An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects.

It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.

A queueing Network approach for capacity planning and patient Scheduling: A case study for the COVID-19 vaccination process in Colombia.

This paper considers the problem of patient scheduling and capacity planning for the vaccination process during the COVID-19 pandemic. The proposed solution is based on a non-linear mathematical modeling approach representing the dynamics of an open Jackson Network and a Generalized Network. To test these models, we proposed three objective functions and analyzed different configurations of the process corresponding to various levels of the models' parameters as well as the conditions present in the case study. To assess the computational performance of the models, we also experimented with larger instances in terms of number of steps or stations used and number of patients scheduled. The computational results show how parameters such as the minimum percentage of patients served, the maximum occupation allowed per station and the objective functions used have an impact on the configuration of the process. The proposed approach can support the decision-making process in vaccination centers to efficiently assign human and material resources to maximize the number of patients vaccinated while ensuring reasonable waiting times, number of patients in queue and servers' utilization rates, which in turn are key to avoid overcrowding and other negative conditions in the system that could increase the risk of infections.

A multilayer network model of Covid-19: Implications in public health policy in Costa Rica.

Successful partnerships between researchers, experts, and public health authorities have been critical to navigate the challenges of the Covid-19 pandemic worldwide. In this collaboration, mathematical models have played a decisive role in informing public policy, with findings effectively translated into public health measures that have shaped the pandemic in Costa Rica. As a result of interdisciplinary and cross-institutional collaboration, we constructed a multilayer network model that incorporates a diverse contact structure for each individual. In July 2020, we used this model to test the effect of lifting restrictions on population mobility after a so-called "epidemiological fence" imposed to contain the country's first big wave of cases. Later, in August 2020, we used it to predict the effects of an open and close strategy (the Hammer and Dance). Scenarios constructed in July 2020 showed that lifting restrictions on population mobility after less than three weeks of epidemiological fence would produce a sharp increase in cases. Results from scenarios in August 2020 indicated that the Hammer and Dance strategy would only work with 50% of the population adhering to mobility restrictions. The development, evolution, and applications of a multilayer network model of Covid-19 in Costa Rica has guided decision-makers to anticipate implementing sanitary measures and contributed to gain valuable time to increase hospital capacity.

A cloud-based platform to predict wind pressure coefficients on buildings.

Natural ventilation (NV) is a key passive strategy to design energy-efficient buildings and improve indoor air quality. Therefore, accurate modeling of the NV effects is a basic requirement to include this technique during the building design process. However, there is an important lack of wind pressure coefficients (C p ) data, essential input parameters for NV models. Besides this, there are no simple but still reliable tools to predict C p data on buildings with arbitrary shapes and surrounding conditions, which means a significant limitation to NV modeling in real applications. For this reason, the present contribution proposes a novel cloud-based platform to predict wind pressure coefficients on buildings. The platform comprises a set of tools for performing fully unattended computational fluid dynamics (CFD) simulations of the atmospheric boundary layer and getting reliable C p data for actual scenarios. CFD-expert decisions throughout the entire workflow are implemented to automatize the generation of the computational domain, the meshing procedure, the solution stage, and the post-processing of the results. To evaluate the performance of the platform, an exhaustive validation against wind tunnel experimental data is carried out for a wide range of case studies. These include buildings with openings, balconies, irregular floor-plans, and surrounding urban environments. The C p results are in close agreement with experimental data, reducing 60%-77% the prediction error on the openings regarding the EnergyPlus software. The platform introduced shows being a reliable and practical C p data source for NV modeling in real building design scenarios. Electronic Supplementary Material ESM: The appendix is available in the online version of this article at 10.1007/s12273-021-0881-9.

Comparison of an artificial neural network and Gompertz model for predicting the dynamics of deaths from COVID-19 in México.

