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"Ren, Xiang"
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Reimagining local worlds: Wen village conservation and regeneration by Amateur Architecture Studio
This article theorises the local world as a conceptual scaffold for future conservation and regeneration. It aims to catalyse a theoretical dialogue across the East and West to understand how ordinary places and lifeworlds are preserved, reproduced, and possibly reimagined. The local worlds discussed herein are conceptualised as the worlds of many, the worlds of relating, the worlds of structuring and the worlds of becoming. These four cardinal points present a source of open-endedness and futurity for contemporary architectural reinterpretation. The current article examines the characterisation of the Wen village conservation and regeneration project, led by the Amateur Architecture Studio in Zhejiang Province of China (2012–2016), as an architectural reimagination of local worlds, with a strong sensitivity to the ordinary places and lifeworlds of rural China and juxtapositions of new local worlds within old worlds. By piecing together the flows and fragments of the architectural process, this article shows how locally situated designs and dynamics have shaped the project in both its formation and afterlife, both ethically and contemporarily. Using close readings of popular and oral accounts from both architect’s and users’ perspectives, this article extends the case study by broadening theoretical conversations about contemporary architecture’s capacity to reimagine local worlds.
Journal Article
Fault diagnosis of power transformers using graph convolutional network
Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy.
Journal Article
The circular RNA circ-ERBIN promotes growth and metastasis of colorectal cancer by miR-125a-5p and miR-138-5p/4EBP-1 mediated cap-independent HIF-1α translation
Background
Circular RNA (circRNAs) and hypoxia have been found to play the key roles in the pathogenesis and progression of cancer including colorectal cancer (CRC). However, the expressions and functions of the specific circRNAs in regulating hypoxia-involved CRC metastasis, and the circRNAs that are relevant to regulate HIF-1α levels in CRC remain elusive.
Methods
qRT-PCR was used to detect the expression of circRNAs and mRNA in CRC cells and tissues. Fluorescence in situ hybridization (FISH) was used to analyze the location of circ-ERBIN. Function-based experiments were performed using circ-ERBIN overexpression and knockdown cell lines in vitro and in vivo, including CCK8, colony formation, EdU assay, transwell, tumor growth and metastasis models. Mechanistically, luciferase reporter assay, western blots and immunohistochemical stainings were performed.
Results
Circ-Erbin was highly expressed in the CRC cells and Circ-Erbin overexpression facilitated the proliferation, migration and metastasis of CRC in vitro and in vivo. Notably, circ-Erbin overexpression significantly promoted angiogenesis by increasing the expression of hypoxia induced factor (HIF-1α) in CRC. Mechanistically, circ-Erbin accelerated a cap-independent protein translation of HIF-1α in CRC cells as the sponges of miR-125a-5p and miR-138-5p, which synergistically targeted eukaryotic translation initiation factor 4E binding protein 1(4EBP-1).
Conclusions
Our findings uncover a key mechanism for circ-Erbin mediated HIF-1α activation by miR-125a-5p-5p/miR-138-5p/4EBP-1 axis and circ-ERBIN is a potential target for CRC treatment.
Journal Article
Enhanced Phosphate Capture by Thermally Modified Calcium Aluminate Decahydrate: Optimization, Performance and Mechanism
Adsorption is a promising technology for phosphate removal to alleviate eutrophication. In this study, thermally modified calcium aluminate decahydrate (TCAH) was prepared via low-temperature thermal treatment of calcium aluminate decahydrate (CAH
) to develop a cost-effective and high-performance phosphate adsorbent. The optimal modification temperature was determined to be 120 °C, which reduced the crystallinity of CAH
, enhanced its porosity, and induced the formation of amorphous calcium aluminate phases. Batch adsorption experiments showed that TCAH exhibited a maximum adsorption capacity of 199.80 mg P/g at 25 °C. The adsorption kinetics followed the pseudo-second-order model, while the adsorption isotherms were well fitted by the Redlich-Peterson model. TCAH maintained high removal efficiency over a wide pH range of 3.0-11.0 and showed high selectivity against common coexisting anions. Characterizations using SEM-EDS, XRD, FTIR and XPS suggested that phosphate removal by TCAH was dominated by synergistic amorphous precipitation and inner-sphere complexation. In tests with real phosphorus-releasing liquor derived from excess sludge, TCAH achieved nearly complete phosphate removal at a dosage of 5 g/L within 6 h. Owing to its readily available raw materials, low preparation temperature, and outstanding phosphate capture performance, TCAH is a promising candidate for efficient phosphate capture and recovery from wastewater.
