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IntroductionIndividual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.MethodsIn this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.Results(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients (r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R2, RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

ABSTRACTSeeking economical, efficient and environmentally friendly nitrogen fertigation management strategies is extremely important for sustainable maize production. A two-year field experiment was conducted to investigate the effects of four nitrogen fertilization methods N0: no nitrogen application; FP: applying 234 kg N ha−1 at sowing; NF1: applying 210 kg N ha−1, 50% N at sowing and 50% N fertigation at the V8 stage; and NF4: applying 210 kg N ha−1 sowing: 30%, fertigation at V8: 20%, V12: 20%, R1: 20%, R3: 10% on maize yield, nitrogen and water use efficiency, economic benefits and the environmental assessment in Northeast China. The results showed that compared with the FP treatment, the NF1 and NF4 treatments significantly increased maize yield by 13.2% and 18.6%, increased recovery efficiency of nitrogen (REN) by 38.6% and 68.4%, agronomic nitrogen efficiency (AEN) by 58.6% and 78.2%, water use efficiency (WUE) by 23.8% and 26.9%, and net income by 16.1% and 25.7%, respectively. Additionally, the treatments reduced the apparent nitrogen loss by 69.5% and 96.1%, nitrogen footprint by 34.4% and 37.4% and carbon footprint by 17.0% and 20.8%, respectively. The yield, REN and net income of NF4 were significantly higher than those of NF1 by 4.8%, 21.6% and 8.3%, respectively, and the nitrogen and carbon footprints of NF4 were significantly lower than those of NF1 by 4.6% and 4.6%, respectively. The NF4 treatment was recommended as an effective method for improving yield and net income and reducing the environmental cost while achieving sustainable maize production.

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson’s correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB.

AbstractIntroduction Blood bone metabolic biomarkers are noninvasive indices for evaluating metabolic bone diseases. We investigated the relationships between blood bone metabolic biomarkers and anemia in chronic kidney disease (CKD) patients and analyzed the effects of parathyroidectomy (PTX) on the above indices.Methods In this cross-sectional study, 100 healthy controls and 239 CKD patients, including 46 secondary hyperparathyroidism (SHPT) patients with PTX, were enrolled. Moreover, a prospective study was conducted in which 28 PTX patients were followed up. The degree of anemia was classified as mild, moderate, or severe based on the tertiles of hemoglobin (Hb) levels of the anemic CKD patients, with cutoff values of 83 g/L and 102 g/L. Bone metabolic biomarkers, including calcium (Ca), phosphorus (P), intact parathyroid hormone (iPTH), fibroblast growth factor 23 (FGF23), and α-klotho, were tested.Results The mean estimated glomerular filtration rate (eGFR) in CKD patients was 25.7 ± 36.0 ml/min/1.73 m2, and 84.10% of CKD patients had anemia. The baseline Hb levels in the mild, moderate, and severe anemia subgroups were 110.86 ± 5.99 g/L, 92.71 ± 5.96 g/L, and 67.38 ± 10.56 g/L, respectively. CKD patients had higher adjusted Ca, P, alkaline phosphatase (ALP), iPTH, and FGF23 levels and lower α-klotho levels than controls. Baseline adjusted Ca, P, iPTH, and α-klotho levels were associated with Hb levels in CKD patients. Blood adjusted Ca, P, and iPTH levels were correlated with anemia severity. After PTX (median interval: 6.88 months), anemia and high blood adjusted Ca, P, iPTH, and FGF23 levels were ameliorated, while α-klotho levels were increased.Conclusions Blood adjusted Ca, P, iPTH, and α-klotho levels were correlated with Hb levels in CKD patients. Correction of bone metabolic disorders may be a therapeutic strategy for anemia treatment.

