compared to undisturbed areas. Accounting for effects of disturbance, ECM fungal diversity and leaf litter depth were highly correlated with increased disease incidence in C. calophylla. In the face of global change, increased virulence of an endemic pathogen has emerged in this Mediterranean-type forest.

Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning.

Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.

DIABETIC RETINOPATHY DIAGNOSIS USING KERNEL FUZZY C MEANS WITH CONVOLUTIONAL NEURAL NETWORK BASED RESIDUAL MODEL

Presently, Internet of Things (IoT) becomes popular owing to diverse its application scenarios like transports, building, healthcare, etc. This study introduces an efficient IoT based diabetic retinopathy (DR) diagnosis model using Kernel Fuzzy C Means Segmentation and Residual Network. The proposed model involves a sequence of processes namely image acquisition, pre-processing, segmentation, feature extraction and classification. At the initial stage, retinal fundus image acquisition takes place which captures the retina image of the patient using head mounted camera. Next, kernel fuzzy c-means (KFCM) based segmentation process is applied to identify the diseased area. Then, the features are extracted using convolutional neural network (CNN) based residual network (ResNet) model. Finally, softmax function is employed to carry out the classification task. The validation of the presented model takes place using Kaggle DR dataset and the experimental results verified the superior performance of the presented model. The obtained results indicated that the KFCM-CNNR model has resulted to a maximum accuracy of 96.89%, sensitivity of 93.12% and specificity of 98.16%.

Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves.

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

Dental fluorosis of children aged 8-12 in diseased areas in Xi’an City from 2014 to 2018

Objective To evaluate the dynamic prevalence of dental fluorosis of children and levels of fluoride in drinking water after improvement of water in Xi’an City, to provide scientific basis for water fluoridation improvement. Methods A total of 35 fluorosis endemic villages were selected as fixed monitor sites in 2014-2018, the ways of water improvement were surveyed, water fluorine content were detected and the prevalence of dental fluorosis in children aged 8 to 12 years were examined. Results Rates of excess fluoride in drinking water from 2014 to 2018 were 22.86%, 14.29%, 11.43%, 11.43% and 8.57%, the difference were significant ( χ 2 = 16.44, P <0.01).The dental fluorosis detection rates of children aged 8 to 12 years were 20.89%, 18.22%, 17.46%, 18.13% and 16.76% in 2014-2018 which showed a obvious descending trend by year ( χ 2 = 10.02, P <0.01). The detection rate of dental fluorosis in children aged 8 and 9 years showed a decreasing trend by year ( χ 2 = 6.53, 4.54, P <0.05). The difference of total rate of dental fluorisis, rate of mild cases rate of moderate-to-severe cases were statistically between the villages without qualified water and the villages with normal fluorine water ( χ 2 = 179.22, 167.93, 10.35, P <0.01). The rate of detection in the villages with the water fluorine exceed standard in 2014-2018 showed a declining trend year by year ( χ 2 = 28.50, P <0.01). The detection rate were significant different across water improvement methods ( χ 2 = 197.76, P <0.01). Detection rate of dental fluorosis decreased from 2014 to 2018 in the areas with municipal water supply showed a decreasing after year ( χ 2 = 12.16, P <0.01). Conclusion The improvement of municipal water supply shows significant effects on water fluorosis control, the detection rate of water fluoride and children’s dental fluorosis in some villages with the other ways of water improvement are still higher than expected, the continuously monitor of fluoride content in water and dental fluorosis in children should be strengthened. 【摘要】 目的 动态监测西安市饮水型氟中毒病区改水后水氟和儿童氟斑牙病情变化趋势,为改水降氟提供科学依据。 方法 2014—2018年在西安市选择 10 个区县的 35 个病区村作为固定监测点,调査改水方式、水氟含量和 8~ 12岁儿童氟 斑牙病情。 结果 2014—2018 年病区村水氟含量超标率分别为 22.86%, 14.29%,11.43%, 11.43%和 8.57%,差异有统计学 意义( χ 2 = 16.44, P <0.01)。各年度间 8~12岁儿童氟斑牙总检出率依次为 20.89%, 18.22%, 17.46%, 18.13%, 16.76%,且呈 逐年下降趋势( χ 2 趋势 = 10.02, P <0.01)。 8,9岁组儿童氟斑牙检出率各年度间比较,呈逐年下降的趋势( χ 2 趋势 值分别为 6.53, 4.54, P 值均<0.05)。水氟含量超标村儿童氟斑牙总检出率、轻度及以下、中度及以上检出率与水氟含量合格村比较,差异 有统计学意义( χ 2 值分别为 179.22,167.93,10.35, P 值均<0.01)。水氟含量超标病区村各年度间的氟斑牙检出率呈逐年 下降趋势( χ 2 趋势 =28.50, P <0.01)。市政供水、低氟井与除氟处理 3 种改水方式儿童氟斑牙检出率差异有统计学意义( χ 2 = 197.76, P <0.01)。市政供水各年度间儿童氟斑牙检出率呈逐年下降趋势( χ 2 趋势 = 12.16, P <0.01)。 结论 市政供水改水降 氟效果显著,其他改水方式仍有部分病区村水氟及儿童氟斑牙检出率未达标,应继续加强改水后水氟含量和儿童氟斑牙病 情监测。

Enhanced Regional Clustering Algorithm in Seizure Location Identification

Epilepsy is a chronic condition that is characterized by frequent occurrence of seizure. The treatment of epilepsy using anticonvulsants that suppress the rapid neuron spikes in brain is promising; however, a permanent fix is always lacked. The region on brain that is responsible for seizure if identified exactly, the diseased area can be expelled and that could be a permanent fix. Generally, IEEG (Intracranial Electroencephalogram) an invasive procedure is adopted to diagnose the location of seizure, though the results are very reliable and considered as a golden standard, the procedure is a complex and risky one. To overcome these difficulties, fMRI (Functional magnetic resonance imaging) is used to read the internal anatomical and metabolic nature of brain, an algorithm (erKNOTS, enhanced regional K based Numbering Out of Time Slices) that analyse each individual voxel is developed and implemented. The result obtained by the developed algorithm is found to be in agreement with those obtained through IEEG. The findings were further validated with Regional homogeneity and Functional connectivity.

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

Release of a liver anticancer drug, sorafenib from its PVA/LDH- and PEG/LDH-coated iron oxide nanoparticles for drug delivery applications

The use of nanocarriers composed of polyethylene glycol- and polyvinyl alcohol-coated vesicles encapsulating active molecules in place of conventional chemotherapy drugs can reduce many of the chemotherapy-associated challenges because of the increased drug concentration at the diseased area in the body. The present study investigated the structure and magnetic properties of iron oxide nanoparticles in the presence of polyvinyl alcohol and polyethylene glycol as the basic surface coating agents. We used superparamagnetic iron oxide nanoparticles (FNPs) as the core and studied their effectiveness when two polymers, namely polyvinyl alcohol (PVA) and polyethylene glycol (PEG), were used as the coating agents together with magnesium–aluminum-layered double hydroxide (MLDH) as the nanocarrier. In addition, the anticancer drug sorafenib (SO), was loaded on MLDH and coated onto the surface of the nanoparticles, to best exploit this nano-drug delivery system for biomedical applications. Samples were prepared by the co-precipitation method, and the resulting formation of the nanoparticles was confirmed by X-ray, FTIR, TEM, SEM, DLS, HPLC, UV–Vis, TGA and VSM. The X-ray diffraction results indicated that all the as-synthesized samples contained highly crystalline and pure Fe3O4. Transmission electron microscopy analysis showed that the shape of FPEGSO-MLDH nanoparticles was generally spherical, with a mean diameter of 17 nm, compared to 19 nm for FPVASO-MLDH. Fourier transform infrared spectroscopy confirmed the presence of nanocarriers with polymer-coating on the surface of iron oxide nanoparticles and the existence of loaded active drug consisting of sorafenib. Thermogravimetric analyses demonstrated the thermal stability of the nanoparticles, which displayed enhanced anticancer effect after coating. Vibrating sample magnetometer (VSM) curves of both produced samples showed superparamagnetic behavior with the high saturation magnetization of 57 emu/g for FPEGSO-MLDH and 49 emu/g for FPVASO-MLDH. The scanning electron microscopy (SEM) images showed a narrow size distribution of both final samples. The SO drug loading and the release behavior from FPEGSO-MLDH and FPVASO-MLDH were assessed by ultraviolet–visible spectroscopy. This evaluation showed around 85% drug release within 72 h, while 74% of sorafenib was released in phosphate buffer solution at pH 4.8. The release profiles of sorafenib from the two designed samples were found to be sustained according to pseudo-second-order kinetics. The cytotoxicity studies confirmed the anti-cancer activity of the coated nanoparticles loaded with SO against liver cancer cells, HepG2. Conversely, the drug delivery system was less toxic than the pure drug towards fibroblast-type 3T3 cells.

