Prediction Of Fate Research Articles

Utility of mangled extremity severity score in severely injured lower limbs

Introduction: Massive lower extremity trauma,in particular open tibial fractures with associated vascular injuries, present an immediate and complex decision-making challenge between a limb salvage attempt and primary amputation. Medical and surgical advances of the past two decades have improved the ability to reconstruct severely injured limb. Limbs that once would have been amputated are now routinely managed with complex reconstruction protocol. Mangled extremity severity score is one of the scoring systems to predict the fate of limbs after severe limb injuries.Methods: Patients fulfilling the inclusion criteria were evaluated with MESS, at the same time treatment protocol for management of injuries of lower limb were followed independently by attending orthopedic surgeon. Mean MESS for salvaged and amputated limbs were calculated and its reliability for prediction of fate of injured limb was assessed using software SPSS v16.Result: The age of patient ranges from 10 to 65 yrs with mean age 35.83. The most common mechanism of injury was Road Traffic Accident followed by fall from height. The mean MESS score for salvaged limbs was 4.18 and for amputated limbs was 8.12 suggesting significant difference in mean scores. The sensitivity (the probability that limbs requiring amputation will have MESS at or above 7) was found to be 75%. The specificity of MESS (the probability that salvage limbs will have MESS < 7) was 95.45%.Conclusion: MESS is a reliable indicator in decision making process whether a limb can be salvaged or needs amputation. The mangled lower extremity with the score of less than 7 may be salvaged and 7 or more may need amputation.JSSN 2015; 18 (1), Page: 23-25

Microbial and Biogeochemical Dynamics in Glacier Forefields Are Sensitive to Century-Scale Climate and Anthropogenic Change

The recent retreat of glaciers and ice sheets as a result of global warming exposes forefield soils that are rapidly colonised by microbes. These ecosystems are dominant in high-latitude carbon and nutrient cycles as microbial activity drives biogeochemical transformations within these newly exposed soils. Despite this, little is known about the response of these emerging ecosystems and associated biogeochemical cycles to projected changes in environmental factors due to human impacts. Here, we applied the model SHIMMER to quantitatively explore the sensitivity of biogeochemical dynamics in the forefield of Midtre Lovenbreen, Svalbard, to future changes in climate and anthropogenic forcings including soil temperature, snow cover, and nutrient and organic substrate deposition. Model results indicated that the rapid warming of the Arctic, as well as an increased deposition of organic carbon and nutrients, may impact primary microbial colonisers in Arctic soils. Warming and increased snow-free conditions resulted in enhanced bacterial production and an accumulation of biomass that was sustained throughout 200 years of soil development. Nitrogen deposition stimulated growth during the first 50 years of soil development following exposure. Increased deposition of organic carbon sustained higher rates of bacterial production and heterotrophic respiration leading to decreases in net ecosystem production and thus net CO2 efflux from soils. Pioneer microbial communities were particularly susceptible to future changes. All future climate simulations encouraged a switch from allochthonously-dominated young soils (<40 years) to microbially-dominated older soils, due to enhanced heterotrophic degradation of organic matter. Critically, this drove remineralisation and increased nutrient availability. Overall, we show that human activity, especially the burning of fossil fuels and the enhanced deposition of nitrogen and organic carbon, has the potential to considerably affect the biogeochemical development of recently exposed Arctic soils in the present day and for centuries into the future. These effects must be acknowledged when attempting to make accurate predictions of the future fate of Arctic soils that are exposed over large expanses of presently ice-covered regions.

Regional Comparison of Multiphase Computed Tomographic Angiography and Computed Tomographic Perfusion for Prediction of Tissue Fate in Ischemic Stroke.

