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As an important topological property for a 3D binary image, the Euler number can be computed by finding specific a voxel block with 2 × 2 × 2 voxels, named the voxel pattern, in the image. In this paper, we introduce three strategies for enhancing the efficiency of a voxel-pattern-based Euler number computing algorithm used for 3D binary images. The first strategy is taking advantage of the voxel information acquired during computation to avoid accessing voxels repeatedly. This can reduce the average number of accessed voxels from 8 to 4 for processing a voxel pattern. Therefore, the efficiency of computation will be improved. The second strategy is scanning every two rows and processing two voxel patterns simultaneously in each scan. In this strategy, only three voxels need to be accessed when a voxel pattern is processed. The last strategy is determining the voxel accessing order in the processing voxel pattern and unifying the processing of the voxel patterns that have identical Euler number increments to one group in the computation. Although this strategy can theoretically reduce the average number of voxels accessed from 8 to 4.25 for processing a voxel pattern, it is more efficient than the above two strategies for moderate- and high-density 3D binary images. Experimental results demonstrated that the three algorithms with each of our proposed three strategies exhibit greater efficiency compared to the conventional Euler number computing algorithm based on finding specific voxel patterns in the image.

BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan–Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.

Background Diethyl aminoethyl hexanoate (DA-6), a plant growth regulator, has many beneficial effects on agricultural production. DA-6 has been applied to many plant species, but the molecular mechanism by which spraying DA-6 after anthesis regulates wheat grain filling is still unknown. Results In this study, we used four DA-6 concentrations: C0 (0 g/L), C2 (2 g/L), C4 (4 g/L), and C6 (6 g/L). The results showed that C4 and C6 led to a significantly higher 1000-grain weight and seed protein content than C0 during two wheat growing seasons. We then subjected samples at 24 days after anthesis (at which point the grain weight increased rapidly) to transcriptome analysis. Flag leaf (L), seed (S), and stem (T) samples under C6 and C0 were used for RNA-seq. The seed samples under C6 compared with C0 (S6vsS0) presented the most differentially expressed genes (DEGs; 2164). Plant hormone signal transduction (p = 1.97 × 10− 4), protein processing in the endoplasmic reticulum (ER; p = 9.04 × 10− 11) and starch and sucrose metabolism (p = 1.90 × 10− 10) pathways were the most markedly enriched pathways in the flag leaves, stems, and seeds, respectively. DEGs involved in sucrose synthesis in the flag leaves, protein processing in ER in the stems, and starch synthesis and protein processing in ER in the seeds were significantly upregulated under C6 compared with C0. Conclusions Overall, we propose a model for spraying DA-6 after anthesis to regulate metabolic pathways in wheat, which provides new insights into wheat in response to DA-6. Electronic supplementary material The online version of this article (10.1186/s12870-019-1925-5) contains supplementary material, which is available to authorized users.

Hepatocellular carcinoma (HCC) is a highly heterogeneous, multigene-driven malignant tumor. ZNF384 is an overexpressed gene with a high frequency of alteration in HCC, but research on the function of ZNF384 in HCC is lacking. In this study, the expression level of ZNF384 in HCC was analyzed through immunohistochemical (IHC) staining, Western blot analysis and qRT-PCR. We also generated ZNF384 knockdown and knockout HCC cell lines using short hairpin RNA (shRNA) and CRISPR/Cas9 systems. MTS, colony formation, and 5-ethynyl-20-deoxyuridine (EdU) assays; flow cytometry; and a xenograft mouse model were used to evaluate the effects of ZNF384 on cell proliferation. Western blot analysis, a dual luciferase reporter assay and a ChIP assay were performed to explore the potential mechanism. We found that overexpression of ZNF384 in HCC and elevated expression of ZNF384 in HCC tissues was significantly correlated with tumor recurrence (P = 0.0097). Kaplan–Meier survival analysis revealed that high expression levels of ZNF384 were correlated with poor overall survival (P = 0.0386). Downregulation of ZNF384 expression suppressed HCC cell proliferation by inhibiting the expression of Cyclin D1. These findings suggest that ZNF384 tends to act as an oncogene in the development of HCC. ZNF384 promotes the proliferation of HCC cells by directly upregulating the expression of Cyclin D1 and might serve as a prognostic predictive factor for HCC patients.

A low false positive (FP) rate is of great importance for the use of a Computer Aided Detection (CAD) system to detect pulmonary nodules in thoracic Computed Tomography (CT). However, due to the variations of nodules in appear and size, it is still a very challenging task to obtain a low FP rate. In this paper, we propose a deep Convolutional Neural Network (CNN) based transfer learning method for FP reduction in pulmonary nodule detection on CT slices. We utilized one of the state-of-the-art CNN models, VGG-16 [4], as a feature extractor to obtain nodule features, and used a support vector machine (SVM) for nodule classification. Firstly we transferred all the layers from a pre-trained VGG-16 model in ImageNet to our target networks. Then, we tuned the last fully connected layers to adjust the computer-vision-task-trained CNN model to pulmonary nodule classification task. The initial CNN filter weights were then optimized using the training data, i.e., the pulmonary nodule patch images and corresponding labels through back-propagation so that they better reflected the modalities in the pulmonary nodule image dataset. Finally, features learned in the fine-tuned CNN were used to train a SVM classifier. The output of the trained SVM was used for final classification. Experimental results show that the overall sensitivity of the proposed method was 87.2% with 0.39 FPs per scan, which is higher than 85.4% with 4 FPs per scan obtained by other state of art method.

Deformed microstructure of the adiabatic shear bands in zirconium alloy impacted by split Hopkinson pressure bar at a strain rate of about 2300 s-1 was characterized systemically. Four different strains of zirconium alloy subjected to impact loading at same strain rate were designed by means of strain stopper rings. At a strain of 0.30, the transformed bands were distinguished at the tip of crack. The transformed bands mainly composed of the ultrafine and equiaxed grains with the mean diameter of about 100~300 nm were found by a dual beam FIB/TEM system. Besides ultrafine grains in the transformed bands, nanometer fine grain was observed and confirmed by HRTEM. Thus, the transformed bands mainly composed of the mixed microstructure including nanometer and ultrafine grains were confirmed, and n phase transformation and amorphization in transformed bands take place.

According to the characteristics of photovoltaic cell output power curve, this paper analyzed and explained the principle of Maximum Power Point Tracking (MPPT) and both advantages and disadvantages of constant voltage tracking method & perturbation observation method. Further, in combination with the advantages of existing maximum power tracking methods, this paper comes up with an improved tracking method which is recognized as maximum power point tracking combined with constant voltage tracking method & variable step-size perturbation observation method. The Simulink simulation results have proven this enhanced tracking method has a better performance in System response and steady state characteristics.

With the complex of the tunnel construction, tunnel construction become more and more difficult, in order to ensure the safety of tunnel construction, the paper introduced a kind of tunnel construction monitoring system based on sensor. The system achieves the real- time monitoring of tunnel construction environment including temperature and humidity, gas concentration, dust concentration, location tracking for construction workers through the wireless communication technology, to control of the real-time status of the tunnel, and ensure timely rescue when the accident occurred.

One of the major problems for computer-aided pulmonary nodule detection in chest radiographs is that a high false-positive (FP) rate exists. In an effort to overcome this problem, a new method based on the MTANN (Massive Training Artificial Neural Network) is proposed in this paper. An MTANN comprises a multi-layer neural network where a linear function rather than a sigmoid function is used as its activity function in the output layer. In this work, a mixture of multiple MTANNs were employed rather than only a single MTANN. 50 MTANNs for 50 different types of FPs were prepared firstly. Then, several effective MTANNs that had higher performances were selected to construct the MTANNs mixture. Finally, the outputs of the multiple MTANNs were combined with a mixing neural network to reduce various different types of FPs. The performance of this MTANNs mixture in FPs reduction is validated on three different versions of commercial CAD software with a validation database consisting of 52 chest radiographs. Experimental results demonstrate that the proposed MTANN approach is useful in cutting down FPs in different CAD software for detecting pulmonary nodules in chest radiographs.

Accurate lung segmentation is an essential step in developing a computer-aided lung disease diagnosis system. However, because of the high variability of computerized tomography (CT) images, it remains a difficult task to accurately segment lung tissue in CT slices using a simple strategy. Motived by the aforementioned, a novel CT lung segmentation method based on the integration of multiple strategies was proposed in this paper. Firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. The proposed method was validated on a CT database consisting of 23 scans, including a number of 883 2D slices (the number of slices per scan is 38 slices), by comparing it to the commonly used lung segmentation method. Experimental results show that the proposed method accurately segmented lung regions in CT slices.

