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The Altiplano-Puna Plateau holds several shallow lakes, which are very sensitive to climate changes. This work is focused on a high-altitude lake system called Lagunas de Vilama (LVS), located in a complex climatic transition area with scarcity of continuous and homogeneous instrumental records. The objective of this study is to determine the regional spatial-temporal variability of precipitation and evaluate the seasonal and interannual lake responses. We use a lake-surfaces record derived from Landsat images to investigate links with regional precipitations and different climatic forcings. The results reveal that austral summer and autumn precipitations control the variability of the annual lake-surfaces. Also, we found intra-annual and interannual lags in the lake responses to precipitations, and identified several wet and dry stages. Our results show negative trends in precipitations and lake-surfaces, whose were strengthened by a shift to a warm phase of the Atlantic Multidecadal Oscillation in the 1990s. The El Nino Southern Oscillation, Pacific Decadal Oscillation, and Southern Annular Mode also exert a strong influence in the region. This study demonstrates that the variability of LVS lakes is strongly related to the South American Monsoon System dynamics and large-scale climate fordngs from the Pacific and Atlantic Oceans. This work provides novel indices which demonstrated to be good indicators of regional hydroclimatological variability for this region of South America.
Fatty acids are widely used to study trophic interactions in food web assemblages. Generally, it is assumed that there is a very small modification of fatty acids from one trophic step to another, making them suitable as trophic biomarkers. However, recent literature provides evidence that many fishes possess genes encoding enzymes with a role in bioconversion, thus the capability for bioconversion might be more widespread than previously assumed. Nonetheless, empirical evidence for biosynthesis occurring in natural populations remains scarce. In this study, we investigated different feeding types of perch (Perca fluviatilis) that are specialized on specific resources with different levels of highly unsaturated fatty acids (HUFAs), and analyzed the change between HUFA proportions in perch muscle tissue compared to their resources. Perch showed matching levels to their resources for EPA, but ARA and especially DHA were accumulated. Compound-specific stable isotope analyses helped us to identify the origin of HUFA carbon. Our results suggest that perch obtain a substantial amount of DHA via bioconversion when feeding on DHA-poor benthic resources. Thus, our data indicate the capability of bioconversion of HUFAs in a natural freshwater fish population.
Seed dispersal plays an important role in population dynamics in agricultural ecosystems, but the effects of surrounding vegetation height on seed dispersal and population connectivity on the landscape scale have rarely been studied. Understanding the effects of surrounding vegetation height on seed dispersal will provide important information for land-use management in agricultural landscapes to prevent the spread of undesired weeds or enhance functional connectivity. We used two model species, Phragmites australis and Typha latifolia, growing in small natural ponds known as kettle holes, in an agricultural landscape to evaluate the effects of surrounding vegetation height on wind dispersal and population connectivity between kettle holes. Seed dispersal distance and the probability of long-distance dispersal (LDD) were simulated with the mechanistic WALD model under three scenarios of "low", "dynamic" and "high" surrounding vegetation height. Connectivity between the origin and target kettle holes was quantified with a connectivity index adapted from Hanski and Thomas (1994). Our results show that mean seed dispersal distance decreases with the height of surrounding matrix vegetation, but the probability of long-distance dispersal (LDD) increases with vegetation height. This indicates an important vegetation-based trade-off between mean dispersal distance and LDD, which has an impact on connectivity. Matrix vegetation height has a negative effect on mean seed dispersal distance but a positive effect on the probability of LDD. This positive effect and its impact on connectivity provide novel insights into landscape level (meta-)population and community dynamics - a change in matrix vegetation height by land-use or climatic changes could strongly affect the spread and connectivity of wind-dispersed plants. The opposite effect of vegetation height on mean seed dispersal distance and the probability of LDD should therefore be considered in management and analyses of future land-use and climate change effects.
The aim of this study was to assess the ability of the FFQ to describe reliable and valid dietary pattern (DP) scores. In a total of 134 participants of the European Prospective Investigation into Cancer and Nutrition-Potsdam study aged 35-67 years, the FFQ was applied twice (baseline and after 1 year) to assess its reliability. Between November 1995 and March 1997, twelve 24-h dietary recalls (24HDR) as reference instrument were applied to assess the validity of the FFQ. Exploratory DP were derived by principal component analyses. Investigated predefined DP were the Alternative Healthy Eating Index (AHEI) and two Mediterranean diet indices. From dietary data of each FFQ, two exploratory DP were retained, but differed in highly loading food groups, resulting in moderate correlations (r 0 center dot 45-0 center dot 58). The predefined indices showed higher correlations between the FFQ (r(AHEI) 0 center dot 62, r(Mediterranean Diet Pyramid Index (MedPyr)) 0 center dot 62 and r(traditional Mediterranean Diet Score (tMDS)) 0 center dot 51). From 24HDR dietary data, one exploratory DP retained differed in composition to the first FFQ-based DP, but showed similarities to the second DP, reflected by a good correlation (r 0 center dot 70). The predefined DP correlated moderately (r 0 center dot 40-0 center dot 60). To conclude, long-term analyses on exploratory DP should be interpreted with caution, due to only moderate reliability. The validity differed extensively for the two exploratory DP. The investigated predefined DP showed a better reliability and a moderate validity, comparable to other studies. Within the two Mediterranean diet indices, the MedPyr performed better than the tMDs in this middle-aged, semi-urban German study population.
Identification of protein complexes from protein-protein interaction (PPI) networks is a key problem in PPI mining, solved by parameter-dependent approaches that suffer from small recall rates. Here we introduce GCC-v, a family of efficient, parameter-free algorithms to accurately predict protein complexes using the (weighted) clustering coefficient of proteins in PPI networks. Through comparative analyses with gold standards and PPI networks from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we demonstrate that GCC-v outperforms twelve state-of-the-art approaches for identification of protein complexes with respect to twelve performance measures in at least 85.71% of scenarios. We also show that GCC-v results in the exact recovery of similar to 35% of protein complexes in a pan-plant PPI network and discover 144 new protein complexes in Arabidopsis thaliana, with high support from GO semantic similarity. Our results indicate that findings from GCC-v are robust to network perturbations, which has direct implications to assess the impact of the PPI network quality on the predicted protein complexes. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them.
Shape-memory hydrogels (SMH) are multifunctional, actively-moving polymers of interest in biomedicine. In loosely crosslinked polymer networks, gelatin chains may form triple helices, which can act as temporary net points in SMH, depending on the presence of salts. Here, we show programming and initiation of the shape-memory effect of such networks based on a thermomechanical process compatible with the physiological environment. The SMH were synthesized by reaction of glycidylmethacrylated gelatin with oligo(ethylene glycol) (OEG) alpha,omega-dithiols of varying crosslinker length and amount. Triple helicalization of gelatin chains is shown directly by wide-angle X-ray scattering and indirectly via the mechanical behavior at different temperatures. The ability to form triple helices increased with the molar mass of the crosslinker. Hydrogels had storage moduli of 0.27-23 kPa and Young's moduli of 215-360 kPa at 4 degrees C. The hydrogels were hydrolytically degradable, with full degradation to water-soluble products within one week at 37 degrees C and pH = 7.4. A thermally-induced shape-memory effect is demonstrated in bending as well as in compression tests, in which shape recovery with excellent shape-recovery rates R-r close to 100% were observed. In the future, the material presented here could be applied, e.g., as self-anchoring devices mechanically resembling the extracellular matrix.
Insulin is the main anabolic hormone secreted by 13-cells of the pancreas stimulating the assimilation and storage of glucose in muscle and fat cells. It modulates the postprandial balance of carbohydrates, lipids and proteins via enhancing lipogenesis, glycogen and protein synthesis and suppressing glucose generation and its release from the liver. Resistance to insulin is a severe metabolic disorder related to a diminished response of peripheral tissues to the insulin action and signaling. This leads to a disturbed glucose homeostasis that precedes the onset of type 2 diabetes (T2D), a disease reaching epidemic proportions. A large number of studies reported an association between elevated circulating fatty acids and the development of insulin resistance. The increased fatty acid lipid flux results in the accumulation of lipid droplets in a variety of tissues. However, lipid intermediates such as diacylglycerols and ceramides are also formed in response to elevated fatty acid levels. These bioactive lipids have been associated with the pathogenesis of insulin resistance. More recently, sphingosine 1-phosphate (S1P), another bioactive sphingolipid derivative, has also been shown to increase in T2D and obesity. Although many studies propose a protective role of S1P metabolism on insulin signaling in peripheral tissues, other studies suggest a causal role of S1P on insulin resistance. In this review, we critically summarize the current state of knowledge of S1P metabolism and its modulating role on insulin resistance. A particular emphasis is placed on S1P and insulin signaling in hepatocytes, skeletal muscle cells, adipocytes and pancreatic 13-cells. In particular, modulation of receptors and enzymes that regulate S1P metabolism can be considered as a new therapeutic option for the treatment of insulin resistance and T2D.
Global change is shifting the timing of biological events, leading to temporal mismatches between biological events and resource availability. These temporal mismatches can threaten species' populations. Importantly, temporal mismatches not only exert strong pressures on the population dynamics of the focal species, but can also lead to substantial changes in pairwise species interactions such as host-pathogen systems. We adapted an established individual-based model of host-pathogen dynamics. The model describes a viral agent in a social host, while accounting for the host's explicit movement decisions. We aimed to investigate how temporal mismatches between seasonal resource availability and host life-history events affect host-pathogen coexistence, that is, disease persistence. Seasonal resource fluctuations only increased coexistence probability when in synchrony with the hosts' biological events. However, a temporal mismatch reduced host-pathogen coexistence, but only marginally. In tandem with an increasing temporal mismatch, our model showed a shift in the spatial distribution of infected hosts. It shifted from an even distribution under synchronous conditions toward the formation of disease hotspots, when host life history and resource availability mismatched completely. The spatial restriction of infected hosts to small hotspots in the landscape initially suggested a lower coexistence probability due to the critical loss of susceptible host individuals within those hotspots. However, the surrounding landscape facilitated demographic rescue through habitat-dependent movement. Our work demonstrates that the negative effects of temporal mismatches between host resource availability and host life history on host-pathogen coexistence can be reduced through the formation of temporary disease hotspots and host movement decisions, with implications for disease management under disturbances and global change.
Manganese (Mn) and zinc (Zn) are not only essential trace elements, but also potential exogenous risk factors for various diseases. Since the disturbed homeostasis of single metals can result in detrimental health effects, concerns have emerged regarding the consequences of excessive exposures to multiple metals, either via nutritional supplementation or parenteral nutrition. This study focuses on Mn-Zn-interactions in the nematode Caenorhabditis elegans (C. elegans) model, taking into account aspects related to aging and age-dependent neurodegeneration.