Search

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields. [ABSTRACT FROM AUTHOR]

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. [ABSTRACT FROM AUTHOR]

Smart fluorescent materials that can respond to environmental stimuli are of great importance in the fields of information encryption and anti‐counterfeiting. However, traditional fluorescent materials usually face problems such as lack of tunable fluorescence and insufficient surface‐adaptive adhesion, hindering their practical applications. Herein, inspired by the glowing sucker octopus, we present a novel strategy to fabricate a reversible fluorescent eutectogel with high transparency, adhesive and self‐healing performance for conformal information encryption and anti‐counterfeiting. Using anthracene as luminescent unit, the eutectogel exhibits photoswitchable fluorescence and can therefore be reversibly written/erased with patterns by non‐contact stimulation. Additionally, different from mechanically irreversible adhesion via glue, the eutectogel can adhere to various irregular substrates over a wide temperature range (−20 to 65 °C) and conformally deform more than 1000 times without peeling off. Furthermore, by exploiting surface‐adaptive adhesion, high transparency and good stretchability of the eutectogel, dual encryption can be achieved under UV and stretching conditions to further improve the security level. This study should provide a promising strategy for the future development of advanced intelligent anti‐counterfeiting materials. [ABSTRACT FROM AUTHOR]

Smart fluorescent materials that can respond to environmental stimuli are of great importance in the fields of information encryption and anti‐counterfeiting. However, traditional fluorescent materials usually face problems such as lack of tunable fluorescence and insufficient surface‐adaptive adhesion, hindering their practical applications. Herein, inspired by the glowing sucker octopus, we present a novel strategy to fabricate a reversible fluorescent eutectogel with high transparency, adhesive and self‐healing performance for conformal information encryption and anti‐counterfeiting. Using anthracene as luminescent unit, the eutectogel exhibits photoswitchable fluorescence and can therefore be reversibly written/erased with patterns by non‐contact stimulation. Additionally, different from mechanically irreversible adhesion via glue, the eutectogel can adhere to various irregular substrates over a wide temperature range (−20 to 65 °C) and conformally deform more than 1000 times without peeling off. Furthermore, by exploiting surface‐adaptive adhesion, high transparency and good stretchability of the eutectogel, dual encryption can be achieved under UV and stretching conditions to further improve the security level. This study should provide a promising strategy for the future development of advanced intelligent anti‐counterfeiting materials. [ABSTRACT FROM AUTHOR]

Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology. [ABSTRACT FROM AUTHOR]

Crop chlorophyll content measuring plays a vital role in monitoring crop growth and optimizing agricultural inputs such as water and fertilizer. However, traditional methods for measuring chlorophyll content primarily rely on labor-intensive chemical analysis. These methods not only involve destructive sampling but also are time-consuming, often resulting in obtaining monitoring results after the optimal growth period of crops. Unmanned aerial vehicle (UAV) remote sensing technology offers the potential for rapidly acquiring chlorophyll content estimations over large areas. Currently, most studies only utilize single features from UAV data and employ traditional machine learning algorithms to estimate chlorophyll content, while the potential of multisource feature fusion and stacking ensemble learning in chlorophyll content estimation research remains largely unexplored. Therefore, this study collected UAV spectral features, thermal features, structural features, as well as chlorophyll content data during maize jointing, trumpet, and big trumpet stages, creating a multisource feature dataset. Subsequently, chlorophyll content estimation models were built based on four machine learning algorithms, namely, ridge regression (RR), light gradient boosting machine (LightGBM), random forest regression (RFR), and stacking ensemble learning. The research results demonstrate that (1) the multisource feature fusion approach achieves higher estimation accuracy compared to the single-feature method, with R2 ranging from 0.699 to 0.754 and rRMSE ranging from 8.36% to 9.47%; and (2) the stacking ensemble learning outperforms traditional machine learning algorithms in chlorophyll content estimation accuracy, particularly when combined with multisource feature fusion, resulting in the best estimation results. In summary, this study proves the effective improvement in chlorophyll content estimation accuracy through multisource feature fusion and stacking ensemble learning. The combination of these methods provides reliable estimation of chlorophyll content using UAV remote sensing technology and brings new insights to precision agriculture management in this field. [ABSTRACT FROM AUTHOR]

Purpose: D-dimer levels are associated with tumor progression and prognosis in various cancers. However, there are few research about the relationship between D-dimer and neuroblastoma (NB). This study assessed the relationships of D-dimer levels with clinical features and overall survival (OS) in patients with NB. Methods: Information about the clinical features of 365 patients and the prognosis of 301 patients was collected. The relationship between D-dimer levels and clinical features or OS was analyzed. We constructed the risk score based on Cox regression analysis and verified the predictive efficacy of the model through ROC curve and calibration curve. Results: The results showed that D-dimer levels were significantly increased in patients with nonmediastinal tumor, tumor larger than 10 cm, stage 3–4 disease, bone marrow metastasis, unfavorable histology, bone metastasis, NMYC amplification, and the high-risk group (all P < 0.05). The Kaplan–Meier survival analysis showed that there were significant differences in 3- and 5-year OS (87.4% vs. 32.3%, 79.3% vs. 32.3%, P < 0.0001) between the low D-dimer and high D-dimer groups. In the high-risk group, the OS of high D-dimer was significantly lower than that of low D-dimer (P < 0.0001). All cases were divided into the training cohort (N = 211) and the validation cohort (N = 90). Multivariate analysis further suggested that D-dimer level, bone metastasis, and NMYC status were independent prognostic factors for OS (all P < 0.05). Based on the above three factors, we constructed the risk score in the training cohort. Survival analysis showed that compared with the other groups, the group with 11 scores had the worst prognosis (3-year OS 0%, P < 0.0001). The time-dependent ROC analysis and calibration curve indicated that the risk score had good accuracy. Conclusions: Patients with high D-dimer levels tended to have unfavorable clinical characteristics and poor prognosis. [ABSTRACT FROM AUTHOR]

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]

Background. Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients. Methods. Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA). Results. The methylated genes' signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test, P < 0.001) in the training group, and the validation of the signature's risk stratification ability was carried out in the test group (log-rank test, P < 0.01 , median survival time: 30.48 vs. >120.36 months). The methylated genes' signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas. Conclusions. The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival. [ABSTRACT FROM AUTHOR]

