A Distortion Correction Method Based on Actual Camera Imaging Principles.

In the human-robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

Comparative analysis of tumor biology and prognosis in mucinous and signet-ring cell colon cancers versus classical adenocarcinoma.

Background: Limited information is currently available on the natural history and prognosis of two distinct histological subtypes of adenocarcinoma (AC) in the colon: mucinous adenocarcinoma (MAC) and signet-ring cell carcinoma (SRCC). Therefore, the aim of this study is to examine the clinicopathological characteristics of colon MAC and SRCC, comparing them to classical AC, using a large cohort of cases from the United States. Methods: Patients diagnosed with colon AC, MAC, or SRCC from the SEER database between 2000 and 2018 were included in our study. Incidence trends, patient demographics, tumor characteristics, treatment, and survival were analyzed. Results: In our study, we analyzed a total of 310,813 patients with colon cancers, including 271,382 cases of classical AC, 34,750 cases of MAC, and 4,681 cases of SRCC. Over the study period, we observed a decline in the age-adjusted incidence rates of colon AC, MAC, and SRCC. Notably, the MAC and SRCC cohorts differed significantly from AC in terms of patient characteristics, tumor locations, and treatment patterns. Patients with MAC and SRCC had poorer survival outcomes compared to those with AC. Factors associated with worse survival included older age, male sex, poorly differentiated tumors, advanced stage, and the presence of MAC or SRCC histology. On the other hand, surgical intervention was associated with improved survival. Conclusion: Our study underscores the significance of recognizing the distinct features and outcomes associated with different histological subtypes of colon cancer. Further research is warranted to delve into the underlying biological traits that contribute to these differences and to develop more tailored treatment strategies.

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review.

As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object's features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

Guanine-rich RNA binding protein GRSF1 inhibits myoblast differentiation through repressing mitochondrial ROS production.

Guanine-rich RNA sequence binding factor 1 (GRSF1) is a member of the RNA-binding protein (RBP) family. GRSF1 regulates RNA metabolism through RNA processing, transport and translation in the cytoplasm and mitochondria. However, its role in myogenesis has not been investigated. Here, we demonstrated that the expression of mitochondrial GRSF1 was negatively related to the differentiation of mouse skeletal myoblasts. Interference with GRSF1 promotes myogenesis both in vitro and in vivo without affecting MyoD expression or cell proliferation. Further studies illustrated that GRSF1 regulated myogenic differentiation through direct targeting of mitochondrial GPX4, a key regulator of the cellular redox status, leading to the modulation of ROS levels, which is important for myogenesis. Our findings underscore a critical function for GRSF1 during skeletal myogenesis linked to its regulation of muscle redox homeostasis.

Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras.

BACKGROUND: Unmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management. METHOD: In this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial-temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated. Random forest (RF) model for AGB estimation as well as the PB was then developed. Furthermore, variable importance and sensitivity analysis of UAV variables were performed to study the potential of improving model robustness and prediction accuracies. RESULTS: It was found that the canopy height extracted from the CSM (Hcsm) exhibited a high correlation with the ground-measured canopy height, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. We also observed that several VIs were highly correlated with AGB, and the modified normalized difference spectral index extracted from the multispectral image achieved the highest correlation. RF model with fusing RGB and multispectral image data substantially improved the prediction results of AGB and PB with the prediction of root mean square error (RMSEP) reduced by 8.33-16.00%. The best prediction results for AGB and PB were achieved with the coefficient of determination (r2), the RMSEP and relative RMSE (RRMSE) of 0.90, 0.21 kg/m2 and 14.05%, and 0.68, 0.10 kg/m2 and 12.11%, respectively. In addition, the result confirmed that the sensitivity analysis could simplify the prediction model without reducing the prediction accuracy. CONCLUSION: These findings demonstrate the feasibility of applying lightweight UAV with dual image-frame snapshot cameras for rice biomass estimation, and its potential for high throughput analysis of plant growth-related traits in precision agriculture as well as the advanced breeding program.

Hyperspectral Imaging for Predicting the Internal Quality of Kiwifruits Based on Variable Selection Algorithms and Chemometric Models.

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm-partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness (R pre = 0.9812, RPD = 5.17) and SSC (R pre = 0.9523, RPD = 3.26) at 380-1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874-1734 nm for predicting pH (R pre = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries.

Near-infrared (874-1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972-1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.