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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag-York-Molli\'e model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%.

Spatial confounding usually refers to an issue that often arises in the context of fitting a spatial linear mixed model: the collinearity between the fixed effects and the spatial random effects. As a major consequence, this can lead to the inference of erroneous covariate-response associations, with the resulting scientific or social impact. Different methods have been proposed in the literature to alleviate spatial confounding. However, recent research suggests that restricted spatial regression, the most commonly used to date, performs worse than the original spatial linear mixed model, and should be avoided for this purpose. In this paper, we propose the use of the Bayesian version of the standard Lasso method for alleviating spatial confounding. Specifically, we propose a joint model that includes the fixed-effects modeling process (not including spatial random effects) and the spatial linear mixed modeling framework subject to a penalty on the effect of the covariate on the response. The model proposed is tested for the Slovenia dataset, which is usually used for the study of this topic. The model provides an estimate of the covariate-response association of interest which is close to that inferred from the fixed-effects model, while allowing for the inclusion of spatial random effects, thus performing better in terms of goodness-of-fit and spatial smoothing than the fixed-effects model. A simulation study is also carried out, which allows us to verify that the covariate effect estimated through the model proposed is coherent with the one given by the fixed-effects model. The simulation study also enables us to reinforce the idea that finding the most suitable modeling framework for estimating a covariate-response association in a spatial context is not an easy task.

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Analyzing how the proximity to certain features or particular places in a city increases or decreases crime risk across space is a fundamental issue in quantitative criminology from both explanatory and predictive perspectives. Regarding the latter, the detection of high-risk cells is of special interest for practical reasons. There are several statistical modeling approaches that can be implemented in order to fulfil these two main objectives. The purpose of this study is to compare risk terrain modeling (RTM), a method widely used among quantitative criminologists, with a non-linear effects model that considers a non-linear function of distances to the selected places. To this end, a dataset containing crime events recorded in Valencia (Spain) along four years was used to perform the comparison. Several socio-demographic covariates and a selection of places in the city were considered for modeling crime counts with both the RTM and the non-linear approaches. The two modeling techniques were moderately coherent with regard to detecting certain types of places as responsible of higher (or lower) crime rates, but several differences arose. Furthermore, the non-linear model was more accurate than RTM to predict future crime occurrences for each of the three crime types that were considered for the analysis. In conclusion, the possibility of modeling the effect of a place on crime risk through non-linear functions appears as one competitive alternative or at least complement to RTM that may deserve further consideration.

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatiotemporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels. Innovation, University, Science and Digital Society Council, Valencia Innovation Agency (AVI) 1.715 JCR (2021) Q2, 56/125 Statistics & Probability 0.951 SJR (2021) Q1, 518/2489 Medicine (miscellaneous) No data IDR 2021 UEV

For some time now, chemistry has not had a good reputation in society. The population identifies this discipline with industries and pollution, while its positive contributions are usually ignored. This fear of chemistry may affect the opinion of society toward not only science, but also the scientific work itself. To assess the prevalence of this social phenomenon among them, a group of 351 preuniversity students was surveyed and the answers provided were analyzed based on their characteristics (age, sex, and location). The results support the usefulness of the developed questionnaire and show that there already exists a tendency to reject everything “containing chemicals” or that “is not natural” at these ages. An influence of the students’ sociocultural environment and years of schooling is noticed concerning the ideas they manifest. In light of this situation, the potential implications for the learning and teaching processes of chemistry are commented on.

Epidermal necrolysis (EN) compromises a spectrum of life-threatening dermatoses (Stevens-Johnson Syndrome [SJS], overlap syndrome and toxic epidermal necrolysis [TEN]). Currently, no active therapeutic regimen with unequivocal benefit exists for SJS/TEN. SCORTEN is the widely-used prognostic scale specific for SJS/TEN. Nevertheless, a new prognostic scale, the ABCD-10, has been recently proposed. In this context, acute renal failure (ARF) seems to be an important comorbidity that could influence prognosis in SJS/TEN patients more than it is assumed by these two scales. Our objectives were to compare the accuracy of the SCORTEN and ABCD-10 scales in predicting the mortality in SJS/TEN, and to investigate the influence of renal failure on prognosis. The prognostic results of 18 patients with EN treated in two referral centers between 2013 and 2018 are presented. SCORTEN, ABCD-10 and renal function values were retrospectively collected for all patients. Out of the 18 patients who were analyzed, nine (50%) received only supportive therapy, four were treated with etanercept 50 mg in a single dose (22.2%) and five with corticosteroids (27.8%). Five patients developed ARF. Predicted mortality was 3.48 for SCORTEN and 2.33 for ABCD-10. Eventually, four patients died (22.2%), all had ARF and none of them received active treatment. Despite study limitations and in the absence of active treatment of choice, SCORTEN behaved as a reliable predictor of mortality in patients with EN, outperforming the newer ABCD-10. ARF was an early event associated with a poor prognosis, which could represent a prognostic marker to consider in the future.

