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Phase response curves are important for analysis and modeling of oscillatory dynamics in various applications, particularly in neuroscience. Standard experimental technique for determining them requires isolation of the system and application of a specifically designed input. However, isolation is not always feasible and we are compelled to observe the system in its natural environment under free-running conditions. To that end we propose an approach relying only on passive observations of the system and its input. We illustrate it with simulation results of an oscillator driven by a stochastic force.
Phase response curves are important for analysis and modeling of oscillatory dynamics in various applications, particularly in neuroscience. Standard experimental technique for determining them requires isolation of the system and application of a specifically designed input. However, isolation is not always feasible and we are compelled to observe the system in its natural environment under free-running conditions. To that end we propose an approach relying only on passive observations of the system and its input. We illustrate it with simulation results of an oscillator driven by a stochastic force.
Yedoma-extremely ice-rich permafrost with massive ice wedges formed during the Late Pleistocene-is vulnerable to thawing and degradation under climate warming. Thawing of ice-rich Yedoma results in lowering of surface elevations. Quantitative knowledge about surface elevation changes helps us to understand the freeze-thaw processes of the active layer and the potential degradation of Yedoma deposits. In this study, we use C-band Sentinel-1 InSAR measurements to map the elevation changes over ice-rich Yedoma uplands on Sobo-Sise Island, Lena Delta with frequent revisit observations (as short as six or 12 days). We observe significant seasonal thaw subsidence during summer months and heterogeneous inter-annual elevation changes from 2016-17. We also observe interesting patterns of stronger seasonal thaw subsidence on elevated flat Yedoma uplands by comparing to the surrounding Yedoma slopes. Inter-annual analyses from 2016-17 suggest that our observed positive surface elevation changes are likely caused by the delayed progression of the thaw season in 2017, associated with mean annual air temperature fluctuations.
Yedoma-extremely ice-rich permafrost with massive ice wedges formed during the Late Pleistocene-is vulnerable to thawing and degradation under climate warming. Thawing of ice-rich Yedoma results in lowering of surface elevations. Quantitative knowledge about surface elevation changes helps us to understand the freeze-thaw processes of the active layer and the potential degradation of Yedoma deposits. In this study, we use C-band Sentinel-1 InSAR measurements to map the elevation changes over ice-rich Yedoma uplands on Sobo-Sise Island, Lena Delta with frequent revisit observations (as short as six or 12 days). We observe significant seasonal thaw subsidence during summer months and heterogeneous inter-annual elevation changes from 2016-17. We also observe interesting patterns of stronger seasonal thaw subsidence on elevated flat Yedoma uplands by comparing to the surrounding Yedoma slopes. Inter-annual analyses from 2016-17 suggest that our observed positive surface elevation changes are likely caused by the delayed progression of the thaw season in 2017, associated with mean annual air temperature fluctuations.
Manganese (Mn) is an essential nutrient for intracellular activities; it functions as a cofactor for a variety of enzymes, including arginase, glutamine synthetase (GS), pyruvate carboxylase and Mn superoxide dismutase (Mn-SOD). Through these metalloproteins, Mn plays critically important roles in development, digestion, reproduction, antioxidant defense, energy production, immune response and regulation of neuronal activities. Mn deficiency is rare. In contrast Mn poisoning may be encountered upon overexposure to this metal. Excessive Mn tends to accumulate in the liver, pancreas, bone, kidney and brain, with the latter being the major target of Mn intoxication. Hepatic cirrhosis, polycythemia, hypermanganesemia, dystonia and Parkinsonism-like symptoms have been reported in patients with Mn poisoning. In recent years, Mn has come to the forefront of environmental concerns due to its neurotoxicity. Molecular mechanisms of Mn toxicity include oxidative stress, mitochondrial dysfunction, protein misfolding, endoplasmic reticulum (ER) stress, autophagy dysregulation, apoptosis, and disruption of other metal homeostasis. The mechanisms of Mn homeostasis are not fully understood. Here, we will address recent progress in Mn absorption, distribution and elimination across different tissues, as well as the intracellular regulation of Mn homeostasis in cells. We will conclude with recommendations for future research areas on Mn metabolism.
One important organizational property of morphology is competition. Different means of expression are in conflict with each other for encoding the same grammatical function. In the current study, we examined the nature of this control mechanism by testing the formation of comparative adjectives in English during language production. Event-related brain potentials (ERPs) were recorded during cued silent production, the first study of this kind for comparative adjective formation. We specifically examined the ERP correlates of producing synthetic relative to analytic comparatives, e.g. angriervs. more angry. A frontal, bilaterally distributed, enhanced negative-going waveform for analytic comparatives (vis-a-vis synthetic ones) emerged approximately 300ms after the (silent) production cue. We argue that this ERP effect reflects a control mechanism that constrains grammatically-based computational processes (viz. more comparative formation). We also address the possibility that this particular ERP effect may belong to a family of previously observed negativities reflecting cognitive control monitoring, rather than morphological encoding processes per se.
Recovering genomics clusters of secondary metabolites from lakes using genome-resolved metagenomics
(2018)
Metagenomic approaches became increasingly popular in the past decades due to decreasing costs of DNA sequencing and bioinformatics development. So far, however, the recovery of long genes coding for secondary metabolites still represents a big challenge. Often, the quality of metagenome assemblies is poor, especially in environments with a high microbial diversity where sequence coverage is low and complexity of natural communities high. Recently, new and improved algorithms for binning environmental reads and contigs have been developed to overcome such limitations. Some of these algorithms use a similarity detection approach to classify the obtained reads into taxonomical units and to assemble draft genomes. This approach, however, is quite limited since it can classify exclusively sequences similar to those available (and well classified) in the databases. In this work, we used draft genomes from Lake Stechlin, north-eastern Germany, recovered by MetaBat, an efficient binning tool that integrates empirical probabilistic distances of genome abundance, and tetranucleotide frequency for accurate metagenome binning. These genomes were screened for secondary metabolism genes, such as polyketide synthases (PKS) and non-ribosomal peptide synthases (NRPS), using the Anti-SMASH and NAPDOS workflows. With this approach we were able to identify 243 secondary metabolite clusters from 121 genomes recovered from our lake samples. A total of 18 NRPS, 19 PKS, and 3 hybrid PKS/NRPS clusters were found. In addition, it was possible to predict the partial structure of several secondary metabolite clusters allowing for taxonomical classifications and phylogenetic inferences. Our approach revealed a high potential to recover and study secondary metabolites genes from any aquatic ecosystem.
A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.
A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará’s research foundation for meteorology)and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation,(b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.
The desiccation-tolerant plant Haberlea rhodopensis can withstand months of darkness without any visible senescence. Here, we investigated the molecular mechanisms of this adaptation to prolonged (30 d) darkness and subsequent return to light. H. rhodopensis plants remained green and viable throughout the dark treatment. Transcriptomic analysis revealed that darkness regulated several transcription factor (TF) genes. Stress-and autophagy-related TFs such as ERF8, HSFA2b, RD26, TGA1, and WRKY33 were up-regulated, while chloroplast-and flowering-related TFs such as ATH1, COL2, COL4, RL1, and PTAC7 were repressed. PHYTOCHROME INTERACTING FACTOR4, a negative regulator of photomorphogenesis and promoter of senescence, also was down-regulated. In response to darkness, most of the photosynthesis-and photorespiratory-related genes were strongly down-regulated, while genes related to autophagy were up-regulated. This occurred concomitant with the induction of SUCROSE NON-FERMENTING1-RELATED PROTEIN KINASES (SnRK1) signaling pathway genes, which regulate responses to stress-induced starvation and autophagy. Most of the genes associated with chlorophyll catabolism, which are induced by darkness in dark-senescing species, were either unregulated (PHEOPHORBIDE A OXYGENASE, PAO; RED CHLOROPHYLL CATABOLITE REDUCTASE, RCCR) or repressed (STAY GREEN-LIKE, PHEOPHYTINASE, and NON-YELLOW COLORING1). Metabolite profiling revealed increases in the levels of many amino acids in darkness, suggesting increased protein degradation. In darkness, levels of the chloroplastic lipids digalactosyldiacylglycerol, monogalactosyldiacylglycerol, phosphatidylglycerol, and sulfoquinovosyldiacylglycerol decreased, while those of storage triacylglycerols increased, suggesting degradation of chloroplast membrane lipids and their conversion to triacylglycerols for use as energy and carbon sources. Collectively, these data show a coordinated response to darkness, including repression of photosynthetic, photorespiratory, flowering, and chlorophyll catabolic genes, induction of autophagy and SnRK1 pathways, and metabolic reconfigurations that enable survival under prolonged darkness.