The present work is focused on modeling and predicting the cumulative number of deaths from COVID-19 in México by comparing an artificial neural network (ANN) with a Gompertz model applying multiple optimization algorithms for the estimation of coefficients and parameters, respectively. For the modeling process, the data published by the daily technical report COVID-19 in Mexico from March 19th to September 30th were used. The data published in the month of October were included to carry out the prediction. The results show a satisfactory comparison between the real data and those obtained by both models with a R2 > 0.999. The Levenberg-Marquardt and BFGS quasi-Newton optimization algorithm were favorable for fitting the coefficients during learning in the ANN model due to their fast and precision, respectively. On the other hand, the Nelder-Mead simplex algorithm fitted the parameters of the Gompertz model faster by minimizing the sum of squares. Therefore, the ANN model better fits the real data using ten coefficients. However, the Gompertz model using three parameters converges in less computational time. In the prediction, the inverse ANN model was solved by a genetic algorithm obtaining the best precision with a maximum error of 2.22% per day, as opposed to the 5.48% of the Gompertz model with respect to the real data reported from November 1st to 15th. Finally, according to the coefficients and parameters obtained from both models with recent data, a total of 109,724 cumulative deaths for the inverse ANN model and 100,482 cumulative deaths for the Gompertz model were predicted for the end of 2020.

Migration and Democratization in Brazil: The Case of Electoral Participation and Competition.

Deepening democratization in Brazil has coincided with sustained flows of domestic migration, which raises an important question of whether migration deepens or depresses democratic development in migrant-sending regions. Whereas earlier perspectives have viewed migration as a political "brain drain," we contend that out-migration can generate resources that promote democratic processes back home. We investigate the role of migration in two aspects of democratization: electoral participation and competition. The analyses are based on spatial panel data models of mayoral election results across all municipalities between 1996 and 2012. The results show that migration increases electoral participation and competition in migrant-sending localities in Brazil. This study also identifies the sociopolitical context that conditions the impact of migration: the effect is most often present in the context of rural-urban migration and is more pronounced in sending localities with less democratic political structures. Moreover, using spatial network models, we find evidence for the transmission of political remittances from migration destination municipalities to origin municipalities. The present study extends the research on the migration-development nexus to the political arena, thus demonstrating the value of integrating demographic processes into explanations of political change.

Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models.

This work presents the modeling and prediction of cases of COVID-19 infection in Mexico through mathematical and computational models using only the confirmed cases provided by the daily technical report COVID-19 MEXICO until May 8th. The mathematical models: Gompertz and Logistic, as well as the computational model: Artificial Neural Network were applied to carry out the modeling of the number of cases of COVID-19 infection from February 27th to May 8th. The results show a good fit between the observed data and those obtained by the Gompertz, Logistic and Artificial Neural Networks models with an R2 of 0.9998, 0.9996, 0.9999, respectively. The same mathematical models and inverse Artificial Neural Network were applied to predict the number of cases of COVID-19 infection from May 9th to 16th in order to analyze tendencies and extrapolate the projection until the end of the epidemic. The Gompertz model predicts a total of 47,576 cases, the Logistic model a total of 42,131 cases, and the inverse artificial neural network model a total of 44,245 as of May 16th. Finally, to predict the total number of COVID-19 infected until the end of the epidemic, the Gompertz, Logistic and inverse Artificial Neural Network model were used, predicting 469,917, 59,470 and 70,714 cases, respectively.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity.

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Phylogenetic, molecular evolution and structural analyses of the WFDC1/prostate stromal protein 20 (ps20).

