Study of the Structure-Related Functions of Eukaryotic Primase-POL ALPHA Complex During Replication

During eukaryotic replication primase•polymerase α (prim•polα) complex synthesizes de novo chimeric primers composed of about 10 nt RNA and 20 nt DNA, which are subsequently extended by main replicative DNA polymerases (pol), polε and polδ, on leading and lagging strands, respectively. It is estimated that prim•polα initiates more than 10 millions of lagging strand Okazaki fragments in human genome in each replication cycle. A concerted action of the two active sites, RNA pol and DNA pol, is required to ensure the efficient priming. A remarkable feature of the prim•polα complex is the “programmed” synthesis of the chimeric primer, where the lengths of the RNA and DNA parts are tightly regulated. It is likely achieved by emerging intrinsic structural features of the complex and components of replication fork. To get a better understanding of the mechanism and biological importance of priming by the primase•polα, we utilized biochemical and genetic approaches to examine the protein-protein interactions, de novo synthesis and primer extension by prim•polα and the genome stability in primase mutants. A direct interaction between the N-terminal domain of the human primase accessory subunit (p58N) and the C-terminal domain of the polα (p180C) catalytic subunit was found. The function of the C-terminal domain of primase containing Fe-S cluster and linker connecting it with p58N in the regulation of primase and polα synthesis was revealed. A novel interaction between the C-terminal domain with the 5’ triphosphate group of the RNA primer as well as the phosphodiester backbone of the template at the primer/template junction defines the length of the RNA primer, and the position where polα synthesis starts. We describe two mechanisms of decrease of apparent processivity of polα induced by divalent metal ions. The increased genomic instability in yeast mutants defective in primer initiation during Okazaki fragment synthesis led to a hypothesis on mutation-prone Okazaki fragment maturation when priming is delayed

MicroRNA-587 antagonizes 5-FU-induced apoptosis and confers drug resistance by regulating PPP2R1B expression in colorectal cancer.

Drug resistance is one of the major hurdles for cancer treatment. However, the underlying mechanisms are still largely unknown and therapeutic options remain limited. In this study, we show that microRNA (miR)-587 confers resistance to 5-fluorouracil (5-FU)-induced apoptosis in vitro and reduces the potency of 5-FU in the inhibition of tumor growth in a mouse xenograft model in vivo. Further studies indicate that miR-587 modulates drug resistance through downregulation of expression of PPP2R1B, a regulatory subunit of the PP2A complex, which negatively regulates AKT activation. Knockdown of PPP2R1B expression increases AKT phosphorylation, which leads to elevated XIAP expression and enhanced 5-FU resistance; whereas rescue of PPP2R1B expression in miR-587-expressing cells decreases AKT phosphorylation/XIAP expression, re-sensitizing colon cancer cells to 5-FU-induced apoptosis. Moreover, a specific and potent AKT inhibitor, MK2206, reverses miR-587-conferred 5-FU resistance. Importantly, studies of colorectal cancer specimens indicate that the expression of miR-587 and PPP2R1B positively and inversely correlates with chemoresistance, respectively, in colorectal cancer. These findings indicate that the miR-587/PPP2R1B/pAKT/XIAP signaling axis has an important role in mediating response to chemotherapy in colorectal cancer. A major implication of our study is that inhibition of miR-587 or restoration of PPP2R1B expression may have significant therapeutic potential to overcome drug resistance in colorectal cancer patients and that the combined use of an AKT inhibitor with 5-FU may increase efficacy in colorectal cancer treatment

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page

Comparisons of Friction Characteristics of a Lightly Loaded Pin Sliding Over Magnetic Disks Coated With Polar and Non-Polar PFPE Lubricants

