Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients

The Antimicrobial Activity and Characterization of Bioactive Compounds in Peganum harmala L. Based on HPLC and HS-SPME-GC-MS

Peganum harmala L. is a perennial herb of the Tribulus family and its aerial parts and seeds can be used as medicine in the traditional medicine of China. However, the differences in chemical components and antibacterial activity between different parts have not been reported. In this study, the chemical composition of the different parts of P. harmala was characterized by high-performance liquid chromatography (HPLC) and headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The antimicrobial activities of the different parts and some isolated components were also carried out on 12 bacterial strains and phytopathogenic fungi. The HPLC results revealed that the contents of harmine and harmaline in the seeds were higher than that in the aerial parts. A total of 94 volatile organic compounds (VOCs) were tentatively identified by HS-SPME-GC-MS for the first time. The major components were methyl hexadecanoate, p-xylene, octane, (Z)-9-octadecanoate, ethylbenzene, methyl octadecanoate, ethyl hexadecanoate, and methyl tetradecanoate. At the concentration of 800 μg·mL−1, the methanol extracts of seeds showed stronger antimicrobial activities with a wide antimicrobial spectrum, inhibiting Escherichia coli (ATCC 24433), Xanthomonas oryzae (ACCC 11602), and Xanthomonas axonopodis with inhibitory rates of more than 90%. Furthermore, harmine and harmaline showed better antibacterial activities against all the bacteria. These findings indicated that alkaloids from P. harmala could account for antimicrobial activity, which could be used as lead molecules in the development of new antimicrobial drugs

The comparisons of memory performances under different sizes of rule sets.

<p>(A)The memory storages of HTEMC algorithm and PTIAL algorithm were compared when the number of rules increases from 100 to 1000. (B)The memory storages of HTEMC algorithm and PTIAL algorithm were compared when the number of rules increases from 500 to 5000.</p

Symbols and their definitions.

<p>Symbols and their definitions.</p

The accuracy comparison.

<p>The accuracy comparison.</p

The memory access comparison.

<p>The memory access comparison.</p