Optimizing the photon ratio of red, green, and blue LEDs for lettuce seedlings: a mixture design approach.

BACKGROUND: Light control technology has been developed and studied for decades in controlled environment agriculture (CEA) for successful crop production. The effects of the light spectrum on plant growth can vary because plants have spectral specific responses, and mixed light elicits interactive combination effects. Response surface methodology (RSM) can be utilized with the design of experiments to optimize a response influenced by multiple inputs with limited data. In this study, we aimed to identify the optimal photon ratio in combination of red (R), green (G), and blue (B) light-emitting diodes (LEDs) for growing lettuce seedlings using RSM and a seedling-indicating parameter by performing a similarity analysis of response surfaces that elucidated the response tendency of different factors, such as light quality. RESULTS: The highest shoot fresh weight was obtained from the R treatment (red LED 100%) at the end of the seedling stage. However, the RGB141 (photon ratio of R:G:B = 1:4:1) treatment during the seedling stage resulted in the highest shoot fresh weight at the final harvest. The value of the leaf area multiplied by the leaf chlorophyll concentration (SPAD) was selected as the seedling-indicating parameter. The optimal RGB photon ratio that maximized this parameter was R:G:B = 30.6:44.0:25.4, and this ratio was verified by conducting identical cultivation experiments. During the first 6 days after transplanting, SPAD gradually increased in R-treated seedlings, while the optimal treatment maintained the value at a higher constant level, which supported our result of shoot fresh weight at harvest. CONCLUSIONS: Thus, we confirmed that the mixture design method allowed us to optimize the combined RGB photon ratios for the seedling stage in order to maximize the growth index of mature lettuce plants and to select an appropriate seedling-indicating parameter that represents the final harvest results to benefit crop production in CEA.

Deep Dichromatic Model Estimation Under AC Light Sources.

The dichromatic reflection model has been popularly exploited for computer vison tasks, such as color constancy and highlight removal. However, dichromatic model estimation is an severely ill-posed problem. Thus, several assumptions have been commonly made to estimate the dichromatic model, such as white-light (highlight removal) and the existence of highlight regions (color constancy). In this paper, we propose a spatio-temporal deep network to estimate the dichromatic parameters under AC light sources. The minute illumination variations can be captured with high-speed camera. The proposed network is composed of two sub-network branches. From high-speed video frames, each branch generates chromaticity and coefficient matrices, which correspond to the dichromatic image model. These two separate branches are jointly learned by spatio-temporal regularization. As far as we know, this is the first work that aims to estimate all dichromatic parameters in computer vision. To validate the model estimation accuracy, it is applied to color constancy and highlight removal. Both experimental results show that the dichromatic model can be estimated accurately via the proposed deep network.

Ghost-Free Deep High-Dynamic-Range Imaging Using Focus Pixels for Complex Motion Scenes.

Multi-exposure image fusion inevitably causes ghost artifacts owing to inaccurate image registration. In this study, we propose a deep learning technique for the seamless fusion of multi-exposed low dynamic range (LDR) images using a focus-pixel sensor. For auto-focusing in mobile cameras, a focus-pixel sensor originally provides left (L) and right (R) luminance images simultaneously with a full-resolution RGB image. These L/R images are less saturated than the RGB images because they are summed up to be a normal pixel value in the RGB image of the focus pixel sensor. These two features of the focus pixel image, namely, relatively short exposure and perfect alignment are utilized in this study to provide fusion cues for high dynamic range (HDR) imaging. To minimize fusion artifacts, luminance and chrominance fusions are performed separately in two sub-nets. In a luminance recovery network, two heterogeneous images, the focus pixel image and the corresponding overexposed LDR image, are first fused by joint learning to produce an HDR luminance image. Subsequently, a chrominance network fuses the color components of the misaligned underexposed LDR input to obtain a 3-channel HDR image. Existing deep-neural-network-based HDR fusion methods fuse misaligned multi-exposed inputs directly. They suffer from visual artifacts that are observed mostly in saturated regions because pixel values are clipped out. Meanwhile, the proposed method reconstructs missing luminance with aligned unsaturated focus pixel image first, and thus, the luma-recovered image provides the cues for accurate color fusion. The experimental results show that the proposed method not only accurately restores fine details in saturated areas, but also produce ghost-free high-quality HDR images without pre-alignment.

Deep Dichromatic Guided Learning for Illuminant Estimation.

A new dichromatic illuminant estimation method using a deep neural network is proposed. Previous methods based on the dichromatic reflection model commonly suffer from inaccurate separation of specularity, thus being limited in their use in a real-world. Recent deep neural network-based methods have shown a significant improvement in the estimation of the illuminant color. However, why they succeed or fail is not explainable easily, because most of them estimate the illuminant color at the network output directly. To tackle these problems, the proposed architecture is designed to learn dichromatic planes and their confidences using a deep neural network with novel losses function. The illuminant color is estimated by a weighted least mean square of these planes. The proposed dichromatic guided learning not only achieves compelling results among state-of-the-art color constancy methods in standard real-world benchmark evaluations, but also provides a map to include color and regional contributions for illuminant estimation, which allow for an in-depth analysis of success and failure cases of illuminant estimation.

