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Cotton bud is one of the most important cash crops with many uses, such as the textile and cotton fabric raw material. However, manual cotton topping cannot fully meet the large-scale production in recent years, due to the efficiency and labor costs. Intelligent mechanical topping can be expected to serve as an inevitable trend during cotton topping. There is a high demand to accurately identify the cotton top bud under the complex environment (such as light and shadow), particularly for the various shape characteristics of cotton top core in the process of cotton cap removal. In this study the accurate recognition was proposed to locate the cotton top bud in the complex environment using an improved YOLOv5s. A dataset was also constructed to contain 3103 cotton top buds with different morphological characteristics under a complex environment. Three categories were divided: single, multi-plant and screened top bud under different occlusion areas, weather and light conditions. Systematic training and analysis were then performed on the data. An improved YOLOv5s model was proposed to reduce the error and leakage detection for the sufficient features of small targets. Multi-scale target features were also effectively extracted using only three detection heads in the original YOLOv5s. At the same time, the ideal extraction of the improved YOLOv5s was obtained with the deepening of the network layer. The object detection and CPP-CBAM attention mechanism were first added to the original structure, in order to improve the shallow and deep feature fusion rate. Secondly, the regression of prediction frame was enhanced to avoid the information loss of the real frame. The loss function SIOU boundary frame regression was added to further accelerate the prediction frame regression speed for the detection accuracy. The SIOU loss function was introduced by the vector angle between the real and predicted frame including angle, distance, and shape loss function. As such, the aspect ratio between the predicted and real frame was considered in the CIOU loss function with the direction between them in the SIOU. The high accuracy of recognition rate was obtained on the top bud, resulting in the less degrading into an IOU loss function. In addition, an accurate identification was performed on the cotton top bud that was seriously obscured by the leaves. The results show that the average accuracy (mAP) reached 92.8% for the recognition of cotton top bud by the improved model. Furthermore, the average accuracy was improved by 2.1, 3.3, 2, and 2.4 percentage points, respectively, compared with Fast R-CNN, YOLOv3, YOLOv5s, and YOLOv6. The improved detection model can be expected to accurately and efficiently identify the cotton top bud under a complex environment in real time. The small size of the model parameters and the high identification speed were suitable for the migration deployment of cotton topping machinery, and the operation of the laser topping robot. The finding can also provide technical and theoretical support to the subsequent operation of cotton laser topping machines, particularly for the mechanized real-time topping. [ABSTRACT FROM AUTHOR]

【Objective】The present paper aimed to study the effects of three rice genes(OsGS3,OsGW2 and OsGN1 a) knockout on the field agronomic characters and quality of Jijing 809. 【Method】A randomized block test was used to compare the agronomic traits, quality and yield of the three gene knockout mutants with the wild type. 【Result】Compared with wild type, the tiller number and seed setting rate of mutants were significantly decreased, but the plant height, grain length, grain width and 1000 grain weight were significantly increased, and the yield was increased by more than 12 %. In addition, the change of brown rice rate was not significant, milled rice rate and head milled rice rate were significantly reduced, chalky grain rate and chalkiness degree were significantly increased, which affected the appearance and quality of milled rice. But amylose content, protein content and taste value did not change significantly. 【Conclusion】 The results suggested that three rice genes(OsGS3,OsGW2 and OsGN1 a) knockout could improve some characters and yield of rice, which provided practical evidence for the application of genome directed editing in breeding. [ABSTRACT FROM AUTHOR]

Objective To study the variation of E6 and E7 genes in HPV16 mutant strains in Panxi region of China and their genetic relationship with the worlds major HPV16 mutant lineages. Methods Womens cervical cell samples were collected and genotyped in Affiliated Hospital of Panzhihua University. The genetic sequences of E6 and E7 in HPV16 from 57 specimens were detected and analyzed in a molecular phylogenetic analysis. Results HPV16 variants in Panxi region belong to Asian or European mutant lineage. Among them. 16 types of nucleotide mutations were observed from E6 and E7 genes.including 9 missense mutations and 7 synonymous mutations. The mutation rate of E6 gene was 12.3% (7/57),and all 6 mutations in E6 gene were missense mutations. E7 gene had a mutation rate of 94.7% (54/57) and involved three missense mutations. A86G mutation in E7 gene may significantly change the carcinogenic ability of HPV16. Conclusion In Panxi region,the mutation types of E6 and E7 in HPV16 are a-bundant. All HPV16 mutants in Panxi region belong to European or Asian lineage and have many branches in their molecular phylogenetic tree. [ABSTRACT FROM AUTHOR]

