Exogenous insulin enhances humoural immune responses in short-day,but not long-day,Siberian hamsters (Phodopus sungorus)

Many animals experience marked seasonal fluctuations in environmental conditions. In response, animals display adaptive alterations in physiology and behaviour, including seasonal changes in immune function. During winter, animals must reallocate finite energy stores from relatively costly, less exigent systems (e.g. reproduction and immunity) to systems critical for immediate survival (e.g. thermoregulation). Seasonal changes in immunity are probably mediated by neuroendocrine factors signalling current energetic state. One potential hormonal candidate is insulin, a metabolic hormone released in response to elevated blood glucose levels. The aim of the present study was to explore the potential role of insulin in signalling energy status to the immune system in a seasonally breeding animal, the Siberian hamster (Phodopus sungorus). Specifically, exogenous insulin was administered to male hamsters housed in either long ‘summer-like’ or short ‘winter-like’ days. Animals were then challenged with an innocuous antigen and immune responses were measured. Insulin treatment significantly enhanced humoural immune responses in short, but not long days. In addition, insulin treatment increased food intake and decreased blood glucose levels across photoperiodic treatments. Collectively, these data support the hypothesis that insulin acts as an endocrine signal integrating seasonal energetic changes and immune responses in seasonally breeding rodents.     

Trading Off Global Fuel Supply,CO2 Emissions and Sustainable Development

The United Nations Conference on Climate Change (Paris 2015) reached an international agreement to keep the rise in global average temperature ‘well below 2°C’ and to ‘aim to limit the increase to 1.5°C’. These reductions will have to be made in the face of rising global energy demand. Here a thoroughly validated dynamic econometric model (Eq 1) is used to forecast global energy demand growth (International Energy Agency and BP), which is driven by an increase of the global population (UN), energy use per person and real GDP (World Bank and Maddison). Even relatively conservative assumptions put a severe upward pressure on forecast global energy demand and highlight three areas of concern. First, is the potential for an exponential increase of fossil fuel consumption, if renewable energy systems are not rapidly scaled up. Second, implementation of internationally mandated CO₂ emission controls are forecast to place serious constraints on fossil fuel use from ~2030 onward, raising energy security implications. Third is the challenge of maintaining the international ‘pro-growth’ strategy being used to meet poverty alleviation targets, while reducing CO₂ emissions. Our findings place global economists and environmentalists on the same side as they indicate that the scale up of CO₂ neutral renewable energy systems is not only important to protect against climate change, but to enhance global energy security by reducing our dependence of fossil fuels and to provide a sustainable basis for economic development and poverty alleviation. Very hard choices will have to be made to achieve ‘sustainable development’ goals.     

The Potential Wind Power Resource in Australia: A New Perspective

Australia’s wind resource is considered to be very good, and the utilization of this renewable energy resource is increasing rapidly: wind power installed capacity increased by 35% from 2006 to 2011 and is predicted to account for over 12% of Australia’s electricity generation in 2030. Due to this growth in the utilization of the wind resource and the increasing importance of wind power in Australia’s energy mix, this study sets out to analyze and interpret the nature of Australia’s wind resources using robust metrics of the abundance, variability and intermittency of wind power density, and analyzes the variation of these characteristics with current and potential wind turbine hub heights. We also assess the extent to which wind intermittency, on hourly or greater timescales, can potentially be mitigated by the aggregation of geographically dispersed wind farms, and in so doing, lessen the severe impact on wind power economic viability of long lulls in wind and power generated. Our results suggest that over much of Australia, areas that have high wind intermittency coincide with large expanses in which the aggregation of turbine output does not mitigate variability. These areas are also geographically remote, some are disconnected from the east coast’s electricity grid and large population centers, which are factors that could decrease the potential economic viability of wind farms in these locations. However, on the eastern seaboard, even though the wind resource is weaker, it is less variable, much closer to large population centers, and there exists more potential to mitigate it’s intermittency through aggregation. This study forms a necessary precursor to the analysis of the impact of large-scale circulations and oscillations on the wind resource at the mesoscale.     