Ambient light robust gamut mapping for optical see-through displays.

An optical see-through (OST) display is affected more severely by ambient light than any other type of displays when placed in an outdoor environment with bright illuminance because of its transparency and thus, its inherent color distortion can worsen. It is hard to directly apply existing gamut mapping methods to an OST display because of its morphological gamut characteristic and the effect of ambient light. In this paper, we propose a new robust gamut mapping method which works against bright ambient light. The process is divided into two steps: lightness mapping (LM) and chroma reproduction. LM aligns the lightness level of sRGB gamut with OST gamut and partitions the region of OST gamut based on the relative size of the sRGB gamut and its lightness value. The second step (chroma reproduction) determines an appropriate chroma reproduction method (gamut compression or extension) and a proper direction for gamut mapping based on the characteristics of each region in order to minimize the effects of ambient light. The quality of color reproduction is qualitatively and quantitatively evaluated based on accurate measurements of the displayed colors. It has been experimentally confirmed that the proposed gamut mapping method can reduce color distortion more than the existing parametric gamut mapping algorithms.

Noise-Robust Iterative Back-Projection.

Noisy image super-resolution (SR) is a significant challenging process due to the smoothness caused by denoising. Iterative back-projection (IBP) can be helpful in further enhancing the reconstructed SR image, but there is no clean reference image available. This paper proposes a novel back-projection algorithm for noisy image SR. Its main goal is to pursuit the consistency between LR and SR images. We aim to estimate the clean reconstruction error to be back-projected, using the noisy and denoised reconstruction errors. We formulate a new cost function on the principal component analysis (PCA) transform domain to estimate the clean reconstruction error. In the data term of the cost function, noisy and denoised reconstruction errors are combined in a region-adaptive manner using texture probability. In addition, the sparsity constraint is incorporated into the regularization term, based on the Laplacian characteristics of the reconstruction error. Finally, we propose an eigenvector estimation method to minimize the effect of noise. The experimental results demonstrate that the proposed method can perform back-projection in a more noise-robust manner than the conventional IBP, and harmoniously work with any other SR methods as a post-processing.

Improving Color Constancy in an Ambient Light Environment Using the Phong Reflection Model.

We present a physics-based illumination estimation approach explicitly designed to handle natural images under ambient light. Existing physics-based color constancy methods are theoretically perfect but do not handle real-world images well because the majority of these methods assume a single illuminant. Therefore, specular pixels selected using existing methods produce estimated dichromatic lines that are thick or curvilinear in the presence of ambient light, thus generating significant errors. Based on the Phong reflection model, we show that a group of specular pixels on a uniformly colored object, although they are subject to intensity thresholding, produce a unique dichromatic line length depending on the geometry of each image path. Assuming that the longest dichromatic line is the most desirable when estimating the chromaticity of an illuminant, ambient-robust specular pixels are also found on the same path on which the longest dichromatic line segment is generated. Therefore, we propose a method to find the optimal image path in which the specular pixels produce the longest dichromatic line. Even though the number of collected specular pixels is reduced using the proposed method, they are proven to be more accurate when determining the illuminant chromaticity even in the existing methods. Experiments with an established benchmark data set and a self-produced image set find that the proposed method is better able to locate the illuminant chromaticity compared with the state-of-the-art color constancy methods.

Potentiation of the anticancer effects of everolimus using a dual mTORC1/2 inhibitor in hepatocellular carcinoma cells.

There is lots of evidence to support the critical involvement of mTOR signaling in the carcinogenesis of hepatocellular carcinoma (HCC). However, it has not been determined how the roles of individual mTORC1 and mTORC2 inhibitors played in the HCC therapeutics. We thus compared the effects of everolimus, Ku0063794, and a combination of the two therapies on HCC cells, using various in vitro studies (HepG2, Hep3B, and Huh7 cells), ex vivo culturing of HCC tissues obtained from patients, and the in vivo mouse xenograft model of HCC cells. Our in vitro, ex vivo, and in vivo experiments consistently demonstrated that everolimus and Ku0063794 combination therapy was superior to individual monotherapies, as manifested by higher reduction of proliferation, migration, and invasion of HCC cells, and the higher inhibition of EMT process as well. Although individual monotherapies could not inhibit SIRT1 (positive regulator of EMT) expression, the combination therapy significantly inhibited SIRT1 expression. However, overexpression of SIRT1 mitigated the EMT-inhibiting effect of the combination therapy, suggesting that the combination therapy inhibits the EMT by way of suppressing SIRT1 expression. Therefore, when considering everolimus as an anti-HCC agent, the improved anticancer effects provided by combining it with an inhibitor of both mTORC1 and mTORC2 should be recognized.

