Search Results (5)

In this research, the authors combine multiobjective evaluation metrics in the (1 + 1) evolutionary strategy with three novel methods of the Pareto optimal procedure to address the learning-to-rank (LTR) problem. From the results obtained, the Cauchy distribution as a random number generator for mutation step sizes outperformed the other distributions used. The aim of using the chosen Pareto optimal methods was to determine which method can give a better exploration–exploitation trade-off for the solution space to obtain the optimal or near-optimal solution. The best combination for that in terms of winning rate is the Cauchy distribution for mutation step sizes with method 3 of the Pareto optimal procedure. Moreover, different random number generators were evaluated and analyzed versus datasets in terms of NDCG@10 for testing data. It was found that the Levy generator is the best for both the MSLR and the MQ2007 datasets, while the Gaussian generator is the best for the MQ2008 dataset. Thus, random number generators clearly affect the performance of ES-Rank based on the dataset used. Furthermore, method 3 had the highest NDCG@10 for MQ2008 and MQ2007, while for the MSLR dataset, the highest NDCG@10 was achieved by method 2. Along with this paper, we provide a Java archive for reproducible research. Full article

Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input, without explicit understanding of relevance between queries and documents, especially in pairwise based ranking approach. Thus, it is an interesting question whether we can devise an algorithm that effectively describes the relation between queries and documents to learn a better ranking model without incurring huge parameter costs. In this paper, we present a Gaussian Embedding model for Ranking (GERank), an architecture for co-embedding queries and documents, such that each query or document is represented by a Gaussian distribution with mean and variance. Our GERank optimizes an energy-based loss based on the pairwise ranking framework. Additionally, the KL-divergence is utilized to measure the relevance between queries and documents. Experimental results on two LETOR datasets and one TREC dataset demonstrate that our model obtains a remarkable improvement in the ranking performance compared with the state-of-the-art retrieval models. Full article

Dried blood spots (DBSs), a micro-sampling technique whereby a drop of blood is collected on filter paper has multiple advantages over conventional blood sampling regarding the sampling itself, as well as transportation and storage. This is the first paper describing the development and validation of a method for the determination of 23 mycotoxins and phase I metabolites in DBSs from pigs and broiler chickens using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The targeted mycotoxins belong to groups for which the occurrence in feed is regulated by the European Union, namely, aflatoxins, ochratoxin A and several Fusarium mycotoxins, and to two groups of unregulated mycotoxins, namely Alternaria mycotoxins and Fusarium mycotoxins (enniatins and beauvericin). The impact of blood haematocrit, DBS sampling volume and size of the analysed DBS disk on the validation results was assessed. No effects of variation in size of the analysed disk, haematocrit and spotted blood volume were observed for most mycotoxins, except for the aflatoxins and β-zearalanol (BZAL) at the lowest haematocrit (26%) level and for the enniatins (ENNs) at the lowest volume (40 µL). The developed method was transferred to an LC-high resolution mass spectrometry instrument to determine phase II metabolites. Then, the DBS technique was applied in a proof-of-concept toxicokinetic study including a comparison with LC-MS/MS data from plasma obtained with conventional venous blood sampling. A strong correlation (r > 0.947) was observed between plasma and DBS concentrations. Finally, DBSs were also applied in a pilot exposure assessment study to test their applicability under field conditions. Full article

Applying post-harvest control measures such as adding mycotoxin detoxifying agents is a frequently-used mitigation strategy for mycotoxins. EFSA states that the efficacy of these detoxifiers needs to be tested using specific biomarkers for exposure. However, the proposed biomarkers for exposure are not further optimized for specific target species. Hence, the goal of this study was (a) to evaluate the most suitable biomarkers for deoxynivalenol (DON) and zearalenone (ZEN) in porcine plasma, urine and feces; and DON, aflatoxin B1 (AFB1) and ochratoxin A (OTA) in plasma and excreta of broiler chickens and (b) to determine the efficacy of a candidate detoxifier, as a proof-of-concept study. Therefore, a mixture of mycotoxins was administered as a single oral bolus with or without detoxifying agent. In accordance with literature AFB1, OTA, and DON-sulphate (DON-S) proved optimal biomarkers in broilers plasma and excreta whereas, in pigs DON-glucuronide (DON-GlcA) and ZEN-glucuronide (ZEN-GlcA) proved the optimal biomarkers in plasma, DON and ZEN-GlcA in urine and, ZEN in feces. A statistically significant reduction was seen between control and treatment group for both AFB1 and DON in broiler plasma, under administration of the mycotoxin blend and detoxifier dose studied suggesting thus, beneficial bioactivity. Full article

A reliable and practical multi-method was developed for the quantification of mycotoxins in plasma, urine, and feces of pigs, and plasma and excreta of broiler chickens using liquid chromatography–tandem mass spectrometry. The targeted mycotoxins belong to the regulated groups, i.e., aflatoxins, ochratoxin A and Fusarium mycotoxins, and to two groups of emerging mycotoxins, i.e., Alternaria mycotoxins and enniatins. In addition, the developed method was transferred to a LC-high resolution mass spectrometry instrument to qualitatively determine phase I and II metabolites, for which analytical standards are not always commercially available. Sample preparation of plasma was simple and generic and was accomplished by precipitation of proteins alone (pig) or in combination with removal of phospholipids (chicken). A more intensive sample clean-up of the other matrices was needed and consisted of a pH-dependent liquid–liquid extraction (LLE) using ethyl acetate (pig urine), methanol/ethyl acetate/formic acid (75/24/1, v/v/v) (pig feces) or acetonitrile (chicken excreta). For the extraction of pig feces, additionally a combination of LLE using acetone and filtration of the supernatant on a HybridSPE-phospholipid cartridge was applied. The LC-MS/MS method was in-house validated according to guidelines defined by the European and international community. Finally, the multi-methods were successfully applied in a specific toxicokinetic study and a screening study to monitor the exposure of individual animals. Full article