Search

Ionic Liquid Electrospray Thrusters (ILETs) exhibit distinct emission characteristics when operating in different emission mode under different conditions, which can significantly impact their performance. Therefore, to optimize the performance of ILETs and make them suitable for a wide range of space missions, it is necessary to investigate the emission mode characteristics of ILETs. In this study, the ionic liquid 1-Ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMI-Im) was used to conduct experiments at varying supply voltage and flow rate conditions. The emitted current was measured and the liquid surface was imaged using a high-speed camera. The emission mode was estimated using algebraic calculations. Experimental results revealed that ILETs operate in the Ion-droplet Mixed mode at low flow rates, while the emission mode transforms into the Droplet mode as the flow rate increases. Similarly, as the supply voltage increases, the emission mode transforms from the Droplet mode to the Ion-droplet Mixed mode. The liquid surface height decreased with the supply voltage increase in each mode, and it increased with the flow rate in the Droplet mode. Notably, the liquid surface height in the Ion-droplet Mixed mode was similar to that in the Droplet mode at the lowest flow rate. Furthermore, the ion ratio significantly affected the thrust and specific impulse of ILETs. • Estimates the emission modes and calculates the particle components rapidly with algebraic calculation. • Ion-droplet mixed mode occurs when the flow rate is low, and droplet mode occurs when flow rate increases. • The liquid surface height of ion-droplet mixed mode is close to the droplet mode with the lowest flow rate. • The performances of ion-droplet mixed mode with low ion ratio are close to droplet mode. • The thrust and specific impulse of ion-droplet mixed mode are different due to different ion current ratio. [ABSTRACT FROM AUTHOR]

• ASFV MGF360-13L is a negative regulator by controlling cGAS-STING signaling pathway. • ASFV MGF360-13L suppresses the cGAS-STING-IFN-I axis by degrading STING. • ASFV MGF360-13L is likely to mediate the degradation of STING through the autophagic pathway. [ABSTRACT FROM AUTHOR]

Background Associating protein complexes to human inherited diseases is critical for better understanding of biological processes and functional mechanisms of the disease. Many protein complexes have been identified and functionally annotated by computational and purification methods so far, however, the particular roles they were playing in causing disease have not yet been well determined. Results In this study, we present a novel method to identify associations between protein complexes and diseases. First, we construct a disease-protein heterogeneous network based on data integration and laplacian normalization. Second, we apply a random walk with restart on heterogeneous network (RWRH) algorithm on this network to quantify the strength of the association between proteins and the query disease. Third, we sum over the scores of member proteins to obtain a summary score for each candidate protein complex, and then rank all candidate protein complexes according to their scores. With a series of leave-one-out cross-validation experiments, we found that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We test our approach with breast cancer and select top 20 highly ranked protein complexes, 17 of the selected protein complexes are evidenced to be connected with breast cancer. Conclusions Our proposed method is effective in identifying disease-related protein complexes based on data integration and laplacian normalization. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The purpose of this study was to measure and analyze interobserver disagreement in rating diagnostic characteristics of pulmonary nodules on computed tomography scans using the Lung Imaging Database Consortium and Image Database Resource Initiative (LIDC/IDRI) database, and then to provide investigators with understanding the variability in rating diagnostic characteristics among radiologists.Materials and Methods: A histogram-based accumulated nodule-level approach is proposed to measure interobserver disagreement in rating diagnostic characteristics of pulmonary nodules among radiologists. The mean rating differences of radiologists on nodule level are calculated; next, a histogram of the accumulated nodule-level disagreements is constructed; and finally, mean, variance, skewness, and kurtosis statistics based on the histogram are extracted to analyze and summary interobserver disagreement in terms of the assessment of diagnostic characteristics of radiologists. Using the developed computer scheme, the disagreement of radiologists in rating all of 1880 distinct nodules from 1018 computed tomography scans are analyzed using original ratings as well as combined ratings according to the LIDC/IDRI instruction.Results: The interobserver disagreement in rating diagnostic characteristics according to the defined categories of the LIDC/IDRI is substantial. The mean values of disagreement range from 0.0052 to 0.2341. The highest disagreement lies in rating subtlety characteristics, whereas internal structure receives the lowest disagreement of 0.0052. The calcification, texture, spiculation, lobulation, malignancy, sphericity, and margin receive disagreements of 0.0393, 0.1351, 0.1616, 0.1943, 0.2144, 0.2174, and 0.2228, respectively.Conclusions: Disagreements exist across radiologists in rating diagnostic characteristics of pulmonary nodules, and the disagreement levels vary from each other. Agreement among radiologists is improved by combining ratings according to the LIDC/IDRI instruction. For investigators, understanding and appreciating the disagreement level of each diagnostic characteristic is required when using them in related researches. [ABSTRACT FROM AUTHOR]

