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Current climate changes imply an imminent risk for forest species. In this context, somatic embryogenesis is a valuable tool to study the response of plants to different abiotic stresses. Based on this, we applied a high-temperature regime (50 °C, 5 min) during the maturation of Pinus radiata D. Don embryogenic masses in order to evaluate the development of an epigenetic memory months later. Therefore, somatic plants (SP) resulting from somatic embryos (ses) maturated at control temperature and cultivated in a greenhouse were submitted to heat stress (40 °C, 2 h, 10 days; 23 °C, 10 days) or at a control temperature (23 °C, 20 days); while another 20 SP resulting from ses maturated in the two temperature regimes and cultivated in the greenhouse were submitted to drought stress or weekly irrigated. All plants were evaluated for relative water content, water potential, electrolyte leakage, stomatal conductance, transpiration, methylation (5-mC) and hydroxymethylation (5-hmC) levels. The results showed that the SP obtained from ses maturated at 50 °C showed an adaptation to drought stress based on water potential and transpiration. Furthermore, SP kept under heat stress in a greenhouse showed lower 5-hmC levels than SP kept at 23 °C. Furthermore, the 5-hmC and 5-hmC/5-mC ratio showed a significantly negative correlation with changes in water potential; and a significantly negative correlation was observed between the levels of stomatal conductance and 5-mC. We conclude that the manipulation of conditions during the maturation process in somatic embryogenesis modulates the physiological characteristics of the SP obtained. This research was funded by MINECO (Spanish Government) project (AGL2016-76143-C4-3R), CYTED (P117RT0522), and MINECO (BES-2017-081249, 'Ayudas para contratos predoctorales para la formación de doctores'). MULTIFOREVER (Project MULTIFOREVER is supported under the umbrella of ERA-NET cofound Forest Value by ANR (FR), FNR (DE), MINCyT (AR), MINECO-AEI (ES), MMM (FI) and VINNOVA (SE). Forest value has received funding from the European Union's Horizon 2020 research and innovation programmed under Agreement No. 773324.

Vegetative propagation through somatic embryogenesis is an effective method to produce elite varieties and can be applied as a tool to study the response of plants to different stresses. Several studies show that environmental changes during embryogenesis could determine future plant development. Moreover, we previously reported that physical and chemical conditions during somatic embryogenesis can determine the protein, hormone and metabolite profiles, as well as the micromorphological and ultrastructural organization of embryonal masses and somatic embryos. In this sense, phytohormones are key players throughout the somatic embryogenesis process as well as during numerous stress-adaptation responses. In this work, we first applied different higherature regimes (30 °C, 4 weeks; 40 °C, 4 days; 50 °C, 5 min) during induction of Pinus radiata D. Don somatic embryogenesis, together with control temperature (23 °C). Then, the somatic plants regenerated from initiated embryogenic cell lines and cultivated in greenhouse conditions were subjected to drought stress and control treatments to evaluate survival, growth and several physiological traits (relative water content, water potential, photosynthesis, stomatal conductance and transpiration). Based on those preliminary results, even more extreme higherature regimes were applied during induction (40 °C, 4 h; 50 °C, 30 min; 60 °C, 5 min) and the corresponding cytokinin profiles of initiated embryonal masses from different lines were analysed. The results showed that the temperature regime during induction had delayed negative effects on drought resilience of somatic plants as indicated by survival, photosynthetic activity and water- use efficiency. However, high temperatures for extended periods of time enhanced subsequent plant growth in well-watered conditions. Higherature regime treatments induced significant differences in the profile of total cytokinin bases, N6-isopentenyladenine, cis-zeatin riboside and trans-zeatin riboside. We concluded that phytohormones could be potential regulators of stress-response processes during initial steps of somatic embryogenesis and that they may have delayed implications in further developmental processes, determining the performance of the generated plants. This research was funded by MINECO (Spanish Government) project (AGL2016-60476143-C4-3R), CYTED (P117RT0522), DECO (Basque government, ‘Ayudas de formación a jóvenes investigadores y tecnólogos’) and MULTIFOREVER (Forest Value, ERANET program, EU). Project MULTIFOREVER is supported under the umbrella of ERA-NET Cofund ForestValue by ANR (FR), FNR (DE), MINCyT (AR), MINECO-AEI (ES), MMM (FI) and VINNOVA (SE). ForestValue has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°773324. This work was also supported by the Internal Grant Agency of Palacký University (IGA_PrF_2019_020) and from European Regional Development Fund-Project ‘Plants as a tool for sustainable global development’ (No. CZ.02.1.01/0.0/0.0/16_019/0000827).

