Low Energy Beam Measurements Using PHIL Accelerator at LAL, Comparison with PARMELA Simulations

http://accelconf.web.cern.ch/AccelConf/PAC2011/papers/wep210.pdfInternational audiencePHIL ("PHo­to-In­jec­tor at LAL") is a new elec­tron beam ac­cel­er­a­tor at LAL. This ac­cel­er­a­tor is ded­i­cat­ed to test and char­ac­ter­ize elec­tron RF-guns and to de­liv­er elec­tron beam to users. This ma­chine has been de­signed to pro­duce and char­ac­terise low en­er­gy (E<10 MeV), small emit­tance (e<10 p.​mm.​mrad), high bril­liance elec­trons bunch at low rep­e­ti­tion fre­quen­cy (n<10Hz). The first beam has been ob­tained on the 4th of Novem­ber 2009. The cur­rent RF-gun test­ed on PHIL is the Al­phaX gun, a 2.5 cell S-band cav­i­ty de­signed by LAL for the plas­ma ac­cel­er­a­tor stud­ies per­formed at the Strath­clyde uni­ver­si­ty. This paper will pre­sent the first Al­phaX RF-gun char­ac­ter­i­za­tions per­formed at LAL on PHIL ac­cel­er­a­tor, and will show com­par­isons be­tween mea­sure­ments and PARMELA sim­u­la­tions

Planck early results. VI. The High Frequency Instrument data processing

We describe the processing of the 336 billion raw data samples from the High Frequency Instrument (HFI) which we performed to produce six temperature maps from the first 295 days of Planck-HFI survey data. These maps provide an accurate rendition of the sky emission at 100, 143, 217, 353, 545 and 857GHz with an angular resolution ranging from 9.9 to 4.4 . The white noise level is around 1.5 μK degree or less in the 3 main CMB channels (100–217 GHz). The photometric accuracy is better than 2% at frequencies between 100 and 353 GHz and around 7% at the two highest frequencies. The maps created by the HFI Data Processing Centre reach our goals in terms of sensitivity, resolution, and photometric accuracy. They are already sufficiently accurate and well-characterised to allow scientific analyses which are presented in an accompanying series of early papers. At this stage, HFI data appears to be of high quality and we expect that with further refinements of the data processing we should be able to achieve, or exceed, the science goals of the Planck project

Consensus on the use and interpretation of cystic fibrosis mutation analysis in clinical practice

It is often challenging for the clinician interested in cystic fibrosis (CF) to interpret molecular genetic results, and to integrate them in the diagnostic process. The limitations of genotyping technology, the choice of mutations to be tested, and the clinical context in which the test is administered can all influence how genetic information is interpreted. This paper describes the conclusions of a consensus conference to address the use and interpretation of CF mutation analysis in clinical settings

Effects of eight neuropsychiatric copy number variants on human brain structure

peer reviewedMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions. © 2021, The Author(s)