Ultrasound-Mediated Drug Delivery in Cancer Therapy: A Review.

The use of ultrasound as a medical diagnostic tool began in the 1940s. Ever since, the medical applications of ultrasound have included imaging, tumor ablation, and lithotripsy; however, an ever-increasing body of literature demonstrates that ultrasound has potential in other medical applications, including targeted drug delivery. Site-specific drug delivery involves delivering drugs to diseased areas with a high degree of precision, which is particularly advantageous in cancer treatment as it would minimize the adverse side effects experienced by patients. This review addresses the ability of ultrasound to induce localized and controlled drug release from nanocarriers, namely micelles and liposomes, utilizing thermal and/or mechanical effects. The interactions of ultrasound with micelles and liposomes, the effects of the lipid composition, and ultrasound parameters on the release of encapsulated drugs are discussed. In addition, a survey of the literature detailing some in vitro and in vivo ultrasound triggered drug delivery systems is presented.

Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting

Apple fruit sorting has been an important postharvest process carried on for the sorting of diseased apple fruits. A fuzzy cluster-based thresholding (FCBT) method for segmenting the region of interest from an apple image has been proposed for sorting apples in this study. As the first step, the acquired RGB colour image of an apple fruit was converted into a greyscale image. Then, five different fuzzy cluster bins with overlapped pixel ranges were taken and greypixel values were binned into them. A cluster with the maximum number of pixels was selected for calculating the threshold value. The region of interest from the apple image was then segmented using the proposed FCBT value. Features extracted from the segmented images were given as input to a fully-connected deep neural network for a classification. The performance of the FCBT method was compared with similar greyscale thresholding methods like Otsu's and Kapur's methods. The visual segmentation accuracy and the execution speed showed that the FCBT outperformed the other methods in segmenting the diseased area. A fully-connected deep neural network model with the FCBT image extracted features as input values gave a 98.33% accuracy rate in sorting the apple images.

Inside Cover

AbstractCross‐polarised optical coherence tomography is a promising technique to reveal certain pathologic conditions in tissue. However, endoscopic devices with flexible small probes to detect diseased areas intraoperatively barely exist. We present an extension of a commercially available swept‐source OCT system. Applying a common‐path probe, the polarisation of reference and sample arm does not need to be further balanced. Due to a depolariser, the system is independent of the inputpolarisation.Further details can be found in the article by Katharina Blessing, Judith Schirmer, Gargi Sharma, and Kanwarpal Singh. (e202000135). image

Effect of northern corn leaf blight severity on Fusarium ear rot incidence of maize