Within different brain regions, we determine the comparative value of multiphase computed tomographic angiography (mCTA) and computed tomographic perfusion (CTP) in predicting follow-up infarction. Patients with M1-middle cerebral artery occlusions were prospectively included in this multicenter study. Regional analysis was performed for each patient within Alberta Stroke Program Early CT Score regions M2 to M6. Regional pial vessel filling was assessed on mCTA in 3 ways: (1) Washout of contrast within pial vessels; (2) Extent of maximal pial vessel enhancement compared with contralateral hemisphere; (3) Delay in maximal pial vessel enhancement compared with contralateral hemisphere. Cerebral blood flow, cerebral blood volume, and Tmax data were extracted within these Alberta Stroke Program Early CT Score regions. Twenty-four- to 36-hour magnetic resonance imaging/CT was assessed for infarct in each Alberta Stroke Program Early CT Score region (defined as >20% infarction within that region). Mixed effects logistic regression models were used to compare mCTA and CTP parameters when predicting brain infarction. Area under the receiver operating characteristics was used to assess discriminative value of statistical models. Seventy-seven patients were included. mCTA parameter washout and CTP parameter Tmax were significantly associated with follow-up infarction in all models (P<0.05). The area under the receiver operating characteristic for mCTA models ranged from 92% to 94% and was not different compared with all CTP models (P>0.05). Mean Tmax and cerebral blood volume values were significantly different between each washout score (P<0.01) and each delay score category (P<0.01). Mean Tmax, cerebral blood flow, and cerebral blood volume values were significantly different between each extent score category (P<0.05). Similar to CTP, multiphase CTA can be used to predict tissue fate regionally in acute ischemic stroke patients.

Reconstructing Early Erythroid Development In Vivo Using Single-Cell Transcriptomics

Erythroid differentiation may be divided into two broad stages: early development, and terminal differentiation. Early development was first explored using the colony-formation potential of hematopoietic tissue. This approach identified multi-potential progenitors (MPP) and unipotential erythroid progenitors that form 'bursts' (BFUe) and smaller colonies (CFUe). Early erythroid development is followed by erythroid terminal differentiation (ETD), which profoundly remodels erythroblasts into enucleated red cells. The molecular study of ETD was fundamentally transformed with the development of cell-surface marker strategies that identify sequential stage-specific erythroblasts in hematopoietic tissue. By contrast, there have been no strategies that systematically identify the entire cellular and molecular trajectory of the early erythroid lineage as it first arises from the MPP and progresses to the point where the ETD program is activated.To address this gap, we undertook single-cell transcriptomics using the InDrop-seq platform (Klein et al. Cell 161:1187 2015). We analyzed Kit+ cells in the bone marrow of mice in the basal state, mice stimulated with Erythropoietin (Epo) for 48 hours, or fetal liver cells. We used Next-Generation Sequencing data to construct k-nearest neighbor (kNN) graphs of cell states for each condition, and extracted the erythroid trajectory using Population Balance Analysis (PBA), a novel computational approach that predicts differentiation fates from single cell RNA profiles.We identified early erythroid developmental stages based on the modeled probability of erythroid commitment, gene expression dynamics, and the architecture of the kNN graph. By screening for appropriate cell surface markers, we developed a flow-cytometric strategy that isolates sub-populations corresponding to regions of the kNN graph, including sub-regions of the erythroid trajectory. This allowed validation of gene expression patterns and of cell fate predictions using in vitro colony formation assays.The earliest stage in the erythroid trajectory, the Erythroid/Basophil MPP stage (E/B-MPP) contains progenitors that emerge from MPPs, predicted to remain mutli-potential but be biased towards bipotential erythroid/basophil and eryrthroid/megakaryocytic fates. This stage is characterized by rapidly changing gene expression profiles. Genes whose expression correlates with the probability of erythroid commitment include both known transcriptional regulators of erythropoiesis such as GATA1, GATA2, Ldb1 and Klf1, as well as novel candidates.Downstream from the E/B-MPP, the two-dimensional projection of the kNN graph becomes a narrow bottleneck, indicating a transient stage. Here the erythroid trajectory contains cells with rapidly increasing probability of erythroid commitment (Emerging Erythroid Progenitors, EEP). The bottleneck connects to a bulge-like region in which progenitors have an extremely high probability of attaining the erythroid fate (committed erythroid progenitors, CEP). This region contains the majority of the marrow's committed erythroid progenitors, and functions as an amplification module, increasing in size in Epo-stimulated marrow and in the fetal liver. Functionally, cells in the bottleneck region give rise to multifocal erythroid colonies (early and late BFUe), whereas cells in the CEP amplification module give rise to unifocal erythroid colonies, including the CFUe. Therefore, the ability of a progenitor to give rise to either multifocal or unifocal colonies correlates closely with molecular stage.Cells in the CEP module express a unique set of genes, induced at the module entry, and repressed at its exit. These include growth-factor receptors mst1r, ryk and il17ra. Their ligands, MSP, Wnt5a and IL17a, are novel regulators of erythropoiesis, either stimulating or inhibiting the formation of erythroid colonies. Exit from the CEP module is marked by a rapid switch, in which the repression of CEP genes coincides with induction of the ETD program. Remarkably, this switch is synchronized with expression of G1/S and S phase genes, underlying a role for S phase progression in ETD activation (Pop et al., PLoS Biology 2010).Our work charts the erythroid trajectory of murine hematopoietic tissue, identifying developmental milestones, setting the stage for their molecular study and for discovery of novel erythroid regulators. [Display omitted] DisclosuresKlein:OneCell Bio: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.