ABSTRACT This paper deals with the measurement of friction force exerted on molecularly thin lubricant film surfaces using a specially arranged pin-on-disk type friction tester. The measurements were carried out by sliding a 1.5-mm-diameter glass ball slider on a rotating disk surface with small loading force. Polar and non-polar PFPE lubricants were dip-coated on magnetic disks covered with diamond-like-carbon (DLC) film. Lubricant film thickness was varied to constitute multiple layered film structures on the DLC surface. To clarify the stratified effect of thin lubricant film on friction, a lightly loading force and a slow rotational speed were selected. The tested results showed that the friction force on non-polar lubricant surfaces increase slightly for mono-layer and multi-layer cases, while the friction force on polar lubricants show steady and gradual increase with increasing loading force. We conclude that friction force at small loading force is dependent intimately on the thickness, molecular weight and end-group functionality

Hierarchical temperature imaging using pseudoinversed convolutional neural network aided TDLAS tomography

As an in situ combustion diagnostic tool, Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for imaging of two-dimensional temperature distributions in reactive flows. Compared with the computational tomographic algorithms, Convolutional Neural Networks (CNNs) have been proofed to be more robust and accurate for image reconstruction, particularly in case of limited access of laser beams in the Region of Interest (RoI). In practice, flame in the RoI that requires to be reconstructed with good spatial resolution is commonly surrounded by low-temperature background. Although the background is not of high interest, spectroscopic absorption still exists due to heat dissipation and gas convection. Therefore, we propose a Pseudo-Inversed CNN (PI-CNN) for hierarchical temperature imaging that (a) uses efficiently the training and learning resources for temperature imaging in the RoI with good spatial resolution, and (b) reconstructs the less spatially resolved background temperature by adequately addressing the integrity of the spectroscopic absorption model. In comparison with the traditional CNN, the newly introduced pseudo inversion of the RoI sensitivity matrix is more penetrating for revealing the inherent correlation between the projection data and the RoI to be reconstructed, thus prioritising the temperature imaging in the RoI with high accuracy and high computational efficiency. In this paper, the proposed algorithm was validated by both numerical simulation and lab-scale experiment, indicating good agreement between the phantoms and the high-fidelity reconstructions.Comment: Submitted to IEEE Transactions on Instrumentation and Measuremen

Ultrathin MgB2 films fabricated on Al2O3 substrate by hybrid physical-chemical vapor deposition with high Tc and Jc

Ultrathin MgB2 superconducting films with a thickness down to 7.5 nm are epitaxially grown on (0001) Al2O3 substrate by hybrid physical-chemical vapor deposition method. The films are phase-pure, oxidation-free and continuous. The 7.5 nm thin film shows a Tc(0) of 34 K, which is so far the highest Tc(0) reported in MgB2 with the same thickness. The critical current density of ultrathin MgB2 films below 10 nm is demonstrated for the first time as Jc ~ 10^6 A cm^{-2} for the above 7.5 nm sample at 16 K. Our results reveal the excellent superconducting properties of ultrathin MgB2 films with thicknesses between 7.5 and 40 nm on Al2O3 substrate.Comment: 7 pages, 4 figures, 2 table

Rethinking Knowledge Graph Propagation for Zero-Shot Learning

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the graph structure share statistical strength allowing generalization to new classes when faced with a lack of data. However, multi-layer architectures, which are required to propagate knowledge to distant nodes in the graph, dilute the knowledge by performing extensive Laplacian smoothing at each layer and thereby consequently decrease performance. In order to still enjoy the benefit brought by the graph structure while preventing dilution of knowledge from distant nodes, we propose a Dense Graph Propagation (DGP) module with carefully designed direct links among distant nodes. DGP allows us to exploit the hierarchical graph structure of the knowledge graph through additional connections. These connections are added based on a node's relationship to its ancestors and descendants. A weighting scheme is further used to weigh their contribution depending on the distance to the node to improve information propagation in the graph. Combined with finetuning of the representations in a two-stage training approach our method outperforms state-of-the-art zero-shot learning approaches.Comment: The first two authors contributed equally. Code at https://github.com/cyvius96/adgpm. To appear in CVPR 201