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Abstract The COVID-19 pandemic has been the largest pandemic of the past century, and various genetic factors have played a significant role in this context. This study aimed to analyze the frequency and association between specific SNPs rs3806268 (NLRP3), rs4925543 (NLRP3), rs12150220 (NLRP1), rs455060 (NLRC4), rs699 (AGT), rs1137101 (LEPR), and rs1801133 (MTHFR) and severe/critical outcomes in Brazilian patients with COVID-19. A total of 100 patients were included in the study, comprising 66 cases and 34 controls. DNA was extracted, sequenced, and genotyped via next-generation sequencing (NGS). For non-parametric data, the Mann-Whitney and Kruskal-Wallis tests were used. Fisher’s test and multivariate logistic regression, considering AIC and BIC criteria, were employed for risk analysis. Odds Ratios (OR) were calculated, with significance set at p

The use of cure-rate survival models has grown in recent years. Even so, proposals to perform the goodness of fit of these models have not been so frequent. However, residual analysis can be used to check the adequacy of a fitted regression model. In this context, we provide Cox–Snell residuals for Poisson-exponentiated Weibull regression with cure fraction. We developed several simulations under different scenarios for studying the distributions of these residuals. They were applied to a melanoma dataset for illustrative purposes.

The article introduces a new family by combining the Marshall and Olkin-G and Gamma-G classes. It has only two extra shape parameters and can be a better model than other existing classes of distributions. Simulations are performed to verify the consistency of the estimators. Its flexibility is shown by means of two real data sets.

The modified Kies flexible generalized family is developed. It can incorporate bimodal and bathtub shapes, and its properties are derived from those of the exponentiated-G class. Within this family, a special case is discussed in terms of its properties and a regression model. The parameters are estimated using the maximum likelihood method, and some simulations are carried out to check their consistency. The usefulness of the proposals is proven by means of three real data sets.

The ongoing transition within forest management towards more biodiversity-oriented practices, such as close-to-nature forestry and retention forestry, may benefit forest fauna such as forest-specialized ground beetles (Coleoptera: Carabidae). However, it remains unclear how forest carabids are jointly affected by these practices in Central European montane forests, which host particularly sensitive, range-restricted carabid species, and where biodiversity-oriented forestry is widely applied. Moreover, roe deer (Capreolus capreolus), the most common large herbivore in these forests, is intensively managed to reduce browsing pressure, but it is yet unknown how this may affect carabids, alongside the effect of silviculture. On 66 1-ha plots in the Black Forest region of Germany, we sampled carabids with pitfall traps, measured roe deer abundances using camera trapping, and measured several structural variables directly related to close-to-nature and retention practices, as well as variables describing microclimate and landscape-level forest cover. We found that the carabid assemblage was dominated by forest specialists, with little influence from fragmentation of the surrounding forest. Higher broadleaf share (and canopy cover for montane specialists) was correlated with higher carabid activity-density. Increasing stand maturity (and lying deadwood volume for montane specialists), was correlated with higher species richness. Plots with higher roe deer abundances showed higher carabid richness and activity-density. Assemblage composition changed along the altitudinal gradient, and both richness and activity-density increased with elevation. Thus, carabid communities, including montane specialists and several species of conservation interest, stand to benefit from close-to-nature and retention practices, if applied throughout the altitude range of montane forests. Forest carabids may additionally profit from maintaining higher roe deer abundances, but further research is needed to understand this causal link, as well as to weigh the costs and benefits of deer culling for forest biodiversity.

This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets.

We define and study the four-parameter logistic Burr XII distribution. It is obtained by inserting the three-parameter Burr XII distribution as the baseline in the logistic-X family and may be a useful alternative method to model income distribution and could be applied to other areas. We illustrate that the new distribution can have decreasing and upside-down-bathtub hazard functions and that its density function is an infinite linear combination of Burr XII densities. Some mathematical properties of the proposed model are determined, such as the quantile function, ordinary and incomplete moments, and generating function. We also obtain the maximum likelihood estimators of the model parameters and perform a Monte Carlo simulation study. Further, we present a parametric regression model based on the introduced distribution as an alternative to the location-scale regression model. The potentiality of the new distribution is illustrated by means of two applications to income data sets.

