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Congestive heart failure (CHF) is a leading cause of morbidity, mortality and healthcare costs, impacting $>$23 million individuals worldwide. Large electronic health records data provide an opportunity to improve clinical management of diseases, but statistical inference on large amounts of relevant personal data is still challenging. Thus, accurately identifying influential risk factors is pivotal to reducing information dimensionality. Bayesian variable selection in survival regression is a common approach towards solving this problem. Here, we propose placing a beta prior directly on the model coefficient of determination (Bayesian $R^2$), which induces a prior on the global variance of the predictors and provides shrinkage. Through reparameterization using an auxiliary variable, we are able to update a majority of the parameters with Gibbs sampling, simplifying computation and quickening convergence. Performance gains over competing variable selection methods are showcased through an extensive simulation study. Finally, the method is applied in a mediation analysis to identify community context attributes impacting time to first congestive heart failure diagnosis of patients enrolled in University of North Carolina Cardiovascular Device Surveillance Registry. The model has high predictive performance and we find that factors associated with higher socioeconomic inequality increase risk of heart failure.

A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{\Lambda\Lambda}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$\Lambda$ hypernuclear event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$\Lambda$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$\Lambda$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.

Observations of groundwater pollutants, such as arsenic or Perfluorooctane sulfonate (PFOS), are riddled with left censoring. These measurements have impact on the health and lifestyle of the populace. Left censoring of these spatially correlated observations are usually addressed by applying Gaussian processes (GPs), which have theoretical advantages. However, this comes with a challenging computational complexity of $\mathcal{O}(n^3)$, which is impractical for large datasets. Additionally, a sizable proportion of the data being left-censored creates further bottlenecks, since the likelihood computation now involves an intractable high-dimensional integral of the multivariate Gaussian density. In this article, we tackle these two problems simultaneously by approximating the GP with a Gaussian Markov random field (GMRF) approach that exploits an explicit link between a GP with Mat\'ern correlation function and a GMRF using stochastic partial differential equations (SPDEs). We introduce a GMRF-based measurement error into the model, which alleviates the likelihood computation for the censored data, drastically improving the speed of the model while maintaining admirable accuracy. Our approach demonstrates robustness and substantial computational scalability, compared to state-of-the-art methods for censored spatial responses across various simulation settings. Finally, the fit of this fully Bayesian model to the concentration of PFOS in groundwater available at 24,959 sites across California, where 46.62\% responses are censored, produces prediction surface and uncertainty quantification in real time, thereby substantiating the applicability and scalability of the proposed method. Code for implementation is made available via GitHub.

This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using $\alpha$-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training., Comment: 32 pages, 13 figures

Understanding the effects of interventions, such as restrictions on community and large group gatherings, is critical to controlling the spread of COVID-19. Susceptible-Infectious-Recovered (SIR) models are traditionally used to forecast the infection rates but do not provide insights into the causal effects of interventions. We propose a spatiotemporal model that estimates the causal effect of changes in community mobility (intervention) on infection rates. Using an approximation to the SIR model and incorporating spatiotemporal dependence, the proposed model estimates a direct and indirect (spillover) effect of intervention. Under an interference and treatment ignorability assumption, this model is able to estimate causal intervention effects, and additionally allows for spatial interference between locations. Reductions in community mobility were measured by cell phone movement data. The results suggest that the reductions in mobility decrease Coronavirus cases 4 to 7 weeks after the intervention., Comment: 25 pages, 1 figure, 3 tables

Unobserved spatial confounding variables are prevalent in environmental and ecological applications where the system under study is complex and the data are often observational. Instrumental variables (IVs) are a common way to address unobserved confounding; however, the efficacy of using IVs on spatial confounding is largely unknown. This paper explores the effectiveness of IVs in this situation -- with particular attention paid to the spatial scale of the instrument. We show that, in case of spatially-dependent treatments, IVs are most effective when they vary at a finer spatial resolution than the treatment. We investigate IV performance in extensive simulations and apply the model in the example of long term trends in the air pollution and cardiovascular mortality in the United States over 1990-2010. Finally, the IV approach is also extended to the spatial interference setting, in which treatments can affect nearby responses., Comment: 27 pages, 5 figures, 9 tables

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided.

