SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

Nowadays, gathering high-quality training data from multiple data controllers with privacy preservation is a key challenge to train high-quality machine learning models. The potential solutions could dramatically break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academia researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short). These two solutions have their advantages and limitations when we evaluate them from privacy preservation, ways of communication, communication overhead, format of data, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of training machine learning models over multiple data sources with privacy-preserving (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, parties supported, data partitioning, threat model, and supported machine learning models, to show the advantages and limitations. Next, we introduce the state-of-the-art platforms which support online training over multiple data sources. Finally, we discuss the potential directions to resolve the problem of TMMPP.Comment: 17 pages, 4 figure

Shifts in Soil Microbial Community Composition, Function, and Co-occurrence Network of Phragmites australis in the Yellow River Delta

Soil microorganisms play vital roles in regulating biogeochemical processes. The composition and function of soil microbial community have been well studied, but little is known about the responses of bacterial and fungal communities to different habitats of the same plant, especially the inter-kingdom co-occurrence pattern including bacteria and fungi. Herein, we used high-throughput sequencing to investigate the bacterial and fungal communities of five Phragmites australis habitats in the Yellow River Delta and constructed their inter-kingdom interaction network by network analysis. The results showed that richness did not differ significantly among habitats for either the bacterial or fungal communities. The distribution of soil bacterial community was significantly affected by soil physicochemical properties, whereas that of the fungal community was not. The main functions of the bacterial and fungal communities were to participate in the degradation of organic matter and element cycling, both of which were significantly affected by soil physicochemical properties. Network analysis revealed that bacteria and fungi participated in the formation of networks through positive interactions; the role of intra-kingdom interactions were more important than inter-kingdom interactions. In addition, rare species acted as keystones played a critical role in maintaining the network structure, while NO3−−N likely played an important role in maintaining the network topological properties. Our findings provided insights into the inter-kingdom microbial co-occurrence network and response of the soil microbial community composition and function to different P. australis habitats in coastal wetlands, which will deepen our insights into microbial community assembly in coastal wetlands

Exploring the potential association and experimental validation of disrupted circadian rhythms with polycystic ovary syndrome via meta-analysis and bioinformatics: a possible pathogenic mechanism

BackgroundPolycystic ovary syndrome (PCOS) has been extensively studied as a common female endocrine disease. In recent years, the relationship between circadian rhythm and PCOS has gradually drawn attention, although the precise nature of this connection remains unclear. The aim of this study was to explore further links between circadian rhythm and PCOS and to identify potential mediators of the pathogenesis of PCOS.MethodWe analyzed the available data on PCOS and circadian rhythm disorders. Consequently, we identified potential transcription factors (NPAS2, INSIG1, H3F3B, SCML1) through bioinformatics and verified them in a well-established PCOS mouse model.ResultsLuteinizing hormone (LH), testosterone (T), and melatonin (ML) exhibited substantial changes in the PCOS patients compared to healthy controls, with ML serving as a crucial biomarker in circadian rhythms. PCR results from ovarian tissues demonstrated altered expression of circadian core oscillator in the PCOS mouse model, with NPAS2 expression aligning with the bioinformatics analysis trend. We used quercetin (QUE) as a treatment and observed that it improved the disturbed expression of circadian core oscillations.ConclusionOur research revealed the correlation between circadian rhythm disruptions and PCOS, identified potential targets, and provided unique insights into the pathogenesis of circadian rhythm-related PCOS. The improvement of circadian core oscillations in the QUE group offers a novel strategy for the treatment of PCOS

Unraveling the metabolic potential of biocontrol fungi through omics data: a key to enhancing large-scaleapplication strategies

Biological control of pests and pathogens has attracted much attention due to its green, safe and effective characteristics. However, it faces the dilemma of insignificant effects in large-scale applications. Therefore, an in-depth exploration of the metabolic potential of biocontrol fungi based on big omics data is crucial for a comprehensive and systematic understanding of the specific modes of action operated by various biocontrol fungi. This article analyzes the preferences for extracellular carbon and nitrogen source degradation, secondary metabolites (nonribosomal peptides, polyketide synthases) and their product characteristics and the conversion relationship between extracellular primary metabolism and intracellular secondary metabolism for eight different filamentous fungi with characteristics appropriate for the biological control of bacterial pathogens and phytopathogenic nematodes. Further clarification is provided that Paecilomyces lilacinus, encoding a large number of hydrolase enzymes capable of degrading pathogen protection barrier, can be directly applied in the field as a predatory biocontrol fungus, whereas Trichoderma, as an antibiosis-active biocontrol control fungus, can form dominant strains on preferred substrates and produce a large number of secondary metabolites to achieve antibacterial effects. By clarifying the levels of biological control achievable by different biocontrol fungi, we provide a theoretical foundation for their application to cropping habitats