Breaking Lander-Waterman’s Coverage Bound

Lander-Waterman’s coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector’s problem which proves that for such genome, the total number of bases to be sequenced should be O(G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman’s result and prove that by combining the sequencing and computing processes, one can re-sequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced.     

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.     

Implications of Network Topology on Stability

In analogy to chemical reaction networks, I demonstrate the utility of expressing the governing equations of an arbitrary dynamical system (interaction network) as sums of real functions (generalized reactions) multiplied by real scalars (generalized stoichiometries) for analysis of its stability. The reaction stoichiometries and first derivatives define the network’s “influence topology”, a signed directed bipartite graph. Parameter reduction of the influence topology permits simplified expression of the principal minors (sums of products of non-overlapping bipartite cycles) and Hurwitz determinants (sums of products of the principal minors or the bipartite cycles directly) for assessing the network’s steady state stability. Visualization of the Hurwitz determinants over the reduced parameters defines the network’s stability phase space, delimiting the range of its dynamics (specifically, the possible numbers of unstable roots at each steady state solution). Any further explicit algebraic specification of the network will project onto this stability phase space. Stability analysis via this hierarchical approach is demonstrated on classical networks from multiple fields.     

Turbine Sound May Influence the Metamorphosis Behaviour of Estuarine Crab Megalopae

It is now widely accepted that a shift towards renewable energy production is needed in order to avoid further anthropogenically induced climate change. The ocean provides a largely untapped source of renewable energy. As a result, harvesting electrical power from the wind and tides has sparked immense government and commercial interest but with relatively little detailed understanding of the potential environmental impacts. This study investigated how the sound emitted from an underwater tidal turbine and an offshore wind turbine would influence the settlement and metamorphosis of the pelagic larvae of estuarine brachyuran crabs which are ubiquitous in most coastal habitats. In a laboratory experiment the median time to metamorphosis (TTM) for the megalopae of the crabs Austrohelice crassa and Hemigrapsus crenulatus was significantly increased by at least 18 h when exposed to either tidal turbine or sea-based wind turbine sound, compared to silent control treatments. Contrastingly, when either species were subjected to natural habitat sound, observed median TTM decreased by approximately 21–31% compared to silent control treatments, 38–47% compared to tidal turbine sound treatments, and 46–60% compared to wind turbine sound treatments. A lack of difference in median TTM in A. crassa between two different source levels of tidal turbine sound suggests the frequency composition of turbine sound is more relevant in explaining such responses rather than sound intensity. These results show that estuarine mudflat sound mediates natural metamorphosis behaviour in two common species of estuarine crabs, and that exposure to continuous turbine sound interferes with this natural process. These results raise concerns about the potential ecological impacts of sound generated by renewable energy generation systems placed in the nearshore environment.     

The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research

In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the ‘genetically moderated treatment effect’ (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework ‘Triangulation WIthin a STudy’ (TWIST)’ in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.     

Predictive Modelling to Identify Near-Shore,Fine-Scale Seabird Distributions during the Breeding Season

During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney’s coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney’s seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making.     

Seasonality of archaeal proteorhodopsin and associated Marine Group IIb ecotypes (Ca. Poseidoniales) in the North Western Mediterranean Sea

