Msb r‐cnn: A multi‐stage balanced defect detection network

Deep learning networks are applied for defect detection, among which Cascade R‐CNN is a multi‐stage object detection network and is state of the art in terms of accuracy and efficiency. However, it is still a challenge for Cascade R‐CNN to deal with complex and diverse defects, as the widely varied shapes of defects lead to inefficiency for the traditional convolution filter to extract features. Additionally, the imbalance in features, losses and samples cause lower accuracy. To address the above challenges, this paper proposes a multi‐stage balanced R‐CNN (MSB R‐CNN) for defect detection based on Cascade R‐CNN. Firstly, deformable convolution is adopted in different stages of the backbone network to improve its adaptability to the varying shapes of the defect. Then, the features obtained by the backbone network are refined and enhanced by the balanced feature pyramid. To overcome the imbalance of classification and regression loss, the balanced L1 loss is applied at different stages to correct it. Finally, for the sample selection, the interaction of union (IoU) balanced sampler and the online hard example mining (OHEM) sampler are combined at different stages to make the sampling more reasonable, which can bring a better accuracy and convergence effect to the model. The results of our experiments on the DAGM2007 dataset has shown that our network (MSB R‐CNN) can achieve a mean average precision (mAP) of 67.5%, an increase of 1.5% mAP, compared to Cascade R‐CNN

Clonal evolution in liver cancer at single-cell and single-variant resolution.

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship

Small and Medium-Scale River Flood Controls in Highly Urbanized Areas: A Whole Region Perspective

While rapid urbanization promotes social and economic development, it poses a serious threat to the health of rivers, especially the small and medium-scale rivers. Flood control for small and medium-scale rivers in highly urbanized areas is particularly important. The purpose of this study is to explore the most effective flood control strategy for small and medium-scale rivers in highly urbanized areas. MIKE 11 and MIKE 21 were coupled with MIKE FLOOD model to simulate flooding with the flood control standard, after which the best flooding control scheme was determined from a whole region perspective (both the mainstream and tributary conditions were considered). The SheGong River basin located near the Guangzhou Baiyun international airport in Guangzhou city over south China was selected for the case study. The results showed that the flooding area in the basin of interest accounts for 42% of the total, with maximum inundation depth up to 0.93 m under the 20-year return period of the designed flood. The flood-prone areas are the midstream and downstream where urbanization is high; however the downstream of the adjacent TieShan River is still able to bear more flooding. Therefore, the probable cost-effective flood control scheme is to construct two new tributaries transferring floodwater in the mid- and downstream of the SheGong River into the downstream of the TieShan River. This infers that flood control for small and medium-scale rivers in highly urbanized areas should not simply consider tributary flood regimes but, rather, involve both tributary and mainstream flood characters from a whole region perspective

Preliminary X-ray crystallographic analysis of a nitric oxide-inducible lactate dehydrogenase from Staphylococcus aureus

A nitric oxide-inducible lactate dehydrogenase from S. aureus, Sa-LDH-1, has been cloned, expressed, purified and crystallized in space group C2, with unit-cell parameters a = 131.4, b = 74.4, c = 103.2 Å, β = 133.4°

Automatic echocardiographic evaluation of the probability of pulmonary hypertension using machine learning

Abstract Echocardiography, a simple and noninvasive tool, is the first choice for screening pulmonary hypertension (PH). However, accurate assessment of PH, incorporating both the pulmonary artery pressures and additional signs for PH remained unsatisfied. Thus, this study aimed to develop a machine learning (ML) model that can automatically evaluate the probability of PH. This cohort included data from 346 (275 for training set and internal validation set and 71 for external validation set) patients with suspected PH patients and receiving right heart catheterization. Echocardiographic images on parasternal short axis‐papillary muscle level (PSAX‐PML) view from all patients were collected, labeled, and preprocessed. Local features from each image were extracted and subsequently integrated to build a ML model. By adjusting the parameters of the model, the model with the best prediction effect is finally constructed. We used receiver‐operating characteristic analysis to evaluate model performance and compared the ML model with the traditional methods. The accuracy of the ML model for diagnosis of PH was significantly higher than the traditional method (0.945 vs. 0.892, p = 0.027 [area under the curve [AUC]]). Similar findings were observed in subgroup analysis and validated in the external validation set (AUC = 0.950 [95% CI: 0.897−1.000]). In summary, ML methods could automatically extract features from traditional PSAX‐PML view and automatically assess the probability of PH, which were found to outperform traditional echocardiographic assessments