Testing model transformation programs using metamorphic testing

Model transformations are crucial for the success of Model Driven Engineering. Testing is a prevailing technique of verifying the correctness of model transformation programs. A major challenge in model transformation testing is the oracle problem, which refers to the difficulty or high cost in determining the correctness of the output models. Metamorphic Testing alleviates the oracle problem by making use of the relationships among the inputs and outputs of multiple executions of the target function. This paper investigates the effectiveness and feasibility of metamorphic testing in testing model transformation programs. Empirical results show that metamorphic testing is an effective testing method for model transformation programs

Effect of initial water content on soil failure mechanism of loess mudflow disasters

The frequency of mudflow disasters induced by rainfall in the Loess Plateau is increasing with the occurrence of global warming. The initial water content is one of the basic properties of soil, which affects the initiation of loess mudflow. In this work, the field study of the debris flow gullies in Yan’an City, Shaanxi Province, China, was conducted, and the main factors that induce gully loess mudflow were summarized. Based on the investigation results, a flume model was designed to carry out flume tests with different initial soil water contents. The experimental results demonstrate the following. (1) Different initial soil water contents lead to different soil failure models. The damage of soil by water flow when the soil water content is in the range of 0−5% is mainly gully erosion; that within the range of 10−15% is mainly rill surface erosion; that within the range of 20−25% is mainly dam breach failure. (2) When the water content of loess is equal to or less than 5% or equal to or greater than 20%, soil can promote the formation of loess mudflow, and the destruction of soil is more likely to cause mudflow disasters. In contrast, when the water content is within 10−15%, loess mudflow is not easily produced. The research results of the initial water content provide not only theoretical support for the study of loess mudflow disasters, but also a reference for the prevention and control of loess mudflow disasters in the Loess Plateau

Cytochrome P450 enzymes in the black-spotted frog (Pelophylax nigromaculatus): molecular characterization and upregulation of expression by sulfamethoxazole

Cytochrome P450 (CYP) enzymes are crucial for the detoxification of xenobiotics, cellular metabolism, and homeostasis. This study investigated the molecular characterization of CYP enzymes in the black-spotted frog, Pelophylax nigromaculatus, and examined the regulation of CYP expression in response to chronic exposure to the antibiotic sulfamethoxazole (SMX) at various environmental concentrations (0, 1, 10, and 100 μg/L). The full-length cDNA of Pn-CYP26B1 was identified. The sequence included open reading frames of 1,536 bp, encoding proteins comprising 511 amino acids. The signature motif, FxxGxxxCxG, was highly conserved when compared with a number of selected animal species. SMX significantly upregulated the expression of the protein CYP26B1 in frog livers at concentrations of 1 and 10 μg/L. SMX showed an affinity for CYP26B1 of −7.6 kcal/mol, indicating a potential mechanism for SMX detoxification or adaptation of the frog. These findings contributed to our understanding of the environmental impact of antibiotics on amphibian species and underscored the importance of CYP enzymes in maintaining biochemical homeostasis under exposure to xenobiotic stress

DJ-1 can inhibit microtubule associated protein 1 B formed aggregates

<p>Abstract</p> <p>Background</p> <p>Abnormal accumulation and aggregation of microtubule associated proteins (MAPs) plays an important role in the pathogenesis of neurodegenerative diseases. Loss-of-function mutation of DJ-1/Park7 can cause early onset of PD. DJ-1, a molecular chaperone, can inhibit α-synuclein aggregation. Currently, little is known whether or not loss of function of DJ-1 contributes to abnormal MAPs aggregation in neurodegenerative disorders such as PD.</p> <p>Results</p> <p>We presented evidence that DJ-1 could bind to microtubule associated protein1b Light Chain (MAP1b-LC). Overexpression of DJ-1 prevented MAP1b-LC aggregation in HEK293t and SH-SY5Y cells while DJ-1 knocking down (KD) enhanced MAP1b-LC aggregation in SH-SY5Y cells. The increase in insoluble MAP1b-LC was also observed in the DJ-1 null mice brain. Moreover, in the DJ-1 KD SH-SY5Y cells, overexpression of MAP1B-LC led to endoplasmic reticulum (ER) stress-induced apoptosis.</p> <p>Conclusion</p> <p>Our results suggest that DJ-1 acts as a molecular chaperone to inhibit MAP1B aggregation thus leading to neuronal apoptosis. Our study provides a novel insight into the mechanisms that underly the pathogenesis of Parkinson's disease (PD).</p

Overcoming Wnt–β-catenin dependent anticancer therapy resistance in leukaemia stem cells

Leukaemia stem cells (LSCs) underlie cancer therapy resistance but targeting these cells remains difficult. The Wnt–β-catenin and PI3K–Akt pathways cooperate to promote tumorigenesis and resistance to therapy. In a mouse model in which both pathways are activated in stem and progenitor cells, LSCs expanded under chemotherapy-induced stress. Since Akt can activate β-catenin, inhibiting this interaction might target therapy-resistant LSCs. High-throughput screening identified doxorubicin (DXR) as an inhibitor of the Akt–β-catenin interaction at low doses. Here we repurposed DXR as a targeted inhibitor rather than a broadly cytotoxic chemotherapy. Targeted DXR reduced Akt-activated β-catenin levels in chemoresistant LSCs and reduced LSC tumorigenic activity. Mechanistically, β-catenin binds multiple immune-checkpoint gene loci, and targeted DXR treatment inhibited expression of multiple immune checkpoints specifically in LSCs, including PD-L1, TIM3 and CD24. Overall, LSCs exhibit distinct properties of immune resistance that are reduced by inhibiting Akt-activated β-catenin. These findings suggest a strategy for overcoming cancer therapy resistance and immune escape

General Comparison of Seismic Design between the Chinese Code and the European code

To promote overseas projects, it is necessary for designers to understand and distinguish the similarities and differences between the Chinese standard GB50011(Edition 2016) and the European standard EN1998. By referring to relevant papers, comparing the ground types, response spectrum, structural importance factors, seismic precaution level and seismic zoning between the GB50011(Edition 2016) and EN1998, it can be concluded that the overall seismic design concepts in the Chinese and European codes are similar but there are some small differences in ground type classification, impact of ground type on seismic action, response spectrum, importance factor, seismic precautionary criterion, seismic precautionary measures, and seismic zone

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods