Wheat Ears Counting in Field Conditions Based on Multi-Feature Optimization and TWSVM

The number of wheat ears in the field is very important data for predicting crop growth and estimating crop yield and as such is receiving ever-increasing research attention. To obtain such data, we propose a novel algorithm that uses computer vision to accurately recognize wheat ears in a digital image. First, red-green-blue images acquired by a manned ground vehicle are selected based on light intensity to ensure that this method is robust with respect to light intensity. Next, the selected images are cut to ensure that the target can be identified in the remaining parts. The simple linear iterative clustering method, which is based on superpixel theory, is then used to generate a patch from the selected images. After manually labeling each patch, they are divided into two categories: wheat ears and background. The color feature “Color Coherence Vectors,” the texture feature “Gray Level Co-Occurrence Matrix,” and a special image feature “Edge Histogram Descriptor” are then exacted from these patches to generate a high-dimensional matrix called the “feature matrix.” Because each feature plays a different role in the classification process, a feature-weighting fusion based on kernel principal component analysis is used to redistribute the feature weights. Finally, a twin-support-vector-machine segmentation (TWSVM-Seg) model is trained to understand the differences between the two types of patches through the features, and the TWSVM-Seg model finally achieves the correct classification of each pixel from the testing sample and outputs the results in the form of binary image. This process thus segments the image. Next, we use a statistical function in Matlab to get the exact a precise number of ears. To verify these statistical numerical results, we compare them with field measurements of the wheat plots. The result of applying the proposed algorithm to ground-shooting image data sets correlates strongly (with a precision of 0.79–0.82) with the data obtained by manual counting. An average running time of 0.1 s is required to successfully extract the correct number of ears from the background, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on wheat seedlings

Own-Race Faces Capture Attention Faster than Other-Race Faces: Evidence from Response Time and the N2pc

Studies have shown that people are better at recognizing human faces from their own-race than from other-races, an effect often termed the Own-Race Advantage. The current study investigates whether there is an Own-Race Advantage in attention and its neural correlates. Participants were asked to search for a human face among animal faces. Experiment 1 showed a classic Own-Race Advantage in response time both for Chinese and Black South African participants. Using event-related potentials (ERPs), Experiment 2 showed a similar Own-Race Advantage in response time for both upright faces and inverted faces. Moreover, the latency of N2pc for own-race faces was earlier than that for other-race faces. These results suggested that own-race faces capture attention more efficiently than other-race faces

The impact of stereotype threat on endogenous poverty-elimination dynamics in generationally poor individuals

IntroductionThe study examines the impact of stereotype threat on generationally poor individuals and its effect on achievement motivation. It also explores the extent to which self-affirmation has an intervention effect on the negative impact of stereotype threat.Methods and resultsIn Study 1, statements that contained negative stereotypes were used to elicit stereotype threat in generationally poor individuals; the results show that stereotype threat reduced the performance of generationally poor individuals in a mental-rotation task. Study 2 used a questionnaire to measure the endogenous dynamics of generationally poor individuals attempting to escape poverty after experiencing stereotype threat; participants in the stereotype-threat group showed lower-level endogenous poverty-elimination dynamics than those in the control group. In Study 3, a self-affirmation intervention was administered to the stereotype-threat group after the stereotype threat was induced. Participants in the self-affirmation group were shown to have higher-level endogenous poverty-elimination dynamics than those in the control group.DiscussionThese findings confirm the negative effect of stereotype threat on endogenous poverty-elimination dynamics and verify the effectiveness of self-affirmation in mitigating the negative effects of stereotype threat

