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The upgrading of the petroleum cracking distillates using cheap methane rather than naturally unavailable hydrogen is an economically attractive process. This work clearly demonstrates the feasibility of methanotreating by directly using methane over zeolite-supported catalyst to convert olefins. The introduction of methane decreases the fraction of olefins and increases the fraction of paraffins in the liquid products obtained from the reaction using 1-decene as the olefin model compound when Ag-Ga/ZSM-5 is charged. It also improves the quality of liquid product in terms of higher H/C atomic ratio, higher heating value and lower bromine number. The interaction between olefins and methane on the surface of the catalyst is evidenced by DRIFT spectroscopy. When propylene is used as the olefin model compound, the shift of the peaks due to the interaction between adsorbed propylene species and catalyst surface as well as the generation of new peaks after the introduction of methane are observed. When 1-decene is used as the model compound, the suppressed intensity of CC signal and enhanced alkane CH bending peak intensity when CH4 is present indicate the conversion of olefins into paraffins. The improved particle dispersion of the charged catalyst upon the reaction under CH4 environment is also noticed from the TEM images, which might be closely related to its better olefin upgrading performance. The outcomes from this study might create an alternative route for cracked distillates upgrading and provide a positive attempt for more valuable methane utilization.

Three complexes with the ditopic ligand 4′-[4-(quinolin-8-yloxymethyl)phenyl]-2,2′:6′,2′′-terpyridine (abbreviated as L), [Ni(L)2](CH3COO)2 (1), [Cd(L)2](ClO4)2 (2), and [Cu2(L)2](ClO4)4·4DMF (3), were synthesized and characterized by elemental analysis, IR spectroscopy, and structurally analyzed by X-ray single-crystal diffraction. Interestingly, in complexes 1 and 2, two ligands adopt a tridentate chelating pattern where the oxaquinoline group is non-coordinated and coordinate with one MII ion (M = Ni for 1, M = Cd for 2) to form a mononuclear unit. In complex 3, two ligands bridge two CuII ions by pyridyl N atoms, ethereal O atoms, and quinolyl N atoms in a head-to-tail mode to generate a dinuclear [Cu2L2] unit. Moreover, extended 1D and 2D supramolecular architectures are further constructed in 1–3 by multiple secondary interactions such as aromatic stacking and hydrogen bonding. Notably, the structural diversity of complexes 1–3 can be properly assigned to the central metal ions that have distinct coordination preferences. In addition, luminescent properties of the ligand and complex 2 were also investigated.

OBJECTIVE To evaluate the feasibility and safety of laparoscopic liver resection in obese patients, we compared the operative outcomes between obese and nonobese patients, also between laparoscopic liver resection and open liver resection of obese and nonobese patients. MATERIALS AND METHODS A total of 86 patients suffering from liver resection in our department from January 2013 to December 2014 were divided into 3 groups: the obese patients group for laparoscopic liver resection, the nonobese patients group for laparoscopic liver resection and the obese patients group for open liver resection. Characteristics and clinic data of 3 groups were studied. RESULTS Characteristics of patients and clinic data were equivalent between the 3 groups. The groups were well matched in age, sex distribution, and liver function (P>0.05). There were no significant differences in the operative time, estimated blood loss, time to oral intake, and postoperative hospital stay in the 3 groups. Tumor diameter of laparoscopic liver resection groups in obese patients was smaller than open liver resections groups in obese patients (P

