Mechanisms contributing to the deep chlorophyll maximum in Lake Superior

The seasonal appearance of a deep chlorophyll maximum (DCM) in Lake Superior is a striking phenomenon that is widely observed; however its mechanisms of formation and maintenance are not well understood. As this phenomenon may be the reflection of an ecological driver, or a driver itself, a lack of understanding its driving forces limits the ability to accurately predict and manage changes in this ecosystem. Key mechanisms generally associated with DCM dynamics (i.e. ecological, physiological and physical phenomena) are examined individually and in concert to establish their role. First the prevailing paradigm, “the DCM is a great place to live”, is analyzed through an integration of the results of laboratory experiments and field measurements. The analysis indicates that growth at this depth is severely restricted and thus not able to explain the full magnitude of this phenomenon. Additional contributing mechanisms like photoadaptation, settling and grazing are reviewed with a one-dimensional mathematical model of chlorophyll and particulate organic carbon. Settling has the strongest impact on the formation and maintenance of the DCM, transporting biomass to the metalimnion and resulting in the accumulation of algae, i.e. a peak in the particulate organic carbon profile. Subsequently, shade adaptation becomes manifest as a chlorophyll maximum deeper in the water column where light conditions particularly favor the process. Shade adaptation mediates the magnitude, shape and vertical position of the chlorophyll peak. Growth at DCM depth shows only a marginal contribution, while grazing has an adverse effect on the extent of the DCM. The observed separation of the carbon biomass and chlorophyll maximum should caution scientists to equate the DCM with a large nutrient pool that is available to higher trophic levels. The ecological significance of the DCM should not be separated from the underlying carbon dynamics. When evaluated in its entirety, the DCM becomes the projected image of a structure that remains elusive to measure but represents the foundation of all higher trophic levels. These results also offer guidance in examine ecosystem perturbations such as climate change. For example, warming would be expected to prolong the period of thermal stratification, extending the late summer period of suboptimal (phosphorus-limited) growth and attendant transport of phytoplankton to the metalimnion. This reduction in epilimnetic algal production would decrease the supply of algae to the metalimnion, possibly reducing the supply of prey to the grazer community. This work demonstrates the value of modeling to challenge and advance our understanding of ecosystem dynamics, steps vital to reliable testing of management alternatives.

Long-Range Pollution Transport: Trans-Atlantic Mechanisms and Lagrangian Modeling Methods

Over the past several decades, it has become apparent that anthropogenic activities have resulted in the large-scale enhancement of the levels of many trace gases throughout the troposphere. More recently, attention has been given to the transport pathway taken by these emissions as they are dispersed throughout the atmosphere. The transport pathway determines the physical characteristics of emissions plumes and therefore plays an important role in the chemical transformations that can occur downwind of source regions. For example, the production of ozone (O3) is strongly dependent upon the transport its precursors undergo. O3 can initially be formed within air masses while still over polluted source regions. These polluted air masses can experience continued O3 production or O3 destruction downwind, depending on the air mass's chemical and transport characteristics. At present, however, there are a number of uncertainties in the relationships between transport and O3 production in the North Atlantic lower free troposphere. The first phase of the study presented here used measurements made at the Pico Mountain observatory and model simulations to determine transport pathways for US emissions to the observatory. The Pico Mountain observatory was established in the summer of 2001 in order to address the need to understand the relationships between transport and O3 production. Measurements from the observatory were analyzed in conjunction with model simulations from the Lagrangian particle dispersion model (LPDM), FLEX-PART, in order to determine the transport pathway for events observed at the Pico Mountain observatory during July 2003. A total of 16 events were observed, 4 of which were analyzed in detail. The transport time for these 16 events varied from 4.5 to 7 days, while the transport altitudes over the ocean ranged from 2-8 km, but were typically less than 3 km. In three of the case studies, eastward advection and transport in a weak warm conveyor belt (WCB) airflow was responsible for the export of North American emissions into the FT, while transport in the FT was governed by easterly winds driven by the Azores/Bermuda High (ABH) and transient northerly lows. In the fourth case study, North American emissions were lofted to 6-8 km in a WCB before being entrained in the same cyclone's dry airstream and transported down to the observatory. The results of this study show that the lower marine FT may provide an important transport environment where O3 production may continue, in contrast to transport in the marine boundary layer, where O3 destruction is believed to dominate. The second phase of the study presented here focused on improving the analysis methods that are available with LPDMs. While LPDMs are popular and useful for the analysis of atmospheric trace gas measurements, identifying the transport pathway of emissions from their source to a receptor (the Pico Mountain observatory in our case) using the standard gridded model output, particularly during complex meteorological scenarios can be difficult can be difficult or impossible. The transport study in phase 1 was limited to only 1 month out of more than 3 years of available data and included only 4 case studies out of the 16 events specifically due to this confounding factor. The second phase of this study addressed this difficulty by presenting a method to clearly and easily identify the pathway taken by only those emissions that arrive at a receptor at a particular time, by combining the standard gridded output from forward (i.e., concentrations) and backward (i.e., residence time) LPDM simulations, greatly simplifying similar analyses. The ability of the method to successfully determine the source-to-receptor pathway, restoring this Lagrangian information that is lost when the data are gridded, is proven by comparing the pathway determined from this method with the particle trajectories from both the forward and backward models. A sample analysis is also presented, demonstrating that this method is more accurate and easier to use than existing methods using standard LPDM products. Finally, we discuss potential future work that would be possible by combining the backward LPDM simulation with gridded data from other sources (e.g., chemical transport models) to obtain a Lagrangian sampling of the air that will eventually arrive at a receptor.

