Source-Sink Dynamics in a Two-Patch SI Epidemic Model with Life Stages and No Recovery from Infection.

This study presents a comprehensive analysis of a two-patch, two-life stage SI model without recovery from infection, focusing on the dynamics of disease spread and host population viability in natural populations. The model, inspired by real-world ecological crises like the decline of amphibian populations due to chytridiomycosis and sea star populations due to Sea Star Wasting Disease, aims to understand the conditions under which a sink host population can present ecological rescue from a healthier, source population. Mathematical and numerical analyses reveal the critical roles of the basic reproductive numbers of the source and sink populations, the maturation rate, and the dispersal rate of juveniles in determining population outcomes. The study identifies basic reproduction numbers R 0 for each of the patches, and conditions for the basic reproduction numbers to produce a receiving patch under which its population. These findings provide insights into managing natural populations affected by disease, with implications for conservation strategies, such as the importance of maintaining reproductively viable refuge populations and considering the effects of dispersal and maturation rates on population recovery. The research underscores the complexity of host-pathogen dynamics in spatially structured environments and highlights the need for multi-faceted approaches to biodiversity conservation in the face of emerging diseases.

How to Obtain and Compare Metagenome-Assembled Genomes.

Metagenome-assembled genomes, or MAGs, are genomes retrieved from metagenome datasets. In the vast majority of cases, MAGs are genomes from prokaryotic species that have not been isolated or cultivated in the lab. They, therefore, provide us with information on these species that are impossible to obtain otherwise, at least until new cultivation methods are devised. Thanks to improvements and cost reductions of DNA sequencing technologies and growing interest in microbial ecology, the rise in number of MAGs in genome repositories has been exponential. This chapter covers the basics of MAG retrieval and processing and provides a practical step-by-step guide using a real dataset and state-of-the-art tools for MAG analysis and comparison.

Global proteome of the saprophytic strain Leptospira biflexa and comparative analysis with pathogenic strain Leptospira interrogans uncover new pathogenesis mechanisms.

Leptospira is a genus of bacteria that includes free-living saprophytic species found in water or soil, and pathogenic species, which are the etiologic agents of leptospirosis. Besides all the efforts, there are only a few proteins described as virulence factors in the pathogenic strain L. interrogans. This work aims to perform L. biflexa serovar Patoc1 strain Paris global proteome and to compare with the proteome database of pathogenic L. interrogans serovar Copenhageni strain Fiocruz L1-130. We identified a total of 2327 expressed proteins of L. biflexa by mass spectrometry. Using the Get Homologues software with the global proteome of L. biflexa and L. interrogans, we found orthologous proteins classified into conserved, low conserved, and specific proteins. Comparative bioinformatic analyses were performed to understand the biological functions of the proteins, subcellular localization, the presence of signal peptide, structural domains, and motifs using public softwares. These results lead to the selection of 182 low conserved within the saprophyte, and 176 specific proteins of L. interrogans. It is anticipated that these findings will indicate further studies to uncover virulence factors in the pathogenic strain. This work presents for the first time the global proteome of saprophytic strain L. biflexa serovar Patoc, strain Patoc1. SIGNIFICANCE: The comparative analysis established an array of specific proteins in pathogenic strain that will narrow down the identification of immune protective proteins that will help fight leptospirosis.

Global proteome of the saprophytic strain Leptospira biflexa and comparative analysis with pathogenic strain Leptospira interrogans uncover new pathogenesis mechanisms

Leptospira is a genus of bacteria that includes free-living saprophytic species found in water or soil, and pathogenic species, which are the etiologic agents of leptospirosis. Besides all the efforts, there are only a few proteins described as virulence factors in the pathogenic strain L. interrogans. This work aims to perform L. biflexa serovar Patoc1 strain Paris global proteome and to compare with the proteome database of pathogenic L. interrogans serovar Copenhageni strain Fiocruz L1–130. We identified a total of 2327 expressed proteins of L. biflexa by mass spectrometry. Using the Get Homologues software with the global proteome of L. biflexa and L. interrogans, we found orthologous proteins classified into conserved, low conserved, and specific proteins. Comparative bioinformatic analyses were performed to understand the biological functions of the proteins, subcellular localization, the presence of signal peptide, structural domains, and motifs using public softwares. These results lead to the selection of 182 low conserved within the saprophyte, and 176 specific proteins of L. interrogans. It is anticipated that these findings will indicate further studies to uncover virulence factors in the pathogenic strain. This work presents for the first time the global proteome of saprophytic strain L. biflexa serovar Patoc, strain Patoc1.

Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A wavelet approach.

Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country's Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission's spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.

Aterectomía rotacional complicada en una paciente anciana con enfermedad coronaria multivaso. Reporte de caso

La aterectomía rotacional es un procedimiento específico utilizado para el manejo de lesiones coronarias complejas, especialmente cuando existe calcificación de las arterias coronarias (CAC). Esta técnica fue muy utilizada hasta la última década del siglo pasado; actualmente, aunque es poco usada, juega un papel importante en pacientes que podrían ser candidatos a revascularización quirúrgica pero que, por diferentes patologías ―como la enfermedad ateromatosa difusa, en la que se requieren estents largos, reestenosis in-stent, lesiones ostiales calcificadas y oclusiones totales crónicas―, se rechaza la opción quirúrgica. La aterectomía rotacional es un método que utiliza una fresa recubierta de diamante para reducir el volumen de las placas ateroescleróticas y la calcificación de los vasos. Este dispositivo dispersa la placa en microfragmentos, con lo que se consigue un aumento del diámetro luminal. Estos fragmentos, que tienen un diámetro mínimo, pasan predominantemente a la circulación capilar y luego son absorbidos por el sistema reticuloendotelial. Dentro de las complicaciones de esta técnica destacan la disección arterial, el atrapamiento del dispositivo, la bradicardia y la microperforación de arterias coronarias. Esta última puede ser corregida con el uso de trombina, de grasa subcutánea o de perlas. Este artículo reporta el caso de una paciente anciana con enfermedad coronaria multivaso asociada a calcificación extensa de todas las arterias coronarias, por una historia de radioterapia recibida en años anteriores por cáncer de mama y que, al no ser candidata a terapia quirúrgica por cirugía cardiovascular, requirió manejo con aterectomía rotacional que se vio complicada por microperforación de una arteria coronaria, pero que posteriormente evolucionó de manera satisfactoria.

Rotational atherectomy is a specific procedure for managing complex coronary artery lesions, especially when there is coronary artery calcification (CAC). This technique was widely used until the last decade of the 20th century; however, although it is rarely used, it currently plays an important role in patients who could be candidates for surgical revascularization but reject surgeries due to different pathologies-such as diffuse atheromatous disease requiring long stents, in-stent restenosis, calcified ostial lesions and chronic total occlusions. Rotational atherectomy is a method that uses a diamond-coated burr to reduce the volume of atherosclerotic plaques and calcification of vessels. This device breaks up plaque into microfragments, leading to an increase in lumen diameter. These fragments, which have a tiny diameter, pass predominantly into the capillary circulation and are then absorbed by the reticuloendothelial system. Among the complications of this technique are arterial dissection, device entrapment, bradycardia and microperforation of coronary arteries. The latter can be corrected with the use of thrombin, subcutaneous fat or beads. This article reports the case of an elderly female patient with multivessel coronary artery disease associated with extensive calcification of all the coronary arteries secondary to radiotherapy received in previous years for breast cancer. The patient, not being a candidate for cardiovascular surgery, required a rotational atherectomy that resulted in a microperforation of a coronary artery but with good subsequent progress.

Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study.

BACKGROUND: Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. OBJECTIVE: This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. METHODS: A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. RESULTS: In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. CONCLUSIONS: On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.

A nonlinear relapse model with disaggregated contact rates: Analysis of a forward-backward bifurcation.

Throughout the progress of epidemic scenarios, individuals in different health classes are expected to have different average daily contact behavior. This contact heterogeneity has been studied in recent adaptive models and allows us to capture the inherent differences across health statuses better. Diseases with reinfection bring out more complex scenarios and offer an important application to consider contact disaggregation. Therefore, we developed a nonlinear differential equation model to explore the dynamics of relapse phenomena and contact differences across health statuses. Our incidence rate function is formulated, taking inspiration from recent adaptive algorithms. It incorporates contact behavior for individuals in each health class. We use constant contact rates at each health status for our analytical results and prove conditions for different forward-backward bifurcation scenarios. The relationship between the different contact rates heavily influences these conditions. Numerical examples highlight the effect of temporarily recovered individuals and initial conditions on infected population persistence.

A mathematical model with nonlinear relapse: conditions for a forward-backward bifurcation.

