Lake-atmosphere interactions at Alqueva reservoir : a case study in the summer of 2014

The study of lake-atmosphere interactions was the main purpose of a 2014 summer experiment at Alqueva reservoir in Portugal. Near-surface fluxes of momentum, heat and mass [water vapour (H2O) and carbon dioxide (CO2)] were obtained with the new Campbell Scientific's IRGASON Integrated Open-Path CO2/H2O Gas Analyser and 3D Sonic Anemometer between 2 June and 2 October. On average, the reservoir was releasing energy in the form of sensible and latent heat flux during the study period. At the end of the 75 d, the total evaporation was estimated as 490.26 mm. A high correlation was found between the latent heat flux and the wind speed (R = 0.97). The temperature gradient between air and water was positive between 12 and 21 UTC, causing a negative sensible heat flux, and negative during the rest of the day, triggering a positive sensible heat flux. The reservoir acted as a sink of atmospheric CO2 with an average rate of -0.026 mg m(-2) s(-1). However, at a daily scale we found an unexpected uptake between 0 and 9 UTC and almost null flux between 13 and 19 UTC. Potential reasons for this result are further discussed. The net radiation was recorded for the same period and water column heat storage was estimated using water temperature profiles. The energy balance closure for the analysed period was 81%. In-water solar spectral downwelling irradiance profiles were measured with a new device allowing measurements independent of the solar zenith angle, which enabled the computation of the attenuation coefficient of light in the water column. The average attenuation coefficient for the photosynthetically active radiation spectral region varied from 0.849 +/- 0.025 m(-1) on 30 July to 1.459 +/- 0.007 m(-1) on 25 September.Peer reviewe

Artificial intelligence – the new suggestion for biomedicine, dentistry and healthcare

The development of technologies based on Artificial Intelligence (AI) and their application in medicine is growing rapidly. Innovations in digital technology, telemedicine, 5G technology and artificial intelligence (AI) create new opportunities for the development of the healthcare system. The aim of the present study is to explore the possibilities for the application of artificial intelligence in biomedicine, dentistry, healthcare and healthcare. In recent years there have been many major innovations, including the introduction of many new information and communication technologies. Digital innovations, including the further inclusion of telemedicine, the development of 5th generation wireless networks (5G) and artificial intelligence (AI) approaches, create an exceptional ecosystem for new health opportunities. The digital health sector creates a favorable environment for the provision of health services at a very high level

On a Second Critical Point in the First Order Metal - Insulator Transitions

For the first order Metal Insulator Transitions we show that together with the d.c conductance zero there is a second critical point, where the dielectric constant becomes zero and further turns negative. At this point the metallic reflectivity sharply increases. The two points can be separated by a Phase Separation State in a 3D disordered system, but may tend to merge in 2D. For illustration we evaluate the dielectric function in a simple effective medium approximation and show that at the second point it turns negative. We reproduce the experimental data on a typical Mott insulator like MnO, demonstrating the presence of the two points clearly. We discuss other experiments for studies of the phase separation state and a similar phase separation in superconductors with insulating inclusions.Comment: 4pages, 2 figure

Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and earth system models

Ocean color remote sensing of chlorophyll concentration has revolutionized our understanding of the biology of the oceans. However, a comprehensive understanding of the structure and function of oceanic ecosystems requires the characterization of the spatio-temporal variability of various phytoplankton functional types (PFTs), which have differing biogeochemical roles. Thus, recent bio-optical algorithm developments have focused on retrieval of various PFTs. It is important to validate and inter-compare the existing PFT algorithms; however direct comparison of retrieved variables is non-trivial because in those algorithms PFTs are defined differently. Thus, it is more plausible and potentially more informative to focus on emergent properties of PFTs, such as phenology. Furthermore, ocean color satellite PFT data sets can play a pivotal role in informing and/or validating the biogeochemical routines of Earth System models. Here, the phenological characteristics of 10 PFT satellite algorithms and 7 latest-generation climate models from the Coupled Model Inter-comparison Project (CMIP5) are inter-compared as part of the International Satellite PFT Algorithm Inter-comparison Project. The comparison is based on monthly satellite data (mostly SeaWiFS) for the 2003-2007 period. The phenological analysis is based on the fraction of microplankton or a similar variable for the satellite algorithms and on the carbon biomass due to diatoms for the climate models. The seasonal cycle is estimated on a per-pixel basis as a sum of sinusoidal harmonics, derived from the Discrete Fourier Transform of the variable time series. Peak analysis is then applied to the estimated seasonal signal and the following phenological parameters are quantified for each satellite algorithm and climate model: seasonal amplitude, percent seasonal variance, month of maximum, and bloom duration. Secondary/double blooms occur in many areas and are also quantified. The algorithms and the models are quantitatively compared based on these emergent phenological parameters. Results indicate that while algorithms agree to a first order on a global scale, large differences among them exist; differences are analyzed in detail for two Longhurst regions in the North Atlantic: North Atlantic Drift Region (NADR) and North Atlantic Subtropical Gyre West (NASW). Seasonal cycles explain the most variance in zonal bands in the seasonally-stratified subtropics at about 30 latitude in the satellite PFT data. The CMIP5 models do not reproduce this pattern, exhibiting higher seasonality in mid and high-latitudes and generally much more spatially homogeneous patterns in phenological indices compared to satellite data. Satellite data indicate a complex structure of double blooms in the Equatorial region and mid-latitudes, and single blooms on the poleward edges of the subtropical gyres. In contrast, the CMIP5 models show single annual blooms over most of the ocean except for the Equatorial band and Arabian Sea

Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and Earth System Models

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordOcean color remote sensing of chlorophyll concentration has revolutionized our understanding of the biology of the oceans. However, a comprehensive understanding of the structure and function of oceanic ecosystems requires the characterization of the spatio-temporal variability of various phytoplankton functional types (PFTs), which have differing biogeochemical roles. Thus, recent bio-optical algorithm developments have focused on retrieval of various PFTs. It is important to validate and inter-compare the existing PFT algorithms; however direct comparison of retrieved variables is non-trivial because in those algorithms PFTs are defined differently. Thus, it is more plausible and potentially more informative to focus on emergent properties of PFTs, such as phenology. Furthermore, ocean color satellite PFT data sets can play a pivotal role in informing and/or validating the biogeochemical routines of Earth System Models. Here, the phenological characteristics of 10 PFT satellite algorithms and 7 latest-generation climate models from the Coupled Model Inter-comparison Project (CMIP5) are inter-compared as part of the International Satellite PFT Algorithm Inter-comparison Project. The comparison is based on monthly satellite data (mostly SeaWiFS) for the 2003–2007 period. The phenological analysis is based on the fraction of microplankton or a similar variable for the satellite algorithms and on the carbon biomass due to diatoms for the climate models. The seasonal cycle is estimated on a per-pixel basis as a sum of sinusoidal harmonics, derived from the Discrete Fourier Transform of the variable time series. Peak analysis is then applied to the estimated seasonal signal and the following phenological parameters are quantified for each satellite algorithm and climate model: seasonal amplitude, percent seasonal variance, month of maximum, and bloom duration. Secondary/double blooms occur in many areas and are also quantified. The algorithms and the models are quantitatively compared based on these emergent phenological parameters. Results indicate that while algorithms agree to a first order on a global scale, large differences among them exist; differences are analyzed in detail for two Longhurst regions in the North Atlantic: North Atlantic Drift Region (NADR) and North Atlantic Subtropical Gyre West (NASW). Seasonal cycles explain the most variance in zonal bands in the seasonally-stratified subtropics at about 30° latitude in the satellite PFT data. The CMIP5 models do not reproduce this pattern, exhibiting higher seasonality in mid and high-latitudes and generally much more spatially homogeneous patterns in phenological indices compared to satellite data. Satellite data indicate a complex structure of double blooms in the Equatorial region and mid-latitudes, and single blooms on the poleward edges of the subtropical gyres. In contrast, the CMIP5 models show single annual blooms over most of the ocean except for the Equatorial band and Arabian Sea.NASAEuropean Space Agency (ESA

