On the Generalized Bertrand Curves in Euclidean NN-spaces

In this article, we give a necessary condition for a $C^\infty$-special Frenet curve in $\mathbb{R}^{N}$ being a generalized Bertrand curve

Twin-Free GaAs Nanosheets by Selective Area Growth: Implications for Defect-Free Nanostructures

Highly perfect, twin-free GaAs nanosheets grown on (111)B surfaces by selective area growth (SAG) are demonstrated. In contrast to GaAs nanowires grown by (SAG) in which rotational twins and stacking faults are almost universally observed, twin formation is either suppressed or eliminated within properly oriented nanosheets are grown under a range of growth conditions. A morphology transition in the nanosheets due to twinning results in surface energy reduction, which may also explain the high twin-defect density that occurs within some III–V semiconductor nanostructures, such as GaAs nanowires. Calculations suggest that the surface energy is significantly reduced by the formation of {111}-plane bounded tetrahedra after the morphology transition of nanowire structures. By contrast, owing to the formation of two vertical {11̅0} planes which comprise the majority of the total surface energy of nanosheet structures, the energy reduction effect due to the morphology transition is not as dramatic as that for nanowire structures. Furthermore, the surface energy reduction effect is mitigated in longer nanosheets which, in turn, suppresses twinning

Albumin fibrillization induces apoptosis via integrin/FAK/Akt pathway

[[abstract]]Background: Numerous proteins can be converted to amyloid-like fibrils to increase cytotoxicity and induce apoptosis, but the methods generally require a high concentration of protein, vigorous shaking, or fibril seed. As well, the detailed mechanism of the cytotoxic effects is not well characterized. In this study, we have developed a novel process to convert native proteins into the fibrillar form. We used globular bovine serum albumin (BSA) as a model protein to verify the properties of the fibrillar protein, investigated its cellular effects and studied the signaling cascade induced by the fibrillar protein. Results: We induced BSA, a non-cytotoxic globular protein, to become fibril by a novel process involving Superdex-200 column chromatography in the presence of anionic or zwittergenic detergent(s). The column pore size was more important than column matrix composite in fibril formation. The fibrillar BSA induced apoptosis in BHK-21 cell as well as breast cancer cell line T47D. Pre-treating cells with anti-integrin antibodies blocked the apoptotic effect. Fibrillar BSA, but not globular BSA, bound to integrin, dephosphorylated focal adhesion kinase (FAK), Akt and glycogen synthase kinase-3β (GSK-3β). Conclusion: We report on a novel process for converting globular proteins into fibrillar form to cause apoptosis by modulating the integrin/FAK/Akt/GSK-3β/caspase-3 signaling pathway. Our findings may be useful for understanding the pathogenesis of amyloid-like fibrils and applicable for the development of better therapeutic agents that target the underlying mechanism(s) of the etiologic agents. ? 2009 Huang et al; licensee BioMed Central Ltd

THE BALANCE EFFECT OF REARFOOT WEDGES WITH DIFFERENT HEIGHT FOR COLLEGIATE STUDENTS WITH CHRONIC ANKLE INSTABILITY: PILOT STUDY

Chronic ankle instability (CAI) is caused by recurrent lateral ankle sprain. Foot orthotic is one option of treatment. The purpose of this study was to determinate the balance effect of rearfoot wedges with different height in collegiate students with chronic ankle instability. Eight collegiate students with CAI subjects were voluntarily particapated in this study. The area of center of pressure was used as balance variable of outcome measurement. Seven height of rearfoot wedge was used to test, included 0°, 2°, 4°, 6° of medial wedge and 2°, 4°, 6° of lateral wedge. One-way ANOVA was used to analyze the difference among sevent height of wedge intervention in CAI group. The results were showed no significantly difference among seven height of wedge intervention. However, we found a trend of balance improvement with the wedge intervention, especially in 4 degrees of medial wedge intervention

DCB-3503, a Tylophorine Analog, Inhibits Protein Synthesis through a Novel Mechanism

