CasGCN: Predicting future cascade growth based on information diffusion graph

Sudden bursts of information cascades can lead to unexpected consequences such as extreme opinions, changes in fashion trends, and uncontrollable spread of rumors. It has become an important problem on how to effectively predict a cascade' size in the future, especially for large-scale cascades on social media platforms such as Twitter and Weibo. However, existing methods are insufficient in dealing with this challenging prediction problem. Conventional methods heavily rely on either hand crafted features or unrealistic assumptions. End-to-end deep learning models, such as recurrent neural networks, are not suitable to work with graphical inputs directly and cannot handle structural information that is embedded in the cascade graphs. In this paper, we propose a novel deep learning architecture for cascade growth prediction, called CasGCN, which employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism on both the extracted features and the temporal information before conducting cascade size prediction. We conduct experiments on two real-world cascade growth prediction scenarios (i.e., retweet popularity on Sina Weibo and academic paper citations on DBLP), with the experimental results showing that CasGCN enjoys a superior performance over several baseline methods, particularly when the cascades are of large scale

Coverage-guided testing for recurrent neural networks

Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerability to input perturbation is also known. Aligning with a view from software defect detection, this article aims to develop a coverage-guided testing approach to systematically exploit the internal behavior of RNNs, with the expectation that such testing can detect defects with high possibility. Technically, the long short-term memory network (LSTM), a major class of RNNs, is thoroughly studied. A family of three test metrics are designed to quantify not only the values but also the temporal relations (including both stepwise and bounded-length) exhibited when LSTM processing inputs. A genetic algorithm is applied to efficiently generate test cases. The test metrics and test case generation algorithm are implemented into a tool testRNN, which is then evaluated on a set of LSTM benchmarks. Experiments confirm that testRNN has advantages over the state-of-the-art tool DeepStellar and attack-based defect detection methods, owing to its working with finer temporal semantics and the consideration of the naturalness of input perturbation. Furthermore, testRNN enables meaningful information to be collected and exhibited for users to understand the testing results, which is an important step toward interpretable neural network testing

Table_1_Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.xlsx

BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.</p

Table_3_Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.xlsx

BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.</p

DataSheet_1_Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.zip

BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.</p

Formal verification of robustness and resilience of learning-enabled state estimation systems

This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural networks for processing sensory input. We study LE-SESs from the perspective of formal verification, which determines the satisfiabilty of a system model against the specified properties. Over LE-SESs, we investigate two key properties – robustness and resilience – and provide their formal definitions. To enable formal verification, we reduce the LE-SESs to a novel class of labelled transition systems, named {PO}2-LTS in the paper, and formally express the properties as constrained optimisation objectives. We prove that the verification problems are NP-complete. Based on {PO}2-LTS and the optimisation objectives, practical verification algorithms are developed to check the satisfiability of the properties on the LE-SESs. As a major case study, we interrogate a real-world dynamic tracking system which uses a single Kalman Filter (KF) – a special case of Bayes filter – to localise and track a ground vehicle. Its perception system, based on convolutional neural networks, processes a high-resolution Wide Area Motion Imagery (WAMI) data stream. Experimental results show that our algorithms can not only verify the properties of the WAMI tracking system but also provide representative examples, the latter of which inspired us to take an enhanced LE-SESs design where runtime monitors or joint-KFs are required. Experimental results confirm the improvement in the robustness of the enhanced design. </p

Table_2_Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.xlsx

BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.</p

Image_1_Significance of monocyte infiltration in patients with gastric cancer: A combined study based on single cell sequencing and TCGA.jpeg

BackgroundGastric cancer is still one of the most lethal tumor diseases in the world. Despite some improvements, the prognosis of patients with gastric cancer is still not accurately predicted.MethodsBased on single cell sequencing data, we conducted a detailed analysis of gastric cancer patients and normal tissues to determine the role of monocytes in the progression of gastric cancer. WCGA facilitated our search for Grade-related genes in TCGA. Then, according to the marker genes and cell differentiation genes of monocytes, we determined the cancer-promoting genes of monocytes. Based on LASSO regression, we established a prognostic model using TCGA database. The accuracy of the model was verified by PCA, ROC curve, survival analysis and prognostic analysis. Finally, we evaluated the significance of the model in clinical diagnosis and treatment by observing drug sensitivity, immune microenvironment and immune checkpoint expression in patients with different risk groups.ResultsMonocytes were poorly differentiated in tumor microenvironment. It mainly played a role in promoting cancer in two ways. One was to promote tumor progression indirectly by interacting with other tumor stromal cells. The other was to directly connect with tumor cells through the MIF and TNF pathway to play a tumor-promoting role. The former was more important in these two ways. A total of 292 monocyte tumor-promoting genes were obtained, and 12 genes were finally included in the construction of the prognosis model. A variety of validation methods showed that our model had an accurate prediction ability. Drug sensitivity analysis could provide guidance for clinical medication of patients. The results of immune microenvironment and immune checkpoint also indicated the reasons for poor prognosis of high-risk patients.ConclusionIn conclusion, we provided a 12-gene risk score formula and nomogram for gastric cancer patients to assist clinical drug therapy and prognosis prediction. This model had good accuracy and clinical significance.</p

Silk–Hydroxyapatite Nanoscale Scaffolds with Programmable Growth Factor Delivery for Bone Repair

Osteoinductive biomaterials are attractive for repairing a variety of bone defects, and biomimetic strategies are useful toward developing bone scaffolds with such capacity. Here, a multiple biomimetic design was developed to improve the osteogenesis capacity of composite scaffolds consisting of hydroxyapatite nanoparticles (HA) and silk fibroin (SF). SF nanofibers and water-dispersible HA nanoparticles were blended to prepare the nanoscaled composite scaffolds with a uniform distribution of HA with a high HA content (40%), imitating the extracellular matrix (ECM) of bone. Bone morphogenetic protein-2 (BMP-2) was loaded in the SF scaffolds and HA to tune BMP-2 release. <i>In vitro</i> studies showed the preservation of BMP-2 bioactivity in the composite scaffolds, and programmable sustained release was achieved through adjusting the ratio of BMP-2 loaded on SF and HA. <i>In vitro</i> and <i>in vivo</i> osteogenesis studies demonstrated that the composite scaffolds showed improved osteogenesis capacity under suitable BMP-2 release conditions, significantly better than that of BMP-2 loaded SF–HA composite scaffolds reported previously. Therefore, these biomimetic SF–HA nanoscaled scaffolds with tunable BMP-2 delivery provide preferable microenvironments for bone regeneration

Pathway and Molecular Mechanisms for Malachite Green Biodegradation in <em>Exiguobacterium</em> sp. MG2

<div><p>Malachite green (MG), N-methylated diaminotriphenylmethane, is one of the most common dyes in textile industry and has also been used as an effective antifungal agent. However, due to its negative impact on the environment and carcinogenic effects to mammalian cells, there is a significant interest in developing microbial agents to degrade this type of recalcitrant molecules. Here, an <em>Exiguobacterium</em> sp. MG2 was isolated from a river in Yunnan Province of China as one of the best malachite green degraders. This strain had a high decolorization capability even at the concentration of 2500 mg/l and maintained its stable activity within the pH range from 5.0 to 9.0. High-pressure liquid chromatography, liquid chromatography-mass spectrometry and gas chromatography–mass spectrometry were employed to detect the catabolic pathway of MG. Six intermediate products were identified and a potential biodegradation pathway was proposed. This pathway involves a series of reactions of N-demethylation, reduction, benzene ring-removal, and oxidation, which eventually converted N-methylated diaminotriphenylmethane into N, N-dimethylaniline that is the key precursor to MG. Furthermore, our molecular biology experiments suggested that both triphenylmethane reductase gene <em>tmr</em> and cytochrome P450 participated in MG degradation, consistent with their roles in the proposed pathway. Collectively, our investigation is the first report on a biodegradation pathway of triphenylmethane dye MG in bacteria.</p> </div