Macroscopic coherent structures in a stochastic neural network: from interface dynamics to coarse-grained bifurcation analysis

We study coarse pattern formation in a cellular automaton modelling a spatially-extended stochastic neural network. The model, originally proposed by Gong and Robinson (Phys Rev E 85(5):055,101(R), 2012), is known to support stationary and travelling bumps of localised activity. We pose the model on a ring and study the existence and stability of these patterns in various limits using a combination of analytical and numerical techniques. In a purely deterministic version of the model, posed on a continuum, we construct bumps and travelling waves analytically using standard interface methods from neural field theory. In a stochastic version with Heaviside firing rate, we construct approximate analytical probability mass functions associated with bumps and travelling waves. In the full stochastic model posed on a discrete lattice, where a coarse analytic description is unavailable, we compute patterns and their linear stability using equation-free methods. The lifting procedure used in the coarse time-stepper is informed by the analysis in the deterministic and stochastic limits. In all settings, we identify the synaptic profile as a mesoscopic variable, and the width of the corresponding activity set as a macroscopic variable. Stationary and travelling bumps have similar meso- and macroscopic profiles, but different microscopic structure, hence we propose lifting operators which use microscopic motifs to disambiguate them. We provide numerical evidence that waves are supported by a combination of high synaptic gain and long refractory times, while meandering bumps are elicited by short refractory times

Detection of spatiotemporal variation in ranavirus distribution using eDNA

Amphibian population declines have been associated with emerging diseases including ranaviruses, which can cause mass die‐offs across entire amphibian communities. Understanding and mitigating disease spread requires knowledge of spatial and temporal patterns of pathogen distribution, but also how environmental factors influence pathogen occurrence. We applied environmental DNA (eDNA) detection tools to survey spatial and temporal distributions of ranaviruses by sampling 103 waterbodies in southeastern Ontario, Canada and assessed the role of abiotic factors as predictors of pathogen occurrence. Ten waterbodies sampled during June–August (>30 km between sites) revealed that ranavirus was marginally more prevalent (p = .055) during the latter part of the summer. Ninety‐three sites sampled at a finer scale (<10 km between sites) exhibited seasonal variability in ranavirus detection (site prevalence: 56% May; 66% July). Occupancy modeling revealed that wetland size and elevation influenced ranavirus occurrence while sampling date and water temperature influenced probability of detection. These findings indicate that biotic factors, such as host density and alternative hosts, should be investigated further as likely determinants of ranavirus prevalence across the landscape. Further, these results highlight the sensitivity of eDNA for detecting widespread presence of ranavirus and that abiotic factors may have a limited role in determining its prevalence and infectivity

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior

The association of hydration status with physical signs, symptoms and survival in advanced cancer-The use of Bioelectrical Impedance Vector Analysis (BIVA) technology to evaluate fluid volume in palliative care: An observational study

Background Hydration in advanced cancer is a controversial area; however, current hydration assessments methods are poorly developed. Bioelectrical impedance vector analysis (BIVA) is an accurate hydration tool; however its application in advanced cancer has not been explored. This study used BIVA to evaluate hydration status in advanced cancer to examine the association of fluid status with symptoms, physical signs, renal biochemical measures and survival. Materials and methods An observational study of 90 adults with advanced cancer receiving care in a UK specialist palliative care inpatient unit was conducted. Hydration status was assessed using BIVA in addition to assessments of symptoms, physical signs, performance status, renal biochemical measures, oral fluid intake and medications. The association of clinical variables with hydration was evaluated using regression analysis. A survival analysis was conducted to examine the influence of hydration status and renal failure. Results The hydration status of participants was normal in 43 (47.8%), 'more hydrated' in 37 (41.1%) and 'less hydrated' in 10 (11.1%). Lower hydration was associated with increased symptom intensity (Beta = -0.29, p = 0.04) and higher scores for physical signs associated with dehydration (Beta = 10.94, p = 0.02). Higher hydration was associated with oedema (Beta = 2.55, p<0.001). Median survival was statistically significantly shorter in 'less hydrated' patients (44 vs. 68 days; p = 0.049) and in pre-renal failure (44 vs. 100 days; p = 0.003). Conclusions In advanced cancer, hydration status was associated with clinical signs and symptoms. Hydration status and pre-renal failure were independent predictors of survival. Further studies can establish the utility of BIVA as a standardised hydration assessment tool and explore its potential research application, in order to inform the clinical management of fluid balance in patients with advanced cancer

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing

Using modeling to understand how athletes in different disciplines solve the same problem: Swimming versus running versus speed skating

Every new competitive season offers excellent examples of human locomotor abilities, regardless of the sport. As a natural consequence of competitions, world records are broken every now and then. World record races not only offer spectators the pleasure of watching very talented and highly trained athletes performing muscular tasks with remarkable skill, but also represent natural models of the ultimate expression of human integrated muscle biology, through strength, speed, or endurance performances. Given that humans may be approaching our species limit for muscular power output, interest in how athletes improve on world records has led to interest in the strategy of how limited energetic resources are best expended over a race. World record performances may also shed light on how athletes in different events solve exactly the same problem-minimizing the time required to reach the finish line. We have previously applied mathematical modeling to the understanding of world record performances in terms of improvements in facilities/equipment and improvements in the athletes' physical capacities. In this commentary, we attempt to demonstrate that differences in world record performances in various sports can be explained using a very simple modeling process

Does Glycine max leaves or Garcinia Cambogia promote weight-loss or lower plasma cholesterol in overweight individuals: a randomized control trial

<p>Abstract</p> <p>Background</p> <p>Natural food supplements with high flavonoid content are often claimed to promote weight-loss and lower plasma cholesterol in animal studies, but human studies have been more equivocal. The aim of this study was firstly to determine the effectiveness of natural food supplements containing <it>Glycine max </it>leaves extract (EGML) or <it>Garcinia cambogia </it>extract (GCE) to promote weight-loss and lower plasma cholesterol. Secondly to examine whether these supplements have any beneficial effect on lipid, adipocytokine or antioxidant profiles.</p> <p>Methods</p> <p>Eighty-six overweight subjects (Male:Female = 46:40, age: 20~50 yr, BMI > 23 < 29) were randomly assigned to three groups and administered tablets containing EGML (2 g/day), GCE (2 g/day) or placebo (starch, 2 g/day) for 10 weeks. At baseline and after 10 weeks, body composition, plasma cholesterol and diet were assessed. Blood analysis was also conducted to examine plasma lipoproteins, triglycerides, adipocytokines and antioxidants.</p> <p>Results</p> <p>EGML and GCE supplementation failed to promote weight-loss or any clinically significant change in %body fat. The EGML group had lower total cholesterol after 10 weeks compared to the placebo group (p < 0.05). EGML and GCE had no effect on triglycerides, non-HDL-C, adipocytokines or antioxidants when compared to placebo supplementation. However, HDL-C was higher in the EGML group (p < 0.001) after 10 weeks compared to the placebo group.</p> <p>Conclusions</p> <p>Ten weeks of EGML or GCE supplementation did not promote weight-loss or lower total cholesterol in overweight individuals consuming their habitual diet. Although, EGML did increase plasma HDL-C levels which is associated with a lower risk of atherosclerosis.</p