Spatial structure increases the waiting time for cancer

Cancer results from a sequence of genetic and epigenetic changes which lead to a variety of abnormal phenotypes including increased proliferation and survival of somatic cells, and thus, to a selective advantage of pre-cancerous cells. The notion of cancer progression as an evolutionary process has been experiencing increasing interest in recent years. Many efforts have been made to better understand and predict the progression to cancer using mathematical models; these mostly consider the evolution of a well-mixed cell population, even though pre-cancerous cells often evolve in highly structured epithelial tissues. We propose a novel model of cancer progression that considers a spatially structured cell population where clones expand via adaptive waves. This model is used to asses two different paradigms of asexual evolution that have been suggested to delineate the process of cancer progression. The standard scenario of periodic selection assumes that driver mutations are accumulated strictly sequentially over time. However, when the mutation supply is sufficiently high, clones may arise simultaneously on distinct genetic backgrounds, and clonal adaptation waves interfere with each other. We find that in the presence of clonal interference, spatial structure increases the waiting time for cancer, leads to a patchwork structure of non-uniformly sized clones, decreases the survival probability of virtually neutral (passenger) mutations, and that genetic distance begins to increase over a characteristic length scale, determined here. These characteristic features of clonal interference may help to predict the onset of cancers with pronounced spatial structure and to interpret spatially-sampled genetic data obtained from biopsies. Our estimates suggest that clonal interference likely occurs in the progressing colon cancer, and possibly other cancers where spatial structure matters.Comment: 21 page

Combinatorial effects of Alpha- and Gamma-Protocadherins on neuronal survival and dendritic self-avoidance

The clustered Protocadherins (Pcdhs) comprise 58 cadherin-related proteins encoded by three tandemly-arrayed gene clusters, Pcdh-alpha, -beta, and --gamma (Pcdha, Pcdhb, Pcdhg). Pcdh isoforms from different clusters are combinatorially expressed in neurons. They form multimers that interact homophilically, and mediate a variety of developmental processes, including neuronal survival, synaptic maintenance, axonal tiling and dendritic self-avoidance. Most studies have analyzed clusters individually. Here, we assess functional interactions between Pcdha and Pcdhg clusters. To circumvent neonatal lethality associated with deletion of Pcdhgs, we used Crispr-Cas9 genome editing in mice to combine a constitutive Pcdha mutant allele with a conditional Pcdhg allele. We analyzed roles of Pcdhas and Pcdhgs in the retina and cerebellum from mice (both sexes) lacking one or both clusters. In retina, Pcdhgs are essential for survival of inner retinal neurons and dendrite self-avoidance of starburst amacrine cells, while Pcdhas are dispensable for both processes. Deletion of both Pcdha and Pcdhg clusters led to far more dramatic defects in survival and self-avoidance than Pcdhg deletion alone. Comparisons of an allelic series of mutants support the conclusion that Pcdhas and Pcdhgs function together in a dose-dependent and cell-type specific manner to provide a critical threshold of Pcdh activity. In the cerebellum, Pcdhas and Pcdhgs also act synergistically to mediate self-avoidance of Purkinje cell dendrites, with modest but significant defects in either single mutant and dramatic defects in the double mutant. Together, our results demonstrate complex patterns of redundancy between Pcdh clusters and the importance of Pcdh cluster diversity in postnatal CNS development

