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Background: With the rapid development of power system, oil-immersed transformers are widely used in the substation and distribution system. The faults of oil-immersed transformers are large threat to the power system. Therefore, it is significant that the faults of oil-immersed transformers can be diagnosed accurately. Objective: To accurately diagnose the faults of oil-immersed transformers through machine learning methods and swarm intelligent algorithms. Methods: To accurately diagnose the faults of oil-immersed transformers, a fault diagnosis method based on T-distributed stochastic neighbor embedding and support vector machine is proposed. The improved beetle antennae search algorithm is used to optimize the parameters of support vector machine. Firstly, the non-coding ratio method is used to obtain nine-dimensional characteristic indices. Secondly, the original nine-dimensional data are reduced to three-dimensional by T-distributed stochastic neighbor embedding. Lastly, the data after dimensionality reduction are used as the input of the support vector machine optimized by improved beetle antennae search algorithm and the fault types of transformers can be diagnosed. Results: The accuracy rate is 94.53% and the operation time is about 1.88s. The results indicate that the method proposed by this paper is reasonable. Conclusion: The experimental results show that the method proposed by this paper has a high accuracy rate and low operation time. Mixed faults that are difficult to diagnose also can be diagnosed by this paper's method. In the era of big data, there is a lot of data of transformer, so the method proposed in this paper has certain engineering significance.

When solving multi-objective optimization problems with complex Pareto fronts characteristics, previous work generally ignores the information related to Pareto fronts provided by the population during the evolution, which is detrimental to efficiently tackle them. In order to taking full advantage of information associated with a population evolution, a multi-objective evolutionary optimization method based on online perceiving Pareto front characteristics is proposed in this study. To this end, the information associated with the Pareto front of an optimization problem is first extracted from the population. Following that, the characteristics of the Pareto front in concavity/convexity and continuity are perceived online. For the purpose of each sub-front containing only one characteristic, a Pareto front is divided based on the concavity/convexity and continuity. According to the characteristic of each sub-front, different reference points are selected to refine the distribution of reference vectors. Finally, a multi-objective evolutionary algorithm is designed targeting the characteristics of the Pareto front. The performance of the proposed method is evaluated by comparing it with 8 state-of-the-art optimizers on 31 test problems. Further, the experimental results demonstrate that the proposed method is competitive in handling multi-objective optimization problems with irregular Pareto fronts.

Platelets promote tumor growth and metastasis in several tumor types. Recent research has found platelets can extravasate and infiltrate into the tumor stroma and interact with the tumor microenvironment. The prognostic role of platelet infiltration in colorectal cancer (CRC) remains controversial. A pan-cancer survival analysis was performed to find the potential prognostic value of platelet infiltration in patients with cancer. A survival analysis and a nomogram prognostic model were established to further confirm the results with data from our center. The correlations between patient outcomes and tumor-infiltrating platelets (TIPs) were identified by immunohistochemical staining for CD42b. The prognostic accuracy and discriminative ability of the nomogram were determined by the concordance index (C-index) and a calibration curve. The pan-cancer survival analysis showed platelet infiltration can lead to a poor prognosis in patients with several types of cancers, including CRC. Platelet infiltration was associated with overall survival (OS) and disease-free survival (DFS) in both primary and validation cohorts. The C-index values of the nomogram for predicting OS and DFS were 0.774 and 0.769, respectively, which were higher than that of the TNM staging system alone. Our study found platelet infiltration has a potential prognostic value regarding postsurgical survival in CRC patients. The proposed nomogram resulted in a more accurate prognostic prediction for postsurgical CRC patients.

Pore structure characterization of deep-buried coals and its implications on methane adsorption capacity are of great significance for further understanding the adsorption mechanism of methane mole...