We constructed a Susceptible-Addicted-Reformed model and explored the dynamics of nonlinear relapse in the Reformed population. The transition from susceptible considered at-risk is modeled using a strictly decreasing general function, mimicking an influential factor that reduces the flow into the addicted class. The basic reproductive number is computed, which determines the local asymptotically stability of the addicted-free equilibrium. Conditions for a forward-backward bifurcation were established using the basic reproductive number and other threshold quantities. A stochastic version of the model is presented, and some numerical examples are shown. Results showed that the influence of the temporarily reformed individuals is highly sensitive to the initial addicted population.

An implementation of a multilayer network model for the Covid-19 pandemic: A Costa Rica study.

We present a numerical implementation for a multilayer network to model the transmission of Covid-19 or other diseases with a similar transmission mechanism. The model incorporates different contact types between individuals (household, social and sporadic networks) and includes an SEIR type model for the transmission of the virus. The algorithm described in this paper includes the main ideas of the model used to give public health authorities an additional tool for the decision-making process in Costa Rica by simulating extensive possible scenarios and projections. We include two simulations: a study of the effect of restrictions on the transmission of the virus and a Costa Rica case study that was shared with the Costa Rican health authorities.

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques.

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.

Digital Platform to Continuously Monitor Patients Using a Smartwatch: Preliminary Report.

BACKGROUND: Monitoring vital signs such as oximetry, blood pressure, and heart rate is important to follow the evolution of patients. Smartwatches are a revolution in medicine allowing the collection of such data in a continuous and organic way. However, it is still a challenge to make this information available to health care professionals to make decisions during clinical follow-up. OBJECTIVE: This study aims to build a digital solution that displays vital sign data from smartwatches, collected remotely, continuously, reliably, and from multiple users, with trigger warnings when abnormal results are identified. METHODS: This is a single-center prospective study following the guidelines "Evaluating digital health products" from the UK Health Security Agency. A digital platform with 3 different applications was created to capture and display data from the mobile phones of volunteers with smartwatches. We selected 80 volunteers who were followed for 24 weeks each, and the synchronization interval between the smartwatch and digital solution was recorded for each vital sign collected. RESULTS: In 14 weeks of project progress, we managed to recruit 80 volunteers, with 68 already registered in the digital solution. More than 2.8 million records have already been collected, without system downtime. Less than 5% of continuous heart rate measurements (bpm) were synchronized within 2 hours. However, approximately 70% were synchronized in less than 24 hours, and 90% were synchronized in less than 119 hours. CONCLUSIONS: The digital solution is working properly in its role of displaying data collected from smartwatches. Vital sign values are being monitored by the research team as part of the monitoring of volunteers. Although the digital solution proved unsuitable for monitoring urgent events, it is more than suitable for use in outpatient clinical use. This digital solution, which is based on cloud technology, can be applied in the future for telemonitoring in regions lacking health care professionals. Accuracy and reliability studies still need to be performed at the end of the 24-week follow-up.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica.

Objective: To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods: Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results: The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions: Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica

[ABSTRACT]. Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

[RESUMEN]. Objetivo. Resumir los resultados de las investigaciones realizadas en Costa Rica en las que se aplicaron métodos matemáticos y estadísticos para estudiar la dinámica de transmisión de las enfermedades transmitidas por mosquitos. Métodos. Se seleccionaron y analizaron tres artículos con análisis matemáticos y estadísticos sobre enfermedades transmitidas por vectores en Costa Rica. En estos artículos se muestra el valor y la pertinencia de emplear diferentes métodos cuantitativos para comprender la dinámica de la enfermedad y brindar apoyo a la toma de decisiones. Resultados. Los resultados de estas investigaciones: 1) muestran la repercusión en los informes de casos de dengue cuando surge un segundo agente patógeno, como el chikunguña; 2) recuperan parámetros clave en la dinámica del Zika mediante la inferencia bayesiana; y 3) muestran el uso de los algoritmos de aprendizaje automático y las variables climáticas para pronosticar el riesgo relativo de dengue en cinco lugares diferentes. Conclusiones. Los modelos matemáticos y estadísticos permiten describir la dinámica de transmisión de las enfermedades transmitidas por mosquitos, mediante la provisión de información cuantitativa para brindar apoyo a los métodos de prevención y control y a la planificación de la asignación de recursos.