Biomass is an important indicator in estimating the growth and yield of crops. Mastering the growth state of crops by evaluating and monitoring biomass is crucial. Correlation analysis was used to identify hyperspectral sensitive bands with the best correlation with biomass, and a hyperspectral vegetation index was constructed. Plant height data was coupled with hyperspectral reflectance and vegetation index data, and a biomass inversion model was constructed using partial least squares (PLS), support vector machine (SVM), and random forest (RF) regression analysis. The R2, root mean square error (RMSE), and normalized root mean square error (NRMSE) were calculated to verify the accuracy of the model. Plant height coupled with hyperspectral reflectance and vegetation index data, R2, RMSE, and NRMSE of the biomass model establishment and validation, compared with the hyperspectral data alone, improved model accuracy of the three algorithms for maize (Zea mays L.) biomass inversion coupled with plant height data with the relative improvement in accuracy of 13.58, 9.6, and 1.05%, respectively. Additionally, the model becomes more stable, overcoming the spectral saturation of canopy to a certain extent. It provides supporting information for accurate farmland planning, growth monitoring, and yield estimation. Core Ideas: Use of plant height coupled hyperspectral reflectance data to perform the biomass inversion of maize.Development of biomass inversion model.Calculate R2, RMSE, and NRMSE to verify the model accuracy. [ABSTRACT FROM AUTHOR]

• The DLR method applied to ultrasound videos of the diaphragm contributes to predicting weaning outcomes. • DLR enables accurate prediction of weaning outcome by automatically segmenting the diaphragm region and combining radiomics features with clinical parameters. • The approaches can enhance patient care and assist doctors in making decisions in the ICU. We proposed an automatic method based on deep learning radiomics (DLR) on shear wave elastography (SWE) and B-mode ultrasound videos of diaphragm for two classification tasks, one for differentiation between the control and patient groups, and the other for weaning outcome prediction. We included a total of 581 SWE and B-mode ultrasound videos, of which 466 were from the control group of 179 normal subjects, and 115 were from the patient group of 35 mechanically ventilated subjects in the intensive care unit (ICU). Among the patient group, 17 subjects successfully weaned and 18 failed. The deep neural network of U-Net was utilized to automatically segment diaphragm regions in dual-modal videos of SWE and B-mode. High-throughput radiomics features were then extracted, the statistical test and least absolute shrinkage and selection operator (LASSO) were applied for feature dimension reduction. The optimal classification models for the two tasks were established using the support vector machine (SVM). The automatic segmentation model achieved Dice score of 87.89 %. A total of 4524 radiomics features were extracted, 10 and 20 important features were left after feature dimension reduction for constructing the two classification models. The best areas under receiver operating characteristic curves of the two models reached 84.01 % and 94.37 %, respectively. Our proposed DLR methods are innovative for automatic segmentation of diaphragm regions in SWE and B-mode videos and deep mining of high-throughput radiomics features from dual-modal images. The approaches have been proved to be effective for prediction of weaning outcomes. [ABSTRACT FROM AUTHOR]

Real-time monitoring of the nutritional status of potato crops enables rational and efficient decision-making about planting patterns and fertilization strategies that maximize yields. Chlorophyll is a useful index for measuring potatoes' nutritional status. Therefore, rapid and accurate estimation of chlorophyll content can be used to guide efforts to improve potato crop quality and yields. Here, we use hyperspectral potato crop data collected by an unmanned aerial vehicle (UAV) and correlate the data with vegetation indexes, spectral position, and area characteristic parameters, spectral resolution, and other index parameters to comprehensively analyse the chlorophyll content of experimental potato crops at different growth stages. We establish a model for estimating chlorophyll content and verify the accuracy of the model by using partial least squares, stepwise regression analysis, support vector machine, and random forest analytical methods. This study provides a new method for estimating the chlorophyll content of crops by using hyperspectral data. We find that the partial least squares (PLS) model based on hyperspectral reflection characteristic variables is optimal for estimating chlorophyll content during the budding and tuber stages of potato growth. The optimal model during tuber formation and starch accumulation is the stepwise regression model on the basis of vegetation indexes and spectral position and area characteristic parameters. Comprehensive results show that compared with the single index parameter, the comprehensive index parameter can be used to estimate the chlorophyll content of potatoes with higher accuracy and better effect; it can also be used to monitor the nutritional status of potatoes. [ABSTRACT FROM AUTHOR]

• Multi-source data fusion is useful for estimating maize biomass. • Combining sensor data and chlorophyll levels improves the estimation of biomass. • Estimated effects vary between growth stages, with better results in later stages. • CatBoost algorithm has potential in biomass estimation. The rapid and accurate estimation of maize above-ground biomass (AGB) is pivotal for precise agricultural management. The rapid evolution of unmanned aerial vehicles (UAVs) and sensor technology has introduced a novel method for obtaining AGB information. Nevertheless, individual sensors may lack comprehensive data, leading to reduced AGB estimation accuracy in certain scenarios. This study collected UAV multi-spectral (MS) and thermal infrared (TIR) data, alongside soil and plant analyzer development (SPAD) values, from maize across multiple growth stages (jointing, trumpet, and big trumpet) during 2022 and 2023. Diverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB. The efficacy of CatBoost was evaluated and benchmarked against Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. For the entire growth, findings reveal that the fusion of multi-source sensor data (MS + TIR) can mitigate the data insufficiency in single-sensor estimations. The resulting R2 values range from 0.608 to 0.817. Optimal estimation outcomes were achieved by the fusion of multi-source sensor data with SPAD values (MS + TIR + SPAD), yielding R2 values ranging from 0.685 to 0.872. For a single growth stage, there are variations in the estimation accuracy across different growth stages. From the jointing stage to the big trumpet stage, the estimation accuracy consistently increases, with the highest accuracy observed during the big trumpet stage, with R2 ranging from 0.721 to 0.901. Additionally, in alignment with the results for the entire growth stage, the fusion of multi-source sensor data with SPAD values still yields the highest estimation accuracy during different growth stages. In a comparison of different machine learning algorithms, for both the entire growth stage and single growth stages, SVR, RFR, and CatBoost achieved R2 values ranging from 0.305 to 0.824, 0.368 to 0.881, and 0.451 to 0.901, respectively. Notably, the CatBoost algorithm exhibited heightened estimation accuracy. The fusion of multi-source sensor data with SPAD values combined with the CatBoost algorithm results in accurate and reliable maize AGB estimation accuracy. This high-throughput approach to crop phenotyping is characterized by speed and accuracy and serves as a valuable reference for rapidly acquiring AGB information in this geographical region. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization. [ABSTRACT FROM AUTHOR]

