Alteration of lncRNA RHPN1-AS1 predicts clinical prognosis and regulates the progression of bladder cancer via modulating miR-485-5p.

BACKGROUND: Exploring effect biomarkers that monitor tumor progression and predict the prognosis could benefit the clinical management of bladder cancer and improve the postoperative life of patients. This study aimed to estimate the function of long non-coding (lnc)RNA RHPN1-AS1 (RHPN1-AS1) in bladder cancer and the potential molecular mechanism. METHODS: The expression of RHPN1-AS1 was evaluated in bladder cancer tissues from 115 patients and cells by polymerase chain reaction. The clinical significance of RHPN1-AS1 was assessed and its effect was also estimated in cell proliferation, migration, and invasion. The underlying molecular mechanism was explored by the dual-luciferase reporter assay. RESULTS: The expression of RHPN1-AS1 was 2.91-fold elevated in bladder cancer, which showed a close correlation with advanced tumor node metastasis stage (P = 0.013) and the presence of lymph node metastasis (P = 0.018). RHPN1-AS1 also served as a poor prognostic indicator (hazard ratio = 2.563) for bladder cancer. The knockdown of RHPN1-AS1 significantly suppressed the proliferation and metastasis ability of bladder cancer cells. Moreover, miR-485-5p was found to mediate the function of RHPN1-AS1 in bladder cancer, which was considered the underlying regulatory mechanism. CONCLUSIONS: RHPN1-AS1 serves as a prognostic biomarker and tumor promoter in bladder cancer via modulating miR-485-5p, which might be a reliable target of bladder cancer therapy.

Surviving high temperatures: The crucial role of vesicular inhibitory amino acid transporter in Asian honeybee, Apis cerana.

Asian honeybees (Apis cerana) play a crucial role as pollinators to service for the ecological stability. However, their proliferation and growth are significantly impacted by environmental temperature stress. This study delves into the function of the Apis cerana vesicular inhibitory amino acid transporter gene (AcVIAAT) in safeguarding Asian honeybees against high-temperature stress. The AcVIAAT gene exhibits positive responsiveness in honeybees subjected to varying thermal conditions by triggering the genes associated with oxidative stress. Molecular docking, co-immunoprecipitation, and ELISA verify the capacity of the AcVIAAT protein to interact with γ-aminobutyric acid (GABA), a key inhibitory neurotransmitter. Administering GABA to honeybees significantly improves their survival rate under high-temperature stress and also simultaneously upregulating oxidative stress-related genes. Therefore, these findings reveal that the AcVIAAT gene enhances the thermoregulatory capacity of honeybees by modulating oxidative stress-related genes through facilitating GABA transport. The characterization of six non-synonymous SNPs in the AcVIAAT gene among A.cerana populations distributed across both the northern and southern regions indicates a potential association between gene variation and environmental adaptation. Our results contribute to elucidating the molecular mechanisms underlying high-temperature tolerance in Asian honeybees and provide a promising genetic marker for enhancing heat tolerance through genetic improvement.

Enhanced Hydrogen Production via Photothermal-Coupled Ultrasound Pyrolysis of Waste Bio-Oil.

The creation of hydrogen using the lower-cost feedstock, waste organics (WOs), e. g. kitchen waste bio-oil, is a win-win solution, because it can both solve energy problems and reduce environmental pollution. Ultrasound has received considerable interest in organic decomposition; however, the application of ultrasound alone is not a good choice for the hydrogen production from WOs, because of the energy consumption and efficiency. To boost the hydrogen production based on ultrasonic cavitation cracking of bio-oil, photothermal materials are introduced into the hydrogen production system to form localized hot spots. Materials carbon black (CB), carbon nanotubes (CNT), and silicon dioxide (SiO2) all exhibit significant enhancing effects on the hydrogen production from bio-oil, and the CB exhibits the most significant strengthening effect among these materials. When the dosage of CB is 5 mg, hydrogen production rate is 180.1 µmol h-1, representing a notable 1.7-fold increase compared to the production rate without CB. In the presence of light and ultrasound, the hydrogen production rate can be increased by 66.7-fold compared to the situation where only light is present without ultrasound.

The Design and Experimentation of a Differential Grain Moisture Detection Device for a Combined Harvester.

To conveniently implement the online detection of grain moisture in combined harvesters and the address the influence of the no-load measurement baseline, thereby enhancing detection accuracy and measurement continuity, this study developed a differential grain moisture detection device. For its convenient installation and integration on combined harvesters, a single-pole plate measurement element with a 1.6 mm thick epoxy resin coated with a 2-ounce copper film was designed, and a grain moisture detection device was constructed based on the STM32F103 microprocessor (STMicroelectronics International NV, Geneva, Switzerland). To enhance the device's interference resistance, a differential amplification measurement circuit integrated with high-frequency excitation was designed using a reference capacitance. To improve the resolution of the measurement circuit, Malab simulations were conducted at different excitation frequencies, ultimately selecting 30 kHz as the system's excitation signal frequency. To validate the effectiveness of the measurement circuit, validity tests were performed on the constructed sensor, which showed that the sensor's measurement voltage could effectively distinguish the moisture levels in grains, with a determination coefficient (R²) reaching 0.9978. To address the errors in moisture measurement caused by changes in grain temperature, an interaction experiment of the effect of moisture content and temperature on the measurement voltage was conducted using an integrated temperature sensor, resulting in the construction of a moisture content calculation model. Both the indoor static detection and field testing of the moisture detection device were conducted, indicating that the maximum average error in static measurements was 0.3%, with a maximum relative error of 0.47%, and the average relative error in field tests was ≤0.4%.

Efficacy of deep brain stimulation for Tourette syndrome and its comorbidities: A meta-analysis.

Tourette Syndrome (TS) is a neurodevelopmental disorder characterized by multiple motor and vocal tics, often accompanied by comorbid disorders. Optional treatments for patients with TS include behavioral therapy, pharmacotherapy, and neurostimulation techniques. Deep brain stimulation (DBS) has been considered a therapeutic approach for refractory TS and its comorbid symptoms. However, systematic comparison is necessary to understand the therapeutic effect of DBS among patients with TS with various comorbid symptoms, demographic characteristics, or stimulation targets. Consequently, our research aimed to assess the clinical efficacy of DBS in alleviating the symptoms of TS and its comorbidities. A systematic literature search was conducted across five databases: PubMed, Web of Science, MEDLINE, Embase, and PsycINFO. The primary outcome was the mean change in the global score of the Yale Global Tic Severity Scale (YGTSS), which assesses the severity of tics. The secondary outcomes included mean improvement of comorbid symptoms, such as obsessive-compulsive behaviors (OCB), depression symptoms and anxiety symptoms. In total, 51 studies with 673 participants were included in this meta-analysis. Overall, the DBS led to a significant improvement in tic symptoms (p â€‹< â€‹0.001), as well as the comorbid obsessive-compulsive, depression, and anxiety symptoms with effect sizes of 1.88, 0.88, 1.04, and 0.76 accordingly. In the subgroup analysis, we found that striatum stimulation led to a more significant improvement in OCB in patients with TS compared to that observed with thalamic stimulation (p â€‹= â€‹0.017). The relationship between sex, age, and target with the improvement of tics, depression, and anxiety was not statistically significant (p â€‹= â€‹0.923, 0.438, 0.591 for different male proportions; p â€‹= â€‹0.463, 0.425, 0.105 for different age groups; p â€‹= â€‹0.619, 0.113, 0.053 for different targets). In conclusion, DBS is an efficient treatment option for TS, as well as the comorbid OCB, depression symptoms, and anxiety symptoms. It is important to highlight that stimulating the striatum is more effective in managing obsessive-compulsive symptoms compared to stimulating the thalamus.

Dilated Heterogeneous Convolution for Cell Detection and Segmentation Based on Mask R-CNN.

Owing to the variable shapes, large size difference, uneven grayscale, and dense distribution among biological cells in an image, it is very difficult to accurately detect and segment cells. Especially, it is a serious challenge for some microscope imaging devices with limited resources owing to a large number of learning parameters and computational burden when using the standard Mask R-CNN. In this work, we propose a mask R-DHCNN for cell detection and segmentation. More specifically, Dilation Heterogeneous Convolution (DHConv) is proposed by designing a novel convolutional kernel structure (i.e., DHConv), which integrates the strengths of the heterogeneous kernel structure and dilated convolution. Then, the traditional homogeneous convolution structure of the standard Mask R-CNN is replaced with the proposed DHConv module to it adapt to shape and size differences encountered in cell detection and segmentation tasks. Finally, a series of comparison and ablation experiments are conducted on various biological cell datasets (such as U373, GoTW1, SIM+, and T24) to verify the effectiveness of the proposed method. The results show that the proposed method can obtain better performance than some state-of-the-art methods in multiple metrics (including AP, Precision, Recall, Dice, and PQ) while maintaining competitive FLOPs and FPS.

Clinical characteristics and surgical outcomes of low-grade epilepsy-associated brain tumors.

Background: Low-grade epilepsy-associated brain tumors (LEATs) are found to be the second most common lesion-related epilepsy. Malignant potential of LEATs is very low and the overall survival is good, so the focus of treatment is focused more on seizure outcome rather than oncological prognosis. Objectives: This study was conducted to evaluate the risk factors of seizure outcomes after resection in patients with LEATs. Design: A retrospective study. Methods: A retrospective analysis of patients with LEATs who underwent resective surgery in our three epilepsy centers between October 2010 and April 2023 with a minimum follow-up of 1 year. Demography, clinical characters, neurophysiology, and molecular neuropathology were assessed for association with postoperative seizure outcomes at 1-, 2-, and 5-year follow-up. Synthetic minority oversampling technique (SMOTE) algorithm model was performed to handle the imbalance of data distribution. Gaussian Naïve Bayes (GNB) algorithms were created as a basis for classifying outcomes according to observation indicators. Results: A total of 111 patients were enrolled in the cohort. The most common pathology was ganglioglioma (n = 37, 33.3%). The percentage of patients with seizure freedom was 91.0% (101/111) at 1-year follow-up, 87.5% (77/88) at 2-year follow-up, and 79.1% (53/67) at 5-year follow-up. Partial resection had a significantly poor seizure outcome compared to total resection and supratotal resection (p < 0.05). The epileptiform discharge on post-resective intraoperative electrocorticography (ECoG) or postoperative scalp electroencephalography (EEG) were negative factors on postoperative seizure freedom at 1-, 2-, or 5-year follow-ups (p < 0.05). The area under the receiver-operating characteristic curve value of the GNB-SMOTE model was 0.95 (95% CI, 0.876-1.000), 0.892 (95% CI, 0.656-0.934), and 0.786 (95% CI, 0.491-0.937) at 1-, 2-, and 5-year follow-up, respectively. Conclusion: The partial resection, post-resective intraoperative ECoG, and postoperative scalp EEG were valuable indicators of poor seizure outcomes. The utilization of post-resective intraoperative ECoG is beneficial to improve seizure outcomes. Based on the data diversity and completeness of three medical centers, a multivariate correlation analysis model was established based on GNB algorithm.

A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis.