A disease model for wheezing disorders in preschool children based on clinicians' perceptions

BACKGROUND: Wheezing disorders in childhood vary widely in clinical presentation and disease course. During the last years, several ways to classify wheezing children into different disease phenotypes have been proposed and are increasingly used for clinical guidance, but validation of these hypothetical entities is difficult. METHODOLOGY/PRINCIPAL FINDINGS: The aim of this study was to develop a testable disease model which reflects the full spectrum of wheezing illness in preschool children. We performed a qualitative study among a panel of 7 experienced clinicians from 4 European countries working in primary, secondary and tertiary paediatric care. In a series of questionnaire surveys and structured discussions, we found a general consensus that preschool wheezing disorders consist of several phenotypes, with a great heterogeneity of specific disease concepts between clinicians. Initially, 24 disease entities were described among the 7 physicians. In structured discussions, these could be narrowed down to three entities which were linked to proposed mechanisms: a) allergic wheeze, b) non-allergic wheeze due to structural airway narrowing and c) non-allergic wheeze due to increased immune response to viral infections. This disease model will serve to create an artificial dataset that allows the validation of data-driven multidimensional methods, such as cluster analysis, which have been proposed for identification of wheezing phenotypes in children. CONCLUSIONS/SIGNIFICANCE: While there appears to be wide agreement among clinicians that wheezing disorders consist of several diseases, there is less agreement regarding their number and nature. A great diversity of disease concepts exist but a unified phenotype classification reflecting underlying disease mechanisms is lacking. We propose a disease model which may help guide future research so that proposed mechanisms are measured at the right time and their role in disease heterogeneity can be studied.

Virosomes can enter cells by non-phagocytic mechanisms

Phagocytosis of fine particles (1 mum) by macrophages is a ligand-receptor-mediated, actin-based process, whereas the entering of smaller particles (mechanisms. Virosomes with a diameter of 0.12-0.18 mum are widely used as carrier systems for drugs, vectors, and plasmids in cancer therapy or for vaccines. We investigated their interactions with airway cells, in particular penetration into monocyte-derived macrophages. The microscopic analysis of phagocytic cells incubated with virosomes and polystyrene particles showed that virosomes and particles penetrated cells even in the presence of cytochalasin D, a drug inhibiting actin-based phagocytosis. The charge of the virosomes and particles did not influence their penetration. Also, different inhibitors of endocytotic pathways did not prevent the particles and virosomes from penetrating into the cells. Additionally, to study the ability of virosomes to overcome the epithelial airway barrier, a triple cell co-culture model composed of epithelial cells, monocyte-derived macrophages and dendritic cells of the respiratory tract was used. We found virosomes and polystyrene particles in both populations of antigen-presenting cells, monocyte-derived macrophages, and dendritic cells, in the latter even if they were not directly exposed. In conclusion, virosomes are readily taken up by monocyte-derived macrophages, both by conventional phagocytosis and by actin-independent mechanisms. Further, they can penetrate the airway barrier and reach resident dendritic cells. Therefore, virosomes are promising vaccine candidates.