[RESUMO]. Objetivo. Resumir os resultados de estudos realizados na Costa Rica em que foram aplicados métodos matemáticos e estatísticos para estudar a dinâmica de transmissão de doenças transmitidas por mosquitos. Métodos. Foram selecionados e revisados três artigos com análises matemáticas e estatísticas sobre doenças transmitidas por vetores na Costa Rica. Esses artigos mostram o valor e a pertinência do uso de diferentes métodos quantitativos para compreender a dinâmica das doenças e apoiar a tomada de decisões. Resultados. Os resultados dessas investigações: 1) mostram o impacto nas notificações de casos de dengue quando surge um segundo patógeno, como o chikungunya; 2) recuperam parâmetros-chave na dinâmica do zika, usando a inferência bayesiana; e 3) mostram o uso de algoritmos de aprendizagem por máquina e variáveis climáticas para prever o risco relativo da dengue em cinco locais diferentes. Conclusões. A modelagem matemática e estatística permite a descrição da dinâmica de transmissão de doenças transmitidas por mosquitos ao oferecer informações quantitativas para apoiar métodos de prevenção e/ou controle e o planejamento da alocação de recursos.

Antioxidant, Anti-Inflammatory and Cytotoxic Activity of Phenolic Compound Family Extracted from Raspberries (Rubus idaeus): A General Review.

Raspberries (Rubus idaeus) possess a wide phenolic family profile; this serves the role of self-protection for the plant. Interest in these compounds have significantly increased, since they have been classified as nutraceuticals due to the positive health effects provided to consumers. Extensive chemical, in vitro and in vivo studies have been performed to prove and validate these benefits and their possible applications as an aid when treating several chronic degenerative diseases, characterized by oxidative stress and an inflammatory response. While many diseases could be co-adjuvanted by the intake of these phenolic compounds, this review will mainly discuss their effects on cancer. Anthocyanins and ellagitannins are known to provide a major antioxidant capacity in raspberries. The aim of this review is to summarize the current knowledge concerning the phenolic compound family of raspberries, and topics discussed include their characterization, biosynthesis, bioavailability, cytotoxicity, antioxidant and anti-inflammatory activities.

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.

Projecting the impact of Covid-19 variants and vaccination strategies in disease transmission using a multilayer network model in Costa Rica.

For countries starting to receive steady supplies of vaccines against SARS-CoV-2, the course of Covid-19 for the following months will be determined by the emergence of new variants and successful roll-out of vaccination campaigns. To anticipate this scenario, we used a multilayer network model developed to forecast the transmission dynamics of Covid-19 in Costa Rica, and to estimate the impact of the introduction of the Delta variant in the country, under two plausible vaccination scenarios, one sustaining Costa Rica's July 2021 vaccination pace of 30,000 doses per day and with high acceptance from the population and another with declining vaccination pace to 13,000 doses per day and with lower acceptance. Results suggest that the introduction and gradual dominance of the Delta variant would increase Covid-19 hospitalizations and ICU admissions by [Formula: see text] and [Formula: see text], respectively, from August 2021 to December 2021, depending on vaccine administration and acceptance. In the presence of the Delta variant, new Covid-19 hospitalizations and ICU admissions are estimated to increase around [Formula: see text] and [Formula: see text], respectively, in the same period if the vaccination pace drops. Our results can help decision-makers better prepare for the Covid-19 pandemic in the months to come.

Mathematical and statistical models for the control of mosquito-borne diseases: the experience of Costa Rica / Modelos matemáticos y estadísticos para el control de enfermedades transmitidas por mosquitos: la experiencia de Costa Rica / Modelos matemáticos e estatísticos para o controle de doenças transmitidas por mosquitos: a experiência da Costa Rica

ABSTRACT Objective. To summarize the results of research conducted in Costa Rica in which mathematical and statistical methods were implemented to study the transmission dynamics of mosquito-borne diseases. Methods. Three articles with mathematical and statistical analysis on vector-borne diseases in Costa Rica were selected and reviewed. These papers show the value and relevance of using different quantitative methods to understand disease dynamics and support decision-making. Results. The results of these investigations: 1) show the impact on dengue case reports when a second pathogen emerges, such as chikungunya; 2) recover key parameters in Zika dynamics using Bayesian inference; and 3) show the use of machine learning algorithms and climatic variables to forecast the dengue relative risk in five different locations. Conclusions. Mathematical and statistical modeling enables the description of mosquito-borne disease transmission dynamics, providing quantitative information to support prevention/control methods and resource allocation planning.