• We utilize the semi-supervised strategy to alleviate the gaps between synthetic and real-world images. To reduce model size, we adopt recursive strategy to reuse model parameters. In addition, the proposed LSNet is capable of learning rich hierarchical features at a coarse-to-fine image residual estimation and improve model robustness. • Our semi-supervised framework has a supervised branch and an unsupervised branch. On both branches, we design a cascaded sub-network, which effectively handles different types of rainy images via a multi-stage processing. On the supervised branch, we also introduce perceptual loss to improve image qualities. To preserve structures and details, we use Total Variation (TV) loss to constrain the unsupervised branch • Our proposed LSNet has the advantages of strong robustness and efficient calculation. Experimental results show that the proposed model achieves an excellent balance among visual quality, inference speed and model parameters. As demonstrated by extensive experiments, our model promotes the performances of real-world applications, e.g., object detction. Deep learning technologies have shown their advantages in Single Image Rain Removal (SIRR) tasks. However, the derained results of most methods are limited to some challenges. First, due to the lack of real-world rainy/clean image pairs, many methods seriously rely on the labeled synthetic training images and will not effectively remove complex rain streaks in real-world scenarios. Second, most existing SIRR models require high computing power, which considerably limits their real-world applications. To address these issues, we propose a Lightweight Semi-supervised Network (LSNet) for SIRR. Our LSNet utilizes a compact semi-supervised framework to improve generalization ability in real-world rainy images removal. Meanwhile, in our semi-supervised framework, we also design a cascaded sub-network, which progressively removes complex rain streaks via a multi-stage manner. Specially, the multi-stage manner is based on a series of cascaded blocks, where we conduct recursive learning strategy to reduce model parameters. Extensive experimental results demonstrate that our method achieves comparable performance to the state-of-the-arts while has fewer parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Temperature, alkalinity and ore type decide the melt and flow characteristics. • The liquid-phase fluidity of mixed iron ore behaved best. • Visual upside-down measurement method can fully assess the flow characteristic. • Improving sample fluidity promotes the pores' diffusion, break and overflow. The liquid phase formed during the sintering of iron ore greatly influences the quality of the sintered ore. This paper aims to study the melt and flow characteristics of iron ore and analyze the variation law of pore structure with fluidity of iron ore during sintering. First, the melt characteristic and liquid phase flow index of five iron ores and their mixtures were investigated, with the mixed ores performing best. A method for measuring the flow characteristic was developed to study mixed iron ores with different alkalinity. This measurement method allowed the simultaneous acquisition of flow velocity, characteristic temperature and bottom curvature to comprehensively assess the flow characteristic. Finally, the effect of the flow characteristic on the pore structure and distribution of sintered ores was investigated by X-ray computed tomography. The results showed that improving the fluidity of the sintered ores would promote the diffusion and overflow of pores. [ABSTRACT FROM AUTHOR]

• Deconstructable beam-to-column composite joints are tested under monotonic and cyclic loadings. • The static and seismic structural performances of deconstructable beam-to-column composite joints are investigated. • The influence of force-transmission sleeve on structural performance of deconstructable beam-to-column composite joints is evaluated. • The deconstructability of deconstructable beam-to-column composite joints is assessed. Deconstructable composite beams, in which bolted connectors are introduced between prefabricated concrete slabs and steel beams, offer promising demolition efficiency. Compared with conventional cast-in-situ composite floor systems, such novel composite beams provide favourable economic performance and sustainability by considerably improving the recyclability of structural components. In this study, four full-scale deconstructable beam-to-column composite joints with deconstructable beams were designed, manufactured, and tested under monotonic and cyclic loading. The structural performances—crack pattern, failure mode, strain distribution and moment–rotation response, energy dissipation, and deterioration evolution—were investigated thoroughly. The effects of the loading scheme and a force-transmission sleeve on the structural performances were simultaneously studied. The moment–rotation responses showed that the composite joints possess outstanding strength, rotation capacity, ductility, and energy dissipation ability. The deformability satisfies the minimum rotation capacity specified by Eurocodes 3 and 4. Strain analysis demonstrated that the composite beams provide favourable composite shear interactions. The installation of the force-transmission sleeve improved the strength of a composite joint owing to the incorporation of steel bars split by the column into the moment-resisting system. These deconstructable composite joints can achieve favourable deconstructability and demolition efficiency at the termination of service life, and thus, serve as novel structural members. [ABSTRACT FROM AUTHOR]