The WFDC1 gene is frequently down-regulated or lost in prostate cancer, and the encoded protein, ps20, has been implicated in epithelial cell behaviour and angiogenesis. However, ps20 remains largely uncharacterised with respect to its structure and interacting partners. This study characterised the evolution, functionality and structural characteristics of WFDC1/ps20 using phylogenetic reconstruction and other computational approaches. Bayesian phylogenetic analyses suggested that ps20 appeared in a common ancestor of deuterostomes-protostomes. The rate of evolutionary change within the coding regions of vertebrate WFDC1 genes and the synteny conservation in mammals differed from that of other vertebrate clades, indicating a possible functional diversity of ps20 homologues. A gene set enrichment analysis of the genes around WFDC1 (conserved synteny) showed functional relationships between the WFDC1, CDH13, CRISPLD2, IRF8 and TFPI2 genes. The molecular evolution of ps20 has been driven by purifying selection, particularly in the segments corresponding to exons 3 and 4, which encode the most conserved regions of the protein. A co-evolution analysis showed that residues within these regions co-vary with each other during the evolution of ps20. These results show that the regions corresponding to exons 3 and 4 are ps20-specific structure-function modules. Homology modelling of the exon 2-encoded polypeptide and subsequent dynamics calculus using a Gaussian network model showed that residues with high conformational flexibility are part of a loop region involved in protein-protein recognition, given the similarity with other serine protease inhibitors. Residues C96, R94, L105, and C66 are critical for the integrity and functionality of this ps20 region.

Some Surprising Implications of NMR-directed Simulations of Substrate Recognition and Binding by Cytochrome P450cam (CYP101A1).

Cytochrome P450cam (CYP101A1) catalyzes the stereospecific 5-exo hydroxylation of d-camphor by molecular oxygen. Previously, residual dipolar couplings measured for backbone amide 1H-15N correlations in both substrate-free and bound forms of CYP101A1 were used as restraints in soft annealing molecular dynamic simulations in order to identify average conformations of the enzyme with and without substrate bound. Multiple substrate-dependent conformational changes remote from the enzyme active site were identified, and site-directed mutagenesis and activity assays confirmed the importance of these changes in substrate recognition. The current work makes use of perturbation response scanning (PRS) and umbrella sampling molecular dynamic of the residual dipolar coupling-derived CYP101A1 structures to probe the roles of remote structural features in enforcing the regio- and stereospecific nature of the hydroxylation reaction catalyzed by CYP101A1. An improper dihedral angle Ψ was defined and used to maintain substrate orientation in the CYP101A1 active site, and it was observed that different values of Ψ result in different PRS response maps. Umbrella sampling methods show that the free energy of the system is sensitive to Ψ, and bound substrate forms an important mechanical link in the transmission of mechanical coupling through the enzyme structure. Finally, a qualitative approach to interpreting PRS maps in terms of the roles of secondary structural features is proposed.

A Network Model to Explore the Effect of the Micro-environment on Endothelial Cell Behavior during Angiogenesis.

Angiogenesis is an important adaptation mechanism of the blood vessels to the changing requirements of the body during development, aging, and wound healing. Angiogenesis allows existing blood vessels to form new connections or to reabsorb existing ones. Blood vessels are composed of a layer of endothelial cells (ECs) covered by one or more layers of mural cells (smooth muscle cells or pericytes). We constructed a computational Boolean model of the molecular regulatory network involved in the control of angiogenesis. Our model includes the ANG/TIE, HIF, AMPK/mTOR, VEGF, IGF, FGF, PLCγ/Calcium, PI3K/AKT, NO, NOTCH, and WNT signaling pathways, as well as the mechanosensory components of the cytoskeleton. The dynamical behavior of our model recovers the patterns of molecular activation observed in Phalanx, Tip, and Stalk ECs. Furthermore, our model is able to describe the modulation of EC behavior due to extracellular micro-environments, as well as the effect due to loss- and gain-of-function mutations. These properties make our model a suitable platform for the understanding of the molecular mechanisms underlying some pathologies. For example, it is possible to follow the changes in the activation patterns caused by mutations that promote Tip EC behavior and inhibit Phalanx EC behavior, that lead to the conditions associated with retinal vascular disorders and tumor vascularization. Moreover, the model describes how mutations that promote Phalanx EC behavior are associated with the development of arteriovenous and venous malformations. These results suggest that the network model that we propose has the potential to be used in the study of how the modulation of the EC extracellular micro-environment may improve the outcome of vascular disease treatments.