Many spatial phenomena exhibit treatment interference where treatments at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial interference, taking into account that treatments at nearby locations are more influential than treatments at locations further apart. Under the no unmeasured confounding assumption, we show that a generalized propensity score is sufficient to remove all measured confounding. To reduce dimensionality issues, we propose a Bayesian spline-based regression model accounting for a sufficient set of variables for the generalized propensity score. A simulation study demonstrates the accuracy and coverage properties. We apply the method to estimate the causal effect of wildland fires on air pollution in the Western United States over 2005--2018., Comment: Submitted. 15 pages, 2 figures

Wildland fire smoke exposures are an increasing threat to public health, and thus there is a growing need for studying the effects of protective behaviors on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals when and where they experience the exposure and subsequently study the effectiveness, but also pose novel methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality and ways to protect their health and record their own health symptoms and actions taken to reduce smoke exposure. We propose a new, doubly robust estimator of the structural nested mean model parameter that accounts for spatially- and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework is flexible enough to handle informative missingness by inverse probability weighting of estimating functions. We evaluate the new method using extensive simulation studies and apply it to Smoke Sense data reported by the citizen scientists to increase the knowledge base about the relationship between health preventive measures and improved health outcomes. Our results estimate how the protective behaviors effects vary over space and time and find that protective behaviors have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the USA.

Wildland fire smoke contains hazardous levels of fine particulate matter PM2.5, a pollutant shown to adversely effect health. Estimating fire attributable PM2.5 concentrations is key to quantifying the impact on air quality and subsequent health burden. This is a challenging problem since only total PM2.5 is measured at monitoring stations and both fire-attributable PM2.5 and PM2.5 from all other sources are correlated in space and time. We propose a framework for estimating fire-contributed PM2.5 and PM2.5 from all other sources using a novel causal inference framework and bias-adjusted chemical model representations of PM2.5 under counterfactual scenarios. The chemical model representation of PM2.5 for this analysis is simulated using Community Multi-Scale Air Quality Modeling System (CMAQ), run with and without fire emissions across the contiguous U.S. for the 2008-2012 wildfire seasons. The CMAQ output is calibrated with observations from monitoring sites for the same spatial domain and time period. We use a Bayesian model that accounts for spatial variation to estimate the effect of wildland fires on PM2.5 and state assumptions under which the estimate has a valid causal interpretation. Our results include estimates of absolute, relative and cumulative contributions of wildfire smoke to PM2.5 for the contiguous U.S. Additionally, we compute the health burden associated with the PM2.5 attributable to wildfire smoke.

Fine particulate matter, PM$_{2.5}$, has been documented to have adverse health effects and wildland fires are a major contributor to PM$_{2.5}$ air pollution in the US. Forecasters use numerical models to predict PM$_{2.5}$ concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in the presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.

A novel method for simultaneous measurement of masses, Q-values, isomer excitation energies, half-lives and decay branching ratios of exotic nuclei has been demonstrated. The method includes first use of a stopping cell as an ion trap, combining containment of precursors and decay-recoils for variable durations in a cryogenic stopping cell (CSC), and afterwards the identification and counting of them by a multiple-reflection time-of-flight mass spectrometer (MR-TOF-MS). Feasibility has been established by recording the decay and growth of $^{216}$Po and $^{212}$Pb (alpha decay) and of $^{119m2}$Sb (t$_{1/2}$ = 850$\pm$90 ms) and $^{119g}$Sb (isomer transition), obtaining half-lives and branching ratios consistent with literature values. Hardly any non-nuclear-decay losses have been observed in the CSC for up to $\sim$10 seconds, which exhibits its extraordinary cleanliness. For $^{119}$Sb, this is the first direct measurement of the ground and second isomeric state masses, resolving the discrepancies in previous excitation energy data. These results pave the way for the measurement of branching ratios of exotic nuclei with multiple decay channels., Comment: 11 pages, 9 figures, 3 tables. Submitted to European Physics Journal A

Mass measurements of fission and projectile fragments, produced via $^{238}$U and $^{124}$Xe primary beams, have been performed with the multiple-reflection time-of-flight mass spectrometer (MR-TOF-MS) of the FRS Ion Catcher with a mass resolving powers (FWHM) up to 410,000 and an uncertainty of $6\cdot 10^{-8}$. The nuclides were produced and separated in-flight with the fragment separator FRS at 300 to 1000 MeV/u and thermalized in a cryogenic stopping cell. The data-analysis procedure was developed to determine with highest accuracy the mass values and the corresponding uncertainties for the most challenging conditions: down to a few events in a spectrum and overlapping distributions, characterized only by a broader common peak shape. With this procedure, the resolution of low-lying isomers is increased by a factor of up to three compared to standard data analysis. The ground-state masses of 31 short-lived nuclides of 15 different elements with half-lives down to 17.9~ms and count rates as low as 11 events per nuclide were determined. This is the first direct mass measurement for seven nuclides. The excitation energies and the isomer-to-ground state ratios of six isomeric states with excitation energies down to about 280~keV were measured. For nuclides with known mass values, the average relative deviation from the literature values is $(2.9 \pm 6.2) \cdot 10^{-8}$. The measured two-neutron separation energies and their slopes near and at the N=126 and Z=82 shell closures indicate a strong element-dependent binding energy of the first neutron above the closed proton shell Z=82. The experimental results deviate strongly from the theoretical predictions, especially for N=126 and N=127.

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modelling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modelling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modelling provides a computationally efficient approach for estimating population-average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present methodology for design selection, make comparisons to existing approaches in the literature and assess the robustness of the designs to modelling assumptions.

Excitation-energy spectra of $^{11}$C nuclei near the $\eta^\prime$-meson production threshold have been measured by missing-mass spectroscopy using the $^{12}$C($p$,$d$) reaction. A carbon target has been irradiated with a 2.5 GeV proton beam supplied by the synchrotron SIS-18 at GSI to produce $\eta^\prime$ meson bound states in $^{11}$C nuclei. Deuterons emitted at $0^\circ$ in the reaction have been momentum-analyzed by the fragment separator (FRS) used as a high-resolution spectrometer. No distinct structure due to the formation of $\eta^\prime$-mesic states is observed although a high statistical sensitivity is achieved in the experimental spectra. Upper limits on the formation cross sections of $\eta^\prime$-mesic states are determined, and thereby a constraint imposed on the $\eta^\prime$-nucleus interaction is discussed., Comment: 13 pages, 14 figures, accepted for publication in Physical Review C

Excitation spectra of $^{11}$C were measured in the $^{12}$C$(p,d)$ reaction near the $\eta'$ emission threshold. A proton beam extracted from the synchrotron SIS-18 at GSI with an incident energy of 2.5 GeV impinged on a carbon target. The momenta of deuterons emitted at 0 degrees were precisely measured with the fragment separator FRS operated as a spectrometer. In contrast to theoretical predictions on the possible existence of deeply bound $\eta'$ mesic states in carbon nuclei, no distinct structures were observed associated with the formation of bound states. The spectra were analyzed to set stringent constraints on the formation cross section and on the hitherto barely-known $\eta'$-nucleus interaction., Comment: 6 pages, 4 figures

Optimum experimental design theory has recently been extended for parameter estimation in copula models. However, the choice of the correct dependence structure still requires wider analyses. In this work the issue of copula selection is treated by using discrimination design techniques. The new proposed approach consists in the use of $D_s$-optimality following an extension of corresponding equivalence theory. We also present some examples and highlight the strength of such a criterion as a way to discriminate between various classes of dependences.