Traffic sign poles are crucial components of the highway system, and their maintenance mainly relies on the subjective judgment of staff, which is low-efficiency and will lead to mistaken inspections. This paper proposes a convenient and effective method to monitor the health of sign poles by using a mobile phone. It is unknown whether a mobile phone can be used as a conventional acceleration sensor. Therefore, the performance of the mobile phone was initially tested to ensure its suitability for acquiring the acceleration data of the traffic sign pole. The results show that the acceleration sensor of mobile phones is high-performance and can be used as a traditional acceleration sensor under the similar sampling frequency. The mobile phone can measure the 1st, 2nd and 5th natural frequencies of the traffic sign pole. Although the 1st and 5th natural frequencies have a large error with simulation, the 2nd natural frequency is accurate and stable. The looseness of the base connection greatly impacts on the 2nd natural frequency, which can be used as a measure of the loose base connection. The 2nd natural frequency was measured for 21 times and found to conform to a normal distribution. The relationship between the 2nd natural frequency and base connection loosening was investigated, which fitted to the equation. The fitting result is good and can be used to predict the degree of sign pole base connection loosening. Therefore, the mobile phone based convenient health monitoring method for traffic sign poles is feasible. [ABSTRACT FROM AUTHOR]

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas. [ABSTRACT FROM AUTHOR]

A class of switched multi‐agent tracking problems with variable tracking number is explored. During the whole tracking course, tracking agents switch and the tracking number varies over different time intervals. Consequently, tracking errors jump and the dimension of Laplacian matrix of the multi‐agent system changes, which increase the difficulty of analysis. To solve this problem, the authors proposed a switched tracking strategy and designed an average norm of tracking errors as a Lyapunov function. Thereafter, conditions guaranteeing the stability of each subsystem and overall stability of the switched tracking system are obtained. Finally, the effectiveness of the proposed tracking strategy and validity of the theoretical results are verified by a numerical simulation. [ABSTRACT FROM AUTHOR]

The liquefaction in saturated sands under blast loading is an important research topic in geotechnical engineering. The effect of blast loading on the liquefaction characteristics of saturated sands was studied through a large-scale millisecond delay blasting (MDB) liquefaction field test. Based on the field test, a numerical model was established by the LS-DYNA finite element program to study the propagation of blast waves and the effects of explosive mass, scaled distance, and blast delay on the liquefaction characteristics of saturated sands. The results show that there was a significant sand boiling phenomenon in saturated sands under blast loading, and high values of the excess pore water pressure ratio were observed at monitoring points during the field test. The numerical simulation results show that the scaled distance and the blast delay significantly affected the excess pore water pressure. While the blast delay increased from 110 ms to 330 ms, the excess pore water pressure of the central area can increase by more than 18%. Based on the field and numerical tests, an empirical equation is established that can take into account both the scaled distance and blast delay, which provides a theoretical basis for future large-scale liquefaction sites by MDB. • Liquefaction and sand boiling in saturated sands by millisecond delay blasting (MDB) was observed. • Based on field test, the effects of explosive mass and blast delay on liquefaction were investigated by LS-DYNA. • The empirical equation of the liquefaction caused by MDB was established. [ABSTRACT FROM AUTHOR]

The digital era has accelerated corporate innovation activity. Under the aim of "carbon neutrality", green technological innovation pushed enterprises toward resource conservation and reduced energy consumption. Although digital technology could increase the utilization of resources in the whole view, it remains unclear under what conditions the data affects green technological innovation performance (GTIP) effectively, and how data governance empowers different organizational management levels by delegating digital information in this progress. This study draws on the knowledge management theory and dynamic capability theory to explore the relationship between data governance and green technological innovation performance in firms. To examine our research model, annual reports of Chinese firms from 2013 to 2019 are analyzed. Results from Poisson fixed-effect model analysis indicate that data governance in general has no direct significant effect on green innovation performance, but for detail data governance items, green innovation reaches maximum, then decreases under the influence of decentralization of data governance, while a slightly U-shaped curve happens when centralization of data governance adopts, but these two curves are asymmetrical contradicted. Our analysis also highlights the important role of corporate environmental proactivity, as it positively moderates the relationship between two types of data governance and firm's green technological innovation performance. Besides, we examine this project by using three-dimensional plot method and Johnson-Neyman check, then find these regressions are robust in our analysis. Finally, implications and recommendations for data governance research and practices are presented. • Centralization of data governance shows slightly U-shaped relationship with GTIP. • Decentralization of data governance shows inverted U-shaped relationship with GTIP. • Corporate environmental proactivity moderates two non-linear relationships above. • Both granted patent and eliminated sample check support these results. [ABSTRACT FROM AUTHOR]

Crop nitrogen content inversion based on UAV (Unmanned Aerial Vehicle) hyperspectral data is vital for addressing global food supply challenges. Many studies have estimated nitrogen content via simple linear regression using a single vegetation index, multiple vegetation indices, hyperspectral representation, and simple structural transformation. Utilizing UAV hyperspectral data of the winter wheat canopy and leveraging the benefits of fractional differential processing to enhance spectral details, traditional hyperspectral vegetation indices were established, and characteristic parameters derived from spectral position and canopy area were extracted. Then, winter wheat plant nitrogen content models with different spectral information characteristics were highlighted, optimally selected and verified. Results reveal that when the original canopy spectrum is processed using the fractional differential, the association between hyperspectral reflectance and nitrogen levels in winter wheat plants can be effectively enhanced. Fractional differential spectra represent outstanding effects on refining spectral details. The findings provide valuable insights into the potential of hyperspectral fractional differential spectra to enhance the precision of nitrogen estimation in winter wheat. [ABSTRACT FROM AUTHOR]

A significant amount of global electric power generation is produced from the combustion of fossil fuels. Steam boilers are one of the most important components for steam and electricity production. The objective of this paper is to establish a theoretical framework for the sustainability analysis of a utility boiler. These analyses can be used by decision-makers to diagnose and optimize the sustainability of a utility boiler. Seven utility boiler systems are analyzed using energy and embodied solar energy (emergy) principles in order to evaluate their environmental efficiencies. They include a subcritical coal fired boiler, a supercritical coal fired boiler, an oil fired boiler, a natural gas fired boiler, a concentrating solar power boiler utilizing a tower configuration, a biomass boiler, and a refuse derived fuel boiler. Their relative environmental impacts were compared. The results show that the natural gas boiler has significantly lower CO 2 emission than an equivalent coal or oil fired boiler. The refuse derived fuel boiler has about the same CO 2 emissions as the natural gas boiler. The emergy sustainability index of a utility boiler system is determined as the measure of its sustainability from an environmental perspective. Our analyses results indicate that the natural gas boiler has a relatively high emergy sustainability index compared to other fossil fuel boilers. Converting existing coal boilers to natural gas boilers is a feasible option to achieve better sustainability. The results also show that the biomass boiler has the best emergy sustainability index and it will remain a means to utilize the renewable energy within the Rankine steam cycle. Before solar boiler technology can be widely used in the United States’ utility power industry, its capital cost and the O&M cost should be reduced. Using the results of this research, decision-makers can make better-informed, environmentally-efficient selections of future utility scale boilers. [ABSTRACT FROM AUTHOR]

The objective of this paper is to establish a theoretical framework for the exergy analysis and advanced exergy analysis of a real biomass boiler. These analyses can be used for both the diagnosis and optimization of a biomass boiler as well as for the design of a new biomass boiler. Conventional exergy analysis is performed to recognize the source(s) of inefficiency and irreversibility and identify exergy destruction in different components of the biomass boiler. An advanced exergy analysis is performed to provide comprehensive information about the avoidable exergy destruction and real fuel-saving potential for each component, as well as the overall system. Sensitivity studies of several design parameters including the excess air, biomass moisture and steam parameters were evaluated. The results show that the maximum exergy destruction occurs in the combustion process, followed by the Water Walls (WW) & Radiant Superheater (RSH) and the Low Temperature Superheater (LTSH). The fuel-saving and exergy efficiency improvement strategies for different components are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a novel readout circuit based on low-noise charge transfer amplifier (LCTA) to suppress the readout noise in multi-bit quanta image sensor. A switched capacitor circuit is used in LCTA to reduce the kT/C noise introduced by its load capacitor, and make the noise from ADC further suppressed due to the increase of its output swing. The digital correlated double sampling of single-slope ADC is used to eliminate the fixed noise in LCTA. The low noise readout circuit is implemented by a 110 nm CMOS process. The simulation results show that the random noise of LCTA is reduced by 19% at a gain of 2 V/V compared to the conventional differential charge transfer amplifier. The increased output swing reduces the random noise power of ADC by 61%. In addition, the LCTA equivalent input fixed noise with 29.8 μV rms is eliminated. The proposed low-noise readout circuit improves the signal-to-noise ratio by 4.37 dB at small-signal conditions. [ABSTRACT FROM AUTHOR]

Objective To study the effect of carbonated drinks on primary and permanent teeth replacement in Children. Method Dog tooth enamel samples were soaked in coca-cola, sprite and pure soda, and the calcium, phosphorus level were analysed. Dental papilla stem cells were separated and cultured in the conditioned medium by adding three drinks. PCR and western blot were used to detect mRNA and protein levels of activator of nuclear factor-k B receptor ligand (RANKL), osteoprotegerin (OPG) and vitamin D receptor (VDR), then the possible role of each gene and interactions relationship were analyzed. Results Compared with saline, coca-cola and sprite showed their significantly decalcification and dephosphorization role, while plain soda water showed calcium and phosphorus protective effect. These three drinks had no effect on mRNA and protein levels of RANKL gene (P > 0.05). Coca-cola and sprite can reduce OPG mRNA and protein levels, and at the same time increase transcription and expression of the VDR gene. Plain soda water has no effect on the OPG gene manifestation, but can significantly reduce the transcription and translation level of the VDR gene. Conclusion Carbonated drinks may affect the dental health of the children's primary and permanent teeth replacement by regulating bone related gene expression and vitamin D receptor family. [ABSTRACT FROM AUTHOR]

The timely and accurate acquisition of winter wheat acreage is crucial for food security. This study investigated the feasibility of extracting the spatial distribution map of winter wheat in Henan Province by using synthetic aperture radar (SAR, Sentinel-1A) and optical (Sentinel-2) images. Firstly, the SAR images were aggregated based on the growth period of winter wheat, and the optical images were aggregated based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS-NDVI) curve. Then, five spectral features, two polarization features, and four texture features were selected as feature variables. Finally, the Google Earth Engine (GEE) cloud platform was employed to extract winter wheat acreage through the random forest (RF) algorithm. The results show that: (1) aggregated images based on the growth period of winter wheat and sensor characteristics can improve the mapping accuracy and efficiency; (2) the extraction accuracy of using only SAR images was improved with the accumulation of growth period. The extraction accuracy of using the SAR images in the full growth period reached 80.1%; and (3) the identification effect of integrated images was relatively good, which makes up for the shortcomings of SAR and optical images and improves the extraction accuracy of winter wheat. [ABSTRACT FROM AUTHOR]

In order to further improve the energy utilization of the range extender electric vehicle (REEV), the energy management strategy of the REEV under different heat demands and operating temperatures is studied. Firstly, for reducing the extra fuel consumption caused by engine operating temperature, the influence of engine lubricating oil temperature on the equivalence factor is analyzed, and a novel method is proposed, which can determine the equivalence factor by three parameters, including engine temperature, operating condition type, and battery state of charge (SOC). Secondly, to reduce the influence of frequent engine start/stop on fuel consumption, a method to establish the penalty factor based on battery SOC is proposed. Finally, the engine temperature rise law under low-temperature environment and the compressor drive mode determination method under high-temperature environment are studied according to the heat demand under different ambient temperatures. An adaptive equivalent consumption minimization strategy (ECMS) considering the effect of temperature under different heat demands is proposed on the basis of the above research. The simulation under the worldwide harmonized light vehicle test cycle (WLTC) drive cycle reveals that by using the adaptive ECMS, the vehicle operating cost is reduced by 5.69% in normal temperature environment, 0.53%-2.39% in a low-temperature environment, and 0.2%-2.01% in a high-temperature environment. [ABSTRACT FROM AUTHOR]

Abstract: Although it has been hypothesized that autoimmune-like phenomena may play a critical role in the protective immune responses to both human and animal malaria, there are still no evidence-based data to support this view. In this study we demonstrate that the majority of anti-single stranded (ss) DNA autoantibody secreting B cells were confined to B220+CD21+CD23− cells and that these cells expanded significantly in the spleen of C57BL/6 mice infected with Plasmodium yoelii 17X non-lethal (PyNL). To determine the role of ssDNA-binding autoantibody secreting B cell responses in murine malaria, we conjugated generation 6 (poly) amidoamine dendrimer nanoparticles with ssDNA to deplete ssDNA-binding autoreactive B cells in vivo. Our data revealed that 55.5% of mice died after DNA-coated nanoparticle-mediated in vivo depletion of ssDNA-specific autoreactive B cells and subsequent challenge using PyNL. Adoptive transfer of B cells with ssDNA specificity to mice, followed by PyNL infection, caused a later appearance and inhibition of parasitemia. The possible mechanism by which the ssDNA-binding autoantibody secreting B cells is involved in the protection against murine malaria has also been demonstrated. [Copyright &y& Elsevier]