Spread of Zika virus in the Americas.

We use a data-driven global stochastic epidemic model to analyze the spread of the Zika virus (ZIKV) in the Americas. The model has high spatial and temporal resolution and integrates real-world demographic, human mobility, socioeconomic, temperature, and vector density data. We estimate that the first introduction of ZIKV to Brazil likely occurred between August 2013 and April 2014 (90% credible interval). We provide simulated epidemic profiles of incident ZIKV infections for several countries in the Americas through February 2017. The ZIKV epidemic is characterized by slow growth and high spatial and seasonal heterogeneity, attributable to the dynamics of the mosquito vector and to the characteristics and mobility of the human populations. We project the expected timing and number of pregnancies infected with ZIKV during the first trimester and provide estimates of microcephaly cases assuming different levels of risk as reported in empirical retrospective studies. Our approach represents a modeling effort aimed at understanding the potential magnitude and timing of the ZIKV epidemic and it can be potentially used as a template for the analysis of future mosquito-borne epidemics.

The Social Context Network Model in Psychiatric and Neurological Diseases.

The role of contextual modulations has been extensively studied in basic sensory and cognitive processes. However, little is known about their impact on social cognition, let alone their disruption in disorders compromising such a domain. In this chapter, we flesh out the social context network model (SCNM), a neuroscientific proposal devised to address the issue. In SCNM terms, social context effects rely on a fronto-temporo-insular network in charge of (a) updating context cues to make predictions, (b) consolidating context-target associative learning, and (c) coordinating internal and external milieus. First, we characterize various social cognition domains as context-dependent phenomena. Then, we review behavioral and neural evidence of social context impairments in behavioral variant frontotemporal dementia (bvFTD) and autism spectrum disorder (ASD), highlighting their relation with key SCNM hubs. Next, we show that other psychiatric and neurological conditions involve context-processing impairments following damage to the brain regions included in the model. Finally, we call for an ecological approach to social cognition assessment, moving beyond widespread abstract and decontextualized methods.

Lamina specific loss of inhibition may lead to distinct neuropathic manifestations: a computational modeling approach

Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

Modelling influenza A(H1N1) 2009 epidemics using a random network in a distributed computing environment.

In this paper we propose the use of a random network model for simulating and understanding the epidemics of influenza A(H1N1). The proposed model is used to simulate the transmission process of influenza A(H1N1) in a community region of Venezuela using distributed computing in order to accomplish many realizations of the underlying random process. These large scale epidemic simulations have recently become an important application of high-performance computing. The network model proposed performs better than the traditional epidemic model based on ordinary differential equations since it adjusts better to the irregularity of the real world data. In addition, the network model allows the consideration of many possibilities regarding the spread of influenza at the population level. The results presented here show how well the SEIR model fits the data for the AH1N1 time series despite the irregularity of the data and returns parameter values that are in good agreement with the medical data regarding AH1N1 influenza virus. This versatile network model approach may be applied to the simulation of the transmission dynamics of several epidemics in human networks. In addition, the simulation can provide useful information for the understanding, prediction and control of the transmission of influenza A(H1N1) epidemics.

On the predictive ability of mechanistic models for the Haitian cholera epidemic.

Predictive models of epidemic cholera need to resolve at suitable aggregation levels spatial data pertaining to local communities, epidemiological records, hydrologic drivers, waterways, patterns of human mobility and proxies of exposure rates. We address the above issue in a formal model comparison framework and provide a quantitative assessment of the explanatory and predictive abilities of various model settings with different spatial aggregation levels and coupling mechanisms. Reference is made to records of the recent Haiti cholera epidemics. Our intensive computations and objective model comparisons show that spatially explicit models accounting for spatial connections have better explanatory power than spatially disconnected ones for short-to-intermediate calibration windows, while parsimonious, spatially disconnected models perform better with long training sets. On average, spatially connected models show better predictive ability than disconnected ones. We suggest limits and validity of the various approaches and discuss the pathway towards the development of case-specific predictive tools in the context of emergency management.