Therapeutic potential of human adipose stem cells in a rat myocardial infarction model.

PURPOSE: Stem cell transplantation is expected to have good effects in the treatment of myocardial infarction (MI). We tested the effect of the transplantation of human adipose-derived cells (ASCs) in Sprague-Dawley (SD) rats with myocardial infarctions. MATERIALS AND METHODS: ASCs were isolated from the waste of elective abdominal surgery. The MI model was set up in SD rats by permanent ligation of the left anterior descending coronary artery. One week after MI, either 1 x 10(6) ASCs or an equal volume of phosphate-buffered saline (PBS) was injected into the infarct zone. Cardiac function was assessed by echocardiography, 1 day, 1 week, 2 weeks, and 4 weeks after treatment. Four weeks after transplantation, immunohistochemistry was performed. RESULTS: Left ventricular function, including fractional shortening (FS), and ejection fraction (EF) showed a significant improvement in the ASCs transplantation group compared to the PBS group 4 weeks after treatment (p < 0.05). The anterior wall thickness of the left ventricle was significantly thicker in the ASCs transplantation group compared to the PBS group (p < 0.01). Multiple troponin T staining, and irregular, small amounts of connexin 43 expression also was observed in the ASCs transplantation group. Infarcted myocardium showed higher capillary density in the ASCs transplantation group than in the PBS injected group (p < 0.01). CONCLUSION: This study provides encouraging evidence that transplantation of ASCs can improve cardiac function of infarct myocardium in rat models with a limitation of cardiac remodeling, improved wall thickness, and increased neovascularization.

Ferroelectricity in highly ordered arrays of ultra-thin-walled Pb(Zr,Ti)O3 nanotubes composed of nanometer-sized perovskite crystallites.

We report the first unambiguous ferroelectric properties of ultra-thin-walled Pb(Zr,Ti)O 3 (PZT) nanotube arrays, each with 5 nm thick walls and outer diameters of 50 nm. Ferroelectric switching behavior with well-saturated hysteresis loops is observed in these ferroelectric PZT nanotubes with P r and E c values of about 1.5 microC cm (-2) and 86 kV cm (-1), respectively, for a maximum applied electric field of 400 kV cm (-1). These PZT nanotube arrays (10 (12) nanotubes cm (-2)) might provide a competitive approach toward the development of three-dimensional capacitors for the terabyte ferroelectric random access memory.

Hepatic angiosarcoma manifested as recurrent hemoperitoneum.

Angiosarcoma is a rare tumor that account for less than 1% of all sarcomas. Although hepatic angiosarcoma usually presents with unspecific symptoms, it rapidly progresses and has a high mortality. We report a rare case of primary hepatic angiosarcoma manifested as recurrent hemoperitoneum.

Changes in the activity of the multifunctional beta-glycosyl hydrolase (Cel44C-Man26A) from Paenibacillus polymyxa by removal of the C-terminal region to minimum size.

Paenibacillus polymyxa GS01 secretes Cel44C-Man26A as a multifunctional enzyme with cellulase, xylanase, lichenase, and mannanase activities. Cel44C-Man26A consists of 1,352 amino acids in which present a catalytic domain (CD) of the glycosyl hydrolase family 44 (GH44), fibronectin domain type 3 (Fn3), catalytic domain of glycosyl hydrolase family 26 (GH26), and a cellulose-binding module type 3 (CBM3). A truncated Cel44C-Man26A protein, consisting of 549 amino acid residues, reacted as a multifunctional mature enzyme despite the absence of the 10 amino acids containing GH44, Fn3, GH26, and CBM3. However, the multifunctional activity was not found in the mature Cel44C-Man26A protein truncated to less than 548 amino acids. The truncated Cel44C-Man26A proteins showed the optimum pH for the lichenase activity was pH 7.0, pH 6.0 for the xylanase and mannanase, and pH 5.0 for the cellulase. The truncated Cel44C-Man26A proteins exhibited enzymatic activity 40-120% higher than the full-length Cel44C.

Two separated ileal adenocarcinomas in neurofibromatosis type 1.

Patients with neurofibromatosis-1 (NF-1) have increased susceptibility to a variety of malignancies. Here, we document a rare case of two separated ileal adenocarcinomas in NF-1. The adenocarcinomas were surrounded by a diffuse tubular adenomatous lesion of the mucosa, and ganglion cells were scattered in the NF background. We found this case meaningful for several reasons: two separated adenocarcinomas arising in an unusual ileal segment, the association with precancerous tubular adenoma, and the presence of ganglion cells, which suggests ganglioneuromatosis in NF-1.