Functional morphology of the caudal skeleton in teleostean fishes

The basic function of the caudal skeleton in teleostean fishes is to support the caudal fin, but its parts contribute to this function in somewhat different ways. The main axis for this support is the upturned terminal end of the vertebral column, which ends at the base of the uppermost principal rays. The uroneural struts just ahead of this axis provide support for it. The parts of the caudal skeleton behind and below this upturned axis, the hypurals and parhypural, not only support the caudal rays but also provide a means for differential movements between the upper and lower parts of the fin base. This basic caudal skeleton varies with the position of the fish in the sequence of teleosten evolution, the way in which the fish uses its caudal fin, and to some extent with the shape of the fin.     

The temperature-dependent swelling of elastin

It is suggested that the temperature-dependent swelling behavior of water-swollen elastin is due entirely to the interaction of the numerous nonpolar groups in the elastin protein wiht the aqueous swelling solvent (i.e., ahydrophobic interaction). Flory-Rehner theory for network swelling was used to test this hypothesis. Calculated values for the solvent–polymer interaction parameter, χ1, derived from swelling data indicate that water is a very poor solvent for elastin at all temperatures over the range 0–70° C. Comparison of the calculated _χ1 values with theoretical values for the free energy of interaction of nonpolar solutes and water strongly suggests that the swelling behavior of elastin can be attributed quantitatively to hydrophobic interactions. The implications of these results for the structure and elastic mechanism of elastin are discussed.     

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.

     

VARIATION IN ANATOMICAL AND MATERIAL PROPERTIES EXPLAINS DIFFERENCES IN HYDRODYNAMIC PERFORMANCES OF FOLIOSE RED MACROALGAE (RHODOPHYTA)1

Over the last two decades, many studies on functional morphology have suggested that material properties of seaweed tissues may influence their fitness. Because hydrodynamic forces are likely the largest source of mortality for seaweeds in high wave energy environments, tissues with material properties that behave favorably in these environments are likely to be selected for. However, it is very difficult to disentangle the effects of materials properties on seaweed performance because size, shape, and habitat also influence mechanical and hydrodynamic performance. In this study, anatomical and material properties of 16 species of foliose red macroalgae were determined, and their effects on hydrodynamic performance were measured in laboratory experiments holding size and shape constant. We determined that increased blade thickness (primarily caused by the addition of medullary tissue) results in higher flexural stiffness (EI), which inhibits the seaweed’s ability to reconfigure in flowing water and thereby increases drag. However, this increase is concurrent with an increase in the force required to break tissue, possibly offsetting any risk of failure. Additionally, while increased nonpigmented medullary cells may pose a higher metabolic cost to the seaweed, decreased reconfiguration causes thicker tissues to expose more photosynthetic surface area incident to ambient light in flowing water, potentially ameliorating the metabolic cost of producing these cells. Material properties can result in differential performance of morphologically similar species. Future studies on ecomechanics of seaweeds in wave‐swept coastal habitats should consider the interaction of multiple trade‐offs.     

Mechanical anisotropy of inflated elastic tissue from the pig aorta

Uniaxial and biaxial mechanical properties of purified elastic tissue from the proximal thoracic aorta were studied to understand physiological load distributions within the arterial wall. Stress–strain behaviour was non-linear in uniaxial and inflation tests. Elastic tissue was 40% stiffer in the circumferential direction compared to axial in uniaxial tests and～100% stiffer in vessels at an axial stretch ratio of 1.2 or 1.3 and inflated to physiological pressure. Poisson’s ratio v_θz averaged 0.2 and v_zθ increased with circumferential stretch from ～0.2 to ～0.4. Axial stretch had little impact on circumferential behaviour. In intact (unpurified) vessels at constant length, axial forces decreased with pressure at low axial stretches but remained constant at higher stretches. Such a constant axial force is characteristic of incrementally isotropic arteries at their in vivo dimensions. In purified elastic tissue, force decreased with pressure at all axial strains, showing no trend towards isotropy. Analysis of the force–length–pressure data indicated a vessel with v_θz≈0.2 would stretch axially 2–4% with the cardiac pulse yet maintain constant axial force. We compared the ability of 4 mathematical models to predict the pressure-circumferential stretch behaviour of tethered, purified elastic tissue. Models that assumed isotropy could not predict the stretch at zero pressure. The neo-Hookean model overestimated the non-linearity of the response and two non-linear models underestimated it. A model incorporating contributions from orthogonal fibres captured the non-linearity but not the zero-pressure response. Models incorporating anisotropy and non-linearity should better predict the mechanical behaviour of elastic tissue of the proximal thoracic aorta.     

Elastic proteins: biological roles and mechanical properties

The term 'elastic protein' applies to many structural proteins with diverse functions and mechanical properties so there is room for confusion about its meaning. Elastic implies the property of elasticity, or the ability to deform reversibly without loss of energy; so elastic proteins should have high resilience. Another meaning for elastic is 'stretchy', or the ability to be deformed to large strains with little force. Thus, elastic proteins should have low stiffness. The combination of high resilience, large strains and low stiffness is characteristic of rubber-like proteins (e.g. resilin and elastin) that function in the storage of elastic-strain energy. Other elastic proteins play very different roles and have very different properties. Collagen fibres provide exceptional energy storage capacity but are not very stretchy. Mussel byssus threads and spider dragline silks are also elastic proteins because, in spite of their considerable strength and stiffness, they are remarkably stretchy. The combination of strength and extensibility, together with low resilience, gives these materials an impressive resistance to fracture (i.e. toughness), a property that allows mussels to survive crashing waves and spiders to build exquisite aerial filters. Given this range of properties and functions, it is probable that elastic proteins will provide a wealth of chemical structures and elastic mechanisms that can be exploited in novel structural materials through biotechnology.     

The viscoelastic basis for the tensile strength of elastin

Purified aortic elastin displays failure behaviour characteristic of an amorphous, noncrystalizing elastomer with failure properties showing a strong dependence on viscoelastic behaviour. Tensile breaking stresses and breaking strains measured over a range of temperatures, hydration levels, and strain rates are reducible to single curves by the application of shift factors obtained from dynamic mechanical tests. The breaking stress of rubbery elastin is similar to that found in other elastomers, but glassy elastin is about an order of magnitude less strong than expected. We suggest elastin's ability to be strengthened through viscous dissipation of strain energy and crack tip blunting is limited by its fibrillar structure.     

Consequences of forced silking

The forced silking of a spider to obtain major ampullate (MA) silk for experiments is a standard practice; however, this method may have profound effects on the resulting silk's properties. Experiments were performed to determine the magnitude of the difference in the forces required to draw silk from the MA gland between unrestrained spiders descending on their draglines and restrained spiders from which MA silk was drawn with a motor. The results show that freely falling spiders can spool silk with as little as 0.1 body weights of force, which generates a stress that is about 2% of the silk's tensile strength. In contrast, forcibly silked spiders apply as much as 4 body weights of force with an internal braking mechanism, and this force creates silk stresses in excess of 50% of the silk's tensile strength. The large forces observed in forced silking should strongly affect the draw alignment of the polymer network in the newly spun fibers, and this may account for the differences in material properties observed between naturally spun and forcibly spun MA silks. In addition, the heat produced by the internal friction brake during forced silking may set the upper limit of forced silking speed.     

Predator-induced plasticity in guppy (<Emphasis Type="Italic">Poecilia reticulata</Emphasis>) life history traits

A number of invertebrates show predator-induced plasticity in life-history and morphological traits that are considered adaptive. Evidence is accumulating that vertebrates may also adjust their life-history traits in response to predators; however, some of the patterns of plasticity, which appear to be an adaptive response specifically to the risk of size-selective predation, may instead result from reduced foraging in response to predator presence. Here, we describe a study of predator-induced plasticity in guppies (Poecilia reticulata). We have predicted that the plastic response to cues from a small, gape-limited, natural predator of guppies, the killlifish (Rivulus hartii), would be the opposite of that caused by reduced food intake. We have found that male guppies increased their size at maturity, both length and mass, in response to the non-lethal presence of this predator. This pattern of plasticity is the opposite of that observed in response to reduced food intake, where male guppies reduce size at maturity. The increase in size at maturity that we observed would likely reduce predation on adult male guppies by this native predator because it is gape-limited and can only eat juvenile and small adult guppies. This size advantage would be important especially because male guppies grow very little after maturity. Therefore, the pattern of plasticity that we observed is likely adaptive. In contrast, female guppies showed no significant response in size at first parturition to the experimental manipulation; however, we did find evidence suggesting that females may produce more, smaller offspring in response to cues from this predator.