Abstract: Various subsets of T-cell receptor (TCR) γδ+ T cells expressing distinct TCR-Vγ chains are found in many different anatomical locations. These TCR γδ+ T cells perform multiple functions as an essential part of the immune system. In particular, protection against malaria infection by TCR γδ+ T cells is of special interest. In the present study, we confirmed that the Vγ7+ γδ T cells, which are an unconventional subset usually localized within the intestine, are recruited to the liver and spleen at the late stage of malaria infection, and contribute to protection against malaria infection via a multifunctional approach in apoptosis inhibitor-deficient mice. [Copyright &y& Elsevier]

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology. [ABSTRACT FROM AUTHOR]

Abstract: DO22–Al3Ti can be transformed into the related cubic L12 structure, with more slip systems, by the addition of ternary elements, such as the rare earth Lanthanum. It is believed that combined processing by mechanical alloying and annealing is conducive to the structural evolution and crystal growth. Appropriate and economical processing parameters were determined as: milling time of 40 h, annealing temperature of 750 °C and annealing time of 40–60 min. With increased milling time, fine and uniform particles, which originally possess a flake-like morphology, are produced. The Vickers hardness of the as-milled Al–Ti and Al–Ti–La powders increases rapidly at the initial milling stage, then increases slowly at final stage due to lattice distortion and solid solution strengthening. However, the Vickers hardness of the two materials decreases with increased of annealing time. The Vickers hardness of the Al–Ti powders is higher than that of the Al–Ti–La powders. [Copyright &y& Elsevier]

Abstract: Mice were fed with a 5% low-protein diet for two weeks, at which point tumor inoculation was conducted. Following this inoculation, the 5% low-protein diet was continued. On the other hand, control mice were fed with a normal diet (25% protein) and such diet was continued after tumor inoculation. In comparison with control mice, mice fed with the 5% low-protein diet showed a prominent prolongation of survival rate when injected with both EL4 and 3LL tumors. Interestingly, CD1d(−/−) mice, which primarily lack natural killer T (NKT) cells, did not show the prolongation of survival rate even when they received a 5% low-protein diet. The most striking phenomenon seen in tumor-bearing mice fed with the 5% low-protein diet was the suppression of tumor metastasis to the liver and lung. Such suppression was not seen in CD1d(−/−) mice who were fed with a 5% low-protein diet. Phenotypic study revealed that the proportion of NKT cells after tumor inoculation decreased in the mice fed with a normal diet. However, such decrease did not occur in mice fed with the 5% low-protein diet. Reflecting the activation of NKT cells by feeding, tumor cytotoxicity and cytokine production were also augmented by the 5% low-protein diet. These results suggest that a low-protein diet has the potential to augment the innate immunity against tumors, especially mediated by the activation of NKT cells. [Copyright &y& Elsevier]

It was previously reported that NKT cells, which are mainly present in the liver of mice, are also present in the uterus and that these uterine NKT cells are associated with abortion under overactivated conditions. In this study, we further examined their phenotypic and functional properties. In parallel with the progression of pregnancy, the number of uterine lymphocytes increased. This increase accompanied an increase in the number of TCRαβ+ T cells and NKT cells in the uterus, although the number of NKT cells decreased at late pregnancy. Approximately one-third of the TCRαβ+ cells were NKT cells at the early pregnant stage, while this ratio decreased toward late pregnancy. These uterine NKT cells were found to respond to α-galactosylceramide (α-GalCer) differently in comparison with liver NKT cells. In contrast to the apoptotic response of liver NKT cells on day 1 after α-GalCer injection, uterine NKT cells expanded prominently without such apoptosis. The majority of liver NKT cells were CD4+. In contrast, almost all of the uterine NKT cells were double negative CD4−8−. However, similar to liver NKT cells, uterine NKT cells used an invariant chain of Vα14Jα281 gene for TCRα. The resistance against apoptosis might be due to the high expression of bcl-2 on uterine NKT cells after α-GalCer activation. Other evidence was that macrophages which existed in the pregnant uterus carried an activation marker, CD69, and could potentially produce many cytokines by their activation. The present results suggest that uterine NKT cells and surrounding macrophages are quite different in function from those in the liver. [Copyright &y& Elsevier]

• A novel model (LabTNet) is proposed to estimate crop biochemical traits. • LabTNet jointly estimates multiple wheat traits by hyperspectral data. • LabTNet exhibits superior predictive performance than existing methods. • Grad-CAM analyzed hyperspectral attention regions of LabTNet. Accurate estimation of crop leaf and canopy biochemical traits, such as leaf dry matter content (Cm), leaf equivalent water thickness (Cw), leaf area index (LAI), dry leaf biomass (DLB), leaf total water content (LW), and fresh leaf biomass (FLB), is essential for monitoring crop growth accurately. The vegetation spectral feature technique combined with statistical regression methods is widely employed for remote sensing crop biochemical traits mapping. However, the crop canopy spectral reflectance is influenced by various crop biochemical traits and uncertainties in geometric changes of light and soil background effects. Consequently, the remote-sensing estimation of crop biochemical traits is limited. A potential solution involves training a deep learning model to understand the physical relationship between crop biochemical traits and canopy spectral reflectance based on a physical radiative transfer model (RTM). The primary focus of this study is to propose a winter-wheat leaf and canopy biochemical traits analysis and mapping method based on hyperspectral remote sensing, utilizing a deep learning network for leaf area index and leaf biochemical traits deep learning network (LabTNet). This study consists of four main tasks: (1) Field-based measurements of winter-wheat spectra and biochemical traits were conducted in two growing seasons. A PROSAIL RTM was also employed to generate a simulated dataset representing comprehensive and complex winter-wheat field conditions. (2) The LabTNet deep learning model was pre-trained using the simulated spectra dataset to acquire knowledge of the physical relationship between crop biochemical traits and canopy spectral reflectance derived from the RTM. Subsequently, the model was re-trained using the field-based spectra dataset from two growing seasons, employing a transfer learning technique. (3) An analysis was conducted to assess the performance of LabTNet against traditional statistical regression methods in estimating crop leaf and canopy biochemical traits. The study used the gradient-weighted class activation mapping (Grad-CAM) technique to analyze the attention regions of input spectra (454:8:950 nm, 960:10:1300 nm, 1450:10:1750 nm, 2000:10:2350 nm) by different convolutional neural network layers in LabTNet, aiming to enhance the interpretability of deep learning models. (4) Winter-wheat leaf and canopy biochemical traits (Cw, Cm, LAI, DLB, LW, and FLB) were mapped using the LabTNet deep learning model. Our research has the following conclusions: (1) Combining the RTM and deep learning techniques yields higher winter-wheat biochemical trait estimates than traditional statistical regression methods. (2) Different LabTNet deep learning model layers focus on distinct areas of canopy reflectance, corresponding to the sensitive regions for various winter-wheat biochemical traits. (3) LabTNet demonstrates similar winter-wheat leaf and canopy biochemical traits estimation performance using visible and near-infrared (VNIR) reflectance data and full-spectral (FS) range hyperspectral reflectance as inputs (Cw: R 2 = 0.603–0.653, RMSE = 0.0015–0.0015 cm; Cm: R 2 = 0.511–0.560, RMSE = 0.0006–0.0007 g/m2; LAI: R 2 = 0.773–0.793, RMSE = 0.65–0.66 m2/m2; LW: R 2 = 0.842–0.847, RMSE = 67.93–70.73 g/m2; DLB: R 2 = 0.747–0.762, RMSE = 21.10–21.89 g/m2; FLB: R 2 = 0.831–0.840, RMSE = 86.26–90.30 g/m2). The combined use of UAV hyperspectral remote sensing and the LabTNet model proves effective in providing high-performance winter-wheat leaf and canopy biochemical trait maps, offering valuable insights for agricultural management. [ABSTRACT FROM AUTHOR]

