Biochemical and Ultrastructural Changes in the Hepatopancreas of Bellamya aeruginosa (Gastropoda) Fed with Toxic Cyanobacteria

This study was conducted to investigate ultrastructural alterations and biochemical responses in the hepatopancreas of the freshwater snail Bellamya aeruginosa after exposure to two treatments: toxic cyanobacterium (Microcystis aeruginosa) and toxic cyanobacterial cells mixed with a non-toxic green alga (Scendesmus quadricauda) for a period of 15 days of intoxication, followed by a 15-day detoxification period. The toxic algal suspension induced a very pronounced increase of the activities of acid phosphatases, alkaline phosphatases and glutathione S-transferases (ACP, ALP and GST) in the liver at the later stage of intoxication. During the depuration, enzymatic activity tended to return to the levels close to those in the control. The activity of GST displayed the most pronounced response among different algal suspensions. Severe cytoplasmic vacuolization, condensation and deformation of nucleus, dilation and myeloid-like in mitochondria, disruption of rough endoplasmic reticulum, proliferation of lysosome, telolysosomes and apoptotic body were observed in the tissues. All cellular organelles began recovery after the snails were transferred to the S. quadricauda. The occurrence of a large amount of activated lysosomes and heterolysosomes and augment in activity of detoxification enzyme GST might be an adaptive mechanism to eliminate or lessen cell damage caused by hepatotoxicity to B. aeruginosa

Balanced Boolean Functions with Optimum Algebraic Immunity and High Nonlinearity

In this paper, three constructions of balanced Boolean functions with optimum algebraic immunity are proposed. The cryptographical properties such as algebraic degree and nonlinearity of the constructed functions are also analyzed

Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond

We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks, where the protocols might differ between one another, yet a procedure of obtaining local gradients is implicitly shared. We first consider the honest-but-curious threat model, in which the detailed implementation of protocol is neglected and only the shared procedure is assumed, which we abstract as an oracle. We find that even under this general setting, single-dimension feature and label can still be recovered from the other party under suitable constraints of batch size, thus demonstrating the potential vulnerability of all frameworks following the same philosophy. Then we look into a popular instantiation of the protocol based on Homomorphic Encryption (HE). We propose an active attack that significantly weaken the constraints on batch size in the previous analysis via generating and compressing auxiliary ciphertext. To address the privacy leakage within the HE-based protocol, we develop a simple-yet-effective countermeasure based on Differential Privacy (DP), and provide both utility and privacy guarantees for the updated algorithm. Finally, we empirically verify the effectiveness of our attack and defense on benchmark datasets. Altogether, our findings suggest that all vertical federated learning frameworks that solely depend on HE might contain severe privacy risks, and DP, which has already demonstrated its power in horizontal federated learning, can also play a crucial role in the vertical setting, especially when coupled with HE or secure multi-party computation (MPC) techniques