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

Hexaaqua­cobalt(II) 3,3′-dicarb­oxy­biphenyl-4,4′-dicarboxyl­ate

In the crystal structure of the title compound, [Co(H2O)6](C16H8O8), both the cation and anion are centrosymmetric. The Co cation displays a CoO6 octa­hedral geometry formed by six water mol­ecules. In the anion, the two carboxyl groups are oriented at dihedral angles of 4.8 (5) and 10.4 (7)° with respect to the benzene ring. Very strong O—H⋯O hydrogen bonds between the protonated and deprotonated carboxylate groups occur. Neighbouring cations and anions are connected through O—H⋯O hydrogen bonds into a three-dimensional supra­molecular structure

New Therapeutic Approaches for the Treatment of Rheumatoid Arthritis may Rise from the Cholinergic Anti-Inflammatory Pathway and Antinociceptive Pathway

Due to the complex etiology of rheumatoid arthritis (RA), it is difficult to be completely cured at the current stage although many approaches have been applied in clinics, especially the wide application of nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). New drug discovery and development via the recently discovered cholinergic anti-inflammatory and antinociceptive pathways should be promising. Based on the above, the nicotinic acetylcholine receptor agonists maintain the potential for the treatment of RA. Therefore, new therapeutic approaches may rise from these two newly discovered pathways. More preclinical experiments and clinical trials are required to confirm our viewpoint