Factors associated with serum magnesium and vascular stiffness in maintenance hemodialysis patients

Introduction : We evaluated the associated factors of serum magnesium in patients on maintenance hemodialysis (MHD). Furthermore, we evaluated the relationship between low serum magnesium and arteriosclerosis in these patients. Methods : In 129 patients on MHD, we evaluated the blood levels of magnesium, brachial‐ankle pulse wave velocity (ba‐PWV), ankle‐brachial index (ABI), and intima‐media thickness of the common carotid artery (IMT). Findings : In MHD patients, the serum level of magnesium was significantly correlated with age, calcium, TNF‐α, albumin, and ba‐PWV but not with ABI or IMT. In the multiple regression analysis, albumin (P = 0.0001, β = 0.31) and calcium (P = 0.029, β = 0.18) were selected as significant predictors of the magnesium level in MHD patients. Furthermore, the serum level of magnesium, as well as systolic blood pressure (P = 0.0001, β = 0.32) and age (P = 0.005, β = 0.25), were selected as significant (P = 0.012, β = ?0.22) predictors of ba‐PWV in MHD patients. Discussion : In MHD patients, the serum magnesium level was associated with the serum levels of calcium and albumin. Furthermore, a low serum magnesium level in MHD patients was associated with the index of vascular stiffness.     

Graphene oxide-decorated Fe<Subscript>2</Subscript>(MoO<Subscript>4</Subscript>)<Subscript>3</Subscript> microflowers as a promising anode for lithium and sodium storage

Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe₂(MoO₄)₃ (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g^–1 at 200 mA·g^–1 after 50 cycles and a capacity of 685 mAh·g^–1 at a high current density of 10 A·g^–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g^–1 at 100 mA·g^–1, maintaining a discharge capacity of 307 mAh·g^–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.

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Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Boosting support vector machines for imbalanced data sets

Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. We then counter the excessive bias introduced by this approach with a boosting algorithm. We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.     

Characterization of renal tumours based on Raman spectra classification

In this study, we propose to evaluate the potential of Raman spectroscopy (RS) to assess renal tumours at surgery. Different classes of Raman renal spectra acquired during a clinical protocol are discriminated using support vector machines classifiers. The influence on the classification scores of various preprocessing steps generally involved in RS are also investigated and evaluated in the particular context of renal tumour characterization. Encouraging results show the interest of RS to evaluate kidney cancer and suggest the potential of this technique as a surgical assistance during partial nephrectomy.     

基于参考点和投票规则的多目标粒子群优化群体决策

针对巨量可选方案的群体决策问题,提出了一个新的基于参考点和投票规则的多目标粒子群优化算法。该算法把个体与参考点的支配关系或者距离作为一个重要因素,在选择引导者的锦标赛方法,局部最优更新规则,以及外部种群档案剪枝规则中都嵌入了基于支配关系或距离因素的投票规则,以找到群体决策解,并且提高搜索效率。仿真结果表明该算法有效。     

高分辨率遥感卫星数据传输中信道编码技术分析与研究

根据RS编译码原理,结合高分辨率遥感卫星在轨影像实际数据压缩情况,提出将RS(255,243)和RS(34,26)两种编码算法进行联合使用的方案,以解决数据传输中误码问题,仿真试验证明该方法在解决有干扰条件下的星地数据传输时,可有效防止和减少误码扩散。     

Proteomic profile of differentially expressed plasma proteins from dystrophic mice and following suberoylanilide hydroxamic acid treatment

Purpose: Histone Deacetylase Inhibitors (DI) ameliorates dystrophic muscle regeneration restoring muscular strength in the mdx mouse model of Duchenne muscular dystrophy (DMD). The further development of these compounds as drugs for DMD treatment is currently hampered by the lack of knowledge about DIs effect in large dystrophic animal models and that of suitable biomarkers to monitor their efficacy. Experimental design: In this study we applied proteomic analysis to identify differentially expressed proteins present in plasma samples from mdx mice treated with the Suberoylanilide hydroxamic acid (SAHA) and relative normal controls (WT). Results: Several differentially expressed proteins were identified between untreated wild type and mdx mice. Among these, fibrinogen, epidermal growth factor 2 receptor, major urinary protein and glutathione peroxidase 3 (GPX3) were constitutively up‐regulated in mdx, while complement C3, complement C6, gelsolin, leukaemia inhibitory factor receptor (LIFr), and alpha 2 macroglobulin were down‐regulated compared to WT mice. SAHA determined the normalization of LIFr and GPX3 protein level while apoliprotein E was de novo up‐regulated in comparison to vehicle‐treated mdx mice. Conclusions and clinical relevance: Collectively, these data unravel potential serological disease biomarkers of mdx that could be useful to monitor muscular dystrophy response to DI treatment.     

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.     

Gambling versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial‐and‐learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.