The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, and the color is consistent with the background, which can be challenging to obtain the number of wheat ears required. In this paper, the performance of Faster regions with convolutional neural networks (Faster R-CNN) and RetinaNet to predict the number of wheat ears for wheat at different growth stages under different conditions is investigated. The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy. Secondly, using the collected image data for recognition, the R2 of RetinaNet and Faster R-CNN after transfer learning is 0.9722 and 0.8702, respectively, indicating that the recognition accuracy of the RetinaNet method is higher on different data sets. We also tested wheat ears at both the filling and maturity stages; our proposed method has proven to be very robust (the R2 is above 90). This study provides technical support and a reference for automatic wheat ear recognition and yield estimation. [ABSTRACT FROM AUTHOR]

In this study, numerical simulations were carried out to analyze the influence of caves in different positions and shapes, in combination with structural planes, on the stability of the slope and the failure characteristics of a rock slope in a deep foundation pit with high inclination structural planes and cave development. The schemes for substituting a single karst cave for karst caves were constructed. Based on the penetration failure characteristics of karst caves between parallel structural planes, calculation methods for the safety factor of the rock foundation pits and the upper bound of the lateral pressure of supporting structures under the combined influence of the caves and structural planes have been developed, which can be used to assess the safety factor of a rock mass and to calculate the lateral pressure under complex geological conditions. The example of Xuzhou Metro Line 3 Sanhuan South Road Station is taken as an example to verify. The displacement calculation results show that the proposed rock and soil pressure calculation method is close to the actual measured value. [ABSTRACT FROM AUTHOR]

Fake news spreads at unprecedented speeds through online social media, raising many concerns and negative impacts on a variety of domains. To control this issue, Fake News Detection (FND) naturally becomes the chief task while multimodal FND has recently attracted more attention due to the fast-growing multimodal content in online social media. Commonly, the existing multimodal FND methods directly fuse the unimodal features into the multimodal features before detecting the veracity, however, such methods result in the neutralization effect problem caused by the veracity conflict between different modalities. To alleviate this problem, we propose a novel multimodal FND method, namely MultImodal fake NEws detectoR with Unimodal Veracity Signals (M iner - uvs). The basic idea of M iner - uvs is to incorporate an unimodal veracity classifier that can predict the veracity labels of each modality, and then use the unimodal veracity labels to weigh the unimodal features, so as to achieve more discriminative multimodal features. Because the unimodal veracity labels are unknown, we formulate the unimodal veracity classification as the Positive and Unlabeled (PU) learning problem; and solve it by using a variational PU learning method with a contrastive multimodal alignment regularization. To evaluate the effectiveness of M iner - uvs , we compare it with dozens of existing multimodal FND methods across benchmark datasets. Experimental results demonstrate that M iner - uvs consistently outperforms the existing competitors. • We describe the neutralization effect problem of previous multimodal FND methods. • We propose a new model MINER-UVS with the PU learning and feature fusion techniques. • Extensive experiments are conducted to indicate the effectiveness of MINER-UVS. [ABSTRACT FROM AUTHOR]

