No abstract

Co single-atom (CoSA) catalysts of the CoN4 moiety usually show an unsatisfactory oxygen reduction reaction (ORR) activity due to poor O2 activation. Herein, we open up a novel strategy to...

Curcumin exhibits a surprisingly wide range of chemo-preventive and chemo-therapeutic activities. Curcumin has undergone more than 40 clinical trials for the treatment of inflammatory diseases and various human cancers. However, phase I/II clinical trials have shown that curcumin exhibit poor bioavailability in humans. Major reasons resulting in the low plasma and tissue levels of curcumin appear to be its poor absorption, fast metabolism, and rapid systemic elimination. It is suggested that the β-diketone moiety is responsible for the instability and weak pharmacokinetic profiles of curcumin. To attenuate the fast metabolism of curcumin, numerous approaches have been considered, including the adjuvant, the liposomal curcumin, curcumin nanoparticles and phospholipid complex, and structural modification to prepare the analogues without the β-diketone moiety. Particularly, the latter called mono-carbonyl analogs of curcumin (MACs) has been reported to has an enhanced stability in vitro and an improved pharmacokinetic profile in vivo. Thus, MACs have attracted a high attention for development of new curcumin-based agents with both enhanced bioactivities and pharmacokinetic profiles. A number of MACs have shown potential anticancer and anti-inflammatory activity in various models. Several of them have been studied intensively in order to develop novel agents. This review covers 607 MACs as well as their biological activities reported in the past two decades.

No abstract

a b s t r a c tPrior studies of multichannel ECoG from animals showed that beta and gamma oscillations carried perceptual information in both local and global spatial patterns of amplitude modulation, when the subjects were trained to discriminate conditioned stimuli (CS). Here the hypothesis was tested that similar patterns could be found in the scalp EEG human subjects trained to discriminate simultaneous visual-auditory CS. Signals were continuously recorded from 64 equispaced scalp electrodes and band-pass filtered. The Hilbert transform gave the analytic phase, which segmented the EEG into temporal frames, and the analytic amplitude, which expressed the pattern in each frame as a feature vector. Methods applied to the ECoG were adapted to the EEG for systematic search of the beta-gamma spectrum, the time period after CS onset, and the scalp surface to locate patterns that could be classified with respect to type of CS. Spatial patterns of EEG amplitude modulation were found from all subjects that could be classified with respect to stimulus combination type significantly above chance levels. The patterns were found in the beta range (15-22 Hz) but not in the gamma range. They occurred in three short bursts following CS onset. They were non-local, occupying the entire array. Our results suggest that the scalp EEG can yield information about the timing of episodically synchronized brain activity in higher cognitive function, so that future studies in brain-computer interfacing can be better focused. Our methods may be most valuable for analyzing data from dense arrays with very high spatial and temporal sampling rates.

Artificial neural networks (ANNs) are generally considered as the most promising pattern recognition method to process the signals from a chemical sensor array of electronic noses, which makes the system more bionics. This paper presents a chaotic neural network entitled KIII, which modeled olfactory systems, applied to an electronic nose to discriminate six typical volatile organic compounds (VOCs) in Chinese rice wines. Thirty-two-dimensional feature vectors of a sensor array consisting of eight sensors, in which four features were extracted from the transient response of each TGS sensor, were input into the KIII network to investigate its generalization capability for concentration influence elimination and sensor drift counteraction. In comparison with the conventional back propagation trained neural network (BP-NN), experimental results show that the KIII network has a good performance in classification of these VOCs of different concentrations and even for the data obtained 1 month later than the training set. Its robust generalization capability is suitable for electronic nose applications to reduce the influence of concentration and sensor drift.

By co-condensation of IMes [IMes = N,N′-bis(2,6-dimethylphenyl)imidazol-2-ylidene]-bridged organosilane and bis(triethoxysilyl)ethane in the presence of template, a new mesoporous ethane−silica with a built-in bulky N-heterocyclic carbene (NHC) precursor in the framework was synthesized. N2 sorption, XRD, and TEM characterizations revealed that the synthesized material had an ordered mesostructure. FT-IR and solid state NMR investigations confirmed that the IMes moiety was covalently integrated with the solid materials. Such a functionalized material was able to coordinate Pd(OAc)2, leading to an active solid catalyst for Suzuki−Miyaura couplings of challenging aryl chlorides and benzyl chlorides under the relatively mild conditions. By using isopropyl alcohol as solvent and KO t Bu as base, a 78% yield for biphenyl was achieved in the presence of 0.5 mol % Pd at 80 °C within 24 h. This solid catalyst could be reused eight times without a significant decrease in activity. The high recyclability may be attributed to the functionalized, stable nanopores that efficiently prevent the in situ formed Pd nanoparticles from the aggregation into the less active large particles in the catalytic reaction. This study not only supplies a novel functionalized periodic mesoporous organosilica (PMO) but also provides an efficient solid catalyst for Suzuki−Miyaura couplings of challenging substrates.

Chloroplast is a typical plant cell organelle where photosynthesis takes place. In this study, a total of 1,808 chloroplast core proteins in Arabidopsis thaliana were reliably identified by combining the results of previously published studies and our own predictions. We then constructed a chloroplast protein interaction network primarily based on these core protein interactions. The network had 22,925 protein interaction pairs which involved 2,214 proteins. A total of 160 previously uncharacterized proteins were annotated in this network. The subunits of the photosynthetic complexes were modularized, and the functional relationships among photosystem I (PSI), photosystem II (PSII), light harvesting complex of photosystem I (LHC I) and light harvesting complex of photosystem I (LHC II) could be deduced from the predicted protein interactions in this network. We further confirmed an interaction between an unknown protein AT1G52220 and a photosynthetic subunit PSI-D2 by yeast two-hybrid analysis. Our chloroplast protein interaction network should be useful for functional mining of photosynthetic proteins and investigation of chloroplast-related functions at the systems biology level in Arabidopsis.