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1 On the Potential Role of MRI Biomarkers of COPD to Guide Bronchoscopic Lung Volume Reduction 2017-10-17             

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Publication date: Available online 16 October 2017
Source:Academic Radiology

Author(s): Colin J. Adams, Dante P.I. Capaldi, Robert Di Cesare, David G. McCormack, Grace Parraga

Rationale and Objectives In patients with severe emphysema and poor quality of life, bronchoscopic lung volume reduction (BLVR) may be considered and guided based on lobar emphysema severity. In particular, x-ray computed tomography (CT) emphysema measurements are used to identify the most diseased and the second–most diseased lobes as BLVR targets. Inhaled gas magnetic resonance imaging (MRI) also provides chronic obstructive pulmonary disease (COPD) biomarkers of lobar emphysema and ventilation abnormalities. Our objective was to retrospectively evaluate CT and MRI biomarkers of lobar emphysema and ventilation in patients with COPD eligible for BLVR. We hypothesized that MRI would provide complementary biomarkers of emphysema and ventilation that help determine the most appropriate lung lobar targets for BLVR in patients with COPD. Materials and Methods We retrospectively evaluated 22 BLVR-eligible patients from the Thoracic Imaging Network of Canada cohort (diffusing capacity of the lung for carbon monoxide = 37 ± 12%predicted, forced expiratory volume in 1 second = 34 ± 7%predicted, total lung capacity = 131 ± 17%predicted, and residual volume = 216 ± 36%predicted). Lobar CT emphysema, measured using a relative area of <−950 Hounsfield units (RA950) and MRI ventilation defect percent, was independently used to rank lung lobe disease severity. Results In 7 of 22 patients, there were different CT and MRI predictions of the most diseased lobe. In some patients, there were large ventilation defects in lobes not targeted by CT, indicative of a poorly ventilated lung. CT and MRI classification of the most diseased and the second–most diseased lobes showed a fair-to-moderate intermethod reliability (Cohen κ = 0.40–0.59). Conclusions In this proof-of-concept retrospective analysis, quantitative MRI ventilation and CT emphysema measurements provided different BLVR targets in over 30% of the patients. The presence of large MRI ventilation defects in lobes next to CT-targeted lobes might also change the decision to proceed or to guide BLVR to a different lobar target.





2 Development of web-based WERM-S module for estimating spatially distributed rainfall erosivity index (EI30) using RADAR rainfall data 2017-10-17             

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Publication date: February 2018
Source:CATENA, Volume 161

Author(s): Avay Risal, Kyoung Jae Lim, Rabin Bhattarai, Jae E. Yang, Huiseong Noh, Rohit Pathak, Jonggun Kim

Despite technological advances, soil erosion modeling is a very complicated process as the amount and rate of soil erosion vary considerably over space and time. Universal Soil Loss Equation (USLE) is one of the oldest and popular models used for soil loss estimation worldwide. USLE R-factor is one of the six input parameters accounting for the impact of rainfall amount and intensity on soil erosion in USLE. The USLE R factor is calculated by averaging annual long time rainfall erosivity index (EI30) values, computed by multiplying maximum rainfall intensity during 30min periods and the kinetic energy of the rainfall. The gage rainfall data are used for the determination of such EI30 index, and one representative value is given for the entire area. Due to the spatial and temporal variability of rainfall pattern, the value may vary considerably over space and time. It is required to obtain the rainfall data over a surface (heterogeneous) rather than at a point (homogeneous) so that spatially distributed erosivity index values can be calculated. Even though RADAR can provide spatially and temporally distributed rainfall data, the process of manual erosivity index calculation for each raster pixel is very tedious, time-consuming and practically not feasible. To overcome these limitations, the web-based WERM-S module was developed to compute a spatial EI30 index from the 10-min interval spatial rainfall data. The WERM-S consists of three different Fortran modules (Convert Module, R-factor calculation module, and R-factor ASCII module). The Jaun-ri watershed was selected as the study area to test the module since the RADAR rainfall data was available for 2015. June, July, and August were found to be the months receiving the maximum amount of rainfall and the average erosivity indices for June, July and August were found to be 2096, 1002, and 993MJ·mm/ha-hr-month, respectively. The maximum erosivity index for a pixel within the study area was observed to be 9821MJ·mm/ha-hr-month for June 4382MJ·mm/ha-hr-month for July and 6093MJ·mm/ha-hr-month for August respectively. The higher value of standard deviations of 1850, 950 and 1115MJ·mm/ha-hr-month for June, July, and August were observed respectively representing that the erosivity index of individual space widely deviated from the mean monthly erosivity index. Thus spatial erosivity index is suggested to be used over average annual R factor values to calculate soil loss using USLE. Furthermore, the WERM-S module can be a very useful tool to automatically calculate the spatially distributed rainfall erosivity index from 10-min interval RADAR rainfall data.