Objective: To determine the optimal scan duration for ultrafast DCE-MRI in effectively differentiating benign from malignant breast lesions. Methods: The study prospectively recruited participants who underwent breast ultrafast DCE-MRI from September 2021 to March 2023. A 30-phase breast ultrafast DCE-MRI on a 3.0-T MRI system was conducted with a 4.5-s temporal resolution. Scan durations ranged from 40.5 s to 135.0 s, during which the analysis is performed at three-phase intervals, forming eight dynamic sets (scan duration [SD]40.5s: 40.5 s, SD54s: 54.0 s, SD67.5s: 67.5 s, SD81s: 81.0 s, SD94.5s: 94.5 s, SD108s: 108.0 s, SD121.5s: 121.5 s, and SD135s: 135.0 s). Two ultrafast DCE-MRI parameters, maximum slope (MS) and initial area under the curve in 60 s (iAUC), were calculated for each dynamic set and compared between benign and malignant lesions. Areas under the receiver operating characteristic curve (AUCs) were used to assess their diagnostic performance. Results: A total of 140 women (mean age, 47 ± 11 years) with 151 lesions were included. MS and iAUC from eight dynamic sets exhibited significant differences between benign and malignant lesions (all p < 0.05), except iAUC at SD40.5s. The AUC of MS (AUC = 0.804) and iAUC (AUC = 0.659) at SD67.5s were significantly higher than their values at SD40.5s (AUC = 0.606 and 0.516; corrected p < 0.05). No significant differences in AUCs for MS and iAUC were observed from SD67.5s to SD135s (all corrected p > 0.05). Conclusions: Ultrafast DCE-MRI with a 67.5-s scan duration appears optimal for effectively differentiating malignant from benign breast lesions. Critical relevance statement: By evaluating scan durations (40.5–135 s) and analyzing two ultrafast DCE-MRI parameters, we found a scan duration of 67.5 s optimal for discriminating between these lesions and offering a balance between acquisition time and diagnostic efficacy. Key Points: Ultrafast DCE-MRI can effectively differentiate malignant from benign breast lesions. A minimum of 67.5-sec ultrafast DCE-MRI scan duration is required to differentiate benign and malignant lesions. Extending the scan duration beyond 67.5 s did not significantly improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The antioxidant activity of chitosan (CS) and three water-soluble derivatives was analyzed comparatively by in vitro and in vivo experiments, including hydroxypropyl chitosan (HPCS), quaternary ammonium salt of chitosan (HACC), and carboxymethyl chitosan (CMCS). The results show that chitosan and its water-soluble derivatives have a scavenging ability on DPPH radicals, superoxide radicals, and hydroxyl radicals, and a reducing ability. A remarkable difference (p < 0.05) was found for HACC and HPCS compared with CS on DPPH radicals, hydroxyl radicals, and reducing ability. The antioxidant ability of the four chitosan samples was in the order of HPCS > HACC > CMCS > CS. Furthermore, antioxidant activity of all samples increased gradually in a concentration-dependent manner. The in vivo result indicates that oral CS and its derivatives samples result in a decrease in lipid peroxides (LPO) and free fatty acids (FFA) levels in serum with an increase in superoxide dismutase (SOD) activity. Especially for the HPCS and HACC groups, the LPO, FFA, and SOD activity in serum was different significantly in comparison with the high-fat controlgroup (HF) (p < 0.05). These results indicate that chitosan and its derivatives can be used as good antioxidants, and the antioxidant activity might be related to the molecular structure of chitosan derivatives. [ABSTRACT FROM AUTHOR]

Cell division cycle 23 (CDC23) is a component of the tetratricopeptide repeat (TPR) subunit in the anaphase-promoting complex or cyclosome (APC/C) complex, which participates in the regulation of mitosis in eukaryotes. However, the regulatory model and mechanism by which the CDC23 gene regulates muscle production in pigs are largely unknown. In this study, we investigated the expression of CDC23 in pigs, and the results indicated that CDC23 is widely expressed in various tissues and organs. In vitro cell experiments have demonstrated that CDC23 promotes the proliferation of myoblasts, as well as significantly positively regulating the differentiation of skeletal muscle satellite cells. In addition, Gene Set Enrichment Analysis (GSEA) revealed a significant downregulation of the cell cycle pathway during the differentiation process of skeletal muscle satellite cells. The protein–protein interaction (PPI) network showed a high degree of interaction between genes related to the cell cycle pathway and CDC23. Subsequently, in differentiated myocytes induced after overexpression of CDC23, the level of CDC23 exhibited a significant negative correlation with the expression of key factors in the cell cycle pathway, suggesting that CDC23 may be involved in the inhibition of the cell cycle signaling pathway in order to promote the differentiation process. In summary, we preliminarily determined the function of CDC23 with the aim of providing new insights into molecular regulation during porcine skeletal muscle development. [ABSTRACT FROM AUTHOR]

Canopy temperature is an important parameter for monitoring the physiological status of maize. The study of its change law can help to improve the growth environment and increase the yield and quality. Based on the RGB, thermal infrared, and multispectral images obtained by an Unmanned Aerial Vehicle (UAV), two peak threshold methods were used in this study to eliminate the soil background and obtain the maize canopy information in the study area. Then, by using the radiation transmission model and Planck’s law, the maize canopy temperature was extracted, and the correlation of canopy temperature change with the regional coverage and soil drought degree was analysed. Results indicated that the accuracy of canopy temperature extraction can be effectively enhanced by eliminating the influence of soil background. At jointing and filling stages, the Pearson correlation coefficients r of canopy temperature and coverage were 0.731 and 0.722, respectively, and the slope of the linear fitting model was less than 0, indicating that the coverage has a negative impact on canopy temperature, i.e., the higher the coverage, the lower the canopy temperature, and vice versa. Additionally, statistical analysis of the canopy temperature and temperature vegetation drought index (TVDI) clustering results at the jointing and filling stages demonstrated that the total overlapping rates of the categories were 72.5% and 41.0% respectively, suggesting that maize canopy temperature is related to regional soil drought degree, the change range of canopy temperature increases with the TVDI value, and the soil drought degree at the jointing stage has a large impact on maize canopy temperature change than that at the filling stage. This study provides technical support for extracting and analysing crop canopy temperature change by using UAV thermal infrared technology. [ABSTRACT FROM AUTHOR]

The lncRNA distal-less homeobox 6 antisense 1 (DLX6-AS1) has been reported to be an oncogenic lncRNA in diverse malignant cancers; however, whether it has oncogenic role in neuroblastoma(NB) remain largely unknown. This study explored the expression status, function and potential mechanism of DLX6-AS1 in NB. In the current study, a total of 70 human NB tissues and matched adjacent non-tumor tissues were collected. Quantitative PCR (qPCR) was performed to study the expression differences of DLX6-AS1 in tissues and NB cell lines. Proliferation, migration, invasion and EMT status of transfected NB cells were evaluated by WST-1 assay, colony formation unit assay, Transwell assay and qPCR, respectively. The interaction between DLX6-AS1 and its potential targets was confirmed by luciferase reporter assay. Xenograft models were established to evaluate tumor proliferation in vivo. We found that the expression of DLX6-AS1 was significantly increased in both NB tissues and cell lines, and elevated DLX6AS1 expression was positively correlated with advanced stage and poor survival. Proliferation rate, migration and invasion ability, as well as EMT process of NB cells was inhibited after DLX6-AS1 knockdown, meanwhile, the tumor growth in vivo was impaired after DLX6-AS1 inhibition. Further analysis showed that DLX6-AS1 regulates the expression of YAP1 by sponging miR-497-5p. DLX6-AS1 directly interacts with miR-497-5p and reduces the binding of miR-497-5p to YAP1 3′UTR, thus inhibiting the degradation of YAP1 by miR-497-5p. This work demonstrates that DLX6-AS1 partially enhances the proliferation, migration and invasion abilities of NB cells through the miR-497-5p/YAP1 pathway, DLX6-AS1 might act as a promising therapeutic target for NB. [ABSTRACT FROM AUTHOR]

Steel strands are crucial components of a prestressed structure, but their performance degrades inevitably with time. Among the existing nondestructive testing methods for measuring the stress state of steel strands, ultrasonic guided wave methods have received the most research attention and are most likely to be applied to actual engineering. This work views the propagation of ultrasonic guided wave as an individual system, and the variations in the stress state in steel strands can be related to the parameters gained by the system identification model. The finite element simulation and experimental results show the propagation characteristics of the guided wave are influenced distinctly by the stress state. The index constructed by the system parameters exhibits good monotonic linear with stress level variation, and the slope of the fitted line from experimental data is similar to the simulation result. The sensor placement and loading path have a limited effect on the stress state and the proposed index. [ABSTRACT FROM AUTHOR]

Umbilical hernia (UH) is a prevalent congenital disorder in pigs, resulting in considerable economic losses and severe animal welfare issues. In the present study, we conducted a genome‐wide association study (GWAS) using the GeneSeek 50K Chip in 2777 pigs (Duroc, n = 1267; Landrace, n = 696; and Yorkshire, n = 814) to explore the candidate genes underlying the risk of umbilical hernia in pigs. After quality control analyses, 2748 animals and 48 524 single nucleotide polymorphisms (SNPs) were retained for subsequent GWAS analysis using the FarmCPU model. The heritability of umbilical hernias was estimated to 0.51 ± 0.04, indicating a reasonable basis for investigating genetic markers associated with this disorder. We identified 54 SNPs and 517 candidate genes that showed significant associations with susceptibility to umbilical hernia across the combined population of the three pig breeds. Gene enrichment analyses highlighted several crucial pathways for platelet degranulation, inflammatory mediator regulation of TRP channels and ion transport. These findings provide further insights into the underlying genetic architecture of umbilical hernias in pigs. [ABSTRACT FROM AUTHOR]

• We investigate better topic representations of topic modeling by considering words discriminative power. • We propose three top topical words reranking methodologies, namely SDW, SDWTS and CHI. • Empirical results demonstrate the proposed methods can output more representative top word lists for topics. The top word list, i.e., the top- M words with highest marginal probabilities in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list are not always representative. The objective of this paper is to find more representative top word lists for topics. To achieve this, we rerank the words in a given topic by further considering marginal probabilities on words over every other topic. The reranking list of top- M words is used to be a novel topic representation for topic models. We investigate three reranking methodologies, using (1) standard deviation weight, (2) standard deviation weight with topic size and (3) Chi Square χ 2 statistic selection. Experimental results on real-world collections indicate that our representations can extract more representative words for topics, agreeing with human judgements. [ABSTRACT FROM AUTHOR]

Short texts, such as Twitter social media posts, have become increasingly popular on the Internet. Inferring topics from massive numbers of short texts is important to many real-world applications. A single short text often contains a few words, making traditional topic models less effective. A recently developed biterm topic model (BTM) effectively models short texts by capturing the rich global word co-occurrence information. However, in the sparse short-text context, many highly related words may never co-occur. BTM may lose many potential coherent and prominent word co-occurrence patterns that cannot be observed in the corpus. To address this problem, we propose a novel relational BTM (R-BTM) model, which links short texts using a similarity list of words computed employing word embeddings. To evaluate the effectiveness of R-BTM, we compare it against the existing short-text topic models on a variety of traditional tasks, including topic quality, clustering and text similarity. Experimental results on real-world datasets indicate that R-BTM outperforms baseline topic models for short texts. [ABSTRACT FROM AUTHOR]

Myogenic differentiation is a complex biological process that is regulated by multiple factors, among which long noncoding RNAs (lncRNAs) play an essential role. However, in-depth studies on the regulatory mechanisms of long noncoding RNAs (lncRNAs) in myogenic differentiation are limited. In this study, we characterized the role of the novel lncRNA TCONS_00323213, which is upregulated during porcine skeletal muscle satellite cell (PSC) differentiation in myogenesis. We found that TCONS_00323213 affected the proliferation and differentiation of PSC in vitro. We performed quantitative polymerase chain reaction (qPCR), 5-ethynyl-20-deoxyuridine (EdU), western blotting, immunofluorescence staining, pull-down assays, and cleavage under targets and tagmentation (CUT and Tag) assays to clarify the effects and action mechanisms of TCONS_00323213. LncRNA TCONS_00323213 inhibited myoblast proliferation based on analyses of cell survival rates during PSC proliferation. Functional analyses revealed that TCONS_00323213 promotes cell differentiation and enhances myogenin (MyoG), myosin heavy chain (MyHC), and myocyte enhancer factor 2 (MEF2C) during myoblast differentiation. As determined by pull-down and RNA immunoprecipitation (RIP) assays, the lncRNA TCONS_00323213 interacted with PBX/Knotted Homeobox 2 (PKNOX2). CUT and Tag assays showed that PKNOX2 was significantly enriched on the MyoG promoter after lncRNA TCONS_00323213 knockdown. Our findings demonstrate that the interaction between lncRNA TCONS_00323213 and PKNOX2 relieves the inhibitory effect of PKNOX2 on the MyoG promoter, increases its expression, and promotes PSC differentiation. This novel role of lncRNA TCONS_00323213 sheds light on the molecular mechanisms by which lncRNAs regulate porcine myogenesis. [ABSTRACT FROM AUTHOR]

As predators, the macronutrients spiders extract from their prey play important roles in their mating and reproduction. Previous studies of macronutrients on spider mating and reproduction focus on protein, the potential impact of prey lipid content on spider mating and reproduction remains largely unexplored. Here, we tested the influence of prey varying in lipid content on female mating, sexual cannibalism, reproduction, and offspring fitness in the wolf spider Pardosa pseudoannulata. We acquired 2 groups of fruit fly Drosophila melanogaster that differed significantly in lipid but not protein content by supplementing cultural media with a high or low dose of sucrose on which the fruit flies were reared (HL: high lipid and LL: low lipid). Subadult (i.e. 1 molt before adult) female spiders that fed HL flies matured with significantly higher lipid content than those fed LL flies. We found that the mated females fed with HL flies significantly shortened pre-oviposition time and resulted in a significantly higher fecundity. However, there was no significant difference in female spiders varying in lipid content on other behaviors and traits, including the latency to courtship, courtship duration, mating, copulation duration, sexual cannibalism, offspring body size, and survival. Hence, our results suggest that the lipid content of prey may be a limiting factor for female reproduction, but not for other behavioral traits in the wolf spiders P. pseudoannulata. [ABSTRACT FROM AUTHOR]