SI3-1: 15 suggested theoretical configurations for the calculation of MDs (defined with the name projects). The selected configuration for the projects used in this study are also indicated in Table SI2-1 and are available at SI3-1.
SI3-2: The experiments employed a dataset containing 152 representatives, non-homologous proteins (see SI3-2 to review the protein files). (Fleming and Richards, 2000).
SI3-3: The evaluation of this application in protein science requires the use of two datasets. The first data set, employed as a training set, was proposed by Ouyang (Ouyang and Liang, 2008) and contains 80 proteins (the case “2BLM” was removed since it only considered an alpha carbon representation). The second dataset, employed as a test set, was proposed by Ruiz-Blanco (Ruiz-Blanco et al., 2015) and contains 17 proteins.
SI3-4: For the generation of the models, a dataset of 204 proteins was employed. (Chou, 1999) The original dataset was split into two groups: a training set with 149 proteins and a test set containing 55 proteins to ensure a proper comparison (Marrero Ponce et al., 2015a)
Data for the manuscript "Foraging decisions as multi-armed bandit problems: applying reinforcement learning algorithms to foraging data". This is an excel file with 4 tabs, each containing the data set for a group size (i.e., 10, 25, 50, and 100 larvae).
To establish a quality benchmark dataset for developing a predictor to identify the functional types of membrane proteins, the sequences were collected from UniProtKB/
Swiss-Prot release on 2018_04 at http://www.uniprot.org/according to the following steps (Lin et al. 2013). Proteins belonging to all eight types were collected. Those proteins annotated with ‘‘fragment’’ were removed; meanwhile, those proteins with the length of sequence less than 50 residues were also excluded, in case of the influence of the fragment. Sequences annotated with ambiguous or uncertain terms, such as ‘‘potential,’’ ‘‘probable,’’‘‘probably,’’ ‘‘maybe,’’ or ‘‘by similarity,’’ were removed for further consideration.
The Dataset 4 is divided as training dataset and testing dataset with 1332 and 1033 respectively.
Genetic insect control, such as self-limiting RIDL2 (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered ('GE') insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control.,Zip file of README text and R scriptsThis zip file contains R scripts containing code and functions for running simulations of the model. The README file describes what each file does.Watkinson-Powell_Alphey_JTB2017_code.zip,
Contributors:Ross, Caitlin, Rychtář, Jan, Rueppell, Olav
Social organization correlates with longevity across animal taxa. This correlation has been explained by selection for longevity by social evolution. The reverse causality is also conceivable but has not been sufficiently considered. We constructed a simple, spatially structured population model of asexually reproducing individuals to study the effect of temporal life history structuring on the evolution of cooperation. Individuals employed fixed strategies of cooperation or defection towards all neighbours in a basic Prisoner׳s Dilemma paradigm. Individuals aged and transitioned through different life history stages asynchronously without migration. An individual׳s death triggered a reproductive event by one immediate neighbour. The specific neighbour was chosen probabilistically according to the cumulative payoff from all local interactions. Varying the duration of pre-reproductive, reproductive, and post-reproductive life history stages, long-term simulations allowed a systematic evaluation of the influence of the duration of these specific life history stages. Our results revealed complex interactions among the effects of the three basic life history stages and the benefit to defect. Overall, a long post-reproductive stage promoted the evolution of cooperation, while a prolonged pre-reproductive stage has a negative effect. In general, the total length of life also increased the probability of the evolution of cooperation. Thus, our specific model suggests that the timing of life history transitions and total duration of life history stages may affect the evolution of cooperative behaviour. We conclude that the causation of the empirically observed association of life expectancy and sociality may be more complex than previously realized.,Raw Data for Simulation OutcomesThis file contains a table in which we list all outcomes for the different simulations that we performed to study the effect of life history structuring on the evolution of cooperation.RossYJTBI8048_DryadDataFile.xlsx,