The TNF-α-238G > A single-nucleotide polymorphism protects against memory decline in older adults with Type 2 diabetes.

Inflammatory markers predict memory dysfunction in elderly patients, but their contribution to memory deficits in adults with Type 2 diabetes mellitus (T2DM) is less well understood. The present study determined whether specific single-nucleotide polymorphisms in the promoter region of tumor necrosis factor-alpha (TNF-α) predict verbal memory in older patients with T2DM. Immediate and delayed verbal memory were assessed using word list and paragraph recall tests in a cohort of subjects with T2DM during 2 sessions, separated by 48 weeks. The presence of the TNF-α-238A allele, which has been shown to decrease gene expression, consistently predicted better baseline performance and protected against memory decline over a period of 48 weeks. Therefore, inflammatory mediators may be important modulators of memory function in individuals with T2DM. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Modeling the short-circuit current density of polymer solar cells based on P3HT:PCBM blend

We have investigated the short-circuit current density of organic solar cells based on poly (3-hexylthiophene)(P3HT)/6,6-phenyl C61-butyric acid methyl ester (PCBM) blend. In order to model charge collection efficiencies with respect to short circuit density in such blends, a full optical modeling of the cell is performed. From the distribution of the electromagnetic field, we compute the rate of exciton generation. This exciton generation rate is used as input in the transport equations of holes and electrons. Charge densities at steady state are obtained as solutions are used for computing short-circuit current densities generated in the cell. The dependence of short-circuit current densities versus the thickness of the blend is analyzed and compared with our experimental data and with data extracted from the literature.     

Methotrexate Alters the Expression of microRNA in Fibroblast-like Synovial Cells in Rheumatoid Arthritis

We aimed to investigate the effect of methotrexate (MTX) on microRNA modulation in rheumatoid arthritis fibroblast-like synovial cells (RA-FLS). RA-FLS were treated with MTX for 48 h. We then performed miRNA array analysis to investigate differentially expressed miRNAs. Transfection with miR-877-3p precursor and inhibitor were used to investigate the functional role of miR-877-3p in RA-FLS. Gene ontology analysis was used to investigate the cellular processes involving miR-877-3p. The production of cytokines/chemokines was screened by multiplex cytokine/chemokine bead assay and confirmed by ELISA and quantitative real-time PCR. The migratory and proliferative activities of RA-FLS were analyzed by wound healing assay and MKI-67 expression. MTX treatment altered the expression of 13 miRNAs (seven were upregulated and six were downregulated). Among them, quantitative real-time PCR confirmed that miR-877-3p was upregulated in response to MTX (1.79 ± 0.46-fold, p < 0.05). The possible target genes of miR-877-3p in RA-FLS revealed by the microarray analysis were correlated with biological processes. The overexpression of miR-877-3p decreased the production of GM-CSF and CCL3, and the overexpression of miR-877-3p inhibited migratory and proliferative activity. MTX altered the miR-877-3p expression on RA-FLS, and this alteration of miR-877-3p attenuated the abundant production of cytokines/chemokines and proliferative property of RA-FLS.     

Sea surface temperature forecasts using on-line local learning algorithm in upwelling regions

When forecasting geophysical time series, long records are not always available. Therefore, the possible physical events may not be evenly represented in the available dataset and a unique neural network modelling the phenomenon of interest may not be accurate. As an alternative, a local learning algorithm is presented in this paper. It is applied to the prediction of sea surface temperature in an upwelling area. It provides encouraging results when applied to forecasts for eight days ahead.     

Statistical analysis of a database of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.     

Anti-smoking educational game using avatars as visualized possible selves

Few social smokers envision themselves being affected by the negative consequences of smoking despite well-known facts that smoking causes serious illnesses and death. However, as smoking habits quickly develop, social smokers cannot be free from the negative consequences of smoking. In this study, we pose the following question: “Would showing social smokers’ possible future as a consequence of smoking help them alter their current smoking behaviors?” Thus, using the theoretical concept of possible selves, an anti-smoking educational game was created in which players could see changes to the appearance of their future selves as a consequence of smoking. We used a 2 (Future face: Showing vs. Not showing) × 2 (Self avatar: Self-avatar vs. Other-avatar) between-subjects design for the experiment. Results indicated that participants who viewed the future face, compared to who did not, reported more negative attitudes toward social smoking and greater intention to quit smoking. The main effect of the self avatar was insignificant; however, seeing the future face in the self-avatar condition led to an increase in perceived risks compared to other-avatar condition. The implications of using avatars as visualized possible selves in health promotion are discussed.     

交织复用空时码在协作中继网络中的应用

本文研究了一种基于交织复用(IDM)的分布式空时编码方案,对一种适用于该方案的迭代接收机结构进行性能分析。同时通过仿真,对所提出的协作编码方案进行性能评估。在相似环境下,通过比较本方案与其它协作空时码的性能,表明本方案由于采用了非正交的空时编码,具有多方面的优越性。同时它还可以采用灵活的发送天线数配置,反映到协作通信的场景,也就是对中继终端的数量没有特殊要求。     

Zooming On All Actors: Automatic Focus+Context Split Screen Video Generation

Recordings of stage performances are easy to capture with a high‐resolution camera, but are difficult to watch because the actors' faces are too small. We present an approach to automatically create a split screen video that transforms these recordings to show both the context of the scene as well as close‐up details of the actors. Given a static recording of a stage performance and tracking information about the actors positions, our system generates videos showing a focus+context view based on computed close‐up camera motions using crop‐and zoom. The key to our approach is to compute these camera motions such that they are cinematically valid close‐ups and to ensure that the set of views of the different actors are properly coordinated and presented. We pose the computation of camera motions as convex optimization that creates detailed views and smooth movements, subject to cinematic constraints such as not cutting faces with the edge of the frame. Additional constraints link the close up views of each actor, causing them to merge seamlessly when actors are close. Generated views are placed in a resulting layout that preserves the spatial relationships between actors. We demonstrate our results on a variety of staged theater and dance performances.     

Segmentation-based multi-class semantic object detection

In this paper we study the problem of the detection of semantic objects from known categories in images. Unlike existing techniques which operate at the pixel or at a patch level for recognition, we propose to rely on the categorization of image segments. Recent work has highlighted that image segments provide a sound support for visual object class recognition. In this work, we use image segments as primitives to extract robust features and train detection models for a predefined set of categories. Several segmentation algorithms are benchmarked and their performances for segment recognition are compared. We then propose two methods for enhancing the segments classification, one based on the fusion of the classification results obtained with the different segmentations, the other one based on the optimization of the global labelling by correcting local ambiguities between neighbor segments. We use as a benchmark the Microsoft MSRC-21 image database and show that our method competes with the current state-of-the-art.