Microplastics (MPs) contribute to the overall pollution of water sources, affecting not only aquatic ecosystems but also water for human consumption (WHC). Currently, there needs to be a global consensus on safe levels of microplastics in WHC, which will allow regulatory efforts and risk assessments to be carried out. Therefore, this study aims to characterize MP particles in WHC of the Lisbon water supply system (LWSS) and compare two approaches to quantify these particles (length and width of the particles, and the area equivalent diameter (AED) of the particles). The quantification of MP particles was made via micro-FTIR (Fourier Transform Infrared Spectroscopy) on transmission mode after water filtration on 5 µm silicon filters. Thirty-eight WHC samples from the LWSS showed MPs up to 836 MPs/L, with an average value of 196 MPs/L. The most representative polymer was polyethylene (PE, 77.2%). The other eight polymers were also quantified. The length and width of MPs ranged between 84 µm and 41 µm, respectively. The AED of MPs ranged between 24 µm and 405 µm. The MP dimensions of both approaches can differ significantly.

We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the outer loop of the channel and substitution at position 428 from serine to proline (S428P), respectively. Heterologous expression of P632L and S428P into HEK cells produced no hERG current compared to the wild type (WT). Moreover, the co-transfection of WT and P632L yielded no hERG current; however, the co-transfection of WT and S428P yielded partial hERG current. Action potentials were prolonged in a complete or partial blockade of hERG current from computer simulations which was more severe in Purkinje than ventricular myocytes. Three dimensional simulations revealed a higher susceptibility to reentry in the presence of hERG current blockade. Our experimental findings suggest that both P632L and S428P mutations may impair the KCNH2 gene. The Purkinje cells exhibit a more severe phenotype than ventricular myocytes, and the hERG current blockade renders the ventricles an arrhythmogenic substrate from computer modeling.

The socio-economic development of a country as well as the state of its health, its communications network, cultural level and health care organisation together affect what is known as the health indicators. Portugal belongs to the group of UNICEF countries with the best indicators in what concerns infant mortality and death below the age of five. It is still, however, 25th and 28th respectively at world level. The authors review some of the factors which have most contributed towards the progress of the indicators over the last twenty years, namely, the role played by general practitioners, the creation of health centers, the development of a maternal-infant assistance programme and the activity of Central and District Hospitals. They analyse the recent maternal-infant programme and discuss the main short and mid-term problems in the perinatal and paediatric age groups. The socio-economic changes seen in Portuguese society, together with sociological changes and those observed in the field of pathology, call for a new approach in the organisation of care which includes the need for a new dialogue between the different health structures of a society, namely, through maintaining the functional coordinating maternal-infant units, the creation of the same units for paediatrics, new efforts by the general paediatrician, the community paediatrician and the sub-specialised paediatrician together with the general practitioners within a specific action programme of a Health Unit.

The Weibull is a popular distribution that models monotonous failure rate data. In this work, we introduce the four-parameter Weibull extended Weibull distribution that presents greater flexibility, thus modeling data with bathtub-shaped and unimodal failure rate. Some of its mathematical properties such as quantile function, linear representation and moments are provided. The maximum likelihood estimation is adopted to estimate its parameters, and the log-Weibull extended Weibull regression model is presented. In addition, some simulations are carried out to show the consistency of the estimators. We prove the greater flexibility and performance of this distribution and the regression model through applications to influenza and hepatitis data. The new models perform much better than some of their competitors.

Proportional hazards models and, in some situations, accelerated failure time models, are not suitable for analyzing data when the failure ratio between two individuals is not constant. We present a Weibull accelerated failure time model with covariables on the location and scale parameters. By considering the effects of covariables not only on the location parameter, but also on the scale, a regression should be able to adequately describe the difference between treatments. In addition, the deviance residuals adapted for data with the interval censored and the exact time of failure proved to be satisfactory to verify the fit of the model. This information favors the Weibull regression as an alternative to the proportional hazards models without masking the effect of the explanatory variables.

There are two major theories for setting up ecological communities, the Niche Theory and the Neutral Theory. Both seek to explain the main factors that form a community, which is a great challenge, since each community has its particularities and the environment has different ways to manifest. We devised a process-oriented study that sought to establish the role of environmental niche driven by coastal upwelling in the assembly of reef fish communities from exposed and sheltered environments a few kilometers apart, in the region of Arraial do Cabo (southwestern Atlantic). A multivariate hierarchical generalized linear mixed model fitted with Bayesian inference was applied to abundance and presence-absence data from visual census, together with environmental data from satellite and reanalysis. We found a stronger contribution of random effects to abundance variance with 24% for sites and 20.7% for sheltered vs. exposed locations, and weaker environmental effects with 7.1% for surface chlorophyll-a concentration (SCC) and 5.4% for sea surface temperature (SST). Environmental effects had a stronger contribution in the presence-absence model, with 20.1% for SCC and 14.6% for SST. The overall influence of the upwelling environment across all species was negative, e.g., Gymnothorax moringa and Canthigaster figueiredoi showing negative responses to SCC and Parablennius pilicornis and Malacoctenus delalandii to SST. The joint action of migration-niche mechanisms is inferred from the dominance of spatio-temporal structure, limited influence of life history traits and phylogeny, explaining around 95% of species niches in the abundance model. Our results bring new evidence for the importance of different filters for community assembly other than the environment, such as phylogenetic history and dispersal. We also discuss the balance between niche (environment) and neutral (stochasticity) processes for the assembly of reef fish communities in a tropical-subtropical transition zone.

Motivated by the recent popularization of the beta prime distribution, a more flexible generalization is presented to fit symmetrical or asymmetrical and bimodal data, and a non-monotonic failure rate. Thus, the Weibull-beta prime distribution is defined, and some of its structural properties are obtained. The parameters are estimated by maximum likelihood, and a new regression model is proposed. Some simulations reveal that the estimators are consistent, and applications to censored COVID-19 data show the adequacy of the models.

Detecting and locating high impedance faults (HIF) in overhead distribution networks (ODN) remains one of the biggest challenges for manufacturers and researchers due to the complexity of this phenomenon, where the electrical current magnitude is similar to that of the loads. To simulate HIF, the selection of the HIF model is important, because it has to correctly reproduce the characteristics of this phenomenon, so that it does not negatively influence the simulations results. Therefore, HIF models play a fundamental role in proposing solutions and validating the effectiveness of the proposed methods to detect and localize HIF in ODN. This paper presents a systematic review of HIF models. It is intended to facilitate the selection of the HIF model to be considered. The models are validated based on experimental data from medium voltage (MV) laboratories, specifically, recorded waveforms from two HIF tests conducted in an MV lab were analyzed and compared with three established HIF models. The efficacy of these models was assessed against MV lab test data to ensure a precise representation of both transient and steady-state conditions for fault conductance and current waveforms. The findings show that the two nonlinear resistor models better approximate the waveforms obtained in the experimental tests performed in this study.

Regression analysis can be appropriate to describe a nonlinear relationship between the response variable and the explanatory variables. This article describes the construction of a partially linear regression model with two systematic components based on the exponentiated odd log-logistic normal distribution. The parameters are estimated by the penalized maximum likelihood method. Simulations for some parameter settings and sample sizes empirically prove the accuracy of the estimators. The superiority of the proposed regression model over other regression models is shown by means of agronomic experimentation data. The predictive performance of the new model is compared with two machine learning techniques: decision trees and random forests. These methods achieved similar prediction performance, i.e., none stands out as a better predictor. In this sense, the objective of the research is to choose the best method. If the objective is only predictive, the decision tree can be used due to its simplicity. For inference purposes, the regression model is recommended, which can provide much more information regarding the relationship of the variables under study.

We study the Stacy-G family, which extends the gamma-G class and provides four of the most well-known forms of the hazard rate function: increasing, decreasing, bathtub, and inverted bathtub. We provide some of its structural properties. We estimate the parameters by maximum likelihood, and perform a simulation study to verify the asymptotic properties of the estimators for the Burr-XII baseline. We construct the log-Stacy-Burr XII regression for censored data. The usefulness of the new models is shown through applications to uncensored and censored real data.

A new two-parameter distribution is defined for modeling non-monotone lifetime data. It is constructed based on the logistic-G family and the exponential distribution. Its hazard rate properties are different than those of the well-known distributions. Some of its statistical properties are presented. An extended regression based on the logarithm of the random variable of the introduced distribution is defined. The new regression can provide better fits than other special regressions for analyzing real data. The performance of the maximum likelihood estimates is investigated from a simulation study. Three lifetime data sets are used to prove empirically the usefulness of the new models.

In this paper, we propose a new flexible survival model called the destructive zero-inflated power series cure rate model. This new model describes a realistic interpretation for the biological mechanism of the occurrence of the event of interest in studies of carcinogenesis in the presence of the competing latent causes. The maximum likelihood method is used for estimating the model parameters. For different sample sizes, various scenarios are simulated to evaluate the precision of the estimates. The usefulness of the new cure rate survival model is illustrated by means of a cutaneous melanoma data set.

This paper introduces and studies a unique probability distribution. The maximum likelihood estimation, the ordinary least squares, the weighted least squares, and the Anderson–Darling estimation methods all take into account a number of financial risk indicators, including the value-at-risk, tail-value-at-risk, tail variance, tail mean-variance, and mean excess loss function. These four approaches were used in a simulation study and an application to insurance claims data for the actuarial evaluation. The well-known Nikulin–Rao–Robson statistic is taken into consideration for distributional validation under the whole set of data. Three complete actual datasets and a simulation study are used to evaluate the Nikulin–Rao–Robson test statistic. An updated version of the Nikulin–Rao–Robson statistic is taken into consideration for censored distributional validation. Three censored actual datasets and a thorough simulation analysis are used to evaluate the novel Nikulin–Rao–Robson test statistic.

Abstract Manually annotating audio files for bird species richness estimation or machine learning validation is a time‐intensive task. A premium is placed on the subselection of files that will maximize the efficiency of unique additional species identified, to be used for future analyses. Using acoustic data collected in 17 plots, we created 60 subsetting scenarios across three gradients: intensity (minutes in an hour), day phase (dawn, morning, or both), and duration (number of days) for manual annotation. We analyzed the effect of these variables on observed bird species richness and assemblage composition at both the local and entire study area scale. For reference, results were also compared to richness and composition estimated by the traditional point count method. Intensity, day phase, and duration all affected observed richness in decreasing respective order. These variables also significantly affected observed assemblage composition (in the same order of effect size), but only the day phase produced compositional dissimilarity that was due to phenological traits of individual bird species, rather than differences in species richness. All annotation scenarios requiring equal sampling effort to point counts yielded higher species richness than the point count method. Our results show that a great majority of species can be obtained by annotating files at high sampling intensities (every 3 or 6 min) in the morning period (post‐dawn) over a duration of two days. Depending on a study's aim, different subsetting parameters will produce different assemblage compositions, potentially omitting rare or crepuscular species, species representing additional functional groups and natural history guilds, or species of higher conservation concern. We do not recommend one particular subsetting regime for all research objectives, but rather present multiple scenarios for researchers to understand how intensity, day phase, and duration interact to identify the best subsetting regime for one's particular research interests.

Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. These candidates are generated by following a set of probability rules. The set of rules is then evolved over a number of iterations to generate better candidates regarding some optimality criteria. This approach rivals that of generated distribution, obtained by adding parameters to existing probability distributions. There are two main advantages for this method. The first is that it is possible to explicitly control the complexity of the candidate functions, by specifying which mathematical functions and operators can be used and how lengthy the mathematical expression of the candidate can be. The second advantage is that this approach deals with model selection and estimation at the same time. The overall performance in both simulated and real data was very satisfying. For the real data applications, the PIPE algorithm obtained better likelihoods for the data when compared to existing models, but with remarkably simpler mathematical expressions.

Gap junctions (GJs), essential structures for cell-cell communication, are made of two hemichannels (commonly called connexons), one on each adjacent cell. Found in almost all cells, GJs play a pivotal role in many physiological and cellular processes, and have even been linked to the progression of diseases, such as cancer. Modulation of GJs is under investigation as a therapeutic strategy to kill tumor cells. Furthermore, GJs have also been studied for their key role in activating anti-cancer immunity and propagating radiation- and oxidative stress-induced cell death to neighboring cells, a process known as the bystander effect. While, gap junction (GJ)-based therapeutic strategies are being developed, one major challenge has been the paradoxical role of GJs in both tumor progression and suppression, based on GJ composition, cancer factors, and tumoral context. Therefore, understanding the mechanisms of action, regulation, and the dual characteristics of GJs in cancer is critical for developing effective therapeutics. In this review, we provide an overview of the current understanding of GJs structure, function, and paradoxical pro- and anti-tumoral role in cancer. We also discuss the treatment strategies to target these GJs properties for anti-cancer responses, via modulation of GJ function.

