Heteroskedasticity and Spatiotemporal Dependence Robust Inference for Linear Panel Models with Fixed Effects

This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator that is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we embed the level of smoothing and the sample size in two different limiting sequences. In the first case where the level of smoothing increases with the sample size, the proposed covariance estimator is consistent and the associated Wald statistic converges to a chi square distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. In the second case where the level of smoothing is fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the Wald statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of a convenient F-approximation to this distribution. For bandwidth selection, we employ and optimize a modified asymptotic mean square error criterion. The fl exibility of our estimator and the proposed bandwidth selection procedure make our estimator adaptive to the dependence structure. This adaptiveness effectively automates the selection of covariance estimators. Simulation results show that our proposed testing procedure works reasonably well in finite samples.Adaptiveness, HAC estimator, F-approximation, Fixed-smoothing asymptotics, Increasing-smoothing asymptotics, Panel data, Optimal bandwidth, Robust inference, Spatiotemporal dependence

Guest Artist Recital, Dr. Sun Min Kim, piano

Assistant professor of piano, Indiana University of Pennsylvani

Following Dynamics of Protein/Nucleic Acid Interactions in Real-Time

Methyl-CpG binding protein 2 (MeCP2) is a protein associated with transcriptional repression of other proteins and its various mutants are found in Rett syndrome patients, which is a severe neurodevelopmental disease found in 1/15,000 females. It has specific binding affinity to methyl-CpG domain but also bind to architectural DNA structure. Here, its binding dynamics to Holliday junction structure was investigated by using single molecular detections; scanning confocal fluorescence microscope and wide-field evanescent field fluorescence microscope. Through these studies, we could distinguish the difference of transitions of the substrate with and without MeCP2. The effect of this MeCP2 binding to 4WJ will be discussed in terms of the structures and transition times

Characterizing uptake, distribution and fate of CdSe/ZnS quantum dots in Synnechococcus elongatus PCC7942

One of the challenges in developing a framework for characterizing nanoparticle toxicity is that the number of nanoparticles and their superficial derivatives is very large and continues to expand rapidly. Multiple factors such as size, geometry, surface chemistry, nanoscale topology, electromagnetic activity, and aggregation and degradation processes can modify the original nanoparticle and change its behavior significantly. Secondly, the type of environments and organisms these nanoparticles may be subjected to are also numerous and complex. Thirdly, the number of analytical and computational techniques available to the researcher today spans physical, chemical, biomolecular, ecological and ‘-omics’ based approaches. Thus any combination of nanoparticle, model organismal system and analytical technique is a potential route of investigation and can produce important broad empirical information on the impact of nanomaterials on living systems. This study is an extension of the analytical framework called DIMER, which involves characterizing the dispersion, imbibition, metabolism, elimination and recycle of nanoparticles to study its life cycle of in the environment. Cadmium selenide quantum dots coated with zinc sulfide were chosen as a model nanoparticle. Similarly the cyanobacterium Synechococcus elongatus PCC 7942 were chosen as the model host organism. This study characterizes the uptake, distribution and fate of both water insoluble and water soluble CdSe/ZnS quantum dots in cyanobacteria. To quantify the toxicological impact of quantum dots on cells, cell growth rate, membrane destabilization, viability and the activity of photosynthetic pigments were characterized. For characterization of uptake and distribution, flow cytometry, laser scanning confocal microscopy and transmission electron microscopy were used. When quantum dots are dispersed into the environment, their imbibition, metabolism, degradation and elimination from cells depends on their surface coating. Consequently, water soluble quantum dots, which are coated with a hydrophilic coating, showed dramatically reduced degradation rates and resulting hazardous effects on the cells even when observed directly in contact with the cells. However, water insoluble quantum dots were immediately toxic to the cells. The observed toxicity was largely indistinguishable from cadmium toxicity, which is a degradation product of the quantum dot. The primary impact observed is that the cadmium destroys the photosynthetic machinery of the cells. Given the central role of cyanobacteria in many aquatic ecosystems, such damage has serious implications to an ecosystem. Additionally, the cadmium toxicity is persistent in the environment. Once contaminated, the growth media continues to inhibit the growth of new cyanobacteria indicating a long-lasting, toxic effect on the environment

RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A-to-I) and "APOBEC"(C-to-U). By utilizing high-throughput sequencing technologies, the biological function of RNA-editing has been actively investigated. Currently, RNA-editing is considered to be a key regulator that controls various cellular functions, such as protein activity, alternative splicing pattern of mRNA, and substitution of miRNA targeting site. DARNED, a public RDD database, reported that there are more than 300-thousands RNA-editing sites detected in human genome(hg19). Moreover, multiple studies suggested that RNA-editing events occur in highly specific conditions. According to DARNED, 97.62 % of registered editing sites were detected in a single tissue or in a specific condition, which also supports that the RNA-editing events occur condition-specifically. Since RNA-seq can capture the whole landscape of transcriptome, RNA-seq is widely used for RDD prediction. However, significant amounts of false positives or artefacts can be generated when detecting RNA-editing from RNA-seq. Since it is difficult to perform experimental validation at the whole-transcriptome scale, there should be a powerful computational tool to distinguish true RNA-editing events from artefacts. Result We developed RDDpred, a Random Forest RDD classifier. RDDpred reports potentially true RNA-editing events from RNA-seq data. RDDpred was tested with two publicly available RNA-editing datasets and successfully reproduced RDDs reported in the two studies (90 %, 95 %) while rejecting false-discoveries (NPV: 75 %, 84 %). Conclusion RDDpred automatically compiles condition-specific training examples without experimental validations and then construct a RDD classifier. As far as we know, RDDpred is the very first machine-learning based automated pipeline for RDD prediction. We believe that RDDpred will be very useful and can contribute significantly to the study of condition-specific RNA-editing. RDDpred is available at http://biohealth.snu.ac.kr/software/RDDpred

THE FACTORS AFFECTING LIFE EXPECTANCY IN SOUTH KOREA

The aim of the research is to examine the relationship between each of four factors affecting life expectancy and life expectancy and thereby provide primary data for government policy. First, the research chose four factors among many factors affecting life expectancy. The four factors chosen are as follows: infant mortality, educational attainment, electric power consumption, and internet. And multiple regression analysis was conducted with data of South Korea drawn from World Bank databank. Regression specification error test (RESET) also conducted so as to check whether a regression model specified is adequate. The findings show that life expectancy has a positive relationship with educational attainment, electric power consumption, and education, while there is a negative relationship between infant mortality and life expectancy. It also shows that an effect of electric power consumption on life expectancy is so weak. But infant mortality has the strongest effect on life expectancy in the four factors. Given that infant mortality reflects a level of health care, it could be assumed that life expectancy in South Korea would be much mediated by health care

THE MOTIVATION FOR LEARNING KOREAN AMONG THE TAIWANESE

The aim of the research is to identify what key factors could be in learning Korean in Taiwan. The findings present the developmental direction of Korean language education and by extension marketing strategy of relevant industry. First, the research chose ten motivational factors among many factors in learning a foreign language. The ten factors chosen are as follows: interest, desire, fellowship, communication, instrumental value, prospectiveness, the personality of teacher, class time, method of teaching, and curriculum. And Decision-making trial and evaluation laboratory method (DEMATEL) was conducted with data collected from fourteen respondents who have been studying Korean at private institute in Taipei, Taiwan. The findings show that interest, desire, communication, the personality of teacher, and curriculum are key motivational factors. Especially, as for importance itself, it shows that interest is most important motivational factor. But notable is the fact that five remaining factors have an effect on five key factors in its degree. Thus the result of research suggests that relevant institutes should prioritize five key motivational factors, but five remaining factors need to be taken into account

Phosphorylation of α-syntrophin is responsible for its subcellular localization and interaction with dystrophin in muscle cells

79-85Syntrophin is a well-known adaptor protein that links intracellular proteins with the dystrophin-glycoprotein complex (DGC) at the sarcolemma. However, little is known about the underlying mechanism that regulates the intracellular localization of α-syntrophin and its interaction with dystrophin. In this study, we demonstrate that α-syntrophin phosphorylation determines its intracellular localization and interaction with dystrophin in muscle cells. α-Syntrophin, a predominant isoform in skeletal muscles, directly interacts with ion channels, enzymes, receptors, and DGC proteins. Despite α-syntrophin being a potential signaling molecule, most studies focus on its function as a dystrophin-associated protein. However, we previously reported that α-syntrophin has a variety of DGC-independent functions to modulate cell migration, differentiation, survival, and protein stability. According to the results of the in vitro phosphorylation assays using subcellular fractions, the phosphorylated α-syntrophin accumulated only at the plasma membrane, and this event occurred regardless of dystrophin expression. However, the α-syntrophin interacting with dystrophin at the membrane was not in a phosphorylated state. We also identified that protein kinase C (PKC) was involved in the phosphorylation of α-syntrophin, which restricted α-syntrophin to interact with dystrophin. In conclusion, we demonstrate that the phosphorylation of α-syntrophin by PKC regulates its intracellular localization and interaction with dystrophin