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore . Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions. [ABSTRACT FROM AUTHOR]

Rationale and Objectives The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) is the largest publicly available computed tomography (CT) image reference data set of lung nodules. In this article, a comprehensive data analysis of the data set and a uniform data model are presented with the purpose of facilitating potential researchers to have an in-depth understanding to and efficient use of the data set in their lung cancer–related investigations. Materials and Methods A uniform data model was designed for representation and organization of various types of information contained in different source data files. A software tool was developed for the processing and analysis of the database, which 1) automatically aligns and graphically displays the nodule outlines marked manually by radiologists onto the corresponding CT images; 2) extracts diagnostic nodule characteristics annotated by radiologists; 3) calculates a variety of nodule image features based on the outlines of nodules, including diameter, volume, and degree of roundness, and so forth; 4) integrates all the extracted nodule information into the uniform data model and stores it in a common and easy-to-access data format; and 5) analyzes and summarizes various feature distributions of nodules in several different categories. Using this data processing and analysis tool, all 1018 CT scans from the data set were processed and analyzed for their statistical distribution. Results The information contained in different source data files with different formats was extracted and integrated into a new and uniform data model. Based on the new data model, the statistical distributions of nodules in terms of nodule geometric features and diagnostic characteristics were summarized. In the LIDC/IDRI data set, 2655 nodules ≥3 mm, 5875 nodules <3 mm, and 7411 non-nodules are identified, respectively. Among the 2655 nodules, 1) 775, 488, 481, and 911 were marked by one, two, three, or four radiologists, respectively; 2) most of nodules ≥3 mm (85.7%) have a diameter <10.0 mm with the mean value of 6.72 mm; and 3) 10.87%, 31.4%, 38.8%, 16.4%, and 2.6% of nodules were assessed with a malignancy score of 1, 2, 3, 4, and 5, respectively. Conclusions This study demonstrates the usefulness of the proposed software tool to the potential users for an in-depth understanding of the LIDC/IDRI data set, therefore likely to be beneficial to their future investigations. The analysis results also demonstrate the distribution diversity of nodules characteristics, therefore being useful as a reference resource for assessing the performance of a new and existing nodule detection and/or segmentation schemes. [ABSTRACT FROM AUTHOR]

Discovering motifs in protein–protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request. [ABSTRACT FROM AUTHOR]

Rationale and Objectives The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. Materials and Methods A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Results Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. Conclusions The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency. [ABSTRACT FROM AUTHOR]

Predicting essential proteins is highly significant because organisms can not survive or develop even if only one of these proteins is missing. Improvements in high-throughput technologies have resulted in a large number of available protein–protein interactions. By taking advantage of these interaction data, researchers have proposed many computational methods to identify essential proteins at the network level. Most of these approaches focus on the topology of a static protein interaction network. However, the protein interaction network changes with time and condition. This important inherent dynamics of the protein interaction network is overlooked by previous methods. In this paper, we introduce a new method named CDLC to predict essential proteins by integrating dynamic local average connectivity and in-degree of proteins in complexes. CDLC is applied to the protein interaction network of Saccharomyces cerevisiae . The results show that CDLC outperforms five other methods (Degree Centrality (DC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), PeC and Co-Expression Weighted by Clustering coefficient (CoEWC)). In particular, CDLC could improve the prediction precision by more than 45% compared with DC methods. CDLC is also compared with the latest algorithm CEPPK, and a higher precision is achieved by CDLC. CDLC is available as Supplementary materials. The default settings of active threshold and alpha-parameter are 0.8 and 0.1, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Mining sequential generator patterns and mining closed sequential patterns are important to sequence mining. They address difficulties in the mining of sequential patterns by reducing the number of sequential rules, and the results are often used to generate non-redundant sequential rules. This paper proposes an efficient method for mining non-redundant sequential rules from an attributed prefix-tree. The proposed method has two phases. In the first phase, it builds a prefix-tree that stores all the sequential patterns from a given sequence database. Then in the second phase, it mines non-redundant sequential rules from this prefix-tree. In the prefix-tree building process, each node on the prefix-tree has a field that indicates whether this node is a minimal sequential generator pattern, and another field that indicates whether this node is a closed sequential pattern. By traversing the prefix-tree, non-redundant sequential rules can be easily mined from a minimal sequential generator pattern X to a closed sequential pattern Y such that X is a prefix of Y. In addition, a good pruning mechanism is proposed to reduce the search space and the execution time in the mining process. Experimental results show that the proposed method is more efficient than existing methods in mining non-redundant sequential rules. [Copyright &y& Elsevier]

