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We present a novel method to extract classification features from functional magnetic resonance imaging (fMRI) data collected at rest or during the performance of a task. By combining a two-level feature identification scheme with kernel principal component analysis (KPCA) and Fisher’s linear discriminant analysis (FLD), we achieve high classification rates in discriminating healthy controls from patients with schizophrenia. Experimental results using leave-one-out cross-validation show that features extracted from the default mode network (DMN) lead to a classification accuracy of over 90% in both data sets. Moreover, using a majority vote method that uses multiple features, we achieve a classification accuracy of 98% in auditory oddball (AOD) task and 93% in rest data. Several components, including DMN, temporal, and medial visual regions, are consistently present in the set of features that yield high classification accuracy. The features we have extracted thus show promise to be used as biomarkers for schizophrenia. Results also suggest that there may be different advantages to using resting fMRI data or task fMRI data.

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We describe a framework based on Wirtinger calculus for adaptive signal processing that enables efficient derivation of algorithms by directly working in the complex domain and taking full advantage of the power of complex-domain nonlinear processing. We establish the basic relationships for optimization in the complex domain and the real-domain equivalences for first-and secondorder derivatives by extending the work of Brandwood and van den Bos. Examples in the derivation of first-and second-order update rules are given to demonstrate the versatility of the approach.

We studied a novel bioflocculant, PX, that is produced from Bacillus Bacillus circulans X3, and has excellent flocculating activity with regard to its characterization and flocculating properties. The bioflocculant was purified from supernatant by ethanol precipitation, dialysis and gel permeation chromatography (GPC). The major component of PX was an acid polysaccharide including uronic (19.8%), pyruvic (6.5%) and acetic acids (0.7%). It consisted of galactose, mannose, xylitol, rhamnose and galacturonic acid in an approximate molar ration of 5:4.1:3:2:1.2. The molecular weight of PX was about 4.85 9 10 4 Da as determined by GPC. The infrared spectrum of the bioflocculant indicated the presence of carboxyl, hydroxyl, amino and methoxyl groups. Studies of the flocculating properties revealed that it was stable at 60-100°C and pH 4-10. Moreover, it could flocculate a kaolin suspension over a wide range of pH and temperature in the presence of CaCl 2 .

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The control of food browning and growth of disease-causing microorganisms is critical to maintaining the quality and safety of food. Tyrosinase is the key enzyme in food browning. The inhibitory effect of methyl trans-cinnamate on the activity of tyrosinase has been investigated. Methyl trans-cinnamate can strongly inhibit both monophenolase and diphenolase activity of mushroom tyrosinase. When the concentration of methyl trans-cinnamate reached 2.5 mM, the lag time was lengthened from 32 to 160 s and the steady-state activity was lost about 65%. The IC(50) value was 1.25 mM. For the diphenolase activity, the inhibition of methyl trans-cinnamate displayed a reversible and noncompetitive mechanism. The IC(50) value was 1.62 mM, and the inhibition constant (K(I)) was determined to be 1.60 mM. Moreover, the antibacterial activity against Escherichia coli, Bacillus subtilis and Staphyloccocus aureus and antifungal activity against Candida albicans were tested. The results showed that methyl trans-cinnamate possessed an antimicrobial ability.