Fitting spatio-temporal models for areal data is crucial in many fields such as cancer epidemiology. However, when data sets are very large, many issues arise. The main objective of this paper is to propose a general procedure to analyze high-dimensional spatio-temporal count data, with special emphasis on mortality/incidence relative risk estimation. We present a pragmatic and simple idea that permits to fit hierarchical spatio-temporal models when the number of small areas is very large. Model fitting is carried out using integrated nested Laplace approximations over a partition of the spatial domain. We also use parallel and distributed strategies to speed up computations in a setting where Bayesian model fitting is generally prohibitively time-consuming and even unfeasible. Using simulated and real data, we show that our method outperforms classical global models. We implement the methods and algorithms that we develop in the open-source R package bigDM where specific vignettes have been included to facilitate the use of the methodology for non-expert users. Our scalable methodology proposal provides reliable risk estimates when fitting Bayesian hierarchical spatio-temporal models for high-dimensional data.

The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important. This work has been supported by Projects MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033). Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Spatio-temporal disease mapping studies the distribution of mortality or incidence risks in space and its evolution in time, and it usually relies on fitting hierarchical Poisson mixed models. These models are complex for practitioners as they generally require adding constraints to correctly identify and interpret the different model terms. However, including constraints may not be straightforward in some recent software packages. This paper focuses on NIMBLE, a library of algorithms that contains among others a configurable system for Markov chain Monte Carlo (MCMC) algorithms. In particular, we show how to fit different spatio-temporal disease mapping models with NIMBLE making emphasis on how to include sum-to-zero constraints to solve identifiability issues when including spatio-temporal interactions. Breast cancer mortality data in Spain during the period 1990-2010 is used for illustration purposes. A simulation study is also conducted to compare NIMBLE with R-INLA in terms of parameter estimates and relative risk estimation. The results are very similar but differences are observed in terms of computing time. This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033

The concept of spatial confounding is closely connected to spatial regression, although no general definition has been established. A generally accepted idea of spatial confounding in spatial regression models is the change in fixed effects estimates that may occur when spatially correlated random effects collinear with the covariate are included in the model. Different methods have been proposed to alleviate spatial confounding in spatial linear regression models, but it is not clear if they provide correct fixed effects estimates. In this article, we consider some of those proposals to alleviate spatial confounding such as restricted regression, the spatial+ model, and transformed Gaussian Markov random fields. The objective is to determine which one provides the best estimates of the fixed effects. Dowry death data in Uttar Pradesh in 2001, stomach cancer incidence data in Slovenia in the period 1995–2001 and lip cancer incidence data in Scotland between the years 1975–1980 are analyzed. Several simulation studies are conducted to evaluate the performance of the methods in different scenarios of spatial confounding. Results reflect that the spatial+ method seems to provide fixed effects estimates closest to the true value although standard errors could be inflated Open Access funding provided by Universidad Pública de Navarra. This work has been supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033.

Univariate spatio-temporal models for areal count data have received great attention in recent years for estimating risks. However, models for studying multivariate responses are less commonly used mainly due to the computational burden. In this article, multivariate spatio-temporal P-spline models are proposed to study different forms of violence against women. Modeling distinct crimes jointly improves the precision of estimates over univariate models and allows to compute correlations among them. The correlation between the spatial and the temporal patterns may suggest connections among the different crimes that will certainly benefit a thorough comprehension of this problem that affects millions of women around the world. The models are fitted using integrated nested Laplace approximations and are used to analyze four distinct crimes against women at district level in the Indian state of Maharashtra during the period 2001-2013. Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.130 39/501100011033.

