A new ultra low-level HPGe activity counting setup in the Felsenkeller shallow-underground laboratory

A new ultra low-level counting setup has been installed in the shallow-underground laboratory Felsenkeller in Dresden, Germany. It includes a high-purity germanium detector (HPGe) of 163\% relative efficiency within passive and active shields. The passive shield consists of 45m rock overburden (140 meters water equivalent), 40 cm of low-activity concrete, and a lead and copper castle enclosed by an anti-radon box. The passive shielding alone is found to reduce the background rate to rates comparable to other shallow-underground laboratories. An additional active veto is given by five large plastic scintillation panels surrounding the setup. It further reduces the background rate by more than one order of magnitude down to 116$\pm$1 kg$^{-1}$ d$^{-1}$ in an energy interval of 40-2700 keV. This low background rate is unprecedented for shallow-underground laboratories and close to deep underground laboratories.Comment: Submitted to Astroparticle Physics; corrected typo in abstrac

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

Genetic modifiers affecting severity of epilepsy caused by mutation of sodium channel Scn2a

Mutations in the voltage-gated sodium channels SCN1A and SCN2A are responsible for several types of human epilepsy. Variable expressivity among family members is a common feature of these inherited epilepsies, suggesting that genetic modifiers may influence the clinical manifestation of epilepsy. The transgenic mouse model Scn2a Q54 has an epilepsy phenotype as a result of a mutation in Scn2a that slows channel inactivation. The mice display progressive epilepsy that begins with short-duration partial seizures that appear to originate in the hippocampus. The partial seizures become more frequent and of longer duration with age and often induce secondary generalized seizures. Clinical severity of the Scn2a Q54 phenotype is influenced by genetic background. Congenic C57BL/6J.Q54 mice exhibit decreased incidence of spontaneous seizures, delayed seizure onset, and longer survival in comparison with [C57BL/6J × SJL/J]F 1 .Q54 mice. This observation indicates that strain SJL/J carries dominant modifier alleles at one or more loci that determine the severity of the epilepsy phenotype. Genome-wide interval mapping in an N 2 backcross revealed two modifier loci on Chromosomes 11 and 19 that influence the clinical severity of of this sodium channel-induced epilepsy. Modifier genes affecting clinical severity in the Scn2a Q54 mouse model may contribute to the variable expressivity seen in epilepsy patients with sodium channel mutations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46986/1/335_2005_Article_49.pd