How Minds Work: A Cognitive Theory of Everything - a Tutorial on the IDA Model of Cognition

The IDA model of cognition is a fully integrated artificial cognitive system reaching across the full spectrum of cognition, from low-level perception/action to high-level reasoning. Extensively based on empirical data, it accurately reflects the full range of cognitive processes found in natural cognitive systems. As a source of plausible explanations for very many cognitive processes, the IDA model provides an ideal tool to think with about how minds work. This online tutorial offers a reasonably full account of the IDA conceptual model, including background material. It also provides a high-level account of the underlying computational “mechanisms of mind” that constitute the IDA computational model.

Zebrafish (Danio rerio) as a model organism for investigating endocrine disruption

Endocrine-disrupting compounds (EDCs) are widespread in the aquatic environment and can cause alterations in development, physiological homeostasis and health of vertebrates. Zebrafish, Danio rerio, has been suggested as a model species to identify targets as well as modes of EDC action. In fact, zebrafish has been found useful in EDC screening, in EDC effects assessment and in studying targets and mechanisms of EDC action. Since many of the environmental EDCs interfere with the sex steroid system of vertebrates, most EDC studies with zebrafish addressed disruption of sexual differentiation and reproduction. However, other targets of EDCs action must not be overlooked. For using a species as a toxicological model, a good knowledge of the biological traits of this species is a pre-requisite for the rational design of test protocols and endpoints as well as for the interpretation and extrapolation of the toxicological findings. Due to the genomic resources available for zebrafish and the long experience with zebrafish in toxicity testing, it is easily possible to establish molecular endpoints for EDC effects assessment. Additionally, the zebrafish model offers a number of technical advantages including ease and cost of maintenance, rapid development, high fecundity, optical transparency of embryos supporting phenotypic screening, existence of many mutant strains, or amenability for both forward and reverse genetics. To date, the zebrafish has been mainly used to identify molecular targets of EDC action and to determine effect thresholds, while the potential of this model species to study immediate and delayed physiological consequences of molecular interactions has been instrumentalized only partly. One factor that may limit the exploitation of this potential is the still rather fragmentary knowledge of basic biological and endocrine traits of zebrafish. Information on species-specific features in endocrine processes and biological properties, however, need to be considered in establishing EDC test protocols using zebrafish, in extrapolating findings from zebrafish to other vertebrate species, and in understanding how EDC-induced gene expression changes translate into disease.

Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods. The model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents. The choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.

Fetal programming of pulmonary vascular dysfunction in mice: role of epigenetic mechanisms.

Insults during the fetal period predispose the offspring to systemic cardiovascular disease, but little is known about the pulmonary circulation and the underlying mechanisms. Maternal undernutrition during pregnancy may represent a model to investigate underlying mechanisms, because it is associated with systemic vascular dysfunction in the offspring in animals and humans. In rats, restrictive diet during pregnancy (RDP) increases oxidative stress in the placenta. Oxygen species are known to induce epigenetic alterations and may cross the placental barrier. We hypothesized that RDP in mice induces pulmonary vascular dysfunction in the offspring that is related to an epigenetic mechanism. To test this hypothesis, we assessed pulmonary vascular function and lung DNA methylation in offspring of RDP and in control mice at the end of a 2-wk exposure to hypoxia. We found that endothelium-dependent pulmonary artery vasodilation in vitro was impaired and hypoxia-induced pulmonary hypertension and right ventricular hypertrophy in vivo were exaggerated in offspring of RDP. This pulmonary vascular dysfunction was associated with altered lung DNA methylation. Administration of the histone deacetylase inhibitors butyrate and trichostatin A to offspring of RDP normalized pulmonary DNA methylation and vascular function. Finally, administration of the nitroxide Tempol to the mother during RDP prevented vascular dysfunction and dysmethylation in the offspring. These findings demonstrate that in mice undernutrition during gestation induces pulmonary vascular dysfunction in the offspring by an epigenetic mechanism. A similar mechanism may be involved in the fetal programming of vascular dysfunction in humans.

Differential regulation of human 3β-hydroxysteroid dehydrogenase type 2 for steroid hormone biosynthesis by starvation and cyclic AMP stimulation: studies in the human adrenal NCI-H295R cell model

Human steroid biosynthesis depends on a specifically regulated cascade of enzymes including 3β-hydroxysteroid dehydrogenases (HSD3Bs). Type 2 HSD3B catalyzes the conversion of pregnenolone, 17α-hydroxypregnenolone and dehydroepiandrosterone to progesterone, 17α-hydroxyprogesterone and androstenedione in the human adrenal cortex and the gonads but the exact regulation of this enzyme is unknown. Therefore, specific downregulation of HSD3B2 at adrenarche around age 6-8 years and characteristic upregulation of HSD3B2 in the ovaries of women suffering from the polycystic ovary syndrome remain unexplained prompting us to study the regulation of HSD3B2 in adrenal NCI-H295R cells. Our studies confirm that the HSD3B2 promoter is regulated by transcription factors GATA, Nur77 and SF1/LRH1 in concert and that the NBRE/Nur77 site is crucial for hormonal stimulation with cAMP. In fact, these three transcription factors together were able to transactivate the HSD3B2 promoter in placental JEG3 cells which normally do not express HSD3B2. By contrast, epigenetic mechanisms such as methylation and acetylation seem not involved in controlling HSD3B2 expression. Cyclic AMP was found to exert differential effects on HSD3B2 when comparing short (acute) versus long-term (chronic) stimulation. Short cAMP stimulation inhibited HSD3B2 activity directly possibly due to regulation at co-factor or substrate level or posttranslational modification of the protein. Long cAMP stimulation attenuated HSD3B2 inhibition and increased HSD3B2 expression through transcriptional regulation. Although PKA and MAPK pathways are obvious candidates for possibly transmitting the cAMP signal to HSD3B2, our studies using PKA and MEK1/2 inhibitors revealed no such downstream signaling of cAMP. However, both signaling pathways were clearly regulating HSD3B2 expression.

Development of an ex vivo protocol to model bone fracture in laying hens resulting from collisions.

Fractures of the keel bone, a bone extending ventrally from the sternum, are a serious health and welfare problem in free range laying hens. Recent findings suggest that a major cause of keel damage within extensive systems is collisions with internal housing structures, though investigative efforts have been hindered by difficulties in examining mechanisms and likely influencing factors at the moment of fracture. The objectives of this study were to develop an ex vivo impact protocol to model bone fracture in hens caused by collision, to assess impact and bird-related factors influencing fracture occurrence and severity, and to identify correlations of mechanical and structural properties between different skeletal sites. We induced keel bone fractures in euthanized hens using a drop-weight impact tester able to generate a range of impact energies, producing fractures that replicate those commonly found in commercial settings. The results demonstrated that impact energies of a similar order to those expected in normal housing were able to produce fractures, and that greater collision energies resulted in an increased likelihood of fractures and of greater severity. Relationships were also seen with keel's lateral surface bone mineral density, and the peak reactive force (strength) at the base of the manubrial spine. Correlations were also identified between the keel and long bones with respect to both strength and bone mineral density. This is the first study able to relate impact and bone characteristics with keel bone fracture at the moment of collision. Greater understanding of these relationships will provide means to reduce levels of breakage and severity in commercial systems.

Traumatization and chronic pain: a further model of interaction.

Up to 80% of patients with severe posttraumatic stress disorder are suffering from "unexplained" chronic pain. Theories about the links between traumatization and chronic pain have become the subject of increased interest over the last several years. We will give a short summary about the existing interaction models that emphasize particularly psychological and behavioral aspects of this interaction. After a synopsis of the most important psychoneurobiological mechanisms of pain in the context of traumatization, we introduce the hypermnesia-hyperarousal model, which focuses on two psychoneurobiological aspects of the physiology of learning. This hypothesis provides an answer to the hitherto open question about the origin of pain persistence and pain sensitization following a traumatic event and also provides a straightforward explanatory model for educational purposes.

SIGNALING MECHANISMS THAT CONTROL GAP JUNCTIONAL COUPLING BETWEEN RETINAL NEURONS

Gap junctions between neurons form the structural substrate for electrical synapses. Connexin 36 (Cx36, and its non-mammalian ortholog connexin 35) is the major neuronal gap junction protein in the central nervous system (CNS), and contributes to several important neuronal functions including neuronal synchronization, signal averaging, network oscillations, and motor learning. Connexin 36 is strongly expressed in the retina, where it is an obligatory component of the high-sensitivity rod photoreceptor pathway. A fundamental requirement of the retina is to adapt to broadly varying inputs in order to maintain a dynamic range of signaling output. Modulation of the strength of electrical coupling between networks of retinal neurons, including the Cx36-coupled AII amacrine cell in the primary rod circuit, is a hallmark of retinal luminance adaptation. However, very little is known about the mechanisms regulating dynamic modulation of Cx36-mediated coupling. The primary goal of this work was to understand how cellular signaling mechanisms regulate coupling through Cx36 gap junctions. We began by developing and characterizing phospho-specific antibodies against key regulatory phosphorylation sites on Cx36. Using these tools we showed that phosphorylation of Cx35 in fish models varies with light adaptation state, and is modulated by acute changes in background illumination. We next turned our focus to the well-studied and readily identifiable AII amacrine cell in mammalian retina. Using this model we showed that increased phosphorylation of Cx36 is directly related to increased coupling through these gap junctions, and that the dopamine-stimulated uncoupling of the AII network is mediated by dephosphorylation of Cx36 via protein kinase A-stimulated protein phosphatase 2A activity. We then showed that increased phosphorylation of Cx36 on the AII amacrine network is driven by depolarization of presynaptic ON-type bipolar cells as well as background light increments. This increase in phosphorylation is mediated by activation of extrasynaptic NMDA receptors associated with Cx36 gap junctions on AII amacrine cells and by Ca2+-calmodulin-dependent protein kinase II activation. Finally, these studies indicated that coupling is regulated locally at individual gap junction plaques. This work provides a framework for future study of regulation of Cx36-mediated coupling, in which increased phosphorylation of Cx36 indicates increased neuronal coupling.

A model of the roles of essential kinases in the induction and expression of late long-term potentiation.

The induction of late long-term potentiation (L-LTP) involves complex interactions among second-messenger cascades. To gain insights into these interactions, a mathematical model was developed for L-LTP induction in the CA1 region of the hippocampus. The differential equation-based model represents actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II (CAMKII) in the vicinity of the synapse, and activation of transcription by CaM kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic weight. Simulations suggest that steep, supralinear stimulus-response relationships between stimuli (e.g., elevations in [Ca(2+)]) and kinase activation are essential for translating brief stimuli into long-lasting gene activation and synaptic weight increases. Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. For example, the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to inhibit ERK activation. The model also appears useful to clarify similarities and differences between hippocampal L-LTP and long-term synaptic strengthening in other systems.

A reduced model clarifies the role of feedback loops and time delays in the Drosophila circadian oscillator.

Although several detailed models of molecular processes essential for circadian oscillations have been developed, their complexity makes intuitive understanding of the oscillation mechanism difficult. The goal of the present study was to reduce a previously developed, detailed model to a minimal representation of the transcriptional regulation essential for circadian rhythmicity in Drosophila. The reduced model contains only two differential equations, each with time delays. A negative feedback loop is included, in which PER protein represses per transcription by binding the dCLOCK transcription factor. A positive feedback loop is also included, in which dCLOCK indirectly enhances its own formation. The model simulated circadian oscillations, light entrainment, and a phase-response curve with qualitative similarities to experiment. Time delays were found to be essential for simulation of circadian oscillations with this model. To examine the robustness of the simplified model to fluctuations in molecule numbers, a stochastic variant was constructed. Robust circadian oscillations and entrainment to light pulses were simulated with fewer than 80 molecules of each gene product present on average. Circadian oscillations persisted when the positive feedback loop was removed. Moreover, elimination of positive feedback did not decrease the robustness of oscillations to stochastic fluctuations or to variations in parameter values. Such reduced models can aid understanding of the oscillation mechanisms in Drosophila and in other organisms in which feedback regulation of transcription may play an important role.

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH4) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO2) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH4 fluxes from wetland systems. Even though modelling of wetland extent and CH4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.