3 Ultrafast Laplace NMR with hyperpolarized xenon gas 2017-10-17             

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Publication date: Available online 16 October 2017
Source:Microporous and Mesoporous Materials

Author(s): Otto Mankinen, Julia Hollenbach, Susanna Ahola, Jörg Matysik, Ville-Veikko Telkki

Laplace NMR, consisting of diffusion and relaxation experiments, provides detailed information about dynamics of fluids in porous materials. Recently, we showed that two-dimensional Laplace NMR experiments can be carried out with a single scan based on spatial encoding. The method shortens the experiment time by one to three orders of magnitude, and therefore it is called ultrafast Laplace NMR. Furthermore, the single-scan approach facilitates significantly the use of nuclear spin hyperpolarization for boosting the sensitivity of the experiment, because a laborious hyperpolarization procedure does not need to be repeated. Here, we push the limits of the ultrafast Laplace NMR method by applying it, for the first time, in the investigation of a gas phase substance, namely hyperpolarized xenon gas. We show that, regardless of the fast diffusion of gas, layer-like spatial encoding is feasible, and an ultrafast diffusion – T 2 relaxation correlation experiment reveals significantly different signals of free gas and gas adsorbed in a mesoporous controlled pore glass (CPG). The observed diffusion coefficients are many orders of magnitude larger than those detected earlier from liquid phase substances, emphasizing the extended application range of the method. The challenges in the methodology, caused by the fast diffusion, are also discussed.

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4 Ultrafast 2D NMR: Methods and Applications 2017-10-17             

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Publication date: Available online 16 October 2017
Source:Annual Reports on NMR Spectroscopy

Author(s): Boris Gouilleux, Laetitia Rouger, Patrick Giraudeau

Multidimensional NMR (nD NMR) has become one of the most powerful spectroscopic tools to deliver diverse structural and functional insights into organic and biomolecules as well as on raw materials. A long-standing concern of nD NMR is related to its long experiment duration, arising from the need to sample the indirect dimension(s) in a multistep fashion. For decades, the NMR community has been developing numerous strategies to speed up nD NMR experiments and therefore extend their scope of applications. Among them is the ultrafast (UF) NMR methodology, capable of delivering arbitrary homo- or heteronuclear multidimensional spectra in a single scan. Since the initial concept was published in 2002, the performance of this subsecond 2D NMR approach has been greatly improved so that UF NMR is nowadays a relevant analytical tool used in broad variety of situations whereby the experiment duration is crucial. Following a description of the principles of UF NMR, the present review aims at emphasizing the numerous methodological developments that this approach has undergone so far in 2017. Thereafter, the high versatility of UF NMR is highlighted through the review of the applications that have been reported in a variety of settings and disciplines, in isotropic as well as anisotropic media.





5 Crystal field distortion of La3+ ion-doped Mn-Cr ferrite 2017-10-17             

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Publication date: 1 February 2018
Source:Journal of Magnetism and Magnetic Materials, Volume 447

Author(s): M.H. Abdellatif, G.M. El-Komy, A.A. Azab, M. Salerno

Ion doping in crystals can result in lattice modifications triggering interesting magnetic and optical properties of the material, understood as a compensation of the crystal deformation and microstrain. We investigated the spinel structure of Mn-Cr ferrite after doping with La3+ ions. The structure was first characterized by X-ray diffraction and high-resolution transmission electron microscopy. Raman scattering spectra were taken that could also be interpreted in terms of crystal field distortion due to La3+ ion doping. On assessing the magneto-impedance of the doped ferrite, it showed giant magneto-impedance behavior, with a strong drop of over 50%. The saturation magnetization was characterised by vibrating sample magnetometer and was found to be 20.25emu/g with remnant magnetization of 1.47emu/g.

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6 Stimuli responsive ion gels based on polysaccharides and other polymers prepared using ionic liquids and deep eutectic solvents 2017-10-17             

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Publication date: 15 January 2018
Source:Carbohydrate Polymers, Volume 180

Author(s): Kamalesh Prasad, Dibyendu Mondal, Mukesh Sharma, Mara G. Freire, Chandrakant Mukesh, Jitkumar Bhatt

Ion gels and self-healing gels prepared using ionic liquids (ILs) and deep eutectic solvents (DESs) have been largely investigated in the past years due to their remarkable applications in different research areas. Herewith we provide an overview on the ILs and DESs used for the preparation of ion gels, highlight the preparation and physicochemical characteristics of stimuli responsive gel materials based on co-polymers and biopolymers, with special emphasis on polysaccharides and discuss their applications. Overall, this review summarizes the fundamentals and advances in ion gels with switchable properties prepared using ILs or DESs, as well as their potential applications in electrochemistry, in sensing devices and as drug delivery vehicles.





7 Image reconstruction of dynamic infrared single-pixel imaging system 2017-10-17             

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Publication date: 1 March 2018
Source:Optics Communications, Volume 410

Author(s): Qi Tong, Yilin Jiang, Haiyan Wang, Limin Guo

Single-pixel imaging technique has recently received much attention. Most of the current single-pixel imaging is aimed at relatively static targets or the imaging system is fixed, which is limited by the number of measurements received through the single detector. In this paper, we proposed a novel dynamic compressive imaging method to solve the imaging problem, where exists imaging system motion behavior, for the infrared (IR) rosette scanning system. The relationship between adjacent target images and scene is analyzed under different system movement scenarios. These relationships are used to build dynamic compressive imaging models. Simulation results demonstrate that the proposed method can improve the reconstruction quality of IR image and enhance the contrast between the target and the background in the presence of system movement.





8 Developing stretchable and graphene-oxide-based hydrogel for the removal of organic pollutants and metal ions 2017-10-16             

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Publication date: March 2018
Source:Applied Catalysis B: Environmental, Volume 222

Author(s): Chencheng Dong, Jie Lu, Bocheng Qiu, Bin Shen, Mingyang Xing, Jinlong Zhang

It remains challenging to process the industrial wastewater of high consistence of organic pollutants and difficult decomposition of heavy metal ions. In this study, we develop a functional nanocomposite hydrogel with a highly photocatalytic Fenton reaction activity for the degradation of organic pollutants and adsorption for the heavy metal ions. The hydrogel is made up of Fe3O4 nanoparticles, reduced graphene oxide (RGO) and polyacrylamide (PAM), which is prepared by a two-step chemical synthetic method, and exhibits the outstanding mechanical strength, Photo-Fenton activity, adsorptive property and reversibility. For the degradation of organic dyes, the Fe3O4/RGO/PAM hydrogel can degrade the 20mg/L Rhodamine B (RhB) for 90% within 60min under visible light irradiation, and even after 10 times cycle test, the degradation rate for RhB still keeps at 90%. Meanwhile, it can degrade the actual sewage of fine chemical wastewater, whose COD (Chemical Oxygen Demand) decreases from 10400 to 2840mg/L after one hour’s visible irradiation. For the synchronous removal of organic pollutants and heavy metal ions over hydrogel, the degradation data of 20mg/L RhB can be up to 90% with 20min under visible light irradiation, and the removal rate of various metal ions can reach up to 34.8%–66.3% after continual two days’ adsorption. This study provides a new pathway to process the industrial wastewater of high consistence and difficult decomposition.

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9 Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce 2017-10-16             

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Publication date: July 2018
Source:Information Fusion, Volume 42

Author(s): Sergio Ramírez-Gallego, Alberto Fernández, Salvador García, Min Chen, Francisco Herrera

We live in a world were data are generated from a myriad of sources, and it is really cheap to collect and storage such data. However, the real benefit is not related to the data itself, but with the algorithms that are capable of processing such data in a tolerable elapse time, and to extract valuable knowledge from it. Therefore, the use of Big Data Analytics tools provide very significant advantages to both industry and academia. The MapReduce programming framework can be stressed as the main paradigm related with such tools. It is mainly identified by carrying out a distributed execution for the sake of providing a high degree of scalability, together with a fault-tolerant scheme. In every MapReduce algorithm, first local models are learned with a subset of the original data within the so-called Map tasks. Then, the Reduce task is devoted to fuse the partial outputs generated by each Map. The ways of designing such fusion of information/models may have a strong impact in the quality of the final system. In this work, we will enumerate and analyze two alternative methodologies that may be found both in the specialized literature and in standard Machine Learning libraries for Big Data. Our main objective is to provide an introduction of the characteristics of these methodologies, as well as giving some guidelines for the design of novel algorithms in this field of research. Finally, a short experimental study will allow us to contrast the scalability issues for each type of process fusion in MapReduce for Big Data Analytics.





10 Orthogonal circulant structure and chaotic phase modulation based analog to information conversion 2017-10-16             

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Publication date: March 2018
Source:Signal Processing, Volume 144

Author(s): Ren Wang, Jingbo Guo, Henry Leung

Modulated wideband converter (MWC) is used to implement analog to information conversion (AIC) to realize sub-Nyquist sampling of sparse multiband analog signal. In this paper, we combine orthogonal circulant matrix with chaos to develop an orthogonal circulant structure and chaotic phase modulation based modulated wideband converter (OCSCPM-MWC). Firstly, random waveforms of different channels in the system correspond to the row vectors of measurement matrix. We use the frequency-domain approach to construct orthogonal circulant matrix. The waveforms of different channels can be generated by various cyclic shifts of the same original waveform and orthogonal to each other. This can greatly reduce the degrees of freedom of the random waveforms, and a superior recovery performance can be obtained. Secondly, we construct chaotic phases of generating elements of orthogonal circulant matrix. The input signal is modulated according to the power spectrum of chaos. We derive the theoretical condition for the orthogonal circulant measurement matrix to satisfy expected restricted isometry property (ExRIP). In addition, we analyze the impact of measurement noise and signal noise on the OCSCPM-MWC system, respectively. Computer simulations are carried out under different situations and they confirm the effectiveness of the proposed approach.





 
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