One-step preparation of enantiopure L- or D-amino acid benzyl esters avoiding the use of banned solvents

The enantiomers of amino acid benzyl esters are very important synthetic intermediates. Many of them are currently prepared by treatment with benzyl alcohol and p-toluenesulfonic acid in refluxing benzene or carbon tetrachloride, to azeotropically remove water, and then precipitated as tosylate salt by adding diethyl ether. Here, we report a very efficient preparation of eight l- or d-amino acid benzyl esters (Ala, Phe, Tyr, Phg, Val, Leu, Lys, Ser), in which these highly hazardous solvents are dismissed using cyclohexane as a water azeotroping solvent and ethyl acetate to precipitate the tosylate salt. With some work-up modifications and lower yield, the procedure can be applied also to methionine. Chiral HPLC analysis shows that all the benzyl esters, including the highly racemizable ones such as those of phenylglycine, tyrosine and methionine, are formed enantiomerically pure under these new reaction conditions thus validating the solvents replacement. Contrariwise, toluene cannot be used in place of benzene or carbon tetrachloride because leading to partially or totally racemized amino acid benzyl esters depending on the polar effect of the amino acid \u3b1-side chain as expressed by Taft\u2019s substituent constant (\u3c3*)

On clustering levels of a hierarchical categorical risk factor

Handling nominal covariates with a large number of categories is challenging for both statistical and machine learning techniques. This problem is further exacerbated when the nominal variable has a hierarchical structure. The industry code in a workers' compensation insurance product is a prime example hereof. We commonly rely on methods such as the random effects approach (Campo and Antonio, 2023) to incorporate these covariates in a predictive model. Nonetheless, in certain situations, even the random effects approach may encounter estimation problems. We propose the data-driven Partitioning Hierarchical Risk-factors Adaptive Top-down (PHiRAT) algorithm to reduce the hierarchically structured risk factor to its essence, by grouping similar categories at each level of the hierarchy. We work top-down and engineer several features to characterize the profile of the categories at a specific level in the hierarchy. In our workers' compensation case study, we characterize the risk profile of an industry via its observed damage rates and claim frequencies. In addition, we use embeddings (Mikolov et al., 2013; Cer et al., 2018) to encode the textual description of the economic activity of the insured company. These features are then used as input in a clustering algorithm to group similar categories. We show that our method substantially reduces the number of categories and results in a grouping that is generalizable to out-of-sample data. Moreover, when estimating the technical premium of the insurance product under study as a function of the clustered hierarchical risk factor, we obtain a better differentiation between high-risk and low-risk companies

Insurance pricing with hierarchically structured data: An illustration with a workers' compensation insurance portfolio

Actuaries use predictive modeling techniques to assess the loss cost on a contract as a function of observable risk characteristics. State-of-the-art statistical and machine learning methods are not well equipped to handle hierarchically structured risk factors with a large number of levels. In this paper, we demonstrate the data-driven construction of an insurance pricing model when hierarchically structured risk factors, contract-specific as well as externally collected risk factors are available. We examine the pricing of a workers' compensation insurance product with a hierarchical credibility model (Jewell, 1975), Ohlsson's combination of a generalized linear and a hierarchical credibility model (Ohlsson, 2008) and mixed models. We compare the predictive performance of these models and evaluate the effect of the distributional assumption on the target variable by comparing linear mixed models with Tweedie generalized linear mixed models. For our case-study the Tweedie distribution is well suited to model and predict the loss cost on a contract. Moreover, incorporating contract-specific risk factors in the model improves the predictive performance and the risk differentiation in our workers' compensation insurance portfolio.Comment: 39 pages (including Appendix), 18 figures and 3 table

Patient-specific CFD simulation of intraventricular haemodynamics based on 3D ultrasound imaging

Background: The goal of this paper is to present a computational fluid dynamic (CFD) model with moving boundaries to study the intraventricular flows in a patient-specific framework. Starting from the segmentation of real-time transesophageal echocardiographic images, a CFD model including the complete left ventricle and the moving 3D mitral valve was realized. Their motion, known as a function of time from the segmented ultrasound images, was imposed as a boundary condition in an Arbitrary Lagrangian-Eulerian framework. Results: The model allowed for a realistic description of the displacement of the structures of interest and for an effective analysis of the intraventricular flows throughout the cardiac cycle. The model provides detailed intraventricular flow features, and highlights the importance of the 3D valve apparatus for the vortex dynamics and apical flow. Conclusions: The proposed method could describe the haemodynamics of the left ventricle during the cardiac cycle. The methodology might therefore be of particular importance in patient treatment planning to assess the impact of mitral valve treatment on intraventricular flow dynamics

Patient-specific CFD models for intraventricular flow analysis from 3D ultrasound imaging : comparison of three clinical cases

Background: As the intracardiac flow field is affected by changes in shape and motility of the heart, intraventricular flow features can provide diagnostic indications. Ventricular flow patterns differ depending on the cardiac condition and the exploration of different clinical cases can provide insights into how flow fields alter in different pathologies. Methods: In this study, we applied a patient-specific computational fluid dynamics model of the left ventricle and mitral valve, with prescribed moving boundaries based on transesophageal ultrasound images for three cardiac pathologies, to verify the abnormal flow patterns in impaired hearts. One case (P1) had normal ejection fraction but low stroke volume and cardiac output, P2 showed low stroke volume and reduced ejection fraction, P3 had a dilated ventricle and reduced ejection fraction. Results: The shape of the ventricle and mitral valve, together with the pathology influence the flow field in the left ventricle, leading to distinct flow features. Of particular interest is the pattern of the vortex formation and evolution, influenced by the valvular orifice and the ventricular shape. The base-to-apex pressure difference of maximum 2 mmHg is consistent with reported data. Conclusion: We used a CFD model with prescribed boundary motion to describe the intraventricular flow field in three patients with impaired diastolic function. The calculated intraventricular flow dynamics are consistent with the diagnostic patient records and highlight the differences between the different cases. The integration of clinical images and computational techniques, therefore, allows for a deeper investigation intraventricular hemodynamics in patho-physiology. (C) 2016 Elsevier Ltd. All rights reserved

Strain-level metagenomic data analysis of enriched in vitro and in silico spiked food samples : paving the way towards a culture-free foodborne outbreak investigation using STEC as a case study

Culture-independent diagnostics, such as metagenomic shotgun sequencing of food samples, could not only reduce the turnaround time of samples in an outbreak investigation, but also allow the detection of multi-species and multi-strain outbreaks. For successful foodborne outbreak investigation using a metagenomic approach, it is, however, necessary to bioinformatically separate the genomes of individual strains, including strains belonging to the same species, present in a microbial community, which has up until now not been demonstrated for this application. The current work shows the feasibility of strain-level metagenomics of enriched food matrix samples making use of data analysis tools that classify reads against a sequence database. It includes a brief comparison of two database-based read classification tools, Sigma and Sparse, using a mock community obtained by in vitro spiking minced meat with a Shiga toxin-producing Escherichia coli (STEC) isolate originating from a described outbreak. The more optimal tool Sigma was further evaluated using in silico simulated metagenomic data to explore the possibilities and limitations of this data analysis approach. The performed analysis allowed us to link the pathogenic strains from food samples to human isolates previously collected during the same outbreak, demonstrating that the metagenomic approach could be applied for the rapid source tracking of foodborne outbreaks. To our knowledge, this is the first study demonstrating a data analysis approach for detailed characterization and phylogenetic placement of multiple bacterial strains of one species from shotgun metagenomic WGS data of an enriched food sample

First detection of a plasmid located carbapenem resistant bla(VIM-1) gene in E. coli isolated from meat products at retail in Belgium in 2015

Carbapenemase-producing Enterobacteriaceae (CPE) confer resistance to antibiotics that are of critical importance to human medicine. There have only been a few reported cases of CPEs in the European food chain. We report the first detection of a carbapenemase-producing Escherichia coli (ST 5869) in the Belgian food chain. Our aim was to characterize the origin of the carbapenem resistance in the E. coli isolate. The isolate was detected during the screening of 178 minced pork samples and was shown to contain the carbapenemase gene bla(VIM-1) by PCR and Sanger sequencing. Whole genome short and long read sequencing (MiSeq and MinION) was performed to characterize the isolate. With a hybrid assembly we reconstructed a 190,205 bp IncA/C2 plasmid containing bla(VIM-1) (S15FP06257_p), in addition to other critically important resistance genes. This plasmid showed only low similarity to plasmids containing bla(VIM-1) previously reported in Germany. Moreover, no sequences existed in the NCBI nucleotide database that completely covered S15FP06257_p. Analysis of the bla(VIM-1) gene cassette demonstrated that it likely originated from an integron of a Klebsiella plasmid reported previously in a clinical isolate in Europe, suggesting that the meat could have been contaminated by human handling in one of the steps of the food chain. This study shows the relevance of fully reconstructing plasmids to characterize their genetic content and to allow source attribution. This is especially important in view of the potential risk of antimicrobial resistance gene transmission through mobile elements as was reported here for the of public health concern bla(VIM-1)

A practical method to implement strain-level metagenomics-based foodborne outbreak investigation and source tracking in routine

The management of a foodborne outbreak depends on the rapid and accurate identification of the responsible food source. Conventional methods based on isolation of the pathogen from the food matrix and target-specific real-time polymerase chain reactions (qPCRs) are used in routine. In recent years, the use of whole genome sequencing (WGS) of bacterial isolates has proven its value to collect relevant information for strain characterization as well as tracing the origin of the contamination by linking the food isolate with the patient’s isolate with high resolution. However, the isolation of a bacterial pathogen from food matrices is often time-consuming and not always successful. Therefore, we aimed to improve outbreak investigation by developing a method that can be implemented in reference laboratories to characterize the pathogen in the food vehicle without its prior isolation and link it back to human cases. We tested and validated a shotgun metagenomics approach by spiking food pathogens in specific food matrices using the Shiga toxin-producing Escherichia coli (STEC) as a case study. Different DNA extraction kits and enrichment procedures were investigated to obtain the most practical workflow. We demonstrated the feasibility of shotgun metagenomics to obtain the same information as in ISO/TS 13136:2012 and WGS of the isolate in parallel by inferring the genome of the contaminant and characterizing it in a shorter timeframe. This was achieved in food samples containing different E. coli strains, including a combination of different STEC strains. For the first time, we also managed to link individual strains from a food product to isolates from human cases, demonstrating the power of shotgun metagenomics for rapid outbreak investigation and source tracking