After the demonstration of the feasibility of hypernuclear spectroscopy with heavy-ion beams, the HypHI Collaboration will next focus on the study of proton- and neutron-rich hypernuclei. The use of a fragment separator for the production and separation of rare isotope beams is a crucial aspect to producing hypernuclei far from the stability line. Precise spectroscopy of exotic hypernuclei is planned to be carried out at the GSI and later at the FAIR facility with the FRS and Super-FRS fragment separators. A systematic study and an optimization analysis were performed in order to determine optimal experimental conditions for producing hypernuclei with high isospin. The optimal conditions are obtained based on theoretical models for the heavy-ion induced reaction and hypernuclei production. Experimental efficiencies for the production of exotic secondary beams were also taken into account via Monte Carlo simulations of the fragment separator. The developed methodology is presented to deduce the expected yields of $^8_\Lambda$Be and subsequently other proton-rich and neutron-rich hypernuclei., Comment: 9 pages, 7 figures, 1 table, accepted to PRC (proofed version)

The possible existence of \eta'-nucleus bound states has been put forward through theoretical and experimental studies. It is strongly related to the \eta' mass at finite density, which is expected to be reduced because of the interplay between the $U_A(1)$ anomaly and partial restoration of chiral symmetry. The investigation of the C(p,d) reaction at GSI and FAIR, as well as an overview of the experimental program at GSI and future plans at FAIR are discussed., Comment: 7 pages, 3 figures; talk at the International Conference on Exotic Atoms and Related Topics (EXA2014), Vienna, Austria, 15-19 September 2014. in Hyperfine Interactions (2015)

At the Mainz Microtron MAMI, the first high-resolution pion spectroscopy from decays of strange systems was performed by electron scattering off a Be-9 target in order to study the ground-state masses of Lambda-hypernuclei. Positively charged kaons were detected by a short-orbit spectrometer with a broad momentum acceptance at zero degree forward angles with respect to the beam, efficiently tagging the production of strangeness in the target nucleus. In coincidence, negatively charged decay-pions were detected by two independent high-resolution spectrometers. About 10^3 pionic weak decays of hyperfragments and hyperons were observed. The pion momentum distribution shows a monochromatic peak at p_pi ~ 133 MeV/c, corresponding to the unique signature for the two-body decay of hyperhydrogen Lambda H-4 -> He-4 + pi-, stopped inside the target. Its binding energy was determined to be B_Lambda = 2.12 +- 0.01 (stat.) +- 0.09 (syst.) MeV with respect to the H-3 + Lambda mass.

The mass of the {\eta}' meson is theoretically expected to be reduced at finite density, which indicates the existence of {\eta}'-nucleus bound states. To investigate these states, we perform missing-mass spectroscopy for the (p, d) reaction near the {\eta}' production threshold. The overview of the experimental situation is given and the current status is discussed., Comment: 6 pages, 3 figures; talk at II Symposium on applied nuclear physics and innovative technologies, September 24th - 27th, 2014, Jagiellonian University, Krak\'ow Poland; to appear in Acta Physica Polonica B

Climate change is expected to alter the distribution of ambient ozone levels and temperatures which, in turn, may impact public health. Much research has focused on the effect of short-term ozone exposures on mortality and morbidity while controlling for temperature as a confounder, but less is known about the joint effects of ozone and temperature. The extent of the health effects of changing ozone levels and temperatures will depend on whether these effects are additive or synergistic. In this paper we propose a spatial, semi-parametric model to estimate the joint ozone-temperature risk surfaces in 95 US urban areas. Our methodology restricts the ozone-temperature risk surfaces to be monotone in ozone and allows for both nonadditive and nonlinear effects of ozone and temperature. We use data from the National Mortality and Morbidity Air Pollution Study (NMMAPS) and show that the proposed model fits the data better than additive linear and nonlinear models. We then examine the synergistic effect of ozone and temperature both nationally and locally and find evidence of a nonlinear ozone effect and an ozone-temperature interaction at higher temperatures and ozone concentrations., Comment: Published in at http://dx.doi.org/10.1214/14-AOAS754 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