RESUMEN Objetivo. Resumir los resultados de las investigaciones realizadas en Costa Rica en las que se aplicaron métodos matemáticos y estadísticos para estudiar la dinámica de transmisión de las enfermedades transmitidas por mosquitos. Métodos. Se seleccionaron y analizaron tres artículos con análisis matemáticos y estadísticos sobre enfermedades transmitidas por vectores en Costa Rica. En estos artículos se muestra el valor y la pertinencia de emplear diferentes métodos cuantitativos para comprender la dinámica de la enfermedad y brindar apoyo a la toma de decisiones. Resultados. Los resultados de estas investigaciones: 1) muestran la repercusión en los informes de casos de dengue cuando surge un segundo agente patógeno, como el chikunguña; 2) recuperan parámetros clave en la dinámica del Zika mediante la inferencia bayesiana; y 3) muestran el uso de los algoritmos de aprendizaje automático y las variables climáticas para pronosticar el riesgo relativo de dengue en cinco lugares diferentes. Conclusiones. Los modelos matemáticos y estadísticos permiten describir la dinámica de transmisión de las enfermedades transmitidas por mosquitos, mediante la provisión de información cuantitativa para brindar apoyo a los métodos de prevención y control y a la planificación de la asignación de recursos.

RESUMO Objetivo. Resumir os resultados de estudos realizados na Costa Rica em que foram aplicados métodos matemáticos e estatísticos para estudar a dinâmica de transmissão de doenças transmitidas por mosquitos. Métodos. Foram selecionados e revisados três artigos com análises matemáticas e estatísticas sobre doenças transmitidas por vetores na Costa Rica. Esses artigos mostram o valor e a pertinência do uso de diferentes métodos quantitativos para compreender a dinâmica das doenças e apoiar a tomada de decisões. Resultados. Os resultados dessas investigações: 1) mostram o impacto nas notificações de casos de dengue quando surge um segundo patógeno, como o chikungunya; 2) recuperam parâmetros-chave na dinâmica do zika, usando a inferência bayesiana; e 3) mostram o uso de algoritmos de aprendizagem por máquina e variáveis climáticas para prever o risco relativo da dengue em cinco locais diferentes. Conclusões. A modelagem matemática e estatística permite a descrição da dinâmica de transmissão de doenças transmitidas por mosquitos ao oferecer informações quantitativas para apoiar métodos de prevenção e/ou controle e o planejamento da alocação de recursos.

Genome-resolved metagenome and metatranscriptome analyses of thermophilic composting reveal key bacterial players and their metabolic interactions.

BACKGROUND: Composting is an important technique for environment-friendly degradation of organic material, and is a microbe-driven process. Previous metagenomic studies of composting have presented a general description of the taxonomic and functional diversity of its microbial populations, but they have lacked more specific information on the key organisms that are active during the process. RESULTS: Here we present and analyze 60 mostly high-quality metagenome-assembled genomes (MAGs) recovered from time-series samples of two thermophilic composting cells, of which 47 are potentially new bacterial species; 24 of those did not have any hits in two public MAG datasets at the 95% average nucleotide identity level. Analyses of gene content and expressed functions based on metatranscriptome data for one of the cells grouped the MAGs in three clusters along the 99-day composting process. By applying metabolic modeling methods, we were able to predict metabolic dependencies between MAGs. These models indicate the importance of coadjuvant bacteria that do not carry out lignocellulose degradation but may contribute to the management of reactive oxygen species and with enzymes that increase bioenergetic efficiency in composting, such as hydrogenases and N2O reductase. Strong metabolic dependencies predicted between MAGs revealed key interactions relying on exchange of H+, NH3, O2 and CO2, as well as glucose, glutamate, succinate, fumarate and others, highlighting the importance of functional stratification and syntrophic interactions during biomass conversion. Our model includes 22 out of 49 MAGs recovered from one composting cell data. Based on this model we highlight that Rhodothermus marinus, Thermobispora bispora and a novel Gammaproteobacterium are dominant players in chemolithotrophic metabolism and cross-feeding interactions. CONCLUSIONS: The results obtained expand our knowledge of the taxonomic and functional diversity of composting bacteria and provide a model of their dynamic metabolic interactions.

The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica.

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.