Background: PHF21A has been associated with intellectual disability and craniofacial anomalies based on its deletion in the Potocki-Shaffer syndrome region at 11p11.2 and its disruption in three patients with balanced translocations. In addition, three patients with de novo truncating mutations in PHF21A were reported recently. Here, we analyze genomic data from seven unrelated individuals with mutations in PHF21A and provide detailed clinical descriptions, further expanding the phenotype associated with PHF21A haploinsufficiency. Methods: Diagnostic trio whole exome sequencing, Sanger sequencing, use of GeneMatcher, targeted gene panel sequencing, and MiSeq sequencing techniques were used to identify and confirm variants. RT-qPCR was used to measure the normal expression pattern of PHF21A in multiple human tissues including 13 different brain tissues. Protein-DNA modeling was performed to substantiate the pathogenicity of the missense mutation. Results: We have identified seven heterozygous coding mutations, among which six are de novo (not maternal in one). Mutations include four frameshifts, one nonsense mutation in two patients, and one heterozygous missense mutation in the AT Hook domain, predicted to be deleterious and likely to cause loss of PHF21A function. We also found a new C-terminal domain composed of an intrinsically disordered region. This domain is truncated in six patients and thus likely to play an important role in the function of PHF21A, suggesting that haploinsufficiency is the likely underlying mechanism in the phenotype of seven patients. Our results extend the phenotypic spectrum of PHF21A mutations by adding autism spectrum disorder, epilepsy, hypotonia, and neurobehavioral problems. Furthermore, PHF21A is highly expressed in the human fetal brain, which is consistent with the neurodevelopmental phenotype. Conclusion: Deleterious nonsense, frameshift, and missense mutations disrupting the AT Hook domain and/or an intrinsically disordered region in PHF21A were found to be associated with autism spectrum disorder, epilepsy, hypotonia, neurobehavioral problems, tapering fingers, clinodactyly, and syndactyly, in addition to intellectual disability and craniofacial anomalies. This suggests that PHF21A is involved in autism spectrum disorder and intellectual disability, and its haploinsufficiency causes a diverse neurological phenotype.

The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online.

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.

Background/Objectives: For efficient PhotoVoltaic (PV) power generation, computing and information technologies are increasingly used in irradiance forecasting and correction. Methods/Statistical Analysis: Today the majority of PV modules are used for grid-connected power generation, so solar generation forecasting that predicts available PV output ahead is essential for integrating PV resources into electricity grids. This paper proposes a short-term solar power forecasting system that employs Neural Network (NN) models to forecast irradiance and PV power. Results: The proposed system uses the weather observations of a ground weather station, the medium-term weather forecasts of a physical model, and the short-term weather forecasts of the Weather Research and Forecasting (WRF) model as input. To increase prediction accuracy, the proposed system performs forecast corrections and determines the correction coefficients based on the characteristics and temperature of PV modules. The proposed system also analyzes the inclination angle of PV modules to predict PV power outputs. Conclusion/Application: In the future, the proposed forecasting system for solar power generation resources will be further refined and run in real environments.

Motivation: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. Results: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study.