A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy

Above-ground biomass (AGB) provides a vital link between solar energy consumption and yield, so its correct estimation is crucial to accurately monitor crop growth and predict yield. In this work, we estimate AGB by using 54 vegetation indexes (e.g., Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index) and eight statistical regression techniques: artificial neural network (ANN), multivariable linear regression (MLR), decision-tree regression (DT), boosted binary regression tree (BBRT), partial least squares regression (PLSR), random forest regression (RF), support vector machine regression (SVM), and principal component regression (PCR), which are used to analyze hyperspectral data acquired by using a field spectrophotometer. The vegetation indexes (VIs) determined from the spectra were first used to train regression techniques for modeling and validation to select the best VI input, and then summed with white Gaussian noise to study how remote sensing errors affect the regression techniques. Next, the VIs were divided into groups of different sizes by using various sampling methods for modeling and validation to test the stability of the techniques. Finally, the AGB was estimated by using a leave-one-out cross validation with these powerful techniques. The results of the study demonstrate that, of the eight techniques investigated, PLSR and MLR perform best in terms of stability and are most suitable when high-accuracy and stable estimates are required from relatively few samples. In addition, RF is extremely robust against noise and is best suited to deal with repeated observations involving remote-sensing data (i.e., data affected by atmosphere, clouds, observation times, and/or sensor noise). Finally, the leave-one-out cross-validation method indicates that PLSR provides the highest accuracy (R2 = 0.89, RMSE = 1.20 t/ha, MAE = 0.90 t/ha, NRMSE = 0.07, CV (RMSE) = 0.18); thus, PLSR is best suited for works requiring high-accuracy estimation models. The results indicate that all these techniques provide impressive accuracy. The comparison and analysis provided herein thus reveals the advantages and disadvantages of the ANN, MLR, DT, BBRT, PLSR, RF, SVM, and PCR techniques and can help researchers to build efficient AGB-estimation models

Relationship between Axial Length and Levels of TGF-β in the Aqueous Humor and Plasma of Myopic Patients

Purpose. To investigate the levels of transforming growth factor-β (TGF-β) in human aqueous humor (AH) and plasma (PL) of patients with myopia, and verify whether there is an association between these levels and their association with axial length (AL). Methods. Thirty-eight myopic patients who received intraocular collamer lens (ICL) implantation were enrolled in this cross-sectional study. Patients were divided into three groups based on AL with cut-off points of 26 and 28 mm. AH and PL samples were obtained during ICL implantation surgery. The levels of TGF-β1, TGF-β2, and TGF-β3 in the AH and PL samples were measured using Luminex xMAP Technology kits (Milliplex xMAP kits). The protein levels of TGF-βs in both AH and PL samples and their relationships with AL were analyzed. Results. In all, 38 patients (59 eyes) were enrolled and divided into the three groups: group A contained 7 people (10 eyes), group B contained 22 people (37 eyes), and group C contained 9 people (12 eyes). In the AH group, we detected TGF-β1 (P50: 19.97 pg/mL), TGF-β2 (2446.00 pg/mL), and TGF-β3 (26.33 pg/mL); in PL, these concentrations were 8984.00, 523.44, and 210.47 pg/mL, respectively. The levels of TGF-β1 and TGF-β3 in AH were positively associated with AL. None of the three isoforms in PL were related to those in AH or to AL. Conclusions. The levels of TGF-β1 and TGF-β3 in AH were more strongly associated with the severity of myopia than the types of TGF-β in PL

Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study

Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R670 only: R2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management

Engineering T7 bacteriophage as a potential DNA vaccine targeting delivery vector

Abstract Background DNA delivery with bacteriophage by surface-displayed mammalian cell penetrating peptides has been reported. Although, various phages have been used to facilitate DNA transfer by surface displaying the protein transduction domain of human immunodeficiency virus type 1 Tat protein (Tat peptide), no similar study has been conducted using T7 phage. Methods In this study, we engineeredT7 phage as a DNA targeting delivery vector to facilitate cellular internalization. We constructed recombinant T7 phages that displayed Tat peptide on their surface and carried eukaryotic expression box (EEB) as a part of their genomes (T7-EEB-Tat). Results We demonstrated that T7 phage harboring foreign gene insertion had packaged into infective progeny phage particles. Moreover, when mammalian cells that were briefly exposed to T7-EEB-Tat, expressed a significant higher level of the marker gene with the control cells infected with the wide type phage without displaying Tat peptides. Conclusion These data suggested that the potential of T7 phage as an effective delivery vector for DNA vaccine transfer