Aspects of leaf anatomy of kudzu (Pueraria lobata, Leguminosae-Faboideae) related to water and energy balance

Kudzu is a cover crop that has escaped cultivation in some subtropical and warm temperate regions. Kudzu has previously demonstrated broad intraspecific physiological plasticity while colonizing new environments. The objective of this paper was to investigate characteristics of kudzu leaflet anatomy that might contribute to its successful growth in climatically distinct environments, and to escape cultivation as well. Fresh and fixed leaflet strips of field-grown plants were analyzed. The lower epidermis of kudzu showed a higher frequency of stomata (147 ± 19 stomata mm-2) than the upper epidermis (26 ± 17 stomata mm-2). The average number of trichomes per square milimeter was 8 for both the upper and the lower epidermis. The average trichome length was 410 ± 200 mum for the upper epidermis and 460 ± 190 mum for the lower epidermis. Cuticle thickness was not considerably different between lower and upper epidermis. The leaflet blade consisted basically of two layers (upper and lower) of unicellular epidermis, two layers of palisade parenchyma and one layer of spongy parenchyma. One layer of paraveinal mesophyll was found between palisade and spongy parenchyma. In conclusion, leaflets of kudzu present anatomical characteristics that might contribute to the broad physiological plasticity shown by kudzu.

High precision anatomy for MEG.

Precise MEG estimates of neuronal current flow are undermined by uncertain knowledge of the head location with respect to the MEG sensors. This is either due to head movements within the scanning session or systematic errors in co-registration to anatomy. Here we show how such errors can be minimized using subject-specific head-casts produced using 3D printing technology. The casts fit the scalp of the subject internally and the inside of the MEG dewar externally, reducing within session and between session head movements. Systematic errors in matching to MRI coordinate system are also reduced through the use of MRI-visible fiducial markers placed on the same cast. Bootstrap estimates of absolute co-registration error were of the order of 1mm. Estimates of relative co-registration error were <1.5mm between sessions. We corroborated these scalp based estimates by looking at the MEG data recorded over a 6month period. We found that the between session sensor variability of the subject's evoked response was of the order of the within session noise, showing no appreciable noise due to between-session movement. Simulations suggest that the between-session sensor level amplitude SNR improved by a factor of 5 over conventional strategies. We show that at this level of coregistration accuracy there is strong evidence for anatomical models based on the individual rather than canonical anatomy; but that this advantage disappears for errors of greater than 5mm. This work paves the way for source reconstruction methods which can exploit very high SNR signals and accurate anatomical models; and also significantly increases the sensitivity of longitudinal studies with MEG.

How early can we predict Alzheimer's disease using computational anatomy?

Computational anatomy with magnetic resonance imaging (MRI) is well established as a noninvasive biomarker of Alzheimer's disease (AD); however, there is less certainty about its dependency on the staging of AD. We use classical group analyses and automated machine learning classification of standard structural MRI scans to investigate AD diagnostic accuracy from the preclinical phase to clinical dementia. Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative were stratified into 4 groups according to the clinical status-(1) AD patients; (2) mild cognitive impairment (MCI) converters; (3) MCI nonconverters; and (4) healthy controls-and submitted to a support vector machine. The obtained classifier was significantly above the chance level (62%) for detecting AD already 4 years before conversion from MCI. Voxel-based univariate tests confirmed the plausibility of our findings detecting a distributed network of hippocampal-temporoparietal atrophy in AD patients. We also identified a subgroup of control subjects with brain structure and cognitive changes highly similar to those observed in AD. Our results indicate that computational anatomy can detect AD substantially earlier than suggested by current models. The demonstrated differential spatial pattern of atrophy between correctly and incorrectly classified AD patients challenges the assumption of a uniform pathophysiological process underlying clinically identified AD.

Social structure emerges via the interaction between local ecology and individual behaviour.

1. The formation of groups is a fundamental aspect of social organization, but there are still many questions regarding how social structure emerges from individuals making non-random associations. 2. Although food distribution and individual phenotypic traits are known to separately influence social organization, this is the first study, to our knowledge, experimentally linking them to demonstrate the importance of their interaction in the emergence of social structure. 3. Using an experimental design in which food distribution was either clumped or dispersed, in combination with individuals that varied in exploratory behaviour, our results show that social structure can be induced in the otherwise non-social European shore crab (Carcinus maenas). 4. Regardless of food distribution, individuals with relatively high exploratory behaviour played an important role in connecting otherwise poorly connected individuals. In comparison, low exploratory individuals aggregated into cohesive, stable subgroups (moving together even when not foraging), but only in tanks where resources were clumped. No such non-foraging subgroups formed in environments where food was evenly dispersed. 5. Body size did not accurately explain an individual's role within the network for either type of food distribution. 6. Because of their synchronized movements and potential to gain social information, groups of low exploratory crabs were more effective than singletons at finding food. 7. Because social structure affects selection, and social structure is shown to be sensitive to the interaction between ecological and behavioural differences among individuals, local selective pressures are likely to reflect this interaction.

Ecological niche overlap in sister species: how do oil-collecting bees Macropis europaea and M. fulvipes (Hymenoptera: Melittidae) avoid competition and hybridization?

