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Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events. [ABSTRACT FROM AUTHOR]

• Develop a deep learning model (GWNN) for landslide susceptibility mapping. • Compare the performance of SVM, ANN, RF, GWR and GWNN models. • The proposed model can capture the spatial heterogeneity of landslides. • Reliable landslide susceptibility map in Yichang City using the proposed model. Landslides are among the most devastating natural disasters worldwide. Landslide susceptibility mapping (LSM) is a scientific approach for assessing landslides-prone areas. Despite the use of various statistical and machine learning (ML) models to predict landslide susceptibility, few have considered the effect of spatial location on landslides. Geographically weighted regression (GWR) is a common method for modeling spatial heterogeneity, but it may not be effective in solving complex nonlinear problems. This leads to inadequate prediction accuracy for landslide susceptibility at the local scale. This study introduces a geographically weighted neural network (GWNN) model to address this problem. The GWNN model combines the advantages of GWR and neural networks (NN) to assess the effect of landslide conditioning factors on landslides. The effectiveness of the proposed method was evaluated using landslide data collected from Yichang City, Hubei Province. The results show that GWNN exhibits the higher performance (AUC = 0.788) compared with traditional ML models and GWR. Furthermore, the superior ability of GWNN is demonstrated to capture spatial heterogeneity and predict landslide susceptibility accurately at local scales. The proposed method provides a valuable reference for modeling landslide-prone areas that exhibit spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Interleukin-enhancer binding factor 3 (ILF3) is a double-stranded RNA-binding protein that has been reported to contribute to the occurrence and progression of various malignant tumors. The aim of the present study was to evaluate the prognostic value of ILF3 and to apply this knowledge to avoid excessive medical treatment in patients with lung adenocarcinoma (LUAD). ILF3 expression in a discovery set consisting of tumor and peri-tumor tissue microarrays was analyzed using immunohistochemical methods. The mRNA level of ILF3 was subsequently analyzed in a validation set downloaded from The Cancer Genome Atlas. The Kaplan-Meier method, univariate and multivariate Cox analyses, decision curve analysis and nomogram models were used to evaluate the prognostic value of ILF3. ILF3 expression was upregulated in tumor tissues compared with peri-tumor tissues and was negatively associated with the overall survival time of patients with LUAD in the discovery and validation sets. Moreover, ILF3 expression was used for risk stratification in patients with tumor-node-metastasis stages II–IV and poor-to-moderate tumor differentiation. ILF3 expression was identified as an independent predictor of adverse prognosis for patients with LUAD in the discovery and validation sets. Finally, nomogram models for the 3- and 5 year survival time of patients with LUAD revealed that ILF3 expression may be used to improve the predictive accuracy of the prognosis and to avoid excessive medical treatment for certain patients with the disease. Overall, the data obtained in the current study revealed that high ILF3 expression was associated with poor prognosis, and demonstrated that ILF3, as a potential independent risk factor, may improve the hierarchical postoperative management of patients with LUAD. [ABSTRACT FROM AUTHOR]

Abstract: A new type of acid-labile cationic copolymer consisting of a hydrophilic poly(ethylene glycol) (PEG) block and a polymethacrylamide block bearing tertiary amines linked by acid-labile ortho ester rings in side chains (PAOE), with defined chain length, had been synthesized via RAFT polymerization. The copolymers could efficiently bind and condense plasmid DNA at neutral pH into narrowly dispersed nano-scale polyplexes. The hydrolysis of ortho ester group in the side-chains of PAOE followed a distinct exocyclic mechanism and the rate of hydrolysis was much accelerated at mildly acidic pH, resulting in the accelerated disruption of polyplexes and the release of intact plasmid DNA. The three polymers were not toxic to cultured COS-7 cells as measured by MTT assay. As expected, PEG segments of the PEG-b-PAOE copolymers prevented nonspecific transfection of COS-7 cells. Once conjugated to a targeting ligand to enhance cell-specific entry, PEG-b-PAOE with its pH-triggered DNA release properties may achieve efficient intracellular delivery of DNA or other nucleic acid therapeutics. [Copyright &y& Elsevier]

Abstract: At present, some researchers provide a type of fuzzy risk analysis algorithms for dealing with fuzzy risk analysis problems, where the values of the evaluating items are represented by trapezoidal fuzzy numbers. In those algorithms, the main operations are two: one is arithmetic operators of the trapezoidal fuzzy numbers; the other is the similarity of the trapezoidal fuzzy numbers. However, the arithmetic operators of some algorithms do not satisfy some properties of the trapezoidal fuzzy numbers and the similarity of the trapezoidal fuzzy numbers does not coincide with the intuition of the human being. Due to this situation, in this paper, we present an efficient approach for fuzzy risk analysis based on some new arithmetic operators of the trapezoidal fuzzy numbers and propose a new similarity of the trapezoidal fuzzy numbers to deal with fuzzy risk analysis problems. At the same time, we make an experiment to use 30 sets of trapezoidal fuzzy numbers to compare the experimental results of our proposed approach with the existing similarity measures. At last, we use an example to illustrate the efficiency of the new approach. [Copyright &y& Elsevier]

Objective: To investigate the anatomic risk factors of knee in patients with acute non-contact anterior cruciate ligament (aACL) ruptures to develop ramp lesions. Methods: A total of 202 subjects were retrospectively divided into three groups: (1) aACL ruptures combined with ramp lesions group (n = 76); (2) isolated ACL ruptures group (n = 56) and (3) normal controls group (n = 70). Quantitative morphological parameters on MRI were measured including: diameter of medial femoral condyle (MFC), anterior-posterior length and depth of medial tibial plateau (MTP AP length and depth), lateral posterior tibial slope (LPTS) and medial posterior tibial slope (MTPS), asymmetry of LPTS and MPTS (LMPTS), lateral meniscal slope (LMS), and medial meniscal slope (MMS). Results: The MTP AP length, MTP AP length/MFC diameter ratio, MTP depth, LPTS and the asymmetry of LMPTS showed significant differences among the three groups (p < 0.001). The risk factors associated with the ramp lesions including a longer MTP AP length (OR 1.17, 95% CI 1.00–1.44, p = 0.044), increased MTP depth (OR 1.91, 95% CI 1.22–3.00, p = 0.005) and lager ratio (OR 1.11, 95% CI 1.01–1.22, p = 0.036). The highest AUC was the MTP AP length/MFC diameter ratio (0.74; 95% CI, 0.66–0.82). The combination model increased higher accuracy (0.80; 95% CI, 0.72–0.88). Conclusion: Several bony anatomic characteristics of the knee, especially the morphology of medial tibia plateau, are additional risk factors for aACL ruptures to develop ramp lesions. Critical relevance statement: Predictive anatomic risk factors of the knee for patients with acute non-contact anterior cruciate ligament (aACL) ruptures to develop ramp lesions, especially the morphology of medial tibia plateau, are detectable by MRI. Key Points: Ramp lesion development can complicate aACL ruptures and requires specific treatment. Longer AP length and increased MTP depth are risk factors for concurrent ramp lesions. Identification of ramp lesions allows for the most appropriate treatment. [ABSTRACT FROM AUTHOR]

Plants have evolved immune systems to fight against pathogens. However, it is still largely unknown how the plant immunity is finely regulated. Here we identified a BTB/POZ domain-containing protein, namely NbBTB, which is predicted to be a member of the ubiquitin E3 ligase complex. The NbBTB expression is downregulated upon the oomycete pathogen Phytophthora parasitica infection. Overexpression of NbBTB in Nicotiana benthamiana promoted plant susceptibility to P. parasitica infection, and silencing NbBTB increased plant resistance to P. parasitica , indicating that NbBTB negatively modulates plant basal defense. Interestingly, overexpressing or silencing NbBTB did not affect plant resistance to two bacterial pathogens Ralstonia solanacearum and Pseudomonas syringae , suggesting that NbBTB is specifically involved in basal defense against oomycete pathogen. Expression of NbBTB suppressed hypersensitive response (HR) triggered by avirulence proteins from both R. sonanacearum and P. infestans , and silencing NbBTB showed the opposite effect, indicating that NbBTB negatively regulates effector-triggered immunity (ETI). Protein accumulation of avirulence effectors in NbBTB -silenced plants was significantly enhanced, suggesting that NbBTB is likely to negatively modulate ETI by affecting effector protein accumulation. Together, our results demonstrated that NbBTB is a negative regulator in both plant basal defense and ETI. • NbBTB negatively regulates plant resistance to Phytophthora parasitica. • NbBTB suppresses hypersensitive response triggered by pathogen effectors. • NbBTB negatively regulates protein accumulation of avirulence effectors. • NbBTB is a negative regulator in both plant basal defense and ETI. [ABSTRACT FROM AUTHOR]

