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Start date: 2022 × Type: Article × Subject: design ×

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Now showing 1 - 4 of 4
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    Synthesis and Biological Evaluation of New (−)-Gossypol-Derived Schiff Bases and Hydrazones
    (New York, NY [u.a.] : Hindawi, 2017) Vu, Vu Van; Nhung, Trinh Thi; Thanh, Nguyen Thi; Chinh, Luu Van; Tien, Vu Dinh; Thuy, Vu Thu; Thi Thao, Do; Nam, Nguyen Hai; Koeckritz, Angela; Vu, Tran Khac
    A series of 14 new (-)-gossypol Schiff bases and hydrazones have been synthesized via an in situ procedure in high yields. Structural data showed that all target compounds exist as the enamine tautomer. Bioassays showed that several compounds exhibited cytotoxic effects against three human cancer cell lines. Compound 8a showed the greatest cytotoxic effect against hepatocellular carcinoma (HepG2), lung carcinoma (LU-1), and breast cancer (MCF-7) cell lines with IC50 values of 20.93, 13.58, and 9.40 μM, respectively. However, in an antibacterial test, compounds 8a and 8b inhibited Staphylococcus aureus and Bacillus cereus and compound 8e inhibited only Staphylococcus aureus at the same MIC values of 1024 μg/ml.
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    Evolutionary design of explainable algorithms for biomedical image segmentation
    ([London] : Nature Publishing Group UK, 2023) Cortacero, Kévin; McKenzie, Brienne; Müller, Sabina; Khazen, Roxana; Lafouresse, Fanny; Corsaut, Gaëlle; Van Acker, Nathalie; Frenois, François-Xavier; Lamant, Laurence; Meyer, Nicolas; Vergier, Béatrice; Wilson, Dennis G.; Luga, Hervé; Staufer, Oskar; Dustin, Michael L.; Valitutti, Salvatore; Cussat-Blanc, Sylvain
    An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
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    Perspective on statistical effects in the adhesion of micropatterned surfaces
    (Melville, NY : American Inst. of Physics, 2021) Booth, Jamie A.; Hensel, René
    Bioinspired micropatterned adhesives have attracted extensive research interest in the past two decades. In modeling the performance of these adhesives, the common assumption has been that the adhesive strength of each sub-contact is identical. Recent experiments, however, have shown that interfacial defects of different characters lead to a distribution of the adhesive strength within a fibrillar array. Based on experimental observations of detachment events, a statistical model for the distribution of the local adhesive strength and the resulting performance of a micropatterned adhesive are presented. This approach constitutes a paradigm shift, providing better understanding of micropatterned adhesives under real conditions. Examples presented include the prediction of unstable detachments in compliant systems. Future directions are discussed, including the extension of the statistical approach to non-uniform loading and rate-dependent effects, the contribution of suction to adhesion and aging of contacts over specific time periods, as well as the necessity for a more in-depth understanding of defect formation considering surface roughness and other imperfections in the system.
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    Viscoelastic and self-healing behavior of silica filled ionically modified poly(isobutylene-co-isoprene) rubber
    (London : RSC Publishing, 2018) Sallat, Aladdin; Das, Amit; Schaber, Jana; Scheler, Ulrich; Bhagavatheswaran, Eshwaran S.; Stöckelhuber, Klaus W.; Heinrich, Gert; Voit, Brigitte; Böhme, Frank
    Rubber composites were prepared by mixing bromobutyl rubber (BIIR) with silica particles in the presence of 1-butylimidazole. In addition to pristine (precipitated) silica, silanized particles with aliphatic or imidazolium functional groups, respectively, were used as filler. The silanization was carried out either separately or in situ during compounding. The silanized particles were characterized by TGA, 1H-29Si cross polarization (CP)/MAS NMR, and Zeta potential measurements. During compounding, the bromine groups of BIIR were converted with 1-butylimidazole to ionic imidazolium groups which formed a dynamic network by ionic association. Based on DMA temperature and strain sweep measurements as well as cyclic tensile tests and stress-strain measurements it could be concluded that interactions between the ionic groups and interactions with the functional groups of the silica particles strongly influence the mechanical and viscoelastic behavior of the composites. A particularly pronounced reinforcing effect was observed for the composite with pristine silica, which was attributed to acid-base interactions between the silanol and imidazolium groups. In composites with alkyl or imidazolium functionalized silica particles, the interactions between the filler and the rubber matrix form dynamic networks with pronounced self-healing behavior and excellent tensile strength values of up to 19 MPa. This new approach in utilizing filler-matrix interactions in the formation of dynamic networks opens up new avenues in designing new kinds of particle-reinforced self-healing elastomeric materials with high technological relevance.