The dynamics of picocyanobacteria from a hypereutrophic shallow lake is affected by light-climate and small-bodied zooplankton: a 10-year cytometric time-series analysis.

In aquatic systems, an interplay between bottom-up and top-down processes determines the dynamic of picocyanobacteria (Pcy) abundance and community structure. Here, we analyzed a 10-year time series (sampled fortnightly) from a hypereutrophic turbid shallow lake located within the Pampa Region of South America, generating the first long-term record of freshwater Pcy from the Southern Hemisphere. We used a cytometric approach to study Pcy community, and focused on its relations with nutrient and light conditions (bottom-up) and potential grazers (top-down). A novel Pcy abundance seasonality with winter maximums was observed for years with relatively stable hydrological levels, related with decreased abundance of seasonal rotifers during colder seasons. Pcy showed lower abundance and higher cytometric alpha diversity during summer, probably due to a strong predation exerted by rotifers. In turn, a direct effect of the non-seasonal small cladocerans Bosmina spp. decreased Pcy abundance and induced a shift from single-cell Pcy into aggregated forms. This structuring effect of Bosmina spp. was further confirmed by Pcy cytometric (dis)similarity analyses from the time series and in situ experimental data. Remarkably, Pcy showed acclimatization to underwater light variations, resembling the relevance of light in this turbid system.

Neonatal mortality rates in Brazilian municipalities: from 1996 to 2017.

OBJECTIVES: Neonatal mortality is a global public health problem, and the efforts to reduce child mortality is one of the goals of the 2030 Agenda for Sustainable Development, launched in 2015 by the United Nations. The availability of historical neonatal mortality rates (NMR) data in Brazilian municipalities is crucial to evaluate trends at local, regional and national level, identifying gaps and vulnerable territories. Therefore, the objective of this article is to offer an integrated dataset containing monthly data in a historical series from 1996 to 2017 with information on all births, neonatal deaths, and NMR (total, early and late components) enriched with information related to the municipality. DATA DESCRIPTION: It is a dataset of historical data with information on the number of births, the number of neonatal deaths, the neonatal mortality rate (including early and late), and geographic information for each month (between January 1996 and December 2017) and Brazilian municipality.

Heterogeneous local dynamics revealed by classification analysis of spatially disaggregated time series data.

Time series data provide a crucial window into infectious disease dynamics, yet their utility is often limited by the spatially aggregated form in which they are presented. When working with time series data, violating the implicit assumption of homogeneous dynamics below the scale of spatial aggregation could bias inferences about underlying processes. We tested this assumption in the context of the 2015-2016 Zika epidemic in Colombia, where time series of weekly case reports were available at national, departmental, and municipal scales. First, we performed a descriptive analysis, which showed that the timing of departmental-level epidemic peaks varied by three months and that departmental-level estimates of the time-varying reproduction number, R(t), showed patterns that were distinct from a national-level estimate. Second, we applied a classification algorithm to six features of proportional cumulative incidence curves, which showed that variability in epidemic duration, the length of the epidemic tail, and consistency with a cumulative normal density curve made the greatest contributions to distinguishing groups. Third, we applied this classification algorithm to data simulated with a stochastic transmission model, which showed that group assignments were consistent with simulated differences in the basic reproduction number, R0. This result, along with associations between spatial drivers of transmission and group assignments based on observed data, suggests that the classification algorithm is capable of detecting differences in temporal patterns that are associated with differences in underlying drivers of incidence patterns. Overall, this diversity of temporal patterns at local scales underscores the value of spatially disaggregated time series data.