θ13\theta_{13}, δ\delta and the neutrino mass hierarchy at a γ=350\gamma=350 double baseline Li/B β\beta-Beam

We consider a $\beta$-Beam facility where $^8$Li and $^8$B ions are accelerated at $\gamma = 350$, accumulated in a 10 Km storage ring and let decay, so as to produce intense $\bar \nu_e$ and $\nu_e$ beams. These beams illuminate two iron detectors located at $L \simeq 2000$ Km and $L \simeq 7000$ Km, respectively. The physics potential of this setup is analysed in full detail as a function of the flux. We find that, for the highest flux ($10 \times 10^{18}$ ion decays per year per baseline), the sensitivity to $\theta_{13}$ reaches $\sin^2 2 \theta_{13} \geq 2 \times10^{-4}$; the sign of the atmospheric mass difference can be identified, regardless of the true hierarchy, for $\sin^2 2 \theta_{13} \geq 4\times10^{-4}$; and, CP-violation can be discovered in 70% of the $\delta$-parameter space for $\sin^2 2 \theta_{13} \geq 10^{-3}$, having some sensitivity to CP-violation down to $\sin^2 2 \theta_{13} \geq 10^{-4}$ for $|\delta| \sim 90^\circ$.Comment: 35 pages, 20 figures. Minor changes, matches the published versio

New drug candidates for depression - a nationwide population-based study

OBJECTIVE: To investigate whether continued use of non-aspirin NSAID, low-dose aspirin, high-dose aspirin, statins, allopurinol and angiotensin agents decreases the rate of incident depression using Danish nationwide population-based registers. METHODS: All persons in Denmark who purchased the exposure medications of interest between 1995 and 2015 and a random sample of 30% of the Danish population was included in the study. Two different outcome measures were included, (i) a diagnosis of depressive disorder at a psychiatric hospital as in-patient or out-patient and (ii) a combined measure of a diagnosis of depression or use of antidepressants. RESULTS: A total of 1 576 253 subjects were exposed to one of the six drugs of interest during the exposure period from 2005 to 2015. Continued use of low-dose aspirin, statins, allopurinol and angiotensin agents was associated with a decreased rate of incident depression according to both outcome measures. Continued uses of non-aspirin NSAIDs as well as high-dose aspirin were associated with an increased rate of incident depression. CONCLUSION: The findings support the potential of agents acting on inflammation and the stress response system in depression as well as the potential of population-based registers to systematically identify drugs with repurposing potential

Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome

Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks to discover combinations of exposures that lead to an increased risk of a specific outcome in parts of the population. The approach allows for exposures acting alone and in synergy with others. The road map of CoOL involves (i) a pre-computational phase used to define a causal model; (ii) a computational phase with three steps, namely (a) fitting a non-negative model on an additive scale, (b) decomposing risk contributions and (c) clustering individuals based on the risk contributions into subgroups; and (iii) a post-computational phase on hypothesis development, validation and triangulation using new data before eventually updating the causal model. The computational phase uses a tailored neural network for the non-negative model on an additive scale and layer-wise relevance propagation for the risk decomposition through this model. We demonstrate the approach on simulated and real-life data using the R package 'CoOL'. The presentation focuses on binary exposures and outcomes but can also be extended to other measurement types. This approach encourages and enables researchers to identify combinations of exposures as potential causes of the health outcome of interest. Expanding our ability to discover complex causes could eventually result in more effective, targeted and informed interventions prioritized for their public health impact