Northern corn leaf blight (NCLB) caused by Exserohilum turcicum and Fusarium ear rot caused by Fusarium verticillioides, are economically important maize diseases in South Africa. The effect of induced plant stress by NCLB on F. verticillioides ear rot and fumonisin production is unknown. Four field trials were conducted during 2016/2017 and 2017/2018 (November and December planting dates) at the Agricultural Research Council – Grain Crops in Potchefstroom (South Africa). Three maize cultivars with varying resistance levels to NCLB were selected (IMP50-10B – susceptible, BG3292 – moderately susceptible, DKC 61-94BR – resistant). NCLB severities were created through eight treatments: TMT1 – maximum control (three fungicide applications); TMT2 – standard control (two fungicide applications) and TMT3 – natural control (not inoculated or sprayed). The remaining treatments were inoculated with a cocktail of five NCLB races (Race 3, 3N, 23, 23N and 13N): TMT4 (five weeks after planting / WAP); TMT5 (five and six WAP); TMT6 (five, six and seven WAP); TMT7 (six and seven WAP); and TMT8 (seven WAP). Maize ears were naturally infected with F. verticillioides. Fifteen random plants were labelled at dent stage and NCLB severity (%), area under the disease progress curve, ear rot diseased area, ear rot severity (%), ear rot incidence (%) and total fumonisins (FB1+FB2+FB3; ug/kg) were established. Low levels of cob rot severity and fumonisins were obtained in all four trials. NCLB severity did not affect ear rot related parameters measured. Mean fumonisin levels were below the South African tolerance levels. Fumonisin concentrations differed significantly between cultivars but was not affected by NCLB severity or the cultivar x treatment interaction.&#x0D; Significance:&#x0D; &#x0D; This is the first study to investigate the effect of NCLB severity as a predisposing factor of ear rot incidence and severity of maize.&#x0D; The study confirmed that ear rot incidence and severity are not impacted by secondary stressors induced by NCLB, and that the cultivation of NCLB-resistant varieties would not bring about lower ear rot incidences.&#x0D;

Development of Permeable and Transparent Intracranial Electrode Embedded in Hydrogel Substrate

In surgery of epilepsy, a patient's cranium is opened for intracranial electroencephalogram (iEEG) measurement or electrical stimulation to treat a part of a brain causing seizures [1]. Conventionally, intracranial metal electrodes with a silicone rubber substrate have been widely used in iEEG measurements during surgery. However, the substrate of the electrode is stiff and needs to be pressed down to fit on the surface of the brain, which increases the tissue damage during the insertion into in a deep sulcus called a longitudinal cerebral fissure or a Silvian fissure. Besides, the low transparency of the electrode substrate decreases the immediate recognition of the diseased area, and the lack of permeability of that also cause damage to the brain in long term treatment including an implantation of the electrode. Herein, a flexible intracranial electrode using a hydrogel as a substrate was developed [2]. A soft carbon fabric electrode was embedded in a hydrogel composed of poly (vinyl alcohol) (PVA). The PVA hydrogel has excellent flexibility, biocompatibility, water content, and hydrophilicity, which leads to well adhesion and conformability to the brain. Moreover, the transparency of PVA hydrogel is expected to improve the visibility of the brain under the substrate and its permeability ensure adequate fluid delivery.Carbon fabric electrodes were fabricated by cutting the electrodes and lead wires out of the fabric and applying an insulation coating. The non-insulated electrode surface was coated with conductive particle polymer poly (3,4-ethylenedioxythiophene) (PEDOT) to increase the surface area and increase the electrical capacity. This process is to prevent electrolysis from occurring during electrical stimulation[3]. A gel precursor solution was prepared by dissolving PVA in distilled water and adding dimethyl sulfoxide (DMSO). The electrodes were embedded in the precursor solution and gelated by repeated freezing and thawing cycles. DMSO provided an uniform and clear gel, and to avoid its irritation, the obtained gel was soaked and washed well in saline.Figures 1 shows the electrodes placed on the brain model. While the conventional electrodes with silicone substrate hardly followed the shape of the object (Figure 1a), the electrodes with hydrogel substrate followed the object well (Figure 1b). The object was not clear to observe through the conventional silicone substrate, and that substrate did not swell the water droplet placed on the surface (Figure 1c). In contrast, the hydrogel-based substrate was clear after the adhesion onto the brain model, and the water droplets placed on the surface were spread over the entire surface and adsorbed to the hydrogel-based substrate. These results indicated that electrodes with a hydrogel substrate could adhere well to the brain surface while minimizing damage to the tissue, and allowing observation of the affected area during surgery.Our developed electrode with soft, hydrophilic, and soft hydrogel substrate were promising device for the treatment of epilepsy, overcoming the conventional problems and leading to new medical approach.

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall, good agreements between UAV-based estimates and observations were found for canopy cover, WSR, and WLR severities, with statistically significant correlations (p-value (Kendall) &lt; 0.0001). Correlation coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively. While the estimation of canopy cover was most often less accurate (correlation coefficients &lt; 0.20), WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide application, while disease severities were also quantified accurately over the same period. Using such a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping season can help to identify any new disease outbreak and efficaciously control its spread.

