Fast sequential multi-element determination of Ca, Mg, K, Cu, Fe, Mn and Zn for foliar diagnosis using high-resolution continuum source flame atomic absorption spectrometry: Feasibility of secondary lines, side pixel registration and least-squares background correction

The fast sequential multi-element determination of Ca, Mg, K, Cu, Fe, Mn and Zn in plant tissues by high-resolution continuum source flame atomic absorption spectrometry is proposed. For this, the main lines for Cu (324.754 nm), Fe (248.327 nm), Mn (279.482 nm) and Zn (213.857 nm) were selected, and the secondary lines for Ca (239.856 nm), Mg (202.582 nm) and K (404.414 nm) were evaluated. The side pixel registration approach was studied to reduce sensitivity and extend the linear working range for Mg by measuring at wings (202.576 nm; 202.577 nm; 202.578 nm; 202.580 nm: 202.585 nm; 202.586 nm: 202.587 nm; 202.588 nm) of the secondary line. The interference caused by NO bands on Zn at 213.857 nm was removed using the least-squares background correction. Using the main lines for Cu, Fe, Mn and Zn, secondary lines for Ca and K, and line wing at 202.588 nm for Mg, and 5 mL min(-1) sample flow-rate, calibration curves in the 0.1-0.5 mg L-1 Cu, 0.5-4.0 mg L-1 Fe, 0.5-4.0 mg L-1 Mn, 0.2-1.0 mg L-1 Zn, 10.0-100.0 mg L-1 Ca, 5.0-40.0 mg L-1 Mg and 50.0-250.0 mg L-1 K ranges were consistently obtained. Accuracy and precision were evaluated after analysis of five plant standard reference materials. Results were in agreement at a 95% confidence level (paired t-test) with certified values. The proposed method was applied to digests of sugar-cane leaves and results were close to those obtained by line-source flame atomic absorption spectrometry. Recoveries of Ca, Mg, K, Cu, Fe, Mn and Zn in the 89-103%, 84-107%, 87-103%, 85-105%, 92-106%, 91-114%, 96-114% intervals, respectively, were obtained. The limits of detection were 0.6 mg L-1 Ca, 0.4 mg L-1 Mg, 0.4 mg L-1 K, 7.7 mu g L-1 Cu, 7.7 mu g L-1 Fe, 1.5 mu g L-1 Mn and 5.9 mu g L-1 Zn. (C) 2009 Elsevier B.V. All rights reserved.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Sequential Determination of Cd, Cu and Pb in Tea Leaves by Slurry Introduction to Thermospray Flame Furnace Atomic Absorption Spectrometry

A simple procedure for the sequential determination of Cd, Cu and Pb in tea leaves by slurry introduction to thermospray flame furnace atomic absorption spectrometry was developed. Detection limits were 0.05 mg kg-1 for Cd, 2.1 mg kg-1 for Cu and 0.68 mg kg-1 for Pb using 0.67 % (m/v) slurries (100 mg/15 mL). © 2013 Springer Science+Business Media New York.

Exploring potentialities of the HR-CS FAAS technique in the determination of Ni and Pb in mineral waters

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Exploring Liquid Sequential Injection Chromatography To Teach Fundamentals of Separation Methods: A Very Fast Analytical Chemistry Experiment

Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)

Sequential release of BMP-7 and VEGF from the PLGA/AK-Gelatin Composite Scaffolds

Vascular endothelial growth factor (VEGF) and bone morphogenetic proteins (BMP-7) are key regulators of angiogenesis and osteogenesis during bone regeneration. The aim of this study was to investigate the possibility of realizing sequential release of the two growth factors using a novel composite scaffold. Poly(lactic-co-glycolic acid) (PLGA)-Akermanite (AK) microspheres were used to make the composite scaffold, which was then loaded with BMP-7, followed by embedding in a gelatin hydrogel matrix loaded with VEGF. The release profiles of the growth factors were studied and selected osteogenic related markers of bone marrow stromal cells (BMSCs) were analysed. It was shown that the composite scaffolds exhibited a fast initial burst release of VEGF within the first 3 days and a sustained slow release of BMP-7 over the full period of 20 days. The in vitro proliferation and differentiation of the BMSCs cultured in the osteogenic medium were enhanced by 1 to 2 times, resulting from the additionally and sequentially release of growth factors from the PLGA-AK/gelatin composite scaffolds.

