Does Price Signal Quality? Strategic Implications of Price as a Signal of Quality for the Case of Genetically Modified Food

We add to the limited empirical literature on consumers' use of price as a quality signal by testing if the traditional downward-sloping consumption-price relationship fails to hold for GM products using data collected from a nationally representative mail survey featuring several hypothetical product choice scenarios. Statistical evidence is mixed across the three products investigated but suggests that survey respondents use price as a signal of the quality of GM products. Implications for firm strategy are discussed.Conjoint analysis, genetically modified food, pricing strategy, price-quality relationship, Demand and Price Analysis,

Does Price Signal Quality? Strategic Implications of Price as a Signal of Quality for the Case of Genetically Modified Food

When products are differentiated and quality is highly subjective (e.g., fashion or art), novel (e.g., a new feature), or difficult to verify prior to purchase (e.g., credence attributes), consumers may turn to price as a signal of quality. Products containing genetically modified (GM) ingredients meet each of these criteria, i.e., GM ingredients are novel, their presence is difficult to verify, and their impact on subjective quality may be viewed differently across individuals with the same knowledge. We add to the limited empirical literature on consumers' use of price as a quality signal by testing for non-monotonicity of consumer demand in price for GM products using data collected from a nationally representative mail survey featuring several hypothetical product choice scenarios. We find mixed evidence across three products for non-monotonicity of demand in price and argue the results suggest that survey respondents use price as a signal of the quality of GM products for at least one of the three products investigated. Implications for firm strategy and regulation are discussed.Research and Development/Tech Change/Emerging Technologies,

Piper retrofractum

Photoaging occurs by UVB-irradiation and involves production of reactive oxygen species (ROS) and overexpression of matrix metalloproteinases (MMPs), leading to extracellular matrix damage. Piper retrofractum Vahl. is used as a traditional medicine for antiflatulence, expectorant, sedative, and anti-irritant; however, its antiphotoaging effect has not yet been studied. The current study investigated the antiphotoaging effect of standardized Piper retrofractum extract (PRE) on UVB-damaged human dermal fibroblasts and hairless mouse skin. PRE treatment activated the peroxisome proliferator-activated receptor delta (PPARδ) and the adenosine monophosphate-activated protein kinase (AMPK), consequently upregulating mitochondrial synthesis and reducing ROS production. Additionally, PRE inhibited MMPs expression via suppressing mitogen-activated protein kinase (MAPK) and activator protein-1 (AP-1). PRE downregulated UVB-induced inflammatory reactions by inhibiting the nuclear factor-kappa B (NF-κB) activity. PRE also enhanced transforming growth factor-beta (TGF-β) and the Smad signaling pathway, thereby promoting procollagen gene transcription. Furthermore, oral administration of PRE (300 mg/kg/day) similarly regulated the signaling pathways and increased antioxidant enzyme expression, thus attenuating physiological deformations, such as wrinkle formation and erythema response. Collectively, these results suggest that PRE acts as a potent antiphotoaging agent via PPARδ and AMPK activation

??????????????? ????????? ????????? ?????? ??? ????????????

??????????????? ????????? ???????????? ???????????? ?????? ?????????????????? ??????????????? ?????? ???????????? ??????. ??? ??????????????? ??????????????? ??? ?????? ????????? ???????????? ????????? ??????????????? ????????? ???????????????. ?????? ??????????????? ????????? ??? ?????? ????????? ???????????????, ??? ?????? ????????? diethylnitrosamine (DEN)??? C3H/HeN ?????? ?????? ?????? ???????????? ??? ?????? ????????? ????????? ???????????????. DEN?????? ????????? ???????????? ?????? alkaline phosphatase (ALP) ??????, TUNEL positive ???????????? ??????, ??? ???????????? ?????? ???????????? duct??? ??????, ?????????????????? ????????????, Masson???s trichrome ???????????? ????????? ???????????? ???, ?????? ?????? ?????? ??? ????????? ????????? ???????????? ?????? ???????????? ?????? ?????? ????????? ??? ?????????. ?????????, ??????????????? ?????? ???????????? ????????? ?????? ?????????????????? ??????, ?????? ?????? ??? ????????? ???????????? ??????????????? ???????????? ???????????? ???????????? ?????? ?????? ????????? ??? ?????????. ???????????? ???????????? ????????? ???????????? ??????, ???????????? ???????????? ???????????? ?????? ????????? ???, solvent partition ????????? ???????????? ????????? ???????????? hexane, ethyl acetate, water ???????????? ???????????????. ?????? ??????????????? ?????? ????????? ??????????????? ??????????????? ???, ethyl acetate ???????????? ??????????????? ????????? ?????????????????? ??????????????? ?????? ??????????????? ???????????? ????????? ????????? ????????? ??? ?????????. ????????? ethyl acetate???????????? ???????????? ????????? ????????? ??? ?????????, ??????????????? ????????? ??? ?????? ????????? ????????? ?????????. ???????????????, ??????????????? ????????? ????????????????????? ?????? ????????? ??? ?????? ????????? ???????????? ?????? ??????????????? ????????? ??? ?????? ????????? ????????? ????????? ?????? ???????????????. ?????????, ?????? ??????????????? ?????? ???????????? ?????? ??? ?????? ?????? ???????????? ??????????????? ?????? ????????? ????????? ??? ?????? ????????? ???????????? ??????.clos

Measuring the maturity of open access: a preliminary study

Open access is an important part of scholarly communication, and it has been a global phenomenon. The growth of open access brings several signif-icant benefits to the general public as well as researchers, ultimately leads to the advancement of science. For the continuous growth and development of open access, it is necessary to measure the degree of maturity of open ac-cess. However, there is not much discussion about the assessment frame-work for open access. This study aims to propose an assessment framework of open access maturity. For the purpose of this study, we conducted an analysis with a total of 24 literatures relevant to the digital maturity, the ma-turity of open data/open science, and major open access initiatives. For digi-tal maturity, 18 articles were analyzed: 10 articles for generic purpose model, and 8 articles for industry-specific model. In addition, three articles on the maturity of open data/open science were analyzed and three major open ac-cess initiatives. In preliminary analysis results, three dimensions with 13 be-longing items were proposed for measuring the maturity of open access. Three dimensions are OA Policy, OA capability, and Openness quality. For OA policy, there are three items such as OA policy document, OA govern-ance, and OA strategy. For OA Capability, finance for OA, people for OA, culture for OA, and collaboration for OA are proposed. For Openness Quali-ty dimension, six items are suggested: submission and review, author rights, user rights, findability, accessibility, and monitoring

Robust Discriminative Metric Learning for Image Representation

Metric learning has attracted significant attentions in the past decades, for the appealing advances in various realworld applications such as person re-identification and face recognition. Traditional supervised metric learning attempts to seek a discriminative metric, which could minimize the pairwise distance of within-class data samples, while maximizing the pairwise distance of data samples from various classes. However, it is still a challenge to build a robust and discriminative metric, especially for corrupted data in the real-world application. In this paper, we propose a Robust Discriminative Metric Learning algorithm (RDML) via fast low-rank representation and denoising strategy. To be specific, the metric learning problem is guided by a discriminative regularization by incorporating the pair-wise or class-wise information. Moreover, low-rank basis learning is jointly optimized with the metric to better uncover the global data structure and remove noise. Furthermore, fast low-rank representation is implemented to mitigate the computational burden and make sure the scalability on large-scale datasets. Finally, we evaluate our learned metric on several challenging tasks, e.g., face recognition/verification, object recognition, and image clustering. The experimental results verify the effectiveness of the proposed algorithm by comparing to many metric learning algorithms, even deep learning ones

Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe

Prediction of Alzheimer's disease pathophysiology based on cortical thickness patterns

AbstractIntroductionRecent studies have shown that pathologically defined subtypes of Alzheimer's disease (AD) represent distinctive atrophy patterns and clinical characteristics. We investigated whether a cortical thickness–based clustering method can reflect such findings.MethodsA total of 77 AD subjects from the Alzheimer's Disease Neuroimaging Initiative 2 data set who underwent 3-T magnetic resonance imaging, [18F]-fluorodeoxyglucose-positron emission tomography (PET), [18F]-Florbetapir PET, and cerebrospinal fluid (CSF) tests were enrolled. After clustering based on cortical thickness, diverse imaging and biofluid biomarkers were compared between these groups.ResultsThree cortical thinning patterns were noted: medial temporal (MT; 19.5%), diffuse (55.8%), and parietal dominant (P; 24.7%) atrophy subtypes. The P subtype was the youngest and represented more glucose hypometabolism in the parietal and occipital cortices and marked amyloid-beta accumulation in most brain regions. The MT subtype revealed more glucose hypometabolism in the left hippocampus and bilateral frontal cortices and less performance in memory tests. CSF test results did not differ between the groups.DiscussionCortical thickness patterns can reflect pathophysiological and clinical changes in AD

Induction of IL-10-producing CD4(+)CD25(+ )T cells in animal model of collagen-induced arthritis by oral administration of type II collagen

Induction of oral tolerance has long been considered a promising approach to the treatment of chronic autoimmune diseases, including rheumatoid arthritis (RA). Oral administration of type II collagen (CII) has been proven to improve signs and symptoms in RA patients without troublesome toxicity. To investigate the mechanism of immune suppression mediated by orally administered antigen, we examined changes in serum IgG subtypes and T-cell proliferative responses to CII, and generation of IL-10-producing CD4(+)CD25(+ )T-cell subsets in an animal model of collagen-induced arthritis (CIA). We found that joint inflammation in CIA mice peaked at 5 weeks after primary immunization with CII, which was significantly less in mice tolerized by repeated oral feeding of CII before CIA induction. Mice that had been fed with CII also exhibited increased serum IgG(1 )and decreased serum IgG(2a )as compared with nontolerized CIA animals. The T-cell proliferative response to CII was suppressed in lymph nodes of tolerized mice also. Production of IL-10 and of transforming growth factor-β from mononuclear lymphocytes was increased in the tolerized animals, and CD4(+ )T cells isolated from tolerized mice did not respond with induction of IFN-γ when stimulated in vitro with CII. We also observed greater induction of IL-10-producing CD4(+)CD25(+ )subsets among CII-stimulated splenic T cells from tolerized mice. These data suggest that when these IL-10-producing CD4(+)CD25(+ )T cells encounter CII antigen in affected joints they become activated to exert an anti-inflammatory effect