Modeling the interactions between river morphodynamics and riparian vegetation

The study of river-riparian vegetation interactions is an important and intriguing research field in geophysics. Vegetation is an active element of the ecological dynamics of a floodplain which interacts with the fluvial processes and affects the flow field, sediment transport, and the morphology of the river. In turn, the river provides water, sediments, nutrients, and seeds to the nearby riparian vegetation, depending on the hydrological, hydraulic, and geomorphological characteristic of the stream. In the past, the study of this complex theme was approached in two different ways. On the one hand, the subject was faced from a mainly qualitative point of view by ecologists and biogeographers. Riparian vegetation dynamics and its spatial patterns have been described and demonstrated in detail, and the key role of several fluvial processes has been shown, but no mathematical models have been proposed. On the other hand, the quantitative approach to fluvial processes, which is typical of engineers, has led to the development of several morphodynamic models. However, the biological aspect has usually been neglected, and vegetation has only been considered as a static element. In recent years, different scientific communities (ranging from ecologists to biogeographers and from geomorphologists to hydrologists and fluvial engineers) have begun to collaborate and have proposed both semiquantitative and quantitative models of river-vegetation interconnections. These models demonstrate the importance of linking fluvial morphodynamics and riparian vegetation dynamics to understand the key processes that regulate a riparian environment in order to foresee the impact of anthropogenic actions and to carefully manage and rehabilitate riparian areas. In the first part of this work, we review the main interactions between rivers and riparian vegetation, and their possible modeling. In the second part, we discuss the semiquantitative and quantitative models which have been proposed to date, considering both multi- and single-thread river

Controls and limits on bedrock channel geometry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94837/1/jgrf728.pd

Medical conditions in autism spectrum disorders

Autism spectrum disorder (ASD) is a behaviourally defined syndrome where the etiology and pathophysiology is only partially understood. In a small proportion of children with the condition, a specific medical disorder is identified, but the causal significance in many instances is unclear. Currently, the medical conditions that are best established as probable causes of ASD include Fragile X syndrome, Tuberous Sclerosis and abnormalities of chromosome 15 involving the 15q11-13 region. Various other single gene mutations, genetic syndromes, chromosomal abnormalities and rare de novo copy number variants have been reported as being possibly implicated in etiology, as have several ante and post natal exposures and complications. However, in most instances the evidence base for an association with ASD is very limited and largely derives from case reports or findings from small, highly selected and uncontrolled case series. Not only therefore, is there uncertainty over whether the condition is associated, but the potential basis for the association is very poorly understood. In some cases the medical condition may be a consequence of autism or simply represent an associated feature deriving from an underlying shared etiology. Nevertheless, it is clear that in a growing proportion of individuals potentially causal medical conditions are being identified and clarification of their role in etio-pathogenesis is necessary. Indeed, investigations into the causal mechanisms underlying the association between conditions such as tuberous sclerosis, Fragile X and chromosome 15 abnormalities are beginning to cast light on the molecular and neurobiological pathways involved in the pathophysiology of ASD. It is evident therefore, that much can be learnt from the study of probably causal medical disorders as they represent simpler and more tractable model systems in which to investigate causal mechanisms. Recent advances in genetics, molecular and systems biology and neuroscience now mean that there are unparalleled opportunities to test causal hypotheses and gain fundamental insights into the nature of autism and its development

COVID-19: Rapid antigen detection for SARS-CoV-2 by lateral flow assay: A national systematic evaluation of sensitivity and specificity for mass-testing

Background Lateral flow device (LFD) viral antigen immunoassays have been developed around the world as diagnostic tests for SARS-CoV-2 infection. They have been proposed to deliver an infrastructure-light, cost-economical solution giving results within half an hour. Methods LFDs were initially reviewed by a Department of Health and Social Care team, part of the UK government, from which 64 were selected for further evaluation from 1st August to 15th December 2020. Standardised laboratory evaluations, and for those that met the published criteria, field testing in the Falcon-C19 research study and UK pilots were performed (UK COVID-19 testing centres, hospital, schools, armed forces). Findings 4/64 LFDs so far have desirable performance characteristics (orient Gene, Deepblue, Abbott and Innova SARS-CoV-2 Antigen Rapid Qualitative Test). All these LFDs have a viral antigen detection of >90% at 100,000 RNA copies/ml. 8951 Innova LFD tests were performed with a kit failure rate of 5.6% (502/8951, 95% CI: 5.1–6.1), false positive rate of 0.32% (22/6954, 95% CI: 0.20–0.48). Viral antigen detection/sensitivity across the sampling cohort when performed by laboratory scientists was 78.8% (156/198, 95% CI 72.4–84.3). Interpretation Our results suggest LFDs have promising performance characteristics for mass population testing and can be used to identify infectious positive individuals. The Innova LFD shows good viral antigen detection/sensitivity with excellent specificity, although kit failure rates and the impact of training are potential issues. These results support the expanded evaluation of LFDs, and assessment of greater access to testing on COVID-19 transmission. Funding Department of Health and Social Care. University of Oxford. Public Health England Porton Down, Manchester University NHS Foundation Trust, National Institute of Health Research