Power-Law Modeling of Cancer Cell Fates Driven by Signaling Data to Reveal Drug Effects.

Extracellular signals are captured and transmitted by signaling proteins inside a cell. An important type of cellular responses to the signals is the cell fate decision, e.g., apoptosis. However, the underlying mechanisms of cell fate regulation are still unclear, thus comprehensive and detailed kinetic models are not yet available. Alternatively, data-driven models are promising to bridge signaling data with the phenotypic measurements of cell fates. The traditional linear model for data-driven modeling of signaling pathways has its limitations because it assumes that the a cell fate is proportional to the activities of signaling proteins, which is unlikely in the complex biological systems. Therefore, we propose a power-law model to relate the activities of all the measured signaling proteins to the probabilities of cell fates. In our experiments, we compared our nonlinear power-law model with the linear model on three cancer datasets with phosphoproteomics and cell fate measurements, which demonstrated that the nonlinear model has superior performance on cell fates prediction. By in silico simulation of virtual protein knock-down, the proposed model is able to reveal drug effects which can complement traditional approaches such as binding affinity analysis. Moreover, our model is able to capture cell line specific information to distinguish one cell line from another in cell fate prediction. Our results show that the power-law data-driven model is able to perform better in cell fate prediction and provide more insights into the signaling pathways for cancer cell fates than the linear model.

Using a 3-D multicellular simulation of spinal cord injury with live cell imaging to study the neural immune barrier to nanoparticle uptake

Development of nanoparticle (NP) based therapies to promote regeneration in sites of central nervous system (CNS; i.e. brain and spinal cord) pathology relies critically on the availability of experimental models that offer biologically valid predictions of NP fate in vivo. However, there is a major lack of biological models that mimic the pathological complexity of target neural sites in vivo, particularly the responses of resident neural immune cells to NPs. Here, we have utilised a previously developed in vitro model of traumatic spinal cord injury (based on 3-D organotypic slice arrays) with dynamic time lapse imaging to reveal in real-time the acute cellular fate of NPs within injury foci. We demonstrate the utility of our model in revealing the well documented phenomenon of avid NP sequestration by the intrinsic immune cells of the CNS (the microglia). Such immune sequestration is a known translational barrier to the use of NP-based therapeutics for neurological injury. Accordingly, we suggest that the utility of our model in mimicking microglial sequestration behaviours offers a valuable investigative tool to evaluate strategies to overcome this cellular response within a simple and biologically relevant experimental system, whilst reducing the use of live animal neurological injury models for such studies.

