Active normal faults and coupled landscape response: bedrock variability in the southern Gulf of Corinth, central Greece

Fluvial erosion processes control landscape response to climatic and tectonic signals and its propagation into sedimentary basins. Considerable effort has gone into quantifying and modelling the effect of changes in uplift rates on fluvial erosion in bedrock rivers. However, current landscape models, based on stream power, tend to ignore the effects bedrock variability. The lack of available data relating rock strength to bedrock erodibility in fluvial settings has limited our ability to explore this question. Recent attempts at modelling to resolve this issue rely on indirect or theoretical rock strength properties. An alternative approach requires field measurements of rock strength together with geomorphological and tectonic constraints to quantify the effect of rock strength on river evolution. The Gulf of Corinth, central Greece, is one of the fastest extending rifts in the world and tectonic boundary conditions are well constrained. We (1) review published constraints on uplift along the active normal faults on the southern coast of the Gulf, and project uplift away from the faults into three catchments using a viscoelastic dislocation model; (2) test how channel width and slope vary in these rivers upstream of the active faults, and we use this data to estimate the distribution of stream power down-system; (3) systematically measure rock strength, using a Schmidt hammer, to constrain its effect on river response to uplift. All the rivers have knickpoints upstream of the active faults and we show they are responding transiently to active faulting. By assuming that our derived uplift rate equals stream power-driven erosion rate we calculate the erodibility, k, of bedrock. We demonstrate that stream powers in rivers crossing faults in the southern Gulf of Corinth correlate with rock strength and derive a non-linear power relationship between bedrock erodibility k and Schmidt hammer rebound. These findings highlight the need to incorporate bedrock variability into stream power erosion models

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Expansion of airway basal epithelial cells from primary human non-small cell lung cancer tumors

Pre-clinical non-small cell lung cancer (NSCLC) models are poorly representative of the considerable inter- and intra-tumor heterogeneity of the disease in patients. Primary cell-based in vitro models of NSCLC are therefore desirable for novel therapy development and personalized cancer medicine. Methods have been described to generate rapidly proliferating epithelial cell cultures from multiple human epithelia using 3T3-J2 feeder cell culture in the presence of Y-27632, a RHO-associated protein kinase (ROCK) inhibitor, in what are known as "conditional reprograming conditions" (CRC) or 3T3+Y. In some cancer studies, variations of this methodology have allowed primary tumor cell expansion across a number of cancer types but other studies have demonstrated the preferential expansion of normal epithelial cells from tumors in such conditions. Here, we report our experience regarding the derivation of primary NSCLC cell cultures from 12 lung adenocarcinoma patients enrolled in the Tracking Cancer Evolution through Therapy (TRACERx) clinical study and discuss these in the context of improving the success rate for in vitro cultivation of cells from NSCLC tumors. This article is protected by copyright. All rights reserved

Straight from the source's mouth: Controls on field‐constrained sediment export across the entire active Corinth Rift, central Greece

The volume and grain‐size of sediment supplied from catchments fundamentally control basin stratigraphy. Despite their importance, few studies have constrained sediment budgets and grain‐size exported into an active rift at the basin scale. Here, we used the Corinth Rift as a natural laboratory to quantify the controls on sediment export within an active rift. In the field, we measured the hydraulic geometries, surface grain‐sizes of channel bars and full‐weighted grain‐size distributions of river sediment at the mouths of 47 catchments draining the rift (constituting 83% of the areal extent). Results show that the sediment grain‐size increases westward along the southern coast of the Gulf of Corinth, with the coarse‐fraction grain‐sizes (84th percentile of weighted grain‐size distribution) ranging from approximately 19 to 91 mm. We find that the median and coarse‐fraction of the sieved grain‐size distribution are primarily controlled by bedrock lithology, with late Quaternary uplift rates exerting a secondary control. Our results indicate that grain‐size export is primarily controlled by the input grain‐size within the catchment and subsequent abrasion during fluvial transport, both quantities that are sensitive to catchment lithology. We also demonstrate that the median and coarse‐fraction of the grain‐size distribution are predominantly transported in bedload; however, typical sand‐grade particles are transported as suspended load at bankfull conditions, suggesting disparate source‐to‐sink transit timescales for sand and gravel. Finally, we derive both a full Holocene sediment budget and a grain‐size‐specific bedload discharged into the Gulf of Corinth using the grain‐size measurements and previously published estimates of sediment fluxes and volumes. Results show that the bedload sediment budget is primarily comprised (~79%) of pebble to cobble grade (0.475–16 cm). Our results suggest that the grain‐size of sediment export at the rift scale is particularly sensitive to catchment lithology and fluvial mophodynamics, which complicates our ability to make direct inferences of tectonic and palaeoenvironmental forcing from local stratigraphic characteristics

Active Nuclear Receptors Exhibit Highly Correlated AF-2 Domain Motions

Nuclear receptor ligand binding domains (LBDs) convert ligand binding events into changes in gene expression by recruiting transcriptional coregulators to a conserved activation function-2 (AF-2) surface. While most nuclear receptor LBDs form homo- or heterodimers, the human nuclear receptor pregnane X receptor (PXR) forms a unique and essential homodimer and is proposed to assemble into a functional heterotetramer with the retinoid X receptor (RXR). How the homodimer interface, which is located 30 Å from the AF-2, would affect function at this critical surface has remained unclear. By using 20- to 30-ns molecular dynamics simulations on PXR in various oligomerization states, we observed a remarkably high degree of correlated motion in the PXR–RXR heterotetramer, most notably in the four helices that create the AF-2 domain. The function of such correlation may be to create “active-capable” receptor complexes that are ready to bind to transcriptional coactivators. Indeed, we found in additional simulations that active-capable receptor complexes involving other orphan or steroid nuclear receptors also exhibit highly correlated AF-2 domain motions. We further propose a mechanism for the transmission of long-range motions through the nuclear receptor LBD to the AF-2 surface. Taken together, our findings indicate that long-range motions within the LBD scaffold are critical to nuclear receptor function by promoting a mobile AF-2 state ready to bind coactivators

Learning and innovative elements of strategy adoption rules expand cooperative network topologies

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3 Tables, 12 Figures and 116 reference

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

<p>Abstract</p> <p>Background</p> <p>Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.</p> <p>Methods</p> <p>Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.</p> <p>Results</p> <p>NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.</p> <p>Conclusions</p> <p>We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.</p

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications