Edge enhanced depth perception with binocular meta-lens

The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry.Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices.This work demonstrates an intelligent,lightweight,and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing.The miniaturized system comprises a binocular meta-lens,a 532 nm filter,and a CMOS sensor.For disparity computation,we propose a stereo-matching neural network with a novel H-Module.The H-Module incorporates an attention mechanism into the Siamese network.The symmetric architecture,with cross-pixel interaction and cross-view interaction,enables a more comprehensive analysis of contextual information in stereo images.Based on spatial intensity discontinuity,the edge enhancement eliminates illposed regions in the image where ambiguous depth predictions may occur due to a lack of texture.With the assistance of deep learning,our edge-enhanced system provides prompt responses in less than 0.15 seconds.This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling,machine vision,autonomous driving,and robotics development.

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.

Retrieval of Winter Wheat Canopy Carotenoid Content with Ground-and Airborne-Based Hyperspectral Data

Accurate assessment of canopy carotenoid content(CC_(x+c)C)in crops is central to monitor physiological conditions in plants and vegetation stress,and consequently supporting agronomic decisions.However,due to the overlap of absorption peaks of carotenoid(C_(x+c))and chlorophyll(C_(a+b)),accurate estimation of carotenoid using reflectance where carotenoid absorb is challenging.The objective of present study was to assess CC_(x+c)C in winter wheat(Triticum aestivum L.)with ground-and aircraft-based hyperspectral measurements in the visible and near-infrared spectrum.In-situ hyperspectral reflectance were measured and airborne hyperspectral data were acquired during major growth stages of winter wheat in five consecutive field experiments.At the canopy level,a remarkable linear relationship(R^(2)=0.95,p<0.001)existed between C_(x+c) and Ca+b,and correlation between CC_(x+c)C and wavelengths within 400 to 1000 nm range indicated that CC_(x+c)C could be estimated using reflectance ranging from visible to near-infrared wavebands.Results of Cx+c assessment based on chlorophyll and carotenoid indices showed that red edge chlorophyll index(CI red edge)performed with the highest accuracy(R^(2)=0.77,RMSE=22.27μg/cm^(2),MAE=4.97μg/cm^(2)).Applying partial least square regression(PLSR)in CC_(x+c)C retrieval emphasized the significance of reflectance within 700 to 750 nm range in CC_(x+c)C assessment.Based on CI red edge index,use of airborne hyperspectral imagery achieved satisfactory results in mapping the spatial distribution of CC_(x+c)C.This study demonstrates that it is feasible to accurately assess CC_(x+c)C in winter wheat with red edge chlorophyll index provided that C_(x+c) correlated well with C_(a+b) at the canopy scale.it is therefore a promising method for CC_(x+c)C retrieval at regional scale from aerial hyperspectral imagery.

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and pests.In most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in practice.If a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for application.This study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific bands.Through a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on leaves.Then,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio analysis.The validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was proposed.The present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

Research on Fast Semi-Quantitative Risk Assessment Method for High Temperature and High Pressure Gas Wells

Aiming at the problem that the existing risk assessment methods in China cannot simply and accurately assess the safety risk of gas wells, a rapid semi-quantitative risk assessment method for gas wells under high temperature and pressure is studied. Based on the rapid risk assessment method of annulus well with pressure in Chevron Company and the existing risk assessment methods, the well barrier and annulus pressure of high temperature and high pressure gas wells are fully considered. A rapid semi-quantitative risk assessment method for high temperature and high pressure gas wells is established, which takes the annulus pressure value, well service life, annulus pressure recovery after pressure relief, reservoir conditions (formation pressure, production) and well CO2 and H2S content as the key risk indexes. The method is applied in a gas field, and the risk value and risk grade of a practical well are semi-quantitatively evaluated. The overall risk situation of the well is obtained. The research results provide important technical guidance for the safe production of gas well.

The continuous wavelet projections algorithm: A practical spectral-feature-mining approach for crop detection