The Archaea Marine Group II (MGII) is widespread in the world’s ocean where it plays an important role in the carbon cycle. Despite recent discoveries on the group’s metabolisms, the ecology of this newly proposed order (Candidatus Poseidoniales) remains poorly understood. Here we used a combination of time-series metagenome-assembled genomes (MAGs) and high-frequency 16S rRNA data from the NW Mediterranean Sea to test if the taxonomic diversity within the MGIIb family (Candidatus Thalassarchaeaceae) reflects the presence of different ecotypes. The MAGs’ seasonality revealed a MGIIb family composed of different subclades that have distinct lifestyles and physiologies. The vitamin metabolisms were notably different between ecotypes with, in some, a possible link to sunlight’s energy. Diverse archaeal proteorhodopsin variants, with unusual signature in key amino acid residues, had distinct seasonal patterns corresponding to changing day length. In addition, we show that in summer, archaea, as opposed to bacteria, disappeared completely from surface waters. Our results shed light on the diversity and the distribution of the euryarchaeotal proteorhodopsin, and highlight that MGIIb is a diverse ecological group. The work shows that time-series based studies of the taxonomy, seasonality, and metabolisms of marine prokaryotes is critical to uncover their diverse role in the ocean.Subject terms: Microbial ecology, Microbial communities     

Gamma Oscillations as a Neural Signature of Shifting Times in Narrative Language

Verbs and other temporal expressions allow speakers to specify the location of events in time, as well as to move back and forth in time, shifting in a narrative between past, present and future. The referential flexibility of temporal expressions is well understood in linguistics but its neurocognitive bases remain unknown. We aimed at obtaining a neural signature of shifting times in narrative language. We recorded and analyzed event-related brain potentials (ERPs) and oscillatory responses to the adverb ‘now’ and to the second main verb in Punctual (‘An hour ago the boy stole a candy and now he peeled the fruit’) and Iterative (‘The entire afternoon the boy stole candy and now he peeled the fruit’) contexts. ‘An hour ago’ introduces a time frame that lies entirely in the past, ‘now’ shifts the narrative to the present, and ‘peeled’ shifts it back to the past. These two referential shifts in Punctual contexts are expected to leave very similar traces on neural responses. In contrast, ‘The entire afternoon’ specifies a time frame that may encompass past, present and future, such that both ‘now’ and ‘peeled’ are consistent with it. Here, no time shift is required. We found no difference in ERPs between Punctual and Iterative contexts either at ‘now’ or at the second verb. However, reference shifts modulated oscillatory signals. ‘Now’ and the second verb in Punctual contexts resulted in similar responses: an increase in gamma power with a left-anterior distribution. Gamma bursts were absent in Iterative contexts. We propose that gamma oscillations here reflect the binding of temporal variables to the values allowed by constraints introduced by temporal expressions in discourse.     

The First Report on Excellent Cycling Stability and Superior Rate Capability of Na3V2(PO4)3 for Sodium Ion Batteries

Sodium ion batteries are attractive for the rapidly emerging large‐scale energy storage market for intermittent renewable resources. Currently a viable cathode material does not exist for practical non‐aqueous sodium ion battery applications. Here we disclose a high performance, durable electrode material based on the 3D NASICON framework. Porous Na₃V₂(PO₄)₃/C was synthesized using a novel solution‐based approach. This material, as a cathode, is capable of delivering an energy storage capacity of ～400 mWh/g vs. sodium metal. Furthermore, at high current rates (10, 20 and 40 C), it displayed remarkable capacity retention. Equally impressive is the long term cycle life. Nearly 50% of the initial capacity was retained after 30,000 charge/discharge cycles at 40 C (4.7 A/g). Notably, coulombic efficiency was 99.68% (average) over the course of cycling. To the best of our knowledge, the combination of high energy density, high power density and ultra long cycle life demonstrated here has never been reported before for sodium ion batteries. We believe our findings will have profound implications for developing large‐scale energy storage systems for renewable energy sources.     

Methanol Production by a Broad Phylogenetic Array of Marine Phytoplankton

Methanol is a major volatile organic compound on Earth and serves as an important carbon and energy substrate for abundant methylotrophic microbes. Previous geochemical surveys coupled with predictive models suggest that the marine contributions are exceedingly large, rivaling terrestrial sources. Although well studied in terrestrial ecosystems, methanol sources are poorly understood in the marine environment and warrant further investigation. To this end, we adapted a Purge and Trap Gas Chromatography/Mass Spectrometry (P&T-GC/MS) method which allowed reliable measurements of methanol in seawater and marine phytoplankton cultures with a method detection limit of 120 nanomolar. All phytoplankton tested (cyanobacteria: Synechococcus spp. 8102 and 8103, Trichodesmium erythraeum, and Prochlorococcus marinus), and Eukarya (heterokont diatom: Phaeodactylum tricornutum, coccolithophore: Emiliania huxleyi, cryptophyte: Rhodomonas salina, and non-diatom heterokont: Nannochloropsis oculata) produced methanol, ranging from 0.8–13.7 micromolar in culture and methanol per total cellular carbon were measured in the ranges of 0.09–0.3%. Phytoplankton culture time-course measurements displayed a punctuated production pattern with maxima near early stationary phase. Stabile isotope labeled bicarbonate incorporation experiments confirmed that methanol was produced from phytoplankton biomass. Overall, our findings suggest that phytoplankton are a major source of methanol in the upper water column of the world’s oceans.     

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.     

A Molecular Trajectory of α-Actinin Activation

The mechanisms by which living cells respond to mechanical stimuli are not yet fully understood. It has been suggested that mechanosensing proteins play an important role in mechanotransduction because their binding affinities are directly affected by the external stress. α-Actinin is an actin cross-linker and may act as a mechanosensor in adhesion sites. Its interaction with vinculin is suggested to be mechanically regulated. In this study, the free energy of activation is explored using the umbrella sampling method. An activation trajectory is generated in which α-actinin’s vinculin-binding site swings out of the rod domain, leading to approximately an 8 kcal/mol free energy release. The activation trajectory reveals several local and global conformational changes along the activation pathway accompanied by the breakage of a number of key interactions stabilizing the inhibited structure. These results may shed light on the role of α-actinin in cellular mechanotransduction and focal adhesion formation.     

Pine as Fast Food: Foraging Ecology of an Endangered Cockatoo in a Forestry Landscape

Pine plantations near Perth, Western Australia have provided an important food source for endangered Carnaby’s Cockatoos (Calyptorhynchus latirostris) since the 1940s. Plans to harvest these plantations without re-planting will remove this food source by 2031 or earlier. To assess the impact of pine removal, we studied the ecological association between Carnaby’s Cockatoos and pine using behavioural, nutritional, and phenological data. Pine plantations provided high densities of seed (158 025 seeds ha⁻¹) over a large area (c. 15 000 ha). Carnaby’s Cockatoos fed throughout these plantations and removed almost the entire annual crop of pine cones. Peak cockatoo abundance coincided with pine seed maturation. Pine seed had energy and protein contents equivalent to native food sources and, critically, is available in summer when breeding pairs have young offspring to feed. This strong and enduring ecological association clearly suggests that removing pine will have a significant impact on this endangered species unless restoration strategies, to establish alternative food sources, are implemented.     

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.     

Rational guide RNA engineering for small-molecule control of CRISPR/Cas9 and gene editing

It is important to control CRISPR/Cas9 when sufficient editing is obtained. In the current study, rational engineering of guide RNAs (gRNAs) is performed to develop small-molecule-responsive CRISPR/Cas9. For our purpose, the sequence of gRNAs are modified to introduce ligand binding sites based on the rational design of ligand–RNA pairs. Using short target sequences, we demonstrate that the engineered RNA provides an excellent scaffold for binding small molecule ligands. Although the ‘stem–loop 1’ variants of gRNA induced variable cleavage activity for different target sequences, all ‘stem–loop 3’ variants are well tolerated for CRISPR/Cas9. We further demonstrate that this specific ligand–RNA interaction can be utilized for functional control of CRISPR/Cas9 in vitro and in human cells. Moreover, chemogenetic control of gene editing in human cells transfected with all-in-one plasmids encoding Cas9 and designer gRNAs is demonstrated. The strategy may become a general approach for generating switchable RNA or DNA for controlling other biological processes.