Structural neuroimaging of social cognition in progressive non-fluent aphasia and behavioral variant of frontotemporal dementia.

Social cognition impairments are pervasive in the frontotemporal dementias (FTD). These deficits would be triggered by (a) basic emotion and face recognition processes as well as by (b) higher level social cognition (e.g., theory of mind, ToM). Both emotional processing and social cognition impairments have been previously reported in the behavioral variant of FTD (bvFTD) and also in other versions of FTDs, including primary progressive aphasia. However, no neuroanatomic comparison between different FTD variants has been performed. We report selective behavioral impairments of face recognition, emotion recognition, and ToM in patients with bvFTD and progressive non-fluent aphasia (PNFA) when compared to controls. Voxel-based morphometry (VBM) shows a classical impairment of mainly orbitofrontal (OFC), anterior cingulate (ACC), insula and lateral temporal cortices in patients. Comparative analysis of regional gray matter related to social cognition deficits (VBM) reveals a differential pattern of fronto-insulo-temporal atrophy in bvFTD and an insulo-temporal involvement in PNFA group. Results suggest that in spite of similar social cognition impairments reported in bvFTD and PNFA, the former represents an inherent ToM affectation whereas in the PNFA these deficits could be related to more basic processes of face and emotion recognition. These results are interpreted in the frame of the fronto-insulo-temporal social context network model (SCNM).

Relationship between color and tannin content in sorghum grain: application of image analysis and artificial neural network

The relationship between sorghum grain color and tannin content was reported in several references. In this study, 33 phenotypes of sorghum grain differing in seed characteristics were collected and analyzed by Folin-Ciocalteu method. A computer image analysis method was used to determine the color characteristics of all 33 sorghum phenotypes. Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input parameters of color characteristics. The goodness of fit of the models was tested using R², MS error, and bias. The computer image analysis technique was a suitable method to estimate tannin through sorghum grain color strength. Therefore, the color quality of the samples was described according three color parameters: L* (lightness), a* (redness - from green to red) and b* (blueness - from blue to yellow. The developed regression and ANN models showed a strong relationship between color and tannin content of samples. The goodness of fit (in terms of R²), which corresponds to training the ANN model, showed higher accuracy of prediction of ANN compared with the equation established by the regression method (0.96 vs. 0.88). The ANN models in term of MS error showed lower residuals distribution than that of regression model (0.002 vs. 0.006). The platform of computer image analysis technique and ANN-based model may be used to estimate the tannin content of sorghum.

Relationship between color and tannin content in sorghum grain: application of image analysis and artificial neural network

The relationship between sorghum grain color and tannin content was reported in several references. In this study, 33 phenotypes of sorghum grain differing in seed characteristics were collected and analyzed by Folin-Ciocalteu method. A computer image analysis method was used to determine the color characteristics of all 33 sorghum phenotypes. Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input parameters of color characteristics. The goodness of fit of the models was tested using R², MS error, and bias. The computer image analysis technique was a suitable method to estimate tannin through sorghum grain color strength. Therefore, the color quality of the samples was described according three color parameters: L* (lightness), a* (redness - from green to red) and b* (blueness - from blue to yellow. The developed regression and ANN models showed a strong relationship between color and tannin content of samples. The goodness of fit (in terms of R²), which corresponds to training the ANN model, showed higher accuracy of prediction of ANN compared with the equation established by the regression method (0.96 vs. 0.88). The ANN models in term of MS error showed lower residuals distribution than that of regression model (0.002 vs. 0.006). The platform of computer image analysis technique and ANN-based model may be used to estimate the tannin content of sorghum.(AU)