The distribution of lip, oral cavity, and pharynx (LOCP) cancer mortality rates in small domains (defined as the combination of province, age group, and gender) remains unknown in Spain. As many of the LOCP risk factors are preventable, specific prevention programmes could be implemented but this requires a clear specification of the target population. This paper provides an in-depth description of LOCP mortality rates by province, age group and gender, giving a complete overview of the disease. This study also presents a methodological challenge. As the number of LOCP cancer cases in small domains (province, age groups and gender) is scarce, univariate spatial models do not provide reliable results or are even impossible to fit. In view of the close link between LOCP and lung cancer, we consider analyzing them jointly by using shared component models. These models allow information-borrowing among diseases, ultimately providing the analysis of cancer sites with few cases at a very disaggregated level. Results show that males have higher mortality rates than females and these rates increase with age. Regions located in the north of Spain show the highest LOCP cancer mortality rates. The work was supported by Project MTM2017-82553-R (AEI, UE), Project PID2020-113125RB-I00/MCIN/ AEI/10.13039/501100011033 and Proyecto Jóvenes Investigadores PJUPNA2018-11.

Development of hybrid pines of Pinus radiata D. Don for commercial forestry presents an opportunity to diversify the current resource of plant material. Climate change and different land uses pose challenges, making alternative species necessary to guarantee wood and non-wood products in the future. Pinus radiata var. cedrosensis &times, Pinus attenuata hybrid possesses different attributes, such as tolerance to drought conditions, better growth and resistance to snow damage at higher altitudes, and more importantly, different wood quality characteristics. Embryogenic cell lines were successfully initiated reciprocal hybrids using as initial explants megagametophytes, excised zygotic embryos and excised zygotic embryos plus nurse culture. However, the questions raised were: does the initiation environment affect the conversion to somatic plantlets months later? Does the mother tree or the cross have an effect on the conversion to somatic plantlets? In the present work we analysed the maturation rate, number of somatic embryos, germination rate, and the ex-vitro growth in cell lines derived from different initiation treatments, mother tree species, and crosses. Differences were not observed for in vitro parameters such as maturation and germination. However, significant differences were observed due to the mother tree species in relation with the ex-vitro growth rates observed, being higher those in which P. radiata acted as a mother. Moreover, embryogenic cell lines from these hybrids were stored at &minus, 80 &deg, C and regenerated after one and five years.

Climatic variations in the current environmental scenario require plants with tolerance to sudden changes in temperature and a decrease in water availability. Accordingly, this tolerance will enable successful plantations and the maintenance of natural and planted forests. Consequently, in the last two decades, drought tolerance and high temperatures in conifers have been an important target for morphological, physiological, and epigenetic studies. Based on this, our research team has optimized different stages of somatic embryogenesis (SE) in Pinus spp. improving the success of the process. Through this method, we can obtain a large amount of clonal material and then analyze the somatic plants under different conditions ex vitro. The analysis of the morphological and physiological parameters in somatic embryos (ses) and plants with different tolerances to abiotic stress can provide us with valuable information about the mechanisms used by plants to survive under adverse environmental conditions. Thus, the objective of this work was to evaluate the influence of high temperatures (23, 40, 50, and 60 &deg, C, after 12 weeks, 90, 30, 5 min, respectively) on the morphology of somatic embryos obtained from Pinus radiata D.Don (Radiata pine) and Pinus halepensis Mill. (Aleppo pine). In addition, we carried out a physiological evaluation of the somatic plants of P. radiata submitted to heat and water stress in a greenhouse. We observed that the number of somatic embryos was not affected by maturation temperatures in both species. Likewise, P. radiata plants obtained from these somatic embryos survived drought and heat stress in the greenhouse. In addition, plants originating from embryonal masses (EMs) subjected to high maturation temperature (40 and 60 &deg, C) had a significant increase in gs and E. Therefore, it is possible to modulate the characteristics of somatic plants produced by the manipulation of environmental conditions during the process of SE.

In this work we propose a novel scalable Bayesian modeling approach to smooth mortality risks borrowing information from neighbouring regions in high-dimensional spatial disease mapping contexts. The method is based on the well-known divide and conquer approach, so that the spatial domain is divided into D subregions where local spatial models can be fitted simultaneously. Model fitting and inference has been carried out using the integrated nested Laplace approximation (INLA) technique. Male colorectal cancer mortality data in the municipalities of continental Spain have been analyzed using the new model proposals. Results show that the new modeling approach is very competitive in terms of model fitting criteria when compared with a global spatial model, and it is computationally much more efficient. This work has been supported by Project MTM2017-82553-R (AEI/FEDER, UE)

Background Ovarian cancer is a silent and largely asymptomatic cancer, leading to late diagnosis and worse prognosis. The late-stage detection and low survival rates, makes the study of the space-time evolution of ovarian cancer particularly relevant. In addition, research of this cancer in small areas (like provinces or counties) is still scarce. Methods The study presented here covers all ovarian cancer deaths for women over 50 years of age in the provinces of Spain during the period 1989-2015. Spatio-temporal models have been fitted to smooth ovarian cancer mortality rates in age groups [50,60), [60,70), [70,80), and [80,+), borrowing information from spatial and temporal neighbours. Model fitting and inference has been carried out using the Integrated Nested Laplace Approximation (INLA) technique. Results Large differences in ovarian cancer mortality among the age groups have been found, with higher mortality rates in the older age groups. Striking differences are observed between northern and southern Spain. The global temporal trends (by age group) reveal that the evolution of ovarian cancer over the whole of Spain has remained nearly constant since the early 2000s. Conclusion Differences in ovarian cancer mortality exist among the Spanish provinces, years, and age groups. As the exact causes of ovarian cancer remain unknown, spatio-temporal analyses by age groups are essential to discover inequalities in ovarian cancer mortality. Women over 60 years of age should be the focus of follow-up studies as the mortality rates remain constant since 2002. High-mortality provinces should also be monitored to look for specific risk factors.

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects, while obtaining good model predictions. In particular, restricted regression and an apparently—though in fact not—equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India. This work has been supported by Project MTM2017-82553-R (AEI/FEDER, UE). It has also been partially funded by ‘la Caixa’ Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001-2014. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also been partially funded by la Caixa Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Crimes against women in India have been continuously increasing lately as reported by the National Crime Records Bureau. Gender-based violence has become a serious issue to such an extent that it has been catalogued as a high impact health problem by the World Health Organization. However, there is a lack of spatiotemporal analyses to reveal a complete picture of the geographical and temporal patterns of crimes against women. We focus on analysing how the geographical pattern of 'dowry deaths' changes over time in the districts of Uttar Pradesh during the period 2001–2014. The study of the geographical distribution of dowry death incidence and its evolution over time aims to identify specific regions that exhibit high risks and to hypothesize on potential risk factors. We also look into different spatial priors and their effects on final risk estimates. Various priors for the hyperparameters are also reviewed. The risk estimates seem to be robust in terms of the spatial prior and hyperprior choices and final results highlight several districts with extreme risks of dowry death incidence. Statistically significant associations are also found between dowry deaths, sex ratio and some forms of overall crime. This work has been supported by project MTM2017‐82553‐R (Agencia Estatal de Investigación–Fondos de Desarrollo Regional, European Union). It has also been partially funded by the Caixa Foundation (grant 1000010434), Caja Navarra Foundation and National University of Distance Learning, Pamplona, under agreement LCF/PR/PR15/51100007.

Outliers and missing data are commonly found in satellite imagery. These are usually caused by atmospheric or electronic failures, hampering the correct monitoring of remote-sensing data. To avoid distorted data, we propose a procedure called 'spatial functional prediction' (SFP). The SFP procedure consists of the following: (1) aggregating remote-sensing data for reducing the number of missing data and/or outliers; (2) additively decomposing the time series of images into a trend, a seasonal, and an error component; (3) defining the spatial functional data and predicting the trend component using an ordinary kriging; and (4) adding back the seasonal and error components to the predicted trend. The benefits of the SFP procedure are illustrated in the following scenarios: introducing random outliers, random missing data, mixtures of both, and artificial clouds in an extensive simulation study of composite images, and using daily images with real clouds. The following two derived variables are considered: land surface temperature (LST day) and normalized vegetation index (NDVI), which are obtained as remote-sensing data in a region in northern Spain during 2003–2016. The performance of SFP was checked using the root mean squared error (RMSE). A comparison with a procedure based on predicting with thin-plate splines (TpsP) is also made. We conclude that SFP is simpler and faster than TpsP, and provides smaller values of RMSE. This research was supported by Project MTM2017-82553-R (AEI/FEDER, UE), and by 'La Caixa' Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Background and objective Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed. Methods The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference. Results The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided. Conclusions Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.

