Exploring Large Document Repositories with RDF Technology: The DOPE Project

This thesaurus-based search system uses automatic indexing, RDF-based querying, and concept-based visualization of results to support exploration of large online document repositories

The legacy effect of synthetic N fertiliser

Cumulative crop recovery of synthetic fertiliser nitrogen (N) over several cropping seasons (legacy effect) generally receives limited attention. The increment in crop N uptake after the first-season uptake from fertiliser can be expressed as a fraction (∆RE) of annual N application rate. This study aims to quantify ∆RE using data from nine long-term experiments (LTEs). As such, ∆RE is the difference between first season (RE1st) and long-term (RELT) recovery of synthetic fertiliser N. In this study, RE1st was assessed either by the 15N isotope method, or by a zero-N subplot freshly superimposed on a long-term fertilised LTE treatment plot. RELT was calculated by comparing N uptake in the total aboveground crop biomass between a long-term fertilised and long-term control (zero-N) treatment. Using a mixed linear effect model, the effects of climate, crop type, experiment duration, average N rate, and soil clay content on ∆RE were evaluated. Because the experimental setup required for calculation of ∆RE is relatively rare, only nine suitable LTEs were found. Across these nine LTEs in Europe and North America, mean ∆RE was 24.4% (±12.0%, 95% CI) of annual N application, with higher values for winter wheat than for maize. This result shows that fertiliser-N retained in the soil and stubble may contribute substantially to crop N uptake in subsequent years. Our results suggest that an initial recovery of 43.8% (±11%, 95% CI) of N application may increase to around 66.0% (±15%, 95% CI) on average over time. Furthermore, we found that ∆RE was not clearly related to long-term changes in topsoil total N stock. Our findings show that the - often used - first year recovery of synthetic fertiliser N application does not express the full effect of fertiliser application on crop nutrition. The fertiliser contribution to soil N supply should be accounted for when exploring future scenarios on N cycling, including crop N requirements and N balance schemes

Realism about the Wave Function

A century after the discovery of quantum mechanics, the meaning of quantum mechanics still remains elusive. This is largely due to the puzzling nature of the wave function, the central object in quantum mechanics. If we are realists about quantum mechanics, how should we understand the wave function? What does it represent? What is its physical meaning? Answering these questions would improve our understanding of what it means to be a realist about quantum mechanics. In this survey article, I review and compare several realist interpretations of the wave function. They fall into three categories: ontological interpretations, nomological interpretations, and the \emph{sui generis} interpretation. For simplicity, I will focus on non-relativistic quantum mechanics.Comment: Penultimate version for Philosophy Compas

α-Fetoprotein and human chorionic gonadotrophin-β as prognostic markers in neuroendocrine tumour patients

Serum chromogranin A is the most useful general and prognostic tumour marker available for neuroendocrine tumour (NET) patients. The role of other tumour markers is less clear. In order to determine the diagnostic and prognostic value of serum α-fetoprotein (AFP) and human chorionic gonadotrophin-β (hCGβ) in NETs, a database containing biochemical, histological, and survival data on 360 NET patients was constructed. This data was statistically assessed, using Statistical Package for the Social Sciences, to determine the utility of commonly measured tumour markers with particular emphasis on AFP and hCGβ. α-Fetoprotein and hCGβ were raised in 9.5 and 12.3% of patients respectively and jointly raised in 9.1% of patients in whom it was measured. α-Fetoprotein levels associated strongly and positively with tumour grade, serum CgA and hCGβ levels, and worse survival. Human chorionic gonadotrophin-β levels also associated strongly and positively with serum CgA and AFP levels, and worsening survival. α-Fetoprotein and hCGβ are elevated in high-grade NETs, with a rapidly progressive course and poorer survival. They also correlate with chromogranin-A, which is known to be a marker of tumour burden and to have prognostic value. Thus AFP and hCGβ are clinically important in NETs and when elevated are poor prognostic markers

Isolation of Primary Human Hepatocytes from Normal and Diseased Liver Tissue: A One Hundred Liver Experience

Successful and consistent isolation of primary human hepatocytes remains a challenge for both cell-based therapeutics/transplantation and laboratory research. Several centres around the world have extensive experience in the isolation of human hepatocytes from non-diseased livers obtained from donor liver surplus to surgical requirement or at hepatic resection for tumours. These livers are an important but limited source of cells for therapy or research. The capacity to isolate cells from diseased liver tissue removed at transplantation would substantially increase availability of cells for research. However no studies comparing the outcome of human hepatocytes isolation from diseased and non-diseased livers presently exist. Here we report our experience isolating human hepatocytes from organ donors, non-diseased resected liver and cirrhotic tissue. We report the cell yields and functional qualities of cells isolated from the different types of liver and demonstrate that a single rigorous protocol allows the routine harvest of good quality primary hepatocytes from the most commonly accessible human liver tissue samples

Development of Clinical Criteria for Functional Assessment to Predict Primary Nonfunction of High-Risk Livers Using Normothermic Machine Perfusion

Increased use of high-risk allografts is critical to meet the demand for liver transplantation. We aimed to identify criteria predicting viability of organs, currently declined for clinical transplantation, using functional assessment during normothermic machine perfusion (NMP). Twelve discarded human livers were subjected to NMP following static cold storage. Livers were perfused with a packed red cell-based fluid at 37°C for 6 hours. Multilevel statistical models for repeated measures were employed to investigate the trend of perfusate blood gas profiles and vascular flow characteristics over time and the effect of lactate-clearing (LC) and non-lactate-clearing (non-LC) ability of the livers. The relationship of lactate clearance capability with bile production and histological and molecular findings were also examined. After 2 hours of perfusion, median lactate concentrations were 3.0 and 14.6 mmol/L in the LC and non-LC groups, respectively. LC livers produced more bile and maintained a stable perfusate pH and vascular flow &gt;150 and 500 mL/minute through the hepatic artery and portal vein, respectively. Histology revealed discrepancies between subjectively discarded livers compared with objective findings. There were minimal morphological changes in the LC group, whereas non-LC livers often showed hepatocellular injury and reduced glycogen deposition. Adenosine triphosphate levels in the LC group increased compared with the non-LC livers. We propose composite viability criteria consisting of lactate clearance, pH maintenance, bile production, vascular flow patterns, and liver macroscopic appearance. These have been tested successfully in clinical transplantation. In conclusion, NMP allows an objective assessment of liver function that may reduce the risk and permit use of currently unused high-risk livers.</p

An enhanced software tool to support better use of manure nutrients: MANNER- NPK

MANNER-NPK (MANure Nutrient Evaluation Routine) is a decision support tool for quantifying manure (and other organic material) crop available nutrient supply. The user-friendly design of an earlier version of MANNER was retained, but in response to user and stakeholder feedback, additional functionality was included to underpin new and revised nitrogen (N) transformation/loss modules (covering ammonia volatilization, nitrate leaching and nitrous oxide/di-nitrogen emissions, and organic N mineralization) and also to estimate manure phosphorus (as P2O5), potassium (as K2O), sulphur (as SO3) and magnesium (as MgO) supply. Notably, MANNER-NPK provides N availability estimates for following crops through the mineralization of organic N. Validation of the crop available N supply estimates was undertaken by comparing predicted values with data from more than 200 field experimental measurements. For cattle, pig and poultry manures, there was good agreement (P<0.001) between predicted and measured fertilizer N replacement values, indicating that MANNER-NPK provides robust estimates of manure crop available N supply and N losses to the wider environment

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p