A high-energy, high-flux source of gamma-rays from all-optical nonlinear Thomson scattering

γ-Ray sources are among the most fundamental experimental tools currently available to modern physics. As well as the obvious benefits to fundamental research, an ultra-bright source of γ-rays could form the foundation of scanning of shipping containers for special nuclear materials and provide the bases for new types of cancer therapy.

However, for these applications to prove viable, γ-ray sources must become compact and relatively cheap to manufacture. In recent years, advances in laser technology have formed the cornerstone of optical sources of high energy electrons which already have been used to generate synchrotron radiation on a compact scale. Exploiting the scattering induced by a second laser, one can further enhance the energy and number of photons produced provided the problems of synchronisation and compact γ-ray detection are solved.

Here, we report on the work that has been done in developing an all-optical and hence, compact non-linear Thomson scattering source, including the new methods of synchronisation and compact γ-ray detection. We present evidence of the generation of multi-MeV (maximum 16–18 MeV) and ultra-high brilliance (exceeding 10²⁰ photons s⁻¹mm⁻²mrad⁻² 0.1% BW at 15 MeV) γ-ray beams. These characteristics are appealing for the paramount practical applications mentioned above.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.