Structure and morphology of X-ray selected AGN hosts at 1<z<3 in CANDELS-COSMOS field

We analyze morphologies of the host galaxies of 35 X-ray selected active galactic nucleus (AGNs) at $z\sim2$ in the Cosmic Evolution Survey (COSMOS) field using Hubble Space Telescope/WFC3 imaging taken from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). We build a control sample of 350 galaxies in total, by selecting ten non-active galaxies drawn from the same field with the similar stellar mass and redshift for each AGN host. By performing two dimensional fitting with GALFIT on the surface brightness profile, we find that the distribution of S$\`e$rsic index (n) of AGN hosts does not show a statistical difference from that of the control sample. We measure the nonparametric morphological parameters (the asymmetry index A, the Gini coefficient G, the concentration index C and the M20 index) based on point source subtracted images. All the distributions of these morphological parameters of AGN hosts are consistent with those of the control sample. We finally investigate the fraction of distorted morphologies in both samples by visual classification. Only $\sim$15% of the AGN hosts have highly distorted morphologies, possibly due to a major merger or interaction. We find there is no significant difference in the distortion fractions between the AGN host sample and control sample. We conclude that the morphologies of X-ray selected AGN hosts are similar to those of nonactive galaxies and most AGN activity is not triggered by major merger.Comment: 5 pages, 3 figures, accepted for publication in The Astrophysical Journal Letter

Capture of Aphis gossypii Glover (Homoptera: Aphididae) during explosion in a cotton field in response to height and orientation of yellow sticky cards

Aphis gossypii Glover is a polyphagous herbivore that causes serious damage to cottons. Current understanding to trap its population is limited in the field approach. In this study, a two-year-course study was conducted to test the yellow sticky card (YSC) effects of orientation (east, up, west, and bottom) and height (30, 60, and 90 cm over the cotton crown) on numbers of trapped A. gossypii every three days in ninth days after explosion in a cotton plantation in Shihezi, Xinjiang, Northwest China in 2014 and 2015. In 2014, YSCs in the east orientation at a height of 60 cm trapped the highest number of A. gossypii five days after explosion. In 2015, highest number of A. gossypii were trapped by YSCs in the up orientation at a height of 30 cm. Our results showed that YSC in the up orientation at 30-60 cm over the cotton crown can trap the highest number of A. gossypii since the five to seven days after aphid explosion. In conclusion, the spatial placement of sticky cards can be available to trap the maximum number of pests which should be incorporated into observations in continuous years

Estimation of nitrogen in cotton leaves using different hyperspectral region data

As an important index of a plant’s N nutrition, leaf nitrogen content (LNC) can be quickly monitored in real time with hyperspectral information, which is helpful to guide the precise application of N in cotton leaves. In this study, taking cotton dripping in Xinjiang, China, as the object of study, five N application treatments (0, 120, 240, 360, 480 kg·ha-1) were set up, and the hyperspectral data and the N content of main stem functional leaves at the cotton flower and boll stage were collected. The results showed that (1) comparing the correlations of the three types of spectral data from the original spectra, first derivative spectra, and second derivative spectra with the LNC of cotton, the first derivative spectra increased the correlation between the reflectance in the peak and valley ranges of the spectral curves and the LNC of cotton; (2) in the three hyperspectral regions of VIS, NIR, and SWIR, all R2 values of the estimation model for the LNC of cotton established based on the characteristic wavelengths of the original and the first derivative spectra were greater than 0.8, and the model accuracy was better than that of the second derivative spectra; and (3) the normalized root mean square error (n-RMSE) values of the validated model using MLR, PCR, and PLSR regression methods were all in the range of 10–20%, indicating that the established model could well estimate the nitrogen content of cotton leaves. The results of this study demonstrate the potential of the three hyperspectral domains of VIR, NIR, and SWIR to estimate the LNC of cotton and provide a new basis for hyperspectral data application in crop nutrient monitoring

A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves

The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management

Diagnostic study of nitrogen nutrition in cotton based on unmanned aerial vehicle RGB images

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 &lt; F2 &lt; 0.67 indicated nitrogen overapplication, whereas F2 &lt; 0.48 or F2  &gt; 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application

Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples

This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops

Prolonged dual antiplatelet therapy in patients with non-ST-segment elevation myocardial infarction: 2-year findings from EPICOR Asia.

BACKGROUND: Patients with non-ST-segment elevation myocardial infarction (NSTEMI) have a generally poor prognosis and antithrombotic management patterns (AMPs) used post-acute coronary syndrome (ACS) remain unclear. Duration of dual antiplatelet therapy (DAPT) and patient characteristics was evaluated in NSTEMI patients enrolled in EPICOR Asia. HYPOTHESIS: Patients stopping DAPT early may benefit from more intensive monitoring. METHODS: EPICOR Asia was a prospective, real-world, primary data collection, cohort study in adults with an ACS, conducted in eight countries/regions in Asia, with 2 year follow-up. Eligible patients were hospitalized within 48 hours of symptom onset and survived to discharge. We describe AMPs and baseline characteristics in NSTEMI patients surviving ≥12 months with DAPT duration ≤12 and > 12 months post-discharge. Clinical outcomes (composite of death, myocardial infarction, and stroke; and bleeding) were also explored. RESULTS: At discharge, 90.8% of patients were on DAPT (including clopidogrel, 99%). At 1- and 2-year follow-up, this was 79.2% and 60.0%. Patients who stopped DAPT ≤12 months post-discharge tended to be older, female, less obese, have prior cardiovascular disease, and have renal dysfunction. While causality cannot be inferred, the incidence of the composite endpoint over the subsequent 12 months was 10.6% and 3.1% with shorter vs longer use of DAPT, and mortality risk over the same period was 8.4% and 1.6%. CONCLUSIONS: Over 90% of NSTEMI patients were discharged on DAPT, with 60% on DAPT at 2 years. Patients stopping DAPT early were more likely to have higher baseline risk and may therefore benefit from more intensive monitoring during long-term follow-up

Association of healthy lifestyle with incident cardiovascular diseases among hypertensive and normotensive Chinese adults

Background: Whether lifestyle improvement benefits in reducing cardiovascular diseases (CVD) events extend to hypertensive patients and whether these benefits differ between hypertensive and normotensive individuals is unclear. This study aimed to investigate the associations of an overall healthy lifestyle with the subsequent development of CVD among participants with hypertension and normotension. Methods: Using data from the Suzhou subcohort of the China Kadoorie Biobank study of 51,929 participants, this study defined five healthy lifestyle factors as nonsmoking or quitting for reasons other than illness; nonexcessive alcohol intake; relatively higher physical activity level; a relatively healthy diet; and having a standard waist circumference and body mass index. We estimated the associations of these lifestyle factors with CVD, ischemic heart disease (IHD) and ischemic stroke (IS). Results: During a follow-up of 10.1 years, this study documented 6,151 CVD incidence events, 1,304 IHD incidence events, and 2,243 IS incidence events. Compared to those with 0–1 healthy lifestyle factors, HRs for those with 4–5 healthy factors were 0.71 (95% CI: 0.62, 0.81) for CVD, 0.56 (95% CI: 0.42, 0.75) for IHD, and 0.63 (95% CI: 0.51, 0.79) for IS among hypertensive participants. However, we did not observe this association among normotensive participants. Stratified analyses showed that the association between a healthy lifestyle and IHD risk was stronger among younger participants, and the association with IS risk was stronger among hypertensive individuals with lower household incomes. Conclusion: Adherence to a healthy lifestyle pattern is associated with a lower risk of cardiovascular diseases among hypertensive patients, but this benefit is not as pronounced among normotensive patients