This paper aims to reveal the effect of walnut shell (WS) ash on the pore structure characteristics during Zhundong (ZD) coal sintering process. The working temperature was set as 1200 °C. The internal three-dimensional models of samples were obtained based on X-ray computed microtomography (XCT) technology while the pore evolution process and mechanism in the ash sintering process were studied, which has not been studied in previous work. The X-ray diffraction (XRD) results showed that adding 10% WS promoted the formation of gehlenite which inhibited the sintering of ash samples. A large amount of K was converted into gas and significantly promoted the formation of pore structures. Besides, the macroscopic change, pore structure, pore parameters were studied. The XCT results showed that the porosity of ZD samples was maintained at 5.46%–40.92% in the whole sintering process. The internal pore structures in ZD samples were mainly constructed of small pores (D e < 0.3 mm, where D e is the equivalent spherical diameter). After adding 10% WS ash, the porosity was increased to 42.50%–50.30%, and the overall pore size distribution also shifted to the direction of large pores. In particular, there was a super large pore inside the 10% WS samples. • The characteristics of pore structure evolution during ZD coal sintering were figured out based on XCT Technology. • Adding 10% WS ash promoted the formation of gehlenite, which inhibited the sintering of ash samples. • After adding 10% WS ash, the porosity was greatly improved. • A super large pore structure existed in the 10% WS samples. • The size of the overall pore structure increased after adding 10% WS ash. [ABSTRACT FROM AUTHOR]

• The viscosities of dendritic DPSA resins at 90 °C are below 200 mPa·s and stable for 4 h. • The flexural modulus of cured resins is up to 12.65 GPa. • The T d5 and Y r800 of the cured resins in nitrogen are above 700 °C and 93%, respectively. Dendritic poly(silylene arylacetylene) resins based on 1,3,5-triethynylbenzene were synthesized through Grignard reaction. The influences of the content of 1,3,5-triethynylbenzene on the properties of the resin including processability, mechanical properties, heat resistance and thermal stability are studied. Results show that dendritic poly(silylene arylacetylene) resins possess good processability and high mechanical properties while maintaining excellent heat-resistance and thermal stability. The viscosity of dendritic poly(silylene arylacetylene) resins at 90 °C are below 200 mPa·s, and the flexural modulus of the cured DPSA-43H resin is up to 12.65 GPa. The degradation temperatures at 5% weight loss and the residue yield at 800 °C of the cured resins in nitrogen are all above 700 °C and 93%, respectively. [ABSTRACT FROM AUTHOR]

In higher plants, the light-harvesting chlorophyll a / b -binding (Lhc) proteins function in multiple processes that are critical to plant growth, development, and abiotic stress response. However, the Lhc gene family has not been well characterized in the important fruit crop, apple (Malus × domestica Borkh.). In this study, we identified 27 Lhc genes in the apple genome. Phylogenetic analysis showed that the Lhc gene family could be classified into three major subfamilies, each of whose members shared similar conserved motifs. Evolutionary analysis indicated that duplicated MdLhc genes were primarily under purifying selection. MdLhc s were expressed at varying levels in all tissues examined and showed different expression patterns under drought stress. The overexpression of MdLhcb4.3 in transgenic Arabidopsis and apple callus enhanced their tolerance to drought and osmotic stress. Taken together, these results demonstrate the important role of Lhc proteins in the regulation of plant resistance to drought and osmotic stress and provide valuable information for further study of Lhc functions in apple. • This study revealed the presence of 27 apple light-harvesting chlorophyll a / b -binding (Lhc) family genes. • Expression profiles of MdLhc genes in different tissues and upon drought stress were analyzed. • Over-expression of MdLhcb4.3 in transgenic Arabidopsis and apple callus conferred tolerance to drought and osmotic stress, implying that it may be a candidate for stress resistance breeding in apple or other crops. [ABSTRACT FROM AUTHOR]