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19’s global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission.

Background Delta hepatitis is a rare infection with an aggressive disease course. For almost three decades, however, there have been no epidemiological studies in our traditionally endemic area. Aim To investigate the prevalence of delta hepatitis in a sample of patients with chronic hepatitis B virus (HBV) infection followed at a Hepatology Unit in Valencia, Spain. Methods Retrospective evaluation of anti-hepatitis D virus-immunoglobulin G seroprevalence among patients with chronic HBV infection (n = 605) followed at a reference Hepatology Unit in Spain. Results The prevalence of anti-hepatitis D virus-immunoglobulin G among HBV-infected patients was 11.5%: Male (63%) and median age of 52 years. The majority were born in Spain (67%) and primarily infected through intravenous drug use. However, a significant percent (24.5%), particularly those diagnosed in more recent years, were migrants presumably nosocomially infected. Comorbidities such as diabetes (8.5%), obesity/overweight (55%), and alcohol consumption (34%) were frequent. A high proportion of patients developed liver complications such as cirrhosis (77%), liver decompensation (81%), hepatocellular carcinoma (HCC) (16.5%), or required liver transplantation (LT) (59.5%). Diabetes was associated with progression to cirrhosis, LT, and death. Male sex, increasing age, and alcohol were associated with LT and HCC. Compared to HBV mono-infected patients, delta individuals developed cirrhosis and liver decompensation more frequently, with no differences in HCC rates. Conclusion Patients infected in the 1980's were mostly locals infected through intravenous drug use, whereas those diagnosed recently are frequently non-Spanish natives from endemic areas. Regardless of their origin, patients are predominantly male with significant comorbidities, which potentially play a major role in disease progression. We confirm a high rate of subsequent liver complications.

The near-repeat phenomenon usually occurs with any crime. Hence, to implement preventive measures, it is of great interest to figure out at which spatio-temporal scale crimes are more likely to be repeated by offenders. The Knox test is the most used statistical tool for evaluating the presence of the near-repeat phenomenon given a dataset of crimes that are located in space and time. The classic version of this test assumes that crime risk is homogeneous in both space and time, although this assumption rarely holds in reality. Therefore, the main goal of this article is to highlight the necessity of adjusting the standard version of the Knox test, including spatial and temporal effects that allow for the consideration of crime risk heterogeneity. In this regard, a methodology that has already been proposed for addressing this issue is described and adapted. This methodology is then put into practice through a dataset of burglaries recorded in the city of Valencia (Spain) in 2016 and 2017. The results yielded by both versions (standard and adjusted) of the Knox test confirm that the near-repeat phenomenon took place for the burglaries that occurred in Valencia during the period under investigation. However, using the adjusted version of the Knox test leads to a reduction in the number of spatio-temporal intervals that are declared as statistically significant. This fact should be born in mind before making decisions on preventive measures.

BACKGROUND The SCORTEN score is a specific predictor of the probability of death for patients diagnosed with Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). OBJECTIVES To evaluate the overall accuracy of SCORTEN and the influence of several moderators such as age, sex, geographical region and age of the study. METHODS A systematic search was performed on MEDLINE, The Cochrane Library, EMBASE, SCOPUS and Web of Knowledge, with no restriction on language (last update 5 February 2019 for all databases). Original studies on the use of SCORTEN were eligible. The standardized mortality ratio (SMR), defined as the quotient between the number of deaths observed and the number expected following SCORTEN, was taken as the measurement of analysis. RESULTS Sixty-four papers were part of the main meta-analysis carried out in the study. A pooled log(SMR) of -0.0889 (95% CI: -0.2023 to 0.0245) was obtained, suggesting a reasonable behaviour of SCORTEN as a predictor of mortality. The possible influence of several factors in the accuracy of SCORTEN was studied by means of meta-regression models. Multivariate meta-regression allowed finding that the mean age of the patients and the ending year of the study are the only factors that significantly influence SCORTEN predictions. The mean age of the group of patients was associated with a significant increase in the observed/expected ratio, whereas a progressive reduction in the observed/expected ratio can be appreciated over the years. Finally, an underestimation of mortality was found for SCORTEN values of 3 or less and the opposite for those above 3 (SCORTEN range: 0-7). CONCLUSIONS The rarity of the disease and the heterogeneity of the studies included are major limitations. Despite the overall remarkable accuracy displayed by SCORTEN, the influence of several factors, as comorbidities (e.g. renal impairment), involved body surface area and patient's age, seem of enough relevance to consider a redefinition of the scale.

