Federated Generative Learning with Foundation Models

Existing federated learning solutions focus on transmitting features, parameters or gadients between clients and server, which suffer from serious low-efficiency and privacy-leakage problems. Thanks to the emerging foundation generative models, we propose a novel federated learning framework, namely Federated Generative Learning, that transmits prompts associated with distributed training data between clients and server. The informative training data can be synthesized remotely based on received prompts containing little privacy and the foundation generative models. The new framework possesses multiple advantages, including improved communication efficiency, better resilience to distribution shift, substantial performance gains, and enhanced privacy protection, which are verified in extensive experiments on ImageNet and DomainNet datasets

Hemostatic, anti-inflammatory and antibacterial effects of Sanqixiantao dressing in vivo and in vitro

Purpose: To study the hemostatic, anti-inflammatory and antibacterial effects of Sanqixiantao dressing.Methods: Sanqixiantao dressing was prepared by mixting with sanqixiiantao extract (8 %) with membrane-forming matrix (5:4:9:2 volume ratio of polyvinyl alcohol: Na CMC: gelatin: glycerol). Rats with local surface wounds were used to evaluate the effects of Sanqixiantao dressing on hemostatic time, wound healing time and infection rate. Serum levels of tumor necrosis factor (TNF)-α and interleukin (IL) 6 were determined. The anti-inflammatory and analgesic effects of Sanqixiantao extracts were assessed by dimethylbenzene-induced ear edema and acetic acid-induced abdominal writhing tests. In in vitro studies, the effect of the extract on blood clotting time, and its antibacterial activities against six pathogenic bacteria (Escherichia coli, Staphylococous aureus, Pseudomonas aeruginosa, Staphylococcus epidermidis, Clostridium perfringens and Clostridium tetani) were evaluated.Results: Sanqixiantao dressing significantly decreased hemostatic time (p < 0.01), wound healing time (p < 0.01) and infection rate (10 vs 100 %), when compared to control rats. Sanqixiantao extract significantly shortened blood clotting time in vitro (p < 0.01), and showed antibacterial activities against E. coli (minimum inhibitory concentration, MIC: 0.4 mg/mL, MBC: 1.6 mg/mL), S. aureus (MIC: 0.8 mg/mL, minimum bacterial concentration, MBC: 3.2 mg/mL), P. aeruginosa (MIC: 0.8 mg/mL, MBC: 3.2 mg/mL), S. epidermidis (MIC: 1.6 mg/mL, MBC: 3.2 mg/mL). Besides, Sanqixiantao extracts (100, 200, 400 and 600 mg/kg) dose-dependently decreased dimethyl-benzene-induced ear edema and acetic acid-induced abdominal writhes in mice (p < 0.05, p < 0.01, p < 0.01, p < 0.01).Conclusion: The results demonstrate that Sanqixiantao dressing has significant hemostatic, antiinflammatory and antibacterial effects in vivo and in vitro, and thus provide some support for the therapeutic application of Sanqixiantao dressing for treating skin wounds.Keywords: Sanqixiantao dressing, Acute skin wound, Hemostatic, Anti-inflammatory activity, Antibacterial effect, Herbal medicin

Machine Unlearning: Solutions and Challenges

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy violations, security breaches, and performance deterioration. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of machine unlearning research. We categorize existing research into exact unlearning that algorithmically removes data influence entirely and approximate unlearning that efficiently minimizes influence through limited parameter updates. By reviewing the state-of-the-art solutions, we critically discuss their advantages and limitations. Furthermore, we propose future directions to advance machine unlearning and establish it as an essential capability for trustworthy and adaptive machine learning. This paper provides researchers with a roadmap of open problems, encouraging impactful contributions to address real-world needs for selective data removal

Identification of differentially expressed sequences in bud differentiation of oriental hybrid lily cultivar ‘Sorbonne’ via suppression subtractive hybridization

The developmental process of lily flower bud differentiation has been studied in morphology thoroughly, but the mechanism in molecular biology is still ambiguous and few studies on genetic expression have been carried out. Little is known about the physiological responses of flower bud differentiation in Oriental hybrid lily ‘Sorbonne’ (Lilium spp.) during the stages of flower bud differentiation and the genes involved in these responses. In this study, the differences in gene expression between two stages of lily bud differentiation: the stage before bud differentiation (SB) and the stage of bud differentiation (SD) were studied. The suppression subtractive hybridization (SSH) method conducted to generate large-scale expressed sequence tags (EST) was designed to identify gene candidates related to the morphological and physiological differences between the stage before bud differentiation and the stage of bud differentiation of lily. The results showed that the SD could induce differential expression of the genes related to lily flower bud differentiation. EST were isolated, cloned, sequenced and identified using BlastN and BlastX, and indicated that at the stage of the flower bud differentiation, there is an activation of a floral development response at a molecular level, mainly related to low temperature and post-transcriptional regulation of nucleic acids. 24.1% of the isolated sequences are not yet described which showed the lack of genomic information currently available for lily. Sequence analysis revealed that most of the differentially expressed genes are related to metabolism and regulation such as protein synthesis and catabolism of carbohydrate related to flower formation. Some genes also encoded transcription factors. These genes showed high mRNA transcript levels in the stage of flower bud differentiation. This study revealed that unknown genes are putatively involved in the stage of lily flower bud differentiation, which serve as a starting point for understanding the differentiation of lily flower bud.Keywords: Lily flower bud differentiation, gene expression, suppression subtractive hybridization (SSH