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features.However, feature-selection methods that satisfy both requirements are lacking. To address this issue,in this study, a novel method, the continuous wavelet projections algorithm(CWPA), was developed,which has advantages of both continuous wavelet analysis(CWA) and the successive projections algorithm(SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content(R^(2)= 0.8521)and equivalent water thickness(R^(2)= 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Tea plant stresses threaten the quality of tea seriously.The technology corresponding to the fast detection and differentiation of stresses is of great significance for plant protection in tea plantation.In recent years,hyperspectral imaging technology has shown great potential in detecting and differentiating plant diseases,pests and some other stresses at the leaf level.However,the lack of studies at canopy level hampers the detection of tea plant stresses at a larger scale.In this study,based on the canopy-level hyperspectral imaging data,the methods for identifying and differentiating the three commonly occurred tea stresses(i.e.,the tea leafhopper,anthrax and sun burn)were studied.To account for the complexity of the canopy scenario,a stepwise detecting strategy was proposed that includes the process of background removal,identification of damaged areas and discrimination of stresses.Firstly,combining the successive projection algorithm(SPA)spectral analysis and K-means cluster analysis,the background and overexposed non-plant regions were removed from the image.Then,a rigorous sensitivity analysis and optimization were performed on various forms of spectral features,which yielded optimal features for detecting damaged areas(i.e.,YSV,Area,GI,CARI and NBNDVI)and optimal features for stresses discrimination(i.e.,MCARI,CI,LCI,RARS,TCI and VOG).Based on this information,the models for identifying damaged areas and those models for discriminating different stresses were established using K-nearest neighbor(KNN),Random Forest(RF)and Fisher discriminant analysis.The identification model achieved an accuracy over 95%,and the discrimination model achieved an accuracy over 93%for all stresses.The results suggested the feasibility of stress detection and differentiation using canopy-level hyperspectral imaging techniques,and indicated the potential for its extension over large areas.

Tea Plantation Frost Damage Early Warning Using a Two-Fold Method for Temperature Prediction

As the source and main producing area of tea in the world, China has formed unique tea culture, and achievedremarkable economic benefits. However, frequent meteorological disasters, particularly low temperature frostdamage in late spring has seriously threatened the growth status of tea trees and caused quality and yield reduction of tea industry. Thus, timely and accurate early warning of frost damage occurrence in specific tea garden isvery important for tea plantation management and economic values. Aiming at the problems existing in currentmeteorological disaster forecasting methods, such as difficulty in obtaining massive meteorological data, largeamount of calculation for predicted models and incomplete information on frost damage occurrence, this paperproposed a two-fold algorithm for short-term and real-time prediction of temperature using field environmentaldata, and temperature trend results from a nearest local weather station for accurate frost damage occurrence leveldetermination, so as to achieve a specific tea garden frost damage occurrence prediction in a microclimate. Timeseries meteorological data collected from a small weather station was used for testing and parameterization of atwo-fold method, and another dataset acquired from Tea Experimental Base of Zhejiang University was furtherused to validate the capability of a two-fold model for frost damage forecasting. Results showed that comparedwith the results of autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR),the proposed two-fold method using a second order Furrier fitting model and a K-Nearest Neighbor model(K = 3) with three days historical temperature data exhibited excellent accuracy for frost damage occurrence prediction on consideration of both model accuracy and computation (98.46% forecasted duration of frost damage,and 95.38% for forecasted temperature at the onset time). For field test in a tea garden, the proposed methodaccurately predicted three times frost damage occurrences, including onset time, duration and occurrence level.These results suggested the newly-proposed two-fold method was suitable for tea plantation frost damage occurrence forecasting.

Crop growth inhibited by over-liming in tea plantations

Liming is a common strategy applied to attain optimal pH for tea growth in severely acidic soils.Tea however is a calciphobous plant,and the effects of liming on its growth and nutrient uptake remain poorly understand.Therefore,it is necessary to better understand the effects of liming on soil chemical properties and tea nutrient content.In this study,a tea plantation that had exhibited large variation in growth after liming was selected as a study site.We categorized plots into two growth condition groups:Plot 1(poor growth)and Plot 2(excellent growth).Tea nutrient levels,and soil chemical properties were then compared between the two groups.Normalized difference vegetation index(NDVI)and transformed vegetation index(TVI)values were significantly higher and lower,respectively,in Plot 2 than in Plot 1.Yield,number of buds per m2,and 100-bud weight were significantly higher in Plot 2.These results were attributed to higher N,K,and Al concentrations and lower Ca concentrations in leaves,and lower pH and available Ca and higher available Al in soil.Leaf concentrations of K and Al were significantly negatively correlated with leaf concentrations of Ca and soil pH.A positive relationship was observed between leaf concentrations of K and Al,indicating inhibited K and Al uptake due to over-liming,restricting tea growth.In conclusion,our results show that tea growth will be restricted by over-liming,as a result of the high soil pH and Ca concentration inhibiting the K and Al uptake.

Pancreatic ductal adenocarcinoma chemoresistance:From metabolism reprogramming to novel treatment

As pancreatic cancer(PC)is highly malignant,its patients tend to develop metastasis at an early stage and show a poor response to conventional chemotherapies.First-line chemotherapies for PC,according to current guidelines,include fluoropyrimidine-and gemcitabine-based regimens.Accumulating research on drug resistance has shown that biochemical metabolic aberrations in PC,especially those involving glycolysis and glutamine metabolism,are highly associated with chemoresistance.Additionally,lipid metabolism is a major factor in chemoresistance.However,emerging compounds that target these key metabolic pathways have the potential to overcome chemoresistance.This review summarizes how PC develops chemoresistance through aberrations in biochemical metabolism and discusses novel critical targets and pathways within cancer metabolism for new drug research.