Geocoding consists in converting a textual description of a location into coordinates. Hence, geocoding a dataset of events has to be carried out before performing a spatial analysis of some data. ...

The behavior and spatial distribution of crime events can be explained through the characterization of an area in terms of its demography, socioeconomy, and built environment. In particular, recent studies on the incidence of crime in a city have focused on the identification of features of the built environment (specific places or facilities) that may increase crime risk within a certain radius. However, it is hard to identify environmental characteristics that consistently explain crime occurrence across cities and crime types. This article focuses on the assessment of the effect that certain types of places have on the incidence of property crime, robbery, and vandalism in three cities of the Valencian region (Spain): Alicante, Castellon, and Valencia. A nonlinear effects model is used to identify such places and to construct a risk map over the three cities considering the three crime types under research. The results obtained suggest that there are remarkable differences across cities and crime types in terms of the types of places associated with crime outcomes. The identification of high-risk areas allows verifying that crime is highly concentrated, and also that there is a high level of spatial overlap between the high-risk areas corresponding to different crime types.

This paper presents a case study carried out in multiple cities of the Valencian Community (Spain) to determine the effect of sociodemographic and road characteristics on traffic accident risk. The analyzes are performed at the road segment level, considering the linear network representing the road structure of each city as a spatial lattice. The number of accidents observed in each road segment from 2010 to 2019 is taken as the response variable, and a zero-inflated modeling approach is considered. Count overdispersion and spatial dependence are also accounted for. Despite the complexity and sparsity of the data, the fitted models performed considerably well, with few exceptions. The results show that the effect of most of the covariates is highly heterogeneous across cities. In particular, the impact of roundabouts and main roads on accident risk is overall consistent across the cities studied, whereas the role of sociodemography and other road features seems much more uncertain. Furthermore, the distribution of zeros and non-zeros displays a markedly spatial structure, whereas the magnitude of non-zeros is generally unstructured in space. These findings could be useful to establish preventive measures at the street level in the future.

Biologic scaffolds composed of extracellular matrix components have been proposed to repair and reconstruct a variety of tissues in clinical and pre-clinical studies. Injectable gels can fill and conform any three-dimensional shape and can be delivered to sites of interest by minimally invasive techniques. In this study, a biological gel was produced from a decellularized porcine urinary bladder by enzymatic digestion with pepsin. The enzymatic digestion was confirmed by visual inspection after dissolution in phosphate-buffered saline solution and Fourier-transform infrared spectroscopy. The rheological and biological properties of the gel were characterized and compared to those of the MatrigelTM chosen as a reference material. The storage modulus G&rsquo, reached 19.4 &plusmn, 3.7 Pa for the 30 mg/mL digested decellularized bladder gels after ca. 3 h at 37 &deg, C. The results show that the gel formed of the porcine urinary bladder favored the spontaneous differentiation of human and rabbit adipose-derived stem cells in vitro into smooth muscle cells to the detriment of cell proliferation. The results support the potential of the developed injectable gel for tissue engineering applications to reconstruct for instance the detrusor muscle part of the human urinary bladder.