Journal Article
Prediction of Ubiquitination Sites by Using the Composition of k-Spaced Amino Acid Pairs
As one of the most important reversible protein post-translation modifications, ubiquitination has been reported to be involved in lots of biological processes and closely implicated with various diseases. To fully decipher the molecular mechanisms of ubiquitination-related biological processes, an initial but crucial step is the recognition of ubiquitylated substrates and the corresponding ubiquitination sites. Here, a new bioinformatics tool named CKSAAP_UbSite was developed to predict ubiquitination sites from protein sequences. With the assistance of Support Vector Machine (SVM), the highlight of CKSAAP_UbSite is to employ the composition of k-spaced amino acid pairs surrounding a query site (i.e. any lysine in a query sequence) as input. When trained and tested in the dataset of yeast ubiquitination sites (Radivojac et al, Proteins, 2010, 78: 365-380), a 100-fold cross-validation on a 1∶1 ratio of positive and negative samples revealed that the accuracy and MCC of CKSAAP_UbSite reached 73.40% and 0.4694, respectively. The proposed CKSAAP_UbSite has also been intensively benchmarked to exhibit better performance than some existing predictors, suggesting that it can be served as a useful tool to the community. Currently, CKSAAP_UbSite is freely accessible at http://protein.cau.edu.cn/cksaap_ubsite/. Moreover, we also found that the sequence patterns around ubiquitination sites are not conserved across different species. To ensure a reasonable prediction performance, the application of the current CKSAAP_UbSite should be limited to the proteome of yeast.
Journal Article
Visible-Thermal Image Object Detection via the Combination of Illumination Conditions and Temperature Information
Object detection plays an important role in autonomous driving, disaster rescue, robot navigation, intelligent video surveillance, and many other fields. Nonetheless, visible images are poor under weak illumination conditions, and thermal infrared images are noisy and have low resolution. Consequently, neither of these two data sources yields satisfactory results when used alone. While some scholars have combined visible and thermal images for object detection, most did not consider the illumination conditions and the different contributions of diverse data sources to the results. In addition, few studies have made use of the temperature characteristics of thermal images. Therefore, in the present study, visible and thermal images are utilized as the dataset, and RetinaNet is used as the baseline to fuse features from different data sources for object detection. Moreover, a dynamic weight fusion method, which is based on channel attention according to different illumination conditions, is used in the fusion component, and the channel attention and a priori temperature mask (CAPTM) module is proposed; the CAPTM can be applied to a deep learning network as a priori knowledge and maximizes the advantage of temperature information from thermal images. The main innovations of the present research include the following: (1) the consideration of different illumination conditions and the use of different fusion parameters for different conditions in the feature fusion of visible and thermal images; (2) the dynamic fusion of different data sources in the feature fusion of visible and thermal images; (3) the use of temperature information as a priori knowledge (CAPTM) in feature extraction. To a certain extent, the proposed methods improve the accuracy of object detection at night or under other weak illumination conditions and with a single data source. Compared with the state-of-the-art (SOTA) method, the proposed method is found to achieve superior detection accuracy with an overall mean average precision (mAP) improvement of 0.69%, including an AP improvement of 2.55% for the detection of the Person category. The results demonstrate the effectiveness of the research methods for object detection, especially temperature information-rich object detection.
Journal Article