A novel predictive measure that predicts which algorithm is best at any given point in time. Always select the predicted best algorithm to run for the next generation.The performance of our method is very competitive if viewed as another novel "individual" algorithm.A very interesting positive synergistic effect is found between algorithms in our method.A novel performance evaluation method that makes a lot of sense when one has an absolute maximum number of function evaluations allowed in your application.Application on a Heating Ventilation and Air Conditioning design problem demonstrates that the method can be used to solve real world problems with constraints and that it performs better than choosing an algorithm randomly. Many good evolutionary algorithms have been proposed in the past. However, frequently, the question arises that given a problem, one is at a loss of which algorithm to choose. In this paper, we propose a novel algorithm portfolio approach to address the above problem for single objective optimization. A portfolio of evolutionary algorithms is first formed. Covariance Matrix Adaptation Evolution Strategy (CMA-ES), History driven Evolutionary Algorithm (HdEA), Particle Swarm Optimization (PSO2011) and Self adaptive Differential Evolution (SaDE) are chosen as component algorithms. Each algorithm runs independently with no information exchange. At any point in time, the algorithm with the best predicted performance is run for one generation, after which the performance is predicted again. The best algorithm runs for the next generation, and the process goes on. In this way, algorithms switch automatically as a function of the computational budget. This novel algorithm is named Multiple Evolutionary Algorithm (MultiEA). The predictor we introduced has the nice property of being parameter-less, and algorithms switch automatically as a function of budget. The following contributions are made: (1) experimental results on 24 benchmark functions show that MultiEA outperforms (i) Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO); (ii) Population-based Algorithm Portfolio (PAP); (iii) a multiple algorithm approach which chooses an algorithm randomly (RandEA); and (iv) a multiple algorithm approach which divides the computational budget evenly and execute all algorithms in parallel (ExhEA). This shows that it outperforms existing portfolio approaches and the predictor is functioning well. (2) Moreover, a neck to neck comparison of MultiEA with CMA-ES, HdEA, PSO2011, and SaDE is also made. Experimental results show that the performance of MultiEA is very competitive. In particular, MultiEA, being a portfolio algorithm, is sometimes even better than all its individual algorithms, and has more robust performance. (3) Furthermore, a positive synergic effect is discovered, namely, MultiEA can sometimes perform better than the sum of its individual EAs. This gives interesting insights into why an algorithm portfolio is a good approach. (4) It is found that MultiEA scales as well as the best algorithm in the portfolio. This suggests that MultiEA scales up nicely, which is a desirable algorithmic feature. (5) Finally, the performance of MultiEA is investigated on a real world problem. It is found that MultiEA can select the most suitable algorithm for the problem and is much better than choosing algorithms randomly.

The continuous non-revisiting genetic algorithm (cNrGA) uses the entire search history and parameter-less adaptive mutation to significantly enhance search performance. Storing the entire search history is natural and costs little when the number of fitness evaluations is small or moderate. However, if the number of evaluations required is substantial, some memory management is desirable. In this paper, we propose two pruning mechanisms to keep the memory used constant. They are least recently used pruning and random pruning. The basic idea is to prune a unit of memory when the memory threshold is reached and some new search information is required to be stored, thus keeping the overall memory used constant. Meanwhile, both pruning strategies naturally form parameter-less adaptive mutation operators. A study is carried out to evaluate the impact on performance caused by loss of search history information. Experimental results show that (1) both strategies can maintain the performance of cNrGA, up to the empirical limit when 90 % of the search history is not recorded, (2) cNrGA and its variants with constant memory outperform the real-coded genetic algorithm and the standard particle swarm optimization. By pre-extracting all the current prune-able history information and storing them into a list, namely, to-prune-list, the overhead of both pruning strategies becomes small. This suggests that cNrGA can be extended to use in situations when the number of fitness evaluations is much larger than before with no significant effect on statistical performance. This widens the applicability of cNrGA to include more practical problems that require larger number of fitness evaluations before converging to the global optima.

PdO/CeO2 nanosheets encapsulated by a monolayer of a continuous and dense HZSM-5 zeolite membrane were prepared by a facile in situ hydrothermal growth process and used as a highly efficient and thermally stable catalyst for methane combustion. Uncoated PdO/CeO2 suffered severe sintering at high temperature or high oxygen concentration. However, the encapsulation of HZSM-5 significantly improved sintering resistance by the suppressing effects of the HZSM-5 coating for the agglomeration of PdOx nanoparticles, resulting in the outstanding thermal stability of PdO/CeO2. Furthermore, the synthesized hybrid materials also exhibited good oxygen- and water-tolerance for methane combustion due to the oxygen or water barrier. In addition, a reactivation behavior was observed due to the self-redispersion of PdOx on CeO2 nanosheets in the reaction atmosphere at high temperature.

Antimicrobial resistance phenotypes (18 antimicrobials; disk diffusion method) and genotypes (38 antimicrobial resistance genes; PCR) of 20 pathogenic Vibrio parahaemolyticus strains isolated from seafood in Shanghai wholesale markets between 2009 and 2013 were evaluated. Seventeen isolates (85%) were resistant to one or more antimicrobials, and highest resistance was observed to ampicillin (85%) and cephazolin (30%). And the isolates with tdh displayed higher resistant rates than isolates with trh . Eight antimicrobial resistance genes ( str B, aad A2, str A, tet A, flo R, sul I, sul II, and sul III) were detected in these isolates. Surprisingly, the antimicrobial resistance phenotypes and genotypes of these isolates were not consistent: some isolates were resistant to β-lactam or aminoglycoside, whereas the corresponding genes were negative. Comparatively, aminoglycoside, tetracycline and chloramphenicol resistance genes occurred in susceptibility isolates. This research reveals the mismatch phenomenon between the antimicrobial resistance phenotype and genotype of seafood-derived pathogenic V . parahaemolyticus , and that susceptibility isolates might be a potential risk source for storage and transmission of resistance genes.

Electrically conductive and mechanically stretchable materials based on liquid metal-embedded elastomer compositions have been widely studied for stretchable sensors. However, liquid metal (LM) in plastics possessing outstanding mechanical properties have not been reported. Here, we fabricated a super-tough force sensor based on liquid metal-polyvinyl alcohol composites (LM-PVA) which exhibited unique sandwich structure with conductive LM composites between two insulated layers. Uniform dispersion of LM in PVA matrix was achieved due to the high viscosity of PVA solution and the surface interaction of PVA and oxide layer of LM. Besides, soft LM droplets remarkably toughened the PVA plastics with 12.3 times increase in toughness (74 vs 6 MJ/m3). The solvent-processed LM-PVA films showed phenomenal mechanical durability (>1000 cycles of stretching and compressing). Flexible resistance and capacitance sensors can be conveniently constructed to detect daily motions, such as stretching, finger bending and bicycle rolling, owing to its unique electrical sensitivity and mechanical durability. Compared with LM-elastomer sensors, LM-PVA showed excellent abrasive resistance and high temperature resistance. This work highlights the potential of LM-plastics in broad applications and can facilitate the realization of flexible sensing devices under engineering applications.

Due to the shielding effect of the outermost electronic layer 5s and 5p of rare earth, the near-infrared photoluminescence (PL) of Er3+ can not be easily affected by the external environment. However, we have regulated the near-infrared PL of Er3+ by changing temperature, which is ascribed to the phase transition of BaTiO3 from tetragonal to cubic phase and the presence of impurity. It also found that the redshift of wavelength in samples containing tetragonal phase BaTiO3 can reach 13 nm, which is never observed in other materials. The center wavelength shifts from 1530 nm to 1543 nm as the temperature rises from 20 to 200 °C. Moreover, the full width at half maximum (FWHM) of samples containing cubic phase has a growth of 6 nm (up to 85 nm) with increasing temperature. It indicated that temperature-induced phase transition can be an important method to modulate the PL of Er3+ in BaTiO3 with impurity.

The discovery of highly efficient photocatalysts for water splitting remains a challenge of high importance. Here, we report a series of novel organic conjugated polymers (OCPs) that contain complexed non‐noble metals for photocatalytic hydrogen evolution. The conjugated chromophore polymers were synthesized by a Heck coupling between ferrocene and phenantholine derivatives, which absorb broadly, even up to the near‐IR region. The photocatalyst performances were investigated for hydrogen evolution by using Fe3(CO)12 as a water reduction catalyst. H2 evolution rates up to 212.4 μmol h−1, with an apparent quantum yield of 10.3 % at 380 nm, were obtained with a photocatalyst polymer based on alternating ferrocene and 4,7‐bisphenyl‐1,10‐phenanthroline units. The high activity is likely due to favorable electron transfer and coordination of Fe3(CO)12 with phenanthroline in the supramolecular OCPs.

The GI tract is preferentially targeted during acute/early HIV-1 infection. Consequent damage to the gut plays a central role in HIV pathogenesis. The basis for preferential targeting of gut tissues is not well defined. Recombinant proteins and synthetic peptides derived from HIV and SIV gp120 bind directly to integrin α4β7, a gut-homing receptor. Using both cell-surface expressed α4β7 and a soluble α4β7 heterodimer we demonstrate that its specific affinity for gp120 is similar to its affinity for MAdCAM (its natural ligand). The gp120 V2 domain preferentially engages extended forms of α4β7 in a cation -sensitive manner and is inhibited by soluble MAdCAM. Thus, V2 mimics MAdCAM in the way that it binds to α4β7, providing HIV a potential mechanism to discriminate between functionally distinct subsets of lymphocytes, including those with gut-homing potential. Furthermore, α4β7 antagonists developed for the treatment of inflammatory bowel diseases, block V2 binding to α4β7. A 15-amino acid V2 -derived peptide is sufficient to mediate binding to α4β7. It includes the canonical LDV/I α4β7 binding site, a cryptic epitope that lies 7-9 amino acids amino terminal to the LDV/I, and residues K169 and I181. These two residues were identified in a sieve analysis of the RV144 vaccine trial as sites of vaccine -mediated immune pressure. HIV and SIV V2 mAbs elicited by both vaccination and infection that recognize this peptide block V2-α4β7 interactions. These mAbs recognize conformations absent from the β- barrel presented in a stabilized HIV SOSIP gp120/41 trimer. The mimicry of MAdCAM-α4β7 interactions by V2 may influence early events in HIV infection, particularly the rapid seeding of gut tissues, and supports the view that HIV replication in gut tissue is a central feature of HIV pathogenesis.

