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Highly enantioselective halocyclization reactions of indole derivatives, including tryptophols and tryptamines, have been accomplished by means of anionic chiral Co(III) complexes and 1,3-dihalohydantoins (as little as 0.50 equiv). 3-Halo-fused indolines were obtained in excellent yields (up to 98%) and enantioselectivities (up to 98% ee), employing the chiral anion phase-transfer-catalysis strategy.

The blood-spinal cord barrier (BSCB), a physical barrier between the blood and spinal cord parenchyma, prevents the toxins, blood cells, and pathogens from entering the spinal cord and maintains a tightly controlled chemical balance in the spinal environment, which is necessary for proper neural function. A BSCB disruption, however, plays an important role in primary and secondary injury processes related to spinal cord injury (SCI). After SCI, the structure of the BSCB is broken down, which leads directly to leakage of blood components. At the same time, the permeability of the BSCB is also increased. Repairing the disruption of the BSCB could alleviate the SCI pathology. We review the morphology and pathology of the BSCB and progression of therapeutic methods targeting BSCB in SCI.

Recently, the ternary spinel selenide MgSc

Rechargeable aqueous batteries are one of the most promising large-scale energy storage devices because of their environment-friendly properties and high safety advantages without using flammable and poisonous organic liquid electrolyte. In addition, rechargeable Zn-MnO

In this study, a improved high performance liquid chromatography-diode array detector (HPLC-DAD) method for simultaneous quantification of twelve major components belonging to three chemical types was developed and validated, and was applied to quantitatively compare the quality of Radix Paeoniae Alba (RPA) sulfur-fumigated with different durations and purchased from commercial herbal markets. The contents of paeoniflorin, benzoylpaeoniflorin, oxypaeoniflorin, benzoic acid and paeonol decreased whereas that of paeoniflorin sulfonate increased in RPA with the extending of sulfur-fumigation duration. Different levels of paeoniflorin sulfonate were determined in ten of seventeen commercial RPA samples, indicating that these ten samples may be sulfur-fumigated with different durations. Moreover, the relative standard deviation of the contents of each component was higher in the commercial sulfur-fumigated RPA samples than that in commercial non-fumigated RPA samples, and the percentage of the total average content of monoterpene glycosides in the determined analytes was higher in the decoctions of commercial sulfur-fumigated RPA than that in commercial non-fumigated RPA samples. All these results suggested that the established method was precise, accurate and sensitive enough for the global quality evaluation of sulfur-fumigated RPA, and sulfur-fumigation can not only change the proportions of bioactive components, but also cause the reduction of the quality consistency of both raw materials and aqueous decoctions of RPA.

Polypropylene samples with fullerene C60, fullerenol C60(OH)24, 1010, C60/168, C60-OH/168 and 1010/168 as antioxidants were prepared by extrusions. MFR, YI, TGA and OIT of all the samples were tested. According to the results of MFR, during the melt extrusion, fullerene showed excellent stability effect on PP. The antioxidative ability of fullerene was comparable to the traditional antioxidant 1010. The antioxidative ability of fullerenol was not significant in the first extrusion and it accelerated the degradation of PP in the second and the third extrusions. TGA and OIT tests showed that the stability effects of fullerene and fullerenol were slightly lower than antioxidant 1010. In the first time, antioxidant 168 was reported to show great synergistic effects with fullerene and fullerenol as antioxidants, which sussested a simple way to enhance the antioxidative abilities of fullerene and fullerenol.