BACKGROUND: DCB-3503, a tylophorine analog, inhibits the growth of PANC-1 (human pancreatic ductal cancer cell line) and HepG2 (human hepatocellular cancer cell line) tumor xenografts in nude mice. The inhibition of growth leads to cancer cell differentiation instead of cell death. However, the mechanisms of action of tylophorine analogs is unknown. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we show that DCB-3503 suppresses the expression of pro-oncogenic or pro-survival proteins with short half-lives, including cyclin D1, survivin, beta-catenin, p53, and p21, without decreasing their mRNA levels. Proteasome inhibitor reversed the inhibitory effect of DCB-3503 on expression of these proteins. DCB-3503 inhibited the incorporation of radiolabeled amino acid and thymidine, and to a much lesser degree of uridine, in a panel of cell lines. The mechanism of inhibition of protein synthesis is different from that of cycloheximide (CHX) as assayed in cell culture and HeLa in vitro translation system. Furthermore, in contrast to rapamycin, DCB-3503 does not affect protein synthesis through the mTOR pathway. DCB-3503 treatment shifts the sedimentation profiles of ribosomes and mRNAs towards the polysomal fractions while diminishing monosome abundance, indicative of the inhibition of the elongation step of protein synthesis. Preferential down regulation of several studied proteins under these conditions is likely due to the relative short half-lives of these proteins. CONCLUSION/SIGNIFICANCE: The inhibitory effect of DCB-3503 on translation is apparently distinct from any of the current anticancer compounds targeting protein synthesis. Translation inhibitors with novel mechanism could complement current chemotherapeutic agents for the treatment of human cancers and suppress the occurrence of drug resistance

A mixture of anatase and rutile TiO2 nanoparticles induces histamine secretion in mast cells

<p>Abstract</p> <p>Background</p> <p>Histamine released from mast cells, through complex interactions involving the binding of IgE to FcεRI receptors and the subsequent intracellular Ca²⁺signaling, can mediate many allergic/inflammatory responses. The possibility of titanium dioxide nanoparticles (TiO₂NPs), a nanomaterial pervasively used in nanotechnology and pharmaceutical industries, to directly induce histamine secretion without prior allergen sensitization has remained uncertain.</p> <p>Results</p> <p>TiO₂NP exposure increased both histamine secretion and cytosolic Ca²⁺concentration ([Ca²⁺]_C) in a dose dependent manner in rat RBL-2H3 mast cells. The increase in intracellular Ca²⁺levels resulted primarily from an extracellular Ca²⁺influx via membrane L-type Ca²⁺channels. Unspecific Ca²⁺entry via TiO₂NP-instigated membrane disruption was demonstrated with the intracellular leakage of a fluorescent calcein dye. Oxidative stress induced by TiO₂NPs also contributed to cytosolic Ca²⁺signaling. The PLC-IP₃-IP₃receptor pathways and endoplasmic reticulum (ER) were responsible for the sustained elevation of [Ca²⁺]_Cand histamine secretion.</p> <p>Conclusion</p> <p>Our data suggests that systemic circulation of NPs may prompt histamine release at different locales causing abnormal inflammatory diseases. This study provides a novel mechanistic link between environmental TiO₂NP exposure and allergen-independent histamine release that can exacerbate manifestations of multiple allergic responses.</p

How Ontologies Can Help in an eMarketplace

Recently, ontologies have been developed in various business domains with the recent maturing of the Semantic Web technologies. However, ontology-related researches have largely focused on the facilitation of successful matchmaking but not much on traders’ requirement elicitation and potential negotiations in e-marketplaces. Because ontology provides the key knowledge about the inter-relationships among the issues and alternatives of the traders’ requirements, we show how to elicit trade requirements, alternatives, and tradeoff from an agreed ontology. This facilitates the whole business process of the e-marketplace, from matchmaking, recommendation, to negotiation. We further propose a novel methodology for the elicitation of dependencies among traders’ requirements for the formulation of an effective decision plan. As a result, traders can have a better cognition of their requirements and the overall operations of the e-marketplace can be streamlined

Super-resolution generative adversarial network based on the dual dimension attention mechanism for biometric image super-resolution

There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases