The balance of Polo-like kinase 1 in tumorigenesis

Polo-like kinase 1 (Plk1) belongs to a family of conserved serine/threonine kinases with a polo-box domain, which have similar but non-overlapping functions in the cell cycle progression. Plk1 plays a key role to ensure the normal mitosis. Interestingly, overexpression of Plk1 is associated with tumor development and could serve as a prognostic marker for many cancers. Due to Plk1 overexpression, several Plk1 inhibitors have been developed and tested for the cancer treatment. However, in a recent study, it has been suggested that down-regulation of Plk1 could also induce aneuploidy and tumor formation in vivo. Therefore, a normal level of Plk1 is important for mitosis. And caution should be taken when Plk1 inhibitors are used in the clinical trial and their side effects including tumorigenesis should be carefully evaluated

An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm

Aluminum with dispersed nanoparticles by laser additive manufacturing.

While laser-printed metals do not tend to match the mechanical properties and thermal stability of conventionally-processed metals, incorporating and dispersing nanoparticles in them should enhance their performance. However, this remains difficult to do during laser additive manufacturing. Here, we show that aluminum reinforced by nanoparticles can be deposited layer-by-layer via laser melting of nanocomposite powders, which enhance the laser absorption by almost one order of magnitude compared to pure aluminum powders. The laser printed nanocomposite delivers a yield strength of up to 1000 MPa, plasticity over 10%, and Young's modulus of approximately 200 GPa, offering one of the highest specific Young's modulus and specific yield strengths among structural metals, as well as an improved specific strength and thermal stability up to 400 °C compared to other aluminum-based materials. The improved performance is attributed to a high density of well-dispersed nanoparticles, strong interfacial bonding between nanoparticles and Al matrix, and ultrafine grain sizes

Stretching Micro Metal Particles into Uniformly Dispersed and Sized Nanoparticles in Polymer.

There is a longstanding challenge to disperse metal nanoparticles uniformly in bulk polymers for widespread applications. Conventional scale-down techniques often are only able to shrink larger elements (such as microparticles and microfibers) into micro/nano-elements (i.e. nanoparticles and nanofibers) without much altering their relative spatial and size distributions. Here we show an unusual phenomenon that tin (Sn) microparticles with both poor size distribution and spatial dispersion were stretched into uniformly dispersed and sized Sn nanoparticles in polyethersulfone (PES) through a stack and draw technique in thermal drawing. It is believed that the capillary instability plays a crucial role during thermal drawing. This novel, inexpensive, and scalable method overcomes the longstanding challenge to produce bulk polymer-metal nanocomposites (PMNCs) with a uniform dispersion of metallic nano-elements

Application of D-S Evidence Fusion Method in the Fault Detection of Temperature Sensor

Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault