Search

Soil salinization and water scarcity are main restrictive factors for irrigated agriculture development in arid regions. Knowing dynamics of soil water and salt content is an important antecedent in remediating salinized soils and optimizing irrigation management. Previous studies mostly used remote sensing technologies to individually monitor water or salt content dynamics in agricultural areas. Their ability to asses different levels of crop water and salt management has been less explored. Therefore, how to extract effective diagnostic features from remote sensing images derived spectral information is crucial for accurately estimating soil water and salt content. In this study, Linear spectral unmixing method (LSU) was used to obtain the contribution of soil water and salt to each band spectrum (abundance), and endmember spectra from Sentinel-2 images. Calculating spectral indices and selecting optimal spectal combination were individually based on soil water and salt endmember spectra. The estimation models were constructed using six machine learning algorithms: BP Neural Network (BPNN), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Gradient Boost Regression Tree (GBRT), and eXtreme Gradient Boosting tree (XGBoost). The results showed that the spectral indices calculated from endmember spectra were able to effectively characterize the response of crop spectral properties to soil water and salt, which circumvent spectral ambiguity induced by water-salt mixing. NDRE spectral index was a reliable indicator for estimating water and salt content, with determination coefficients (R2) being 0.55 and 0.57, respectively. Compared to other models, LSU-XGBoost model achieved the best performance. This model properly reflected the process of soil water-salt dynamics in farmland during crop growth period. This study provided new methods and ideas for soil water-salt estimation in dry irrigated agricultural areas, and provided decision support for governance of salinized land and optimal management of irrigation.

Understanding soil moisture dynamics is crucial for crop growth. The digital mapping of field soil moisture distribution provides valuable information for agricultural water management. The optical satellite data provides fine scale soil moisture information for a region. However, these data are greatly limited due to cloud contamination and revisit period. Despite the reported beneficial effects of spatiotemporal fusion methods, the accurate estimates of high-resolution soil moisture through spatiotemporal fusion data are still unclear, particularly when using Sentinel-2/3 fusion images. This study introduces a new soil moisture estimation framework integrating spatio-temporal spectral information from Sentinel-2/3 fusion images and machine learning algorithm,and thus provide spatiotemporally continuous soil moisture estimation. The framework includes four fusion methods (ESTARRFM, Fit-FC, FSDAF and STFMF) and four machine learning models (PLSR, SVM, RF and GBRT). The feasibility of the framework was validated in the Hetao Irrigation Area of Inner Mongolia, China. The results showed that the Sentinel-2/3 fused image generated by Fit-FC was visually the closest to the true image, followed by ESTARFM, FSDAF, and STFMF. The spatiotemporal fusion-machine learning estimation framework provided reliable estimation for multi-layer (0 ∼ 20, 20 ∼ 40 and 40 ∼ 60 cm) soil water in the irrigation area. The dense time series of soil water generated by the framework facilitated the detection of irrigation events in the irrigated farmland. Our findings highlighted the effectiveness of Sentinel-2/3 fused images in providing high-resolution continuous daily monitoring of farmland soil water on a large scale. These high spatial–temporal resolution time series are valuable for monitoring crop growth and water resource management, contributing to further expanding the application of satellite remote sensing in precision agriculture.

Aboveground biomass (AGB) is a critical indicator for monitoring the crop growth status and predicting yields. UAV remote sensing technology offers an efficient and non-destructive method for collecting crop information in small-scale agricultural fields. High-resolution hyperspectral images provide abundant spectral-textural information, but whether they can enhance the accuracy of crop biomass estimations remains subject to further investigation. This study evaluates the predictability of winter canola AGB by integrating the narrowband spectra and texture features from UAV hyperspectral images. Specifically, narrowband spectra and vegetation indices were extracted from the hyperspectral images. The Gray Level Co-occurrence Matrix (GLCM) method was employed to compute texture indices. Correlation analysis and autocorrelation analysis were utilized to determine the final spectral feature scheme, texture feature scheme, and spectral-texture feature scheme. Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R2 = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. Compared to the spectra feature scheme, the model’s R2 increased by 11.2%, RMSE decreased by 29%, and NRMSE reduced by 17%. These findings provide a reference for studies on UAV hyperspectral remote sensing monitoring of crop growth status.

Accurate, timely, and continuous soil moisture information is helpful for crop stress diagnosis and irrigation management decision. OPtical TRApezoid Model (OPTRAM) based on optical satellite data has been proven to be an effective method for assessing soil moisture status. However, the applicability of OPTRAM to small-scale field soil moisture assessment remains to be explored. In this study, we propose a strategy for the genetically parameterized OPTRAM and evaluate its applicability on Unmanned Aerial Vehicle (UAV) high-resolution hyspectral data. The results showed that: (1) When OPTRAM was used to genetically parameterized with PROSAIL generated dataset, 46 characteristic narrowband bands (|R|= 0.52–0.78) were determined in the spectral region of near infrared (NIR) (750–850 nm) and SWIR (1060–1080 and 1450–1500 nm); (2) By fine-tuned soil moisture estimation model using transfer learning strategy, the reliable soil moisture estimation was achieved in three crops (R2=0.57–0.64; RMSE=0.008–0.022 m3m−3);(3) Compared to soil moisture estimation model using a single spectral region (NIR or SWIR), the DSWC model that combine NIR and SWIR was more effective for tracking soil moisture; (4) The scale effect was observed when the fine-tuned soil moisture estimation model was applied on the high-resolution UAV images. The model performance was stable in pixel size of 1–7 cm and began to drop at pixel size of 11 cm. The above results advance the application of OPTRAM on small farmland soil moisture assessment and demonstrate the application potential of OPTRAM on narrow-band hyperspectral data. This study provides a new candidate for the use of hyperspectral data to estimate soil moisture, and scientific support for precision agriculture and irrigation scheduling.

The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardized solar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation.

The task of converting facial sketch images to facial images aims to generate reasonable and clear facial images from a given facial sketch image. However, the facial images generated by existing methods are often blurry and suffer from edge overflow issues. In this study, we proposed HE-CycleGAN, a novel facial-image generation network with a symmetric architecture. The proposed HE-CycleGAN has two identical generators, two identical patch discriminators, and two identical edge discriminators. Therefore, HE-CycleGAN forms a symmetrical architecture. We added a newly designed high-frequency feature extractor (HFFE) to the generator of HE-CycleGAN. The HFFE can extract high-frequency detail features from the feature maps’ output, using the three convolutional modules at the front end of the generator, and feed them to the end of the generator to enrich the details of the generated face. To address the issue of facial edge overflow, we have designed a multi-scale wavelet edge discriminator (MSWED) to determine the rationality of facial edges and better constrain them. We trained and tested the proposed HE-CycleGAN on CUHK, XM2VTS, and AR datasets. The experimental results indicate that HE-CycleGAN can generate higher quality facial images than several state-of-the-art methods.

Drip fertigation (DF) is a widely used technology to increase grain yield with water and fertilizer conservation. However, the mechanism of high grain yield (GY) under DF is still unclear. Here, a four-year field experiment assessed the impacts of four treatments (i.e., conventional irrigation and nitrogen application, CK; drip irrigation with conventional nitrogen fertilization, DI; split-nitrogen fertigation with conventional irrigation, SF; and drip fertigation, DF) on maize phenology, leaf photosynthetic rates, grain filling processes, plant biomass, and GY. The results showed that DF significantly increased maize GY by affecting phenology, grain filling traits, aboveground biomass (BIO) accumulation, and translocation. Specifically, DF significantly increased leaf chlorophyll content, which enhanced leaf photosynthetic rates, and together with an increase of leaf area index, promoted BIO accumulation. As a result, the BIO at the silking stage of DF increased by 29.5%, transported biomass increased by 109.2% (1.2 t ha−1), and the accumulation of BIO after silking increased by 23.1% (1.7 t ha−1) compared with CK. Meanwhile, DF prolonged grain filling days, significantly increased the grain weight of 100 kernels, and promoted GY increase. Compared with CK, the four-year averaged GY and BIO increased by 34.3% and 26.8% under DF; a 29.7%, 46.1%, and 24.2% GY increase and a 30.7%, 39.5%, and 29.9% BIO increase were contributed by irrigation, nitrogen, and coupling effects of irrigation and nitrogen, respectively. These results reveal the high yield mechanism of drip-fertigated maize, and are of important significance for promoting the application of drip fertigation.

The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77–0.85). The linear regression model based on single-angle OPIVI was most accurate at −15° (R2 = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R2 of 0.77 and with a root mean square error (RMSE) of 0.38 cm2·cm−2. This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications.

Background: The BRAFV600E mutation is a valuable indicator for thyroid cancer diagnosis. This study aimed to develop a deep convolutional neural network (DCNN) model based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules. Methods: The ultrasound images were obtained from four hospitals between January 2017 and January 2022. We trained and validated the DCNN model based on the primary set from center 1 (979 images, 528 patients). The DCNN network consists of Conv block, Downsample block, Gaussian error linear unit, Global Average Polling, and Full Connected. The predictive performance of this model was evaluated by using areas under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in four independent test sets from center 1 to center 4 (531 images, 282 patients). Heatmaps were used to visualize the most predictive regions of each image. Specimens obtained through fine‐needle aspiration or surgery were used to detect the BRAFV600E mutation. Results: The DCNN model achieved encouraging predictive performance by fivefold cross‐validation (AUC 0.95) in the primary set. This performance was further confirmed in the independent internal test set (AUC 0.93) and three independent external test sets (AUC 0.84–0.88). The deep learning score revealed significant differences between BRAFV600E‐mutant and BRAFV600E‐wild‐type groups (all test sets p < .001). The heatmaps visualized the most predictive region located inside or alongside the thyroid nodules. Conclusion: A DCNN model with encouraging predictive performance was developed based on ultrasound images to predict the BRAFV600E mutation status of thyroid nodules.

Remote sensing (RS) scene classification has always attracted much attention as an elemental and hot topic in the RS community. In recent years, many methods using convolutional neural networks (CNNs) and other advanced machine-learning techniques have been proposed. Their performance is excellent; however, they are disabled when there are noisy labels (i.e., RS scenes with incorrect labels), which is inevitable and common in practice. To address this problem, some specific RS classification models have been developed. Although feasible, their behavior is still limited by the complex contents of RS scenes, excessive noise filtering schemes, and intricate noise-tolerant learning strategies. To further enhance the RS classification results under the noisy scenario and overcome the above limitations, in this paper we propose a multiscale information exploration network (MIEN) and a progressive learning algorithm (PLA). MIEN involves two identical sub-networks whose goals are completing the classification and recognizing possible noisy RS scenes. In addition, we develop a transformer-assistive multiscale fusion module (TAMSFM) to enhance MIEN’s behavior in exploring the local, global, and multiscale contents within RS scenes. PLA encompasses a dual-view negative-learning (DNL) stage, an adaptively positive-learning (APL) stage, and an exhaustive soft-label-learning (ESL) stage. Their aim is to learn the relationships between RS scenes and irrelevant semantics, model the links between clean RS scenes and their labels, and generate reliable pseudo-labels. This way, MIEN can be thoroughly trained under the noisy scenario. We simulate noisy scenarios and conduct extensive experiments using three public RS scene data sets. The positive experimental results demonstrate that our MIEN and PLA can fully understand RS scenes and resist the negative influence of noisy samples.

A modified relation between the intraocular and intracranial pressures is presented by employing the least square method to fit the existing experiments. Relative analysis here indicates that this modified relation not only is better than the previous relation by comparing with the existing experimental data but also overcomes the induced singularity in applying the existing mechanical models to compute the mechanical properties of the lamina cribrosa. The present study will be a beneficial help to understanding the relationship between the intraocular and intracranial pressures and even glaucomatous developing.

Soil salinization poses a significant constraint on the sustainable development of agriculture. While satellite remote-sensing data enables salinity monitoring over large spatial scales, its coarse resolution limits monitoring accuracy. On the other hand, unmanned aerial vehicle (UAV) remote-sensing data offers greater accuracy in salinity monitoring but covers a smaller area compared to satellite remote sensing. To address the need for both high precision and wide-range monitoring, we developed a soil salinity monitoring model integrating ground, UAV, and satellite data. Field experiments were conducted in the Shahaoqu irrigation area, Inner Mongolia, China, from August 11 to 15, 2019. During this period, we collected satellite remote-sensing images, UAV remote-sensing images, and soil salinity data. Spectral bands from the remote-sensing data were utilized to construct separate vegetation and salinity indices, which were further filtered using the variable importance in projection (VIP) algorithm. The soil salinity monitoring model was then constructed using the extreme learning machine (ELM) algorithm. Several soil salinity monitoring models were developed, including SSMM-UAV (based on ground-UAV data at a resolution of 6.5 cm), SSMM-UAV-upscaling (obtained by upscaling the results of SSMM-UAV to a 16 m scale), SSMM-satellite (based on ground-satellite data at a 16 m scale), and SSMM-UAV-satellite (constructed using ground-UAV-satellite data at a 16 m scale). The results revealed that SSMM-UAV accurately monitored soil salinity at the UAV scale, with R2 values exceeding .81 and RMSEs below 0.11% for both the model modelling set and validation set. SSMM-UAV-upscaling demonstrated consistency with SSMM-UAV and effectively represented salinity conditions at the 16 m scale. In contrast, SSMM-satellite exhibited inferior performance, with R2 values of .42 and .32 for the modelling set and validation set, respectively, and RMSEs of 0.15% and 0.17%, respectively. By incorporating ground-UAV-satellite data, SSMM-UAV-satellite improved the R2 of SSMM-satellite by more than .09 and reduced the RMSEs by at least 0.08%. Furthermore, the area covered by satellite data was ca. 85 times larger than that covered by UAV data. The synergistic use of UAV and satellite data in salinity monitoring enhances the accuracy of satellite remote sensing and expands the monitoring range of UAV remote sensing. The findings of this study provide a reference for high precision and largescale salt monitoring through the synergistic integration of ground, UAV, and satellite data. [ABSTRACT FROM AUTHOR]

BackgroundPrimary perivascular epithelioid cell tumors (PEComas) of the female reproductive tract have been primarily reported as case reports owing to their clinical rarity. Limited incidence rates and clinical case data hinder a comprehensive understanding of the risks and invasiveness of this disease. We discuss herein the diagnosis, treatment, and prognosis of this disease to enhance comprehension and therapeutic strategies.MethodsWe conducted a clinical analysis of patients with PEComa treated at the Gynecology Department of The West China Second University Hospital of Sichuan University between May 2018 and January 2024. Diagnosis and treatment were evaluated based on pertinent literature.ResultsOverall, eight patients (seven patients with tumors in the uterus and one patient with tumors in the pelvic cavity) were evaluated. One patient with PEComa of unknown malignant potential and two patients with malignant PEComa underwent hysterectomy and bilateral adnexectomy with or without adjuvant therapy and did not develop recurrence. Meanwhile, three patients who underwent lesion resection only exhibited radiological evidence of new lesions. Furthermore, postoperative imaging identified new pulmonary nodules in three patients.ConclusionAlthough the current criteria are generally effective in assessing the tumor invasiveness of PEComa, emphasizing the significance of complete lesion resection remains crucial. Inadequate treatment significantly increases the risks of recurrence and metastasis. Additionally, the prevalence of pulmonary metastases may have been underestimated. Refining risk stratification to prevent overtreatment of low-grade malignancies or overlooking highly aggressive tumors is an important area for further study.

