Filosofía ambiental de campo: Educación e investigación para la valoración ecológica y ética de los insectos dulceacuícolas (Field environmental philosophy: education and research for the ecological and ethical appreciation of freshwater insects)

In a rapidly changing world, to confront biodiversity losses and the lack of appreciation for and knowledge about the most diverse groups of organisms, it is urgently necessary to stimulate cultural shifts that transcend purely scientific and technological domains. This paper addresses this problem by focusing on one of the least known groups of organisms, and in one of the most remote regions of the planet: freshwater insects in the sub-Antarctic Magellanic ecoregion. The work of this thesis included scientific-ecological and environmental philosophical research that was integrated into formal and non-formal environmental education practices that value freshwater insects, particularly as indicators of climate change. The integration of science and philosophy was done adapting the Field Environmental Philosophy methodology that includes a four-step cycle. Transdisciplinary research on freshwater insects and their sub-Antarctic ecosystems served as a basis for the composition of metaphors and educational activities with schoolchildren, other members of the local community and visitors to Omora Park, in Puerto Williams, Chile. Based on this work, new outdoor educational activities were designed with the objective of awakening the interest of citizens for insects, and nurturing their perceptions about these organisms, their habitats and life habits. In this way, at a local scale this work aims to contribute to greater knowledge, appreciation and conservation of this unique sub-Antarctic biodiversity, and at a global scale it aims to contribute overcoming the under-appreciation for the most diverse group of organisms: the insects

Ensemble Modeling for Aromatic Production in Escherichia coli

Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning

Determinants of non attendance to mammography program in a region with high voluntary health insurance coverage

<p>Abstract</p> <p>Background</p> <p>High participation rates are needed to ensure that breast cancer screening programs effectively reduce mortality. We identified the determinants of non-participation in a public breast cancer screening program.</p> <p>Methods</p> <p>In this case-control study, 274 women aged 50 to 64 years included in a population-based mammography screening program were personally interviewed. Socio-demographic characteristics, health beliefs, health service utilization, insurance coverage, prior mammography and other preventive activities were examined.</p> <p>Results</p> <p>Of the 192 cases and 194 controls contacted, 101 and 173, respectively, were subsequently interviewed. Factors related to non-participation in the breast cancer screening program included higher education (odds ratio [OR] = 5.28; 95% confidence interval [CI95%] = 1.57–17.68), annual dental checks-ups (OR = 1.81; CI95%1.08–3.03), prior mammography at a private health center (OR = 7.27; CI95% 3.97–13.32), gynecologist recommendation of mammography (OR = 2.2; CI95%1.3–3.8), number of visits to a gynecologist (median visits by cases = 1.2, versus controls = 0.92, P = 0.001), and supplemental private insurance (OR = 5.62; CI95% = 3.28–9.6). Among women who had not received a prior mammogram or who had done so at a public center, perceived barriers were the main factors related to non-participation. Among women who had previously received mammograms at a private center, supplemental private health insurance also influenced non-participation. Benign breast symptoms increased the likelihood of participation.</p> <p>Conclusion</p> <p>Our data indicate that factors related to the type of insurance coverage (such as prior mammography at a private health center and supplemental private insurance) influenced non-participation in the screening program.</p

OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions

Computational procedures for predicting metabolic interventions leading to the overproduction of biochemicals in microbial strains are widely in use. However, these methods rely on surrogate biological objectives (e.g., maximize growth rate or minimize metabolic adjustments) and do not make use of flux measurements often available for the wild-type strain. In this work, we introduce the OptForce procedure that identifies all possible engineering interventions by classifying reactions in the metabolic model depending upon whether their flux values must increase, decrease or become equal to zero to meet a pre-specified overproduction target. We hierarchically apply this classification rule for pairs, triples, quadruples, etc. of reactions. This leads to the identification of a sufficient and non-redundant set of fluxes that must change (i.e., MUST set) to meet a pre-specified overproduction target. Starting with this set we subsequently extract a minimal set of fluxes that must actively be forced through genetic manipulations (i.e., FORCE set) to ensure that all fluxes in the network are consistent with the overproduction objective. We demonstrate our OptForce framework for succinate production in Escherichia coli using the most recent in silico E. coli model, iAF1260. The method not only recapitulates existing engineering strategies but also reveals non-intuitive ones that boost succinate production by performing coordinated changes on pathways distant from the last steps of succinate synthesis

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2&nbsp;m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0\u20135 and 5\u201315&nbsp;cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\ub0C (mean&nbsp;=&nbsp;3.0&nbsp;\ub1&nbsp;2.1\ub0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6&nbsp;\ub1&nbsp;2.3\ub0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler ( 120.7&nbsp;\ub1&nbsp;2.3\ub0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications