Differential cell-cycle control by oscillatory versus sustained Hes1 expression via p21

Oscillatory Hes1 expression activates cell proliferation, while high and sustained Hes1 expression induces quiescence, but the mechanism by which Hes1 differentially controls cell proliferation depending on its expression dynamics is unclear. Here, we show that oscillatory Hes1 expression down-regulates the expression of the cyclin-dependent kinase inhibitor p21 (Cdkn1a), which delays cell-cycle progression, and thereby activates the proliferation of mouse neural stem cells (NSCs). By contrast, sustained Hes1 overexpression up-regulates p21 expression and inhibits NSC proliferation, although it initially down-regulates p21 expression. Compared with Hes1 oscillation, sustained Hes1 overexpression represses Dusp7, a phosphatase for phosphorylated Erk (p-Erk), and increases the levels of p-Erk, which can up-regulate p21 expression. These results indicate that p21 expression is directly repressed by oscillatory Hes1 expression, but indirectly up-regulated by sustained Hes1 overexpression, suggesting that depending on its expression dynamics, Hes1 differentially controls NSC proliferation via p21

Visual Place Recognition From Eye Reflection

The cornea in the human eye reflects incoming environmental light, which means we can obtain information about the surrounding environment from the corneal reflection in facial images. In recent years, as the quality of consumer cameras increases, this has caused privacy concerns in terms of identifying the people around the subject or where the photo is taken. This paper investigates the security risk of eye corneal reflection images: specifically, visual place recognition from eye reflection images. First, we constructed two datasets containing pairs of scene and corneal reflection images. The first dataset is taken in a virtual environment. We showed pre-captured scene images in a 180-degree surrounding display system and took corneal reflections from subjects. The second dataset is taken in an outdoor environment. We developed several visual place recognition algorithms, including CNN-based image descriptors featuring a naive Siamese network and AFD-Net combined with entire image feature representations including VLAD and NetVLAD, and compared the results. We found that AFD-Net+VLAD performed the best and was able to accurately determine the scene in 73.08% of the top-five candidate scenes. These results demonstrate the potential to estimate the location at which a facial picture was taken, which simultaneously leads to a) positive applications such as the localization of a robot while conversing with persons and b) negative scenarios including the security risk of uploading facial images to the public

High-density and low-roughness anodic oxide formed on SiC in highly concentrated LiCl aqueous solution

The wide bandgap and high carrier mobility of silicon carbide (SiC), as well as its physical and chemical stability, make it a promising material for a number of applications. One of the key requirements for these applications involves oxide formation on SiC. The usefulness of the oxide produced by anodizing is, however, limited since the anodic oxide formed on SiC in the usual dilute aqueous solution has a low density and high surface roughness. Here, we consider a new parameter in anodic oxide formation by focusing on the concentration of free water in the electrolyte, using a highly concentrated aqueous solution. In a concentrated solution, oxygen evolution, which results in a reduction in the density of the oxide, is suppressed, and the rate of formation of anodic oxide at defect sites effectively decreases to reduce the surface roughness. Furthermore, an interfacial layer with a higher density than SiO₂ is formed between SiC and SiO₂, buffering the difference in density between them. As a result, we successfully obtained an anodic oxide with a relatively high density and low surface roughness. This study provides a new approach to improving the properties of the anodic oxide formed on SiC

Carrier transport properties of nanocrystalline Er3N@C80

Electrical transport properties of the nanocrystalline Er3N@C80 with fcc crystal structure were characterized by measuring both temperature-dependent d.c. conductance and a.c. impedance. The results showed that the Er3N@C80 sample has characteristics of n-type semiconductor and an electron affinity larger than work function of gold metal. The Er3N@C80/Au interface has an ohmic contact behavior and the contact resistance was very small as compared with bulk resistance of the Er3N@C80 sample. The charge carriers in the sample were thermally excited from various trapped levels and both acoustic phonon and ionic scatterings become a dominant process in different temperature regions, respectively. At temperatures below 250 K, the activation energy of the trapped carrier was estimated to be 35.5 meV, and the ionic scattering was a dominant mechanism. On the other hand, at temperatures above 350 K, the activation energy was reduced to 15.9 meV, and the acoustic phonon scattering was a dominant mechanism. In addition, a polarization effect from the charge carrier was observed at low frequencies below 2.0 MHz, and the relative intrinsic permittivity of the Er3N@C80 nanocrystalline lattice was estimated to be 4.6 at frequency of 5.0 MHz

Kernelized Back-Projection Networks for Blind Super Resolution

Since non-blind Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations, SR with the degradation model is required. However, this paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR. This result motivates us to revisit high-performance non-blind SR and extend it to blind SR with blur kernels. This paper proposes two SR networks by integrating kernel estimation and SR branches in an iterative end-to-end manner. In the first model, which is called the Kernel Conditioned Back-Projection Network (KCBPN), the low-dimensional kernel representations are estimated for conditioning the SR branch. In our second model, the Kernelized BackProjection Network (KBPN), a raw kernel is estimated and directly employed for modeling the image degradation. The estimated kernel is employed not only for back-propagating its residual but also for forward-propagating the residual to iterative stages. This forward-propagation encourages these stages to learn a variety of different features in different stages by focusing on pixels with large residuals in each stage. Experimental results validate the effectiveness of our proposed networks for kernel estimation and SR. We will release the code for this work.Comment: The first two authors contributed equally to this wor

CADLIVE Optimizer: Web-based Parameter Estimation for Dynamic Models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models

A RecA-mediated exon profiling method

We have developed a RecA-mediated simple, rapid and scalable method for identifying novel alternatively spliced full-length cDNA candidates. This method is based on the principle that RecA proteins allow to carry radioisotope-labeled probe DNAs to their homologous sequences, resulting in forming triplexes. The resulting complex is easily detected by mobility difference on electrophoresis. We applied this exon profiling method to four selected mouse genes as a feasibility study. To design probes for detection, the information on known exonic regions was extracted from public database, RefSeq. Concerning the potentially transcribed novel exonic regions, RNA mapping experiment using Affymetrix tiling array was performed. As a result, we were able to identify alternative splice variants of Thioredoxin domain containing 5, Interleukin1β, Interleukin 1 family 6 and glutamine-rich hypothetical protein. In addition, full-length sequencing demonstrated that our method could profile exon structures with >90% accuracy. This reliable method can allow us to screen novel splice variants from a huge number of cDNA clone set effectively

CADLIVE optimizer: web-based parameter estimation for dynamic models

Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models