Estimación de la resistencia a compresión simple del jabre estabilizado “in situ” con cemento como material en la formación de explanadas de carreteras

Granite rock has powerful alterations at several meters of depth. The clayed sand resulting is commonly known as jabre. This “in situ” mixture of cement-stabilized soil requires a laboratory formula. Even when the test section is correctly verified, the mechanical properties of the homogeneous mixture of jabre exhibit high degrees of dispersion. The laboratory work undertaken included particle-size analysis and screening, defini­tion of liquid and plastic limits, compressive strength, dry density and moisture content over stabilized samples, modified Proctor, California Bearing Ratio (CBR) and the determination of the workability of the hydrauli­cally bound mixtures. The stress resistance curve was analyzed by means of a multilinear model of unconfined compressive strength (UCS). Since practical engineering only requires UCS for 7 days, in order to gain greater knowledge of the material, other UCS transformations were used at other curing times such as 7, 14 and 28 days.La roca granítica presenta habitualmente un horizonte de alteración con varios metros de potencia. La arena arcillosa resultante como producto de alteración se deno­mina comúnmente jabre. Aunque la fórmula de trabajo de la estabilización de jabre con cemento se verifique correctamente en tramos de prueba, la estabilización “in situ” del jabre con cemento presenta habitualmente elevadas dispersiones. Entre los ensayos de laboratorio efectuados se encuentran los ensayos de análisis granulo­métrico, límites de Atterberg, resistencia a compresión simple (RCS), la densidad y humedad sobre probetas de suelo estabilizado, Proctor modificado, índice CBR (California Bearing Ratio) y el plazo de trabajabilidad de la mezcla con cemento. La curva de endurecimiento del suelo estabilizado fue ajustada mediante un modelo multi­lineal. Aunque tradicionalmente se especifique la RCS a 7 días, buscando definir un mejor comportamiento del material, los autores calcularon otros modelos de jabre estabilizado para roturas a 7, 14 y 28 días

ApoE Receptor 2 Regulates Synapse and Dendritic Spine Formation

Apolipoprotein E receptor 2 (ApoEr2) is a postsynaptic protein involved in long-term potentiation (LTP), learning, and memory through unknown mechanisms. We examined the biological effects of ApoEr2 on synapse and dendritic spine formation-processes critical for learning and memory.In a heterologous co-culture synapse assay, overexpression of ApoEr2 in COS7 cells significantly increased colocalization with synaptophysin in primary hippocampal neurons, suggesting that ApoEr2 promotes interaction with presynaptic structures. In primary neuronal cultures, overexpression of ApoEr2 increased dendritic spine density. Consistent with our in vitro findings, ApoEr2 knockout mice had decreased dendritic spine density in cortical layers II/III at 1 month of age. We also tested whether the interaction between ApoEr2 and its cytoplasmic adaptor proteins, specifically X11α and PSD-95, affected synapse and dendritic spine formation. X11α decreased cell surface levels of ApoEr2 along with synapse and dendritic spine density. In contrast, PSD-95 increased cell surface levels of ApoEr2 as well as synapse and dendritic spine density.These results suggest that ApoEr2 plays important roles in structure and function of CNS synapses and dendritic spines, and that these roles are modulated by cytoplasmic adaptor proteins X11α and PSD-95

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth’s climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedi�tion (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To re�duce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between envi�ronmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shap�ing the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question

Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition

The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90ĝ€¯d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and "hotspots"of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question