Objective: Cervical cancer classification based on human papillomavirus (HPV) infection status is necessary, as the popularity of HPV vaccination is increasing. Hence, this study aimed to explore the clinicopathological features, diagnosis, treatment, and prognosis of gastric-type endocervical adenocarcinoma (GAS) in west China. Methods: We performed a retrospective analysis and follow-up of patients with GAS who were hospitalized in West China Second University Hospital, a tertiary care center in west China, between September 2019 and April 2021. Results: A total of 16 cases were pathologically diagnosed as GAS at the hospital, most of which had no specific clinical manifestations. Among the 16 cases, 3 cases were confirmed preoperatively. The occurrence of full-layer cervix stroma infiltration, lymphatic vessel infiltration, and lymph node metastasis were 87.5%, 100%, and 50%, respectively. Among all cases, 8 cases were classified as stage III according to the International Federation of Gynecology and Obstetrics 2018 Clinical Staging Standard for cervical cancer. A total of 15 patients underwent a transabdominal or laparoscopic extensive hysterectomy, and the remaining 1 patient was pathologically diagnosed with GAS after a total hysterectomy and afterward underwent bilateral oophorectomy and pelvic lymph node dissection as supplementary surgeries. The follow-up data presented the survival of the 16 patients receiving concurrent chemoradiotherapy after surgery. However, GAS recurred in 3 non-medically-controlled patients among the 16 patients in 3 to 6 months after the treatment. Conclusions: GAS is a rare but highly malignant cancer that usually results in a poor prognosis. A few reports of GAS diagnosis and treatment, especially from developing countries, are available. Hitherto clinical routine screening methods might be insufficient to fulfill the requirements of GAS diagnosis owing to the difficulties in preoperative diagnosis and the probability of misdiagnosis. To reduce the rate of misdiagnosis, repeated deep multisite biopsies, cervical curettage, cervical conization, and hysteroscopy might be helpful in suspicious GAS cases.

Background Few patients with prostate cancer benefit from current immunotherapies. Therefore, we aimed to explore new strategies to change this paradigm.Methods Human tissues, cell lines and in vivo experiments were used to determine whether and how N-cadherin impacts the production of programmed death ligand-1 (PD-L1) and indole amine 2,3-dioxygenase (IDO-1) and whether N-cadherin can increase the production of effector (e)Treg cells. Then, we used PC3-bearing humanized non-obese diabetic/severe combined immunodeficiency IL2Rγnull (hNSG) mice with an intravenous injection of human CD34+ hematopoietic stem cells into the tail vein to evaluate whether the N-cadherin antagonist N-Ac-CHAVC-NH2 (designated ADH-1) could improve the therapeutic effect of tumor-infiltrating lymphocyte (TIL)-related treatment.Results N-cadherin dramatically upregulated the expression of PD-L1 and IDO-1 through IFN-γ (interferongamma) signaling and increasing the production of free fatty acids that could promote the generation of eTreg cells. In preclinical experiments, immune reconstitution mediated by TILs slowed tumor growth and extended the survival time; however, this effect disappeared after immune system suppression by PD-L1, IDO-1 and eTreg cells. Furthermore, ADH-1 effectively reduced immunosuppression and enhanced TIL-related therapy.Conclusions These data show that the N-cadherin antagonist ADH-1 promotes TIL antitumor responses. This important hurdle must be overcome for tumors to respond to immunotherapy.

Interleukin-17 (IL-17) is a proinflammatory cytokine that is associated with inflammation, autoimmune disorders, and even tumors. Previous studies revealed that a large group of human malignant tumors have abnormally high IL-17 expression. In the present study, we analyzed two single-nucleotide polymorphisms (SNPs) in the IL17A (rs2275913) and IL17F (rs763780) in 311 cervical cancer patients and 463 controls using TaqMan assays. Our results indicated that the frequencies of AA genotype and A allele of rs2275913 were significantly different between the cervical cancer patients and controls (P=0.008, OR = 1.32, 95% CI, 1.07–1.62). Stratified analyses revealed that the polymorphism of rs2275913 was also associated with positive peritumor intravascular cancer emboli and high clinical stage. The genotype and allele frequencies of rs763780 did not show any difference between patients and controls or relate to patient clinical characteristics. Collectively, these findings suggested that IL17 gene polymorphism rs2275913 was associated with the susceptibility as well as positive peritumor intravascular cancer emboli and high clinical stage of cervical cancer in Chinese women.