We use the Clayton and Frank copulas and the exponentiated odd log-logistic family to define a new flexible bivariate model to fit bimodal and asymmetry data. The copulas allow different distributions for the response variable, thus making analysis more suitable. We present some structural properties of the new model and describe a simulation study to show the consistency of the estimators. We construct a bivariate regression model based on the new family to fit oak lettuce plant data for different concentrations of silicon dioxide and organosilicon compounds. We check the response variables fresh weight and plant height together in order to verify the existing correlation between them. These variables exhibit a bimodal form, and the family used is able to model this behavior. Different marginal distributions are selected, which is an interesting point of the copula methodology. The variables have strong positive dependence, and the experiment is carried out comparing the control treatment with others leading to the following results: (i) the treatment 1-ethoxysilatrane (with concentrations 5 × 10−4 mL·L−1 and 10−3 mL·L−1) is not significant for the response variables; (ii) the treatment amorphous silicon dioxide (with concentrations 50 mg·L−1 and 100 mg·L−1) and the same treatment (with concentrations 5 × 10−3 mL·L−1 and 10−2 mL·L−1) are significant and have positive effects on both responses; (iii) the treatment amorphous silicon dioxide (with concentrations 200 mg·L−1 and 300 mg·L−1) are significant and have negative effects on the response variables. Overall, the proposed bivariate model is suitable for the current data and can be useful in other applications.

Hoechst 33342 (H33342) is a fluorescent probe that is commonly used to stain the DNA of living cells. To do so, it needs to interact with and permeate through cell membranes, despite its high overall charge at physiological pH values. In this work, we address the effect of pH in the association of H33342 with lipid bilayers using a combined experimental and computational approach. The partition of H33342 to 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid membranes was experimentally quantified using fluorescence spectroscopy and isothermal titration calorimetry (ITC) measurements. Quantum chemical calculations were performed to select the most stable isomer of H33342 for the overall charges 0, +1, and +2, expected to predominate across the 5 < pH < 10 range. The interaction of these isomers with POPC bilayers was then studied by both unrestrained and umbrella sampling molecular dynamics (MD) simulations. Both experimental results and computational free energy profiles indicate that the partition coefficient of H33342 displays a small variation over a wide pH range, not exceeding one order of magnitude. The enthalpy variation upon partition to the membrane suggests efficient hydrogen bonding between the probe and the lipid, namely, for the protonated +2 form, which was confirmed in the MD simulation studies. The relatively high lipophilicity obtained for the charged species contrasts with the decrease in their general hydrophobicity as estimated from octanol/water partition. This highlights the distinction between lipophilicity and hydrophobicity, as well as the importance of considering the association with lipid bilayers when predicting the affinity for biomembranes.

Abstract Background Species co-occurrences can have profound effects on the habitat use of species, and therefore habitat structure alone cannot fully explain observed abundances. To account for this aspect of community organization, we developed multi-species abundance models, incorporating the local effect of co-occurring and potentially associated species, alongside with environmental predictors, linked mainly to forest management intensity. We coupled it with a landscape-scale analysis to further examine the role of management intensity in modifying the habitat preferences in connection with the landscape context. Using empirical data from the Black Forest in southern Germany, we focused on the forest bird assemblage and in particular on the cavity-nesting and canopy-foraging guilds. We included in the analysis species that co-occur and for which evidence suggests association is likely. Results Our findings show that the local effect of species associations can mitigate the effects of management intensity on forest birds. We also found that bird species express wider habitat preferences in forests under higher management intensity, depending on the landscape context. Conclusions We suspect that species associations may facilitate the utilization of a broader range of environmental conditions under intensive forest management, which benefits some species over others. Networks of associations may be a relevant factor in the effectiveness of conservation-oriented forest management.

The Cardioid (C) distribution is one of the most important models for modeling circular data. Although some of its structural properties have been derived, this distribution is not appropriate for asymmetry and multimodal phenomena in the circle, and then extensions are required. There are various general methods that can be used to produce circular distributions. This paper proposes four extensions of the C distribution based on the beta, Kumaraswamy, gamma, and Marshall–Olkin generators. We obtain a unique linear representation of their densities and some mathematical properties. Inference procedures for the parameters are also investigated. We perform two applications on real data, where the new models are compared to the C distribution and one of its extensions.

Abstract We define two new flexible families of continuous distributions to fit real data by compoun-ding the Marshall–Olkin class and the power series distribution. These families are very competitive to the popular beta and Kumaraswamy generators. Their densities have linear representations of exponentiated densities. In fact, as the main properties of thirty five exponentiated distributions are well-known, we can easily obtain several properties of about three hundred fifty distributions using the references of this article and five special cases of the power series distribution. We provide a package implemented in R software that shows numerically the precision of one of the linear representations. This package is useful to calculate numerical values for some statistical measurements of the generated distributions. We estimate the parameters by maximum likelihood. We define a regression based on one of the two families. The usefulness of a generated distribution and the associated regression is proved empirically.