First report of Stemphylium leaf spot caused by Stemphylium vesicarium on alfalfa (Medicago sativa) in Peru.

Alfalfa (Medicago sativa) is the most cultivated fodder crop in Peru with 172,000 ha cultivated (MINAM 2019), and Arequipa is the top producing region with 40% of the national production in 2015 (Santamaría et al. 2016). In January-April 2019 (av. 20°C and 70% RH), most alfalfa fields in Majes-Pedregal, Arequipa were affected by an unidentified foliar disease. One of the fields was located at the farm of the Universidad Nacional de San Agustín de Arequipa (16°19'29.6" S, 72°12'59.9" W). Symptoms appeared as elliptical light brown spots witdark brown borders (Fig. S1a and b). The field (~60 × 60 m) was divided into ~30 × 12 m sections and two plants in each section were collected (20 plants total). Plants were digitized and the leaflet diseased area was calculated with ImageJ 1.53a, from which an incidence of 100% and a severity of 38.7 ± 4.4 % were estimated. Microscopical observations at the leaflet spots revealed consistently the presence of oblong multiseptated conidia (23.6-42.8 × 16.5-25.2 µm; av. 33.3 × 20.9 µm; n = 40) of the genus Stemphylium (Simmons 1969; Woudenberg et al. 2017) (Fig. S1c). We obtained 10 pure cultures by placing conidia from the spots directly onto potato dextrose agar medium with the aid of stereoscope and sterile forceps. Two isolates (UNSA-StemV01 and UNSA-StemV02) were incubated further until ascospore production at room temperature with no special light stimulus. After 45 days of growth, globose pseudothecia and ellipsoidal ascospores (25.4-38.7 × 11.2-16.6 µm; av. 31.9 × 13.7 µm; n = 30) formation occurred (Fig. S1d and e). We extracted the DNA from these two isolates using Wizard® Purification Kit (Promega Corp., Madison, WI) and sequenced the internal transcribed spacer 1 and 2 intervening 5.8S rDNA subunit (GenBank accessions: MT371236-37), and the glyceraldehyde-3-phosphate dehydrogenase (MT375513-14) and the calmodulin (MT375515-16) genes, highly resolutive markers to identify Stemphylium species, following Woudenberg et al. (2017). We retrieved sequence data available from 43 isolates of nine Stemphylium species (Han et al. 2019; Woudenberg et al. 2017), and built a mid-point rooted phylogeny with the three-loci concatenated data set (Fig. S2). We identified our isolates as S. vesicarium (Fig. S2). Koch's postulates were fulfilled by spray-inoculation with conidia from isolate UNSA-StemV01 suspended in sterile water (1×104 / mL) to two healthy 50-day old alfalfa plants growing on pots in the university greenhouse (av. 25°C and 70% RH). Two plants sprayed with sterile water without conidia served as control. Symptoms appeared after 21 days of inoculation, and when conidia were re-isolated, they were the same as originally obtained. No symptoms developed in the control plants. This confirmed that S. vesicarium is the causal agent of the alfalfa disease in Majes-Pedregal, identified as Stemphylium leaf spot. revious studies documented S. vesicarium on asparagus and onion in Peru (Castillo Valiente 2018; Vásquez Salas 2018; Vásquez Sangay 2013), but molecular characterization has only been applied to S. lycopersici from potatoes (Woudenberg et al. 2017). Stemphylium vesicarium has been documented in various crops, including alfalfa, and countries in Europe, North America, Africa, Asia and in Australia and New Zealand (Han et al. 2019; Woudenberg et al. 2017). This occurrence is the first report of S. vesicarium on alfalfa in Peru. The disease compromises the quality of this fodder crop, so actions need to be taken in Arequipa.

R-CHOP Versus R-Bendamustine with or without Rituximab Maintenance in Newly Diagnosed Follicular Lymphoma Patients with High SUV at Baseline PET

R-CHOP Versus R-Bendamustine with or without Rituximab Maintenance in Newly Diagnosed Follicular Lymphoma Patients with High SUV at Baseline PET