Efficient Sampling Policy for Selecting a Good Enough Subset

The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure

Tfp1 is required for ion homeostasis, fluconazole resistance and N-Acetylglucosamine utilization in Candida albicans

AbstractThe vacuolar-type H+-ATPase (V-ATPase) is crucial for the maintenance of ion homeostasis. Dysregulation of ion homeostasis affects various aspects of cellular processes. However, the importance of V-ATPase in Candida albicans is not totally clear. In this study, we demonstrated the essential roles of V-ATPase through Tfp1, a putative V-ATPase subunit. Deletion of TFP1 led to generation of an iron starvation signal and reduced total iron content, which was associated with mislocalization of Fet34p that was finally due to disorders in copper homeostasis. Furthermore, the tfp1∆/∆ mutant exhibited weaker growth and lower aconitase activity on nonfermentable carbon sources, and iron or copper addition partially rescued the growth defect. In addition, the tfp1∆/∆ mutant also showed elevated cytosolic calcium levels in normal or low calcium medium that were relevant to calcium release from vacuole. Kinetics of cytosolic calcium response to an alkaline pulse and VCX1 (VCX1 encodes a putative vacuolar Ca2+/H+ exchanger) overexpression assays indicated that the cytosolic calcium status was in relation to Vcx1 activity. Spot assay and concentration-kill curve demonstrated that the tfp1∆/∆ mutant was hypersensitive to fluconazole, which was attributed to reduced ergosterol biosynthesis and CDR1 efflux pump activity, and iron/calcium dysregulation. Interestingly, carbon source utilization tests found the tfp1∆/∆ mutant was defective for growth on N-Acetylglucosamine (GlcNAc) plate, which was associated with ATP depletion due to the decreased ability to catabolize GlcNAc. Taken together, our study gives new insights into functions of Tfp1, and provides the potential to better exploit V-ATPase as an antifungal target

The coordinated roles of miR-26a and miR-30c in regulating TGFβ1-induced epithelial-to-mesenchymal transition in diabetic nephropathy

MicroRNAs (miRNAs) play vital roles in the development of diabetic nephropathy. Here, we compared the protective efficacies of miR-26a and miR-30c in renal tubular epithelial cells (NRK-52E) and determined whether they demonstrated additive effects in the attenuation of renal fibrosis. TGFβ1 suppressed miR-26a and miR-30c expression but up-regulated pro-fibrotic markers in NRK-52E cells, and these changes were also found in the kidney cortex of 40-week-old diabetic Otsuka Long-Evans Tokushima fatty (OLETF) rats. Bioinformatic analyses and luciferase assays further demonstrated that both miR-26a and miR-30c targeted connective tissue growth factor (CTGF); additionally, Snail family zinc finger 1 (Snail1), a potent epithelial-to-mesenchymal transition (EMT) inducer, was targeted by miR-30c. Overexpression of miR-26a and miR-30c coordinately decreased CTGF protein levels and subsequently ameliorated TGFβ1-induced EMT in NRK-52E cells. Co-silencing of miR-26a and miR-30c exhibited the opposite effect. Moreover, miR-26a and miR-30c co-silenced CTGF to decrease ERK1/2 and p38 MAPK activation. Furthermore, miR-26a was up-regulated in urinary extracellular vesicles of diabetic nephropathy patients. Our study provides evidence for the cooperative roles of miR-26a and miR-30c in the pathogenesis of diabetic nephropathy, and the co-targeting of miR-26a and miR-30c could provide a new direction for diabetic nephropathy treatment

Temperature fluctuation and acute myocardial infarction in Beijing: an extended analysis of temperature ranges and differences

PurposeFew studies examined the relationship between temperature fluctuation metrics and acute myocardial infarction (AMI) hospitalizations within a single cohort. We aimed to expand knowledge on two basic measures: temperature range and difference.MethodsWe conducted a time-series analysis on the correlations between temperature range (TR), daily mean temperature differences (DTDmean), and daily mean-maximum/minimum temperature differences (TDmax/min) and AMI hospitalizations, using data between 2013 and 2016 in Beijing, China. The effects of TRn and DTDmeann over n-day intervals were compared, respectively. Subgroup analysis by age and sex was performed.ResultsA total of 81,029 AMI hospitalizations were included. TR1, TDmax, and TDmin were associated with AMI in J-shaped patterns. DTDmean1 was related to AMI in a U-shaped pattern. These correlations weakened for TR and DTDmean with longer exposure intervals. Extremely low (1st percentile) and high (5°C) DTDmean1 generated cumulative relative risk (CRR) of 2.73 (95% CI: 1.56–4.79) and 2.15 (95% CI: 1.54–3.01). Extremely high TR1, TDmax, and TDmin (99th percentile) correlated with CRR of 2.00 (95% CI: 1.73–2.85), 1.71 (95% CI: 1.40–2.09), and 2.73 (95% CI: 2.04–3.66), respectively. Those aged 20–64 had higher risks with large TR1, TDmax, and TDmin, while older individuals were more affected by negative DTDmean1. DTDmean1 was associated with a higher AMI risk in females.ConclusionTemperature fluctuations were linked to increased AMI hospitalizations, with low-temperature extremes having a more pronounced effect. Females and the older adult were more susceptible to daily mean temperature variations, while younger individuals were more affected by larger temperature ranges