Visibility is one of the important meteorological factors, and low visibility affects the safe and efficient operation of expressway. Visibility calculation method based on video data is similar to the perception of human eyes, and has the adaptability to bad weather conditions or complex observation sites. Based on three weather processes of fog in Anhui province on October 17th, December 1st and 7th of 2016, the visibility calculation model were built by using gray mean square error of corresponding video image and visibility observation data of three highway sites in Anhui province. Results show that the modeled visibility can well reflect the actual visibility corresponding with the change of fog process. The proposed visibility calculation method based on video image is similar to the perception of human eyes, i. e., the higher the visibility, the higher the resolution of terrain features for human eyes, and the greater the variance of the monitoring image as well. However, due to the difference in monitoring principle and the environment properties, error exists between the observation data and the calculated results. Theoretically, the error is low under the conditions of fog with uniform spatial distribution, and the visibility meter being close to the surveillance camera. [ABSTRACT FROM AUTHOR]

目的了解婴儿期下呼吸道感染病原菌分布及耐药性,为临床合理选用抗菌药物提供依据。方法回顾性统计2016年1月至2018年12月该院收治的1516份婴儿期下呼吸道感染者痰标本分离的病原菌种类,并对主要菌种的耐药性进行分析。结果共检出病原菌677株,其中革兰阴性菌564株(83.31%),位于前3位的由高至低依次为流感嗜血杆菌、肺炎克雷伯菌、大肠埃希菌;革兰阳性菌99株(14.62%),分别为金黄色葡萄球菌和肺炎链球菌。流感嗜血杆菌对氨苄西林耐药率大于50.00%,对氨曲南、头孢吡肟、头孢曲松、氯霉素、氧氟沙星耐药率小于10.00%;肺炎克雷伯菌、大肠埃希菌对氨苄西林耐药率大于90.00%,对头孢哌酮/舒巴坦、哌拉西林/他唑巴坦、头孢西丁、阿米卡星、亚胺培南、美罗培南耐药率小于10.00%;金黄色葡萄球菌对青霉素耐药率达98.89%,对红霉素、克林霉素、复方新诺明耐药率大于50.00%,对万古霉素100.00%敏感。结论该院婴儿期下呼吸道感染病原菌以革兰阴性菌为主,细菌耐药较为普遍。临床上应根据婴儿期用药特点及药敏试验结果合理使用抗菌药物。 [ABSTRACT FROM AUTHOR]

Objective To study the base sequence mutations in exons 18-21 of epidermal growth factor receptor (EG­FR) gene in the cancer tissues of patients with non-small-cell lung cancer ( NSCLC). Methods The specimens of cancer tissues from 72 NSCLC patients were preserved during the operation. Another 20 specimens of normal lung tissues were col­lected as the controls. The sequences of EGFR exons 18-21 in the cancer tissues and normal lung tissues were detected by direct sequencing. The EGFR mutations were analyzed. We compared the type of base mutation with NCBIs EGFR clinical gene mutation database ( ClinVar) to determine whether the sample mutation type was a pathological mutation and whether it corresponded to a specific drug response type. In addition， we analyzed the relationship between EGFR gene mutations and clinicopathological features of patients with NSCLC. Results The mutation rates of EGFR exons 18-21 in the NSCLC cancer tissues and normal tissues were 58.3% (42/72) and 0，respectively，with significant difference (P <0. 05). Mutations were found in all 4 exons. Sixty specific mutations were found in the detected exons of EGFR. The mutation types included point mutation，deletion mutation，insertion mutation，and insertion/deletion mutation. The mutation rates of ex­ons 18-21 were 8.3% (6/72) , 29.2% (21/72) , 37.5% (27/72) , and 4.2% (3/72) , respectively. Each exon had specific mutation types. There were 4 , 21 , 9 and 1 point mutations in exons 18-21 , respectively. Other specific mutations included 13 insertion/deletion mutations in exon 19 , 2 deletion and 1 insertion/deletion mutations in exon 20 , and 1 inser­tion mutation in exon 21. There were 6 cancer-associated mutations in this study , including 1 benign variation ( c. 2361G > A) with mutation rate of 31. 9% (23/72)，1 drug-sensitive mutation ( c. 2830T > G) with mutation rate of 2. 8% (2/ 72) , and 4 carcinogenic mutations. The overall rate of cancer-associated mutations was 12. 5% (9/72). The targeted drug-sensitive mutations got an overall rate of 2. 8% (2/72) . The mutation of c. 2573T > G in exon 21 caused Leu858Arg variation, which was a sensitive mutation in EGFR-targeted drug therapy. Conclusion There is a high mutation rate of EGFR exons 18-21 in NSCLC cancer tissues. The main mutation types are point mutation, deletion mutation, insertion mu­tation and insertion ／ deletion mutation. Exon 19 has the most mutations，and the mutations of exon 20 are the most com­plex. Of the 6 cancer-associated mutations，only one targeted drug-sensitive mutation is found in exon 21 . [ABSTRACT FROM AUTHOR]

