Mortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology and public health. However, they are frequently available in an aggregated form over irregular geographical units, hindering the visualization of the underlying mortality risk. Also, it can be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked to potential risk factors that are usually measured in a different spatial resolution. In this paper, we propose the use of the penalized composite link model and its mixed model representation. This model considers the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a finer scale, thus reducing the visual bias resulting from the spatial aggregation within original units. We also extend the model by considering individual random effects at the aggregated scale, in order to take into account the overdispersion. We illustrate our novel proposal using two datasets: female deaths by lung cancer in Indiana, USA, and male lip cancer incidence in Scotland counties. We also compare the performance of our proposal with the area-to-point Poisson kriging approach.
We advance an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature spatial patterns in a polyextreme terrestrial environment. Drone flight imagery connected simulated HiRISE imagery to ground surveys, spectroscopy and biosignature mapping to reveal predictable distributions linked to environmental factors. AI/ML models successfully identified geologic features with high probabilities for containing biosignatures at spatial scales relevant to rover-based astrobiology exploration. Targeted approaches augmented by deep learning delivered 56.9-87.5% probabilities of biosignature detection versus <10% for random searches and reduced the physical search space by 85-97%. Libraries of biosignature distributions, detection probabilities, predictive models and search roadmaps for many terrestrial environments will standardize analog science research, enabling agnostic comparisons at all scales. This is vital to preparing, informing and optimizing biosignature quests on Mars, assisting high-stakes mission decisions between competing targets, and maximizing precise selection of high-priority samples. In extreme environments, the distribution of biosignatures is tightly controlled by a complex interdependency of geological, physicochemical, and biological interactions 1-5 . In such environments, microbial populations often occur in non-random spatial distributions closely tied to their
In the actual mining scenario, copper bioleaching, mainly raw mined material known as run‐of‐mine (ROM) copper bioleaching, is the best alternative for the treatment of marginal resources that are not currently considered part of the profitable reserves because of the cost associated with leading technologies in copper extraction. It is foreseen that bioleaching will play acomplementary role in either concentration—as it does in Minera Escondida Ltd. (MEL)—or chloride main leaching plants. In that way, it will be possible to maximize mines with installed solvent‐extraction and electrowinning capacities that have not been operative since the depletion of their oxide ores. One of the main obstacles for widening bioleaching technology applications is thelack of knowledge about the key events and the attributes of the technology’s critical events at the industrial level and mainly in ROM copper bioleaching industrial operations. It is relevant to assess the bed environment where the bacteria–mineral interaction occurs to learn about the limiting factors determining the leaching rate. Thus, due to inability to accurately determine in‐situ key variables, their indirect assessment was evaluated by quantifying microbial metabolic‐associated responses. Several candidate marker genes were selected to represent the predominant components of the microbial community inhabiting the industrial heap and the metabolisms involved in microbial responses to changes in the heap environment that affect the process performance. The microbial community’s predominant components were Acidithiobacillus ferrooxidans, At. thiooxidans, Leptospirillum ferriphilum, and Sulfobacillus sp. Oxygen reduction, CO2 and N2 fixation/uptake, iron and sulfur oxidation, and response to osmotic stress were the metabolisms selected regarding research results previously reported in the system. After that, qPCR primers for each candidate gene were designed and validated. The expression profile of the selected genes vs. environmental key variables in pure cultures, column‐leaching tests, and the industrial bioleaching heap was defined. We presented the results obtained from the industrial validation of the marker genes selected for assessing CO2 and N2 availability, osmotic stress response, as well as ferrous iron and sulfur oxidation activity in the bioleaching heap process of MEL. We demonstrated that molecular markers are useful for assessing limiting factors like nutrients and air supply, and the impact of the quality of recycled solutions. We also learned about the attributes of variables like CO2, ammonium, and sulfate levels that affect the industrial ROM‐scale operation.
Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.
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