Terminal 10q26.12 deletion is associated with neonatal asymmetric crying facies syndrome: a case report and literature review.

BACKGROUND: The terminal 10q26 deletion syndrome is a clinically heterogeneous disorder without identified genotype-phenotype correlations. We reported a case of congenital asymmetric crying facies (ACF) syndrome with 10q26.12qter deletion and discussed their genotype-phenotype correlations and the potentially contributing genes involving the etiology of ACF. METHODS AND RESULTS: We reported a case of neonatal 10q26.12qter deletion and summarized the genotype-phenotype correlations and contributing genes of 10q26.12qter deletion from DECIPHER database and published studies. Meanwhile, we analyzed the potential pathogenic genes contributing to 10q26 deletion syndrome. The female preterm infant harboring 10q26.12qter deletion showed symptoms of abnormal craniofacial appearance with rare congenital asymmetric crying facies, developmental retardation, congenital heart disease, and pulmonary artery hypertension. The deleted region was 13.28 Mb in size as detected by G-banding and array comparative genome hybridization, containing 62 Online Mendelian Inheritance in Man (OMIM) catalog genes. We summarized data from 17 other patients with 10q26.12qter deletion, 11 from the DECIPHER database and 6 from published studies. Patients with monoallelic WDR11 and FGFR2 deletions located in 10q26.12q26.2 were predisposed to craniofacial dysmorphisms, growth retardation, intellectual disability and cardiac diseases. CONCLUSION: ACF is a facial dysmorphism frequently accompanied by other systemic deformities. It is a genetic abnormality that may associate with terminal 10q26.12 deletion. Early cardiac, audiologic, cranial examinations and genetic detection are needed to guide early diagnosis and treatment strategy.

Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types.

Predicting rain from large-scale environmental variables remains a challenging problem for climate models and it is unclear how well numerical methods can predict the true characteristics of rainfall without smaller (storm) scale information. This study explores the ability of three statistical and machine learning methods to predict 3-hourly rain occurrence and intensity at 0.5° resolution over the tropical Pacific Ocean using rain observations the Global Precipitation Measurement (GPM) satellite radar and large-scale environmental profiles of temperature and moisture from the MERRA-2 reanalysis. We also separated the rain into different types (deep convective, stratiform, and shallow convective) because of their varying kinematic and thermodynamic structures that might respond to the large-scale environment in different ways. Our expectation was that the popular machine learning methods (i.e., the neural network and random forest) would outperform a standard statistical method (a generalized linear model) because of their more flexible structures, especially in predicting the highly skewed distribution of rain rates for each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models. One implication of this study is that machine learning tools must be carefully assessed and are not necessarily applicable to solving all big data problems. Another implication is that traditional climate model approaches are not sufficient to predict extreme rain events and that other avenues need to be pursued.