Active Learning with Expert Advice

Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose a framework of active forecasters for online active learning with expert advice, which attempts to extend two regular forecasters, i.e., Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of active learning with expert advice. We prove that the proposed algorithms satisfy the Hannan consistency under some proper assumptions, and validate the efficacy of our technique by an extensive set of experiments.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Variation in Enamel Formation Genes Influences Enamel Demineralization In Vitro in a Streptococcus mutans Biofilm Model

Genetic studies have shown that variations in enamel formation genes are associated with caries susceptibility. The aim of this study was to test in vitro whether variants in these genes are associated with dental enamel demineralization in a Streptococcus mutans biofilm model. DNA and enamel samples were obtained from 213 individuals. DNA was extracted from saliva, and 16 single nucleotide polymorphisms were analyzed. The physical and chemical properties of sound enamel samples and the mineral loss and the lesion depth of the demineralized enamel samples under cariogenic challenge were analyzed. Microhardness, enamel chemicals, mineral loss and demineralization depth were compared between different genotypes at each single nucleotide polymorphism. The GG genotype of TUFT1 (rs17640579) and the GT genotype of MMP20 (rs1612069) exhibited increased microhardness (p = 0.044 and 0.016, respectively). The GG genotype of AMBN (rs7694409) had a higher magnesium level, while the CT genotype of TFIP11 (rs2097470) had a lower magnesium level (p = 0.044 and 0.046, respectively). The GT genotype of MMP20 (rs1612069) had a higher calcium level (p = 0.034). The GG genotype of AMBN (rs13115627), the AG genotype of ENAM (rs12640848) and the AA genotype of MMP20 (rs2292730) had a lower phosphorus level (p = 0.012, 0.006, and 0.023, respectively). The GG genotype of AMBN (rs13115627) was also associated with a higher calcium-phosphorus ratio (p = 0.034). Individuals with the CC genotype of TFIP11 (rs134143) exhibited significantly more mineral loss (p = 0.011) and a deeper lesions (p = 0.042). Individuals with the TT genotype of TFIP11 (rs2097470) had more mineral loss (p = 0.018). Individuals with the GG genotype of TUFT1 (rs17640579) exhibited a shallower demineralization depth (p = 0.047). Individuals with the GT genotype of MMP20 (rs1612069) exhibited a shallower demineralization depth (p = 0.042). Individuals with the GG genotype of ENAM (rs12640848) exhibited less mineral loss (p = 0.01) and a shallower demineralization depth (p = 0.03). Genetic variations in TFIP11, TUFT1, MMP20, and ENAM influenced enamel demineralization in a Streptococcus mutans biofilm model

Large Scale Online Kernel Classification

In this work, we present a new framework for large scale online kernel classification, making kernel methods efficient and scalable for large-scale online learning tasks. Unlike the regular budget kernel online learning scheme that usually uses different strategies to bound the number of support vectors, our framework explores a functional approximation approach to approximating a kernel function/matrix in order to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) the Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) the Nyström Online Gradient Descent (NOGD) algorithm that applies the Nyström method to approximate large kernel matrices. We offer theoretical analysis of the proposed algorithms, and conduct experiments for large-scale online classification tasks with some data set of over 1 million instances. Our encouraging results validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget kernel online learning approaches

Analysis of Small RNAs in Streptococcus mutans under Acid Stress—A New Insight for Caries Research

Streptococcus mutans (S. mutans) is the major clinical pathogen responsible for dental caries. Its acid tolerance has been identified as a significant virulence factor for its survival and cariogenicity in acidic conditions. Small RNAs (sRNAs) are recognized as key regulators of virulence and stress adaptation. Here, we constructed three libraries of sRNAs with small size exposed to acidic conditions for the first time, followed by verification using qRT-PCR. The levels of two sRNAs and target genes predicted to be bioinformatically related to acid tolerance were further evaluated under different acid stress conditions (pH 7.5, 6.5, 5.5, and 4.5) at three time points (0.5, 1, and 2 h). Meanwhile, bacterial growth characteristics and vitality were assessed. We obtained 1879 sRNAs with read counts of at least 100. One hundred and ten sRNAs were perfectly mapped to reported msRNAs in S. mutans. Ten out of 18 sRNAs were validated by qRT-PCR. The survival of bacteria declined as the acid was increased from pH 7.5 to 4.5 at each time point. The bacteria can proliferate under each pH except pH 4.5 with time. The levels of sRNAs gradually decreased from pH 7.5 to 5.5, and slightly increased in pH 4.5; however, the expression levels of target mRNAs were up-regulated in acidic conditions than in pH 7.5. These results indicate that some sRNAs are specially induced at acid stress conditions, involving acid adaptation, and provide a new insight into exploring the complex acid tolerance for S. mutans

Influence of creep aging on structural and mechanical properties of Mg-9Gd-2Nd-0.5Zr alloys

Secondary phase changes after traditional aging and creep aging and corresponding effects on mechanical properties of Mg-9Gd-2Nd-0.5Zr alloys were studied. The results reveal that the presence of stress during the creep aging increases the concentration of dislocations in the alloy and provides abundant nucleus positions for β′ phase, promoting the precipitation rate of β′ phase in the alloy, which plays a critical role in shortening the peak aging time. In addition, the creep aging under unidirectional stress leads to the anisotropy of the solute atomic diffusion coefficient in the alloy, and the diffusion coefficient (Da/D) along the direction of tensile stress gradually decreases. At the same time, the tensile stress reduces the system energy and releases more energy to the β′ variants (C1) perpendicularly to the stress direction, resulting in preferentially-oriented precipitation and a decrease in alloy strength. There is a decrease in yield strength (YS) from 180.1 MPa to 166.8 MPa and ultimate tensile strength (UTS) from 321.3 MPa to 292.3 MPa after doing creep aging. Moreover, the presence of unidirectional stress during creep aging reduces the angle between the texture pole and basal plane texture, promoting the rotation of the grain orientation towards the basal plane in the alloy and increasing the alloy elongation to 9.5%

Cux1+ proliferative basal cells promote epidermal hyperplasia in chronic dry skin disease identified by single-cell RNA transcriptomics

Pathological dry skin is a disturbing and intractable healthcare burden, characterized by epithelial hyperplasia and severe itch. Atopic dermatitis (AD) and psoriasis models with complications of dry skin have been studied using single-cell RNA sequencing (scRNA-seq). However, scRNA-seq analysis of the dry skin mouse model (acetone/ether/water (AEW)-treated model) is still lacking. Here, we used scRNA-seq and in situ hybridization to identify a novel proliferative basal cell (PBC) state that exclusively expresses transcription factor CUT-like homeobox 1 (Cux1). Further in vitro study demonstrated that Cux1 is vital for keratinocyte proliferation by regulating a series of cyclin-dependent kinases (CDKs) and cyclins. Clinically, Cux1+ PBCs were increased in patients with psoriasis, suggesting that Cux1+ PBCs play an important part in epidermal hyperplasia. This study presents a systematic knowledge of the transcriptomic changes in a chronic dry skin mouse model, as well as a potential therapeutic target against dry skin-related dermatoses