Electroanalytical procedure and sorption studies for imazaquin in different soils

ABSTRACTThe objective of the present work is to determine sorption coefficients of the herbicide imazaquin using the differential pulse polarography in the major soil classes occurring in the State of São Paulo, Brazil. This study contributes to the predictions of pesticide fate and transport mechanisms in the environment as well as its exposure and interactions with different soil materials. Imazaquin was analysed directly in soil samples (scan rate 2 mV s–1, mercury drop size 0.37 mm, and pulse height 50 mV) and the detection limits varied from 42 to 58 g L–1 for different soils. Imazaquin showed the KF values varying from 0.8 (for the sandy soil) to 35.5 L kg–1 (for the medium textured soil), with a significant dependence on the soil pH, organic matter content, and clay minerals. The study demonstrates the practical utilisation of the electroanalytical methodology to determine the imazaquin adsorption in soil and consequently assess the impact of this herbicide in the environment, especially for the evaluation of the potential risk of surface and groundwater contamination.

Fate and transport of polychlorinated biphenyls (PCBs) in the River Thames catchment – Insights from a coupled multimedia fate and hydrobiogeochemical transport model

The fate of persistent organic pollutants (POPs) in riverine environments is strongly influenced by hydrology (including flooding) and fluxes of sediments and organic carbon. Coupling multimedia fate models (MMFMs) and hydrobiogeochemical transport models offers unique opportunities for understanding the environmental behaviour of POPs. While MMFMs are widely used for simulating the fate and transport of legacy and emerging pollutants, they use greatly simplified representations of climate, hydrology and biogeochemical processes. Using additional information about weather, river flows and water chemistry in hydrobiogeochemical transport models can lead to new insights about POP behaviour in rivers. As most riverine POPs are associated with suspended sediments (SS) or dissolved organic carbon (DOC), coupled models simulating SS and DOC can provide additional insights about POPs behaviour. Coupled simulations of river flow, DOC, SS and POP dynamics offer the possibility of improved predictions of contaminant fate and fluxes by leveraging the additional information in routine water quality time series. Here, we present an application of a daily time step dynamic coupled multimedia fate and hydrobiogeochemical transport model (The Integrated Catchment (INCA) Contaminants model) to simulate the behaviour of selected PCB congeners in the River Thames (UK). This is a follow-up to an earlier study where a Level III fugacity model was used to simulate PCB behaviour in the Thames. While coupled models are more complex to apply, we show that they can lead to much better representation of POPs dynamics. The present study shows the importance of accurate sediment and organic carbon simulations to successfully predict riverine PCB transport. Furthermore, it demonstrates the important impact of short-term weather variation on PCB movement through the environment. Specifically, it shows the consequences of the severe flooding, which occurred in early 2014 on sediment PCB concentrations in the River Thames.

Characterization of volatile organic compound adsorption on multiwall carbon nanotubes under different levels of relative humidity using linear solvation energy relationship

Multiwall carbon nanotubes (MWCNTs) have been used as an adsorbent for evaluating the gas/solid partitioning of selected volatile organic compounds (VOCs). In this study, 15 VOCs were probed to determine their gas/solid partitioning coefficient (LogKd) using inverse gas chromatography at different relative humidity (RH) levels. Interactions between MWCNTs and VOCs were analyzed by regressing the observed LogKd with the linear solvation energy relationship (LSER). The results demonstrate that the MWCNT carbonyl and carboxyl groups provide high adsorption capacity for the VOCs (LogKd 3.72–5.24g/kg/g/L) because of the π-/n-electron pair interactions and hydrogen-bond acidity. The increasing RH gradually decreased the LogKd and shifted the interactions to dipolarity/polarizability, hydrogen-bond basicity, and cavity formation. The derived LSER equations provided adequate fits of LogKd, which is useful for VOC-removal processes and fate prediction of VOC contaminants by MWCNT adsorption in the environment.

Long-term transients and complex dynamics of a stage-structured population with time delay and the Allee effect

Traditionally, mathematical modeling in population ecology is mainly focused on asymptotic behavior of the model, i.e. as given by the system attractors. Recently, however, transient regimes and especially long-term transients have been recognized as playing a crucial role in the dynamics of ecosystems. In particular, long-term transients are a potential explanation of ecological regime shifts, when an apparently healthy population suddenly collapses and goes extinct. In this paper, we show that the interplay between delay in maturation and a strong Allee effect can result in long-term transients in a single species system. We first derive a simple ‘conceptual’ model of the population dynamics that incorporates both a strong Allee effect and maturation delay. Unlike much of the previous work, our approach is not empirical since our model is derived from basic principles. We show that the model exhibits a high complexity in its asymptotic dynamics including multi-periodic and chaotic attractors. We then show the existence of long-term transient dynamics in the system, when the population size oscillates for a long time between locally stable stationary states before it eventually settles either at the persistence equilibrium or goes extinct. The parametric space of the model is found to have a complex structure with the basins of attraction corresponding to the persistence and extinction states being of a complicated shape. This impedes the prediction of the eventual fate of the population, as a small variation in the maturation delay or the initial population size can either bring the population to extinction or ensure its persistence.

Aerobic and anaerobic microbial degradation of crude (4-methylcyclohexyl)methanol in river sediments

Cyclohexane and some of its derivatives have been a major concern because of their significant adverse human health effects and widespread occurrence in the environment. The 2014 West Virginia chemical spill has raised public attention to (4-methylcyclohexyl)methanol (4-MCHM), one cyclohexane derivative, which is widely used in coal processing but largely ignored. In particular, the environmental fate of its primary components, cis- and trans-4-MCHM, remains largely unexplored. This study aimed to investigate the degradation kinetics and mineralization of cis- and trans-4-MCHM by sediment microorganisms under aerobic and anaerobic conditions. We found the removal of cis- and trans-4-MCHM was mainly attributed to biodegradation with little contribution from sorption. A nearly complete aerobic degradation of 4-MCHM occurred within 14days, whereas the anaerobic degradation was reluctant with residual percentages of 62.6% of cis-4-MCHM and 85.0% of trans-4-MCHM after 16-day incubation. The cis-4-MCHM was degraded faster than the trans under both aerobic and anaerobic conditions, indicating an isomer-specific degradation could occur during the 4-MCHM degradation. Nitrate addition enhanced 4-MCHM mineralization by about 50% under both aerobic and anaerobic conditions. Both cis- and trans-4-MCHM fit well with the first-order kinetic model with respective degradation rates of 0.46–0.52 and 0.19–0.31day−1 under aerobic condition. Respective degradation rates of 0.041–0.095 and 0.013–0.052day−1 occurred under anaerobic condition. One bacterial strain capable of effectively degrading 4-MCHM isomers was isolated from river sediments and identified as Bacillus pumilus at the species level based on 16S rRNA gene sequence and 97% identity. Our findings will provide critical information for improving the prediction of the environmental fate of 4-MCHM and other cyclohexane derivatives with similar structure as well as enhancing the development of feasible treatment technologies to mitigate these compounds.

