Reduced occupancy of the deeply bound 0d(5/2) neutron state in 32Ar

The 9Be(32Ar, 31Ar)X reaction, leading to the 5/2+ ground state of a nucleus at the proton drip line, has a cross section of 10.4(13) mb at a beam energy of 65.1 MeV/nucleon. This translates into a spectroscopic factor that is only 24(3)% of that predicted by the many-body shell-model theory. We introduce refinements to the eikonal reaction theory used to extract the spectroscopic factor to clarify that this very strong reduction represents an effect of nuclear structure. We suggest that it reflects correlation effects linked to the high neutron separation energy (22.0 MeV) for this state.     

Zur raschen Extraktbestimmung bei der technischen Malzanalyse

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Untersuchung der Milch

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Untersuchung von Milch

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Flow behaviour of glass during extrusion

Within their working range the inorganic glasses can be regarded as Newtonian liquids. Their flow behaviour during the extrusion process is therefore generally in good agreement with the Hagen-Poiseuille law, if the boundary condition of a stagnant glass layer at the die wall is fullfilled.     

First observation of F

We measured the excitation function for ¹³O + p elastic scattering to obtain data on the unknown ¹⁴F nucleus. The ground state and several low-lying excited states in ¹⁴F were observed and spin/parity assignments were made. ¹⁴F appears to be much less unstable than was predicted. We compare theoretical predictions for the ¹⁴F level scheme with the experimental results.     

Finding small somatic structural variants in exome sequencing data: a machine learning approach

Genetic variation forms the basis for diversity but can as well be harmful and cause diseases, such as tumors. Structural variants (SV) are an example of complex genetic variations that comprise of many nucleotides ranging up to several megabases. Based on recent developments in sequencing technology it has become feasable to elucidate the genetic state of a person’s genes (i.e. the exome) or even the complete genome. Here, a machine learning approach is presented to find small disease-related SVs with the help of sequencing data. The method uses differences in characteristics of mapping patterns between tumor and normal samples at a genomic locus. This way, the method aims to be directly applicable for exome sequencing data to improve detection of SVs since specific SV detection methods are currently lacking. The method has been evaluated based on a simulation study as well as with exome data of patients with acute myeloid leukemia. An implementation of the algorithm is available at https://github.com/lenz99-/svmod.