Abstract Background Posterior reversible encephalopathy syndrome (PRES) is a rare neurological disorder with complex physiopathological mechanisms that have not been fully understood. Early identification is of great prognostic significance, of which the symptoms and radiological abnormalities can be completely reversed. If the diagnosis and treatment are delayed, ischemia and massive infarction may be developed in some patients. Posterior reversible encephalopathy syndrome (PRES) has been reported mainly in association with postpartum eclampsia, which have been rarely reported, while the association with hypothyroidism has not been reported at home or abroad. Case presentation Here we report on a pregnant 29-year-old with multipara and a chief complication of hypothyroidism. She presented in the emergency department with frequent attacks of severe headache symptoms resulting from reversible cerebral vasoconstriction syndrome (RCVS), accompanied with prenatal eclampsia. PRES was determined by radiological examination. Conclusion To the best of our knowledge, this is the first case of PRES complicated by hypothyroidism and prepartum eclampsia.Clinicians should be alert for the co-occurence of eclampsia, PRES, and RCVS when patients have convulsions after a typical throbbing headache. Moreover, regular monitoring of thyroid function during pregnancy should also occupy certain special attention.

In order to study the optimal extraction process of antioxidants from Malus hupehensis leaves, hydroxyl radical scavenging rate was taken as a reference index, and the extraction conditions of antioxidants from Malus hupehensis leaves was studied by response surface optimization ultrasonic-assisted enzymatic method. The results showed that the best process of ultrasonic-assisted enzyme method was 100 W ultrasonic power, liquid to material ratio 17:1 mL∙g−1, ultrasonic time 32 min, cellulase addition 2.6%, enzymatic digestion time 49 min. The hydroxyl radical scavenging rate was 89.9%±0.06% under this condition, the flavonoid content in the extract was 14.01%, the polyphenol content was 8.93%, and the polysaccharide content was 7.65%, compared with the decoction method, reflux method, enzymatic method and ultrasonic method, the active substance content was increased by more than 10%, the DPPH scavenging rate and hydroxyl radical scavenging rate were increased by more than 20%. It's better to extraction of antioxidants from Malus hupehensis leaves by ultrasonic aided enzymatic method.

Abstract Fast and selective fluorescence imaging for a biomarker to related‐disease diagnosis remains a significant challenge due to complex physical environment. Human carboxylesterase (CE) is expected to be a potential biomarker of hepatocellular carcinoma (HCC) to improve the accuracy of diagnosis. However, existing probes for CE has slow response rate and low selectivity. Herein, the amide group is selected as CE‐responsive sites based on the “substrate‐hydrolysis enzymatic reaction” approach. From a series of off–on probes with leave groups in the amide unit, probe JFast is screened with the optimal combination of rapid response rate and high selectivity toward CE. JFast requires only 150 s to reach the maximum fluorescence at 676 nm in the presence of CE and free from the interference of other esterase. Computational docking simulations indicate the shortest distance between the CE and active site of JFast. Cell and in vivo imaging present that the probe can turn on the liver cancer cells and tumor region precisely. Importantly, JFast is allowed to specifically image orthotopic liver tumor rather than metastatic tumor and distinguish human primary liver cancer tissue from adjacent ones. This study provides a new tool for CE detection and promotes advancements in accurate HCC diagnosis.

Abstract The signal‐to‐noise ratio (SNR) is one of the key features of a fluorescent probe and one that often defines its potential utility for in vivo labeling and analyte detection applications. Here, it is reported that introducing a pyridine group into traditional cyanine‐7 dyes in an asymmetric manner provides a series of tunable NIR fluorescent dyes (Cy‐Mu‐7) characterized by enhanced Stokes shifts (≈230 nm) compared to the parent cyanine 7 dye (

Abstract Background The treatment of a displaced proximal humeral fracture is still a matter of controversy. The purpose of this study was to report outcomes at a long-term follow-up after fixation augmentation using peek (polyether-ether-ketone) cage and locking compression plate (LCP). Methods A total of 27 patients (average age 53.8 years, range 19–86 years) were treated with peek cage and LCP. All of them had a minimum radiographic and clinical follow-up of 1 years. Outcomes were assessed using the Constant-Murley score (CMS), disability of the arm, shoulder and hand (DASH) score. Complications were also recorded during follow-up. Results The average follow-up was 28 months (range 12–48 months). The mean functional outcomes were as follows: CMS, 73.3 (range 61–86); DASH, 45.9 (range 27–68). A total of 4 patients had complications: osteonecrosis developed in one patient, loss of reduction was observed in 1 patient and stiffness was occurred in two patients. Conclusion The use of peek cage and LCP has been a valuable option in the treatment of proximal humeral fractures. The complication rate was acceptable. Suitable void filler in the proximal humerus for reconstructing the medial column integrity attains mechanical stability in reducing the incidence of the complications.

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.

For crop growth monitoring and agricultural management, it is important to use hyperspectral remote sensing techniques to estimate canopy nitrogen content in a timely and accurate manner. The traditional nadir method has limited ability to assess the nitrogen trophic state of cotton shoots, which is not conducive to high-precision nitrogen inversion, whereas the multi-angle remote sensing monitoring method can effectively extract the canopy’s physicochemical information. However, multi-angle spectral information is affected by a variety of factors, which frequently causes shifts in the band associated with nitrogen uptake, and lowers the estimation accuracy. The capacity of the spectral index to estimate aerial nitrogen concentration (ANC) in cotton was therefore investigated in this work under various observation zenith angles (VZAs), and the Relief−F method was employed to select the best spectral band with weight for ANC that is insensitive to VZA. Therefore, in this study, the ability of the spectral index to estimate ANC in cotton was explored under different VZAs, and the Relief-F algorithm was used to optimize the optimal spectral band with weight for ANC that is insensitive to VZA. The angle insensitive nitrogen index (AINI) for various VZAs was calculated using the expression (R530 − R704)/(R1412 + R704). The results show that the correlation between the spectral index and the ANC chosen in this study is stronger than the correlation between off-nadir observations, and the correlation coefficients between Photochemical Reflectance Index (PRI), AINI, and ANC are highest when VZA is −20° and −50° (r = 0.866 and 0.893, respectively). Compared with the traditional vegetation index, AINI had the best correlation with ANC under different VZAs (r > 0.84), and the performance of ANC in the backscatter direction was estimated to be better than that in the forward-scatter direction. At the same time, the ANC estimation model of the optimal indices AINI and PRI was combined with the machine learning method to achieve better accuracy, and the prediction accuracy of the random forest (RF) model was R2 = 0.98 and RMSE = 0.590. This study shows that the AINI index can estimate cotton ANC under different VZAs. Simultaneously, the backscattered direction is revealed to be more conducive to cotton ANC estimation. The findings encourage the use of multi-angle observations in crop nutrient estimation, which will also help to improve the use of ground-based and satellite sensors.

Cotton is the main economic crop in China and is important owing to its use as an industrial raw material and a cash crop. This experiment was conducted in the main cotton-producing area of Xinjiang, China. A hyperspectrometer was used to monitor the canopy spectral reflectance of cotton at different stages of growth. The results showed that the leaf area index (LAI) increased with the increase in the amount of nitrogen fertilizer added during the early full boll stage and decreased with the increase in nitrogen fertilization in the full and late boll stages. Insufficient or excessive fertilization led to a decrease in the LAI. The visible light band indicated that the canopy spectral reflectance decreased, and the amount of fertilizer increased in all the growth stages. The near-infrared band revealed that the canopy spectral reflectance increased with the amount of nitrogen applied during the bud stage, early boll stage, and the most vigorous period of boll growth. During the flowering period, the spectral reflectance followed the order N3 > N4 > N2 > N1 > N0. During the entire growth period of cotton, the values of the cotton LAI predicted using the ratio vegetation index (RVI) model were found to best fit the measured values. The LAI monitoring models of cotton in each growth stage were different. The TVI model is the best in the bud and early boll stages. The NDVI model is the best in the flowering stage, and the DVI model is the best in the full boll stage. This study provides a basis to accurately monitor the LAI in each growth period of cotton.

To determine the best phosphorus (P) fertilizer management strategy in chernozem soil in the semi-arid region of Northeast China, a field experiment under film mulched drip irrigation was conducted for two consecutive years. Five P application methods were tested, including no P fertilizer applied (P0); P fertilizer one-time basal application (P1); one fertigation one time (P2); fertigation twice (P3) and fertigation four times (P4). The shoot dry matter weight, phosphorus accumulation, yield and POlsen in soil were measured regularly during the maize growing season. The results demonstrated that P fertilizer application methods imposed significant effects on dry shoot matter, the apparent P loss, P fertilizer use efficiency and yield of maize (P < 0.05). The yield, P agrinomic efficiency and P recovery efficiency of P4 treatment were significantly higher than P1 treatment by 4.2, 39.7, and 66.4% across two year. In addition, P4 treatment significantly enhanced the shoot dry matter weight after V12 stage, post-silking P uptake and reduced the apparent P loss. In conclusion, P fertigation and a rational frequency (e.g., fertigation four times) enable to keep the maximum grain yield, the shoot dry matter, and recovery efficiency of P fertilizer (66.4%) though changing agronomic methods for efficient acquisition of P toward a sustainable and productive agricultural fertigation system.

Malignant tumors have always been the biggest problem facing human survival, and a huge number of people die from cancer every year. Therefore, the identification and detection of malignant tumors have far-reaching significance for human survival and development. Some substances are abnormally expressed in tumors, such as cyclooxygenase-2 (COX-2), nitroreductase (NTR), pH, biothiols (GSH, Cys, Hcy), hydrogen sulfide (H2S), hydrogen sulfide (H2O2), hypochlorous acid (HOCl) and NADH. Consequently, it is of great value to diagnose and treat malignant tumors due to the identification and detection of these substances. Compared with traditional tumor detection methods, fluorescence imaging technology has the advantages of an inexpensive cost, fast detection and high sensitivity. Herein, we mainly introduce the research progress of fluorescent probes for identifying and detecting abnormally expressed substances in several tumors.

Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R2 value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R2 = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure.

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.

Cu-Zhi-Yi-Hao (CZYH), an empirical formula of traditional Chinese medicine (TCM), has been used for amnesia treatment in clinical practice. However, its underlying pharmacological mechanism has not been fully illuminated. The current study was designed to investigate the neuroprotective effect of CZYH on a β-amyloid 25-35- (Aβ25-35-) induced learning and memory deficit rat model. CZYH (200, 400, or 800 mg/kg), donepezil (1.0 mg/kg), or distilled water was given to Aβ25-35-stimulated animals for 17 days consecutively. The Morris water maze test revealed that CZYH (400 or 800 mg/kg) administration improved the Aβ25-35-induced cognitive impairments in rats, and Nissl staining demonstrated that CZYH mitigated the Aβ-caused neuron loss. In addition, CZYH treatment markedly inhibited the activation of microglia as evidenced by a decreased level of IBA-1 and increased YM-1/2 protein expression. The protein expression levels of TNF-α, IL-1β, and COX-2 were also repressed by CZYH. Besides, CZYH treatment alleviated Aβ-induced IκB-α degradation and NF-κB p65 phosphorylation, as well as reduced the JNK phosphorylation level. In conclusion, the present study suggests that CZYH could improve learning and memory abilities and relieve neuron loss in Aβ25-35-induced rats, at least partly through inhibition of the neuroinflammatory response via inhibiting the JNK-dependent NF-κB activation, indicating that CZYH might be a promising formula for the treatment of AD.

Chronic cerebral hypoperfusion (CCH) is regarded as a high-risk factor for cognitive decline of vascular dementia (VD) as it is conducive to induce beta-amyloid (Aβ) aggregation. Icariside II (ICS II), a plant-derived flavonoid compound, has showed neuroprotective effect on animal models of Alzheimer’s disease (AD) by decreasing Aβ levels. Here, we assessed the effect of ICS II on CCH-induced cognitive deficits and Aβ levels in rats, and the possible underlying mechanisms were also explored. It was disclosed that CCH induced by bilateral common carotid artery occlusion (BCCAO) caused cognitive deficits, neuronal injury and increase of Aβ1-40 and Aβ1-42 levels in the rat hippocampus, while oral administration of ICS II for 28 days abolished the above deficits in the hippocampus of BCCAO rats. Meanwhile, ICS II significantly decreased the expression of beta-amyloid precursor protein (APP) and β-site amyloid precursor protein cleavage enzyme 1 (BACE1), as well as increased the expression of a disintegrin and metalloproteinase domain 10 (ADAM10) and insulin-degrading enzyme (IDE). ICS II also activated peroxisome proliferator-activated receptor (PPAR)α and PPARγ, enhanced the expression of brain-derived neurotrophic factor (BDNF), tyrosine receptor kinase B (TrkB), levels of Akt and cAMP response element binding protein (CREB) phosphorylation. Together, these findings suggested that ICS II attenuates CCH-induced cognitive deficits by inhibiting the amyloidogenic pathway via involvement of BDNF/TrkB/CREB signaling and up-regulation of PPARα and PPARγ in rats.