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Interspecific interactions and the evolution of dispersal are both of interest when considering the potential impact of habitat fragmentation on community ecology, but the interaction between these processes is not well studied. We address this by considering the coevolution of dispersal strategies in a host-parasitoid system. An individual-based host-parasitoid metapopulation model was constructed for a patchy environment, allowing for evolution in dispersal rates of both species. Highly rarefied environments with few suitable patches selected against dispersal in both species, as did relatively static environments. Provided that parasitoids persist, all the variables studied led to stable equilibria in dispersal rates for both species. There was a tendency toward higher dispersal rates in parasitoids because of the asymmetric relationships of the two species to the patches: vacant patches are most valuable for hosts, but unsuitable for parasitoids, which require an established host population to reproduce. High host dispersal rate was favoured by high host population growth rate, and in the parasitoid by high growth rates in both species.

PURPOSE: Quantitative uncertainties are a direct consequence of averaging, a common procedure when building life cycle inventories (LCIs). This averaging can be amongst locations, times, products, scales or production technologies. To date, however, quantified uncertainties at the unit process level have largely been generated using a Numerical Unit Spread Assessment Pedigree (NUSAP) approach and often disregard inherent uncertainties (inaccurate measurements) and spread (variability around means). METHODS: A decision tree for primary and secondary data at the unit process level was initially created. Around this decision tree, a protocol was developed with the recognition that dispersions can be either results of inherent uncertainty, spread amongst data points or products of unrepresentative data. In order to estimate the characteristics of uncertainties for secondary data, a method for weighting means amongst studies is proposed. As for unrepresentativeness, the origin and adaptation of NUSAP to the field of life cycle assessment are discussed, and recommendations are given. RESULTS AND DISCUSSION: By using the proposed protocol, cross-referencing of outdated data is avoided, and user influence on results is reduced. In the meantime, more accurate estimates can be made for horizontally averaged data with accompanying spread and inherent uncertainties, as these deviations often contribute substantially towards the overall dispersion. CONCLUSIONS: In this article, we highlight the importance of including inherent uncertainties and spread alongside the NUSAP pedigree. As uncertainty data often are missing in LCI literature, we here describe a method for evaluating these by taking several reported values into account. While this protocol presents a practical way towards estimating overall dispersion, better reporting in literature is promoted in order to determine real uncertainty parameters.

When desirable retardation of skin penetration of toxic compounds such as pesticides and chemical warfare agents was evaluated using model permeants, retardation was observed under certain conditions, but was not predictable. It was concluded that generic penetration modulation was not a realistic goal. Investigations into skin surface sampling techniques highlighted the difficulties of minimising variability, and comparison of in vitro tape stripping with published in vivo data for a clobetasol propionate cream demonstrated the significance of formulation inhomogeneity. Determination of orally administered doxycycline in the stratum corneum using in vivo skin surface biopsies suggested that it was unlikely that this technique could be of value for most orally administered drugs. Evaporative loss of volatile permeants, such as fragrances, during skin penetration studies makes it impossible to achieve full mass balance. Direct capture of evaporating material was occlusive and significantly affected the amount that permeated through the skin membranes. A simple novel method was therefore developed to allow estimation of evaporative loss under the experimental conditions. Pre-study skin water permeability coefficient (Kp) is frequently used as a membrane integrity check and it is generally assumed that skin of higher water permeability will be of higher permeability for subsequently applied compounds, regardless of differing physiochemical properties. However, it was shown that Kp did not correlate with subsequent test compound permeation for twelve compounds of moderate to high lipophilicity.