BACKGROUND Neoadjuvant therapy (NAT) is becoming increasingly important in locally advanced rectal cancer. Hence, such research has become a problem. AIM To evaluate the downstaging effect of NAT, its impact on postoperative complications and its prognosis with different medical regimens. METHODS Seventy-seven cases from Shanghai Ruijin Hospital affiliated with Shanghai Jiaotong University School of Medicine were retrospectively collected and divided into the neoadjuvant radiochemotherapy (NRCT) group and the neoadjuvant chemotherapy (NCT) group. The differences between the two groups in tumor regression, postoperative complications, rectal function, disease-free survival, and overall survival were compared using the χ2 test and Kaplan-Meier analysis. RESULTS Baseline data showed no statistical differences between the two groups, whereas the NRCT group had a higher rate of T4 (30/55 vs 5/22, P < 0.05) than the NCT groups. Twelve cases were evaluated as complete responders, and 15 cases were evaluated as tumor regression grade 0. Except for the reduction rate of T stage (NRCT 37/55 vs NCT 9/22, P < 0.05), there was no difference in effectiveness between the two groups. Preoperative radiation was not a risk factor for poor reaction or anastomotic leakage. No significant difference in postoperative complications and disease-free survival between the two groups was observed, although the NRCT group might have better long-term overall survival. CONCLUSION NAT can cause tumor downstaging preoperatively or even complete remission of the primary tumor. Radiochemotherapy could lead to better T downstaging and promising overall survival without more complications.

Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.

Background The surgical procedure for laparoscopic right colectomy (LRC) is not standardized. Some published studies show the superiority of ileocolic anastomosis (IIA), but the evidence so far is insufficient. This study aimed to investigate the potential advantages in postoperative recovery and safety of IIA in LRC. Methods A total of 114 patients who underwent LRC with IIA (n = 58) or extracorporeal ileocolic anastomosis (EIA, n = 56) between January 2019 and September 2021 were enrolled. We collected certain factors as clinical features, intraoperative characteristics, oncological outcomes, postoperative recovery, and short-term outcomes. Our primary outcome was time to gastrointestinal (GI) function recovery. Secondary outcomes were postoperative complications within 30 days, postoperative pain, and length of hospital stay. Results Faster GI recovery and less postoperative pain were observed in patients with IIA compared to EIA [time to first flatus: (2.4 ± 0.7) vs (2.8 ± 1.0) days, p p = 0.01; postoperative visual analogue scale score: (3.9 ± 1.0) vs (4.3 ± 0.6), p = 0.02]. No significant differences were detected in oncological outcomes or postoperative complications. IIA, rather than EIA, tended to be performed in patients with higher body mass index [(23.93 ± 3.52) vs (22.36 ± 2.87) kg/m2, p = 0.01]. Conclusions IIA is associated with faster GI function recovery and less postoperative pain and may be more favorable for obese patients.

Optimal discretization of continuously valued attributes is an uncertainty problem. The uncertainty of discretization is propagated and accumulated in the process of data mining, which has a direct influence on the usability and operation of the output results for mining. To address the limitations of existing discretization evaluation indices in describing accuracy and operation efficiency, this work suggests a discretization uncertainty index based on individuals. This method takes the local standard score as the general similarity measure in and between the intervals and evaluates discretization reliability according to the relative position of individuals in each interval. The experiment shows the new evaluation index is consistent with commonly used metrics. Under the premise of guaranteeing the validity of discrete evaluation, the proposed method has greater description accuracy and operation efficiency than extant approaches; it also has more advantages for massive data processing and special distribution detection.

Building extraction has attracted considerable attention in the field of remote sensing image analysis. Fully convolutional network modelling is a recently developed technique that is capable of si...

Colorectal cancer (CRC) is a worldwide disease with worse survival. Our objective is to identify previously unrecognized prognostic factors to better evaluate disease progression. Seven GEO datasets were collected and analysed using R software, followed by KEGG enrichment analysis and TFs network construction. LASSO-COX analysis was performed to select the most useful prognostic features. COX model was used to analyse prognostic factors associated with OS. The survival curve was constructed using Kaplan-Meier analysis. A Nomogram model was also constructed to predict prognosis. A total of 3559 differentially expressed genes (DEGs) and 66 differentially expressed transcription factors were identified. FOXD1 was identified as the most differentially expressed factor of TFs covering the most downstream DEGs and independent risk prognostic factor. Next, FOXD1 expression was detected using immunohistochemical staining in 131 CRC patients' tissue and the association between FOXD1 expression and clinicopathologic features was analysed. High expression of FOXD1 was correlated with TNM stage and pathological differentiation. Multivariate COX regression analyses confirmed that FOXD1 high-expression, TNM stage and tumour differentiation were independent prognostic risk factor of OS and DFS. Patients with high expression of FOXD1 were more likely to have poor overall survival and disease-free survival. The combination of FOXD1 and Plk2 which we have previously reported allowed us to predict the survival of post-surgical CRC patients more accurately, adding to the former prognostic model based on the TNM Stage. The results showed that patients with high expression of both FOXD1 and Plk2 have the worst survival. A combination of FOXD1 and Plk2 can better evaluate patients' survival.

CXCL5 is overexpressed in colorectal cancer (CRC) and promotes distant metastasis and angiogenesis of tumors; however, the underlying mechanism that mediates CXCL5 overexpression in CRC remains unclear. Here, we successfully extracted and identified primary mesenchymal stromal cells (MSCs) and verified the promoting effects of tumor-associated MSCs on CRC proliferation and metastasis in vivo and in vitro. We found that MSCs not only promoted the expression of CXCL5 by secreting CCL7 but also secreted TGF-β to inhibit this process. After secretion, CCL7/CCR1 activated downstream CBP/P300 to acetylate KLF5 to promote CXCL5 transcription, while TGF-β reversed the effect of KLF5 on transcription activation by regulating SMAD4. Taken together, our results indicate that MSCs in the tumor microenvironment promoted the progression and metastasis of CRC and regulated the expression of CXCL5 in CRC cells by secreting CCL7 and TGF-β. KLF5 is the key site of these processes and plays a dual role in CXCL5 regulation. MSCs and their secreted factors may serve as potential therapeutic targets in the tumor environment.

Human retinal microvascular endothelial cells (HRMECs) injury plays an essential role in the pathogenesis of diabetic retinopathy (DR). As one of the crucial pathogenetic factors, oxidative stress induces HRMECs apoptosis and microvascular lesions. Nuclear factor erythroid 2-related factor 2 (Nrf2) acts as a molecular switch in oxidative stress-induced HRMECs injury. The present study was designed to investigate the protective effect and underlying mechanism of carnosol, a potential Nrf2 agonist, in tert-butyl hydroperoxide (t-BHP) induced HRMECs oxidative stress injury. In this study, carnosol was found to inhibit HRMECs injury induced by t-BHP. Transcriptomics and molecular biology illustrated that the mechanism was associated with oxidative stress, vascular system development, apoptosis, cell cycle, cell adhesion, cytoskeleton, and nitric oxide biosynthesis. Carnosol directly scavenged free radicals or activated the Nrf2 signaling pathway to alleviate HRMECs oxidative stress. ML385 pretreatment or Nrf2 small interference RNA (siRNA) inhibited the protective effect of carnosol on HRMECs injury. Moreover, the apoptosis and cell cycle arrest in HRMECs were suppressed by carnosol. Treatment with carnosol could also effectively regulate the adhesion and cytoskeleton. Overall, our data provide a systematic perspective for the mechanism of carnosol against HRMECs oxidative stress injury and reveal that carnosol may be a candidate drug for DR therapy.

Background. Cervical cancer (CC) is a common gynecological malignant tumor. Ferroptosis is a new type of programmed cell death, which plays a crucial part in cancer. However, current knowledge regarding ferroptosis-related long noncoding RNAs (lncRNAs) in CC is still limited. Therefore, our aim is to identify ferroptosis-related lncRNAs, build a steady prediction model, and improve the prediction value of CC. Methods. We obtained RNA expression and ferroptosis-related gene data of female CC patients from TCGA and FerrDb databases, respectively. Then, the ferroptosis-related lncRNAs were obtained by the limma R package and Cytoscape 3.7.1. We constructed the prediction model by Cox regression analysis. Finally, the prediction model was verified by the median risk score, Kaplan–Meier analysis, the time-dependent receiver operating characteristic (ROC) curve, clinical features, and immunoinfiltration analysis. Results. We acquired 1393 ferroptosis-related lncRNAs. The ferroptosis-related lncRNA signature was obtained by multivariate Cox regression analysis, and the patients were divided into a high-risk group and a low-risk group. The prognosis of the high-risk group was worse than that of the low-risk group. We found that the risk score can be used as an independent prognostic index by multivariate Cox regression analysis. The area under the time-dependent ROC curve reached 0.847 at 1 year, 0.906 at 2 years, 0.807 at 3 years, and 0.724 at 5 years in the training cohort. Principal component analysis showed that the diffusion directions of the two groups were different. Gene set enrichment analysis indicated that lncRNAs of two groups may be involved in tumorigenesis. Further analysis showed that high-risk groups were related to immune-related pathways. Ferroptosis-related lncRNAs are related to the proportion of tumor-infiltrating immune cells in CC. Conclusion. We have constructed a ferroptosis-related lncRNA prediction model. The prognostic model had important clinical significance, including improving the predictive value and guiding the individualized treatment of CC patients.

