Seasonal occurrence and individual variability of bull sharks, Carcharhinus leucas, in a marine reserve of the southwestern Gulf of California.

Background: Studying how the bull sharks aggregate and how they can be driven by life history traits such as reproduction, prey availability, predator avoidance and social interaction in a National Park such as Cabo Pulmo, is key to understand and protect the species. Methods: The occurrence variability of 32 bull sharks tracked with passive acoustic telemetry were investigated via a hierarchical logistic regression model, with inference conducted in a Bayesian framework, comparing sex, and their response to temperature and chlorophyll. Results: Based on the fitted model, occurrence probability varied by sex and length. Juvenile females had the highest values, whereas adult males the lowest. A strong seasonality or day of the year was recorded, where sharks were generally absent during September-November. However, some sharks did not show the common pattern, being detected just for a short period. This is one of the first studies where the Bayesian framework is used to study passive acoustic telemetry proving the potential to be used in further studies.

Mesoscale activity drives the habitat suitability of yellowfin tuna in the Gulf of Mexico.

Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries' catch data spanning 2012-2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.

Feline sporotrichosis: social vulnerability and prioritization of geographic areas in Guarulhos, SP, Brazil / Esporotricose felina: vulnerabilidade social e priorização de áreas geográficas em Guarulhos, SP, Brasil

Over the past two decades, many Brazilian cities have been reporting an increasing incidence and spread of feline sporotrichosis. The disease is neglected, and little is known about the causal processes underlying its epidemic occurrence. This study characterized the spatiotemporal dynamics of feline sporotrichosis in Guarulhos. Moreover, we proposed and tested a causal explanation for its occurrence and zoonotic transmission, giving a key role to social vulnerability. A direct acyclic graph represented the causal explanation, while Bayesian spatial models supported its test as well as the attribution of a risk-based priority index to the census tracts of the city. Between 2011 and 2017, the disease grew exponentially and the spatial spread increased. The model findings showed a dose-response pattern between an index of social vulnerability and the incidence of feline sporotrichosis. This pattern was not strictly monotonic, so some census tracts received a higher priority index than others with higher vulnerability. According to our causal explanation, there will not be effective prevention of feline and zoonotic sporotrichosis as long as social inequities continue imposing precarious livelihoods.(AU)

Nas últimas duas décadas, diversas cidades brasileiras têm relatado um aumento na incidência esporotricose felina e sua disseminação. A doença é negligenciada e pouco se sabe sobre os processos causais que estão envolvidos na sua ocorrência epidêmica. Neste estudo, foi caracterizada a dinâmica espaço-temporal da esporotricose felina em Guarulhos. Além disso, é proposta e testada uma explicação causal para sua ocorrência e transmissão zoonótica, atribuindo um papel fundamental à vulnerabilidade social. Um grafo acíclico direcionado representou a explicação causal, enquanto modelos espaciais Bayesianos foram usados para testá-la e para atribuir um índice de prioridade baseado em risco aos setores censitários da cidade. Entre 2011 e 2017, a doença cresceu exponencialmente e a sua disseminação espacial aumentou. Os resultados do modelo mostraram um padrão de dose-resposta entre um índice de vulnerabilidade social e a incidência de esporotricose felina. Esse padrão não foi estritamente monotônico, já que alguns setores censitários receberam um índice de prioridade mais alto do que outros com maior vulnerabilidade. Segundo nossa explicação causal, não haverá prevenção efetiva da esporotricose felina e zoonótica enquanto as iniquidades sociais continuarem impondo condições de vida precárias.(AU)

Trophic interactions will expand geographically but be less intense as oceans warm.