Benign hepatosteatosis, affected by lipid uptake, de novo lipogenesis and fatty acid (FA) oxidation, progresses to non-alcoholic steatohepatitis (NASH) on stress and inflammation. A key macronutrient proposed to increase hepatosteatosis and NASH risk is fructose. Excessive intake of fructose causes intestinal-barrier deterioration and endotoxaemia. However, how fructose triggers these alterations and their roles in hepatosteatosis and NASH pathogenesis remain unknown. Here we show, using mice, that microbiota-derived Toll-like receptor (TLR) agonists promote hepatosteatosis without affecting fructose-1-phosphate (F1P) and cytosolic acetyl-CoA. Activation of mucosal-regenerative gp130 signalling, administration of the YAP-induced matricellular protein CCN1 or expression of the antimicrobial peptide Reg3b (beta) peptide counteract fructose-induced barrier deterioration, which depends on endoplasmic-reticulum stress and subsequent endotoxaemia. Endotoxin engages TLR4 to trigger TNF production by liver macrophages, thereby inducing lipogenic enzymes that convert F1P and acetyl-CoA to FA in both mouse and human hepatocytes. Fructose consumption has greatly increased in recent years and has been linked to the development of hepatic steatosis. Here, the authors show that fructose promotes gut-barrier deterioration and subsequent endotoxaemia that in turn induces hepatic lipogenesis by activation TLR signalling in liver macrophages.

Gait-initiation onset (GI-onset) during sit-to-walk (STW) is commonly defined by mediolateral ground-reaction-force (xGRF) rising and crossing a threshold pre-determined from sit-to-stand peak xGRF. However, after stroke this method [xGRFthresh] lacks validity due to impaired STW performance. Instead, methodologies based upon instance of swing-limb maximum-vertical-GRF [vGRFmaxSWING], maximum-xGRF [xGRFmax], and swing-limb heel-off [firstHEELoff] can be applied, although their validity is unclear. Therefore, we determined these methodologies' validity by revealing the shortest transition-time (seat-off-GI-onset), their utility in routinely estimating GI-onset, and whether they exhibited satisfactory intra-subject reliability. Twenty community-dwelling stroke (60 (SD 14) years), and twenty-one age-matched healthy volunteers (63 (13) years) performed 5 standardised STW trials with 2 force-plates and optical motion-tracking. Transition-time differences across-methods were assessed using Friedman tests with post-hoc pairwise-comparisons. Within-method single-measure intra-subject reliability was determined using ICC3,1 and standard errors of measurement (SEMs). In the healthy group, median xGRFthresh transition-time was significantly shorter than xGRFmax (0.183s). In both the healthy and stroke groups, xGRFthresh transition-times (0.027s, 0.695s respectively) and vGRFmaxSWING (0.080s, 0.522s) were significantly shorter than firstHEELoff (0.293s, 1.085s) (p<0.001 in all cases). GI-onset failed to be estimated in 48% of stroke trials using xGRFthresh. Intra-subject variability was relatively high but was comparable across all estimation methods. The firstHEELoff method yielded significantly longer transition-times. The xGRFthresh method failed to routinely produce an estimation of GI-onset estimation. Thus, with all methods exhibiting low, yet comparable intra-subject repeatability, averaged xGRFmax or vGRFmaxSWING repeated-measures are recommended to estimate GI-onset for both healthy and community-dwelling stroke individuals.

Rising-to-walk is an everyday transitional movement task rarely employed in gait rehabilitation. Sit-to-walk (STW) and sit-to-stand-and-walk (STSW), where a pause separates sit-to-stand and gait-initiation (GI) represent extremes of rising-to-walk behaviour. Delayed GI can indicate pathological impairment but is also observed in healthy individuals. We hypothesise that healthy subjects express consistent biomechanical parameters, among others that differ, during successful rising-to-walk task performance regardless of behaviour. This study therefore sought to identify if any parameters are consistent between STW and STSW in health because they represent normal rise-to-walk performance independent of pause, and also because they represent candidate parameters sensitive enough to monitor change in pathology. Ten healthy volunteers performed 5 trials of STW and STSW. Event timing, ground-reaction-forces (GRFs), whole-body-centre-of-mass (BCoM) displacement, and centre-of-pressure (CoP) to extrapolated BCoM (xCoM) distance (indicator of positional stability) up to the 3rd step were compared between-tasks with paired t-tests. For consistent parameters; agreement between-tasks was assessed using Bland-Altman analyses and minimal-detectable-change (MDC) calculations. Mean vertical GRFs, peak forward momentum and fluidity during rising; CoP-xCoM separation at seat-off, upright, GI-onset, and steps1-2; and forward BCoM velocity were all significantly greater in STW. In contrast, peak BCoM vertical momentum, flexion-momentum time, and 3rd step stability were consistent between tasks and yielded acceptable reliability. STW is a more challenging task due to the merging of rising with GI reflected by greater CoP-xCoM separation compared to STSW indicative of more positional instability. However, BCoM vertical momentum, flexion-momentum time, and step3 stability remained consistent in healthy individuals and are therefore candidates with which to monitor change in gait rehabilitation following pathology. Future studies should impose typical pause-durations observed in pathology upon healthy subjects to determine if the parameters we have identified remain consistent.