A comprehensive, accurate, and timely expression of earth observation (EO) tasks is the primary prerequisite for the response to and the emergency monitoring of disasters, especially floods. However, the existing information model does not fully satisfy the demand for a fine-grain observation expression of EO task, which results in the absence of task process management. The current study proposed an EO task representation model based on meta-object facility to address this problem. The model not only describes the static information of a task, but it also defines the dynamics of an observation task by introducing a functional metamodel. This metamodel describes the full life cycle of a task; it comprises five process methods: birth, separation, combination, updating, and extinction. An earth observation task modeling and management prototype system (EO-TMMS) for conducting a remote sensing satellite sensor observation task representation experiment on flooding was developed. In accordance with the results, the proposed model can describe various EO tasks demands and the full life cycle process of an EO task. Compared with other typical observation task information models, the proposed model satisfies the dynamic and fine-grain process representation of EO tasks, which can improve the efficiency of EO sensor utilization. [ABSTRACT FROM AUTHOR]

The sheer size of all frequent itemsets is one challenging problem in data mining research. Based on both closed itemset and maximal itemset, meta itemset which is a new concise representation of frequent itemset is proposed. It is proved that both closed itemset and maximal itemset are special cases of meta itemset. The set of all closed itemsets and the set of all maximal itemsets form the upper bound and the lower bound of the set of all meta itemsets. Then, property and pruning strategies of meta itemset are discussed. Finally, an efficient algorithm for mining meta itemset is proposed. Experimental results show that the proposed algorithm is effective and efficient. [ABSTRACT FROM AUTHOR]

Abstract: Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. Methods for mining frequent itemsets have been implemented using a BitTable structure. BitTableFI is such a recently proposed efficient BitTable-based algorithm, which exploits BitTable both horizontally and vertically. Although making use of efficient bit wise operations, BitTableFI still may suffer from the high cost of candidate generation and test. To address this problem, a new algorithm Index-BitTableFI is proposed. Index-BitTableFI also uses BitTable horizontally and vertically. To make use of BitTable horizontally, index array and the corresponding computing method are proposed. By computing the subsume index, those itemsets that co-occurrence with representative item can be identified quickly by using breadth-first search at one time. Then, for the resulting itemsets generated through the index array, depth-first search strategy is used to generate all other frequent itemsets. Thus, the hybrid search is implemented, and the search space is reduced greatly. The advantages of the proposed methods are as follows. On the one hand, the redundant operations on intersection of tidsets and frequency-checking can be avoided greatly; On the other hand, it is proved that frequent itemsets, including representative item and having the same supports as representative item, can be identified directly by connecting the representative item with all the combinations of items in its subsume index. Thus, the cost for processing this kind of itemsets is lowered, and the efficiency is improved. Experimental results show that the proposed algorithm is efficient especially for dense datasets. [Copyright &y& Elsevier]

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative to address the problem. Set-Enumeration Tree (SET) is a common data structure used in several MFI mining algorithms. For this kind of algorithm, the process of mining MFI's can also be viewed as the process of searching in set-enumeration tree. To reduce the search space, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first. Firstly, the index array is proposed, and based on bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFI's using breadth-first search at one time, which avoids first-level nodes that would not participate in the answer set and reduces drastically the number of candidate itemsets. Then, for candidate MFI's, depth-first search strategy is used to generate all MFI's. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm is efficient especially for dense datasets. [ABSTRACT FROM AUTHOR]

SPOCK1 encodes a Ca 2+ -binding matricellular glycoprotein which plays an oncogenic role in cancer cells. However, the role of SPOCK1 in the pathogenesis of colorectal cancer (CRC) has not been determined. Here, SPOCK1 was found higher expressed in CRC tissues than that of adjacent normal tissues. Furthermore, up-regulated expression of SPOCK1 in CRC patients was associated with tumor size and TNM stage. In addition, we observed that the depletion of SPOCK1 inhibited proliferation in vitro and tumorigenicity in vivo and promoted apoptosis in cell culture. Our data suggest that inactivation of PI3K/Akt signaling pathway was involved in down-regulation of SPOCK1-mediated suppression of tumor cell proliferation. These results suggest that SPOCK1 expression is correlated with malignant features of CRC and may serve as potential therapeutic and preventive strategies for CRC. [ABSTRACT FROM AUTHOR]