Somatic embryogenesis (SE) provides us a potent biotechnological tool to manipulate the physical and chemical conditions (water availability) along the process and to study their effect in the final success in terms of quantity of somatic embryos produced. In the last years, our research team has been focused on the study of different aspects of the SE in Pinus spp. One of the main aspects affecting SE is the composition of culture media; in this sense, phytohormones play one of the most crucial roles in this propagation system. Many studies in conifers have shown that different stages of SE and somatic embryo development are correlated with distinct endogenous phytohormone profiles under the stress conditions needed for the process (i.e., cytokinins play a regulatory role in stress signaling, which it is essential for radiata pine SE). Based on this knowledge, the aim of this study was to test the effect of different temperatures (18, 23, and 28°C) and gelling agent concentrations (8, 9, and 10 gL-1) during the maturation stage of Pinus radiata SE in maturation and germination rates. Parallel, phytohormone profile of somatic embryos developed was evaluated. In this sense, the highest gellan gum concentration led to significantly lower water availability. At this gellan gum concentration and 23°C a significantly higher number of somatic embryos was obtained and the overall success of the process increased with respect to other treatments assayed. The somatic embryos produced in these conditions showed the highest concentration of iP-type cytokinins and total ribosides. Although, the different conditions applied during maturation of somatic embryos led to different hormonal profiles, they did not affect the ex vitro survival of the resulting somatic plants, where no significant differences were observed. This research was funded by MINECO (Spanish Government) project (AGL2016-76143-C4-3R), CYTED (P117RT0522), and DECO (Basque Government). The Ministry of Education, Youth and Sports of the Czechia provided support for this project via the National Program for Sustainability I (LO1204).

A comparative study of the magnetic properties (magnetic moment, magnetocrystalline anisotropy) and hyperthermia response in Co-Zn spinel nanoparticles is presented. The CoxZn1-xFe2O4 nanoparticles (x = 1, 0.5, 0.4, 0.3, 0.2 and 0.1) were synthesized by co-precipitated method and the morphology and mean crystallite size (around 10 nm) of the nanoparticles were analysed by TEM Microscopy. Regarding the magnetic characterization (SQUID magnetometry), Co-Zn nanoparticles display at room temperature anhysteretic magnetization curves, characteristic of the superparamagnetic behavior. A decrease in the blocking temperature, T-B, with Zn content is experimentally detected that can be ascribed to the reduction in the mean nanoparticle size as x decreases. Furthermore, the reduction in the magnetocrystalline anisotropy with Zn inclusion is confirmed through the analysis of TB versus the mean volume of the nanoparticles and the law of approach to saturation. Maximum magnetization is achieved for x = 0.5 as a result of the cation distribution between octahedral and tetrahedral spinel sites, analysed by neutron diffraction studies. The occurrence of a canted spin arrangement (Yafet-Kittel angle) is introduced to properly fit the magnetic spinel structures. Finally, the heating capacity of these spinel ferrites is analyzed under ac magnetic field (magnetic hyperthermia). Maximum SAR (Specific Absorption Rate) values are achieved for x = 0.5 that should be correlated to the maximum magnetic moment of this composition. The work has been performed within the framework of projects MAT2017-83631-C3-2-R (Spanish "Ministerio de Economía, Industria y Competitividad") and DRUG-MAG, Nueva generación de plataformas teragnósticas contra el cáncer asistidas por partículas superparamagnéticas (Gobierno de Navarra, Departamento de Desarrollo Económico). J. Lopez-Garcia acknowledges ILL for his Ph.D. contract. E. C. Mendonca, J. G. S. Duque thank to Brazilian agencies FAPITEC (PRONEX) and CNPq (455608/2014-8).

Satellite remote sensing data have become available in meteorology, agriculture, forestry, geology, regional planning, hydrology or natural environment sciences since several decades ago, because satellites provide routinely high quality images with different temporal and spatial resolutions. Joining, combining or smoothing these images for a better quality of information is a challenge not always properly solved. In this regard, geostatistics, as the spatiotemporal stochastic techniques of georeferenced data, is a very helpful and powerful tool not enough explored in this area yet. Here, we analyze the current use of some of the geostatistical tools in satellite image analysis, and provide an introduction to this subject for potential researchers. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Project MTM2017-82553-R), the Government of Navarra (Project PI015, 2016 and Project PI043 2017), and by the Fundación Caja Navarra-UNED Pamplona (2016 and 2017).

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (project MTM2017-82553-R) jointly financed with the European Regional Development Fund (FEDER), the Government of Navarre (PI015-2016 and PI043-2017 projects) and the Fundación CAN-Obra Social Caixa-UNED Pamplona 2016 and 2017.

Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference. This work was supported by grants from the Spanish Ministry of Economy and Competitiveness (projects MTM2014-51992-R-MDU- and MTM2016-77501-P -ALQ-, jointly financed with the European Regional Development Fund), the Spanish Ministry of Economy, Industry, and Competitiveness (MTM2017-82553-R jointly financed with the European Regional Development Fund (FEDER). MDU, AA), and the Colombian Administrative Department of Science and Technology (grant 646-2014 for doctoral studies abroad) DAMB.

Summary Knowledge of the current state of the art in information and communication technology of businesses (ICTB) is an important issue for governments, markets and policy makers, because information technology improves access to information and plays an important role in firms’ competitiveness. Statistical agencies use normalized surveys to provide harmonized statistics about the use of technology in enterprises. Classical design-based estimators are appropriate for large domains, because direct estimates are consistent and easy to obtain by using sampling weights. However, to supply estimates for unplanned domains, where the sample size is random, model-based estimators are usually required. In this paper, alternative logistic model-based estimators are suggested to derive small area estimates from ICTB surveys. Final estimates are benchmarked to achieve coherence with direct estimates in larger domains, and standard errors are given by using bootstrap techniques. A Monte Carlo simulation study is conducted to compare the performance of the small area estimators proposed and to evaluate the behaviour of the mean-squared error estimator. Results are illustrated with the 2010 ICTB survey of the Basque country (Spain).

Nowadays, GPS receivers are very reliable because of their good accuracy and precision; however, uncertainty is also inherent in geospatial data. Quality of GPS measurements can be influenced by atmospheric disturbances, multipathing, synchronization of clocks, satellite geometry, geographical features of the observed region, low broadcasting coverage, inadequate transmitting formats, or human or instrumental unknown errors. Assuming that the scenario and technical conditions that can influence the quality of GPS measurements are optimal, that functional and stochastic models that process the signals to a geodetic measurement are correct, and that all the GPS observables are taken in the same conditions, it is still possible to estimate the positional errors as the difference between the real coordinates and those measured by the GPS. In this paper, three spatial linear mixed models, one for each axis, are used for modelling real-time kinematic GPS accuracy and precision, of a multiple-reference-station network in dual-frequency with carrier phase measurements. Along the paper, the proposed models provide an estimate of the “accuracy” in terms of bias defined as the difference between real coordinates and measured coordinates after being processed and “precision” through the standard errors of the estimated differences. This is done using ten different transmitting formats. Mapping and quantifying these differences can be interesting for users and GPS professionals. The performance of these models is illustrated by mapping positional error estimates within the whole region of Navarre, Spain. Sampled data have been taken in 54 out of the 211 geodetic vertex points of this region. Maps show interesting error patterns depending on transmitting formats, the different axes, and the geographical characteristics of the region. Higher differences are found in regions with bad broadcasting coverage, due to the presence of mountains and high degree of humidity.

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. This research was supported by the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R), the Government of Navarre (Project PI015, 2016), and by the Fundación Caja Navarra-UNED Pamplona (2016).

Recently, several models have been proposed for smoothing risks in disease mapping. These models consider different ways of introducing both spatial and temporal dependence as well as spatio-temporal interactions. In this work, a comparison among some autoregressive, moving average, and P-spline models is performed. Firstly, brain cancer mortality data are used to analyze the degree of smoothness introduced by these models. Secondly, two separate simulation studies (one model-based and the other model-free) are carried out to evaluate the model performance in terms of bias, variability, sensitivity, and specificity. We conclude that P-spline models seem to be a good alternative to autoregressive and moving average models when analyzing highly sparse disease mapping data.

