Cross-level Attention with Overlapped Windows for Camouflaged Object Detection

Camouflaged objects adaptively fit their color and texture with the environment, which makes them indistinguishable from the surroundings. Current methods revealed that high-level semantic features can highlight the differences between camouflaged objects and the backgrounds. Consequently, they integrate high-level semantic features with low-level detailed features for accurate camouflaged object detection (COD). Unlike previous designs for multi-level feature fusion, we state that enhancing low-level features is more impending for COD. In this paper, we propose an overlapped window cross-level attention (OWinCA) to achieve the low-level feature enhancement guided by the highest-level features. By sliding an aligned window pair on both the highest- and low-level feature maps, the high-level semantics are explicitly integrated into the low-level details via cross-level attention. Additionally, it employs an overlapped window partition strategy to alleviate the incoherence among windows, which prevents the loss of global information. These adoptions enable the proposed OWinCA to enhance low-level features by promoting the separability of camouflaged objects. The associated proposed OWinCANet fuses these enhanced multi-level features by simple convolution operation to achieve the final COD. Experiments conducted on three large-scale COD datasets demonstrate that our OWinCANet significantly surpasses the current state-of-the-art COD methods

The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD:Multitemporal semantic change detection

We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest

Illustrating the biological functions and diagnostic value of transmembrane protein family members in glioma

BackgroundIt is well-established that patients with glioma have a poor prognosis. Although the past few decades have witnessed unprecedented medical advances, the 5-year survival remains dismally low.ObjectiveThis study aims to investigate the role of transmembrane protein-related genes in the development and prognosis of glioma and provide new insights into the pathogenesis of the diseaseMethodsThe datasets of glioma patients, including RNA sequencing data and relative clinical information, were obtained from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Prognostic transmembrane protein-related genes were identified by univariate Cox analysis. New disease subtypes were recognized based on the consensus clustering method, and their biological uniqueness was verified via various algorithms. The prognosis signature was constructed using the LASSO-Cox regression model, and its predictive power was validated in external datasets by receiver operating characteristic (ROC) curve analysis. An independent prognostic analysis was conducted to evaluate whether the signature could be considered a prognostic factor independent of other variables. A nomogram was constructed in conjunction with traditional clinical variables. The concordance index (C-index) and Decision Curve Analysis (DCA) were used to assess the net clinical benefit of the signature over traditional clinical variables. Seven different softwares were used to compare the differences in immune infiltration between the high- and low-risk groups to explore potential mechanisms of glioma development and prognosis. Hub genes were found using the random forest method, and their expression was based on multiple single-cell datasets.ResultsFour molecular subtypes were identified, among which the C1 group had the worst prognosis. Principal Component Analysis (PCA) results and heatmaps indicated that prognosis-related transmembrane protein genes exhibited differential expression in all four groups. Besides, the microenvironment of the four groups exhibited significant heterogeneity. The 6 gene-based signatures could predict the 1-, 2-, and 3-year overall survival (OS) of glioma patients. The signature could be used as an independent prognosis factor of glioma OS and was superior to traditional clinical variables. More immune cells were infiltrated in the high-risk group, suggesting immune escape. According to our signature, many genes were associated with the content of immune cells, which revealed that transmembrane protein-related genes might influence the development and prognosis of glioma by regulating the immune microenvironment. TMEM158 was identified as the most important gene using the random forest method. The single-cell datasets consistently showed that TMEM158 was expressed in multiple malignant cells.ConclusionThe expression of transmembrane protein-related genes is closely related to the immune status and prognosis of glioma patients by regulating tumor progression in various ways. The interaction between transmembrane protein-related genes and immunity during glioma development lays the groundwork for future studies on the molecular mechanism and targeted therapy of glioma

When China strikes: Quantifying Australian companies' stock price responses to China's trade restrictions