NSAIDs Modulate Clonal Evolution in Barrett's Esophagus

Cancer is considered an outcome of decades-long clonal evolution fueled by acquisition of somatic genomic abnormalities (SGAs). Non-steroidal anti-inflammatory drugs (NSAIDs) have been shown to reduce cancer risk, including risk of progression from Barrett's esophagus (BE) to esophageal adenocarcinoma (EA). However, the cancer chemopreventive mechanisms of NSAIDs are not fully understood. We hypothesized that NSAIDs modulate clonal evolution by reducing SGA acquisition rate. We evaluated thirteen individuals with BE. Eleven had not used NSAIDs for 6.2±3.5 (mean±standard deviation) years and then began using NSAIDs for 5.6±2.7 years, whereas two had used NSAIDs for 3.3±1.4 years and then discontinued use for 7.9±0.7 years. 161 BE biopsies, collected at 5–8 time points over 6.4–19 years, were analyzed using 1Million-SNP arrays to detect SGAs. Even in the earliest biopsies there were many SGAs (284±246 in 10/13 and 1442±560 in 3/13 individuals) and in most individuals the number of SGAs changed little over time, with both increases and decreases in SGAs detected. The estimated SGA rate was 7.8 per genome per year (95% support interval [SI], 7.1–8.6) off-NSAIDs and 0.6 (95% SI 0.3–1.5) on-NSAIDs. Twelve individuals did not progress to EA. In ten we detected 279±86 SGAs affecting 53±30 Mb of the genome per biopsy per time point and in two we detected 1,463±375 SGAs affecting 180±100 Mb. In one individual who progressed to EA we detected a clone having 2,291±78 SGAs affecting 588±18 Mb of the genome at three time points in the last three of 11.4 years of follow-up. NSAIDs were associated with reduced rate of acquisition of SGAs in eleven of thirteen individuals. Barrett's cells maintained relative equilibrium level of SGAs over time with occasional punctuations by expansion of clones having massive amount of SGAs

Global assessment of ocean carbon export by combining satellite observations and food-web models

The export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food-web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional-scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional-scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of ~6 Pg C yr−1. Global export estimates show small variation (typically < 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump

Distributions of phytoplankton carbohydrate, protein and lipid in the world oceans from satellite ocean colour

Energy value of phytoplankton regulates the growth of higher trophic species, affecting the tropic balance and sustainability of marine food webs. Therefore, developing our capability to estimate and monitor, on a global scale, the concentrations of macromolecules that determine phytoplankton energy value, would be invaluable. Reported here are the first estimates of carbohydrate, protein, lipid, and overall energy value of phytoplankton in the world oceans, using ocean-colour data from satellites. The estimates are based on a novel bio-optical method that utilises satellite-derived bio-optical fingerprints of living phytoplankton combined with allometric relationships between phytoplankton cells and cellular macromolecular contents. The annually-averaged phytoplankton energy value, per cubic meter of sub-surface ocean, varied from less than 0.1 kJ in subtropical gyres, to 0.5–1.0 kJ in parts of the equatorial, northern and southern latitudes, and rising to more than 10 kJ in certain coastal and optically complex waters. The annually-averaged global stocks of carbohydrate, protein and lipid were 0.044, 0.17 and 0.108 gigatonnes, respectively, with monthly stocks highest in September and lowest in June, over 1997-2013. The fractional contributions of phytoplankton size classes e.g., picoplankton, nanoplankton and microplankton to surface concentrations and global stocks of macromolecules varied considerably across marine biomes classified as Longhurst provinces. Among these provinces, the highest annually-averaged surface concentrations of carbohydrate, protein, and lipid were in North-East Atlantic Coastal Shelves, whereas, the lowest concentration of carbohydrate or lipid were in North Atlantic Tropical Gyral, and that of protein was in North Pacific Subtropical Gyre West. The regional accuracy of the estimates and their sensitivity to satellite inputs are quantified from the bio-optical model, which show promise for possible operational monitoring of phytoplankton energy value from satellite ocean colour. Adequate in situ measurements of macromolecules and improved retrievals of inherent optical properties from high-resolution satellite images, would be required to validate these estimates at local sites, and to further improve their accuracy in the world oceans