Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer’s Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer’s disease

Novel Antioxidant Peptides Identified from <i>Arthrospira platensis</i> Hydrolysates Prepared by a Marine Bacterium <i>Pseudoalteromonas</i> sp. JS4-1 Extracellular Protease

Crude enzymes produced by a marine bacterium Pseudoalteromonas sp. JS4-1 were used to hydrolyze phycobiliprotein. Enzymatic productions showed good performance on DPPH radical and hydroxyl radical scavenging activities (45.14 ± 0.43% and 65.11 ± 2.64%, respectively), especially small peptides with MWCO Caenorhabditis elegans under adversity. Then, 25 peptides total were identified from F2 by LC-MS/MS, and the peptide with the new sequence of INSSDVQGKY as the most significant component was synthetized and the ORAC assay and cellular ROS scavenging assay both illustrated its excellent antioxidant property

Effect of bicarbonate on CO2 electroreduction over cathode catalysts

CO2 electroreduction (CO2 ER) using renewable energy is ideal for mitigating the greenhouse effect and closing the carbon cycle. Bicarbonate (HCO3−) is most commonly employed as the electrolyte anion because it is known to facilitate CO2 ER. However, its dynamics in the electric double layer remains obscure and requires more in-depth investigation. Herein, we investigate the refined reduction process of bicarbonate by employing in situ attenuated total reflectance surface-enhanced infrared absorption spectroscopy. By comparing the product distributions in Ar-saturated KCl and KHCO3 electrolytes, we confirmed CO production from HCO3− in the absence of an external CO2 source. Notably, in contrast to an electric compulsion, negatively charged HCO3− anions were found to accumulate near the electrode surface. A reduction mechanism of HCO3− is proposed in that HCO3− is not adsorbed over a catalyst, but may be enriched near the electrode surface and converted to CO2 and react over Au and Cu electrodes. The dependence of the CO2 ER activity on the local HCO3− concentration was subsequently discovered, which was in turn dependent on the bulk HCO3− concentration and cathodic potential. In particular, the local HCO3− concentration was limited by the cathodic potential, leading to a plateau in the CO2 ER activity. The proposed mechanism provides insights into the interaction between the catalyst and the electrolyte in CO2 ER