Numerical Modeling of Width of Turbulent Field around a Large Diameter Cylindrical Pier under Supercritical Reynolds Number Based on LBM

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Receptor-Targeting Phthalocyanine Photosensitizer for Improving Antitumor Photocytotoxicity

Photodynamic therapy (PDT) is a promising therapeutic modality which uses a photosensitizer to capture visible light resulting in phototoxicity in the irradiated region. PDT has been used in a number of pathological indications, including tumor. A key desirable feature of the photosensitizer is the high phototoxicity on tumor cells but not on normal cells. In this study, we conjugate a gonadotropin-releasing hormone (GnRH) to a photosensitizer, Zinc phthalocyanine (ZnPc), in order to enhance its specificity to breast cancer, which over-expresses GnRH receptor. ZnPc has unique advantages over other photosensitizers, but is difficult to derivatize and purify as a single isomer. We previously developed a straight-forward way to synthesize mono-substituted β-carboxy-phthalocyanine zinc (ZnPc-COOH). Photophysical and photochemical parameters of this ZnPc-GnRH conjugate including fluorescence quantum yield (Фf), fluorescence decay time (τs) and singlet oxygen quantum yield (ФΔ) were evaluated and found comparable with that of ZnPc, indicating that addition of a GnRH peptide does not significantly alter the generation of singlet oxygen from ZnPc. Cellular uptakes and phototoxicities of this conjugate were tested and found significantly enhanced on human breast cancer cell lines overexpressing GnRH receptors (MDA-MB-231 and MCF-7 cells) compared to cells with low levels of GnRH receptors, such as human embryonic lung fibroblast (HELF) and human liver carcinoma (HepG2) cells. In addition, the cellular uptake of this conjugate toward MCF-7 cells were found clearly alleviated by a GnRH receptor blocker Cetrorelix, suggesting that the cellular uptake of this conjugate was GnRH receptor-mediated. Put together, these findings revealed that coupling ZnPc with GnRH analogue was an effective way to improve the selectivity of ZnPc towards tumors with over-expressed GnRH receptors

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and collision avoidance, our method employs the score-based diffusion model to learn a series of gradient fields. These gradient fields encode the correlations between constraint satisfaction and the spatial relationships of polygons, learned from teacher examples. During the testing phase, packing solutions are generated using a coarse-to-fine refinement mechanism guided by the learned gradient fields. To enhance packing feasibility and optimality, we introduce two key architectural designs: multi-scale feature extraction and coarse-to-fine relation extraction. We conduct experiments on two typical industrial packing domains, considering translations only. Empirically, our approach demonstrates spatial utilization rates comparable to, or even surpassing, those achieved by the teacher algorithm responsible for training data generation. Additionally, it exhibits some level of generalization to shape variations. We are hopeful that this method could pave the way for new possibilities in solving the packing problem