A fast dual algorithm for kernel logistic regression

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Efficient sequential assignments in proteins with reduced dimensionality 3D HN(CA)NH

We present reduced dimensionality (RD) 3D HN(CA)NH for efficient sequential assignment in proteins. The experiment correlates the N-15 and H-1 chemical shift of a residue ('i') with those of its immediate N-terminal (i - 1) and C-terminal (i + 1) neighbors and provides four-dimensional chemical shift correlations rapidly with high resolution. An assignment strategy is presented which combines the correlations observed in this experiment with amino acid type information obtained from 3D CBCA(CO)NH. By classifying the 20 amino acid types into seven distinct categories based on C-13(beta) chemical shifts, it is observed that a stretch of five sequentially connected residues is sufficient to map uniquely on to the polypeptide for sequence specific resonance assignments. This method is exemplified by application to three different systems: maltose binding protein (42 kDa), intrinsically disordered domain of insulin-like growth factor binding protein-2 and Ubiquitin. Fast data acquisition is demonstrated using longitudinal H-1 relaxation optimization. Overall, 3D HN(CA)NH is a powerful tool for high throughput resonance assignment, in particular for unfolded or intrinsically disordered polypeptides.

Rapid analysis of steroidal saponin mixture using electrospray ionization mass spectrometry combined with sequential tandem mass spectrometry

Using electrospray ionization mass spectrometry (MS) combined with sequential tandem MS(ESI-MSn), two major steroidal saponins extracted from Tribulus terrestris were studied, and considerable useful structural information was obtained. The structure of the proposed known steroidal saponin was verified, and the structure of the unknown saponin was investigated using MSn experiments. Some special fragment ions were also observed, and the corresponding fragmentation mechanisms were investigated which are characteristic for steroidal saponins and can give some information on the linkage position of some sugar groups in saponins. This methodology has been established as a powerful tool for the rapid, comparative analysis of mixtures such as crude plant extracts. (C) 1998 John Wiley & Sons, Ltd.

Fast Moving Window Algorithm for QR and Cholesky Decompositions

This paper proposes a fast moving window algorithm for QR and Cholesky decompositions by simultaneously applying data updating and downdating. The developed procedure is based on inner products and entails a similar downdating to that of the Chambers’ approach. For adding and deleting one row of data from the original matrix, a detailed analysis shows that the proposed algorithm outperforms existing ones in terms or computational efficiency, if the number of columns exceeds 7. For a large number of columns, the proposed algorithm is numerically superior compared to the traditional sequential technique.

Processing of short auditory stimuli:The rapid audio sequential presentation paradigm (RASP)

Human listeners seem to be remarkably able to recognise acoustic sound sources based on timbre cues. Here we describe a psychophysical paradigm to estimate the time it takes to recognise a set of complex sounds differing only in timbre cues: both in terms of the minimum duration of the sounds and the inferred neural processing time. Listeners had to respond to the human voice while ignoring a set of distractors. All sounds were recorded from natural sources over the same pitch range and equalised to the same duration and power. In a first experiment, stimuli were gated in time with a raised-cosine window of variable duration and random onset time. A voice/non-voice (yes/no) task was used. Performance, as measured by d', remained above chance for the shortest sounds tested (2 ms); d's above 1 were observed for durations longer than or equal to 8 ms. Then, we constructed sequences of short sounds presented in rapid succession. Listeners were asked to report the presence of a single voice token that could occur at a random position within the sequence. This method is analogous to the "rapid sequential visual presentation" paradigm (RSVP), which has been used to evaluate neural processing time for images. For 500-ms sequences made of 32-ms and 16-ms sounds, d' remained above chance for presentation rates of up to 30 sounds per second. There was no effect of the pitch relation between successive sounds: identical for all sounds in the sequence or random for each sound. This implies that the task was not determined by streaming or forward masking, as both phenomena would predict better performance for the random pitch condition. Overall, the recognition of familiar sound categories such as the voice seems to be surprisingly fast, both in terms of the acoustic duration required and of the underlying neural time constants.