Safflower silk is one of the most important cash crops in the medical treatment and dye. The manual picking of safflower silk cannot fully meet the large-scale production at present. The mechanized harvesting can be expected to improve the safflower harvesting efficiency and labor cost-saving in the industrial planting. The complex environmental factors have made great difficulty to accurately identify and locate the safflower during mechanical picking, including the natural environment (such as the light and shelter), and the safflower characteristics (such as the small and dense target, as well as the different maturity). In this study, an improved YOLOv7 model was proposed to rapidly and accurately locate the safflower recognition in the complex environment. 1500 safflower images were established to divide into three types of samples, silk, bulb, and decay. The small target was found in the datasets with uneven sample size, especially the data of decay. The safflower sample dataset was produced to build the complex environment data for the real picking. Firstly, the Swin Transformer attention mechanism was added to the YOLOv7 network model, in order to improve the detection accuracy of the model for each classification sample, and the ability of the backbone network, especially to extract the small target features. Secondly, the Focal Loss function of the multi-classification was improved in the recognition rate of unbalanced samples under multi-class tasks, particularly for the target confidence loss and classification loss. The attenuation parameters γ was adjusted to balance the sample. The attenuation parameters were determined after experimental verification. Finally, the safflower detection network model was designed to meet the real-time and accurate detection requirements under the complex environment. The test results show that the best performance of the model was achieved in the attenuation parameters of 1.0, where the position of Swin Transformer was the layer 50. The average precision of the improved model reached 88.5% in each category sample, which was 7.0 percentage points higher than before. The average detection accuracy of the unbalanced category sample decay was 15.9 percentage points higher. Compared with the Faster RCNN model, the detection speed increased by three times, while the average accuracy increased by 9.7 percentage points; Compared with the Deformable DETR model, the detection speed increased by about 5 times, and the average accuracy increased by 16.5 percentage points. The improved model performed the best, in terms of the detection efficiency, detection speed, and model size. The improved model can also be expected to accurately detect the safflower, indicating the smaller parameters and the faster recognition speed suitable for the migration deployment on the safflower picking machinery. The finding can provide the technical support for the mechanized picking in real time. In the future research, the balance between species can be promoted to expand the number of samples from the data level in the harvesting machinery. [ABSTRACT FROM AUTHOR]

Xinmei is one of the European varieties with "paradise medicine fruit", because of its rich medicinal and health care and beauty functions. Today, Xinmei have been widely planted in Kashgar and Yili in Xinjiang Uygur autonomous region, China. Picking robots have also been used to harvest Xinmei in recent years. However, rapid and accurate visual detection is still challenging in the complex environment, such as overlap and occlusion. A large number of Xinmei fruits are blocked by leaves and trunks, due to the small target of Xinmei fruit and the dense branches and leaves. The key feature information of Xinmei cannot be detected for the expensive and high economic value. In addition, the more fruits overlapped, the larger the overlapping area among fruits was. In this study, a detection model was proposed for Xinmei in a complex environment using SFF-YOLOv5s. The dataset of Xinmei was constructed in the real orchard environment. The advanced YOLOv5s model was adopted as the network base. Firstly, the Coordinate Attention mechanism CA (Coordinate Attention) was introduced in the C3 module of the Backbone network, in order to extract the key feature information when the Xinmei was blocked by leaves. The number of parameters was reduced to embed on mobile devices. Secondly, the weighted bidirectional feature pyramid network was introduced into the Neck layer, in order to enhance the fusion among different feature layers of the model. The recognition of the model was promoted on the mutually occluding fruit. SIoU loss function was also used to replace the CIoU loss function in the original model, in order to accelerate the convergence speed for the high accuracy of the model. The test results showed that better performance was achieved, where the accuracy of the SFF-YOLOv5s model was 93.4%, the recall rate was 92.9%, the mean average precision (mAP) was 97.7%, the model weight was only 13.6MB, and the average detection time of a single image was 12.1ms. After the CA attention mechanism was added to the C3 module, the average accuracy of the improved model increased by 0.2 percentage points, while the number of parameters was reduced from 7.02 to 6.41M. With the weighted bidirectional feature pyramid network, the average accuracy of the model reached 97.6%, which was 0.5 percentage points higher than that of the original YOLOv5s. When SIoU was used as the loss function, the accuracy of the model was improved by 2 percentage points, compared with the original. Compared with the Faster R-CNN, YOLOv3, YOLOv4, YOLOv5s, YOLOv7, and YOLOv8s models, the average accuracy (mAP) was improved by 3.6, 6.8, 13.1, 0.6, 0.4 and 0.5 percentage points, respectively. The lowest computation and weight were achieved with the high detection speed. Therefore, the optimal performance of the SFF-YOLOv5s model can fully meet the requirements of real-time detection of Xinmei in the complex orchard environment. The finding can provide technical support to the visual perception of xinmei in picking robots. [ABSTRACT FROM AUTHOR]

现阶段，因部分医务人员医德医风缺失，导致医患关系紧张，影响了医院的形象及发展。该文综合分析了医务人员医德医风缺失的主要原因，包括市场经济影响、医疗服务市场的特殊性、医院管理因素及个人因素等，并提出加强医德医风教育、开展督查、落实奖惩、建立服务基层制度等对策。 [ABSTRACT FROM AUTHOR]

Leaf diseases have a severe threat to the quality and yield of grapes. However, there is often difficulty in the disease detection and low application rate of convolutional neural network (CNN) in a complicated field environment. In this study, an accurate and intelligent detection system was established to realize the strong robustness and real-time performance for grape leaf diseases using transfer learning and an updated CNN model. Firstly, 1990 images were captured for the healthy leaves and five types of infected leaves from field conditions. The combined datasets of PlantVillage and AI Challenger were used to pre-train the VGG16 network for the fully trained parameters. Secondly, the batch normalization, global average pooling layer, and Center Loss function were utilized to modify the structure of the pre-trained VGG16 network, where there was no change in the parameters of the thirteen front convolutional layers and pooling layers. The updated CNN was fine-tuned with the augmented images of grape leaves from field conditions. Stochastic gradient descent (SGD) and adaptive moment estimation (Adam) optimizers were adopted at the initial learning rates of 0.01 and 0.001 in the phase of fine-tuning for experimental comparison. Thirdly, different equilibrium parameters of Center Loss function were utilized in Softmax classification layer for optimal performance. The updated CNN model was also compared with the state-of-the-art models. Finally, the optimal CNN model was deployed in mobile phones to carry out in field condition. The experimental results showed that the updated model using Adam optimizer behaved with a higher accuracy and more stable performance than those using the SGD in the fine-tuning training phase. There were a higher accuracy, a lower loss value, and smaller vibration in the trained model with a small initial learning rate of 0.001 than those with a larger initial learning rate of 0.01, indicating that a smaller learning rate was more reasonable for fine-tuning training. In addition, the accuracy of the model was improved by the equilibrium parameter with a certain range in a Center Loss function. When the equilibrium parameter was set as 0.12, optimal performance of the updated CNN model was achieved at the initial learning rate of 0.001, where the average classification precision was 0.980 0, the recall was 0.980 1, the F1 score was 0.980 1, the average accuracy was 98.02%, and the testing time per image was 0.327 s. The accuracy of updated CNN increased by 2.82%, while the detection time was reduced by 66.8%, and the number of parameters decreased by 83.4%, compared with the original VGG16 network. The comprehensive performance of the updated VGG16 model was also better than that of AlexNet, ResNet50, and Inception v3 models, indicating obvious advantages in the accuracy, weight space occupation, and testing time for the detection of grape leaf diseases. It infers that the Batch normalization layer can speed up the learning process, whereas, the Global average pooling layer without fully connected layers can greatly reduce the number of weight parameters of the model. Center Loss function improved the ability of fine classification. After deployed into smart phone platform, the detection system maintained an accuracy of 95.67% and detection time of 0.357 s per image for the portable and intelligent diagnostics of grape leaf diseases. The transfer learning provided the possibility of quickly acquiring high-performance model under the condition of small datasets. The finding can provide precise guide for the prevention and control of grape diseases in fields. [ABSTRACT FROM AUTHOR]

目的 牙周病病例的种植治疗由于骨量受损和有限的牙间隙而较难种植, 本研究的目的是评估三维计算机虚拟 引导技术在牙周病种植术中的临床应用效果。方法 对 2018 年 8 月至 2019 年 4 月该院收治的16名患有牙周病的 32 个拟种植 部位进行完整的数字化工作流程,通过计算机进入点(肩)和顶点(尖端)的坐标及计划植入物和实际植入物的角度偏差来评估准 确性。结果 用计算机设计虚拟引导技术在牙周病种植中可以很好地植入种植体,角度的偏离值为 (4.53±1.89)°, 根部的偏离 值为 (0.97±0.21)mm, 肩部的偏离值为 (0.85±0.19)mm。结论 三维虚拟引导技术的应用有助于提高骨量不足和牙间隙有限 的牙周病患者的种植精准度。 [ABSTRACT FROM AUTHOR]

The experiment was to explore the feasibility of using the extractive liquid sediment (SXBS) produced in pharmaceutical process of Shengxuebao mixture as a feed additive. The research used UPLC-TOF-MS and other technologies to analyze the small molecule chemical composition and nutritional composition of SXBS, and SXBS was added into diet of New Zealand rabbits for 60 d. The effect of SXBS on growth performance, nutrient apparent digestibility, slaughter performance and intestinal flora of New Zealand rabbits were investigated. The feeding safety of SXBS was reflected by blood routine, blood biochemical analysis and histopathological observation. The results showed that SXBS was rich in nutrients such as carbohydrates, crude fiber, crude protein and crude fat, and contained a lot of active ingredients such as flavonoids, amino acids, terpenes, polyphenols and saponins. SXBS had no effect on growth performance of New Zealand rabbits. SXBS could significantly regulate the bacterial and fungal flora structure of New Zealand rabbits' gut, up-regulate linoleic acid, propionic acid, taurine, galactose and other metabolic functions related to the digestion and absorption of intestinal carbohydrates, and increase the abundance of Zygosaccharomyces ruberii. SXBS had no obvious toxic effect on New Zealand rabbits within 60 d, and the safety was good. The study indicates that SXBS can be used in rabbit breeding as a rabbit feed additive for regulating intestinal microecology. [ABSTRACT FROM AUTHOR]

Objective To analyze the relationship between single nucleotide polymorphisms (SNP) of Notch1 gene rs3124599 and susceptibility to lung cancer in patients with primary lung cancer. Methods A total of 78 patients with primary lung cancer (observation group) and 156 healthy subjects (control group) were enrolled. The morning venous blood of the two groups were taken (2 m L). DNA was extracted by phenol-chloroform extraction and Notch1 gene rs3124599 locus typing was analyzed by PCR-PFLPS technique. The clinical data of the two groups were collected. The correlation between various factors and susceptibility to lung cancer was evaluated by conditional logistic regression analysis. The interaction coefficient (γ) was used to evaluate the interaction between environmental factors and gene polymorphism. Results The distribution frequencies of Notch1 gene rs3124599 genotypes AA,AG,GG in the observation group were 25.6% (20/78),39.7% (31/78),and 34.6% (27/78),which were 37.2% (58/156),44.9% (70/156),and 17.9% (28/156) in the control group; the distribution frequencies of alleles A and G in the observation group were 45.5% (71/156)and 54.5% (85/156),respectively,which were 59.6% (186/312) and 40.4% (126/312) in the control group. The differences of the distribution of genotypes and alleles A and G between the two groups were statistically significant (χ2=6.518,3.987; all P < 0.05). The number of smoking,degree of smoking,and the family history of cancer in the observation group were higher than those in the control group (χ2= 23.890,5.647; P < 0.05). Conditional logistic regression analysis showed that individuals with rs3124599 GG genotype had an increased risk of lung cancer as compared with individuals with rs3124599 AA genotype (corrected OR = 1.596,95% CI: 1.058-2.429). The individuals carrying alleles G gene were 1.193 times more likely to develop lung cancer than those carrying A gene (95% CI: 1.132 to 2.501,P <0.05). The rs3124599 locus AG or GG genotype had positive interaction with smoking (γ = 2.296) and drinking (γ =1.130). When the rs3124599 locus AG/GG genotype was associated with a family history of the tumor,the risk of developing lung cancer increased (OR = 3.751,95% CI = 1.665-18. 034). Compared with rs3124599 genotype AA non-smokers,the risk of lung cancer in smokers or smokers carrying AG/GG increased by 4.495 times and 12.134 times,respectively. Conclusion The mutation frequency of allele G in Notch1 gene rs3124599 is higher in lung cancer patients. The allele G of the Notch1 gene rs3124599 (A/G) may be a risk factor for lung cancer,and it has an interaction with the patient’s family history of cancer and smoking history. [ABSTRACT FROM AUTHOR]