Purpuse The COVID-19 outbreak has escalated into the worse pandemic of the present century. The fast spread of the new SARS-CoV-2 coronavirus has caused devastating health and economic crises all over the world, with Spain being one of the worst affected countries in terms of confirmed COVID-19 cases and deaths per inhabitant. In this situation, the Spanish Government declared the lockdown of the country. Methods The variations of air pollution in terms of fine particulate matter (PM2.5) levels in seven representative cities of Spain are analyzed here considering the effect of meteorology during the national lockdown. The possible associations of PM2.5 pollution and climate with COVID-19 accumulated cases were also analyzed. Results While the epidemic curve was flattened, the results of the analysis show that the 4-week Spanish lockdown significantly reduced the PM2.5 levels in only one city despite the drastically reduced human activity. Furthermore, no associations between either PM2.5 exposure or environmental conditions and COVID-19 transmission were found during the early spread of the pandemic. Conclusions A longer period applying human activity restrictions is necessary in order to achieve significant reductions of PM2.5 levels in all the analyzed cities. No effect of PM2.5 pollution or weather on COVID-19 incidence was found for these pollutant levels and period of time. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s40201-022-00786-2.

The COVID-19 outbreak has escalated into the worse pandemic of the present century. The fast spread of the new SARS-CoV-2 coronavirus has caused devastating health and economic crises all over the world, with Spain being one of the worst affected countries in terms of confirmed COVID-19 cases and deaths per inhabitant. In this situation, the Spanish Government declared the lockdown of the country. The variations of air pollution in terms of fine particulate matter (PM2.5) levels in seven cities of Spain are analyzed here considering the effect of meteorology during the national lockdown. The possible associations of PM2.5pollution and climate with COVID-19 accumulated cases were also analyzed. While the epidemic curve was flattened, the results of the analysis show that the 4-week Spanish lockdown significantly reduced the PM2.5levels in only one of the cities despite the drastically reduced human activity in good agreement with our previous study of changes in air quality in terms of CO, SO2, PM10, O3and NO2levels. Furthermore, no associations between either PM2.5exposure or environmental conditions and COVID-19 transmission were found during the early spread of the pandemic.

Highlights • Variations in air quality in terms of CO, SO2, PM10, O3 and NO2 levels were studied • The lockdown of Spain was not able to reduce severe air pollution in all its forms • Significant reductions of NO2 levels were achieved in most cities • Increases of O3 pollution levels were found in several cities, The COVID-19 pandemic has escalated into one of the largest crises of the 21st Century. The new SARS-CoV-2 coronavirus, responsible for COVID-19, has spread rapidly all around the world. The Spanish Government was forced to declare a nationwide lockdown in view of the rapidly spreading virus and high mortality rate in the nation. This study investigated the impact of short-term lockdown during the period from March 15th to April 12th 2020 on the atmospheric levels of CO, SO2, PM10, O3, and NO2 over 11 representative Spanish cities. The possible influence of several meteorological factors (temperature, precipitation, wind, sunlight hours, minimum and maximum pressure) on the pollutants' levels were also considered. The results obtained show that the 4-week lockdown had significant impact on reducing the atmospheric levels of NO2 in all cities except for the small city of Santander as well as CO, SO2, and PM10 in some cities, but resulted in increase of O3 level., Graphical abstract Image, graphical abstract

Background The SCORTEN score is a specific predictor of mortality for patients with Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). There is little evidence in support of the common immunomodulating therapies for SJS/TEN. Objectives To systematically assess the effectiveness of several therapies for SJS/TEN through the SCORTEN score. Methods Databases were searched for original studies on the use of SCORTEN. Six meta-analyses were carried out on patients with SJS/TEN who received supportive care only or in combination with immunomodulating drugs: corticosteroids, cyclosporine, etanercept, immunoglobulins or a combination of corticosteroids with immunoglobulins. A multivariate meta-regression and a network meta-analysis were also performed. Results Of 3893 studies identified, fifty-two involving 2466 patients with SJS/TEN were preselected. Data from thirty-eight of these studies (1827 patients) were finally pooled, and results [log(SMR)] from meta-analyses were as follows: -0.13 (95% CI, -0.42,0.16) for corticosteroids, -0.39 (95% CI, -0.87,0.09) for immunoglobulins, 0.13 (95% CI, -0.15,0.40) for supportive treatment, -0.88 (95% CI, -1.47, -0.29) for cyclosporine, -0.95 (95% CI, -1.82, -0.07) for etanercept and - 0.56 (95% CI, -0.94, -0.19) for immunoglobulins plus corticosteroids. The meta-regression analysis confirmed that cyclosporine and immunoglobulins plus corticosteroids were associated with less deaths than predicted by SCORTEN. In the network meta-analysis, no treatment achieved a significant reduction in the SMR. Limitations Heterogeneity and quality of the included studies. Conclusions Some treatments for SJS/TEN show a better performance, but there is not sufficient evidence to recommend its widespread use in all patients.