The article presents some aspects related to the COVID-19 pandemic in Brazil including public health, challenges facing healthcare workers and adverse impacts on the country’s economy. Its main contribution is the availability of two web applications for online monitoring of the evolution of the pandemic in Brazil and South America. The applications provide the possibility to download data in different formats, view interactive maps and graphs of the cumulative confirmed cases, deaths and lethality rates, in addition to presenting plots of moving averages for states and municipalities. The predictions about new cases and new deaths caused by COVID-19, in states and regions of Brazil, are also reported using GAMLSS models. The forecasts can be easily used by public managers for effective decision-making.

In olive trees, fluctuations in the onset of phenological stages have been reported due to weather conditions. The present study analyses the reproductive phenology of 17 olive cultivars grown in Elvas (Portugal) in 3 consecutive years (2012–2014). Through 2017–2022, the phenological observations continued with four cultivars. The phenological observations followed the BBCH scale. Over the course of the observations, the bud burst (stage 51) occurred gradually later; a few cultivars did not follow this trend in 2013. The flower cluster totally expanded phase (stage 55) was achieved gradually earlier, and the period between stages 51–55 was shortened, especially in 2014. Date of bud burst showed a negative correlation with minimum temperature (Tmin) of November–December, and, in ‘Arbequina’ and ‘Cobrançosa’, the interval stage 51–55 showed a negative correlation with both the Tmin of February and the Tmax of April, whereas in ‘Galega Vulgar’ and ‘Picual’ there was instead a positive correlation with the Tmin of March. These two seemed to be more responsive to early warm weather, whereas ‘Arbequina’ and ‘Cobrançosa’ were less sensitive. This investigation revealed that olive cultivars behaved differently under the same environmental conditions and, in some genotypes, the ecodormancy release may be linked to endogenous factors in a stronger way.

Creativity and innovation in culinary research have gained steady academic interest over the last decade. The scholastic interest in creative innovation ranges from its artistic value to culinary creations, gastronomic experiences, and food science and technology. Creative innovation is important for food enterprises to succeed in a highly competitive market. In the context of the New Nordic Cuisine, entrepreneurs and chefs are constantly challenged to bring something new to the dining table. In this context, the processes of creative innovation remain under researched, particularly in the use of seaweed. As such, using the example of seaweed, a relatively new food in the New Nordic Cuisine, the objective of this corpus based study was to explore creative innovation from a systems integral approach, in order to uncover salient themes that contribute the processes of creative innovation in culinary research, and bringing new foods to market. For a corpus driven study, we built a small corpora of interviews with chefs, and food entrepreneurs. We enquired after what inspired and motivated them when faced with a challenge of bringing a relatively new food to market, or in creating new dishes with new available food technologies. The results suggested that food technology plays a critical role in creative innovation, and the resulting new dishes that can be presented to customers. They also suggested that seaweed in the New Nordic Cuisine is an emerging food concept, and that it is embedded in a social and cultural history and familiarity of the Nordic people.

Theobroma subincanum Mart. popularly known as cupuí, belongs to the family Malvaceae. It is a wild fruit species native to the Amazon and is found in mainland forests, being characterized by its great alimentary and ecological importance. The cupuí presents genes that can be used for the croop breeding of commercial species of the genus, so that many studies have been carried to characterize genotypes through molecular markers, among which ISSRs (Inter Simple Sequence Repeats) have been widely used. In this context the objective of the study was to analyze the genetic diversity among cupuí genotypes, using molecular markers ISSR. Ten markers used amplified 42 fragments, being 35.71% polymorphic, and the genetic dissimilarity, estimated from the Jaccard coefficient, varied from 0.02 to 0.26, indicating the existence of genetic variability among the studied genotypes. The UPGMA grouping method allocated the twenty genotypes of cupuí analyzed in three groups, while the Tocher optimization method distributed the same in eight groups, thus confirming the existence of genetic diversity among the evaluated materials. The two methods of grouping revealed that the MN08, MN09 and MN10 individuals are the most divergent and therefore the most suitable for conservation and breeding programs of the species.

We obtain some properties of the Weibull Marshall–Olkin Lindley distribution. Its hazard rate function can be increasing, decreasing, bathtub-shaped, decreasing-increasing-decreasing and unimodal. We compare the performance of some methods to estimate its model parameters by means of extensive simulations. The potentiality of the new distribution to modeling real-life data is shown using two real data sets.