Badanie promieniowania słonecznego i przenoszenia ciepła przez tkaniny kabinowe przy użyciu metody objętości skończonych

The heat transfer of different fabrics was investigated numerically in the cabin of an aircraft. The discrete ordinate (DO) radiation model was adopted to describe the solar radiation through the cabin window and the fabric’s reflection. The conjugate heat transfer between the air flow and the seat fabric was included to study the influence of the textile type and fabric thickness. Some important parameters such as the temperature, radiative heat flux, and heat transfer coefficient on the fabric surface were evaluated. The results showed that both altering of the textile type and thickness will bring about the variation of temperature on the cushion surface. The carbon fibre yarn seat and thinner padding fabric provide a much more enjoyable environment than others. The air circulation in the cabin can improves the thermal environment to some degree.W pracy zbadano przenikanie ciepła różnych tkanin stosowanych w kabinie samolotu. Do opisu promieniowania słonecznego wpadającego przez okno kabiny i odbicia tkaniny został przyjęty model promieniowania na osi rzędnych dyskretnych (DO). Zbadano wpływ rodzaju tkaniny i grubości tkaniny uwzględniając przenoszenie ciepła koniugatu między przepływem powietrza a tkaniną siedziska. Oceniono niektóre ważne parametry, takie jak: temperatura, strumień ciepła promieniowania i współczynnik przenikania ciepła na powierzchni tkaniny. Wyniki pokazały, że zarówno zmiana rodzaju, jak i grubości tkaniny powoduje zmianę temperatury na powierzchni poduszki. Stwierdzono, że siedzisko z włókna węglowego i cieńsza tkanina wyściełająca zapewniają znacznie przyjemniejsze środowisko, a cyrkulacja powietrza w kabinie może w pewnym stopniu poprawić warunki termiczne

Simultaneously learning affinity matrix and data representations for machine fault diagnosis

Recently, preserving geometry information of data while learning representations have attracted increasing attention in intelligent machine fault diagnosis. Existing geometry preserving methods require to predefine the similarities between data points in the original data space. The predefined affinity matrix, which is also known as the similarity matrix, is then used to preserve geometry information during the process of representations learning. Hence, the data representations are learned under the assumption of a fixed and known prior knowledge, i.e., similarities between data points. However, the assumed prior knowledge is difficult to precisely determine the real relationships between data points, especially in high dimensional space. Also, using two separated steps to learn affinity matrix and data representations may not be optimal and universal for data classification. In this paper, based on the extreme learning machine autoencoder (ELM-AE), we propose to learn the data representations and the affinity matrix simultaneously. The affinity matrix is treated as a variable and unified in the objective function of ELM-AE. Instead of predefining and fixing the affinity matrix, the proposed method adjusts the similarities by taking into account its capability of capturing the geometry information in both original data space and non-linearly mapped representation space. Meanwhile, the geometry information of original data can be preserved in the embedded representations with the help of the affinity matrix. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed method, and the empirical study also shows it is an efficient tool on machine fault diagnosis

ELM embedded discriminative dictionary learning for image classification

Dictionary learning is a widely adopted approach for image classification. Existing methods focus either on finding a dictionary that produces discriminative sparse representation, or on enforcing priors that best describe the dataset distribution. In many cases, the dataset size is often small with large intra-class variability and nondiscriminative feature space. In this work we propose a simple and effective framework called ELM-DDL to address these issues. Specifically, we represent input features with Extreme Learning Machine (ELM) with orthogonal output projection, which enables diverse representation on nonlinear hidden space and task specific feature learning on output space. The embeddings are further regularized via a maximum margin criterion (MMC) to maximize the inter-class variance and minimize intra-class variance. For dictionary learning, we design a novel weighted class specific ℓ1,2 norm to regularize the sparse coding vectors, which promotes uniformity of the sparse patterns of samples belonging to the same class and suppresses support overlaps of different classes. We show that such regularization is robust, discriminative and easy to optimize. The proposed method is combined with a sparse representation classifier (SRC) to evaluate on benchmark datasets. Results show that our approach achieves state-of-the-art performance compared to other dictionary learning methods