Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Review:Wearable Sweat Sensors for Diseases Prevention and Monitoring

Advanced in wireless technologies and flexible materials with great biocompatibility,wearable devices have been utilized in the field of healthcare,sports management,and diseases prevention,which have been widely applied in current electronic equipment.Sweat,as a common metabolite on the skin surface,contains a wealth of biomarkers for disease detection and diagnosis.Therefore,developing wearable sweat sensors can provide a non⁃invasive method for health data collecting,sports monitoring,and clinical diagnosis in a convenient way.Recent research in sweat metabolomics has offered a lot of information for sweat analysis and the wearable sweat sensors with small size,various sensing,and transmission units,and good skin contact has exhibited dynamic multi⁃signal detection.This article introduces the biomarkers in sweat related to different diseases and the current development of sweat sensors for user􀆳s activation monitoring and diseases detection.The barriers and difficulties in the future are also discussed and perspectives in the next generation sweat sensors are proposed.

Intestinal ultrasound as a non-invasive tool to monitor inflammatory bowel disease activity and guide clinical decision making

Intestinal ultrasound(IUS)is a non-invasive,real-time,cross-sectional imaging tool that can be used at the point-of-care to assess disease activity in patients with Crohn’s disease or ulcerative colitis.IUS promotes quick and impactful treatment decisions that can modify disease progression and enhance patient compliance.This review will summarize the technical aspects of IUS,the evidence to support the use of IUS in disease activity monitoring,the comparison of IUS to current standard of care monitoring modalities such as colonoscopy and calprotectin,and the optimal positioning of IUS in a tight-control monitoring strategy.

A Preliminary Study of C-reactive Protein in the Diagnosis and Monitoring of Lyme Disease

Concentrations of C-reactive protein (CRP) in the serum of 14 patients suffering from Lyme diseasc were measured. 86% of these patients were found to have abnormally high concentrations of serum CRP (range 14-158 mg/L). The CRP concentration of a 60-yearold patient abated from 29 mg/L to 13 mg/L after treatrnent. Results suggest that serum CRP concentration can provide a valuable and accurate means for the clinical diagnosis and monitoring of Lyme disease

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

Capsule endoscopy in Crohn's disease: Are we seeing any better?

Crohn’s disease (CD) is a complex, immune-mediated disorder that often requires a multi-modality approach for optimal diagnosis and management. While traditional methods include ileocolonoscopy and radiologic modalities, increasingly, capsule endoscopy (CE) has been incorporated into the algorithm for both the diagnosis and monitoring of CD. Multiple studies have examined the utility of this emerging technology in the management of CD, and have compared it to other available modalities. CE offers a noninvasive approach to evaluate areas of the small bowel that are difficult to reach with traditional endoscopy. Furthermore, CE maybe favored in specific sub segments of patients with inflammatory bowel disease (IBD), such as those with IBD unclassified (IBD-U), pediatric patients and patients with CD who have previously undergone surgery.

Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing regions.The disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as example.Bakanae disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of conidia.Therefore,early disease surveillance plays a crucial role in securing rice production.Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied.In this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease.Phenotypic data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease mechanism.Hyperspectral data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning algorithms.Based on this model,an average accuracy of 92.2%was achieved on the 21st day of infection.It also achieved an accuracy of 79.4%as early as the 9th day.Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance.Collectively,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

Diagnosis of gastroesophageal reflux disease in elderly subjects using 24-houres ophageal pH monitoring

Objective To evaluate the relationship between the parameters of 24 hour esophageal pH monitoring and gastroesophageal reflux disease (GERD) among elderly subjects. Methods Twenty four hour esophageal pH monitoring was carried out in 20 elderly subjects without apparent GERD symptoms (controls) and 69 suspected GERD subjects.Results Normal values of the parameters from 20 elderly controls were obtained. Percent of total time, percent of supine time and percent of upright time in which the pH was <4 (indicating reflux) were less than 3.3%, 1.4%, 5.5%, respectively. The number of reflux episodes and episodes lasting longer than 5 minutes were less than 65 and 2 times respectively. The values obtained in 66 GERD suspected subjects were significantly different from those in norrmal controls. The differences of reflux parameters between the esophagitis group and non esophagitis group, such as percent of total time with pH<4, percent of supine time with pH<4 and number of reflux lasting longer than 5 minutes were also significant. Conclusions About 51.6% patients (34/66) with reflux symptoms but without endoscopic evidence of esophagitis were definitely diagnosed as GERD by esophageal pH monitoring. Duration of esophageal acid exposure correlated with the severity of GERD.

Rapid detection of the rice false smut fungus Ustilaginoidea virens by lateral flow strip-based recombinase polymerase amplification assay

Rice false smut,caused by Ustilaginoidea virens,is a devastating disease that greatly reduces rice yield and quality.However,controlling rice false smut disease is challenging due to the unique infection mode of U.virens.Therefore,there is a need for early diagnosis and monitoring techniques to prevent the spread of this disease.Lateral flow strip-based recombinase polymerase amplification(LF-RPA)overcomes the limitations of current U.virens detection technologies,which are time-consuming,require delicate equipment,and have a high false-positive rate.In this study,we used a comparative genomics approach to identify Uv_3611,a specific gene of U.virens,as the target for the LF-RPA assay.The designed primers and probe efffectively detected the genomic DNA(gDNA)of U.virens and demonstrated no cross-reactivity with related pathogens.Under optimal conditions,the LF-RPA assay demonstrated a sensitivity of 10 pg of U.virens gDNA.Additionally,by incorporating a simplified PEG-NaOH method for plant DNA extraction,the LF-RPA assay enabled the detection of U.virens in rice spikelets within 30 min,without the need for specialized equipment.Furthermore,the LF-RPA assay successfully detected U.virens in naturally infected rice and seed samples in the field.Therefore,the LF-RPA assay is sensitive,efficient,and convenient,and could be developed as a kit for monitoring rice false smut disease in the field.