Practical Aspects of Solving Hybrid Bayesian Networks Containing Deterministic Conditionals

This is the author's final draft. Copyright 2015 WileyIn this paper we discuss some practical issues that arise in solv- ing hybrid Bayesian networks that include deterministic conditionals for continuous variables. We show how exact inference can become intractable even for small networks, due to the di culty in handling deterministic conditionals (for continuous variables). We propose some strategies for carrying out the inference task using mixtures of polyno- mials and mixtures of truncated exponentials. Mixtures of polynomials can be de ned on hypercubes or hyper-rhombuses. We compare these two methods. A key strategy is to re-approximate large potentials with potentials consisting of fewer pieces and lower degrees/number of terms. We discuss several methods for re-approximating potentials. We illustrate our methods in a practical application consisting of solv- ing a stochastic PERT network

Approximating probability density functions in hybrid Bayesian networks with mixtures of truncated exponentials

Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization and Monte Carlo methods for solving hybrid Bayesian networks. Any probability density function (PDF) can be approximated by an MTE potential, which can always be marginalized in closed form. This allows propagation to be done exactly using the Shenoy-Shafer architecture for computing marginals, with no restrictions on the construction of a join tree. This paper presents MTE potentials that approximate standard PDF’s and applications of these potentials for solving inference problems in hybrid Bayesian networks. These approximations will extend the types of inference problems that can be modeled with Bayesian networks, as demonstrated using three examples

Endocrine Disrupting Chemicals in Patients with Chronic Obstructive Pulmonary Diseases

The study of indoor environmental quality as well as the development and progression of chronic respiratory diseases have received a great deal of attention in the past few years. However, most of those surveys focus on the effects of particulate matter (PM) and biological contaminants (fungi and bacteria) and evidences on the effects of endocrine disrupting chemicals (EDCs) in these pathologies are limited. Hence, RESPIRA project aims to contribute towards a better understanding of the role of multiple stressors in respiratory diseases by providing data on the levels and effects of EDCs in patients with Chronic Obstructive Pulmonary Disease (COPD) and control individuals from Estarreja region (NW Portugal). Here we will summarize the results obtained for phenolic compounds (parabens, triclosan and triclocarban) in matched human and indoor environmental samples (house dust) from COPD patients. Overall, the concentrations in dust samples are one to two orders of magnitude higher that the concentrations in human urine. Triclosan was detected in all the dust samples, triclocarban was detected in 82% of the dust samples and parabens in 90% to 100% of the samples. In urine samples, triclosan was detected in 56% of the samples, triclocarban was always bellow detection limit (0.25 ng/mL) and parabens detection frequency varied widely (23-84%). Interestingly, the highest level reported in dust for triclosan (1200 ng/g) corresponded to the house of the patient with the highest triclosan concentration in urine (140 ng/mL).publishe

Personal care products in matched human and environmental samples collected under the framework of RESPIRA Project

The indoor environment is an important source of exposure to microbial communities that may deleteriously affect human respiratory health. Recent studies demonstrated that the microbial community structure can be altered by the use of household products such as antimicrobial agents. Hence, in order to understand the modulation of the indoor microbiome by household products and their joint effect in the respiratory status of COPD patients we evaluated the levels of antimicrobials agents in dust samples and matched urine samples from patients with COPD. Overall, the concentrations in dust samples are one to two orders of magnitude higher that the concentrations in human urine. Triclosan was detected in all the dust samples, triclocarban was detected in 82% of the dust samples and parabens in 90% to 100% of the samples. In urine samples, triclosan was detected in 56% of the samples, triclocarban was always bellow detection limit (0.25 ng/mL) and parabens detection frequency varied widely (23-84%). Interestingly, the highest level reported in dust for triclosan (1200 ng/g) corresponded to the house of the patient with the highest triclosan concentration in urine (140 ng/mL) and at that house high levels of antibiotic resistant bacteria were found. Such results suggest that the use of antimicrobials might be associated with the presence of resistant bacteria and thus deserve to be further studied.publishe

Examining the immune signatures of SARS-CoV-2 infection in pregnancy and the impact on neurodevelopment: Protocol of the SIGNATURE longitudinal study.

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women

Examining the immune signatures of SARS-CoV-2 infection in pregnancy and the impact on neurodevelopment: Protocol of the SIGNATURE longitudinal study

The COVID-19 pandemic represents a valuable opportunity to carry out cohort studies that allow us to advance our knowledge on pathophysiological mechanisms of neuropsychiatric diseases. One of these opportunities is the study of the relationships between inflammation, brain development and an increased risk of suffering neuropsychiatric disorders. Based on the hypothesis that neuroinflammation during early stages of life is associated with neurodevelopmental disorders and confers a greater risk of developing neuropsychiatric disorders, we propose a cohort study of SARS-CoV-2-infected pregnant women and their newborns. The main objective of SIGNATURE project is to explore how the presence of prenatal SARS-CoV-2 infection and other non-infectious stressors generates an abnormal inflammatory activity in the newborn. The cohort of women during the COVID-19 pandemic will be psychological and biological monitored during their pregnancy, delivery, childbirth and postpartum. The biological information of the umbilical cord (foetus blood) and peripheral blood from the mother will be obtained after childbirth. These samples and the clinical characterisation of the cohort of mothers and newborns, are tremendously valuable at this time. This is a protocol report and no analyses have been conducted yet, being currently at, our study is in the recruitment process step. At the time of this publication, we have identified 1,060 SARS-CoV-2 infected mothers and all have already given birth. From the total of identified mothers, we have recruited 537 SARS-COV-2 infected women and all of them have completed the mental health assessment during pregnancy. We have collected biological samples from 119 mothers and babies. Additionally, we have recruited 390 non-infected pregnant women.This work has received support from the Fundación Alicia Koplowitz to realize the epigenetic wide association study and to the clinical assessment to the children. This work has also received public support from the Consejería de Salud y Familias para la financiación de la investigación, desarrollo e innovación (i + d + i) biomédica y en ciencias de la salud en Andalucía (CSyF 2021 - FEDER). Grant Grant number PECOVID- 0195-2020. Convocatoria financiada con Fondo Europeo de Desarrollo Regional (FEDER) al 80% dentro del Programa Operativo de Andalucía FEDER 2014-2020. Andalucía se mueve con Europa. NG-T received payment under Rio Hortega contract CM20-00015 with the Carlos III Health Institute.Peer reviewe

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Relevance for robust Bayesian network MAP-explanations

In the context of explainable AI, the concept of MAP-independence was recently introduced as a means for conveying the (ir)relevance of intermediate nodes for MAP computations in Bayesian networks. In this paper, we further study the concept of MAP-independence, discuss methods for finding sets of relevant nodes, and suggest ways to use these in providing users with an explanation concerning the robustness of the MAP result

Assessment of flood risk in Mediterranean catchments: An approach based on Bayesian Networks

National and international technical reports have demonstrated the increase of extreme event occurrences which becomes more dangerous in coastal areas due to their higher population density. In Spain, flood and storm events are the main reasons for compensation according to the National Insurance Consortium. The aim of this paper is to model the risk of flooding in a Mediterranean catchment in the South of Spain. A hybrid dynamic Object Oriented Bayesian network was learnt based on Mixture of Truncated Exponential models, a scenario of rainfall event was included and the final model validated. OOBN structure allows the catchment to be divided into 5 different units and models each of them independently. It transforms a complex problem into a simple and easily interpretable model. Results show that the model is able to accurately watch the evolution of river level, by predicting its increase and the time the river needs to recover normality, which can be defined as the river resilienc

Inventory management with log-normal demand per unit time

This paper examines optimal policies in a continuous review inventory management system when demand in each time period follows a log-normal distribution. In this scenario, the distribution for demand during the entire lead time period has no known form. The proposed procedure uses the Fenton-Wilkinson method to estimate the parameters for a single log-normal distribution that approximates the probability density function (PDF) for lead time demand, conditional on a specific lead time. Once these parameters are determined, a mixture of truncated exponentials (MTE) function that approximates the lead time demand distribution is constructed. The objective is to include the log-normal distribution in a robust decision support system where the PDF that best fits the historical period demand data is used to construct the lead time demand distribution. Experimental results indicate that when the log-normal distribution is the best fit, the model presented in this paper reduces expected inventory costs by improving optimal policies, as compared to other potential approximations