Water body extraction (WBE) has attracted considerable attention in the field of remote sensing image analysis. Herein, we present an enhanced deep convolutional encoder–decoder (DCED) network (or Deep U-Net) specifically tailored to WBE from remote sensing images by applying superpixel segmentation and conditional random fields (CRFs). First, we preclassify the entire remote sensing image into the water and nonwater areas via Deep U-Net, using the results of class membership probabilities as the unary potential in the CRF model. The pairwise potential of CRF is defined by a linear combination of Gaussian kernels, which forms a fully connected neighbor structure. Next, regional restriction is incorporated into the approach to enhance the consistency of the connected area. We use the simple linear iterative clustering algorithm to generate superpixels and correct the binary classification results by calculating their average posterior probabilities. Finally, a highly efficient approximate inference algorithm, mean-field inference, is generated for the final model. The results from the experimental application to GaoFen-2 images and WorldView-2 images demonstrate that the proposed approach exhibits competitive quantitative and qualitative performance, which effectively reduces salt-and-pepper noise and retains the edge structures of water bodies. Compared to existing state-of-the-art methods, our proposed method achieves superior final results.

In recent years, the application of transmission channel environment on-line monitoring device has played an important role in improving the level of power grid support capability. In order to further optimize the deployment of monitoring devices, it is necessary to establish a sitting method which adequately considers the environmental characteristics. Based on the analysis of the altitude data at transmission line tower location, a sitting method of monitoring device considering terrain characteristics is established. According to altitude data, five terrain categories, including "Uphill", "Downhill", "Top", "Valley" and "Flat", are proposed, and the terrain judgment method of line tower is formed. On this foundation, a sitting method of monitoring device is set up, and the influence of mountainous terrain is considered, typical monitoring blind areas correction is completed. The analysis of calculation results show that this method can focus on mountain coverage on the basis of whole section monitoring at transmission channel, and the density of monitoring device can well match with the terrain. An application of the research can provide guidance for the development of transmission channel monitoring.

Background: Oxaliplatin resistance is a major challenge for treatment of metastatic colorectal cancer (mCRC). Many molecular targeted drugs for refractory CRC have been developed to solve colorectal cancer drug resistance, but their effectiveness and roles in the progression of CRC and oxaliplatin- resistance still not clear.Methods: PDOs derived from CRC patients were constructed to conduct the sensitivity assays of oxaliplatin in vitro. Oxaliplatin resistant PDOs were selected and treated under the combined treatment of ML264(a KLF5 inhibitor) and oxaliplatin to determine the effects of KLF5 inhibition on apoptosis. Using CRC cell lines to investigate downstream mechanisms and xenograft models to confirm whether ML264 can restore oxaliplatin sensitivity of CRC cells in vivo.Results: We successfully constructed CRC PDOs and conducted the sensitivity test of oxaliplatin in PDOs from different patients. We found that ML264 restores oxaliplatin sensitivity in CRC PDOs by restoring the apoptotic response, and this effect was achieved by inhibiting the KLF5/Bcl-2/caspase3 signal pathway. Chromatin immunoprecipitation (ChIP) and luciferase reporter assay verified that KLF5 promoted the transcription of Bcl-2 in CRC cells. KLF5 inhibition also overcomed oxaliplatin resistance in xenograft tumors.Conclusions: Our study demonstrated that ML264 can restores oxaliplatin sensitivity in CRC PDOs by restoring the apoptotic response. KLF5 might be a potential therapeutic target for CRC resistant to oxaliplatin. PDOs have strong potential in evaluating inhibitors and drug combinations therapy in a preclinical environment.

This article presents a novel change detection (CD) approach for high-resolution remote-sensing images, which incorporates visual saliency and random forest (RF). First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis. Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for super-pixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, super-pixel-based CD is implemented by applying RF based on these samples. Experimental results on Quickbird, Ziyuan 3 (ZY3), and Gaofen 2 (GF2) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.