Predicting the occurrence of traffic accidents is essential for establishing preventive measures and reducing the impact of traffic accidents. In particular, it is fundamental to make predictions using fine spatio-temporal units. In this paper, the daily risk of traffic accident occurrence across the road network of Valencia (Spain) is modeled through logistic regression models. The spatio-temporal dependence between the observations is accounted for through the inclusion of lagged binary covariates representing the previous occurrence of a traffic accident within a spatio-temporal window centered at each combination of day and segment of the network. A temporal distance of 28 days and a fifth-order spatial distance are set as the limits of such dependence. Furthermore, the models include fixed effects in terms of several socio-demographic, network-related, and weather-related covariates. Temporal (month and day of the week) and spatial (borough-level) effects are also considered. The predictive quality of the models is examined through the Matthews correlation coefficient and the prediction accuracy index. The results indicate that the incorporation of spatio-temporal dependence improves the predictive ability of the models. However, while the inclusion of temporally-lagged covariates representing short-and mid-term temporal dependence yields more accurate predictions, the higher-order spatial lags barely alter model performance.

espanolEl aprendizaje de la programacion por medio de los ordenadores constituye una gran ventaja a nivel de competencias en la epoca actual. Ademas, en un sentido estrictamente educacional, la programacion puede dotar a los alumnos que la estudian y practican de una mayor capacidad de razonamiento logico, pensamiento estructurado o incluso una mayor imaginacion. Asi pues, el primer objetivo de este trabajo es revisar algunos estudios que senalan las multiples ventajas que puede suponer para el alumnado el aprendizaje de la programacion durante su educacion secundaria. El segundo y principal objetivo es plantear el uso de uno de los lenguajes de programacion mas populares del momento, el R, como una herramienta para tratar contenidos propios de la asignatura de Matematicas. Esto es especialmente interesante debido a la presencia de contenidos en el curriculo que se prestan claramente al diseno de algoritmos y a una experimentacion mayor que la que permite la ensenanza tradicional. Esta metodologia de aprendizaje fue puesta en practica con 33 alumnos espanoles de entre 14 y 15 anos de edad, los cuales utilizaron el lenguaje R para tratar cuestiones relativas a la resolucion de ecuaciones polinomicas. La experiencia permitio comprobar grandes ventajas de la metodologia, aunque tambien algunas desventajas para ciertos alumnos, debido a la complejidad intrinseca de la programacion, como se desprendio del analisis correlacional de la encuesta realizada a los mismos. En cualquier caso, estas desventajas podrian subsanarse mediante una aplicacion de la metodologia mas prolongada en el tiempo. EnglishLearning to program using computers constitutes a great advantage for students in order to gain competencies nowadays. Furthermore, programming can help them to develop skills such as logical reasoning, structured and/or even creative thinking from an educational point of view. Therefore, the first objective of this work is to review several studies that discuss the multiple benefits that students can obtain from learning to program in secondary education. The second and main purpose of this study seeks to show the possibility of learning mathematical concepts with the aid of one of the most popular programming languages currently available, R. This is especially interesting because some mathematical contents in the curriculum are easy to teach via algorithmic design and experimentation. Besides, these contents are unable to be taught in this way when using traditional teaching methods. This learning methodology was tested with 33 students from Spain aged 14-15 years, which used the R programming language to study polynomial equations. The experience provided sufficient information for observing great advantages and yet some disadvantages for certain students due to the intrinsic complexity of programming, as it was revealed through the correlational analysis of the survey that was taken by the participants. In any case, these disadvantages would likely be solved by a longer implementation of the methodology.

One of the most common applications of spatial data analysis is detecting zones, at a certain investigation level, where a point-referenced event under study is especially concentrated. The detection of this kind of zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is typical that hotspot detection procedures are entirely focused on the determination of zones where an event is (overall) highly concentrated. It is less common, by far, that such procedures prioritize the location of zones where a specific type of event is overrepresented in relation to the other types observed, which have been denoted as differential risk hotspots. The R package DRHotNet provides several functionalities to facilitate the detection of differential risk hotspots along a linear network. In this paper, DRHotNet is depicted and its usage in the R console is shown through a detailed analysis of a crime dataset.

