Chromosome 12q24.31-q24.33 deletion causes multiple dysmorphic features and developmental delay: First mosaic patient and overview of the phenotype related to 12q24qter defects

<p>Abstract</p> <p>Background</p> <p>Genomic imbalances of the 12q telomere are rare; only a few patients having 12q24.31-q24.33 deletions were reported. Interestingly none of these were mosaic. Although some attempts have been made to establish phenotype/genotype interaction for the deletions in this region, no clear relationship has been established to date.</p> <p>Results</p> <p>We have clinically screened more than 100 patients with dysmorphic features, mental retardation and normal karyotype using high density oligo array-CGH (aCGH) and identified a ~9.2 Mb hemizygous interstitial deletion at the 12q telomere (Chromosome 12: 46,XY,del(12)(q24.31q24.33) in a severely developmentally retarded patient having dysmorphic features such as low set ears, microcephaly, undescended testicles, bent elbow, kyphoscoliosis, and micropenis. Parents were found to be not carriers. MLPA experiments confirmed the aCGH result. Interphase FISH revealed mosaicism in cultured peripheral blood lymphocytes.</p> <p>Conclusions</p> <p>Since conventional G-Banding technique missed the abnormality; this work re-confirms that any child with unexplained developmental delay and systemic involvement should be studied by aCGH techniques. The FISH technique, however, would still be useful to further delineate the research work and identify such rare mosaicism. Among the 52 deleted genes, <it>P2RX2, ULK1, FZD10, RAN, NCOR2 STX2, TESC, FBXW8</it>, and <it>TBX3 </it>are noteworthy since they may have a role in observed phenotype.</p

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties

Clinical, genetic, and functional characterization of the glycine receptor β-subunit A455P variant in a family affected by hyperekplexia syndrome

Hyperekplexia is a rare neurological disorder characterized by exaggerated startle response affecting newborns with the hallmark characteristics of hypertonia, apnea, and noise or touch-induced non-epileptic seizures. The genetic causes of the disease can vary and several associated genes and mutations have been reported to affect glycine receptors (GlyRs); however, the mechanistic links between GlyRs and hyperekplexia are not yet understood. Here, we describe a patient with hyperekplexia from a consanguineous family. Extensive genetic screening using exome sequencing coupled with autozygome analysis and iterative filtering supplemented by in silico prediction identified that the patient carries the homozygous missense mutation A455P in GLRB, which encodes the GlyR β-subunit. To unravel the physiological and molecular effects of A455P on GlyRs, we used electrophysiology in a heterologous system as well as immunocytochemistry, confocal microscopy, and cellular biochemistry. We found a reduction in glycine-evoked currents in N2A cells expressing the mutation compared to wild type cells. Western blot analysis also revealed a reduced amount of GlyR β protein both in cell lysates and isolated membrane fractions. In line with the above observations, co-immunoprecipitation assays suggested that the GlyR α1-subunit retained co-assembly with βA455P to form membrane-bound heteromeric receptors. Finally, structural modelling showed that the A455P mutation affected the interaction between the GlyR β-subunit transmembrane domain 4 and the other helices of the subunit. Taken together, our study identifies and validates a novel loss-of-function mutation in GlyRs whose pathogenicity is likely to cause hyperekplexia in affected individuals

A comparison of flare forecasting methods, I: results from the “All-clear” workshop

YesSolar flares produce radiation which can have an almost immediate effect on the near-Earth environ- ment, making it crucial to forecast flares in order to mitigate their negative effects. The number of published approaches to flare forecasting using photospheric magnetic field observations has prolifer- ated, with varying claims about how well each works. Because of the different analysis techniques and data sets used, it is essentially impossible to compare the results from the literature. This problem is exacerbated by the low event rates of large solar flares. The challenges of forecasting rare events have long been recognized in the meteorology community, but have yet to be fully acknowledged by the space weather community. During the interagency workshop on “all clear” forecasts held in Boulder, CO in 2009, the performance of a number of existing algorithms was compared on common data sets, specifically line-of-sight magnetic field and continuum intensity images from MDI, with consistent definitions of what constitutes an event. We demonstrate the importance of making such systematic comparisons, and of using standard verification statistics to determine what constitutes a good prediction scheme. When a comparison was made in this fashion, no one method clearly outperformed all others, which may in part be due to the strong correlations among the parameters used by different methods to characterize an active region. For M-class flares and above, the set of methods tends towards a weakly positive skill score (as measured with several distinct metrics), with no participating method proving substantially better than climatological forecasts.This work is the outcome of many collaborative and cooperative efforts. The 2009 “Forecasting the All-Clear” Workshop in Boulder, CO was sponsored by NASA/Johnson Space Flight Center’s Space Radiation Analysis Group, the National Center for Atmospheric Research, and the NOAA/Space Weather Prediction Center, with additional travel support for participating scientists from NASA LWS TRT NNH09CE72C to NWRA. The authors thank the participants of that workshop, in particular Drs. Neal Zapp, Dan Fry, Doug Biesecker, for the informative discussions during those three crazy days, and NCAR’s Susan Baltuch and NWRA’s Janet Biggs for organizational prowess. Workshop preparation and analysis support was provided for GB, KDL by NASA LWS TRT NNH09CE72C, and NASA Heliophysics GI NNH12CG10C. PAH and DSB received funding from the European Space Agency PRODEX Programme, while DSB and MKG also received funding from the European Union’s Horizon 2020 research and in- novation programme under grant agreement No. 640216 (FLARECAST project). MKG also acknowledges research performed under the A-EFFort project and subsequent service implementation, supported under ESA Contract number 4000111994/14/D/MPR. YY was supported by the National Science Foundation under grants ATM 09-36665, ATM 07-16950, ATM-0745744 and by NASA under grants NNX0-7AH78G, NNXO-8AQ90G. YY owes his deepest gratitude to his advisers Prof. Frank Y. Shih, Prof. Haimin Wang and Prof. Ju Jing for long discussions, for reading previous drafts of his work and providing many valuable comments that improved the presentation and contents of this work. JMA was supported by NSF Career Grant AGS-1255024 and by a NMSU Vice President for Research Interdisciplinary Research Grant