Exploratory landscape analysis techniques are widely used methods for the algorithm selection problem. The existing sampling methods for exploratory landscape analysis are usually designed to sample unbiased candidates for measuring the characteristics of the entire search space. In this paper, we discuss the limitation of the unbiased sampling and propose a novel sampling method, which is algorithm based and thus biased. Based on the sampling method, we propose several novel landscape features which are called algorithm based landscape features. The proposed features are compared with the conventional landscape features using supervised and unsupervised learning. The experimental results show that the algorithm based landscape features outperform the conventional landscape features.

Evolutionary algorithms are cost-effective for solving real-world optimization problems, such as NP-hard and black-box problems. Before an evolutionary algorithm can be put into real-world applications, it is desirable that the algorithm was tested on a number of benchmark problems. On the other hand, performance measure on benchmarks can reflect if the benchmark suite is representative. In this paper, benchmarks are generated based on the performance comparison among a set of established algorithms. For each algorithm, its uniquely easy (or uniquely difficult) problem instances can be generated by an evolutionary algorithm. The unique difficulty nature of a problem instance to an algorithm is ensured by the Kruskal-Wallis H-test, assisted by a hierarchical fitness assignment method. Experimental results show that an algorithm performs the best (worst) consistently on its uniquely easy (difficult) problem. The testing results are repeatable. Some possible applications of this work include: 1) to compose an alternative benchmark suite; 2) to give a novel method for accessing novel algorithms; and 3) to generate a set of meaningful training and testing problems for evolutionary algorithm selectors and portfolios.

Background Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement. Results A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines. Conclusions This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy. Electronic supplementary material The online version of this article (10.1186/s12864-018-4837-0) contains supplementary material, which is available to authorized users.

Laser Optoacoustic Ultrasonic Imaging System Assembly (LOUISA-3D) was developed in response to demand of diagnostic radiologists for an advanced screening system for the breast to improve on low sensitivity of x-ray based modalities of mammography and tomosynthesis in the dense and heterogeneous breast and low specificity magnetic resonance imaging. It is our working hypothesis that co-registration of quantitatively accurate functional images of the breast vasculature and microvasculature, and anatomical images of breast morphological structures will provide a clinically viable solution for the breast cancer care. Functional imaging is LOUISA-3D is enabled by the full view 3D optoacoustic images acquired at two rapidly toggling laser wavelengths in the near-infrared spectral range. 3D images of the breast anatomical background is enabled in LOUISA-3D by a sequence of B-mode ultrasound slices acquired with a transducer array rotating around the breast. This creates the possibility to visualize distributions of the total hemoglobin and blood oxygen saturation within specific morphological structures such as tumor angiogenesis microvasculature and larger vasculature in proximity of the tumor. The system has four major components: (i) a pulsed dual wavelength laser with fiberoptic light delivery system, (ii) an imaging module with two arc shaped probes (optoacoustic and ultrasonic) placed in a transparent bowl that rotates around the breast, (iii) a multichannel electronic system with analog preamplifiers and digital data acquisition boards, and (iv) computer for the system control, data processing and image reconstruction. The most important advancement of this latest system design compared with previously reported systems is the full breast illumination accomplished for each rotational step of the optoacoustic transducer array using fiberoptic illuminator rotating around the breast independently from rotation of the detector probe. We report here a pilot case studies on one healthy volunteer and on patient with a suspicious small lesion in the breast. LOUISA3D visualized deoxygenated veins and oxygenated arteries of a healthy volunteer, indicative of its capability to visualize hypoxic microvasculature in cancerous tumors. A small lesion detected on optoacoustic image of a patient was not visible on ultrasound, potentially indicating high system sensitivity of the optoacoustic subsystem to small but aggressively growing cancerous lesions with high density angiogenesis microvasculature. The main breast vasculature (0.5-1 mm) was visible at depth of up to 40-mm with 0.3-mm resolution. The results of LOUISA-3D pilot clinical validation demonstrated the system readiness for statistically significant clinical feasibility study.

It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.

A promising duplex coating was prepared by microarc oxidation (MAO) and enameling processes onto polished Ti6Al4V alloy. The TiO2 ceramic coating deposited by MAO was characterized and then combined with an enameling treatment in order to improve the tribological and corrosion resistance of Ti6Al4V alloy. The morphology, phase composition, and hardness of MAO and MAO/enameling-coated Ti6Al4V alloy were evaluated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and Vickers microhardness tester, respectively. The tribological performance was investigated using a ball-on-disk tribometer. The corrosion resistance was studied using immersion tests and potentiodynamic polarization. Wear tests show that the enamel coating on the MAO-coated surface causes a reduction in the friction coefficient. Immersion tests demonstrate that the duplex coating is more effective in improving the corrosion resistance of Ti6Al4V than the MAO coating especially at high temperature (80 °C). Potentiodynamic polarization curves reveal that the corrosion potential of the duplex coating increases by about 250 mV and the corrosion current density is slightly lower than that of the MAO coating. The duplex coating is superior to the stand-alone MAO coating in improving the tribological and corrosion behavior of Ti6Al4V.

A facile synthetic method of oleic acid decorated Cu 2 O microcrystals has been developed by thermal decomposition of copper formate-octylamine complexes in paraffin using oleic acid as dispersing agent. This new method showed many advantages, which include free-reducing agent, enhancing antioxidant properties of Cu 2 O and good dispersity in paraffin, etc . The phase structure and morphology were investigated by means of XRD, SEM and TEM. It is found that the reaction time and temperature play the important roles in the crystallite morphology. With the increase of the reaction time, the Cu 2 O rhombic dodecahedron is gradually transformed into the spherical particle by intraparticle ripening. The shape evolution of Cu 2 O microcrystals can be accelerated with the increase of temperature.

Vibrio parahaemolyticus, Listeria monocytogenes and Salmonella spp. are important pathogens contaminating seafood in China. In this study, we developed an efficient multiplex real-time PCR for the simultaneous detection of V. parahaemolyticus, L. monocytogenes and Salmonella spp. in raw shrimp without a prior enrichment step. In a test using 28 target and non-target strains only the targets were detected and two calibration curves, for pure cultures and artificially contaminated samples, were used to evaluate the efficiencies of this method. Amplification efficiencies of this multiplex real-time PCR were excellent in pure cultures and artificially contaminated shrimps. The limits of detection in artificially contaminated shrimps were 112 CFU/g for V. parahaemolyticus, 158 CFU/g for L. monocytogenes and 103 CFU/g for Salmonella spp. We validated this multiplex real-time PCR method on 48 commercial samples and the results were comparable to standard culture methods. This efficient multiplex real-time PCR, where each test takes only 50 min after DNA extraction, is a useful tool for high-throughput surveillance of V. parahaemolyticus, L. monocytogenes and Salmonella spp. in seafood products.

To improve the electrocatalytic activity for methanol oxidation, PtCo nanoparticles supported on expanded graphite (EG) were prepared by the solution-phase reduction method with ethylene glycol. The structure of the catalyst was characterized by using transmission electron microscope (TEM) and X-ray diffraction (XRD), and its electrocatalytic performance toward methanol oxidation was investigated by cyclic voltammetry and chronoamperometry. The results show that PtCo alloy nanoparticles with an average size of 2.46 nm have been uniformly dispersed on expanded graphite sheets. As compared to PtCo/MWCNT and commercial Pt/C catalysts, PtCo/EG catalyst has higher electrocatalytic activity, anti-poisoning ability and long-term stability for methanol oxidation in acidic medium, which expected to be a promising anode material for direct methanol fuel cell.

Objective: The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples. Methods: This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM). Results: In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18). Conclusion: We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

Synthetic aperture (SA) ultrasound imaging is a well-known technique in which RF signals for every transmit and receive element combinations is first obtained and a 2-way-focused image is synthetically formed afterwards. Despite its benefits, one of its main shortcomings is reduced penetration depth caused by low acoustic power from single element firing. To address this challenge, we propose a spatial prediction filtering technique called the frequency-space (F-X) prediction filtering (FXPF), which adaptively constructs a spatial filter that rejects random noise and off-axis clutter components directly from the time-aligned channel data. By utilizing the information in the spatial domain, FXPF enhances the signal-to-noise ratio (SNR) by suppressing random noise within the transducer bandwidth, which typically cannot be filtered by temporal bandpass filters.

Highly efficient Pt-Fe/γ-Al

During conventional ultrasound imaging, the need for multiple transmissions for one image and the time of flight for a desired imaging depth limit the frame rate of the system. Using a single plane wave pulse during each transmission followed by parallel receive processing allows for high frame rate imaging. However, image quality is degraded because of the lack of transmit focusing. Beamforming by spatial matched filtering (SMF) is a promising method which focuses ultrasonic energy using spatial filters constructed from the transmit–receive impulse response of the system. Studies by other researchers have shown that SMF beamforming can provide dynamic transmit–receive focusing throughout the field of view. In this paper, we apply SMF beamforming to plane wave transmissions (PWTs) to achieve both dynamic transmit–receive focusing at all imaging depths and high imaging frame rate (>5000 frames per second). We demonstrated the capability of the combined method (PWT + SMF) of achieving two-way focusing mathematically through analysis based on the narrowband Rayleigh–Sommerfeld diffraction theory. Moreover, the broadband performance of PWT + SMF was quantified in terms of lateral resolution and contrast from both computer simulations and experimental data. Results were compared between SMF beamforming and conventional delay-and-sum (DAS) beamforming in both simulations and experiments. At an imaging depth of 40 mm, simulation results showed a 29% lateral resolution improvement and a 160% contrast improvement with PWT + SMF. These improvements were 17% and 48% for experimental data with noise.

Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.

Point estimates, such as the maximum a posteriori (MAP) estimate, are commonly computed in image re- construction tasks. However, such point estimates provide no information about the range of highly probable solutions, namely the uncertainty in the computed estimate. Bayesian inference methods that seek to compute the posterior probability distribution function (PDF) of the object can provide exactly this information, but are generally computationally intractable. Markov Chain Monte Carlo (MCMC) methods, which avoid explicit posterior computation by directly sampling from the PDF, require considerable expertise to run in a proper way. This work investigates a computationally efficient variational Bayesian inference approach for computing the posterior image variance with application to MRI. The methodology employs a sparse object prior model that is consistent with the model assumed in most sparse reconstruction methods. The posterior variance map generated by the proposed method provides valuable information that reveals how data-acquisition parameters and the specification of the object prior affect the reliability of a reconstructed MAP image. The proposed method is demonstrated by use of computer-simulated MRI data.

In this work, the feasible of the recovery of nickel from stainless steelmaking sludge by circular leaching and extraction was investigated. The contents of main elements in the sludge were analyzed first. In the circular leaching process, the valuable metal in the sludge was proved to be leached selectively through controlling the pH of leached solution. When the pH of leached solution was 4.93, the leaching rate of Ni was 19.2%, but Fe and Cr were undetected in the leached solution. Then, trough circular leaching, the solution with high content of Ni and low contents of other metals was obtained. Fe and Cr were accumulated in the leached residue. In the extraction process, Versatic 10 and M84 were used as extractants to treat the leached solution. The extraction rate of Ni was over 80% when the equilibrium pH was 3.5, while the extraction rates of other metals were all below 10%. In this study, Ni was proved to be separable recovery by circular leaching and extracting from the leaching solution.

A Bayesian algorithm selection framework for black box optimization problems is proposed. A set of benchmark problems is used for training. The performance of a set of algorithms on the problems is recorded. In the beginning, an algorithm is randomly selected to run on the given unknown problem. A Bayesian approach is used to measure the similarity between problems. The most similar problem to the given problem is selected. Then the best algorithm for solving it is suggested for the second run. The process repeats until n algorithms have been run. The best solution out of n runs is recorded. We have experimentally evaluated the property and performance of the framework. Conclusions are (1) it can identify the most similar problem efficiently; (2) it benefits from a restart mechanism. It performs better when more knowledge is learned. Thus it is a good algorithm selection framework.

In this research, a nano-Mo2C particle dispersion-strengthened copper alloy was prepared by a novel method, i.e., electron beam physical vapor deposition (EB-PVD) which has advantages of simple technical process and low cost compared with the conventional mechanical alloying method. And the microstructure and properties of the material were investigated. The results show that the copper matrix is composed of columnar crystals with the average width of 7 μm, and the size of Mo2C dispersoid is 1–7 nm. The ultimate tensile strength of the material is 486 MPa, and the electrical conductivity is 82 % IACS. As the temperature increases from 293 to 573 K, the material becomes more brittle.

Different pretreatments (wet, oxidation and complex pretreatments) were used to modify activated carbon honeycomb monolith, and the role of surface properties of support in CO oxidation over Pd/ACHM catalysts was also investigated. Pretreatment was an efficient way to introduce oxygen groups, especially by complex pretreatment. Oxygen-containing groups increased 1.7 times as much as that of original ACHM, and 86% conversion was obtained at 30 °C and 100% relative humidity on supported Pd catalyst prepared from modified ACHM. Oxygen-containing species on the support not only improved Pd dispersion but also affected Pd states. Compared with Pd dispersion, Pd states played a more important role in CO oxidation. The amount of low-valent Pd species (Pd 0 , Pd + 1 ) on catalyst surface was consistent with CO oxidation activity. The presence of NO caused CO oxidation conversion to decrease by 15% which was attributed to the difference between NO and CO adsorption energy on Pd surfaces confirmed by density functional theory calculation. 90 days' 1000 m 3 /h pilot experiment results showed that the catalyst exhibited high activity and stability in the presence of moisture and NO x .

