Volunteer Computing (VC) is a paradigm that takes advantage of idle cycles from computing resources donated by volunteers and connected through the Internet to compute large-scale, loosely coupled simulations. A big challenge in VC projects is the scheduling of work-units across heterogeneous, volatile, and error-prone computers. The design of efficient scheduling policies for VC projects involves subjective and time-demanding tuning that is driven by knowledge of the project designer. VC projects are in need of a faster and project-independent method to automate the scheduling design. To automatically generate a scheduling policy, we must explore the extremely large space of syntactically valid policies. Given the size of this search space, exhaustive search is not feasible. Thus in this paper we propose to solve the problem using an evolutionary method to automatically generate a set of scheduling policies that are project-independent, minimize errors, and maximize throughput in VC projects. Our method includes a genetic algorithm where the representation of individuals, the fitness function, and the genetic operators are specifically tailored to get effective policies in a short time. The effectiveness of our method is evaluated with SimBA, a Simulator of BOINC Applications. In contrast with manually designed scheduling policies that often perform well only for the specific project they were designed for and require months of tuning, our resulting scheduling policies provide better overall throughput across the different VC projects considered in this work and were generated by our method in a time window of one week.

As ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation, their secondary structures have been the focus of many recent studies. Despite the computing power of supercomputers, computationally predicting secondary structures with thermodynamic methods is still not feasible when the RNA molecules have long nucleotide sequences and include complex motifs such as pseudoknots. This paper presents RNAVLab (RNA Virtual Laboratory), a virtual laboratory for studying RNA secondary structures including pseudoknots that allows scientists to address this challenge. Two important case studies show the versatility and functionalities of RNAVLab. The first study quantifies its capability to rebuild longer secondary structures from motifs found in systematically sampled nucleotide segments. The extensive sampling and predictions are made feasible in a short turnaround time because of the grid technology used. The second study shows how RNAVLab allows scientists to study the viral RNA genome replication mechanisms used by members of the virus family Nodaviridae.

The processes by which organic life is consumed and reborn in a complex ecosystem were investigated through a multiomics approach applied to the tripartite Xenorhabdus bacterium- Steinernema nematode- Galleria insect symbiosis. Trophic analyses demonstrate the primary consumers of the insect are the bacteria, and the nematode in turn consumes the bacteria.

Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).