Smooth center-stable/unstable manifolds and foliations of stochastic evolution equations with non-dense domain

The current paper is devoted to the asymptotic behavior of a class of stochastic PDE. More precisely, with the help of the theory of integrated semigroups and a crucial estimate of the random Stieltjes convolution, we study the existence and smoothness of center-unstable invariant manifolds and center-stable foliations for a class of stochastic PDE with non-dense domain through the Lyapunov-Perron method. Finally, we give two examples about a stochastic age-structured model and a stochastic parabolic equation to illustrate our results.Comment: 46 page

The Impact of Exposed Passwords on Honeyword Efficacy

Honeywords are decoy passwords that can be added to a credential database; if a login attempt uses a honeyword, this indicates that the site's credential database has been leaked. In this paper we explore the basic requirements for honeywords to be effective, in a threat model where the attacker knows passwords for the same users at other sites. First, we show that for user-chosen (vs. algorithmically generated, i.e., by a password manager) passwords, existing honeyword-generation algorithms largely fail to achieve reasonable tradeoffs between false positives and false negatives in this threat model. Second, we show that for users leveraging algorithmically generated passwords, state-of-the-art methods for honeyword generation will produce honeywords that are not sufficiently deceptive, yielding many false negatives. Instead, we find that only a honeyword-generation algorithm that uses the same password generator as the user can provide deceptive honeywords in this case. However, when the defender's ability to infer the generator from the (one) account password is less accurate than the attacker's ability to infer the generator from potentially many, this deception can again wane. Taken together, our results provide a cautionary note for the state of honeyword research and pose new challenges to the field

Ag nanoparticles/PPV composite nanofibers with high and sensitive opto-electronic response

The novel Ag nanoparticles/poly(p-phenylene vinylene) [PPV] composite nanofibers were prepared by electrospinning. The transmission electron microscope image shows that the average diameter of composite fibers is about 500 nm and Ag nanoparticles are uniformly dispersed in the PPV matrix with an average diameter of about 25 nm. The Fourier transform infrared spectra suggest that there could be a coordination effect to a certain extent between the Ag atom and the π system of PPV, which is significantly favorable for the dissociation of photoexcitons and the charge transfer at the interface between the Ag nanoparticle and the PPV. The Au top electrode device of the single Ag/PPV composite nanofiber exhibits high and sensitive opto-electronic responses. Under light illumination of 5.76 mW/cm2 and voltage of 20 V, the photocurrent is over three times larger than the dark current under same voltage, which indicates that this kind of composite fiber is an excellent opto-electronic nanomaterial

Towards Plausible Differentially Private ADMM Based Distributed Machine Learning

The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, differentially private ADMM still confronts many research challenges. For example, the guarantee of differential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible differentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the difference of the current solution from that in the last iteration, and if the difference is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate.Comment: Comments: Accepted for publication in CIKM'2

Metabolic clues to aging: exploring the role of circulating metabolites in frailty, sarcopenia and vascular aging related traits and diseases

Background: Physical weakness and cardiovascular risk increase significantly with age, but the underlying biological mechanisms remain largely unknown. This study aims to reveal the causal effect of circulating metabolites on frailty, sarcopenia and vascular aging related traits and diseases through a two-sample Mendelian Randomization (MR) analysis.Methods: Exposures were 486 metabolites analyzed in a genome-wide association study (GWAS), while outcomes included frailty, sarcopenia, arterial stiffness, atherosclerosis, peripheral vascular disease (PAD) and aortic aneurysm. Primary causal estimates were calculated using the inverse-variance weighted (IVW) method. Methods including MR Egger, weighted median, Q-test, and leave-one-out analysis were used for the sensitive analysis.Results: A total of 125 suggestive causative associations between metabolites and outcomes were identified. Seven strong causal links were ultimately identified between six metabolites (kynurenine, pentadecanoate (15:0), 1-arachidonoylglycerophosphocholine, androsterone sulfate, glycine and mannose) and three diseases (sarcopenia, PAD and atherosclerosis). Besides, metabolic pathway analysis identified 13 significant metabolic pathways in 6 age-related diseases. Furthermore, the metabolite-gene interaction networks were constructed.Conclusion: Our research suggested new evidence of the relationship between identified metabolites and 6 age-related diseases, which may hold promise as valuable biomarkers

Using Integrative Analysis of DNA Methylation and Gene Expression Data in Multiple Tissue Types to Prioritize Candidate Genes for Drug Development in Obesity

Obesity has become a major public health issue which is caused by a combination of genetic and environmental factors. Genome-wide DNA methylation studies have identified that DNA methylation at Cytosine-phosphate-Guanine (CpG) sites are associated with obesity. However, subsequent functional validation of the results from these studies has been challenging given the high number of reported associations. In this study, we applied an integrative analysis approach, aiming to prioritize the drug development candidate genes from many associated CpGs. Association data was collected from previous genome-wide DNA methylation studies and combined using a sample-size-weighted strategy. Gene expression data in adipose tissues and enriched pathways of the affiliated genes were overlapped, to shortlist the associated CpGs. The CpGs with the most overlapping evidence were indicated as the most appropriate CpGs for future studies. Our results revealed that 119 CpGs were associated with obesity (p ≤ 1.03 × 10−7). Of the affiliated genes, SOCS3 was the only gene involved in all enriched pathways and was differentially expressed in both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). In conclusion, our integrative analysis is an effective approach in highlighting the DNA methylation with the highest drug development relevance. SOCS3 may serve as a target for drug development of obesity and its complications

Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice

Rice (Oryza sativa) is an important dietary source of both essential micronutrients and toxic trace elements for humans. The genetic basis underlying the variations in the mineral composition, the ionome, in rice remains largely unknown. Here, we describe a comprehensive study of the genetic architecture of the variation in the rice ionome performed using genome-wide association studies (GWAS) of the concentrations of 17 mineral elements in rice grain from a diverse panel of 529 accessions, each genotyped at ∼6.4 million single nucleotide polymorphism loci. We identified 72 loci associated with natural ionomic variations, 32 that are common across locations and 40 that are common within a single location. We identified candidate genes for 42 loci and provide evidence for the causal nature of three genes, the sodium transporter gene Os-HKT1;5 for sodium, Os-MOLYBDATE TRANSPORTER1;1 for molybdenum, and Grain number, plant height, and heading date7 for nitrogen. Comparison of GWAS data from rice versus Arabidopsis (Arabidopsis thaliana) also identified well-known as well as new candidates with potential for further characterization. Our study provides crucial insights into the genetic basis of ionomic variations in rice and serves as an important foundation for further studies on the genetic and molecular mechanisms controlling the rice ionome

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data