Groundwater Contaminant Transport Modeling Using Multiple Adaptive Data Assimilation Techniques

In modeling the behavior of contaminants in a subsurface environment using data assimilation schemes, accurate assignment of model and observation errors are significant for the successful application of the techniques. In this study, a three‐dimensional transport model was used to simulate the advection and dispersiontransport of contaminant in the subsurface. Stochastic data assimilation schemes were coupled with the subsurface contaminant transport model to predict the state of the contaminant. Three data assimilation techniques namely the conventional Ensemble KalmanFilter, the Adaptive Ensemble Kalman Filter and the Hybrid Adaptive Ensemble Kalman Filter were adopted to improve the prediction of the contaminant fate and transport in the groundwater. The Ensemble Kalman Filter applies a Monte Carlo approach to the filtering problem. The adaptive filtering technique employs the diagnostic approach to fine tune the model and observation covariance matrix. The hybrid technique uses combination of the forecast covariance matrix and the invariant background covariance matrix to explore the Ensemble Kalman filter. The impact of the filters on the numerical model is examined by using the Normalized Root Mean Square Error (NRMSE), Average Absolute Bias (AAB) metric, and Maximum Absolute Deviation (MAD) techniques. The AAB evaluation of Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter shows error reduction of 93% and 90%, respectively, while the MAD assessment recorded 94% and 89% improvement respectively, in the numerical model. The results of simulations show that the prediction accuracy of the filters is better than numerical model. The proposed Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter, takes advantage of the adaptive factor and the weighting factor, respectively to improve the prediction efficiency of the Ensemble Kalman filter. Sensitivity analysis performed on Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter using NRMSE indicates that the adaptive factor does not affect the prediction accuracy of the former whereas the weighting factor has influence on the later.

In Silico Models for Ecotoxicity of Pharmaceuticals.

Pharmaceuticals and their active metabolites are one of the significantly emerging environmental toxicants. The major routes of entry of pharmaceuticals into the environment are industries, hospitals, or direct disposal of unwanted or expired drugs made by the patient. The most important and distinct features of pharmaceuticals are that they are deliberately designed to have an explicit mode of action and designed to exert an effect on humans and other living systems. This distinctive feature makes pharmaceuticals and their metabolites different from other chemicals, and this necessitates the evaluation of the direct effects of pharmaceuticals in various environmental compartments as well as to living systems. In this background, the alarming situation of ecotoxicity of diverse pharmaceuticals have forced government and nongovernment regulatory authorities to recommend the application of in silico methods to provide quick information about the risk assessment and fate properties of pharmaceuticals as well as their ecological and indirect human health effects. This chapter aims to offer information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity as well as application of in silico methods to provide information about the basic risk management and fate prediction of pharmaceuticals in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals are also discussed.

Prediction of Early Arterial Recanalization and Tissue Fate in the Selection of Patients With the Greatest Potential to Benefit From Intravenous Tissue-Type Plasminogen Activator.

Our objective is to determine the performance of the combination of likelihood of arterial recanalization and tissue fate to predict functional clinical outcome in patients with acute stroke. Clinical, imaging, and outcome data were collected in 173 patients with acute ischemic stroke who presented within 4.5 hours from symptom onset, in the time window eligible for intravenous tissue-type plasminogen activator. Imaging data included Alberta Score Program Early Computed Tomographic Score (ASPECTS), site of occlusion, volume of ischemic core and penumbra, and recanalization. Outcome data consisted of modified Rankin Scale score at 90 days. We classified patients based on their baseline imaging characteristics and treatment with intravenous tissue-type plasminogen activator (yes/no) according to 5 different hypothetical prognostic algorithms: (1) based on whether patients received intravenous tissue-type plasminogen activator, (2) based on ASPECTS, (3) based on the site of occlusion, (4) based on volume of ischemic core and penumbra, and (5) based on a matrix of predicted recanalization and volume of ischemic core and penumbra. We compared the performance of such algorithms to predict good clinical outcome, defined as modified Rankin Scale score of ≤2 at 90 days. One hundred and twenty-four patients received intravenous tissue-type plasminogen activator; 49 did not. In the group that was treated, 46 (37%) had good outcome as opposed to 38.7% in the nontreated. The algorithm that combined the prediction of recanalization with the volume of ischemic core and penumbra showed the highest accuracy to predict good outcome (77.7%) as opposed to others (range, 43.9%-57.2%) The combination of predicted recanalization and tissue fate proved superior to prognosticate good clinical outcome when compared with other usual predictors.

Degradation of cis- and trans-(4-methylcyclohexyl) methanol in activated sludge

Crude (4-methylcyclohexyl)methanol (MCHM) caused extensive contamination of drinking water, wastewater, and the environment during the 2014 West Virginia Chemical Spill. However, information related to the environmental degradation of cis- and trans-4-MCHM, the main components of the crude 4-MCHM mixture, remains largely unknown. This study is among the first to investigate the degradation kinetics and transformation of 4-MCHM isomers in activated sludge. The 4-MCHM loss was mainly due to biodegradation to form carbon dioxide (CO2), plus acetic, propionic, isobutyric, and isovaleric acids with little contribution from adsorption. The biodegradation of 4-MCHM isomers followed the first-order kinetic model with half-lives higher than 0.50 days. Nitrate augmented the degradation of 4-MCHM isomers, while glucose and acetate decreased their degradation. One 4-MCHM-degrading bacterium isolated from activated sludge was identified as Acinetobacter bouvetii strain EU40 based on 16S rRNA gene sequences. This study will enhance the prediction of the environmental fate of 4-MCHM in water treatment systems.

Lessons learned while building the Deepwater Horizon Database: Toward improved data sharing in coastal science

Process studies and coupled-model validation efforts in geosciences often require integration of multiple data types across time and space. For example, improved prediction of hydrocarbon fate and transport is an important societal need which fundamentally relies upon synthesis of oceanography and hydrocarbon chemistry. Yet, there are no publically accessible databases which integrate these diverse data types in a georeferenced format, nor are there guidelines for developing such a database. The objective of this research was to analyze the process of building one such database to provide baseline information on data sources and data sharing and to document the challenges and solutions that arose during this major undertaking. The resulting Deepwater Horizon Database was approximately 2.4GB in size and contained over 8 million georeferenced data points collected from industry, government databases, volunteer networks, and individual researchers. The major technical challenges that were overcome were reconciliation of terms, units, and quality flags which were necessary to effectively integrate the disparate data sets. Assembling this database required the development of relationships with individual researchers and data managers which often involved extensive e-mail contacts. The average number of emails exchanged per data set was 7.8. Of the 95 relevant data sets that were discovered, 38 (40%) were obtained, either in whole or in part. Over one third (36%) of the requests for data went unanswered. The majority of responses were received after the first request (64%) and within the first week of the first request (67%). Although fewer than half of the potentially relevant datasets were incorporated into the database, the level of sharing (40%) was high compared to some other disciplines where sharing can be as low as 10%. Our suggestions for building integrated databases include budgeting significant time for e-mail exchanges, being cognizant of the cost versus benefits of pursuing reticent data providers, and building trust through clear, respectful communication and with flexible and appropriate attributions.

CranSLIK v2.0: improving the stochastic prediction of oil spill transport and fate using approximation methods

Abstract. Oil spill models are used to forecast the transport and fate of oil after it has been released. CranSLIK is a model that predicts the movement and spread of a surface oil spill at sea via a stochastic approach. The aim of this work is to identify parameters that can further improve the forecasting algorithms and expand the functionality of CranSLIK, while maintaining the run-time efficiency of the method. The results from multiple simulations performed using the operational, validated oil spill model, MEDSLIK-II, were analysed using multiple regression in order to identify improvements which could be incorporated into CranSLIK. This has led to a revised model, namely CranSLIK v2.0, which was validated against MEDSLIK-II forecasts for real oil spill cases. The new version of CranSLIK demonstrated significant forecasting improvements by capturing the oil spill accurately in real validation cases and also proved capable of simulating a broader range of oil spill scenarios.

Volatilisation of pesticides under field conditions: inverse modelling and pesticide fate models.

A substantial fraction of the applied crop protection products on crops is lost to the atmosphere. Models describing the prediction of volatility and potential fate of these substances in the environment have become an important tool in the pesticide authorisation procedure at the EU level. The main topic of this research is to assess the rate and extent of volatilisation of ten pesticides after application on field crops. For eight of the ten pesticides, the volatilisation rates modelled with PEARL (Pesticide Emission Assessment at Regional and Local scales) corresponded well to the calculated rates modelled with ADMS (Atmospheric Dispersion Modelling System). For the other pesticides, large differences were found between the models. Formulation might affect the volatilisation potential of pesticides. Increased leaf wetness increased the volatilisation of propyzamide and trifloxystrobin at the end of the field trial. The reliability of pesticide input parameters, in particular the vapour pressure, is discussed. Volatilisation of propyzamide, pyrimethanil, chlorothalonil, diflufenican, tolylfluanid, cyprodinil and E- and Z-dimethomorph from crops under realistic environmental conditions can be modelled with the PEARL model, as corroborated against field observations. Suggested improvements to the volatilisation component in PEARL should include formulation attributes and leaf wetness at the time of pesticide application. © 2015 Society of Chemical Industry.

Stealth effect of biomolecular corona on nanoparticle uptake by immune cells.

When injected in a biological milieu, a nanomaterial rapidly adsorbs biomolecules forming a biomolecular corona. The biomolecular corona changes the interfacial composition of a nanomaterial giving it a biological identity that determines the physiological response. Characterization of the biomolecular structure and composition has received increasing attention mostly due to its detrimental impact on the nanomaterial's metabolism in vivo. It is generally accepted that an opsonin-enriched biomolecular corona promotes immune system recognition and rapid clearance from circulation. Here we applied dynamic light scattering and nanoliquid chromatography tandem mass spectrometry to thoroughly characterize the biomolecular corona formed around lipid and silica nanoparticles (NPs). Incubation with human plasma resulted in the formation of NP-biomolecular coronas enriched with immunoglobulins, complement factors, and coagulation proteins that bind to surface receptors on immune cells and elicit phagocytosis. Conversely, we found that protein-coated NPs were protected from uptake by macrophage RAW 264.7 cells. This implies that the biomolecular corona formation provides a stealth effect on macrophage recognition. Our results suggest that correct prediction of the NP's fate in vivo will require more than just the knowledge of the biomolecular corona composition. Validation of efficient methods for mapping protein binding sites on the biomolecular corona of NPs is an urgent task for future research.

An in vitro model for the assessment of stem cell fate following implantation within the infarct microenvironment identifies ISL-1 expression as the strongest predictor of c-Kit+ cardiac progenitor cells' therapeutic potential

Cell therapy has the potential to drastically improve clinical outcomes for the 1.45 million patients suffering from a myocardial infarction (MI) each year in the U.S. However, the limitations associated with this treatment – including poor engraftment, significant cell death and poor differentiation potential – have prevented its widespread application clinically. To optimize functional improvements provided by transplanted cells, there is a need to develop methods that increase cellular retention and viability, while supporting differentiation and promoting paracrine signaling. Current in vivo models are expensive, difficult to access and manipulate and are time consuming. We have developed an in vitro model of MI which allows for a straightforward, consistent and relatively accurate prediction of cell fate following injection in vivo. The model demonstrated how the infarct environment impairs cellular engraftment and differentiation, but identified an implantation strategy which enhanced cell fate in vitro. Multivariate linear regression identified variables within the model that regulated vascular differentiation potential including oxygen tension, stiffness and cytokine presence, while cardiac differentiation was more accurately predicted by Isl-1 expression in the original cell isolate than any other variable present within the model system. The model highlighted how the cells' sensitivity to the infarct variables varied from line to line, which emphasizes the importance of the model system for the prediction of cell fate on a patient specific basis. Further development of this model system could help predict the clinical efficacy of cardiac progenitor cell therapy at the patient level as well as identify the optimal strategy for cell delivery.