Deep learning and machine vision technology are widely applied to detect the quality of mechanized soybean harvesting. A clean dataset is the foundation for constructing an online detection learning model for the quality of mechanized harvested soybeans. In pursuit of this objective, we established an image dataset for mechanized harvesting of soybeans. The photos were taken on October 9, 2018, at a soybean experimental field of Liangfeng Grain and Cotton Planting Professional Cooperative in Guanyi District, Liangshan, Shandong, China. The dataset contains 40 soybean images of different qualities. By scaling, rotating, flipping, filtering, and adding noise to enhance the data, we expanded the dataset to 800 frames. The dataset consists of three folders, which store images, label maps, and record files for partitioning the dataset into training, validation, and testing sets. In the initial stages, the author devised an online detection model for soybean crushing rate and impurity rate based on machine vision, and research outcomes affirm the efficacy of this dataset. The dataset can help researchers construct a quality prediction model for mechanized harvested soybeans using deep learning techniques.

Precise Phase Measurement for Fringe Reflection Technique through Optimized Camera Response.

The Fringe Reflection is a robust and non-contact technique for optical measurement and specular surface characterization. The periodic alternation between dark and light cycles of the fringe patterns encodes the geometric information and provides a non-contact method of spatial measurement through phase extraction. Precisely expressing the positions of the points of the fringe pattern is a fundamental requirement for an accurate fringe reflection measurement. However, the nonlinear processes, both in generating the fringe pattern on a screen and capturing it using pixel values, cause inevitable errors in the phase measurement and eventually reduce the system's precision. Aiming at reducing these nonlinear errors, we focus on constructing a new quantity from the pixel values of the photos of the fringe patterns that could linearly respond to the ideal fringe pattern. To this end, we hypothesize that the process of displaying the fringe pattern on a screen using a control function is similar to the process of capturing the pattern and converting the illuminating information into pixel values, which can be described using the camera's response function. This similarity allows us to build a scaled energy quantity that could have a better linear relation with the control function. We optimize the extracted camera response function using an objective to increase the precision and reduce the quoted error. Experiments designed to determine the positions of points along the quartile lines verify the effectiveness of the proposed method in improving fringe reflection measurement precision.

Multi-channel electrohysterography enabled uterine contraction characterization and its effect in delivery assessment.

Uterine contractions are routinely monitored by tocodynamometer (TOCO) at late stage of pregnancy to predict the onset of labor. However, TOCO reveals no information on the synchrony and coherence of contractions, which are important contributors to a successful delivery. The electrohysterography (EHG) is a recording of the electrical activities that trigger the local muscles to contract. The spatial-temporal information embedded in multiple channel EHG signals make them ideal for characterizing the synchrony and coherence of uterine contraction. To proceed, contractile time-windows are identified from TOCO signals and are then used to segment out the simultaneously recorded EHG signals of different channels. We construct sample entropy SamEn and Concordance Correlation based feature ψ from these EHG segments to quantify the synchrony and coherence of contraction. To test the effectiveness of the proposed method, 122 EHG recordings in the Icelandic EHG database were divided into two groups according to the time difference between the gestational ages at recording and at delivery (TTD). Both SamEn and ψ show clear difference in the two groups (p<10-5) even when measurements were made 120 h before delivery. Receiver operating characteristic curve analysis of these two features gave AUC values of 0.834 and 0.726 for discriminating imminent labor defined with TTD ≤ 24 h. The SamEn was significantly smaller in women (0.1433) of imminent labor group than in women (0.3774) of the pregnancy group. Using an optimal cutoff value of SamEn to identify imminent labor gives sensitivity, specificity, and accuracy as high as 0.909, 0.712 and 0.743, respectively. These results demonstrate superiority in comparing to the existing SOTA methods. This study is the first research work focusing on characterizing the synchrony property of contractions from the electrohysterography signals. Despite the very limited dataset used in the validation process, the promising results open a new direction to the use of electrohysterography in obstetrics.

The natural course of bacillus Calmette-Guérin induced bladder lesions: A long-term follow-up study and systematic review.

Objective: Bacillus Calmette-Guérin (BCG) instillation is the standard adjuvant treatment for intermediate- and high-risk non-muscle-invasive bladder cancer after transurethral resection. Nevertheless, its toxicity often causes bladder complications. On follow-up cystoscopy, post-BCG bladder lesions can be pathologically benign, urothelial carcinoma recurrence, or other types of bladder malignancy. Only a small number of case reports have been published on post-BCG bladder lesions. Their clinical features, natural course, and management remain unknown. Methods: We retrospectively studied cystoscopic videos and medical records of BCG-treated bladder cancer patients at our center. During a long-term follow-up, we took biopsies on tumor-like lesions and described their changes. In addition, we summarized previous studies on post-BCG bladder lesions by systematic literature searching and review. Results: We described a series of three cases with post-BCG bladder lesions mimicking tumor recurrence from a total of 38 cases with follow-up data for more than 5 years. Those lesions could last, grow, or disappear spontaneously, and remain pathological benign for years. In systematic review, we identified and analyzed a total of 15 cases with post-BCG bladder lesions with detailed clinical information. Eleven of the 15 were benign and have a good prognosis with nephrogenic adenoma being the most common pathological type. Conclusion: Based on previous studies and our experience, benign lesions after BCG instillation cannot distinguish with cancer recurrence by cystoscopy alone, even under narrow band imaging mode. Nonetheless, given most of them have a good prognosis, random biopsy or transurethral resection might be spared in the patients with long-term negative biopsy and urine cytology.

Survey Results of Honey Bee Colony Losses in Winter in China (2009-2021).

There is growing concern that massive loss of honey bees can cause serious negative effects on biodiversity and ecosystems. Surveys of colony losses have been performed worldwide to monitor the dynamic changes and health status of honey bee colonies. Here, we present the results of surveys regarding winter colony losses from 21 provinces in China from 2009 to 2021, with a total of 1,744,324 colonies managed by 13,704 beekeepers. The total colony losses were low (9.84%; 95% Confidence Interval (CI): 9.60-10.08%) but varied among years, provinces, and scales of apiaries. As little is known about the overwintering mortality of Apis cerana, in this study, we surveyed and compared the loss rates between Apis mellifera and A. cerana in China. We found colonies of A. mellifera suffered significantly lower losses than A. cerana in China. Larger apiaries resulted in higher losses in A. mellifera, whereas the opposite was observed in A. cerana. Furthermore, we used generalized linear mixed-effects models (GLMMs) to evaluate the effects of potential risk factors on winter colony losses and found that the operation size, species, migration, migration×species interaction, and queen problems were significantly related to the loss rates. New queens can increase their colony overwintering survival. Migratory beekeepers and large operations reported lower loss rates.

Spermatophore development in drones indicates the metabolite support for sperm storage in honey bees (Apis cerana).

Developing effective long-term sperm storage strategies to maintain activity requires an understanding of the underlying spermatophore developmental phase in drones. Here we compared the developmental processes and metabolites about seminal vesicles of drones from different parentages (0-24 d)in honeybee colonies, including mated queens, virgin queens, and worker bees. The results showed a similar developmental trend of seminal vesicles in thethree groups of drones on the whole, although there were significant differences in developmental levels, as well as in other indicators. Correlation analysis showed significant positive correlations between seminal vesicle width and sperm viability. The metabolomics of the seminal vesicles in drones from mated queens showed differences of the metabolites in each stage. Particularly, squalene identified among them was validated a protective effect on sperm vitality in vitro experiments. Together the results of these assays support that there were significant differences in the developmental levels of seminal vesicles among the three groups of drones in honeybees, wherein a significant correlation between sperm viability and the developmental levels of seminal vesicles were dissected. The metabolomics analysis and semen storage experiments in vitro display signatures of squalene that may act as an effective protective agent in maintaining sperm viability. Collectively, our findings indicate that spermatophore development in drones provides metabolite support, which contributes to research on the differences of sperm viability among drones in the future.