Polysaccharides and ganoderic acids (GAs) are the major bioactive constituents of Ganoderma species. However, the commercialization of their production was limited by low yield in the submerged culture of Ganoderma despite improvement made in recent years. In this work, twelve Ganoderma strains were screened to efficiently produce polysaccharides and GAs, and Ganoderma lucidum 5.26 (GL 5.26) that had been never reported in fermentation process was found to be most efficient among the tested stains. Then, the fermentation medium was optimized for GL 5.26 by statistical method. Firstly, glucose and yeast extract were found to be the optimum carbon source and nitrogen source according to the single-factor tests. Ferric sulfate was found to have significant effect on GL 5.26 biomass production according to the results of Plackett-Burman design. The concentrations of glucose, yeast extract and ferric sulfate were further optimized by response surface methodology. The optimum medium composition was 55 g/L of glucose, 14 g/L of yeast extract, 0.3 g/L of ferric acid, with other medium components unchanged. The optimized medium was testified in the 10-L bioreactor, and the production of biomass, IPS, total GAs and GA-T enhanced by 85, 27, 49 and 93 %, respectively, compared to the initial medium. The fermentation process was scaled up to 300-L bioreactor; it showed good IPS (3.6 g/L) and GAs (670 mg/L) production. The biomass was 23.9 g/L in 300-L bioreactor, which was the highest biomass production in pilot scale. According to this study, the strain GL 5.26 showed good fermentation property by optimizing the medium. It might be a candidate industrial strain by further process optimization and scale-up study.

Boiling processing is commonly used in post-harvest handling of White Paeony Root (WPR), in order to whiten the herbal materials and preserve the bright color, since such WPR is empirically considered to possess a higher quality. The present study was designed to investigate whether and how the boiling processing affects overall quality of WPR. First, an ultra-high performance liquid chromatography quadrupole/time-of-flight mass spectrometry-based metabolomics approach coupled with multivariate statistical analysis was developed to compare the holistic quality of boiled and un-boiled WPR samples. Second, ten major components in WPR samples boiled for different durations were quantitatively determined using high performance liquid chromatography to further explore the effects of boiling time on the holistic quality of WPR, meanwhile the appearance of the processed herbal materials was observed. The results suggested that the boiling processing conspicuously affected the holistic quality of WPR by simultaneously and inconsistently altering the chemical compositions and that short-time boiling processing between 2 and 10 min could both make the WPR bright-colored and improve the contents of major bioactive components, which were not achieved either without boiling or with prolonged boiling. In conclusion, short-term boiling (2-10 min) is recommended for post-harvest handling of WPR.

Ensemble pruning searches for a selective subset of members that performs as well as, or better than ensemble of all members. However, in the accuracy / diversity pruning framework, generalization ability of target ensemble is not considered, and moreover, there is not clear relationship between them. In this paper, we proof that ensemble formed by members of better generalization ability is also of better generalization ability. We adopt learning with both labeled and unlabeled data to improve generalization ability of member learners. A data dependant kernel determined by a set of unlabeled points is plugged in individual kernel learners to improve generalization ability, and ensemble pruning is launched as much previous work. The proposed method is suitable for both single-instance and multi-instance learning framework. Experimental results on 10 UCI data sets for single-instance learning and 4 data sets for multi-instance learning show that subensemble formed by the proposed method is effective.

Teaching quality is a key metric in college teaching effect and ability evaluation. In many previous literatures, evaluation of such metric is merely depended on subjective judgment of few experts based on their experience, which leads to some false, bias or unstable results. Moreover, pure human based evaluation is expensive that is difficult to extend to large scale. With the application of information technology, much information in college teaching is recorded and stored electronically, which founds the basic of a computer-aid analysis. In this paper, we perform teaching quality evaluation within machine learning framework, focusing on learning and modeling electronic information associated with quality of teaching, to get a stable model described the substantial principles of teaching quality. Artificial Neural Network (ANN) is selected as the main model in this work. Experiment results on real data sets consisted of 4 subjects / 8 semesters show the effectiveness of the proposed method.

By means of the density functional theory calculations, two C88(D2:35)-based endohedral fullerenes, Lu3C2@C88(D2:35) and Lu3N@C88(D2:35) which encapsulate tri-lutetium carbide and tri-lutetium nitride cluster were investigated. For the cores in Lu3C2@C88 and Lu3N@C88, the trivalent C2 and N respectively template a butterfly-shaped endohedral moiety and a planar tri-lutetium cluster within the same D2-symmetric C88 cage. Moreover, Lu3N@C88 - D2 has a closed-shell electronic structure but for LuC3C2@C88 - D2, it owns an unpaired electron mainly localized on the internal Lu3C2 cluster. These results clearly showed that the core unit C2(3) as well as N3- play an important role in constructing molecular structures and electronic features of metallofullerenes. Furthermore, the electrochemical redox potentials, and vibrational frequencies of the two endofullerenes agree well with our experimental results. The electronic structures, ionization energies, electron affinities, inner clusters' dynamic motions of them have been predicted to further disclose the characters of these two metallofullerenes.

Magnetic resonance imaging (MRI) has evolved into one of the most powerful techniques as a noninvasive diagnostic tool by providing high quality anatomical images of soft tissue; and the rapid expansion of medical MRI has prompted the development of contrast agents (CAs). These agents can shorten the relaxation time of nearby water molecules, enhancing the contrast between areas containing the contrast agent and the surrounding tissues, and increasing diagnostic confidence.The discovery of fullerenes have greatly expanded the number of known carbon allotropes, and have been the subject of intense research, both for their unique properties and for their technological applications, especially in materials science, biomedical science, electronics, and nanotechnology. As an important family of fullerenes, endohedral fullerene has exhibited unique properties of both inner clusters and carbon cage. Gadofullerenes have revealed low toxicity, high efficiency and diversity of modification as a new class of MRI contrast agents, which gives them great potential as a next generation of MRI contrast agents. The readily surface modification of carbon cage facilitates specific tissue-targeting, which in turn increase the local contrast enhancement with a limited dosage of administration. The cellular uptake of gadofullerenes will unambiguously improve the diagnosis and therapy of some disease. Herein, the latest findings on water-solubilized gadofullerene materials, such as Gd@C82, Gd@C60, ScxGd3-x@C80(x= 0, 1, 2) and how these findings relate to their future applications in MRI are reviewed and discussed.

Observe the effects of Goutengsan on SOD, MAO-B, GSH-PX, NO, LDH, index of brain, rate of death and so on in rats to study therapeutic effects and mechanism of Goutengsan on Alzheimer dementia (AD) model.One hundred and twenty rats were randomly divided into 6 groups, 3 experimental groups of which were daily administrated with Goutengsan extract whereas the model and control groups were given NS (0.01 mL x g(-1)). Aniracetam at 0.1 g x kg(-1) served as a positive control. At the 5th day after administration, all groups except the control were administrated (ip) with AlCl3 (100 mg x kg(-1) ) for successive 50 days at 1 day interval. After administration, the death rate, body weight, training scores, brain index, MAO-B, SOD, GSH-Px in brain and NO, LDH in serum were determined.The brain index, SOD, GSH-Px activities as well as NO content of drug-treated groups were strikingly higher that of model group, and had not obvious difference from that of normal group except content of LDH was higher.Goutengsan could increase the brain index, cut down the rate of death, stable increase of body weight, promote the endogenous antioxidant activity, enhance the clearance of lipid peroxide and other metabolic waste, inhibit the MAO-B activity, reduced the leakage of LDH and maintain the content of NO at a normal level. Therefore Goutengsan could protect cells, delay senile, improve symptoms of AD.

We report a facile hydrothermal synthetic approach to selectively produce 1-dimensional, 2-dimensional, or 3-dimensional MnO2 nanomaterials reliably and conveniently. The influences of reaction conditions on the morphology and crystallographic forms and the formation mechanism of the as-obtained MnO2 nanostructures have been studied in this work. And the materials produced by this method have excellent crystalline nature. Preliminary electrochemical study indicates that the as-prepared 2-dimensional MnO2 nanomaterials are excellent cathode materials using in lithium batteries.

With the development of information technology in healthcare industry, more and more data has been generated and stored electronically. To fully exploit the information and knowledge, data mining and machine learning methods have been developed and studied. We notice that a large body of healthcare data are lack of supervised information which requires expense human efforts in labelling or scoring them so as to be analysed in a data mining model. In this article, we address the problem of making use of unlabeled or un-scored data, together with only a few supervised data, to improve the performance of analysis model for healthcare decision making. This kind of paradise is called semi-supervised learning in machine learning literatures. We focus on semi-supervised kernel learning and propose to apply the learned kernel in two algorithms, i.e., support vector machine SVM and kernel regularised least squares KRLS. The evaluation results on two publicly available healthcare dataset illustrate the effectiveness of the proposed framework.

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