Emission of carbon nanofiber (CNF) from CNF-containing composite adsorbents

In spite of numerous applications of carbon nanofibers (CNFs) in a variety of fields, the potential release of airborne CNF during their special application, which could lead to workers or end-users exposure, has not been well investigated. In this study, the potential release of CNF from an organic vapour respirator cartridge was evaluated by carbon analysis and microscopy analysis. The cartridge consisted of an AC (Activated Carbon)/CNF composite adsorbent and different types of particulate filters. The composite adsorbent CNF were prepared by chemical vapour deposition (CVD). Air was passed through the prepared cartridge for 12 hours at 12 l/min and particles were collected on sampling filters suitable for measuring organic and elemental carbon (OC/EC) by carbon analysis based on the NIOSH 5040 method. Breakthrough of CNFs was also checked by scanning and transmission electron microscopy (SEM/TEM). This study found only minimal amounts of released elemental carbon while passing the air through the cartridge. Meanwhile TEM photos showed a few CNF structures for AC/CNF composite adsorbents which were not in the critical range in terms of length, aspect ratio, or number. [Authors]

Performance of juveniles of Pseudoplatystoma fasciatum fed graded levels of corn gluten meal

The objective of this work was to evaluate corn gluten meal (CGM) as a substitute for fish meal in diets for striped catfish (Pseudoplatystoma fasciatum) juveniles. Eight isonitrogenous (46% crude protein) and isoenergetic (3,450 kcal kg-1 digestible energy) diets, with increasing levels of CGM - 0, 6, 12, 18, 24, 30, 36, and 42% -, were fed to juvenile striped catfish (113.56±5.10 g) for seven weeks. Maximum values for weight gain, specific growth rate, protein efficiency ratio and feed conversion ratio, evaluated by polynomial quadratic regression, were observed with 10.4, 11.4, 15.4 and 15% of CGM inclusion, respectively. Feed intake decreased significantly from 0.8% CGM. Mesenteric fat index and body gross energy decreased linearly with increasing levels of CGM; minimum body protein contents were observed with 34.1% CGM. Yellow pigmentation of fillets significantly increased until 26.5% CGM, and decreased from this point forth. Both plasma glucose and protein concentrations decreased with increased CGM levels. The inclusion of 10-15% CGM promotes optimum of striped catfish juveniles depending on the parameter evaluated. Yellow coloration in fillets produced by CGM diets can have marketing implications.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Fast Bias Field Correction for 9.4 Tesla Magnetic Resonance Imaging

In this paper the problem of intensity inhomogeneity athigh magnetic field on magnetic resonance images isaddressed. Specifically, rat brain images at 9.4Tacquired with a surface coil are bias corrected. Wepropose a low- pass frequency model that takes intoaccount not only background-object contours but alsoother important contours inside the image. Twopre-processing filters are proposed: first, to create avolume of interest without contours, and second, toextrapolate the image values of such masked area to thewhole image. Results are assessed quantitatively andvisually in comparison to standard low pass filterapproach, and they show as expected better accuracy inenhancing image intensity.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Focal degeneration of astrocytes in amyotrophic lateral sclerosis

Astrocytes emerge as key players in motor neuron degeneration in Amyotrophic Lateral Sclerosis (ALS). Whether astrocytes cause direct damage by releasing toxic factors or contribute indirectly through the loss of physiological functions is unclear. Here we identify in the hSOD1(G93A) transgenic mouse model of ALS a degenerative process of the astrocytes, restricted to those directly surrounding spinal motor neurons. This phenomenon manifests with an early onset and becomes significant concomitant with the loss of motor cells and the appearance of clinical symptoms. Contrary to wild-type astrocytes, mutant hSOD1-expressing astrocytes are highly vulnerable to glutamate and undergo cell death mediated by the metabotropic type-5 receptor (mGluR5). Blocking mGluR5 in vivo slows down astrocytic degeneration, delays the onset of the disease and slightly extends survival in hSOD1(G93A) transgenic mice. We propose that excitotoxicity in ALS affects both motor neurons and astrocytes, favouring their local interactive degeneration. This new mechanistic hypothesis has implications for therapeutic interventions.Cell Death and Differentiation advance online publication, 11 July 2008; doi:10.1038/cdd.2008.99.

Chemical and biological attributes of a lowland soil affected by land leveling

The objective of this work was to evaluate the relationship between soil chemical and biological attributes and the magnitude of cuts and fills after the land leveling process of a lowland soil. Soil samples were collected from the 0 - 0.20 m layer, before and after leveling, on a 100 point grid established in the experimental area, to evaluate chemical attributes and soil microbial biomass carbon (MBC). Leveling operations altered the magnitude of soil chemical and biological attributes. Values of Ca, Mg, S, cation exchange capacity, Mn, P, Zn, and soil organic matter (SOM) decreased in the soil profile, whereas Al, K, and MBC increased after leveling. Land leveling decreased in 20% SOM average content in the 0 - 0.20 m layer. The great majority of the chemical attributes did not show relations between their values and the magnitude of cuts and fills. The relation was quadratic for SOM, P, and total N, and was linear for K, showing a positive slope and indicating increase in the magnitude of these attributes in cut areas and stability in fill areas. The relationships between these chemical attributes and the magnitude of cuts and fills indicate that the land leveling map may be a useful tool for degraded soil recuperation through amendments and organic fertilizers.

