Proposing a Compartmental Model for Leprosy and Parameterizing Using Regional Incidence in Brazil

Hansen’s disease (HD), or leprosy, is still considered a public health risk in much of Brazil. Understanding the dynamics of the infection at a regional level can aid in identification of targets to improve control. A compartmental continuous-time model for leprosy dynamics was designed based on understanding of the biology of the infection. The transmission coefficients for the model and the rate of detection were fit for each region using Approximate Bayesian Computation applied to paucibacillary and multibacillary incidence data over the period of 2000 to 2010, and model fit was validated on incidence data from 2011 to 2012. Regional variation was noted in detection rate, with cases in the Midwest estimated to be infectious for 10 years prior to detection compared to 5 years for most other regions. Posterior predictions for the model estimated that elimination of leprosy as a public health risk would require, on average, 44–45 years in the three regions with the highest prevalence. The model is easily adaptable to other settings, and can be studied to determine the efficacy of improved case finding on leprosy control.     

Inequality of gender,age and disabilities due to leprosy and trends in a hyperendemic metropolis: Evidence from an eleven-year time series study in Central-West Brazil

The present study aimed to investigate the epidemiological situation of leprosy (Hansen’s Disease), in a hyperendemic metropolis in the Central-West region of Brazil. We studied trends over eleven years, both in the detection of the disease and in disabilities, analyzing disparities and/or differences regarding gender and age. This is an ecological time series study conducted in Cuiabá, capital of the state of Mato Grosso. The population consisted of patients diagnosed with leprosy between the years 2008 and 2018. The time series of leprosy cases was used, stratifying it according to gender (male and female), disability grade (G0D, G1D, G2D, and not evaluated) and age. The calendar adjustment technique was applied. For modeling the trends, the Seasonal-Trend decomposition procedure based on Loess (STL) was used. We identified 9.739 diagnosed cases, in which 58.37% were male and 87.55% aged between 15 and 59 years. Regarding detection according to gender, there was a decrease among women and an increase in men. The study shows an increasing trend in disabilities in both genders, which may be related to the delay in diagnosis. There was also an increasing number of cases that were not assessed for disability at the time of diagnosis, which denotes the quality of the services.     

Quantifying Short-Term Foraging Movements in a Marsupial Pest to Improve Targeted Lethal Control and Disease Surveillance

In New Zealand, the introduced marsupial brushtail possum (Trichosurus vulpecula) is a pest species subject to control measures, primarily to limit its ability to transmit bovine tuberculosis (TB) to livestock and for conservation protection. To better define parameters for targeted possum control and TB surveillance, we here applied a novel approach to analyzing GPS data obtained from 44 possums fitted with radio-tracking collars, producing estimates of the animals’ short-term nocturnal foraging patterns based on 1-, 3- or 5-nights’ contiguous data. Studies were conducted within two semi-arid montane regions of New Zealand’s South Island High Country: these regions support low-density possum populations (<2 possums/ha) in which the animals’ home ranges are on average larger than in high-density populations in forested habitat. Possum foraging range width (FRW) estimates increased with increasing monitoring periods, from 150-200m based on a single night’s movement data to 300-400m based on 5 nights’ data. The largest average FRW estimates were recorded in winter and spring, and the smallest in summer. The results suggest that traps or poison-bait stations (for lethal control) or monitoring devices (for TB surveillance), set for > 3 consecutive nights at 150m interval spacings, would likely place >95% of the possums in this type of habitat at risk of encountering these devices, year-round. Modelling control efficacy against operational expenditure, based on these estimations, identified the relative cost-effectiveness of various strategies that could be applied to a typical aerial poisoning operation, to reduce the ongoing TB vectorial risk that possums pose in the High Country regions. These habitat-specific findings are likely to be more relevant than the conventional pest control and monitoring methodologies developed for possums in their more typical forested habitat.     

A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations

We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: It delivers targeted illumination to specified regions of the animal’s body such as its head or tail; it automatically delivers stimuli triggered upon the animal’s behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal’s behavioral response to competing mechanosensory stimuli in the the anterior and posterior gentle touch receptor neurons. Responses to more than 43,418 stimulus events from a range of anterior–posterior intensity combinations were measured. The animal’s probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the anterior stimulation intensity. We also probed the animal’s response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over 9,700 stimulus events were delivered during turning onset at a rate of 9.2 events per worm hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many fold increases in throughput to better constrain quantitative models of sensorimotor processing.

This study resents a new targeted illumination method for the nematode Caenorhabditis elegans, allowing delivery of optogenetic stimulation to specific body parts of many animals at once, automatically triggered by the animals’ behavior.     