Interactions among species are likely to change geographically due to climate-driven species range shifts and in intensity due to physiological responses to increasing temperatures. Marine ectotherms experience temperatures closer to their upper thermal limits due to the paucity of temporary thermal refugia compared to those available to terrestrial organisms. Thermal limits of marine ectotherms also vary among species and trophic levels, making their trophic interactions more prone to changes as oceans warm. We assessed how temperature affects reef fish trophic interactions in the Western Atlantic and modeled projections of changes in fish occurrence, biomass, and feeding intensity across latitudes due to climate change. Under ocean warming, tropical reefs will experience diminished trophic interactions, particularly herbivory and invertivory, potentially reinforcing algal dominance in this region. Tropicalization events are more likely to occur in the northern hemisphere, where feeding by tropical herbivores is predicted to expand from the northern Caribbean to extratropical reefs. Conversely, feeding by omnivores is predicted to decrease in this area with minor increases in the Caribbean and southern Brazil. Feeding by invertivores declines across all latitudes in future predictions, jeopardizing a critical trophic link. Most changes are predicted to occur by 2050 and can significantly affect ecosystem functioning, causing dominance shifts and the rise of novel ecosystems.

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

BACKGROUND: This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. RESULTS: Mainly indicated that variables within the Bayesian network are part of each patient's state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. CONCLUSION: If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.

Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx.

High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available.

SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus.

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015-2016.

We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015-December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.

Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records.

High sampling variability complicates estimation of demographic rates in small areas. In addition, many countries have imperfect vital registration systems, with coverage quality that varies significantly between regions. We develop a Bayesian regression model for small-area mortality schedules that simultaneously addresses the problems of small local samples and underreporting of deaths. We combine a relational model for mortality schedules with probabilistic prior information on death registration coverage derived from demographic estimation techniques, such as Death Distribution Methods, and from field audits by public health experts. We test the model on small-area data from Brazil. Incorporating external estimates of vital registration coverage though priors improves small-area mortality estimates by accounting for underregistration and automatically producing measures of uncertainty. Bayesian estimates show that when mortality levels in small areas are compared, noise often dominates signal. Differences in local point estimates of life expectancy are often small relative to uncertainty, even for relatively large areas in a populous country like Brazil.

Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models.

Wildlife-vehicle collisions (WVC) represent a major threat for wildlife and understanding how WVC spatial patterns relate to surrounding land cover can provide valuable information for deciding where to implement mitigation measures. However, these relations may be heavily biased as many casualties are undetected in roadkill surveys, e.g. due to scavenger activity, which may ultimately jeopardize conservation actions. We suggest using occupancy models to overcome imperfect detection issues, assuming that 'occupancy' represents the preference for crossing the road in a given site, i.e. is a proxy for the roadkill risk; and that the 'detectability' is the joint probability of an animal being hit in the crossing site and its carcass being detected afterwards. Our main objective was to assess the roadkill risk along roads while accounting for imperfect detection issues and relate it to land cover information. We conducted roadkill surveys over 114 km in nine different roads, biweekly, for five years (total of 484 surveys), and developed a Bayesian hierarchical occupancy model to assess the roadkill risk for the six most road-killed taxa for each road section and season (WET and DRY). Overall, we estimated a higher roadkill risk in road sections surrounded by agriculture, open habitats; and a higher detectability within the 4-lane road sections. Our modeling framework has a great potential to overcome the limitations related to imperfect detection when assessing the roadkill risk and may become an important tool to predict which road sections have a higher mortality risk.

Identifying fish diversity hot-spots in data-poor situations.

One of the more challenging tasks in Marine Spatial Planning (MSP) is identifying critical areas for management and conservation of fish stocks. However, this objective is difficult to achieve in data-poor situations with different sources of uncertainty. In the present study we propose a combination of hierarchical Bayesian spatial models and remotely sensed estimates of environmental variables to be used as flexible and reliable statistical tools to identify and map fish species richness and abundance hot-spots. Results show higher species aggregates in areas with higher sea floor rugosity and habitat complexity, and identify clear richness hot-spots. Our findings identify sensitive habitats through essential and easy-to-use interpretation tools, such as predictive maps, which can contribute to improving management and operability of the studied data-poor situations.

Estimation of temporal gait parameters using Bayesian models on acceleration signals.

The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results.

Modeling sensitive parrotfish (Labridae: Scarini) habitats along the Brazilian coast.

In coral reef environments, there is an increasing concern over parrotfish (Labridae: Scarini) due to their rising exploitation by commercial small-scale fisheries, which is leading to significant changes in the reefs' community structure. Three species, Scarus trispinosus (Valenciennes, 1840), Sparisoma frondosum (Agassiz, 1831) and Sparisoma axillare (Steindachner, 1878), currently labeled as threatened, have been intensively targeted in Brazil, mostly on the northeastern coast. Despite their economic importance, ecological interest and worrisome conservation status, not much is known about which variables determine their occurrence. In this study, we adopted a hierarchical Bayesian spatial-temporal approach to map the distribution of these three species along the Brazilian coast, using landing data from three different gears (gillnets, spear guns, and handlines) and environmental variables (bathymetry, shore distance, seabed slope, Sea Surface Temperature and Net Primary Productivity). Our results identify sensitive habitats for parrotfish along the Brazilian coast that would be more suitable to the implementation of spatial-temporal closure measures, which along with the social component fishers could benefit the management and conservation of these species.

Toward an experimental account of argumentation: the case of the slippery slope and the ad hominem arguments.

Argumentation is a crucial component of our lives. Although in the absence of rational debate our legal, political, and scientific systems would not be possible, there is still no integrated area of research on the psychology of argumentation. Furthermore, classical theories of argumentation are normative (i.e., the acceptability of an argument is determined by a set of norms or logical rules), which sometimes creates a dissociation between the theories and people's behavior. We think the current challenge for psychology is to bring together the cognitive and normative accounts of argumentation. In this article, we exemplify this point by analyzing two cases of argumentative structures experimentally studied in the context of cognitive psychology. Specifically, we focus on the slippery slope argument and the ad hominem argument under the frameworks of Bayesian and pragma-dialectics approaches, respectively. We think employing more descriptive and experimental accounts of argumentation would help Psychology to bring closer the cognitive and normative accounts of argumentation with the final goal of establishing an integrated area of research on the psychology of argumentation.

A epidemia de aids no Estado de São Paulo: uma aplicação do modelo espaço-temporal bayesiano completo / The aids epidemic in the State of São Paulo: application of the full Bayesian space-time model

O Estado de São Paulo, por compreender aproximadamente 40 por cento dos casos de aids notificados no Brasil, oferece uma situação propícia para análise espaço-temporal, visando melhor compreensão da disseminação do HIV/aids. Utilizando os casos de aids notificados ao Ministério da Saúde nos anos de 1990 a 2004 para pessoas com idade igual ou superior a 15 anos, tendo como fonte de informação o Sistema de Informação de Agravos e Notificação, Ministério da Saúde, foram estimados os riscos relativos de aids segundo sexo para períodos de 3 anos utilizando modelos bayesianos completos. Os modelos utilizados se mostraram adequados para explicar o processo de disseminação da aids no Estado de São Paulo e evidenciam os processos de feminização e interiorização da doença, além de sugerir que os municípios atualmente mais atingidos se encontram em regiões de pólos de crescimento econômico e possuem população inferior a 50.000 habitantes.

The State of São Paulo accounts for approximately 40 percent of the AIDS cases notified in Brazil and provides a suitable opportunity for space-time analysis aimed at better understanding of the dissemination of HIV/AIDS. Using the AIDS cases notified to the Ministry of Health between 1990 and 2004, among individuals aged 15 years or over, and the Ministry of Health's information system for disease notification (Sistema de Informação de Agravos e Notificação, SINAN) as the information source, the relative risks of AIDS over three-year periods were estimated using full Bayesian models, for each gender. The models used were shown to be adequate for explaining the process of AIDS dissemination in the State of São Paulo and demonstrated the growth among females and in small-sized municipalities. They also suggested that the municipalities currently most affected are in regions of economic growth and have populations of less than 50,000 inhabitants.