Lung cancer has been the most common cancer in the world. Early detection is the most important for reducing the death due to lung cancer. Chest radiography has been widely and frequently used for detection and diagnosis on lung cancer. To assess pathological changes in chest radiographs, radiologists often compare the previous chest radiograph and the current one obtained from the same patient at different times. A temporal subtraction image, which is constructed subtracting the previous radiograph from the current one, is often used to support this comparison work. This paper presents a multilayered partitioning image registration method for chest-radiograph temporal subtraction. First, we used global matching based on the mutual information of ribs. Then, we divide the images after global matching into four groups of temple subareas used multilayered partitioning method. For individual local subarea in each group, we use a genetic algorithm to efficiently find its corresponding area in the previous image. By the result of genetic algorithm, we construct the new matching image according to the four groups of temple subareas, and subtract the matching image to construct the temporal subtraction image.

Lung cancer has been the most common cancer in the world. Early detection is the most important for reducing the death due to lung cancer. Chest radiography has been widely and frequently used for detection and diagnosis on lung cancer. To assess pathological changes in chest radiographs, radiologists often compare the previous chest radiograph and the current one obtained from the same patient at different times. A temporal subtraction image, which is constructed subtracting the previous radiograph from the current one, is often used to support this comparison work. This paper presents a mutual-information-based image registration method for chest-radiograph temporal subtraction. First, we extract the lung regions of original radiographs. Then we get the center lines of lungs by the outer contours of lungs. We set the centre lines of lungs verticality and coincident in horizontal. We extract rib cages from the images of lung regions after centre line aligning. Then we constantly transform the rib cage image of the previous radiograph. As transforming, we calculate the mutual information between two rib cages images. We improve the transform parameters based on the mutual information, and subtract the radiographs translated by using the best parameters to construct the temporal subtraction image.

SUMMARY This paper proposes a new first-scan method for two-scan labeling algorithms. In the first scan, our proposed method first scans every fourth image line, and processes the scan line and its two neighbor lines. Then, it processes the remaining lines from top to bottom one by one. Our method decreases the average number of times that must be checked to process a foreground pixel will; thus, the efficiency of labeling can be improved.

To assess pathological chest change, radiologists compare the same patient's chest radiographs taken at different times. Supporting radiologists' diagnostics, temporal-subtraction images constructed from the previous and current radiographs have enhanced the visualization of pathological change. This paper presents a genetic-algorithm-based temporal subtraction for chest radiographs. First, we extract ribs from previous and current images and use them for global matching of the two images. Then, we divide the lung area in the current image into many subareas. For individual subarea, we use the genetic algorithm for local matching to find its corresponding area in the previous image efficiently. Results demonstrated that pathological change were accurately enhanced in temporal-subtraction images without major misregistration artifacts, accurately visualizing of pathological change and proving useful in improving radiologists, diagnostic performance.

This paper presents a fast linear-time two-scan algorithm for labeling connected components in binary images. In the first scan, provisional labels are assigned to object pixels in the same way as do most conventional labeling algorithms. To improve efficiency, we use corresponding equivalent label sets and a representative label table for resolving label equivalences. When the first scan is finished, all provisional labels belonging to each connected component in a given image are combined in the corresponding equivalent label set, and they are assigned a unique representative label with the representative label table. During the second scan, by use of the completed representative label table, all provisional labels belonging to each connected component are replaced by their representative label. Our algorithm is very simple in principle, and is easy to implement. Experimental results demonstrated that the efficiency of our algorithm is superior to that of other labeling algorithms.

This paper presents an improvement of Herbrand's theorem. We propose a method for specifying a subuniverse of the Herbrand universe of a clause set S for each argument of predicate symbols and function symbols in S. We prove that a clause set S is unsatisfiable if and only if there is a finite unsatisfiable set of ground instances of clauses of S that are derived by only instantiating each variable, which appears as an argument of predicate symbols or function symbols, in S over its corresponding argument's sub-universe of the Herbrand universe of S. Because such sub-universes are usually smaller (sometimes considerably) than the Herbrand universe of S, the number of ground instances may decrease considerably in many cases. We present an algorithm for automatically deriving the sub-universes for arguments in a given clause set, and show the correctness of our improvement. Moreover, we introduce an application of our approach to model generation theorem proving for non-range-restricted problems, show the range-restriction transformation algorithm based on our improvement and provide examples on benchmark problems to demonstrate the power of our approach.