P-hydroxybenzaldehyde protects Caenorhabditis elegans from oxidative stress and β-amyloid toxicity

IntroductionGastrodia elata is the dried tuber of the orchid Gastrodia elata Bl. It is considered a food consisting of a source of precious medicinal herbs, whose chemical composition is relatively rich. Gastrodia elata and its extracted fractions have been shown to have neuroprotective effects. P-hydroxybenzaldehyde (p-HBA), as one of the main active components of Gastrodia elata, has anti-inflammatory, antioxidative stress, and cerebral protective effects, which has potential for the treatment of Alzheimer’s disease (AD). The aim of this study was to verify the role of p-HBA in AD treatment and to investigate its mechanism of action in depth based using the Caenorhabditis elegans (C. elegans) model.MethodsIn this study, we used paralysis, lifespan, behavioral and antistress experiments to investigate the effects of p-HBA on AD and aging. Furthermore, we performed reactive oxygen species (ROS) assay, thioflavin S staining, RNA-seq analysis, qPCR validation, PCR Array, and GFP reporter gene worm experiment to determine the anti-AD effects of p-HBA, as well as in-depth studies on its mechanisms.Resultsp-HBA was able to delay paralysis, improve mobility and resistance to stress, and delay aging in the AD nematode model. Further mechanistic studies showed that ROS and lipofuscin levels, Aβ aggregation, and toxicity were reduced after p-HBA treatment, suggesting that p-HBA ameliorated Aβ-induced toxicity by enhancing antioxidant and anti-aging activity and inhibiting Aβ aggregation. p-HBA had a therapeutic effect on AD by improving stress resistance, as indicated by the down-regulation of NLP-29 and UCR-11 expression and up-regulation of PQN-75 and LYS-3 expression. In addition, the gene microarray showed that p-HBA treatment played a positive role in genes related to AD, anti-aging, ribosomal protein pathway, and glucose metabolism, which were collectively involved in the anti-AD mechanism of p-HBA. Finally, we also found that p-HBA promoted nuclear localization of DAF-16 and increased the expression of SKN-1, SOD-3, and GST-4, which contributed significantly to inhibition of Aβ toxicity and enhancement of antioxidative stress.ConclusionOur work suggests that p-HBA has some antioxidant and anti-aging activities. It may be a viable candidate for the treatment and prevention of Alzheimer’s disease

Research on the uncertainty problem of SDG fault diagnosis based on information flow

Accurate and complete diagnosis of nuclear accidents is the primary link to nuclear emergencies. The signed directed graph (SDG), as a common method of fault diagnosis, has good completeness. However, if there are feedforward and negative feedback control loops, compensation response and reverse response will be generated in the process of fault occurrence and propagation, which makes the state of the compensation variable and reverse variable uncertain, thus destroying the compatibility of the fault propagation path, leading to incomplete SDG reverse reasoning. In order to solve this problem, this paper proposes a SDG fault diagnosis method based on information flow. This method first finds out all the directed edges with the compensation variable or inverse variable node as the endpoint. By comparing the goodness of fit between the information flow on these directed edges and their total information flow curves, the path that contained the directed edge represented by sub-information flow with the highest R2 is obtained, and it is used as the fault propagation path with the maximum probability to determine the state of the compensation variable or inverse variable and fault location. At the same time, this paper extends the SDG model from the structure to build an FR-SDG model that can intuitively describe the node steady-state information under the final response and the fault propagation path with the maximum probability, which improves the diagnosis resolution. Finally, this paper carried out the relevant experiments in the subsystem of the nuclear reactor primary loop by using the simulation system PCTran AP1000, which verified the feasibility of this method

Vocal Emotion of Humanoid Robots: A Study from Brain Mechanism

[[abstract]]—Driven by rapid ongoing advances in humanoid robot, increasing attention has been shifted into the issue of emotion intelligence of AI robots to facilitate the communication between man-machines and human beings, especially for the vocal emotion in interactive system of future humanoid robots. This article explored the brain mechanism of vocal emotion by studying previous researches, and developed an experiment to observe the brain response by fMRI, to analyze vocal emotion of human beings. Findings in this article provided a new approach to design and evaluate the vocal emotion of humanoid robots based on brain mechanism of human beings.[[notice]]補正完畢[[incitationindex]]SCI[[cooperationtype]]國外[[countrycodes]]HR

Solar Driven Gas Phase Advanced Oxidation Processes for Methane Removal – Challenges and Perspectives

Methane (CH(4)) is a potent greenhouse gas and the second highest contributor to global warming. CH(4) emissions are still growing at an alarmingly high pace. To limit global warming to 1.5 °C, one of the most effective strategies is to reduce rapidly the CH(4) emissions by developing large‐scale methane removal methods. The purpose of this perspective paper is threefold. (1) To highlight the technology gap dealing with low concentration CH(4) (at many emission sources and in the atmosphere). (2) To analyze the challenges and prospects of solar‐driven gas phase advanced oxidation processes for CH(4) removal. And (3) to propose some ideas, which may help to develop solar‐driven gas phase advanced oxidation processes and make them deployable at a climate significant scale

Multiple tumor suppressors regulate a HIF-dependent negative feedback loop via ISGF3 in human clear cell renal cancer.

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1, KDM5C/JARID1C, SETD2, and/or BAP1, remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/-cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC. © 2018, Liao et al