Recently, the interest in studying pancreatic cancer mortality has increased due to its high lethality. In this work a detailed analysis of pancreatic cancer mortality in Spanish provinces was performed using recent data. A set of multivariate spatial gender-age-period-cohort models was considered to look for potential candidates to analyze pancreatic cancer mortality rates. The selected model combines features of APC (age-period-cohort) models with disease mapping approaches. To ensure model identifiability sum-to-zero constraints were applied. A fully Bayesian approach based on integrated nested Laplace approximations (INLA) was considered for model fitting and inference. Sensitivity analyses were also conducted. In general, estimated average rates by age, cohort, and period are higher in males than in females. The higher differences according to age between males and females correspond to the age groups [65, 70), [70, 75), and [75, 80). Regarding the cohort, the greatest difference between men and women is observed for those born between the forties and the sixties. From there on, the younger the birth cohort is, the smaller the difference becomes. Some cohort differences are also identified by regions and age-groups. The spatial pattern indicates a North-South gradient of pancreatic cancer mortality in Spain, the provinces in the North being the ones with the highest effects on mortality during the studied period. Finally, the space-time evolution shows that the space pattern has changed little over time. This work has been supported by the Spanish Ministry of Economy and Competitiveness (Project MTM 2014-51992-R) and by the Health Department of the Navarre Government (Project 113, Res.2186/2014). We would like to thank the National Epidemiology Center (area of Environmental Epidemiology and Cancer) for providing the data, originally created by the Spanish Statistical Office. Sí

Smoothing risks is one of the main goals in disease mapping as classical measures, such as standardized mortality ratios, can be extremely variable. However, smoothing risks might hinder the detection of high risk areas, since these two objectives are somewhat contradictory. Most of the work on smoothing risks and detection of high risk areas has been derived using conditional autoregressive (CAR) models. In this work, penalized splines (P-splines) models are also investigated. Confidence intervals for the log-relative risk predictor will be derived as a tool to detect high-risk areas. The performance of P-spline and CAR models will be compared in terms of smoothing (relative bias), sensitivity (ability to detect high risk areas), and specificity (ability to discard false patterns created by noise) through a simulation study based on the well-known Scottish lip cancer data.

Summary Current demand for accountability and efficiency of healthcare organizations, combined with the greater availability of routine data on clinical care and outcomes, has led to an increased focus on statistical methods in healthcare regulation. We consider three different regulatory functions in which statistical analysis plays a vital role: rating organizations, deciding whom to inspect and continuous surveillance for arising problems. A common approach to data standardization based on (possibly overdispersed) Z-scores is proposed, although specific tools are used for assessing performance against a target, combining indicators when screening for inspection, and continuous monitoring using risk-adjusted sequential testing procedures. We pay particular attention to the problem of simultaneously monitoring over 200000 indicators for excess mortality, both with respect to the statistical issues surrounding massive multiplicity, and the organizational aspects of dealing with such a complex but high profile process.

Measuring forest tree biomass is becoming a very important issue due to the general environmental awareness motivated by global warming and climate change. However, weighing a tree is a very complicated, expensive, and destructive process. The tree is divided into several parts, and the total weight is obtained by adding the weight of the different components. The biomass information of a forest is obtained using statistical models, but one of the main difficulties is that the additivity property is not generally satisfied, i.e., when adding the predicted weights for the different tree components, the result does not match up with the total weight predicted for the tree. In this work, alternative methods for obtaining biomass predictions satisfying the additivity property are analyzed. In particular, segmented regression models with a common break point and penalized splines with the same smoothing parameter achieve the additivity property without any further adjustments. Some classical models will be also used for comparison purposes. Results are illustrated with real data from a beech forest (European project FORSEE-020) in the province of Navarre, Spain.

Semiparametric models combining both non-parametric trends and small area random effects are now currently being investigated in small area estimation (SAE). These models can prevent bias when the functional form of the relationship between the response and the covariates is unknown. Furthermore, penalized spline regression can be a good tool to incorporate non-parametric regression models into the SAE techniques, as it can be represented as a mixed effects model. A penalized spline model is considered to analyze trends in small areas and to forecast future values of the response. The prediction mean squared error (MSE) for the fitted and the predicted values, together with estimators for those quantities, are derived. The procedure is illustrated with real data consisting of average prices per squared meter of used dwellings in nine neighborhoods of the city of Vitoria, Spain, during the period 1993-2007. Dwelling prices for the next five years are also forecast. A simulation study is conducted to assess the performance of both the small area trend estimator and the prediction MSE estimators. The results confirm a good behavior of the proposed estimators in terms of bias and variability.

Disease mapping studies have experienced an enormous development in the last twenty years. Both an Empirical Bayes (EB) and a Fully Bayes (FB) approach have been used for smoothing purposes. However, an excess of smoothing might hinder the detection of true high-risk areas. Identifying these extreme regions minimizing the misclassification of background or normal areas, and then, avoiding false alarms is crucial in epidemiology. Bayesian decision rules, based on the posterior distribution of the relative risks, have been investigated for this task, but no similar studies have been conducted under the EB approach. Within this framework, second order correct estimators of the MSE of the log-relative risk predictor can be used to build appropriate confidence intervals for the relative risks. Their ability to detect high-risk areas is investigated through a simulation study using the geographical structure of the well-known Scottish lip cancer data. Bayesian credibility intervals and decision rules, based on the posterior distribution of the relative risks, are also investigated to check if any of the approaches outperforms the others when classifying high-risk regions. The conclusion is that Bayesian decision rules, exploiting the posterior distribution of the relative risks, are more powerful to detect high-risk areas than EB confidence intervals, but no general rules can be defined as a global criterion to be routinely applied in every real setting.

In some diseases it is well-known that a unimodal mortality pattern exists. A clear example in developed countries is breast cancer, where mortality increased sharply until the nineties and then decreased. This clear unimodal pattern is not necessarily applicable to all regions within a country. In this paper, we develop statistical tools to check if the unimodality pattern persists within regions using order restricted inference. Break points as well as con dence intervals are also provided. In addition, a new test for checking monotonicity against unimodality is derived allowing to discriminate between a simple increasing pattern and an up-then-down response pattern. A comparison with the widely used joinpoint regression technique under unimodality is provided. We show that the joinpoint technique could fail when the underlying function is not piecewise linear. Results will be illustrated using age-speci c breast cancer mortality data from Spain in the period 1975-2005., Ministerio de Ciencia e Innovación grant (MTM2012-37129), Spanish Ministry of Science and Innovation project MTM 2011-22664, Ministerio de Ciencia e Innovación and FEDER grant MTM2014-51992-R, Health Department of Navarre Government (Project 113, Res. 2186/2014)

Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning. Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables topics, including univariate and multivariate distributions. After exploring sampling distributions, the authors discuss point estimation, confidence intervals, hypothesis testing, and a wide range of nonparametric methods. With a focus on experimental design, the book also presents fixed- and random-effects models as well as randomized block and two-factor factorial designs. The final chapter describes simple and multiple regression analyses. Demonstrating that R can be used as a powerful teaching aid, this comprehensive text presents extensive treatments of data analysis using parametric and nonparametric techniques. It effectively links statistical concepts with R procedures, enabling the application of the language to the vast world of statistics.

This article aims to estimate the total area occupied by olive trees in a region called Comarca IV, located in a central region of Navarra, Spain. Traditionally, small area linear mixed models have been used for similar purposes using regular quadrats (also called segments) as sampling units, and assuming that the majority of segments are fully included in the study domain. When this does not happen, the sampling units are of different size, and there exists an extra variability that can be very different within areas. In this case it is advisable to include model weights in the model. In this article, we propose a weighted unit level linear mixed model where both the variance components and the coefficients of the model are estimated using these weights. We also discuss, the model performance and compare it with alternatives.

This article discusses and evaluates penalized quasi-likelihood (PQL) estimation techniques for the situation where random effects are correlated, as is typical in mapping studies. This is an approximate fitting technique which uses a Laplace approximation to the integrated mixed model likelihood. It is much easier to implement than usual maximum likelihood estimation. Our results show that the PQL estimates are reasonably unbiased for analysis of mixed Poisson models when there is correlation in the random effects, except when the means are sufficiently small to yield sparse data. However, although the normal approximation to the distribution of the parameter estimates works fairly well for the parameters in the mean it does not perform as well for the variance components. In addition, when the mean mortality counts are small, the estimated standard errors of the variance components tend to become more biased than those for the mean. We illustrate our approaches by applying PQL for mapping mortality in British Columbia, Canada, over the five-year period 1985–1989.

Summary. The purpose of this article is to draw attention to the possible need for inclusion of interaction effects between regions and age groups in mapping studies. We propose a simple model for including such an interaction in order to develop a test for its significance. The assumption of an absence of such interaction effects is a helpful simplifying one. The measure of relative risk related to a particular region becomes easily and neatly summarized. Indeed, such a test seems warranted because it is anticipated that the simple model, which ignores such interaction, as is in common use, may at times be adequate. The test proposed is a score test and hence only requires fitting the simpler model. We illustrate our approaches using mortality data from British Columbia, Canada, over the 5-year period 1985–1989. For this data, the interaction effect between age groups and regions is quite large and significant.