A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods

Host-guest Interaction at Molecular Interfaces: Binding of Cucurbit[7]uril on Ferrocenyl Self-assembled Monolayers on Gold

Ferrocene (Fc) encapsulated cucurbit[7]uril (CB[7]) supramolecular host-guest complex  (Fc@CB[7]) as a synthetic recognition pair has been widely adapted for coupling biomolecules and nanomaterials due to its ultra-high binding affinity. In this paper, we have explored the binding of CB[7] on binary ferrocenylundecanethiolate/octanethiolate self-assembled monolayer on gold  (FcC11S-/C8S-Au), a model system to deepen our understanding of host-guest chemistry at molecular interfaces. It has been shown that upon incubation with CB[7] solution, the redox behavior FcC11S-/C8S-Au changes remarkably, i.e., a new pair of peaks appeared at more positive potential with narrowed widths. The ease of quantitation of surface bound-redox species (Fc+/Fc and  Fc+@CB[7]/ Fc@CB[7]) enabled us to determine the thermodynamic formation constant of  Fc@CB[7] at FcC11S-/C8S-Au (7.3±1.8 × 104 M-1). With time-dependent redox responses, we were able to, for the first time, deduce both the binding and dissociation rate constants, 2.8±0.3 × 103  M-1s-1 and 0.08±0.01 s-1, respectively. These results showed substantial differences both thermodynamically and kinetically for the formation of host-guest inclusion complex at molecular interfaces with respect to solution-diffused, homogenous environments

Identification of key bioactive anti-migraine constituents of Asari radix et rhizoma using network pharmacology and nitroglycerin-induced migraine rat model

Purpose: To elucidate the bioactive constituents of Asari radix et rhizoma (ARR) in treating migraine based on network pharmacology and nitroglycerin-induced migraine rat model. Methods: The potential bioactive constituents of ARR were identified with the aid of literature retrieval and virtual screening, and the migraine-related hub genes were identified using protein-protein interaction and topology analyses. Then, the interaction between the potential bioactive constituents and hub genes was determined with molecular docking and topology, leading to the prediction of the anti-migraine constituents of ARR. Moreover, a rat model of nitroglycerin-induced migraine was used to confirm the prediction by measuring the frequency of head-scratching and head-shaking behavior (FHHB) in the rats. In addition, levels of nitric oxide (NO) and calcitonin gene-related peptide (CGRP) in blood, norepinephrine (NE) and 5-hydroxytryptamine (5-HT) in brain were measured using appropriate commercial kits. Results: Network pharmacology revealed that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key anti-migraine bioactive constituents of ARR. On addition of naringenin-7-O-β-D- glucopyranoside or higenamine to ARR, there was marked enhancement of the mitigating effect of ARR on nitroglycerin-induced abnormalities in levels of NO, CGRP, 5-HT and NE, as well as FHHB in rats (p < 0.05 or 0.01). Conclusion: These findings indicate that naringenin-7-O-β-D-glucopyranoside and higenamine might be the key bioactive and anti-migraine constituents of ARR. However, in addition to naringenin-7-O-β-D- glucopyranoside and higenamine, there were many other anti-migraine constituents in ARR. Therefore, there is need for further investigations on the actual contributions of these two constituents of ARR in treating migraine