The use of probiotic starters remaining viable in unpasteurized and unfiltered beers could significantly increase health benefits. Here, the probiotic Saccharomyces cerevisiae var. boulardii (Scb) and a commercial Saccharomyces cerevisiae (Sc) strain, which is commonly employed in the brewing industry, are compared as single starters. The healthy value of the produced beers and growth performance in a laboratory bioreactor are analysed by determining antioxidant activity, phenolic content and profile, alcohol, biomass growth modelling by the logistic and Gompertz equations, biovolume estimation from 2D microscopy images, and yeast viability after fermentation. Thus, in this study, the craft beer produced with the probiotic yeast possessed higher antioxidant activity, lower alcohol content, similar sensory attributes, much higher yeast viability and more acidification, which is very desirable to reduce contamination risks at large-scale production. Furthermore, Scb exhibited faster growth in the bioreactor culture and larger cell volumes than Sc, which increases the probiotic volume of the final craft beer.

The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of Val&egrave, ncia (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of Val&egrave, ncia&rsquo, s boroughs. It has been carried out using a logistic model after dichotomization of the noise incidence variable. The spatial effects consider first- and second-order neighbors. The temporal effects are included in the model by means of one- and two-week temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses, and recreational activities, among other variables. The results suggest that there is a problem of "social" noise in Val&egrave, ncia that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months severely increase the chances of expecting a noise incident.

Although most of the literature on traffic safety analysis has been developed over areal zones, there is a growing interest in using the specific road structure of the region under investigation, which is known as a linear network in the field of spatial statistics. The use of linear networks entails several technical complications, ranging from the accurate location of traffic accidents to the definition of covariates at a spatial micro-level. Therefore, the primary goal of this study was to display a detailed analysis of a dataset of traffic accidents recorded in Valencia (Spain), which were located into a linear network representing more than 30 km of urban road structure corresponding to one district of the city. A set of traffic-related covariates was constructed at the road segment level for performing the analysis. Several issues and methodological approaches that are inherent to linear networks have been shown and discussed. In particular, the network was defined in a way that allowed the explicit investigation of traffic accidents around road intersections and the consideration of traffic flow directionality. Zero-inflated negative binomial count models accounting for spatial heterogeneity were used. Traffic safety at road intersections was specifically taken into account in the analysis by considering the higher variability and number of zeros that can be observed at these road entities and the differential contribution of the covariates depending on the proximity of a road intersection. To complement the results obtained from the count models fitted, coldspots and hotspots along the network were also detected, with explanatory objectives. The models confirmed that spatial heterogeneity, overdispersion and the close presence of road intersections explain the accident counts observed in the road network analyzed. Hotspot detection revealed that several covariates whose contribution was unclear in the modelling approaches may also be affecting accident counts at the road segment level.

Traffic safety analysis at the macroscopic level usually relies on previously defined areal traffic analysis zones (TAZs) that are used as the units of investigation. Hence, statistical inference is made on the basis of such units, implying that the consideration of a certain TAZ configuration may influence the results and conclusions achieved. Regarding this, the modifiable areal unit problem (MAUP) is a well-known issue in the field of spatial statistics, which refers to the effects that arise in statistical properties and estimations when there is a change in areal units of analysis. In this paper, the consequences of MAUP have been investigated through a dataset of traffic crashes that occurred in Valencia within the years 2014 and 2015 and two common statistical models: a conditional autoregressive model and a geographically weighted regression. In the absence of an established TAZ scheme for the city, four classes of basic spatial units (BSUs) were considered: census tracts, hexagonal units and two types with construction based on the structure of main roads and intersections of the city. Each of these BSU types was specified at different levels of spatial aggregation. The main research objective was to investigate the final effects that changes in BSU type and scale have on model parameter estimations, but also the specific alterations that MAUP causes to data in terms of the distributional characteristics of the response, multicollinearity among the covariates and covariates’ spatial autocorrelation. The results showed the presence and severity of MAUP for the dataset and area that were analysed. Although effects from scale variations were more moderate, changing the BSU type affected the results severely. The joint use of hexagonal units and a conditional autoregressive model achieved the best performance among all the possibilities explored, but the choice of a proper BSU unit should rely on more factors. Despite MAUP effects, educational centres showed a consistent (and negative) association with traffic crashes, a fact possibly related to their distribution across the whole city. Other covariates revealed a positive correlation with crash counts, but these findings were more uncertain given the discrepancies found at different scales and zonings.

