Function annotation of hepatic retinoid x receptor α based on genome-wide DNA binding and transcriptome profiling.

BackgroundRetinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.Principal findingsClose to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.ConclusionsThis study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation

Confidence sets for optimal factor levels of a response surface

Construction of confidence sets for the optimal factor levels is an important topic in response surfaces methodology. In Wan et al. (2015), an exact inline image confidence set has been provided for a maximum or minimum point (i.e., an optimal factor level) of a univariate polynomial function in a given interval. In this article, the method has been extended to construct an exact inline image confidence set for the optimal factor levels of response surfaces. The construction method is readily applied to many parametric and semiparametric regression models involving a quadratic function. A conservative confidence set has been provided as an intermediate step in the construction of the exact confidence set. Two examples are given to illustrate the application of the confidence sets. The comparison between confidence sets indicates that our exact confidence set is better than the only other confidence set available in the statistical literature that guarantees the inline image confidence level

Confidence sets for optimal factor levels of a response surface

Construction of confidence sets for the optimal factor levels is an important topic in response surfaces methodology. In Wan et al. (2015), an exact (1 - a) confidence set has been provided for a maximum or minimum point (i.e., an optimal factor level) of a univariate polynomial function in a given interval. In this article, the method has been extended to construct an exact (1 - a) confidence set for the optimal factor levels of response surfaces. The construction method is readily applied to many parametric and semiparametric regression models involving a quadratic function. A conservative confidence set has been provided as an intermediate step in the construction of the exact confidence set. Two examples are given to illustrate the application of the confidence sets. The comparison between confidence sets indicates that our exact confidence set is better than the only other confidence set available in the statistical literature that guarantees the (1 - a) confidence level

Cold Storage Effects on Fitness of the Whitefly Parasitoids Encarsia sophia and Eretmocerus hayati

Successful biological control of the whitefly Bemisia tabaci involves the mass rearing of biocontrol agents in large numbers for field release. Cold storage of the biocontrol agents is often necessary to provide a sufficient number of biocontrol agents during an eventual pest outbreak. In this study, the fitness of two whitefly parasitoids Encarsia sophia Girault and Dodd (Hymenoptera: Aphelinidae) and Eretmocerus hayati Zolnerowich and Rose (Hymenoptera: Aphelinidae) was evaluated under fluctuating cold storage temperatures. The emergence rate of old pupae of either species was not affected when stored at 12, 10, 8 and 6 °C for 1 week. Cold storage had no effect on the longevity of the emerging adult En. sophia except young pupae stored at 4 °C, while Er. hayati was negatively affected after 2 weeks of storage time at all temperatures. Parasitism by adults emerging from older pupae stored at 12 °C for 1 week was equivalent to the control. Combined with the results for the emergence time, we suggest that the old pupal stage of En. sophia and Er. hayati could be stored at 12 and 10 °C, respectively (transferred every 22 h to 26 ± 1 °C for 2 h), for 1 week, with no or little adverse effect.National Natural Science Foundation of China (NSFC) (31672087); National Key Research and Development Project of China (2017YFC1200600, 2016YFC1201200); International Science & Technology Cooperation Program of China (2015DFG32300); Shenzhen Science and Technology Program (KQTD20180411143628272)info:eu-repo/semantics/publishedVersio

Statistical calibration and exact one-sided simultaneous tolerance intervals for polynomial regression

Statistical calibration using linear regression is a useful statistical tool having many applications. Calibration for infinitely many future y-values requires the construction of simultaneous tolerance intervals (STI’s). As calibration often involves only two variables x and y and polynomial regression is probably the most frequently used model for relating y with x, construction of STI’s for polynomial regression plays a key role in statistical calibration for infinitely many future y-values. The only exact STI’s published in the statistical literature are provided by Mee et al. (1991) and Odeh and Mee (1990). But they are for a multiple linear regression model, in which the covariates are assumed to have no functional relationships. When applied to polynomial regression, the resultant STI’s are conservative. In this paper, one-sided exact STI’s have been constructed for a polynomial regression model over any given interval. The available computer program allows the exact methods developed in this paper to be implemented easily. Real examples are given for illustration

Counting by weighing:know your numbers with confidence

Counting by weighing is often more efficient than counting manually, which is time consuming and prone to human errors, especially when the number of items (e.g. plant seeds, printed labels or coins) is large. Papers in the statistical literature have focused on how to count, by weighing, a random number of items that is close to a prespecified number in some sense. The paper considers the new problem, arising from a consultation with a company, of making inference about the number of 1p coins in a bag with known weight for infinitely many bags, by using the estimated distribution of coin weight from one calibration data set only. Specifically, a lower confidence bound has been constructed on the number of 1p coins for each of infinitely many future bags of 1p coins, as required by the company. As the same calibration data set is used repeatedly in the construction of all these lower confidence bounds, the interpretation of coverage frequency of the lower confidence bounds that is proposed is different from that of a usual confidence set

Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie