GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast

In this data paper, Bird tracking - GPS tracking of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast is described, a species occurrence dataset published by the Research Institute for Nature and Forest (INBO). The dataset (version 5.5) contains close to 2.5 million occurrences, recorded by 101 GPS trackers mounted on 75 Lesser Black-backed Gulls and 26 Herring Gulls breeding at the Belgian and Dutch coast. The trackers were developed by the University of Amsterdam Bird Tracking System (UvA-BiTS, http://www.uva-bits.nl). These automatically record and transmit bird movements, which allows us and others to study their habitat use and migration behaviour in great detail. Our bird tracking network is operational since 2013. It is funded for LifeWatch by the Hercules Foundation and maintained in collaboration with UvA-BiTS and the Flanders Marine Institute (VLIZ). The recorded data are periodically released in bulk as open data (http://dataset.inbo.be/bird-tracking-gull-occurrences), and are also accessible through CartoDB and the Global Biodiversity Information Facility (GBIF).     

Cooperation‐mediated plasticity in dispersal and colonization

Kin selection theory predicts that costly cooperative behaviors evolve most readily when directed toward kin. Dispersal plays a controversial role in the evolution of cooperation: dispersal decreases local population relatedness and thus opposes the evolution of cooperation, but limited dispersal increases kin competition and can negate the benefits of cooperation. Theoretical work has suggested that plasticity of dispersal, where individuals can adjust their dispersal decisions according to the social context, might help resolve this paradox and promote the evolution of cooperation. Here, we experimentally tested the hypothesis that conditional dispersal decisions are mediated by a cooperative strategy: we quantified the density‐dependent dispersal decisions and subsequent colonization efficiency from single cells or groups of cells among six genetic strains of the unicellular Tetrahymena thermophila that differ in their aggregation level (high, medium, and low), a behavior associated with cooperation strategy. We found that the plastic reaction norms of dispersal rate relative to density differed according to aggregation level: highly aggregative genotypes showed negative density‐dependent dispersal, whereas low‐aggregation genotypes showed maximum dispersal rates at intermediate density, and medium‐aggregation genotypes showed density‐independent dispersal with intermediate dispersal rate. Dispersers from highly aggregative genotypes had specialized long‐distance dispersal phenotypes, contrary to low‐aggregation genotypes; medium‐aggregation genotypes showing intermediate dispersal phenotype. Moreover, highly aggregation genotypes showed evidence for beneficial kin‐cooperation during dispersal. Our experimental results should help to resolve the evolutionary conflict between cooperation and dispersal: cooperative individuals are expected to avoid kin‐competition by dispersing long distances, but maintain the benefits of cooperation by dispersing in small groups.     

Amyloid Assemblies of Influenza A Virus PB1-F2 Protein Damage Membrane and Induce Cytotoxicity

PB1-F2 is a small accessory protein encoded by an alternative open reading frame in PB1 segments of most influenza A virus. PB1-F2 is involved in virulence by inducing mitochondria-mediated immune cells apoptosis, increasing inflammation, and enhancing predisposition to secondary bacterial infections. Using biophysical approaches we characterized membrane disruptive activity of the full-length PB1-F2 (90 amino acids), its N-terminal domain (52 amino acids), expressed by currently circulating H1N1 viruses, and its C-terminal domain (38 amino acids). Both full-length and N-terminal domain of PB1-F2 are soluble at pH values ≤6, whereas the C-terminal fragment was found soluble only at pH ≤ 3. All three peptides are intrinsically disordered. At pH ≥ 7, the C-terminal part of PB1-F2 spontaneously switches to amyloid oligomers, whereas full-length and the N-terminal domain of PB1-F2 aggregate to amorphous structures. When incubated with anionic liposomes at pH 5, full-length and the C-terminal part of PB1-F2 assemble into amyloid structures and disrupt membrane at nanomolar concentrations. PB1-F2 and its C-terminal exhibit no significant antimicrobial activity. When added in the culture medium of mammalian cells, PB1-F2 amorphous aggregates show no cytotoxicity, whereas PB1-F2 pre-assembled into amyloid oligomers or fragmented nanoscaled fibrils was highly cytotoxic. Furthermore, the formation of PB1-F2 amyloid oligomers in infected cells was directly reflected by membrane disruption and cell death as observed in U937 and A549 cells. Altogether our results demonstrate that membrane-lytic activity of PB1-F2 is closely linked to supramolecular organization of the protein.     

EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures

Regulatory DNA elements, short genomic segments that regulate gene expression, have been implicated in developmental disorders and human disease. Despite this clinical urgency, only a small fraction of the regulatory DNA repertoire has been confirmed through reporter gene assays. The overall success rate of functional validation of candidate regulatory elements is low. Moreover, the number and diversity of datasets from which putative regulatory elements can be identified is large and rapidly increasing. We generated a flexible and user-friendly tool to integrate the information from different types of genomic datasets, e.g. ATAC-seq, ChIP-seq, conservation, aiming to increase the ease and success rate of functional prediction. To this end, we developed the EMERGE program that merges all datasets that the user considers informative and uses a logistic regression framework, based on validated functional elements, to set optimal weights to these datasets. ROC curve analysis shows that a combination of datasets leads to improved prediction of tissue-specific enhancers in human, mouse and Drosophila genomes. Functional assays based on this prediction can be expected to have substantially higher success rates. The resulting integrated signal for prediction of functional elements can be plotted in a build-in genome browser or exported for further analysis.     

Predicting community characteristics from habitat conditions: fluvial fish and hydraulics

1. One current approach to the prediction of community characteristics is to use models of key local-scale processes (e.g. niche dimensions) affecting individuals and to estimate the effects of these attributes over larger scales. We tested this approach, focusing on how the hydraulic habitat structures fluvial fish communities. 2. We used a recent statistical habitat model to predict fish community characteristics in eleven reaches in the Rhône river basin in France. Predictions were made ‘blindly’ since most reaches were not used to calibrate the model. The model reflects species preferences for local hydraulics. We made predictions of the fish community from the local hydraulic conditions found in the reaches under low flow conditions. The overall abundance and the relative abundance (both as indices) of fish species, specific size classes and species traits (i.e. reproductive, trophic, morphological and others) were predicted. We summarized our predictions of the relative abundance of species as two ‘community structure indices’ using Principal Component Analysis. 3. Our predictions from low-flow hydraulics were compared with long-term observations of fish communities. The relative abundance of species actually observed depended largely on zoogeographic factors within the Rhône basin which could not be predicted by the model. The model predicted 13% of the variance in the indices of relative abundance at the species level and 23% of this variance at the trait level for all zoogeographic regions combined. However, when focused on reaches within a geographic region, the model explained up to 47% of the same variance. Therefore, geographic regions act as ‘filters’ on the relative abundance of species, but hydraulics do affect fish communities within a given geographical context. 4. For the synthetic ‘community structure indices’, we obtained good predictions from hydraulics independently of the geographical context (variance explained up to 95%). These indices were linked to simple key hydraulic characteristics of river reaches (Froude and/or Reynolds number). The indices enabled interpretations of the links between hydraulics, geomorphology, discharge and community patterns. These links were consistent with existing knowledge of species and their traits. 5. In addition to the above validations, the habitat model partly explained the observed effects of impoundment on fish communities. 6. The present results show that stream hydraulics strongly impact fish community structure. Consequently, our findings confirm that community characteristics can be predicted using models of the local-scale habitat requirements of the species forming the community.     

High plant diversity of lowland rainforest vestiges in eastern Madagascar

Based on cartography, floristic inventory and vegetation analysis in the north and south of the Eastern Domain of Madagascar we identified three original tropical rainforest types which are among the world's most biodiverse known sites for plants: the littoral forest on sand, the lowland forest on gneiss and the lowland forest on basalt. Floristic and structural comparisons were conducted on 37 plots of 50×10m. Multivariate analysis indicates that floristic composition is correlated with abiotic factors of rainfall, latitude, soil composition, and marine influence; the structure of the undergrowth is generally homogeneous and the canopy is more or less open. Many reserves must be created on gneiss, sand and basalt all along the eastern coast to preserve biodiversity from the deforestation process. On basalt, this is especially urgent because about only 10 000 ha of a very ancient forest that shelters numerous botanical novelties remain today.     

Endangered stands of thuriferous juniper in the western Mediterranean basin: ecological status, conservation and management

Thuriferous juniper is only found in isolated parts of the western Mediterranean: France (Alps, Pyrenees and Corsican highlands), Spain, Algeria and Morocco. These semi-arid mountain stands, where thuriferous juniper trees grow in low-density open woodland, are seriously endangered: (i) In the Atlas mountains, the thuriferous juniper stands are heavily degraded as a result of the intensive wood removal and livestock activity in these densely populated areas. This situation, which will soon become irreversible unless remedial measures are urgently taken, has produced impoverished soils and hillside instability while contributing to desertification. (ii) In Spain, although livestock activity and cultivation have strongly reduced areas occupied by Juniperus thurifera, stands are still numerous and, in some regions, show a good regeneration due to conservation measures. (iii) In France, the decline in human and livestock activities over recent decades has led to a recolonization of some of the Juniper stands by pines or oak. A forest management system that enables these original stands to survive and regenerate must be undertaken without delay. The dynamics of evolution of these stands is quite different north and south of the Mediterranean. In both cases, conservation measures are urgently required to protect or rehabilitate these original stands with floristic, ecological and socio-economic interest.     

High-performance liquid chromatographic determination of paclitaxel in rat serum: application to a toxicokinetic study

A simple and sensitive high-performance liquid chromatographic method is described for the determination of paclitaxel (Taxol^®) at 230 nm using a Nucleosil C₁₈ (5 μm) column and a methanol–water (70:30, v/v) mobile phase following a single-step extraction from serum with dichloromethane. The assay was validated against the classical criteria and was applied to a toxicokinetic study in rats after one or five, one per week) intraperitoneal administrations of 16 mg/kg Taxol^®.     

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.