Traditional genome-wide association studies (GWASs) usually focus on single-marker analysis, which only accesses marginal effects. Pathway analysis, on the other hand, considers biological pathway gene marker hierarchical structure and therefore provides additional insights into the genetic architecture underlining complex diseases. Recently, a number of methods for pathway analysis have been proposed to assess the significance of a biological pathway from a collection of single-nucleotide polymorphisms. In this study, we propose a novel approach for pathway analysis that assesses the effects of genes using the sequence kernel association test and the effects of pathways using an extended adaptive rank truncated product statistic. It has been increasingly recognized that complex diseases are caused by both common and rare variants. We propose a new weighting scheme for genetic variants across the whole allelic frequency spectrum to be analyzed together without any form of frequency cutoff for defining rare variants. The proposed approach is flexible. It is applicable to both binary and continuous traits, and incorporating covariates is easy. Furthermore, it can be readily applied to GWAS data, exome-sequencing data, and deep resequencing data. We evaluate the new approach on data simulated under comprehensive scenarios and show that it has the highest power in most of the scenarios while maintaining the correct type I error rate. We also apply our proposed methodology to data from a study of the association between bipolar disorder and candidate pathways from Wellcome Trust Case Control Consortium (WTCCC) to show its utility.

The modification of Co3O4 by Bi2O3 significantly enhanced its catalytic performance for CO oxidation. The 20 wt.% Bi2O3-Co3O4 exhibited the best catalytic performance. The results of H2-TPR and CO-TPR revealed that the mobility of oxygen was accelerated greatly and the ability of low-temperature oxygen activation was the crucial factor to improve the catalytic performance. Bi2O3 entered the lattice of Co3O4 caused the structural defect and lattice distortion, which should be the origin of the high O2 activation ability and mobility. Structure-performance correlation demonstrated that the low-temperature oxygen activation was dependent on the defective degree of structure, which was determined by the content of Bi2O3. The catalytic behaviors under different reaction conditions revealed that CO could effectively adsorb on the surface active sites and CO2 could competitively adsorb on the active sites. The ability to supply the active O2 species was suggested to be a key step. The kinetic data showed not only the amount of surface active sites were increased on the surface of Co3O4 but also the catalytic ability of single active site was enhanced greatly.

Regularization parameter and kernel parameters play a crucial role on the accuracy and rapidity of the least squares support vector machine (LS-SVM) algorithm. Existing methods of tuning the parameters for LS-SVM have some disadvantages, such as difficulty of getting global optimum and poor convergent speed. In this work, we develop the parameter design of LS-SVM using quantum particle swarm optimization (QPSO) and apply the designed LS-SVM into node localization. According to the initial values, the optimal parameter values are obtained using the leave-10-out cross validation method. Simulation results show that the proposed method is compared to the coupling simulated annealing (CSA) LS-SVM and the improved particle swarm optimization (IPSO) LS-SVM.

This paper focuses on the structural analysis of concrete canoes. It firstly introduces the background of the concrete canoe competition and some typical FEA methods for concrete canoes. Then the paper establishes and analyzes an FEA model with SAP2000. Considering the similarity between concrete canoes and strip foundations under column, the paper uses the foundation engineering theory of strip foundations under column to determine the support conditions, and the applied loads of concrete canoes under 2 key loading cases. The result is proved to be accurate according to the experience of Tongji University Concrete Canoe Team. Finally, a conjecture of partial plane cross-section assumption of concrete canoe is proposed according to the result of FEA.

Highly efficient In2O3-Co3O4 catalysts were prepared for ultralow-temperature CO oxidation by simultaneously tuning the CO adsorption strength and oxygen activation over a Co3O4 surface, which could completely convert CO to CO2 at temperatures as low as -105 °C compared to -40 °C over pure Co3O4, with enhanced stability.

In this paper, the problem of exponential synchronization for a class of chaotic neural networks which covers the Hopfield neural networks and cellular neural networks with reaction-diffusion terms and time-varying delays is investigated. A feedback control gain matrix is derived to achieve the state synchronization of two identical neural networks with reaction-diffusion terms, and the synchronization condition can be verified if a certain Hamiltonian matrix with no eigenvalue on the imaginary axis.

CO adsorption and O2 activation played important roles in CO oxidation on a supported Pd catalyst, which were dependent on the chemical state of Pd. A series of Pd catalysts supported on Al2O3 with different Pd states were prepared: metal Pd (NCR), PdO (NC), Pd2+ coordinated with Cl− (Pd2+–Cl−, CF), and a mixture of PdO and Pd2+–Cl− (CC) and the activity of CO oxidation was in the order NCR ~ CF > CC ≫ NC. The catalysts were characterized by Brunauer, Emmett and Teller (BET) surface area analysis, X-ray diffraction (XRD), temperature programmed reduction by hydrogen (H2-TPR), X-ray photoelectron spectroscopy (XPS) and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). The results showed that metal Pd (Pd0) could be partially oxidized to Pd+ in the presence of O2, which produced new CO adsorption sites and decreased the CO adsorption strength simultaneously. The cooperation between the enhanced CO adsorption and the decrease in the CO adsorption strength led to high activity for CO oxidation on NCR. For high chemical valence Pd in the species (Pd2+), the chemical environment or coordinated ligand of the Pd species showed large effects on the CO oxidation. The lowest CO oxidation activity on NC occurred when PdO hardly adsorbed CO, meanwhile, PdO was not easily reduced by CO. However, the presence of Cl− significantly promoted the reduction of Pd2+ to Pd+, which increased the amount of CO adsorption and resulted in the higher activity for CO oxidation on CF and CC than PdO. Tuning the CO adsorption by adjusting the chemical state of Pd may be a useful approach to prepare a highly efficient supported Pd catalyst.

