Automatic gauge detection via geometric fitting for safety inspection

For safety considerations in electrical substations, the inspection robots are recently deployed to monitor important devices and instruments with the presence of skilled technicians in the high-voltage environments. The captured images are transmitted to a data station and are usually analyzed manually. Toward automatic analysis, a common task is to detect gauges from captured images. This paper proposes a gauge detection algorithm based on the methodology of geometric fitting. We first use the Sobel filters to extract edges which usually contain the shapes of gauges. Then, we propose to use line fitting under the framework of random sample consensus (RANSAC) to remove straight lines that do not belong to gauges. Finally, the RANSAC ellipse fitting is proposed to find most fitted ellipse from the remaining edge points. The experimental results on a real-world dataset captured by the GuoZi Robotics demonstrate that our algorithm provides more accurate gauge detection results than several existing methods

PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification

Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods. Our code is available at https://github.com/keepgoingjkg/PatchCT.Comment: accepted by ICCV2

Study on Urban Landscape Green Space -- A Case Study of Longquanshan Park

Studying on Qinglongshan Forest Park in Wuhan by typical plot method, 15 typical plots in the park were investigated, and the species richness index, diversity index, evenness index, frequency and important value index were calculated. The results showed that there were 139 species of woody plants belonging to 46 families and 97 genera, including 41 species of evergreen trees, 32 species of deciduous trees, 62 species of evergreen shrubs, 25 species of deciduous shrubs, and 4 species of bamboo belonging to 1 family, 3 genera. Species richness index and Simpson diversity index were all expressed as tree layer > shrub layer, evergreen species > deciduous species. Pielou evenness was tree layer > shrub layer. This study can provide some reference for understanding the existing plant status of forest parks, carrying out science popularization, scientific research, protection and improvement of forest landscape quality

Twisted Epithelial-to-Mesenchymal Transition Promotes Progression of Surviving Bladder Cancer T24 Cells with hTERT-Dysfunction

Human cancer cells maintain telomeres to protect cells from senescence through telomerase activity (TA) or alternative lengthening of telomeres (ALT) in different cell types. Moreover, cellular senescence can be bypassed by Epithelial-to-mesenchymal transition (EMT) during cancer progression in diverse solid tumors. However, it has not been elucidated the characteristics of telomere maintenance and progression ability after long-term culture in bladder cancer T24 cells with hTERT dysfunction.In this study, by using a dominant negative mutant human telomerase reverse transcriptase (hTERT) vector to inhibit TA in bladder cancer T24 cells, we observed the appearance of long phenotype of telomere length and the ALT-associated PML body (APB) complex after the 27(th) passage, indicating the occurrence of ALT-like pathway in surviving T24/DN868A cells with telomerase inhibition. Meanwhile, telomerase inhibition resulted in significant EMT as shown by change in cellular morphology concomitant with variation of EMT markers. Consistently, the surviving T24/DN868A cells showed increased progression ability in vitro and in vivo. In addition, we found Twist was activated to mediate EMT in surviving T24/DN868A samples.Taken together, our findings indicate that bladder cancer T24 cells may undergo the telomerase-to-ALT-like conversion and promote cancer progression at advanced stages through promoting EMT, thus providing novel possible insight into the mechanism of resistance to telomerase inhibitors in cancer treatment

Spatial variation of energy efficiency based on a Super-Slack-Based Measure: Evidence from 104 resource-based cities

Energy efficiency is tied to energy activities and environmental effects and serves as a useful tool for sustainability analysis. Few insights have been acquired for sustainability development from resource-based cities in developed or developing countries. A Super-Slack-Based Measure (Super-SBM) with undesirable outputs is established to account for the total-factor energy efficiency from an energy-economy-environment perspective. Using China as a case study, the spatial variation in energy efficiency from 104 resource-based cities is analysed, furthermore, the results are compared with a scenario that does not consider environmental constraints. Finally, resource-based cities are classified into three categories through K-means clustering technology: high-efficiency region, medium-efficiency region and low-efficiency region. The investigation results show the following: (1) Efficiency disparities exist in resource-based cities under different scenarios, as a whole, the energy efficiency in the scenario two considering by-products of energy activities is obviously lower, which can more truly represent the sustainability of resource-based cities. (2) Most resource-based cities are in low-efficiency zones with substantial room for improvement. Spatial agglomeration effect or spatial spillover effect appears in a few cities. (3) Urban development in developing countries may follow the full life cycle process of local resources. A total of 262 resource-based cities could be roughly categorized into four types. The energy efficiency of growing type is the highest, followed by grow-up type, recessionary type, and regenerative type. (4) The ordering of efficiency in resource-based city is as follows: oil and gas-based > multiple minerals-based > non-metallic-based > nonferrous metal-based > coal-based > forestry-based > ferrous metal-based. The discussion offered in this study for various types of resource-based cities could provide a reference for other cities or developing countries which are in similar industrialization phases and hope for sustainable development

Clinical determinants of the severity of COVID-19: A systematic review and meta-analysis.

ObjectiveWe aimed to systematically identify the possible risk factors responsible for severe cases.MethodsWe searched PubMed, Embase, Web of science and Cochrane Library for epidemiological studies of confirmed COVID-19, which include information about clinical characteristics and severity of patients' disease. We analyzed the potential associations between clinical characteristics and severe cases.ResultsWe identified a total of 41 eligible studies including 21060 patients with COVID-19. Severe cases were potentially associated with advanced age (Standard Mean Difference (SMD) = 1.73, 95% CI: 1.34-2.12), male gender (Odds Ratio (OR) = 1.51, 95% CI:1.33-1.71), obesity (OR = 1.89, 95% CI: 1.44-2.46), history of smoking (OR = 1.40, 95% CI:1.06-1.85), hypertension (OR = 2.42, 95% CI: 2.03-2.88), diabetes (OR = 2.40, 95% CI: 1.98-2.91), coronary heart disease (OR: 2.87, 95% CI: 2.22-3.71), chronic kidney disease (CKD) (OR = 2.97, 95% CI: 1.63-5.41), cerebrovascular disease (OR = 2.47, 95% CI: 1.54-3.97), chronic obstructive pulmonary disease (COPD) (OR = 2.88, 95% CI: 1.89-4.38), malignancy (OR = 2.60, 95% CI: 2.00-3.40), and chronic liver disease (OR = 1.51, 95% CI: 1.06-2.17). Acute respiratory distress syndrome (ARDS) (OR = 39.59, 95% CI: 19.99-78.41), shock (OR = 21.50, 95% CI: 10.49-44.06) and acute kidney injury (AKI) (OR = 8.84, 95% CI: 4.34-18.00) were most likely to prevent recovery. In summary, patients with severe conditions had a higher rate of comorbidities and complications than patients with non-severe conditions.ConclusionPatients who were male, with advanced age, obesity, a history of smoking, hypertension, diabetes, malignancy, coronary heart disease, hypertension, chronic liver disease, COPD, or CKD are more likely to develop severe COVID-19 symptoms. ARDS, shock and AKI were thought to be the main hinderances to recovery

HSIC_<i>CR</i>: A Lightweight Scoring Criterion Based on Measuring the Degree of Causality for the Detection of SNP Interactions

Recently, research on detecting SNP interactions has attracted considerable attention, which is of great significance for exploring complex diseases. The formulation of effective swarm intelligence optimization algorithms is a primary resolution to this issue. To achieve this goal, an important problem needs to be solved in advance; that is, designing and selecting lightweight scoring criteria that can be calculated in O(m) time and can accurately estimate the degree of association between SNP combinations and disease status. In this study, we propose a high-accuracy scoring criterion (HSICCR) by measuring the degree of causality dedicated to assessing the degree. First, we approximate two kinds of dependencies according to the structural equation of the causal relationship between epistasis SNP combination and disease status. Then, inspired by these dependencies, we put forward this scoring criterion that integrates a widely used method of measuring statistical dependencies based on kernel functions (HSIC). However, the computing time complexity of HSIC is O(m2), which is too costly to be an integral part of the scoring criterion. Since the sizes of the sample space of the disease status, SNP loci and SNP combination are small enough, we propose an efficient method of computing HSIC for variables with a small sample in O(m) time. Eventually, HSICCR can be computed in O(m) time in practice. Finally, we compared HSICCR with five representative high-accuracy scoring criteria that detect SNP interactions for 49 simulation disease models. The experimental results show that the accuracy of our proposed scoring criterion is, overall, state-of-the-art

Expression and clinical relations of protein tyrosine phosphatase receptor type S in esophageal squamous cell carcinoma

Protein tyrosine phosphatase receptor type S is a tumor suppressor gene, located at chromosome 19p13.3, frequently inactivated through deletions or epigenetic mechanisms in many types of cancers. In this study, we investigate protein tyrosine phosphatase receptor S (PTPRS) expression level, clinicopathological and prognostic significance in 205 cases of esophageal squamous cell carcinoma (ESCC). Paraffin embedded tissue with immunohistochemistry methods was adopted to exam PTPRS expression in ESCC and paired normal esophageal mucosa tissues on Tissue Microarrays (TMAs). The protein tyrosine phosphatase receptor S was significantly down-regulated in ESCC (58.0%) relative to normal tissues (43.9%) (P=0.006). Statistical analysis revealed that reduced PTPRS expression was significantly associated with TNM stage (P=0.013), invasion depth (P<0.001), local lymph node metastasis (P=0.042) and tumor differentiation (P=0.001). Furthermore, Kaplan-Meier survival analysis revealed that low expression of PTPRS significantly correlated with poor survival of ESCC patients (P=0.002). Cox regression analysis confirmed PTPRS expression as an independent predictor of the overall survival of ESCC patients (HR=1.573, P=0.049). The 5-year overall survival rates in patients with high and low PTPRS expression were 50.6% and 37.2%, respectively. PTPRS deficiency is independently associated with shorter survival and increased recurrence in patients. Our data offer convincing evidence that loss of PTPRS expression may predict an aggressive clinical course in ESCC patients. PTPRS may function as a tumor suppressor and play an important role in ESCC growth and metastasis

The prenatal diagnostic indicators of placenta accreta spectrum disorders

Placenta accreta spectrum (PAS) disorders refers to a heterogeneous group of anomalies distinguished by abnormal adhesion or invasion of chorionic villi through the myometrium and uterine serosa. PAS frequently results in life-threatening complications, including postpartum hemorrhage and hysterotomy. The incidence of PAS has increased recently as a result of rising cesarean section rates. Consequently, prenatal screening for PAS is essential. Despite the need to increase specificity, ultrasound is still considered a primary adjunct. Given the dangers and adverse effects of PAS, it is necessary to identify pertinent markers and validate indicators to improve prenatal diagnosis. This article summarizes the predictors regarding biomarkers, ultrasound indicators, and magnetic resonance imaging (MRI) features. In addition, we discuss the effectiveness of joint diagnosis and the most recent research on PAS. In particular, we focus on (a) posterior placental implantation and (b) accreta after in vitro fertilization-embryo transfer, both of which have low diagnostic rates. At last, we graphically display the prenatal diagnostic indicators and each diagnostic performance