Importance Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. Objective To describe the local spread of SARS-CoV-2 in Valencia, Spain. Design, Setting, and Participants This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Exposures Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. Main Outcomes and Measures The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. Results In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (β2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (β1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. Conclusions and Relevance This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown., This study was supported by the Innovation, Universities, Science and Digital Society Council through the Valencia Innovation Agency (AVI); grant 851255 from the European Research Council under the European Union’s Horizon 2020 research and innovation program (Dr Zanin); grant MDM-2017-0711 from the Spanish State Research Agency through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in Research and Development (Dr Zanin); and from the Universitat de Valencia (Drs Iftimi and Lozano).

In recent years, an increase in the consumer interest has been observed for craft beers, which are unpasteurized and unfiltered. Thus, the use of probiotic starters remaining in the craft beers could significantly increase health benefits. Here, the first study on the use of the probiotic Sacharomyces boulardii (Sb) yeast as single starter for craft brewing at 20 L scale is presented. The probiotic biomass growth was studied in bioreactor batch culture and modelled by the logistic and Gompertz equations. Finally, the probiotic biovolume of Sb was estimated from microscopy images. All these experiments were compared with those obtained with a commercial Sacharomyces cerevisiae (Sc) yeast strain, which is commonly utilised in the brewing industry. The result of this study showed that, the craft beer produced with Sb possessed higher antioxidant activity and more acidification, which is very desirable to reduce contamination risks at large-scale production. Furthermore, Sb exhibited faster growth and larger biovolume than Sc, which increases the probiotic value of the final craft beer.

Due to the possible depletion of fossil fuels in the near future and the necessity of finding new food sources for a growing world population, marine microalgae constitutes a very promising alternative resource, which can also contribute to carbon dioxide fixation. Thus, seven species (Chaetoceros calcitrans, Chaetoceros gracilis, Isochrysis galbana, Nannochloropsis gaditana, Dunaliella salina, Tetraselmis suecica, and Tetraselmis chuii) were grown in five serial batch cultures at a bench scale under continuous illumination. The batch cultures were inoculated with an aliquot that was extracted from a larger-scale culture in order to obtain growth data valid for the entire growth cycle with guaranteed reproducibility. Thus, measurements of optical density at several wavelengths and cell counting with a haemocytometer (Neubauer chamber) were performed every one or two days for 22 days in the five batch cultures of each specie. Modeling of cell growth, the relationship between optical density (OD) and cell concentration and the effect of wavelength on OD was performed. The results of this study showed the highest and lowest growth rate for N. gaditana and T. suecica, respectively. Furthermore, a simple and accurate discrimination method by performing direct single OD measurements of microalgae culture aliquots was developed and is already available for free on internet. PRUCV2017-162-001 grant from the Universidad Católica de Valencia San Vicente Mártir PRUCV2018-231-001 grant from the Universidad Católica de Valencia San Vicente Mártir PRUCV2017-231-001 grant from the Universidad Católica de Valencia San Vicente Mártir PRUCV2018-162-001 grant from the Universidad Católica de Valencia San Vicente Mártir Ciencias del Mar

Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014-2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern. A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.

Traffic safety around school locations is a topic of particular interest given the large number of vulnerable users, such as pedestrians or cyclists, that commute to them at certain times of the day. A dataset of traffic accidents recorded in Valencia (Spain) during 2014 and 2015 is analyzed in order to estimate the effects that school locations produce on traffic risk within their surroundings. The four typologies of school in this city according to the academic levels they offer (All-level, Preschool, Primary, Secondary) are distinguished and taken into consideration for the analysis. Two time windows comprising the starting time in the morning and the evening time once day school has ended are analyzed independently. Several statistical methods are used, including observed vs expected ratios, macroscopic conditional autoregressive modelling, logistic regression in the context of a case-control study design and risk modelling in relation to several school locations. The distances to each type of school and a set of environmental, traffic-related, demographic and socioeconomic covariates are employed for the analysis. The macroscopic modelling of accident counts and the modelling of risk as a function of the distance to each type of school serves to confirm that proximity to a school has an effect on the incidence of traffic accidents in particular time windows. Specifically, school types coexisting in Valencia show differential behaviour in this regard. In addition, several covariates have displayed a positive (bus stop density, complex intersections, main road length) and negative (land use entropy) association with accident counts in the time windows investigated. Finally, the definition of a case-control study design enabled us to observe some differences undetected by the macroscopic approaches that would require further research.