A novel experiment, aiming at demonstrating the feasibility of hypernuclear spectroscopy with heavy-ion beams, was conducted. Using the invariant mass method, the spectroscopy of hypernuclear products of 6Li projectiles on a carbon target at 2 AGeV was performed. Signals of the \Lambda-hyperon and 3\Lambda H and 4\Lambda H hypernuclei were observed for final states of p+\pi^-, 3He+\pi^- and 4He+\pi^-, respectively, with significance values of 6.7, 4.7 and 4.9\sigma. By analyzing the proper decay time from secondary vertex distribution with the unbinned maximum likelihood fitting method, their lifetime values were deduced to be $262 ^{+56}_{-43} \pm 45$ ps for \Lambda, $183 ^{+42}_{-32} \pm 37$ ps for 3\Lambda H, and $140 ^{+48}_{-33}\pm 35 $ ps for 4\Lambda H., Comment: 13 pages, 10 figures, accepted for publication in Nuclear Physics A, v2 : revised few references

Cross section measurements of the exclusive p(e,e'K+)Lambda,Sigma^0 electroproduction reactions have been performed at the Mainz Microtron MAMI in the A1 spectrometer facility using for the first time the Kaos spectrometer for kaon detection. These processes were studied in a kinematical region not covered by any previous experiment. The nucleon was probed in its third resonance region with virtual photons of low four-momenta, Q^2= 0.030-0.055 (GeV/c)^2. The MAMI data indicate a smooth transition in Q^2 from photoproduction to electroproduction cross sections. Comparison with predictions of effective Lagrangian models based on the isobar approach reveal that strong longitudinal couplings of the virtual photon to the N* resonances can be excluded from these models., Comment: 16 pages, 7 figures

The HypHI collaboration aims to perform a precise hypernuclear spectroscopy with stable heavy ion beams and rare isotope beams at GSI and fAIR in order to study hypernuclei at extreme isospin, especially neutron rich hypernuclei to look insight hyperon-nucleon interactions in the neutron rich medium, and hypernuclear magnetic moments to investigate baryon properties in the nuclei. We are currently preparing for the first experiment with $^6$Li and $^{12}$C beams at 2 AGeV to demonstrate the feasibility of a precise hypernuclear spectroscopy by identifying $^{3}_{\Lambda}$H, $^{4}_{\Lambda}$H and $^{5}_{\Lambda}$He. The first physics experiment on these hypernuclei is planned for 2009. In the present document, an overview of the HypHI project and the details of this first experiment will be discussed., Comment: 5 pages, 2 figures, French-Japanese symposium 2008, Paris (France)

The performance of scintillating fibre detectors was studied with electrons at the spectrometer facility of the Mainz microtron MAMI, as well as in a C-12 beam of 2 AGeV energy and in a beam of different particle species at GSI. Multi-anode photomultipliers were used to read out one or more bundles of 128 fibres each in different geometries. For electrons a time resolution of FWHM = 1 ns was measured in a single detector plane with a detection efficiency epsilon > 99%. A time resolution of 310 ps (FWHM) between two planes of fibres was achieved for carbon ions, leading to a FWHM = 220 ps for a single detector. The hit position residual was measured with a width of FWHM = 0.27 mm. The variation in the measured energy deposition was Delta E/E= 15-20% (FWHM) for carbon ions. In addition, the energy response to p/pi/d particles was studied. Based on the good detector performance fibre hodoscopes will be constructed for the KAOS/A1 spectrometer at MAMI and for the HypHI experiment at GSI., Comment: submitted to Nucl. Instrum. Meth. A