In early 2020, China, Australia's top export market, unilaterally imposed trade restrictions on Australian barley, beef, coal, cotton, timber, copper and wine. However, convincing evidence regarding the effects of such trade restrictions on firms is scarce. Leveraging data on daily stock returns from 20 listed Australian and 32 listed Chinese firms that produce the restricted commodities, we provide the first systematic analysis of the firm-level economic impacts of China's trade restrictions on Australian and Chinese firms. We find significant adverse effects on Australian firms' stock returns, leading to almost 20% loss within 10 trading days; however, most firms' stock returns immediately rebounded. In contrast, Chinese firms usually saw significant positive stock returns, leading to almost 30% gains, and the positive abnormal returns continuously increased within 10 trading days. Media coverage and trade dependence substantially impact Australian and Chinese firms' stock returns—industries with stronger trade dependence on China saw greater losses in Australian firms' stock returns. Our results suggest that trade reallocation and deflection are two effective mitigation mechanisms for Australian exporters facing China's trade restrictions

Global path planning of wheeled robots using multi-objective memetic algorithms

Global path planning is a fundamental problem of mobile robotics. The majority of global path planning methods are designed to find a collision-free path from a start location to a target location while optimizing one or more objectives like path length, smoothness, and safety at a time. It is noted that providing multiple tradeoff path solutions of different objectives is much more beneficial to the user's choice than giving a single optimal solution in terms of some specific criterion. This paper proposes a global path planning of wheeled robots using multi-objective memetic algorithms (MOMAs). Particularly, two MOMAs are implemented based on conventional multi-objective genetic algorithms with elitist non-dominated sorting and decomposition strategies respectively to optimize the path length and smoothness simultaneously. Novel path encoding scheme, path refinement, and specific evolutionary operators are designed and introduced to the MOMAs to enhance the search ability of the algorithms as well as guarantee the safety of the candidate paths obtained in complex environments. Experimental results on both simulated and real environments show that the proposed MOMAs are efficient in planning a set of valid tradeoff paths in complex environments

A protocol for generating induced T cells by reprogramming B cells in vivo

Obtaining T cells by reprogramming is one of the major goals in regenerative medicine. Here, we describe a protocol for generating functional T cells from Hoxb5-expressing pro/pre-B cells in vivo. This protocol includes the construction of Hoxb5 recombinant plasmids, retroviral packaging, isolation and viral transduction of pro/pre-B cells, cell transplantation, and phenotypic analysis of induced T cells. The procedure is reproducible and straightforward, providing an approach for generating induced T cells for translational research. Keywords: Hoxb5, Retrovirus, Pro/pre-B cells, T cell

lncRNA-SNHG14 Plays a Role in Acute Lung Injury Induced by Lipopolysaccharide through Regulating Autophagy via miR-223-3p/Foxo3a

lncRNAs play important roles in lipopolysaccharide- (LPS-) induced acute lung injury. But the mechanism still needs further research. In the present study, we investigate the functional role of the lncRNA-SNHG14/miR-223-3p/Foxo3a pathway in LPS-induced ALI and tried to confirm its regulatory effect on autophagy. Transcriptomic profile changes were identified by RNA-seq in LPS-treated alveolar type II epithelial cells. The expression changes of lncRNA-SNHG14/miR-223-3p/Foxo3a were confirmed using qRT-PCR and west blot. The binding relationship of lncRNA-SNHG14/miR-223-3p/and miR-223-3p/Foxo3a was verified using dual-luciferase reporter, RNA immunoprecipitation, and RNA pull-down assays. Using gain-of-function or loss-of-function approaches, the effect of lncRNA-SNHG14/miR-223-3p/Foxo3a was investigated in LPS-induced acute lung injury mice model and in vitro. Increasing of lncRNA-SNHG14 and Foxo3a with reducing miR-223-3p was found in LPS-treated A549 cells and lung tissue collected from the LPS-induced ALI model. lncRNA-SNHG14 inhibited miR-223-3p but promoted Foxo3a expression as a ceRNA. Artificially changes of lncRNA-SNHG14/miR-223-3p/Foxo3a pathway promoted or protected cell injury from LPS in vivo and in vitro. Autophagy activity could be influenced by lncRNA-SNHG14/miR-223-3p/Foxo3a pathway in cells with or without LPS treatment. In conclusion, aberrant expression changes of lncRNA-SNHG14 participated alveolar type II epithelial cell injury and acute lung injury induced by LPS through regulating autophagy. One underlying mechanism is that lncRNA-SNHG14 regulated autophagy by controlling miR-223-3p/Foxo3a as a ceRNA. It suggested that lncRNA-SNHG14 may serve as a potential therapeutic target for patients with sepsis-induced ALI