Oil-collecting bees are found worldwide and always in association with particular oil-producing flowers. In the Western Palearctic, three oil-collecting bee species within the genus Macropis (Hymenoptera, Melittidae) interact in a tight pollination mutualism with species of the only European oil-producing plant genus Lysimachia L. (Myrsinaceae). Two of these oil-collecting bees (Macropis europaea and Macropis fulvipes) show overlapping geographic distributions, comparable morphologies, and similar ecological characteristics (e.g., habitat type, floral preferences). In view of these similarities, we presume that hybridization should occur between the two species unless potential variation among the species' ecological niches prevents it, simultaneously decreasing competition for resources. Using modern genetic analyses and ecological niche modeling on a large bee sampling throughout Europe, we discuss new perspectives on the ecology and evolutionary history of this mutualism.

Spatial Polarization of the Ecological Footprint distribution

The international allocation of natural resources is determined, not by any ethical or ecological criteria, but by the dominance of market mechanisms. From a core-periphery perspective, this allocation may even be driven by historically determined structural patterns, with a core group of countries whose consumption appropriates most available natural resources, and another group, having low natural resource consumption, which plays a peripheral role. This article consists of an empirical distributional analysis of natural resource consumption (as measured by Ecological Footprints) whose purpose is to assess the extent to which the distribution of consumption responds to polarization (as opposed to mere inequality). To assess this, we estimate and decompose different polarization indices for a balanced sample of 119 countries over the period 1961 to 2007. Our results points toward a polarized distribution which is consistent with a core-periphery framework.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Benefits from ecological study methods to taxonomy of enchytraeids in southern Mata Atlântica

The objective of this work was to determine how taxonomy benefited from the ecological quantitative and site-based sampling methods in enchytraeids studies. Enchytraeids (small relatives of earthworms) were sampled in different phases of rain forest regeneration in the southern Mata Atlântica in Paraná, Brazil. The research combined ecological and taxonomic work, because enchytraeids are poorly studied and difficult to identify, and many new species were expected. The provision of large numbers of specimens enabled the test of species diagnoses by investigating the ranges of character variations in a larger series of specimens. Simplified species diagnoses adapted to the local conditions that allowed the identification of all specimens, juveniles included, were developed. Key characters and character states are presented for the three genera: Achaeta, Hemienchytraeus and Guaranidrilus. Among several new species, a rare species, possibly a remnant of the autochthonous forest fauna, was found and described.

Towards an ecological index for tropical soil quality based on soil macrofauna

The objective of this work was to construct a simple index based on the presence/absence of different groups of soil macrofauna to determine the ecological quality of soils. The index was tested with data from 20 sites in South and Central Tabasco, Mexico, and a positive relation between the model and the field observations was detected. The index showed that diverse agroforestry systems had the highest soil quality index (1.00), and monocrops without trees, such as pineapple, showed the lowest soil quality index (0.08). Further research is required to improve this model for natural systems that have very low earthworm biomass (<10 g m-2) and a high number of earthworm species (5-7), as it is in the tropical rain forest, whose soil quality index was medium (0.5). The application of this index will require an illustrated guide for its users. Further studies are required in order to test the use of this index by farmers.

Spatial polarization of the ecological footprint distribution

The international allocation of natural resources is determined, not by any ethical or ecological criteria, but by the dominance of market mechanisms. From a core-periphery perspective, this allocation may even be driven by historically determined structural patterns, with a core group of countries whose consumption appropriates most available natural resources, and another group, having low natural resource consumption, which plays a peripheral role. This article consists of an empirical distributional analysis of natural resource consumption (as measured by Ecological Footprints) whose purpose is to assess the extent to which the distribution of consumption responds to polarization (as opposed to mere inequality). To assess this, we estimate and decompose different polarization indices for a balanced sample of 119 countries over the period 1961 to 2007. Our results points toward a polarized distribution which is consistent with a core-periphery framework. Keywords: Polarization, Core-Periphery, Ecological Footprint

T cell recognition of hapten. Anatomy of T cell receptor binding of a H-2kd-associated photoreactive peptide derivative.

To elucidate the structural basis of T cell recognition of hapten-modified antigenic peptides, we studied the interaction of the T1 T cell antigen receptor (TCR) with its ligand, the H-2Kd-bound Plasmodium berghei circumsporozoite peptide 252-260 (SYIPSAEKI) containing photoreactive 4-azidobenzoic acid (ABA) on P. berghei circumsporozoite Lys259. The photoaffinity-labeled TCR residue(s) were mapped as Tyr48 and/or Tyr50 of complementary determining region 2beta (CDR2beta). Other TCR-ligand contacts were identified by mutational analysis. Molecular modeling, based on crystallographic coordinates of closely related TCR and major histocompatibility complex I molecules, indicated that ABA binds strongly and specifically in a cavity between CDR3alpha and CDR2beta. We conclude that TCR expressing selective Vbeta and CDR3alpha sequences form a binding domain between CDR3alpha and CDR2beta that can accommodate nonpeptidic moieties conjugated at the C-terminal portion of peptides binding to major histocompatibility complex (MHC) encoded proteins.