Laser-ablation laser-induced breakdown spectroscopy (LA-LIBS) based on single Nd:YAG laser is used to analyze copper impurity in silver jewellery with enhanced sensitivity and minimal sample ablation. 6-30 folds signal enhancement can be achieved under the re-excitation of the breakdown laser and the spatial resolution is only determined by the ablation laser. 50 ppm limit of detection of copper is achieved when the crater diameter is 17.2 μm under current experimental condition. This technique gives higher analysis sensitivity under the same sample ablation in comparison with single pulse (SP) LIBS. It is useful for high sensitive element microanalysis of precious samples.

Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is an emerging imaging technique that holds great promise for biomedical imaging. PACT is a hybrid imaging method that can exploit the strong endogenous contrast of optical methods along with the high spatial resolution of ultrasound methods. In its canonical form that is addressed in this article, PACT seeks to estimate the photoacoustically-induced initial pressure distribution within the object. Image reconstruction methods are employed to solve the acoustic inverse problem associated with the image formation process. When an idealized imaging scenario is considered, analytic solutions to the PACT inverse problem are available; however, in practice, numerous challenges exist that are more readily addressed within an optimization-based, or iterative, image reconstruction framework. In this article, the PACT image reconstruction problem is reviewed within the context of modern optimization-based image reconstruction methodologies. Imaging models that relate the measured photoacoustic wavefields to the sought-after object function are described in their continuous and discrete forms. The basic principles of optimization-based image reconstruction from discrete PACT measurement data are presented, which includes a review of methods for modeling the PACT measurement system response and other important physical factors. Non-conventional formulations of the PACT image reconstruction problem, in which acoustic parameters of the medium are concurrently estimated along with the PACT image, are also introduced and reviewed.

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

A comparative study of single-and double-pulse laser induced breakdown spectrometry (LIBS) was carried out to detect the silver element in silver jewelry samples. One Nd:YAG was adopted in the experiment to generate two pulses which were set in an orthogonal configuration. The results show that, for double-laser LIBS, average diameter (3μm) of craters on the sample surface is much smaller and the intensity of silver emission signals are magnified by a factor of 40 compared with the single-pulse LIBS. Due to the superiority in high sensitivity and low energy consumption, double-pulse LIBS presents a bright application prospect in rapid on-site undamaged analysis of precious metal jewelry.

In vitriolic electrolyte, DC power supply is used on 7075 aluminum alloy to have anodic oxidation treatment then let the oxidized samples have a treatment of electroless plating. Surface morphology of the coating and performance of corrosion wear are studied by means of scanning electron microscope (SEM) and friction and wear experimental machine. The results show that unsealed oxide coating have many holes and cracks, which are extremely badly-distributed; corrosion resistance of the oxide coatings which have the treatment of electroless plating have improved, having lower coefficient of friction and better performance of corrosion wear; besides; coefficient of friction of the samples in water is higher than that in salt water, its wear amount is less.

The reactivity of 2-propanol with TiO2 nanocrystals supported on Au(1 1 1) was studied by temperature-programmed desorption (TPD) and scanning tunneling microscopy (STM). The nanocrystals, which are grown through oxidation of a Ti Au surface alloy, had an average height of 1 nm and width of 15 nm with a dominantly hexagonal morphology. The desorption of propanol and propanol-derived products from the TiO2 nanocrystal surfaces was observed in the 270–570 K temperature range and could be distinguished from desorption of propanol from the Au(1 1 1) surface below 270 K. With increasing propanol coverage, the TiO2-related TPD peaks were occupied before the appearance of any Au(1 1 1)-related peaks. Our calculations showed that the TiO2 nanocrystals were saturated at 0.4 ML of local propanol surface concentration, where 1 ML ≡ 5.2 × 1014 cm−2 refers to surface density of five-coordinated Ti atoms on rutile(1 1 0). Our TPD measurements showed that 61% of this adsorbed propanol desorbed molecularly at 310 K, while 23% dehydrated into propene and 6% dehydrogenated to form acetone, both products desorbing in the 370–570 K temperature range. The desorption temperatures of products from supported TiO2 nanocrystals were shown to depend strongly on the morphology of the nanocrystals.