This paper presents a method for multi-agent cooperative reasoning with E-knowledge and I-knowledge. We explain the meanings of E-knowledge and I-knowledge and show their importance for multi-agent cooperative reasoning. We consider how to translate E-knowledge and I-knowledge statements with the possible-world semantics into their corresponding statements in first order logic. Communication among agents is also incorporated into our method. An example is given to show how to use E-knowledge and I-knowledge to describe and solve multi-agent cooperative reasoning problems.

We propose a query processing method for amalgamated knowledge bases. Our query processing method is an extension of the magic sets technique for query processing in amalgamated knowledge bases, augmented with the capabilities of handling amalgamated atoms. Through rewriting rules in a given amalgamated knowledge base, our method offers the advantages associated with top-down as well as bottom-up evaluation. We discuss how to handle amalgamated atoms, consider how to check whether an amalgamated atom is satisfiable in a fact set and how to extend a fact set by inserting an amalgamated atom. We also give the transformation procedures for amalgamated knowledge databases and show the correctness of our method.

This paper considers the problem of generating various calligraphy from some sample fonts. Our method is based on the deformable contour model g-snake. By representing the outline of each stroke of a character with a g-snake, we cast the generation problem into global and local deformation of g-snake under different control parameters, where the local deformation obeys the energy minimization principle of regularization technique. The base values of the control parameters are learned from given sample fonts. The experimental results on alphabet and Japanese characters Hiragana show such processing as a reasonable method for generating calligraphy.

This paper presents an run-based efficient label-equivalence-based connected-component labeling algorithms for threedimensional binary images. Our algorithm is run-based. Instead of assigning each foreground voxel, we assign each run a provisional label. Moreover, we also use run data to label foreground voxels without scanning any background voxel in the second scan. Experimental results have demonstrated that our algorithm is much more efficient than conventional three-dimensional labeling algorithms.

This paper presents a mutual-entropy-based global registration method for temporal subtraction of chest radiographs. Our global registration method consists of two steps. In the first step, we make a rough registration by calculating the mutual entropy of on the whole areas of the previous radiograph and the current radiograph, and in the second step, we make an accurate registration by calculating the mutual entropy of on the rib areas only. Experimental results demonstrated that the visualization of pathological change in the temporal subtraction images constructed by our global registration method is better than that constructed by convenient global registration methods.

Our purpose was to reduce false-positive (FP) detections generated by a computerized lesion detection scheme by using a "mixture of expert" massive-training artificial neural networks (MTANNs). Multiple MTANNs were trained with "ensemble training" for reduction of diverse types of non-lesions. We started from a seed MTANN trained with lesions and non-lesions of a seed type. We applied the trained seed MTANN to lesions and various types of non-lesions to analyze the weakness of the seed MTANN. We arranged the output scores of the MTANN for lesions and non-lesions in an ascending order to form the score scale representing the degree of difficulty in distinction between lesions and non-lesions by the seed MTANN. The score scale was divided into several segments, and ten non-lesions were sampled from the center of each segment so that sets of non-lesion samples covered diverse difficulties. We trained several MTANNs with several sets of non-lesions so that each MTANN became an expert for the non-lesions at a certain level of difficulty. We then combined expert MTANNs with a mixing ANN to form a "mixture of expert" MTANNs. Our database consisted of CT colonography datasets acquired from 100 patients, including 26 polyps. We applied our initial CAD scheme to this CTC database. FP sources included haustral folds, stool, colonic walls, the ileocecal valves, and rectal tubes. The mixture of expert MTANNs distinguished all polyps correctly from more than 50% of the non-polyps. Thus, the mixture of expert MTANNs was able reduce one half of the FPs generated by a computerized polyp detection scheme while the original sensitivity was maintained. We compare the effectiveness of ensemble training with that of training with manually selected cases. The performance of the MTANNs with ensemble training was superior to that of the MTANNs trained with manually selected cases.