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

Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

Research on Zonal Disintegration Characteristics and Failure Mechanisms of Deep Tunnel in Jointed Rock Mass with Strength Reduction Method

To understand the fracture features of zonal disintegration and reveal the failure mechanisms of circle tunnels excavated in deep jointed rock masses, a series of three-dimensional heterogeneous models considering varying joint dip angles are established. The strength reduction method is embedded in the RFPA method to achieve the gradual fracture process, macro failure mode and safety factor, and to reproduce the characteristic fracture phenomenon of deep rock masses, i.e., zonal disintegration. The mechanical mechanisms and acoustic emission energy of surrounding rocks during the different stages of the whole formation process of zonal disintegration affected by different-dip-angle joints and randomly distributed joints are further discussed. The results demonstrate that the zonal disintegration process is induced by the stress redistribution, which is significantly different from the formation mechanism of traditional surrounding rock loose zone; the dip angle of joint set has a great influence on the stress buildup, stress shadow and stress transfer as well as the failure mode of surrounding rock mass; the existence of parallel and random joints lead the newly formed cracks near the tunnel surface to developing along their strikes; the random joints make the zonal disintegration pattern much more complex and affected by the regional joint composition. These will greatly improve our understanding of the zonal disintegration in deep engineering

The Synergistic Effect of Temperature and Loading Rate on Deformation for Thermoplastic Fiber Metal Laminates

The glass fiber reinforced polypropylene/AA2024 hybrid laminates (short for Al/Gf/PP laminates) as structural materials were prepared and formed by hot pressing. The synergistic effects of temperature and loading speed on the laminate deformation under tensile and bending conditions were investigated and analyzed in this study. In tension, stress–strain curves presented bimodal types effected by tensile rates and temperatures. The state of PP resin determines the mechanical behavior of the FMLs. The tensile rate has no effect on FML deformation without heating or over the melting point of PP resin (about 170 °C). The softening point of PP resin (about 100 °C) is characteristic temperature. When the temperature exceeds the softening point but does not reach the melting point, the tensile strength and elongation will demonstrate coordinated growth at a relatively high tensile speed. The efficiency of fiber bridging is affected significantly since the resin is the medium that transfers load from the metal to the fiber. Under bending, the curves presented a waterfall decrement with temperature increment. The softening point of resin matrix is the key in a bending process. When the temperature is near the softening point, deformation is sensitive to both the temperature and the loading speed to a certain extent. If temperature is lower than softening point, deformation is mainly guided by temperature. If the temperature is beyond the softening point, loading speed is in a leading position of deformation. The bending strength gradually increases with loading rate. By using these deformation characteristics, the deformation of the thermoplastic laminates can be controlled in stamping or other plastic forming processes for thermoplastic fiber metal laminates