3,307 research outputs found
Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks
Scientists have long sought to understand how vascular networks supply blood
and oxygen to cells throughout the body. Recent work focuses on principles that
constrain how vessel size changes through branching generations from the aorta
to capillaries and uses scaling exponents to quantify these changes. Prominent
scaling theories predict that combinations of these exponents explain how
metabolic, growth, and other biological rates vary with body size.
Nevertheless, direct measurements of individual vessel segments have been
limited because existing techniques for measuring vasculature are invasive,
time consuming, and technically difficult. We developed software that extracts
the length, radius, and connectivity of in vivo vessels from contrast-enhanced
3D Magnetic Resonance Angiography. Using data from 20 human subjects, we
calculated scaling exponents by four methods--two derived from local properties
of branching junctions and two from whole-network properties. Although these
methods are often used interchangeably in the literature, we do not find
general agreement between these methods, particularly for vessel lengths.
Measurements for length of vessels also diverge from theoretical values, but
those for radius show stronger agreement. Our results demonstrate that vascular
network models cannot ignore certain complexities of real vascular systems and
indicate the need to discover new principles regarding vessel lengths
Revisiting Multi-Subject Random Effects in fMRI: Advocating Prevalence Estimation
Random Effects analysis has been introduced into fMRI research in order to
generalize findings from the study group to the whole population. Generalizing
findings is obviously harder than detecting activation in the study group since
in order to be significant, an activation has to be larger than the
inter-subject variability. Indeed, detected regions are smaller when using
random effect analysis versus fixed effects. The statistical assumptions behind
the classic random effects model are that the effect in each location is
normally distributed over subjects, and "activation" refers to a non-null mean
effect. We argue this model is unrealistic compared to the true population
variability, where, due to functional plasticity and registration anomalies, at
each brain location some of the subjects are active and some are not. We
propose a finite-Gaussian--mixture--random-effect. A model that amortizes
between-subject spatial disagreement and quantifies it using the "prevalence"
of activation at each location. This measure has several desirable properties:
(a) It is more informative than the typical active/inactive paradigm. (b) In
contrast to the hypothesis testing approach (thus t-maps) which are trivially
rejected for large sample sizes, the larger the sample size, the more
informative the prevalence statistic becomes.
In this work we present a formal definition and an estimation procedure of
this prevalence. The end result of the proposed analysis is a map of the
prevalence at locations with significant activation, highlighting activations
regions that are common over many brains
Exploring the Clinical Reasoning of Experienced Occupational Therapists: A Metacognitive Approach
This study explored the clinical reasoning of experienced occupational therapists’ (OTs) perceptions of how practitioners apply anatomy concepts in practice. The research question was: how do OTs apply anatomy concepts during their clinical reasoning processes in everyday practice
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CA1-projecting subiculum neurons facilitate object-place learning.
Recent anatomical evidence suggests a functionally significant back-projection pathway from the subiculum to the CA1. Here we show that the afferent circuitry of CA1-projecting subicular neurons is biased by inputs from CA1 inhibitory neurons and the visual cortex, but lacks input from the entorhinal cortex. Efferents of the CA1-projecting subiculum neurons also target the perirhinal cortex, an area strongly implicated in object-place learning. We identify a critical role for CA1-projecting subicular neurons in object-location learning and memory, and show that this projection modulates place-specific activity of CA1 neurons and their responses to displaced objects. Together, these experiments reveal a novel pathway by which cortical inputs, particularly those from the visual cortex, reach the hippocampal output region CA1. Our findings also implicate this circuitry in the formation of complex spatial representations and learning of object-place associations
TRY plant trait database - enhanced coverage and open access
Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
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