How People Use Social Information to Find out What to Want in the Paradigmatic Case of Inter-temporal Preferences

The weight with which a specific outcome feature contributes to preference quantifies a person’s ‘taste’ for that feature. However, far from being fixed personality characteristics, tastes are plastic. They tend to align, for example, with those of others even if such conformity is not rewarded. We hypothesised that people can be uncertain about their tastes. Personal tastes are therefore uncertain beliefs. People can thus learn about them by considering evidence, such as the preferences of relevant others, and then performing Bayesian updating. If a person’s choice variability reflects uncertainty, as in random-preference models, then a signature of Bayesian updating is that the degree of taste change should correlate with that person’s choice variability. Temporal discounting coefficients are an important example of taste–for patience. These coefficients quantify impulsivity, have good psychometric properties and can change upon observing others’ choices. We examined discounting preferences in a novel, large community study of 14–24 year olds. We assessed discounting behaviour, including decision variability, before and after participants observed another person’s choices. We found good evidence for taste uncertainty and for Bayesian taste updating. First, participants displayed decision variability which was better accounted for by a random-taste than by a response-noise model. Second, apparent taste shifts were well described by a Bayesian model taking into account taste uncertainty and the relevance of social information. Our findings have important neuroscientific, clinical and developmental significance.     

Shrinking Bouma’s window: How to model crowding in dense displays

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma’s law, only the target’s nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model’s outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.     

Connectotyping: Model Based Fingerprinting of the Functional Connectome

A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “connectotype”, or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model’s ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach.     

A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic

In recent years researchers have investigated a growing number of weighted heterogeneous networks, where connections are not merely binary entities, but are proportional to the intensity or capacity of the connections among the various elements. Different degree centrality measures have been proposed for this kind of networks. In this work we propose weighted degree and strength centrality measures (WDC and WSC). Using a reducing factor we correct classical centrality measures (CD) to account for tie weights distribution. The bigger the departure from equal weights distribution, the greater the reduction. These measures are applied to a real network of Italian livestock movements as an example. A simulation model has been developed to predict disease spread into Italian regions according to animal movements and animal population density. Model’s results, expressed as infected regions and number of times a region gets infected, were related to weighted and classical degree centrality measures. WDC and WSC were shown to be more efficient in predicting node’s risk and vulnerability. The proposed measures and their application in an animal network could be used to support surveillance and infection control strategy plans.     

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel’s model integration by combining independently developed models of the retina and lamina neuropils in the fly’s visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel’s ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel’s communication performance both over the number of interface ports exposed by an emulation’s constituent modules and the total number of modules comprised by an emulation.     

Judging the Difference between Attractiveness and Health: Does Exposure to Model Images Influence the Judgments Made by Men and Women?

Recent research has shown facial adiposity (apparent weight in the face) to be a significant predictor of both attractiveness and health, thus making it an important determinant of mate selection. Studies looking at the relationship between attractiveness and health have shown that individuals differentiate between the two by preferring a lower weight for attractiveness than for health in female faces. However, these studies have either been correlational studies, or have investigated weight perceived from only the face. These differences have been discussed with regard to sociocultural factors such as pressure from parents, peers and also media, which has been seen to have the highest influence. While exposure to media images has been shown to influence women’s own-body image, no study has yet directly tested the influence of these factors on people’s preferred weight in other women’s bodies. Here we examine how a short exposure to images of models influences men’s and women’s judgments of the most healthy looking and attractive BMI in Malaysian Chinese women’s bodies by comparing differences in preferences (for attractiveness and health) between groups exposed to images of models of varying attractiveness and body weight. Results indicated that participants preferred a lower weight for attractiveness than for health. Further, women’s but not men’s preferred BMI for attractiveness, but not health, was influenced by the type of media images to which they were exposed, suggesting that short term exposure to model images affect women’s perceptions of attractiveness but not health.     

A historical perspective on malaria control in Brazil

Malaria has always been an important public health problem in Brazil. The early history of Brazilian malaria and its control was powered by colonisation by Europeans and the forced relocation of Africans as slaves. Internal migration brought malaria to many regions in Brazil where, given suitableAnopheles mosquito vectors, it thrived. Almost from the start, officials recognised the problem malaria presented to economic development, but early control efforts were hampered by still developing public health control and ignorance of the underlying biology and ecology of malaria. Multiple regional and national malaria control efforts have been attempted with varying success. At present, the Amazon Basin accounts for 99% of Brazil’s reported malaria cases with regional increases in incidence often associated with large scale public works or migration. Here, we provide an exhaustive summary of primary literature in English, Spanish and Portuguese regarding Brazilian malaria control. Our goal was not to interpret the history of Brazilian malaria control from a particular political or theoretical perspective, but rather to provide a straightforward, chronological narrative of the events that have transpired in Brazil over the past 200 years and identify common themes.     

Occam's Razor in sensorimotor learning

A large number of recent studies suggest that the sensorimotor system uses probabilistic models to predict its environment and makes inferences about unobserved variables in line with Bayesian statistics. One of the important features of Bayesian statistics is Occam''s Razor—an inbuilt preference for simpler models when comparing competing models that explain some observed data equally well. Here, we test directly for Occam''s Razor in sensorimotor control. We designed a sensorimotor task in which participants had to draw lines through clouds of noisy samples of an unobserved curve generated by one of two possible probabilistic models—a simple model with a large length scale, leading to smooth curves, and a complex model with a short length scale, leading to more wiggly curves. In training trials, participants were informed about the model that generated the stimulus so that they could learn the statistics of each model. In probe trials, participants were then exposed to ambiguous stimuli. In probe trials where the ambiguous stimulus could be fitted equally well by both models, we found that participants showed a clear preference for the simpler model. Moreover, we found that participants’ choice behaviour was quantitatively consistent with Bayesian Occam''s Razor. We also show that participants’ drawn trajectories were similar to samples from the Bayesian predictive distribution over trajectories and significantly different from two non-probabilistic heuristics. In two control experiments, we show that the preference of the simpler model cannot be simply explained by a difference in physical effort or by a preference for curve smoothness. Our results suggest that Occam''s Razor is a general behavioural principle already present during sensorimotor processing.     

Unraveling the Gordian knot: genetics and the troubled road to effective therapeutics for Alzheimer’s disease

In the late 20th century, identification of the major protein components of amyloid plaques and neurofibrillary tangles provided a window into the molecular pathology of Alzheimer’s disease, ushering in an era of optimism that targeted therapeutics would soon follow. The amyloid-cascade hypothesis took hold very early, supported by discoveries that dominant mutations in APP, PSEN1, and PSEN2 cause the very rare, early-onset, familial forms of the disease. However, in the past decade, a stunning series of failed Phase-3 clinical trials, testing anti-amyloid antibodies or processing-enzyme inhibitors, prompts the question, What went wrong? The FDA’s recent controversial approval of aducanumab, despite widespread concerns about efficacy and safety, only amplifies the question. The assumption that common, late-onset Alzheimer’s is a milder form of familial disease was not adequately questioned. The differential timing of discoveries, including blood–brain–barrier-penetrant tracers for imaging of plaques and tangles, made it easy to focus on amyloid. Furthermore, the neuropathology community initially implemented Alzheimer’s diagnostic criteria based on plaques only. The discovery that MAPT mutations cause frontotemporal dementia with tauopathy made it even easier to overlook the tangles in Alzheimer’s. Many important findings were simply ignored. The accepted mouse models did not predict the human clinical trials data. Given this lack of pharmacological validity, input from geneticists in collaboration with neuroscientists is needed to establish criteria for valid models of Alzheimer’s disease. More generally, scientists using genetic model organisms as whole-animal bioassays can contribute to building the pathogenesis network map of Alzheimer’s disease.     

Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang,China: A Spatial Analysis

Objectives

Xinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.

Methods

Numbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.

Results

Incidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin’s Local Moran’s I identified the “hotspots” which were consistently located in the southwest regions composed of 20 to 28 districts, and the “coldspots” which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of “hotspots” and “coldspots” with different intensity; 30 county/districts clustered as “hotspots”, while 47 county/districts clustered as “coldspots”. OLS regression model included the “proportion of minorities” and the “per capita GDP” as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR² = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting “proportion of minorities” to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R² = 0.68, AIC = 871), which revealed that “proportion of minorities” was a strong predictor in the south central regions while “per capita GDP” was a strong predictor for the southwest regions.

Conclusion

The SS+TB incidence of Xinjiang had a decreasing trend during 2005–2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters—the “hotspots”, which were consistently located in the southwest regions, and the “coldspots”, which were consistently located in the north central regions. The exploration of socio-demographic predictors identified the “proportion of minorities” and the “per capita GDP” as predictors and may help to guide TB control programs and targeting intervention.     

A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti

Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals’ refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80–1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3–5.9). The hierarchical model also performed better than the commonly used Fisher-Ford’s method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.     

Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation

A mosaic of intact native and human-modified vegetation use can provide important habitat for top predators such as the puma (Puma concolor), avoiding negative effects on other species and ecological processes due to cascade trophic interactions. This study investigates the effects of restoration scenarios on the puma’s habitat suitability in the most developed Brazilian region (São Paulo State). Species Distribution Models incorporating restoration scenarios were developed using the species’ occurrence information to (1) map habitat suitability of pumas in São Paulo State, Southeast, Brazil; (2) test the relative contribution of environmental variables ecologically relevant to the species habitat suitability and (3) project the predicted habitat suitability to future native vegetation restoration scenarios. The Maximum Entropy algorithm was used (Test AUC of 0.84 ± 0.0228) based on seven environmental non-correlated variables and non-autocorrelated presence-only records (n = 342). The percentage of native vegetation (positive influence), elevation (positive influence) and density of roads (negative influence) were considered the most important environmental variables to the model. Model projections to restoration scenarios reflected the high positive relationship between pumas and native vegetation. These projections identified new high suitability areas for pumas (probability of presence >0.5) in highly deforested regions. High suitability areas were increased from 5.3% to 8.5% of the total State extension when the landscapes were restored for ≥ the minimum native vegetation cover rule (20%) established by the Brazilian Forest Code in private lands. This study highlights the importance of a landscape planning approach to improve the conservation outlook for pumas and other species, including not only the establishment and management of protected areas, but also the habitat restoration on private lands. Importantly, the results may inform environmental policies and land use planning in São Paulo State, Brazil.     

Components of Attention in Grapheme-Color Synesthesia: A Modeling Approach

Grapheme-color synesthesia is a condition where the perception of graphemes consistently and automatically evokes an experience of non-physical color. Many have studied how synesthesia affects the processing of achromatic graphemes, but less is known about the synesthetic processing of physically colored graphemes. Here, we investigated how the visual processing of colored letters is affected by the congruence or incongruence of synesthetic grapheme-color associations. We briefly presented graphemes (10–150 ms) to 9 grapheme-color synesthetes and to 9 control observers. Their task was to report as many letters (targets) as possible, while ignoring digit (distractors). Graphemes were either congruently or incongruently colored with the synesthetes’ reported grapheme-color association. A mathematical model, based on Bundesen’s (1990) Theory of Visual Attention (TVA), was fitted to each observer’s data, allowing us to estimate discrete components of visual attention. The models suggested that the synesthetes processed congruent letters faster than incongruent ones, and that they were able to retain more congruent letters in visual short-term memory, while the control group’s model parameters were not significantly affected by congruence. The increase in processing speed, when synesthetes process congruent letters, suggests that synesthesia affects the processing of letters at a perceptual level. To account for the benefit in processing speed, we propose that synesthetic associations become integrated into the categories of graphemes, and that letter colors are considered as evidence for making certain perceptual categorizations in the visual system. We also propose that enhanced visual short-term memory capacity for congruently colored graphemes can be explained by the synesthetes’ expertise regarding their specific grapheme-color associations.     

The impact of climate suitability,urbanisation, and connectivity on the expansion of dengue in 21st century Brazil

Dengue is hyperendemic in Brazil, with outbreaks affecting all regions. Previous studies identified geographical barriers to dengue transmission in Brazil, beyond which certain areas, such as South Brazil and the Amazon rainforest, were relatively protected from outbreaks. Recent data shows these barriers are being eroded. In this study, we explore the drivers of this expansion and identify the current limits to the dengue transmission zone. We used a spatio-temporal additive model to explore the associations between dengue outbreaks and temperature suitability, urbanisation, and connectivity to the Brazilian urban network. The model was applied to a binary outbreak indicator, assuming the official threshold value of 300 cases per 100,000 residents, for Brazil’s municipalities between 2001 and 2020. We found a nonlinear relationship between higher levels of connectivity to the Brazilian urban network and the odds of an outbreak, with lower odds in metropoles compared to regional capitals. The number of months per year with suitable temperature conditions for Aedes mosquitoes was positively associated with the dengue outbreak occurrence. Temperature suitability explained most interannual and spatial variation in South Brazil, confirming this geographical barrier is influenced by lower seasonal temperatures. Municipalities that had experienced an outbreak previously had double the odds of subsequent outbreaks. We identified geographical barriers to dengue transmission in South Brazil, western Amazon, and along the northern coast of Brazil. Although a southern barrier still exists, it has shifted south, and the Amazon no longer has a clear boundary. Few areas of Brazil remain protected from dengue outbreaks. Communities living on the edge of previous barriers are particularly susceptible to future outbreaks as they lack immunity. Control strategies should target regions at risk of future outbreaks as well as those currently within the dengue transmission zone.