Plasmonic bound states in the continuum for unpolarized weak spatially coherent light

Plasmonic resonances empowered by bound states in the continuum(BICs) offer unprecedented opportunities to tailor light–matter interaction. However, excitation of high quality-factor(Q-factor) quasi-BICs is often limited to collimated light at specific polarization and incident directions, rendering challenges for unpolarized focused light. The major hurdle is the lack of robustness against weak spatial coherence and poor polarization of incident light. Here, addressing this limitation, we demonstrate sharp resonances in symmetric plasmonic metasurfaces by exploiting BICs in the parameter space, offering ultraweak angular dispersion effect and polarization-independent performance. Specifically, a high-Q(≈71) resonance with near-perfect absorption(>90%) is obtained for the input of unpolarized focused light covering wide incident angles(from 0° to 30°). Also, giant electric and magnetic field enhancement simultaneously occurs in quasi-BICs. These results provide a way to achieve efficient near-field enhancement using focused light produced by high numerical aperture objectives.

Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

Detection of yellow rust using hyperspectral data is of practical importance for disease control and prevention.As an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and insects.Given the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected plants.The impacts of the mother wavelet type and spectral interval on disease detection were analyzed.The results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow rust.The Mexh wavelet slightly outperformed the others in estimating disease severity.Although the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 nm.Therefore,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow rust.The relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

The development and application of genome editing technology in ruminants: a review

Transgenic ruminants are a valuable resource for both animal breeding and biomedical research.The development of transgenic breeding is proceeding slowly,because it suffers from low efficiency of gene transfer and possible safety problems from uncontrolled random integration.However,new breeding methods combined with genome editing and somatic cell nuclear transfer or microinjection can offer an economic and efficient way to produce gene-edited ruminants,which can serve as bioreactors or have improved disease resistance,animal welfare and product quality.Recent advances in precise genome editing technologies,especially clustered regularly interspaced short palindromic repeat(CRISPR)/Cas9 nucleases,are enabling the systematic development of gene-edited ruminant production.This review covers the development of gene-edited ruminants,the particulars of site-specific engineered nucleases and the state of the art and new insights into practical applications and social acceptance of genome editing technology in ruminants.It is concluded that the production of gene-edited ruminants is feasible and through improvements in genome editing technology it is possible to help feed the world.

Survey on the current status of the diagnosis and treatment of pancreatic cancer in public tertiary hospitals in China:a cross-sectional questionnaire-based,observational study

Objective:Pancreatic cancer is one of the most aggressive digestive system malignant tumors,and its clinical diagnosis and treatment are still challenging.To further understand the current status and improve the multidisciplinary collaboration for diagnosis and treatment of pancreatic cancer in China,we conducted an online questionnaire survey on the diagnosis and treatment status of pancreatic cancer in public tertiary hospitals of China in 2021.Methods:In this cross-sectional questionnaire-based,observational study,online questionnaires with real-name authentication were used to gather data from 500 clinicians,50 pharmacists,and 1000 pancreatic cancer patients in tertiary general hospitals or cancer hospitals nationwide.Results:A total of 485 valid questionnaires were obtained from the clinicians,majority of whom were from economically better developed regions or cities of China.There were multi-disciplinary team treatment(MDT)clinics for pancreatic cancer patients in 60%of the hospitals.Minimally invasive surgeries could be performed in all the surveyed hospitals.However,open surgery was still the mainstream choice in most cases.Gemcitabine-based chemotherapy was the most popular first-line adjuvant regimen for pancreatic cancer.A total of 50 valid questionnaires were collected from pharmacists,48%of them are not satisfactory with the efficacy of the chemotherapeutic drugs,and myelosuppression,liver,and renal damage were the most concerning side effects.In total,1011 valid questionnaires were collected from the patients.Approximately,48.4%of the patients did not know about pancreatic cancer before becoming ill.Over 80%of pancreatic cancer patients reported poor to very poor health-related quality of life,and the estimated overall medical expenses were within<400,000($58823.53)in 80%of the patients.Clinicians,pharmacists,and patients believe that popularizing scientific knowledge of pancreatic cancer,constructing MDT clinics and fast-lane system,and conducting clinical research will help further improve the diagnosis and treatment of pancreatic cancer.Conclusions:The MDT clinics for pancreatic cancer have been well developed in most of the public tertiary hospitals.Minimally invasive pancreatic surgery has developed rapidly in China;however,open surgery is still the mainstream choice for pancreatic cancer.The proportion of adjuvant treatment has been significantly improved,and the gemcitabine-based regimen is the most commonly used first-line regimen.Most of the public still lacks the general knowledge of pancreatic cancer,needing further popularization.The construction of a fast-lane treatment system and conducting of high-level clinical studies are the warm expectations of the clinicians and patients.The real-world situation of the diagnosis and treatment of pancreatic cancer in the other types of hospitals of China needs further exploration.