•Fusing spatial and temporal features is effective in fMRI classification.•Models considering sequence achieve better results than timing invariant models.•fMRI scans longer than one minute contain ...enough information for ADHD diagnosis.•deep-learned features outperform hand-crafted features in fMRI analysis.
Attention Deficit/Hyperactivity Disorder (ADHD) is one kind of neurodevelopmental disorders common in children. Due to the complexity of the pathological mechanism, there is a lack of objective diagnostic methods up to now. This paper aimed to propose automatic ADHD diagnostic method using resting state functional magnetic resonance imaging (rs-fMRI) data with the spatio-temporal deep learning models. Unlike traditional methods, this paper constructed a deep learning method called 4-D CNN based on granular computing which were trained based on derivative changes in entropy, and can calculate granularity at a coarse level by stacking layers. Considering the structure of rs-fMRI as time-series 3-D frames, several models of spatial and temporal granular computing and fusion were proposed, including feature pooling, long short-term memory (LSTM) and spatio-temporal convolution. This paper introduced an approach to augment dataset which can sample one subject's rs-fMRI frames into several relatively short term pieces with a fixed stride. The public dataset of ADHD-200 Consortium was used to train and validate our method. And the results of evaluations showed that our method outperformed traditional methods on the dataset (accuracy: 71.3%, AUC: 0.80). Therefore, our 4-D CNN method can be used to build more accurate automatic assistant diagnosis tool of ADHD.
Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge ...pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets.
With the reduction of oil and gas reserves and the increase of mining difficulty in Northern China, the carbonate rocks in Southern North China Basin are becoming a significant exploration target for ...carbonate reservoirs. However, the development characteristics, formation stages, formation environments and mechanisms of the carbonate reservoirs in Southern North China Basin are still unclear, which caused the failures of many oil and gas exploration wells. This study focused on addressing this unsolved issue from the Ordovician carbonate paleokarst in the Huai-Fu Basin, which is located in the southeast of Southern North China Basin and one of the key areas for oil and gas exploration. Based on petrology, mineralogy and geochemical data, pore types, distribution characteristics, and formation stages of the Ordovician paleokarst were analyzed. Then, in attempt to define the origins of porosity development, the formation environments and mechanisms were illustrated. The results of this study showed that pore types of the Ordovician carbonates in the Huai-Fu Basin are mainly composed of intragranular pores, intercrystalline (intergranular) pores, dissolution pores (vugs), fractures, channels, and caves, which are usually in fault and fold zones and paleoweathering crust. Furthermore, five stages and five formation environments of the Ordovician paleokarst were identified. Syngenetic karst, eogenetic karst, and paleoweathering crust karst were all developed in a relatively open near-surface environment, and their formations are mainly related to meteoric water dissolution. Mesogenetic karst was developed in a closed buried environment, and its formation is mainly related to the diagenesis of organic matters and thermochemical sulfate reduction in the Permian-Carboniferous strata. Hydrothermal (water) karst was developed in a deep-buried and high-temperature environment, where hydrothermal fluids (waters) migrated upward through structures such as faults and fractures to dissolve carbonate rocks and simultaneously deposited hydrothermal minerals and calcites. Lastly, a paleokarst evolution model, combined with the related porosity evolution processes, nicely revealed the Ordovician carbonate reservoir development. This study provides insights and guidance for further oil and gas exploration in the Southern North China Basin, and also advances our understanding of the genesis of carbonate paleokarst around the world.
TGF-β promotes tumor invasion and metastasis through inducing epithelial-mesenchymal transition (EMT) in non-small cell lung cancer (NSCLC). Circular RNAs (circRNAs) are recognized as functional ...non-coding RNAs involved in human cancers. However, whether and how circRNAs contribute to TGF-β-induced EMT and metastasis in NSCLC remain vague. Here, we investigated the regulation and function of Circular RNA hsa_circ_0008305 (circPTK2) in TGF-β-induced EMT and tumor metastasis, as well as a link between circPTK2 and transcriptional intermediary factor 1 γ (TIF1γ) in NSCLC.
Circular RNAs were determined by human circRNA Array analysis, real-time quantitative reverse transcriptase PCR and northern blot. Luciferase reporter, RNA-binding protein immunoprecipitation (RIP), RNA pull-down and fluorescence in situ hybridization (FISH) assays were employed to test the interaction between circPTK2 and miR-429/miR-200b-3p. Ectopic overexpression and siRNA-mediated knockdown of circPTK2, TGF-β-induced EMT, Transwell migration and invasion in vitro, and in vivo experiment of metastasis were used to evaluate the function of circPTK2. Transcription and prognosis analyses were done in public databases.
CircPTK2 and TIF1γ were significantly down-regulated in NSCLC cells undergoing EMT induced by TGF-β. CircPTK2 overexpression augmented TIF1γ expression, inhibited TGF-β-induced EMT and NSCLC cell invasion, whereas circPTK2 knockdown had the opposite effects. CircPTK2 functions as a sponge of miR-429/miR-200b-3p, and miR-429/miR-200b-3p promote TGF-β-induced EMT and NSCLC cell invasion by targeting TIF1γ. CircPTK2 overexpression inhibited the invasion-promoting phenotype of endogenous miR-429/miR-200b-3p in NSCLC cells in response to TGF-β. CircPTK2 overexpression significantly decreased the expression of Snail, an important downstream transcriptional activator of TGF-β/Smad signaling. In an in vivo experiment of metastasis, circPTK2 overexpression suppressed NSCLC cell metastasis. Moreover, circPTK2 expression was dramatically down-regulated and positively correlated with TIF1γ expression in human NSCLC tissues. Especially, circPTK2 was significantly lower in metastatic NSCLC tissues than non-metastatic counterparts.
Our findings show that circPTK2 (hsa_circ_0008305) inhibits TGF-β-induced EMT and metastasis by controlling TIF1γ in NSCLC, revealing a novel mechanism by which circRNA regulates TGF-β-induced EMT and tumor metastasis, and suggesting that circPTK2 overexpression could provide a therapeutic strategy for advanced NSCLC.
Groundwater is an important drinking water resource worldwide. However, mining could influence the natural groundwater geochemistry, reducing the correct discrimination of inrush sources and ...affecting mine safety. To accurately identify potential inrush sources in the future, hydrogeochemical processes and their evolution should be investigated using multiple analytical methods. In the study, based on hydrochemical analysis and multivariate statistical methods, the major ion chemistry of groundwater in multi-aquifer groundwater systems was analyzed in the Huainan coalfield (Anhui Province, China). The results showed that the hydrogeochemical processes in this area consisted of the three subprocesses: dissolution, desulfidation, and cation exchange. For the Cenozoic bottom aquifer and the Ordovician aquifer, the major hydrogeochemical processes are the dissolution of salt rock, silicate, carbonate, and sulfate, followed by cation exchange; for the Permian and Carboniferous aquifers, the main hydrogeochemical processes are desulfidation and cation exchange, followed by dissolution. From the Permian aquifer to the Carboniferous aquifer and then to the Ordovician aquifer, the concentration of SO
4
2−
increased, while the concentration of HCO
3
−
decreased due to enhanced sulfate dissolution and weakened desulfurization. However, long-term mining activities changed the groundwater seepage field and the hydrogeochemical field, causing hydrogeochemical evolutionary processes with obvious zoning. The eastern discharge zone showed the strongest dissolution and the weakest desulfidation and cation exchange; from the eastern discharge zone to the central runoff zone, dissolution weakened, while desulfidation and cation exchange were enhanced; and in the western recharge zone, desulfidation and cation exchange were the strongest, while dissolution was the weakest.
The number of applications (apps) available for smart devices or Android based IoT (Internet of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill-designed or ...malicious apps (malapps) has been growing explosively. To ensure the quality and security of the apps in the markets, many approaches have been proposed in recent years to discriminate malapps from benign ones. Machine learning is usually utilized in classification process. Accurately characterizing apps' behaviors, or so-called features, directly affects the detection results with machine learning algorithms. Android apps evolve very fast. The size of current apps has become increasingly large and the behaviors of apps have become increasingly complicated. The extracting effective and representative features from apps is thus an ongoing challenge. Although many types of features have been extracted in existing work, to the best of our knowledge, no work has systematically surveyed the features constructed for detecting Android malapps. In this paper, we are motivated to provide a clear and comprehensive survey of the state-of-the-art work that detects malapps by characterizing behaviors of apps with various types of features. Through the designed criteria, we collect a total of 1947 papers in which 236 papers are used for the survey with four dimensions: the features extracted, the feature selection methods employed if any, the detection methods used, and the scale of evaluation performed. Based on our in-depth survey, we highlight the issues of exploring effective features from apps, provide the taxonomy of these features and indicate the future directions.
Background
Lung adenocarcinoma (LUAD) has a very high morbidity and mortality rate, and its pathogenesis and treatment are still in the exploratory stage. Fatty acid metabolism plays a significant ...role in tumorigenesis, progression, and immune regulation. However, the gene expression of fatty acid metabolism in patients with LUAD and its relationship with prognosis remain unclear.
Methods
We collected 309 fatty acid metabolism-related genes, established a LUAD risk model based on The Cancer Genome Atlas (TCGA) using Least Absolute Shrinkage Selection Operator (LASSO) regression analysis, and divided LUAD patients into high-risk and low-risk groups, which were further validated using the Gene Expression Omnibus (GEO) database. The nomogram, principal component analysis (PCA), and receiver operating characteristic (ROC) curves showed that the model had the best predictive performance. The ROC curves and calibration plots confirmed that the nomogram had good predictive power. We further analyzed the differences in clinical characteristics, immune cell infiltration, immune-related functions, chemotherapy drug sensitivity, and immunotherapy efficacy between the high-risk and low-risk groups. We also analyzed the enrichment pathways and protein–protein interaction (PPI) networks of different genes in the high-risk and low-risk groups to screen for target genes and further explored the correlation between target genes and differences in survival prognosis, clinical characteristics, gene mutations, and immune cells.
Results
Risk score and staging are independent prognostic factors for patients with LUAD. The high-risk group had lower immune cell infiltration, was more sensitive to chemotherapeutic agents, and had a poorer survival prognosis. We also obtained three pivotal genes with poor survival prognosis in the high expression group, which were strongly associated with clinical symptoms and immune cells.
Conclusion
Risk score and staging are independent prognostic factors for patients with LUAD. The high-risk group had lower immune cell infiltration, was more sensitive to chemotherapeutic agents, and had a poorer survival prognosis. We also obtained three survival prognosis-associated target genes that are closely associated with clinical symptoms and immune cells and may be potential targets for immune-targeted therapy in LUAD.
Karst collapse columns (KCCs) are channels where groundwater and gas gather, and they pose a great threat to mining safety. In this study, a mechanical model and criterion for the roof collapse of ...KCCs were established, and simulations were performed to analyze the formation mechanism of KCCs in the Huainan coalfield of Northern China. The results showed that the roof collapse and upward development of KCCs were facilitated by increasing the cave radius of the KCC basement and decreasing the groundwater pressure, single-layer thickness of the roof strata, lateral pressure coefficient of the rock mass, and cohesion and internal friction angle between fractures. The KCC formation process in the Huainan coalfield could be divided into four stages: (I) early collapse, (II) middle-early rapid collapse, (III) middle-late slow collapse, and (IV) late filling compaction. The simulation results were generally consistent with actual KCCs observed in the Huainan coalfield, which verified the theoretical analysis. The results of this study provide an important reference for the formation mechanism and evolution of KCCs in Northern China.
By introducing networking technologies and services into healthcare infrastructures (e.g., multimodal sensors and smart devices) that are deployed to supervise a person's health condition, the ...traditional healthcare system is being revolutionized toward knowledge-centric connected healthcare (KCCH), where persons will take their own responsibility for their healthcare in a knowledge- centric way. Due to the volume, velocity, and variety of healthcare supervision data generated by these healthcare infrastructures, an urgent and strategic issue is how to efficiently process a person's healthcare supervision data with the right knowledge of the right guardians (e.g., relatives, nurses, and doctors) at the right time. To solve this issue, the naming and routing criterion of medical knowledge is studied. With this offloaded medical knowledge, we propose an edge learning as a service (EdgeLaaS) framework for KCCH to locally process health supervision data. In this framework, edge learning nodes can help the patient choose better advice from the right guardians in real time when some emergencies occur. Two application cases: 1) fast self-help and 2) mobile help pre-calling are studied. Performance evaluations demonstrate the superiority of KCCH and EdgeLaaS, respectively.
•Dissolution mechanisms for multi-genesis of paleokarst have been clarified.•The formation mechanism of karst collapse columns (KCCs) has been revealed.•Three main factors controlling paleokarst and ...KCCs have been illustrated.•The evolution model of paleokarst and KCCs have been established.•Spatial distribution of paleokarst and KCCs has been predicted.
Karst water in the Middle Cambrian-Lower Ordovician carbonates is the ecological resources and domestic water in the Huainan coalfield (Anhui Province, China), but it also directly threatens the safe production of coal mines due to the development of paleokarst and karst collapse columns (KCCs). Most of the Middle Cambrian-Lower Ordovician carbonates in Northern China experienced multistage tectonic movements and were affected by multi-type corrosive fluids, but very few studies focused on the effect of multistage fluid-rock reaction on the formation of paleokarst and KCCs. To investigate the formation mechanisms and characteristics of paleokarst and KCCs, this study integrated petrographic studies, isotope geochemistry (C and O) and minor elements (Ba, Mn, and Sr), and clarified the sources and types of corrosive fluids. Through this study, meteoric water, formation water, hydrothermal fluids, and mixing fluids were determined as the four main types of corrosive fluids that formed pores, vugs, fractures, caves, and KCCs in the Middle Cambrian-Lower Ordovician carbonates. The meteoric dissolution is controlled by carbonic acid solution recharge conditions which affect the karst development in the Cambrian and Ordovician paleoweathering crusts and local carbonate outcrops. Hydrothermal fluids with high-temperature, high-pressure and high-corrosivity can develop a strong hydrothermal pore-fracture system in the Cambrian carbonate, which is the reason that KCCs can develop in the Cambrian strata in the Huainan coalfield. The mixing dissolution is controlled by sulfuric acid dissolution and usually occurs in the water tables and the fault and fracture zones, which are conducive to the development of caves and KCCs. In addition to the above four corrosive fluids, the development of paleokarst and KCCs in the Huainan area is also controlled by stratigraphic lithology and geological structures, especially for faults and fractures, which are the main migration channels of corrosive fluids. An evolution model of paleokarst and KCCs was established, providing a plausible interpretation for better understanding of the spatial distribution of paleokarst and KCCs. In practice, this study provides critical references for predicting the spatial distribution of paleokarst and KCCs in Northern China coalfields, as well as the exploration and development of karst water around the world.
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