Factors that Influence Subdural Hemorrhage Secondary to Intracranial Arachnoid Cysts in Children.

OBJECTIVE: This study aimed to investigate factors that influence subdural haemorrhage (SDH) secondary to intracranial arachnoid cysts (IACs) in children. METHODS: Data of children with unruptured IACs (IAC group) and those with SDH secondary to IACs (IAC-SDH group) were analyzed. Nine factors, sex, age, birth type (vaginal or caesarean), symptoms, side (left, right, or midline), location (temporal or nontemporal), image type (I, II, or III), volume, and maximal diameter, were selected. IACs were classified as types I, II, and III according to their morphological changes observed on computed tomography images. RESULTS: There were 117 boys (74.5%) and 40 girls (25.5%); 144 (91.7%) patients comprised the IAC group and 13 (8.3%) comprised the IAC-SDH group. There were 85 (53.8%) IACs on the left side, 53 (33.5%) on the right side, 20 (12.7%) in the midline region, and 91 (58.0%) in the temporal region. The univariate analysis showed significant differences in age, birth type, symptoms, cyst location, cyst volume, and cyst maximal diameter (P < 0.05) between the 2 groups. Logistic regression using the synthetic minority oversampling technique model showed that image type III and birth type were independent factors that influenced SDH secondary to IACs (ß0 = 4.143; ß for image type = -3.979; ß for birth type = -2.542) and that the representative area under the receiver-operating characteristic curve value was 0.948 (95% confidence interval, 0.898-0.997). CONCLUSIONS: IACs are more common in boys than in girls. They can be divided into 3 groups according to their morphological changes on computed tomography images. Image type III and caesarean delivery were independent factors that influenced SDH secondary to IACs.

Comparison of the mitochondrial genomes of three geographical strains of Apis laboriosa indicates high genetic diversity in the black giant honeybee (Hymenoptera: Apidae).

Apis laboriosa is the largest honeybee that lives mainly on cliff faces, with strong migratory ability. In this study, we firstly sequenced and assembled two complete mitochondrial genomes of A. laboriosa isolated from two distant locations in China (Chongqing and Shangri-La regions). Combined with the published mitochondrial genome of A. laboriosa from Nepal, comparative genomic analyses were conducted to gain insight into the genetic diversity of giant honeybees from different geographical distributions. The mitochondrial genomes of A. laboriosa from Chongqing and Shangri-La regions were 15,579 and 15,683 bp in length, respectively, both larger than that from Nepal with the length of 15,510 bp. Three mitochondrial genomes all harbor 37 common genes and present the same AT bias and the frequency of codon usage. However, the fragments including COX1, SSUrRNA, LSUrRNA, and the AT-rich region of the mitochondrial genome from Shangri-La region demonstrate distinctive insertions and deletions compared to those from Chongqing and Nepal regions. Phylogenetic trees of mitochondrial genomes show that A. laboriosa from Chongqing is most closely related to that from Nepal, rather than to Shangri-La. Genetic distance between Shangri-La and Chongqing or Nepal was even larger than that between the various subspecies of Apis mellifera. Overall, these results unmark that A. laboriosa in different geographical distributions can exhibit high genetic diversity at the mitochondrial genomic level, and therein, A. laboriosa from Shangri-La may be the subspecies. All these studies will contribute to our understanding of the geographical distribution and genetic differentiation of black giant honeybee in Asian region.

Adaptive Diffusion Pairwise Fused Lasso LMS Algorithm Over Networks.

The topic of identification for sparse vector in a distributed way has triggered great interest in the area of adaptive filtering. Grouping components in the sparse vector has been validated to be an efficient way for enhancing identification performance for sparse parameter. The technique of pairwise fused lasso, which can promote similarity between each possible pair of nonnegligible components in the sparse vector, does not require that the nonnegligible components have to be distributed in one or multiple clusters. In other words, the nonnegligible components may be randomly scattered in the unknown sparse vector. In this article, based on the technique of pairwise fused lasso, we propose the novel pairwise fused lasso diffusion least mean-square (PFL-DLMS) algorithm, to identify sparse vector. The objective function we construct consists of three terms, i.e., the mean-square error (MSE) term, the regularizing term promoting the sparsity of all components, and the regularizing term promoting the sparsity of difference between each pair of components in the unknown sparse vector. After investigating mean stability condition of mean-square behavior in theoretical analysis, we propose the strategy of variable regularizing coefficients to overcome the difficulty that the optimal regularizing coefficients are usually unknown. Finally, numerical experiments are conducted to verify the effectiveness of the PFL-DLMS algorithm in identifying and tracking sparse parameter vector.

CASF-Net: Cross-attention and cross-scale fusion network for medical image segmentation.

BACKGROUND: Automatic segmentation of medical images has progressed greatly owing to the development of convolutional neural networks (CNNs). However, there are two uncertainties with current approaches based on convolutional operations: (1) how to eliminate the general limitations that CNNs lack the ability of modeling long-range dependencies and global contextual interactions, and (2) how to efficiently discover and integrate global and local features that are implied in the image. Notably, these two problems are interconnected, yet previous approaches mainly focus on the first problem and ignore the importance of information integration. METHODS: In this paper, we propose a novel cross-attention and cross-scale fusion network (CASF-Net), which aims to explicitly tap the potential of dual-branch networks and fully integrate the coarse and fine-grained feature representations. Specifically, the well-designed dual-branch encoder hammers at modeling non-local dependencies and multi-scale contexts, significantly improving the quality of semantic segmentation. Moreover, the proposed cross-attention and cross-scale module efficiently perform multi-scale information fusion, being capable of further exploring the long-range contextual information. RESULTS: Extensive experiments conducted on three different types of medical image segmentation tasks demonstrate the state-of-the-art performance of our proposed method both visually and numerically. CONCLUSIONS: This paper assembles the feature representation capabilities of CNN and transformer and proposes cross-attention and cross-scale fusion algorithms. The promising results show new possibilities of using cross-fusion mechanisms in more downstream medical image tasks.

Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3.

Wheat, one of the most important food crops in the world, is usually harvested mechanically by combine harvesters. The impurity rate is one of the most important indicators of the quality of wheat obtained by mechanized harvesting. To realize the online detection of the impurity rate in the mechanized harvesting process of wheat, a vision system based on the DeepLabV3+ model of deep learning for identifying and segmenting wheat grains and impurities was designed in this study. The DeepLabV3+ model construction considered the four backbones of MobileNetV2, Xception-65, ResNet-50, and ResNet-101 for training. The optimal DeepLabV3+ model was determined through the accuracy rate, comprehensive evaluation index, and average intersection ratio. On this basis, an online detection method of measuring the wheat impurity rate in mechanized harvesting based on image information was constructed. The model realized the online detection of the wheat impurity rate. The test results showed that ResNet-50 had the best recognition and segmentation performance; the accuracy rate of grain identification was 86.86%; the comprehensive evaluation index was 83.63%; the intersection ratio was 0.7186; the accuracy rate of impurity identification was 89.91%; the comprehensive evaluation index was 87.18%; the intersection ratio was 0.7717; and the average intersection ratio was 0.7457. In terms of speed, ResNet-50 had a fast segmentation speed of 256 ms per image. Therefore, in this study, ResNet-50 was selected as the backbone network for DeepLabV3+ to carry out the identification and segmentation of mechanically harvested wheat grains and impurity components. Based on the manual inspection results, the maximum absolute error of the device impurity rate detection in the bench test was 0.2%, and the largest relative error was 17.34%; the maximum absolute error of the device impurity rate detection in the field test was 0.06%; and the largest relative error was 13.78%. This study provides a real-time method for impurity rate measurement in wheat mechanized harvesting.