Modeling the self-organization property of keratin intermediate filaments

Keratin intermediate filaments (IFs) fulfill an important function of structural support in epithelial cells. The necessary mechanical attributes require that IFs be organized into a crosslinked network and accordingly, keratin IFs are typically organized into large bundles in surface epithelia. For IFs comprised of keratins 5 and 14 (K5, K14), found in basal keratinocytes of epidermis, bundling can be self-driven through interactions between K14's carboxy-terminal tail domain and two regions in the central α-helical rod domain of K5. Here, we exploit theoretical principles and computational modeling to investigate how such cis-acting determinants best promote IF crosslinking. We develop a simple model where keratin IFs are treated as rigid rods to apply Brownian dynamics simulation. Our findings suggest that long-range interactions between IFs are required to initiate the formation of bundlelike configurations, while tail domain-mediated binding events act to stabilize them. Our model explains the differences observed in the mechanical properties of wild-type versus disease-causing, defective IF networks. This effort extends the notion that the structural support function of keratin IFs necessitates a combination of intrinsic and extrinsic determinants, and makes specific predictions about the mechanisms involved in the formation of crosslinked keratin networks in vivo.     

Distinguishing cell types or populations based on the computational analysis of their infrared spectra

Infrared (IR) spectroscopy of intact cells results in a fingerprint of their biochemistry in the form of an IR spectrum; this has given rise to the new field of biospectroscopy. This protocol describes sample preparation (a tissue section or cytology specimen), the application of IR spectroscopy tools, and computational analysis. Experimental considerations include optimization of specimen preparation, objective acquisition of a sufficient number of spectra, linking of the derived spectra with tissue architecture or cell type, and computational analysis. The preparation of multiple specimens (up to 50) takes 8 h; the interrogation of a tissue section can take up to 6 h (～100 spectra); and cytology analysis (n = 50, 10 spectra per specimen) takes 14 h. IR spectroscopy generates complex data sets and analyses are best when initially based on a multivariate approach (principal component analysis with or without linear discriminant analysis). This results in the identification of class clustering as well as class-specific chemical entities.     

Exploring the difficulties of studying futures in ecology: what do ecological scientists think?

In a mainly experimental science based traditionally on hypothesis testing such as ecology, studying futures may be difficult. However, in the last few decades, predicting the consequences of global changes on the dynamics and function of ecological systems has become a major challenge in ecological research. To study how ecological scientists deal with potential difficulties in studying futures, we adopted a reflexive viewpoint on how scientists address the study of ecological futures. To do so we questioned a panel of ecological scientists on their practical involvement and point of view. Quantitative and qualitative analyses of their responses showed that predictions or predictive models were the dominant theme. Many quantitative models, based on statistical correlations, empirical rules or processes have been developed and their methodological limitations explored by the researchers we interviewed. In a small proportion of studies, qualitative scenarios have been elaborated to explore the range of possible futures. Interviewees emphasized the problem of dealing with ecological complexity and multiple future possibilities. Specificities of futures compared to past or present events were not fully identified. In fact, researchers studying futures mainly adopted a reductionist approach, trying to simplify complex ecological systems. But methods and tools promoted by such an approach to science may not always be appropriate to deal with future ecological complexity. Indeed, an emphasis on prediction prevents ecologists from acknowledging the multiplicity and undetermined nature of futures.     

Spatial pattern of central African rainforests can be predicted from average tree size

When considering all trees irrespective of their species, natural tropical rain forests typically exhibit spatial patterns that range from random to regular. The regularity is often interpreted as a footprint of tree competition. Using 23 permanent sample plots totalling 61 ha in the rain forests of central Africa, we characterized their spatial patterns and modelled those that exhibited regularity by a Strauss point process. This Strauss process is obtained as a Markov point process whose interaction function is an exponential function of a competition index commonly used in forestry. The parameter of this Strauss process characterizes the strength of competition. The 23 plots in central Africa differed in tree density and basal area, and could be discriminated depending on the type of spatial patterns: plots having a large basal area with respect to their density had a non regular pattern, whereas those having a small basal area with respect to their density had a regular pattern. For those plots that exhibited regularity, average tree size could be used to predict the strength of competition. The parameter of the Strauss process was significantly related to the average size by a linear relationship, such that competition decreases as average tree size increases. This relationship extrapolated to a null value of the Strauss parameter when average tree size reaches 32 cm in diameter. This relationship between average tree size and spatial pattern is a testable feature for future studies on the relationship between competition and spatial pattern in natural forests.     

Improving indicator species analysis by combining groups of sites

Indicator species are species that are used as ecological indicators of community or habitat types, environmental conditions, or environmental changes. In order to determine indicator species, the characteristic to be predicted is represented in the form of a classification of the sites, which is compared to the patterns of distribution of the species found at the sites. Indicator species analysis should take into account the fact that species have different niche breadths: if a species is related to the conditions prevailing in two or more groups of sites, an indicator species analysis undertaken on individual groups of sites may fail to reveal this association. In this paper, we suggest improving indicator species analysis by considering all possible combinations of groups of sites and selecting the combination for which the species can be best used as indicator. When using a correlation index, such as the point‐biserial correlation, the method yields the combination where the difference between the observed and expected abundance/frequency of the species is the largest. When an indicator value index (IndVal) is used, the method provides the set of site‐groups that best matches the observed distribution pattern of the species. We illustrate the advantages of the method in three different examples. Consideration of combinations of groups of sites provides an extra flexibility to qualitatively model the habitat preferences of the species of interest. The method also allows users to cross multiple classifications of the same sites, increasing the amount of information resulting from the analysis. When applied to community types, it allows one to distinguish those species that characterize individual types from those that characterize the relationships between them. This distinction is useful to determine the number of types that maximizes the number of indicator species.