Aquaculture has become an agronomic activity with noticeable development around the world to respond to the simultaneous decrease of fish captures and the increasing demand of aquatic products for ...human consumption. However, different problems limit the development of this industry and one of those is the time required for most of the cultured fish species to achieve economically viable the commercial size. The knowledge up to date of the regulatory systems involved in controlling growth has improved very much but, it is still necessary to devote efforts to transform the basic information in application to fish culture production. The aim of the present review is to summarize the knowledge acquired with the studies about the GH/IGF axis and other hormones regarding their function on the regulation of fish muscle development and growth. To this end, GH and IGFs effects in muscle cells on metabolism and development are examined, as well as the contribution of IGF-I binding proteins, IGF-I receptors and their downstream regulated molecules like TOR and its relation with cell proliferation and differentiation and the myogenic regulatory factors. The effect of regulatory molecules on cultured myocytes are reviewed as well as in vivo responses, including the model of sustained and maintained swimming. Key aspects we consider should be further investigated to complete the scenario of the regulation of fish muscle are also proposed.
•The GH/IGF axis regulates growth and metabolism in fish muscle•Thyroid hormones and steroids exert important roles controlling muscle growth•IGF-I and IGF-II stimulate nutrients uptake and differentially regulate myogenesis•TOR and proteolytic systems' members can be valuable markers of growth condition•Moderate and sustained swimming provokes in fish better growth and flesh quality
In this study, a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice is provided. To this end, the similarity of middle-tropospheric variables—which are ...important for the development of both dynamical and statistical downscaling schemes—from 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalysis data on a daily time scale is assessed. For estimating the distributional similarity, two comparable scores are used: the two-sample Kolmogorov–Smirnov statistic and the probability density function (PDF) score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson correlation coefficient. As the most important results demonstrated, the PDF score is found to be inappropriate if the underlying data follow a mixed distribution. By providing global similarity maps for each variable under study, regions where reanalysis data should not assumed to be “perfect” are detected. In contrast to the geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost every region of the world. Moreover, for the latter these differences not only occur in the mean, but also in higher-order moments. However, when considering standardized anomalies, distributional and serial dissimilarities are negligible over most extratropical land areas. Since transformed reanalysis data are not appropriate for regional climate models—in opposition to statistical approaches—their results are expected to be more sensitive to reanalysis choice.
The present paper is a follow-on of the work presented in Manzanas et al. (Clim Dyn 53(3–4):1287–1305, 2019) which provides a comprehensive intercomparison of alternatives for the post-processing ...(statistical adjustment, calibration and downscaling) of seasonal forecasts for a particularly interesting region, Southeast Asia. To answer the questions that were raised in the preceding work, apart from Bias Adjustment (BA) and ensemble Re-Calibration (RC) methods—which transform directly the variable of interest,—we include here more complex Perfect Prognosis (PP) and Model Outputs Statistics (MOS) downscaling techniques—which operate on a selection of large-scale model circulation variables linked to the local observed variable of interest. Moreover, we test the suitability of BA and PP methods for the post-processing of daily—not only seasonal—time-series, which are often needed in a variety of sectoral applications (crop, hydrology, etc.) or to compute specific climate indices (heat waves, fire weather index, etc.). In addition, we also undertake an assessment of the effect that observational uncertainty may have for statistical post-processing. Our results indicate that PP methods (and to a lesser extent MOS) are highly case-dependent and their application must be carefully analyzed for the region/season/application of interest, since they can either improve or degrade the raw model outputs. Therefore, for those cases for which the use of these methods cannot be carefully tested by experts, our overall recommendation would be the use of BA methods, which seem to be a safe, easy to implement alternative that provide competitive results in most situations. Nevertheless, all methods (including BA ones) seem to be sensitive to observational uncertainty, especially regarding the reproduction of extremes and spells. For MOS and PP methods, this issue can even lead to important regional differences in interannual skill. The lessons learnt from this work can substantially benefit a wide range of end-users in different socio-economic sectors, and can also have important implications for the development of high-quality climate services.
Socio-ecological systems maintain reciprocal interactions between biophysical and socioeconomic structures. As a result of these interactions key essential services for society emerge. Urban ...expansion is a direct driver of land change and cause serious shifts in socio-ecological relationships and the associated lifestyles. The framework of rural-urban gradients has proved to be a powerful tool for ecological research about urban influences on ecosystems and on sociological issues related to social welfare. However, to date there has not been an attempt to achieve a classification of municipalities in rural-urban gradients based on socio-ecological interactions. In this paper, we developed a methodological approach that allows identifying and classifying a set of socio-ecological network configurations in the Region of Madrid, a highly dynamic cultural landscape considered one of the European hotspots in urban development. According to their socio-ecological links, the integrated model detects four groups of municipalities, ordered along a rural-urban gradient, characterized by their degree of biophysical and socioeconomic coupling and different indicators of landscape structure and social welfare. We propose the developed model as a useful tool to improve environmental management schemes and land planning from a socio-ecological perspective, especially in territories subject to intense urban transformations and loss of rurality.
Graphic scheme that summarizes the social welfare pattern identified in the rural-urban gradient of Madrid Region. Curve tendencies of social welfare proxies are based on statistically significant values obtained for each socio-ecological group. Landscape pattern of each type of municipalities is indicated along the abscissa axis. Display omitted
•We used a method that detects socio-ecological networks in rural-urban gradients.•The method links socio-ecological types with spatial patterns and social welfare.•Our model highlights the coupling between landscape and socioeconomic structures.•The results detect municipality types along a rural-urban gradient in Madrid Region.•The proposed model can improve land planning from a socio-ecological perspective.
Gestational Diabetes Mellitus (GDM) is a highly prevalent maternal pathology characterized by maternal glucose intolerance during pregnancy that is, associated with severe complications for both ...mother and offspring. Several risk factors have been related to GDM; one of the most important among them is genetic predisposition. Numerous single nucleotide polymorphisms (SNPs) in genes that act at different levels on various tissues, could cause changes in the expression levels and activity of proteins, which result in glucose and insulin metabolism dysfunction. In this review, we describe various SNPs; which according to literature, increase the risk of developing GDM. These SNPs include: (1) those associated with transcription factors that regulate insulin production and excretion, such as rs7903146 (
) and rs5015480 (
); (2) others that cause a decrease in protective hormones against insulin resistance such as rs2241766 (
) and rs6257 (
); (3) SNPs that cause modifications in membrane proteins, generating dysfunction in insulin signaling or cell transport in the case of rs5443 (
) and rs2237892 (
); (4) those associated with enzymes such as rs225014 (
) and rs9939609 (
) which cause an impaired metabolism, resulting in an insulin resistance state; and (5) other polymorphisms, those are associated with growth factors such as rs2146323 (
) and rs755622 (
) which could cause changes in the expression levels of these proteins, producing endothelial dysfunction and an increase of pro-inflammatory cytokines, characteristic on GDM. While the pathophysiological mechanism is unclear, this review describes various potential effects of these polymorphisms on the predisposition to develop GDM.
Many existing approaches for multisite weather generation try to capture several statistics of the observed data (such as pairwise correlations) in order to generate spatially and temporarily ...consistent series. In this work, we analyze the application of Bayesian networks to this problem, focusing on precipitation occurrence and considering a simple case study to illustrate the potential of this new approach. We use Bayesian networks to approximate the multivariate (multisite) probability distribution of observed gauge data, which is factorized according to the relevant (marginal and conditional) dependencies. This factorization allows the simulation of synthetic samples from the multivariate distribution, thus providing a sound and promising methodology for multisite precipitation series generation.
Key Points
Bayesian networks are introduced as a novel machine learning methodology for multisite precipitation occurrence generation
Their performance is assessed using several measures in terms of spatial and temporal coherence
The proposed methodology shows promise, with improvement on several spatiotemporal aspects against existing models
Avian coccidiosis continues to be one of the costliest diseases of commercial poultry. Understanding the epidemiology of Eimeria species in poultry flocks and the resistance profile to common ...anticoccidials is important to design effective disease prevention and control strategies. This study examined litter samples to estimate the prevalence and distribution of Eimeria species among broiler farms in 4 geographic regions of Colombia. A total of 245 litter samples were collected from 194 broiler farms across representative regions of poultry production between March and August 2019. The litter samples were processed for oocysts enumeration and speciation after sporulation. End-point polymerase chain reaction (PCR) analysis was conducted to confirm the presence of Eimeria species. Anticoccidial sensitivity was determined with 160 Ross AP males in 5 treatment groups: noninfected, nonmedicated control (NNC), infected, nonmedicated control (INC), infected salinomycin treated (SAL, dose: 66 ppm), infected diclazuril treated (DIC, dose: 1 ppm), and infected methylbenzocuate-Clopidol treated (MET.CLO, dose: 100 ppm), All birds were orally inoculated with 1 × 106 sporulated oocysts using a 1 mL syringe, except for the NNC- group who received 1ml of water.Eimeria spp. were found in 236 (96.3%) out of 245 individual houses, representing 180 (92.8%) out of 194 farms. Eimeria acervulina was the most prevalent species (35.0%) followed by Eimeria tenella (30.9%), Eimeria maxima (20.4%), and other Eimeria spp. (13.6%). However, mixed species infections were common, with the most prevalent combination being mixtures of E. acervulina, E. maxima, E. tenella, and other species in 31.4% of the Eimeria-positive samples. PCR analysis identified E. acervulina, E. maxima, E. tenella, Eimeria necatrix, Eimeria mitis, and Eimeria praecox with variable prevalence across farms and regions. Anticoccidial sensitivity testing of strains of Eimeria isolated from 1 region, no treatment difference (P > 0.05) was observed in final weight (BW), weight gain (BWG) or feed conversion (FCR). For the global resistance index (GI) classified SAL and MET.CLO as good efficacy (85.79 and 85.49, respectively) and DIC as limited efficacy (74.52%). These results demonstrate the ubiquitous nature of Eimeria spp. and identifies the current state of sensitivity to commonly used anticoccidials in a region of poultry importance for Colombia.
Increasingly frequent "megafires" in North America's dry forests have prompted proposals to restore historical fire regimes and ecosystem resilience. Restoration efforts that reduce tree densities ...(eg via logging) could have collateral impacts on declining old-forest species, but whether these risks outweigh the potential effects of large, severe fires remains uncertain. We demonstrate the effects of a 2014 California megafire on an iconic old-forest species, the spotted owl (Strix occidentalis). The probability of owl site extirpation was seven times higher after the fire (0.88) than before the fire (0.12) at severely burned sites, contributing to the greatest annual population decline observed during our 23-year study. The fire also rendered large areas of forest unsuitable for owl foraging one year post-fire. Our study suggests that megafires pose a threat to old-forest species, and we conclude that restoring historical fire regimes could benefit both old-forest species and the dry forest ecosystems they inhabit in this era of climate change.
Accessibility of multispectral, multitemporal imagery combined with recent advances in cloud computing and machine learning approaches have enhanced our ability to model habitat characteristics ...across broad spatial and temporal scales. We integrated a large dataset of known nest and roost sites of a threatened species, the Mexican spotted owl (Strix occidentalis lucida), in the southwestern USA with Landsat imagery processed using the Continuous Change Detection and Classification (CCDC) time series algorithm on Google Earth Engine. We then used maximum entropy modeling (Maxent) to classify the landscape into four 'spectral similarity' classes that reflected the degree to which 30-m pixels contained a multispectral signature similar to that found at known owl nest/roost sites and mapped spectral similarity classes from 1986-2020. For map interpretation, we used nationally consistent forest inventory data to evaluate the structural and compositional characteristics of each spectral similarity class. We found a monotonic increase of structural characteristics typically associated with owl nesting and roosting over classes of increasing similarity, with the 'very similar' class meeting or exceeding published minimum desired management conditions for owl nesting and roosting. We also found an increased rate of loss of forest vegetation typical of owl nesting and roosting since the beginning of the 21st century that can be partly attributed to increased frequency and extent of large (≥400 ha) wildfires. This loss resulted in a 38% reduction over the 35-year study period in forest vegetation most similar to that used for owl nesting and roosting. Our modelling approach using cloud computing with time series of Landsat imagery provided a cost-effective tool for landscape-scale, multidecadal monitoring of vegetative components of a threatened species' habitat. Our approach could be used to monitor trends in the vegetation favored by any other species, provided that high-quality location data such as we presented here are available.
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