Effect of a Non-Newtonian Load on Signature S

The quartz crystal microbalance (QCM) is increasingly used for monitoring the interfacial interaction between surfaces and macromolecules such as biomaterials, polymers, and metals. Recent QCM applications deal with several types of liquids with various viscous macromolecule compounds, which behave differently from Newtonian liquids. To properly monitor such interactions, it is crucial to understand the influence of the non-Newtonian fluid on the QCM measurement response. As a quantitative indicator of non-Newtonian behavior, we used the quartz resonator signature, S2, of the QCM measurement response, which has a consistent value for Newtonian fluids. We then modified De Kee’s non-Newtonian three-parameter model to apply it to our prediction of S2 values for non-Newtonian liquids. As a model, we chose polyethylene glycol (PEG400) with the titration of its volume concentration in deionized water. As the volume concentration of PEG400 increased, the S2 value decreased, confirming that the modified De Kee’s three-parameter model can predict the change in S2 value. Collectively, the findings presented herein enable the application of the quartz resonator signature, S2, to verify QCM measurement analysis in relation to a wide range of experimental subjects that may exhibit non-Newtonian behavior, including polymers and biomaterials

The Genome Sequence of 'Mycobacterium massiliense' Strain CIP 108297 Suggests the Independent Taxonomic Status of the Mycobacterium abscessus Complex at the Subspecies Level

Members of the Mycabacterium abscessus complex are rapidly growing mycobacteria that are emerging as human pathogens. The M. abscassus complex was previously composed of three species, namely M. abscessus sensu strict, 'M. massiliense', and M. bolletii', In 2011, 'M. massiliense' and 'M. bolletre' were united and reclassified as a single subspecies within M. abscessus: M. abscessus subsp. bolletii. However, the placement of 'M. massiliense' Within the boundary of M. abscessus subsp. balletii remains highly controversial with regard to clinical aspects. In this study, we revisited the taxonomic status of members of the M. abscessus complex based on comparative analysis of he whole-genome sequences of 53 strains, The genome sequence of the previous type strain of 'Mycobacterium massiliense' (CIP 108297) was determined using next-generation sequencing. The genome tree based on average nucleotide identity (AN I) values supported the differentiation of M. bolletii' and M. massiliense' at the subspecies level. The genome tree also clearly illustrated that 'M. bolletil' and 'M. massiliense' form a distinct phylogenetic clade within the radiation of the M. abscessus complex. The genomic distances observed in this study suggest that the current M. abscessus subsp. bolletii taxon should be divided into two subspecies, M. abscessus subsp. massiliense subsp. nov. and M. abscessus subsp. bolletii, to correspondingly accommodate the previously known 'M. assiliense' and 'M. bolletii' strains.

(E)-2,2′-[3-(2-Nitro­phen­yl)prop-2-ene-1,1-di­yl]bis­(3-hy­droxy-5,5-dimethyl­cyclo­hex-2-en-1-one)

In the title compound, C25H29NO6, each of the cyclo­hexenone rings adopts a half-chair conformation. Each of the pairs of hy­droxy and carbonyl O atoms are oriented to allow for the formation of intra­molecular O—H⋯O hydrogen bonds, which are typical of xanthene derivatives. The nitro group is rotationally disordered over two orientations in a 0.544 (6):0.456 (6) ratio. In the crystal, weak inter­molecualr C—H⋯O hydrogen bonds link mol­ecules into layers parallel to the ab plane

Automatic mandibular canal detection using a deep convolutional neural network

The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques for automatic prediction and detection are being widely researched. Particularly in dentistry, for various reasons, automated mandibular canal detection has become highly desirable. The positioning of the inferior alveolar nerve (IAN), which is one of the major structures in the mandible, is crucial to prevent nerve injury during surgical procedures. However, automatic segmentation using Cone beam computed tomography (CBCT) poses certain difficulties, such as the complex appearance of the human skull, limited number of datasets, unclear edges, and noisy images. Using work-in-progress automation software, experiments were conducted with models based on 2D SegNet, 2D and 3D U-Nets as preliminary research for a dental segmentation automation tool. The 2D U-Net with adjacent images demonstrates higher global accuracy of 0.82 than naïve U-Net variants. The 2D SegNet showed the second highest global accuracy of 0.96, and the 3D U-Net showed the best global accuracy of 0.99. The automated canal detection system through deep learning will contribute significantly to efficient treatment planning and to reducing patients’ discomfort by a dentist. This study will be a preliminary report and an opportunity to explore the application of deep learning to other dental fields.Peer reviewe

2,2-[(E)-3,3-Diphenyl­prop-2-ene-1,1-di­yl]bis­(3-hy­droxy­cyclo­hex-2-en-1-one)

In the title compound, C27H26O4, each of the cyclo­hexenone rings adopts a half-chair conformation. The dihedral angle between the two phenyl rings is 89.53 (5)°. The hy­droxy and carbonyl O atoms face each other and are orientated to allow the formation of two intra­molecular O—H⋯O hydrogen bonds, which are typical of xanthene derivatives