A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services

A novel conceptual framework is presented that proposes to apply trait‐based approaches to predicting the impact of environmental change on ecosystem service delivery by multi‐trophic systems. Development of the framework was based on an extension of the response–effect trait approach to capture functional relationships that drive trophic interactions. The framework was populated with worked examples to demonstrate its flexibility and value for linking disparate data sources, identifying knowledge gaps and generating hypotheses for quantitative models.     

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce

Background

Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment.

Results

On average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance.

Conclusions

By using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone.

     

Experimental evidence of minimal effects on octocoral hosts caused by the introduced ophiuroid Ophiothela mirabilis

Coral Reefs - Highly abundant and widespread populations of the introduced eastern Pacific ophiuroid brittle star Ophiothela mirabilis occur in southeast Florida, extending the range of this new...     

Fitness Beats Truth in the Evolution of Perception

Acta Biotheoretica - Does natural selection favor veridical percepts—those that accurately (if not exhaustively) depict objective reality? Perceptual and cognitive scientists standardly claim...     

Corticioid basidiomycetes associated with bark beetles,including seven new Entomocorticium species from North America and Cylindrobasidium ipidophilum,comb. nov

Antonie van Leeuwenhoek - Seven new Entomocorticium species (Peniophoraceae) are described based on morphology and phylogenetic analyses. Along with the type species (E. dendroctoni),...     

Enhanced sampling protocol to elucidate fusion peptide opening of SARS-CoV-2 spike protein

Large-scale conformational transitions in the spike protein S2 domain are required during host-cell infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although conventional molecular dynamics simulations have been extensively used to study therapeutic targets of SARS-CoV-2, it is still challenging to gain molecular insight into the key conformational changes because of the size of the spike protein and the long timescale required to capture these transitions. In this work, we have developed an efficient simulation protocol that leverages many short simulations, a dynamic selection algorithm, and Markov state models to interrogate the structural changes of the S2 domain. We discovered that the conformational flexibility of the dynamic region upstream of the fusion peptide in S2 is coupled to the proteolytic cleavage state of the spike protein. These results suggest that opening of the fusion peptide likely occurs on a submicrosecond timescale after cleavage at the S2′ site. Building on the structural and dynamical information gained to date about S2 domain dynamics, we provide proof of principle that a small molecule bound to a seam neighboring the fusion peptide can slow the opening of the fusion peptide, leading to a new inhibition strategy for experiments to confirm. In aggregate, these results will aid the development of drug cocktails to inhibit infections caused by SARS-CoV-2 and other coronaviruses.     

Genetic differentiation among populations of the blackfin goodea Goodea atripinnis (Cyprinodontiformes: Goodeidae): implications for its evolutionary history

Central Mexico is characterized by a complex topography that is the result of historic and contemporary tectonic and climatic factors. These events have influenced the evolutionary history of numerous freshwater fishes in the region. Nonetheless, recent studies have shown that life-history traits and ecological characteristics of species may influence dispersal capabilities and the degree of genetic connectivity. Goodea (Cyprinodontiformes: Goodeidae) is one of the most widely distributed and environmentally tolerant genera of goodeids. In this study, the authors analysed variation in the mitochondrial cytochrome b gene to evaluate the phylogeographic relationships, genetic structure, genetic diversity and demographic history of Goodea from across its distribution range. They found low genetic differentiation and identified shared haplotypes among several regions. Geographic segregation was found in samples southwest and northeast of the Lower Lerma region, with some internal isolated groups showing phylogeographic differentiation and unique haplotypes. The AMOVA best explained genetic structure when grouped by haplogroups rather than when grouped by recognized biogeographic regions. Several regions showed null genetic diversity, raising the possibility of dispersal mediated by humans. Finally, Bayesian Skyline Plot analysis showed a population expansion for the Southwest haplogroup, except for the Armería population and sub-group II of the Northeast haplogroup. All this suggests a recent colonization of Goodea atripinnis throughout some of the biogeographic regions currently inhabited by this species.