The mussel industry faces challenges such as low and inconsistent levels of larvae settlement and poor-quality spat, leading to variable production. However, mussel farming remains a vital sustainable and environmentally responsible method for producing protein, fostering ecological responsibility in the aquaculture sector. We investigate the population connectivity and larval dispersion of blue mussels ( Mytilus edulis ) in Scottish waters, as a case study, using a multidisciplinary approach that combined genetic data and particle modelling. This research allows us to develop a thorough understanding of blue mussel population dynamics in mid-latitude fjord regions, to infer gene-flow patterns, and to estimate population divergence. Our findings reveal a primary south-to-north particle transport direction and the presence of five genetic clusters. We discover a significant and continuous genetic material exchange among populations within the study area, with our biophysical model’s outcomes aligning with our genetic observations. Additionally, our model reveals a robust connection between the southwest coast and the rest of the west coast. This study will guide the preservation of mussel farming regions, ensuring sustainable populations that contribute to marine ecosystem health and resilience. A study of mussel populations in Scottish waters found that mussel larvae move from south to north and form five distinct genetic groups. This helps preserve mussel farms and marine ecosystems.

Many if not all models of disease transmission on networks can be linked to the exact state-based Markovian formulation. However the large number of equations for any system of realistic size limits their applicability to small populations. As a result, most modelling work relies on simulation and pairwise models. In this paper, for a simple SIS dynamics on an arbitrary network, we formalise the link between a well known pairwise model and the exact Markovian formulation. This involves the rigorous derivation of the exact ODE model at the level of pairs in terms of the expected number of pairs and triples. The exact system is then closed using two different closures, one well established and one that has been recently proposed. A new interpretation of both closures is presented, which explains several of their previously observed properties. The closed dynamical systems are solved numerically and the results are compared to output from individual-based stochastic simulations. This is done for a range of networks with the same average degree and clustering coefficient but generated using different algorithms. It is shown that the ability of the pairwise system to accurately model an epidemic is fundamentally dependent on the underlying large-scale network structure. We show that the existing pairwise models are a good fit for certain types of network but have to be used with caution as higher-order network structures may compromise their effectiveness.

Cardiovascular disease remains the leading cause of mortality and healthcare expenditures in the United States. It is also a major contributor to premature mortality, years lived with disability, and rising healthcare costs around the world. Despite the availability of proven therapies and interventions that could vastly decrease the burden of cardiovascular disease and cardiometabolic conditions, their implementation is poor, with generally less than half of patients being treated with the most effective therapies. Implementation science offers promise in bridging this gap and mitigating disparities. However, even though small studies have shown that there are effective methods to improve the implementation of evidence-based therapies, these methods have not been scaled to make an impact at the level of health systems or nationally. A coordinated, multi-stakeholder approach is essential to identify barriers to implementation on a broad scale and, more critically, to develop and deploy practical solutions. The Duke Clinical Research Institute conducted an Implementation Summit entitled “Scalability, Spread, and Sustainability” to explore strategies for advancing the uptake of evidence-based interventions for cardiometabolic diseases in healthcare in the United States. This manuscript presents the participants’ multi-stakeholder perspective on the steps necessary to improve the implementation of evidence-based therapies in cardiometabolic disease. Key recommendations include focused efforts on evidence generation around broad implementation strategies, dissemination of the evidence generated, uptake of evidence into usual care settings, and investment in training the current and next generations of leaders in implementation.

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune.