Integrated Regulation of Class II Human Endogenous Retroviruses in a Breast Cancer Cell Line

Endogenous retroviruses (ERVs) are still regarded as foreign invaders by most biologists. Because of structural and positional homology of ERVs in human and ape genomes, they have been considered molecular evidences of common ancestry. Using a breast cancer cell line, we analyzed the regulatory features of a group of human endogenous retroviruses (HERV-K), and found that they contain multiple sequence motifs subjecting them to regulation by sex hormones, a stem cell-specific transcription factor (OCT4), and DNA methylation. Mutation of the OCT4 motif abrogates their response to sex hormones, while methylation of a progesterone-response element enhances receptor-binding. We also found that solo LTRs of HERVK enable hormonal regulation of downstream cellular genes. The findings support the hypothesis that ERVs are integral parts of eukaryotic genomes and are designed to regulate interspersed genes, especially in reproduction and development

On-line part deformation prediction based on deep learning

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data

Effect of Interfacial Atomic Mixing on the Thermal Conductivity of Multi-Layered Stacking Structure

Multi-layered stacking structures and atomic mixing interfaces were constructed. The effects of various factors on the thermal conductivity of different lattice structures were studied by non-equilibrium molecular dynamics simulations, including the number of atomic mixing layers, temperature, total length of the system, and period length. The results showed that the mixing of two and four layers of atoms can improve the thermal conductivities of the multi-layer structure with a small total length due to a phonon bridge mechanism. When the total length of the system is large, the thermal conductivity of the multi-layer structure with atomic mixing interfaces decreases significantly compared with that of the perfect interfaces. The interfacial atom mixing destroys the phonon coherent transport in the multi-layer structure and decreases the thermal conductivity to some extent. The thermal conductivity of the multi-layer structure with perfect interfaces is significantly affected by temperature, whereas the thermal conductivity of the multi-layer structures with atomic mixing is less sensitive to temperature

Laplace neural operator for complex geometries

Neural operators have emerged as a new area of machine learning for learning mappings between function spaces. Recently, an expressive and efficient architecture, Fourier neural operator (FNO) has been developed by directly parameterising the integral kernel in the Fourier domain, and achieved significant success in different parametric partial differential equations. However, the Fourier transform of FNO requires the regular domain with uniform grids, which means FNO is inherently inapplicable to complex geometric domains widely existing in real applications. The eigenfunctions of the Laplace operator can also provide the frequency basis in Euclidean space, and can even be extended to Riemannian manifolds. Therefore, this research proposes a Laplace Neural Operator (LNO) in which the kernel integral can be parameterised in the space of the Laplacian spectrum of the geometric domain. LNO breaks the grid limitation of FNO and can be applied to any complex geometries while maintaining the discretisation-invariant property. The proposed method is demonstrated on the Darcy flow problem with a complex 2d domain, and a composite part deformation prediction problem with a complex 3d geometry. The experimental results demonstrate superior performance in prediction accuracy, convergence and generalisability.Comment: 21 pages, 15 figure

Special issue on digital enterprise technologies (editorial)

The special issue includes carefully selected papers presented at the 9th International Conference on Digital Enterprise Technology (DET2016) which was held on 29-31 March, 2016 in Nanjing, China. Authors were invited to re-write, extend and significantly improve their papers presented at DET2016. The main aim of the conference is to provide an international forum for the exchange of leading edge scientific knowledge and industrial experiences, regarding the development, integration and applications of the various aspects of Digital Enterprise Technologies, in the global manufafturing of the knowledge economy era

Spcial issue on Digital Enterprise Technologies in Manufacturing

The guest editors are delighted to present this special issue on Digital Enterprise Technologies in Manufacturing to international researchers and practioners in the manufacturing and related technology and service sectors. The special issue includes carefully selected papers presented at the 9th International Conference on Digital Enterprise Technology held on 29-31 of March 2016 in Nanjing, China. Authors were invited to re-write, extend and significantly improve their papers presented at DET2016

Transcription of Human Endogenous Retroviruses During the Menstrual Cycle Suggests Coordinated Hormonal Regulation

Scattered among human and animal genomes are a class of repetitive genetic elements called endogenous retroviruses (ERVs), which are generally considered remnants of ancient viral infections. Because humans and chimpanzees share ERVs at similar genomic positions, evolutionists use these elements as another argument for common ancestry. From a creationist perspective, ERVs may have been created in strategic locations of the genome to perform essential functions, such as synchronized regulation of interspersed genetic elements. Since some human endogenous retroviruses (HERVs) contain putative steroid hormone-response elements, it would be reasonable that expression of such HERVs would be controlled by sex hormones, and might even demonstrate temporal patterns during the female menstrual cycle. Accordingly, we quantified the transcription dynamics of multiple HERV elements in peripheral blood leukocytes using SYBR Green-based RT-PCR in male and female human subjects. Preliminary data indicated that expression of HERVs indeed followed a temporal pattern in females. Moreover, transcription activity of ERV genes was strongly correlated with blood levels of progesterone. The same pattern was demonstrated for HERV-K elements and the syncytin-1 gene encoded by ERVWE1. These results suggest coordinated regulation of some ERV elements by progesterone in the female

Special issue: Best in China–Smart manufacturing

The guest editors are delighted to present this special issue to international researchers and practioners in the manufacturing and related industries, especially in high precision technologies for high value specialised products from micro to very large sizes

Effect of Tube Geometry and Curvature on Film Condensation in the Presence of a Noncondensable Gas

Based on the double boundary layer theory, a generalized mathematical model was developed to study the distributions of gas film, liquid film, and heat transfer coefficient along the tube surface with different geometries and curvatures for film condensation in the presence of a noncondensable gas. The results show that: (i) for tubes with the same geometry, gas film thickness, and liquid film thickness near the top of the tube decrease with the increasing of curvature and the heat transfer rate increases with it. (ii) For tubes with different geometries, one need to take into account all factors to compare their overall heat transfer rate including gas film thickness, liquid film thickness and the separating area. Besides, the mechanism of the drainage and separation of gas film and liquid film was analyzed in detail. One can make a conclusion that for free convection, gas film never separate since parameter A is always positive, whereas liquid film can separate if parameter B becomes negative. The separating angle of liquid film decreases with the increasing of curvature

A new method for inferencing and representing workpiece residual stress field using monitored deformation force data

The residual stress inside stock materials is one of the fundamental properties related to the quality of manufactured parts, in terms of geometric/dimensional stability and fatigue life. For parts of large size and high precision requirements, accurate measuring and predicting the residual stress field has been a major challenge. Existing technologies to measure residual stress field are either strain-based measurement methods or non-destructive methods with low efficiency and accuracy. This paper reports a new non-destructive method for inferencing the residual stress field based on deformation forces. The residual stress field of a workpiece was inferred based on the characteristics that the deformation forces reflected the overall effect of unbalanced residual stress field after material removal operations. The relationship between deformation forces and the residual stress field was modeled based on the principle of virtual work, and the residual stress field inference problem was solved by an enforced regularization method. The theoretical verification is presented and actual experiment cases tested, shown reliable accuracy and flexibility for large aviation structural parts. The underlying principle of the method provides an important reference for predicting and compensating workpiece deformation caused by residual stress using dynamic machining monitoring data in the digital and intelligent manufacturing context