Federated Unlearning via Active Forgetting

The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness

Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems

With the growing privacy concerns in recommender systems, recommendation unlearning, i.e., forgetting the impact of specific learned targets, is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as the unlearning target. However, we find that attackers can extract private information, i.e., gender, race, and age, from a trained model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as the unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to degrade attacking performance and make target attributes indistinguishable. In this paper, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance. Specifically, we investigate two types of distinguishability measurements, i.e., user-to-user and distribution-to-distribution. We use the stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed methods

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The existing research on UOF is limited and fails to deal with the root cause of the UOF issue: the learning process between advantaged and disadvantaged users is unfair. To tackle this issue, we propose an In-processing User Constrained Dominant Sets (In-UCDS) framework, which is a general framework that can be applied to any backbone recommendation model to achieve user-oriented fairness. We split In-UCDS into two stages, i.e., the UCDS modeling stage and the in-processing training stage. In the UCDS modeling stage, for each disadvantaged user, we extract a constrained dominant set (a user cluster) containing some advantaged users that are similar to it. In the in-processing training stage, we move the representations of disadvantaged users closer to their corresponding cluster by calculating a fairness loss. By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously. Comprehensive experiments on three real-world datasets demonstrate that In-UCDS outperforms the state-of-the-art methods, leading to a fairer model with better overall recommendation performance

Experimental Infection of Rabbits with Rabbit and Genotypes 1 and 4 Hepatitis E Viruses

Background: A recent study provided evidence that farmed rabbits in China harbor a novel hepatitis E virus (HEV) genotype. Although the rabbit HEV isolate had 77-79% nucleotide identity to the mammalian HEV genotypes 1 to 4, their genomic organization is very similar. Since rabbits are used widely experimentally, including as models of infection, we investigated whether they constitute an appropriate animal model for human HEV infection.Methods: Forty-two SPF rabbits were divided randomly into eleven groups and inoculated with six different isolates of rabbit HEV, two different doses of a second-passage rabbit HEV, and with genotype 1 and 4 HEV. Sera and feces were collected weekly after inoculation. HEV antigen, RNA, antibody and alanine aminotransferase in sera and HEV RNA in feces were detected. The liver samples were collected during necropsy subject to histopathological examination.Findings: Rabbits inoculated with rabbit HEV became infected with HEV, with viremia, fecal virus shedding and high serum levels of viral antigens, and developed hepatitis, with elevation of the liver enzyme, ALT. The severity of disease corresponded to the infectious dose (genome equivalents), with the most severe hepatic disease caused by strain GDC54-18. However, only two of nine rabbits infected with HEV genotype 4, and none infected with genotype 1, developed hepatitis although six of nine rabbits inoculated with the genotype 1 HEV and in all rabbits inoculated with the genotype 4 HEV seroconverted to be positive for anti-HEV IgG antibody by 14 weeks post-inoculation.Conclusions: These data indicate that rabbits are an appropriate model for rabbit HEV infection but are not likely to be useful for the study of human HEV. The rabbit HEV infection of rabbits may provide an appropriate parallel animal model to study HEV pathogenesis

Long-range imaging LiDAR with multiple denoising technologies

The ability to capture and record high-resolution images over long distances is essential for a wide range of applications, including connected and autonomous vehicles, defense and security operations, as well as agriculture and mining industries. Here, we demonstrate a self-assembled bistatic long-range imaging LiDAR system. Importantly, to achieve high signal-to-noise ratio (SNR) data, we employed a comprehensive suite of denoising methods including temporal, spatial, spectral, and polarization filtering. With the aid of these denoising technologies, our system has been validated to possess the capability of imaging under various complex usage conditions. In terms of distance performance, the test results achieved ranges of over 4000 m during daylight with clear weather, 19,200 m at night, 6700 m during daylight with haze, and 2000 m during daylight with rain. Additionally, it offers an angular resolution of 0.01 mrad. These findings demonstrate the potential to offer comprehensive construction strategies and operational methodologies to individuals seeking long-range LiDAR data

Microbial Intervention as a Novel Target in Treatment of Non-Alcoholic Fatty Liver Disease Progression

Background/Aims: Emerging evidence suggests a close link between gut microbiota and non-alcoholic fatty liver disease (NAFLD). In this study, we aimed to investigate the association between gut microbiota and the DNA methylation of adiponectin (an adipocyte-specific adipocytokine) in rats, following diet-induced NAFLD. Methods: 50 male SD rats were randomly divided into five groups with or without a high fat diet (HFD), antibiotics, and probiotics, in order to establish an imbalanced gut microbiota and probiotic treatment model in NAFLD rats. After 13 weeks of treatment, blood, liver, and cecal tissue samples were collected. Serum lipids, liver function indexes by biochemical analyzers, and changes in liver pathology with hematoxylin-eosin (HE) and masson staining were detected. Furthermore, the serum adiponectin by enzyme-linked immunosorbent assay (ELISA) and liver adiponectin methylation levels in the promoter regions by pyrophosphate sequencing were determined. High throughput Illumina sequencing targeted microbial 16S genes, bioinformatics and statistical analysis identified cecal-associated gut microbiota. Results: HFD with antibiotic exposure showed the most severe steatohepatitis and a severe gut microbiota alteration. Reduced bacterial diversity was also seen and the abundances of Firmicutes, Lactobacillus, Cyanobacteria, Acidobacteria, Chlamydiae, Chlamydiales, Rubrobacteria, Verrucomicrobia, Blautia, Shewanella, Bacteroides, Bacteroides acidifaciens, and Bacteroides uniformis, were shown to be partly reversed by probiotic treatment. Decreased serum adiponectin levels and increased DNA methylation levels of adiponectin promoter regions were also markedly associated with the NAFLD progression during gut microbiota alteration. Conclusion: Our results suggested that both gut microbiota alteration and adiponectin variability may be drivers of NAFLD progression and that targeting the gut microbiota, such as via administration of a probiotic, may delay NAFLD progression via adiponectin

Water-Driven Assembly of Laser Ablation-Induced Au Condensates as Mesomorphic Nano- and Micro-Tubes

Reddish Au condensates, predominant atom clusters and minor amount of multiply twinned particles and fcc nanoparticles with internal compressive stress, were produced by pulsed laser ablation on gold target in de-ionized water under a very high power density. Such condensates were self-assembled as lamellae and then nano- to micro-diameter tubes with multiple walls when aged at room temperature in water for up to 40 days. The nano- and micro-tubes have a lamellar- and relaxed fcc-type wall, respectively, both following partial epitaxial relationship with the co-existing multiply twinned nanoparticles. The entangled tubes, being mesomorphic with a large extent of bifurcation, flexibility, opaqueness, and surface-enhanced Raman scattering, may have potential encapsulated and catalytic/label applications in biomedical systems

Prostaglandin E2 in the Regulation of Water Transport in Renal Collecting Ducts

The kidney plays a central role in the regulation of the body water balance. The process of targeting the water channel aquaporin-2 (AQP2) on the apical plasma membrane of the collecting duct (CD) principal cells is mainly regulated by the antidiuretic peptide hormone arginine vasopressin (AVP), which is responsible for the maintenance of water homeostasis. Recently, much attention has been focused on the local factors modulating renal water reabsorption by AQP2 in the collecting ducts, especially prostaglandin E2 (PGE2). PGE2 is a lipid mediator involved in a variety of physiological and pathophysiological processes in the kidney. The biological function of PGE2 is mainly mediated by four G-protein-coupled receptors, namely EP1-4, which couple to drive separate intracellular signaling pathways. Increasing evidence demonstrates that PGE2 is essential for renal water transport regulation via multiple mechanisms. Each EP receptor plays a unique role in regulating water reabsorption in renal collecting ducts. This brief review highlights the role of PGE2 in the regulation of water reabsorption and discusses the involvement of each EP receptor subtype in renal collecting duct. A better understanding of the role of PGE2 in renal water transport process may improve disease management strategies for water balance disorders, including nephrogenic diabetes insipidus

Fabrication of Phosphate-Imprinted PNIPAM/SiO₂ Hybrid Particles and Their Phosphate Binding Property

A SiO2 microsphere imprinted by phosphate ions was prepared with the use of phosphate ion as the template molecule and tetraethoxysilane as the precursor. Thereafter, the imprinted SiO2 microspheres were modified with 3-(trimethoxysilyl)propyl methacrylate (TMSPMA@SiO2), followed by introducing the double bond. In the presence of TMSPMA@SiO2, using N-isopropylacrylamide as monomer, and potassium persulfate as initiator, polymer/inorganic hybrid particles (PNIPAM/SiO2) were prepared. Fourier transform infrared spectroscopy, thermogravimetric analysis, nitrogen adsorption-desorption test, and transmission electron microscope were employed for the characterization of molecular imprinted SiO2 microspheres and PNIPAM/SiO2 hybrid particles. The effects of phosphate concentration, pH value, and adsorption temperature on the phosphate binding properties of PNIPAM/SiO2 hybrid particles were studied by UV-vis spectrophotometer. The experimental results shed light on the fact that the PNIPAM structure is beneficial for the improvement of the adsorption ability of phosphate-imprinted SiO2 microspheres. With the increase in the initial phosphate concentration, the adsorption capacity of hybrid particles to phosphate ions increased to 274 mg/g at pH = 7 and 15 °C. The acid condition and the temperature below the low critical solution temperature (LCST) of PNIPAM are favorable to the adsorption of phosphate ions by PNIPAM/SiO2 hybrid particles, and the maximum adsorption capacity can reach 287 mg/g (at pH = 5 and 15 °C). The phosphate imprinted polymer/inorganic hybrid material is expected to be put to use in the fields of phosphate ions adsorption, separation, and recovery