Log-Regularly Varying Scale Mixture of Normals for Robust Regression

Linear regression with the classical normality assumption for the error distribution may lead to an undesirable posterior inference of regression coefficients due to the potential outliers. This paper considers the finite mixture of two components with thin and heavy tails as the error distribution, which has been routinely employed in applied statistics. For the heavily-tailed component, we introduce the novel class of distributions; their densities are log-regularly varying and have heavier tails than those of Cauchy distribution, yet they are expressed as a scale mixture of normal distributions and enable the efficient posterior inference by Gibbs sampler. We prove the robustness to outliers of the posterior distributions under the proposed models with a minimal set of assumptions, which justifies the use of shrinkage priors with unbounded densities for the coefficient vector in the presence of outliers. The extensive comparison with the existing methods via simulation study shows the improved performance of our model in point and interval estimation, as well as its computational efficiency. Further, we confirm the posterior robustness of our method in the empirical study with the shrinkage priors for regression coefficients.Comment: 62 page

On Data Augmentation for Models Involving Reciprocal Gamma Functions

In this paper, we introduce a new and efficient data augmentation approach to the posterior inference of the models with shape parameters when the reciprocal gamma function appears in full conditional densities. Our approach is to approximate full conditional densities of shape parameters by using Gauss’s multiplication formula and Stirling’s formula for the gamma function, where the approximation error can be made arbitrarily small. We use the techniques to construct efficient Gibbs and Metropolis-Hastings algorithms for a variety of models that involve the gamma distribution, Student’s t-distribution, the Dirichlet distribution, the negative binomial distribution, and the Wishart distribution. The proposed sampling method is numerically demonstrated through simulation studies

Pop2 phosphorylation at S39 contributes to the glucose repression of stress response genes, HSP12 and HSP26

The S. cerevisiae Pop2 protein is an exonuclease in the Ccr4-Not complex that is a conserved regulator of gene expression. Pop2 regulates gene expression post-transcriptionally by shortening the poly(A) tail of mRNA. A previous study has shown that Pop2 is phosphorylated at threonine 97 (T97) by Yak1 protein kinase in response to glucose limitation. However, the physiological importance of Pop2 phosphorylation remains unknown. In this study, we found that Pop2 is phosphorylated at serine 39 (S39) under unstressed conditions. The dephosphorylation of S39 was occurred rapidly after glucose depletion, and the addition of glucose to the glucose-deprived culture recovered this phosphorylation, suggesting that Pop2 phosphorylation at S39 is regulated by glucose. This glucose-regulated phosphorylation of Pop2 at S39 is dependent on Pho85 kinase. We previously reported that Pop2 takes a part in the cell wall integrity pathway by regulating LRG1 mRNA; however, S39 phosphorylation of Pop2 is not involved in LRG1 expression. On the other hand, Pop2 phosphorylation at S39 is involved in the expression of HSP12 and HSP26, which encode a small heat shock protein. In the medium supplemented with glucose, Pop2 might be phosphorylated at S39 by Pho85 kinase, and this phosphorylation contributes to repress the expression of HSP12 and HSP26. Glucose starvation inactivated Pho85, which resulted in the derepression of HSP12 and HSP26, together with other glucose sensing mechanisms. Our results suggest that Pho85-dependent phosphorylation of Pop2 is a part of the glucose sensing system in yeast

Characteristics of dietary intake in relation to the consumption of home-produced foods among farm women in two rural areas of Kenya: A preliminary study

The present study aimed to clarify the differences in nutritional intake in relation to the consumption of local food products and dietary patterns between two rural Kenyan regions, Kitui and Vihiga, where different ethnic groups live in different agro-ecological zones. A participant observation study with weighted dietary records was conducted in August 2019. Enumerators stayed in each targeted household for approximately one week and measured the ingredients and dishes. We compared the dietary intake of farm women in charge of meal preparation (n = 21) between the two regions and examined the contribution of each dish to the intake and the degree of home production for each food item. The results showed no difference in energy intake, but vitamin B2, B12, and C intakes were significantly higher in Vihiga, influenced by their consuming small fish and a variety of homegrown leafy vegetables. The people in Kitui consumed large quantities of homegrown pigeon peas, largely contributing to their nutritional intake. Dietary patterns were similar; common staple foods and tea with sugar accounted for about 40% of energy and protein intakes and fruit consumption was low. There was no difference in foods purchased frequently. These results suggested that promoting locally available fruits and vegetables would contribute to a sustainable supply of adequate micronutrients. Further studies are required to develop strategies to promote healthy dietary habits and improve health status

Methamphetamine induces endoplasmic reticulum stress related gene CHOP/Gadd153/ddit3 in dopaminergic cells

We examined the toxicity of methamphetamine and dopamine in CATH.a cells, which were derived from mouse dopamine-producing neural cells in the central nervous system. Use of the quantitative real-time polymerase chain reaction revealed that transcripts of the endoplasmic reticulum stress related gene (CHOP/Gadd153/ddit3) were considerably induced at 24–48 h after methamphetamine administration (but only under apoptotic conditions), whereas dopamine slightly induced CHOP/Gadd153/ddit3 transcripts at an early stage. We also found that dopamine and methamphetamine weakly induced transcripts for the glucose-regulated protein 78 gene (Grp78/Bip) at the early stage. Analysis by immunofluorescence microscopy demonstrated an increase of　CHOP/Gadd153/ddit3 and Grp78/Bip proteins at 24 h after methamphetamine administration. Treatment of CATH.a cells with methamphetamine caused a re-distribution of dopamine inside the cells, which mimicked the presynaptic activity of neurons with cell bodies located in the ventral tegmental area or the substantia nigra. Thus, we have demonstrated the existence of endoplasmic reticulum stress in a model of presynaptic dopaminergic neurons for the first time. Together with the recent evidence suggesting the importance of presynaptic toxicity, our findings provide new insights into the mechanisms of dopamine toxicity, which might represent one of the most important mechanisms of methamphetamine toxicity and addiction

Log-regularly varying scale mixture of normals for robust regression

Linear regression that employs the assumption of normality for the error distribution may lead to an undesirable posterior inference of regression coefficients due to potential outliers. A finite mixture of two components, one with thin and one with heavy tails, is considered as the error distribution in this study. For the heavily-tailed component, the novel class of distributions is introduced; their densities are log-regularly varying and have heavier tails than the Cauchy distribution. Yet, they are expressed as a scale mixture of normals which enables the efficient posterior inference when using a Gibbs sampler. The robustness of the posterior distributions is proved under the proposed models using a minimal set of assumptions, which justifies the use of shrinkage priors with unbounded densities for the coefficient vector in the presence of outliers. An extensive comparison with the existing methods via simulation study shows the improved performance of the proposed model in point and interval estimation, as well as its computational efficiency. Further, the posterior robustness of the proposed method is confirmed in an empirical study with shrinkage priors for regression coefficients

Automation of yeast spot assays using an affordable liquid handling robot

The spot assay of the budding yeast Saccharomyces cerevisiae is an experimental method that is used to evaluate the effect of genotypes, medium conditions, and environmental stresses on cell growth and survival. Automation of the spot assay experiments from preparing a dilution series to spotting to observing spots continuously has been implemented based on large laboratory automation devices and robots, especially for high-throughput functional screening assays. However, there has yet to be an affordable solution for the automated spot assays suited to researchers in average laboratories and with high customizability for end-users. To make reproducible spot assay experiments widely available, we have automated the plate-based yeast spot assay of budding yeast using Opentrons OT-2 (OT-2), an affordable liquid-handling robot, and a flatbed scanner. We prepared a 3D-printed mount for the Petri dish to allow for precise placement of the Petri dish inside the OT-2. To account for the uneven height of the agar plates, which were made by human hands, we devised a method to adjust the z-position of the pipette tips based on the weight of each agar plate. During the incubation of the agar plates, a flatbed scanner was used to automatically take images of the agar plates over time, allowing researchers to quantify and compare the cell density within the spots at optimal time points a posteriori. Furthermore, the accuracy of the newly developed automated spot assay was verified by performing spot assays with human experimenters and the OT-2 and quantifying the yeast-grown area of the spots. This study will contribute to the introduction of automated spot assays and the automated acquisition of growth processes in conventional laboratories that are not adapted for high-throughput laboratory automation