Enhanced MDIR receiver for space-time dispersive channels

A particular property of the matched desiredimpulse response receiver is introduced in this paper, namely,the fact that full exploitation of the diversity is obtained withmultiple beamformers when the channel is spatially and timelydispersive. This particularity makes the receiver specially suitablefor mobile and underwater communications. The new structureprovides better performance than conventional and weightedVRAKE receivers, and a diversity gain with no needs of additionalradio frequency equipment. The baseband hardware neededfor this new receiver may be obtained through reconfigurabilityof the RAKE architectures available at the base station. Theproposed receiver is tested through simulations assuming UTRAfrequency-division-duplexing mode.

Gliotransmitters Travel in Time and Space.

The identification of the presence of active signaling between astrocytes and neurons in a process termed gliotransmission has caused a paradigm shift in our thinking about brain function. However, we are still in the early days of the conceptualization of how astrocytes influence synapses, neurons, networks, and ultimately behavior. In this Perspective, our goal is to identify emerging principles governing gliotransmission and consider the specific properties of this process that endow the astrocyte with unique functions in brain signal integration. We develop and present hypotheses aimed at reconciling confounding reports and define open questions to provide a conceptual framework for future studies. We propose that astrocytes mainly signal through high-affinity slowly desensitizing receptors to modulate neurons and perform integration in spatiotemporal domains complementary to those of neurons.

Conditional maximum likelihood timing recovery: estimators and bounds

This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulationthat is systematically applied in the literature for the derivationof non-data-aided (NDA) timing-error-detectors (TEDs). A new CML TED is derived and proved to be self-noise free, in contrast to the conventional low-SNR-UML TED. In addition, the paper provides a derivation of the conditional Cramér–Rao Bound (CRB ), which is higher (less optimistic) than the modified CRB (MCRB)[which is only reached by decision-directed (DD) methods]. It is shown that the CRB is a lower bound on the asymptotic statisticalaccuracy of the set of consistent estimators that are quadratic with respect to the received signal. Although the obtained boundis not general, it applies to most NDA synchronizers proposed in the literature. A closed-form expression of the conditional CRBis obtained, and numerical results confirm that the CML TED attains the new bound for moderate to high Eg/No.

Incidence and management of pulmonary embolism following spinal surgery occurring while under chemical thromboprophylaxis.

Patients undergoing spinal surgery are at risk of developing thromboembolic complications even though lower incidences have been reported as compared to joint arthroplasty surgery. Deep vein thrombosis (DVT) has been studied extensively in the context of spinal surgery but symptomatic pulmonary embolism (PE) has engaged less attention. We prospectively followed a consecutive cohort of 270 patients undergoing spinal surgery at a single institution. From these patients, only 26 were simple discectomies, while the largest proportion (226) was fusions. All patients received both low molecular weight heparin (LMWH) initiated after surgery and compressive stockings. PE was diagnosed with spiral chest CT. Six patients developed symptomatic PE, five during their hospital stay. In three of the six patients the embolic event occurred during the first 3 postoperative days. They were managed by the temporary insertion of an inferior vena cava (IVC) filter thus allowing for a delay in full-dose anticoagulation until removal of the filter. None of the PE patients suffered any bleeding complication as a result of the introduction of full anticoagulation. Two patients suffered postoperative haematomas, without development of neurological symptoms or signs, requiring emergency evacuation. The overall incidence of PE was 2.2% rising to 2.5% after exclusion of microdiscectomy cases. The incidence of PE was highest in anterior or combined thoracolumbar/lumbar procedures (4.2%). There is a large variation in the reported incidence of PE in the spinal literature. Results from the only study found in the literature specifically monitoring PE suggest an incidence of PE as high as 2.5%. Our study shows a similar incidence despite the use of LMWH. In the absence of randomized controlled trials (RCT) it is uncertain if this type of prophylaxis lowers the incidence of PE. However, other studies show that the morbidity of LMWH is very low. Since PE can be a life-threatening complication, LMWH may be a worthwhile option to consider for prophylaxis. RCTs are necessary in assessing the efficacy of DVT and PE prophylaxis in spinal patients.

Second-order parameter estimation

This work provides a general framework for the design of second-order blind estimators without adopting anyapproximation about the observation statistics or the a prioridistribution of the parameters. The proposed solution is obtainedminimizing the estimator variance subject to some constraints onthe estimator bias. The resulting optimal estimator is found todepend on the observation fourth-order moments that can be calculatedanalytically from the known signal model. Unfortunately,in most cases, the performance of this estimator is severely limitedby the residual bias inherent to nonlinear estimation problems.To overcome this limitation, the second-order minimum varianceunbiased estimator is deduced from the general solution by assumingaccurate prior information on the vector of parameters.This small-error approximation is adopted to design iterativeestimators or trackers. It is shown that the associated varianceconstitutes the lower bound for the variance of any unbiasedestimator based on the sample covariance matrix.The paper formulation is then applied to track the angle-of-arrival(AoA) of multiple digitally-modulated sources by means ofa uniform linear array. The optimal second-order tracker is comparedwith the classical maximum likelihood (ML) blind methodsthat are shown to be quadratic in the observed data as well. Simulationshave confirmed that the discrete nature of the transmittedsymbols can be exploited to improve considerably the discriminationof near sources in medium-to-high SNR scenarios.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Semivariogram models for estimating fig fly population density throughout the year

The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae) throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, hole effect, K-Bessel, J-Bessel, and stable semivariogram models, using ordinary kriging interpolation. The models with the best fit were selected by cross-validation. Each data set (months) has a particular spatial dependence structure, which makes it necessary to define specific models of semivariograms in order to enhance the adjustment to the experimental semivariogram. Therefore, it was not possible to determine a standard semivariogram model; instead, six theoretical models were selected: circular, Gaussian, hole effect, K-Bessel, J-Bessel, and stable.