ABSTRACT
Here we report the green synthesis of gelatin/protein hybrid nanogels containing gold nanoparticles (AuNPs) that collectively exhibit metal-enhanced luminescence/fluorescence (MEL/MEF). The gelatin/protein nanogels, prepared by genipin cross-linking of preformed gelatin/protein polyion complexes (PICs), exhibited sizes ranging between 50 and 200â¯nm, depending on the weight ratio of gelatin and protein. These nanogels serve as reducing and stabilizing agents for the AuNPs, allowing for nucleation in a gel network that exhibits colloidal stability and MEL/MEF. AuNP/gelatin/HRP and AuNP/gelatin/LTF nanogels presented an ~11-fold enhancement of bioluminescence in an HRP-luminol system and a ~50-fold fluorescence enhancement when compared to free LTF in cell uptake experiments. These hybrid nanogels show promise for optically enhanced diagnosis and other therapeutic applications.
Subject(s)
Gold/chemistry , Green Chemistry Technology , Luminescence , Luminescent Measurements , Metal Nanoparticles/chemistry , Nanogels/chemistry , Animals , Mice , RAW 264.7 CellsABSTRACT
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Subject(s)
Algorithms , Artificial Intelligence , Forecasting , Fuzzy Logic , Logistic Models , Models, Econometric , Pattern Recognition, Automated/methods , Computer Simulation , TaiwanABSTRACT
Electrophysiological correlates of the processing facial expressions were investigated in subjects performing the rapid serial visual presentation (RSVP) task. The peak latencies of the event-related potential (ERP) components P1, vertex positive potential (VPP), and N170 were 165, 240, and 240 ms, respectively. The early anterior N100 and posterior P1 amplitudes elicited by fearful faces were larger than those elicited by happy or neutral faces, a finding which is consistent with the presence of a 'negativity bias.' The amplitude of the anterior VPP was larger when subjects were processing fearful and happy faces than when they were processing neutral faces; it was similar in response to fearful and happy faces. The late N300 and P300 not only distinguished emotional faces from neutral faces but also differentiated between fearful and happy expressions in lag2. The amplitudes of the N100, VPP, N170, N300, and P300 components and the latency of the P1 component were modulated by attentional resources. Deficient attentional resources resulted in decreased amplitude and increased latency of ERP components. In light of these results, we present a hypothetical model involving three stages of facial expression processing.
