A pilot study on occurrence of Yersinia enterocolitica and Yersinia pseudotuberculosis in Latvian pigs at slaughtering

The aim of the study was to detect the distribution of presumptive pathogenic Yersinia spec1es in pigs of Latvian origm. In total tonsils of 108 pigs were collected from 6 farms m two abattoirs situated in different parts of Latvia. Samples were investigated by using direct plating on the selective CIN media and cold enrichment technique for 2 weeks. All presumptive isolates were confirmed biochemically. During the direct plating only 58% of cultures of Y.enterocolitica and 4 Y. pseudotuberculosts were recovered. Y. enterocolitica was isolated from the pig tonsil samples orig1nated from all six farms The distribution of positive samples among different farms varied from 15 to 45%. Y. pseudotuberculosis was recovered from 3 out of 6 herds studied rangmg from 5 to 25% on each positive farm. The mean prevalence of Y.enterocolitica and Y. pseudotuberculosis in all six farms was 31% and 8% respectively Results of study indicate that none of the investigated herds was free of potentially pathogenic Yersinia. The presence of Yersinia species in pigs indicates that a possibility for contamination with bacteria occurs during the offal removal of and meat mspection of carcasses. Further investigations on pathogenic properties and slaughtenng techniques at the slaughterhouses involved in this study should be continued

N-learners problem: Fusion of concepts

We are given N learners each capable of learning concepts (subsets) of a domain set X in the sense of Valiant, i.e. for any c {element of} C {improper subset} 2{sup X}, given a finite set of examples of the form ; ;...; generated according to an unknown probability distribution P{sub X} on X, each learner produces a close approximation to c with a high probability. We are interested in combining the N learners using a single fuser or consolidator. We consider the paradigm of passive fusion, where each learner is first trained with the sample without the influence of the consolidator. The composite system is constituted by the fuser and the individual learners. We consider two cases: open and closed fusion. In open fusion the fuser is given the sample and the hypotheses of the individual learners; we show that the fusion rule can be obtained by formulating this problem as another learning problem. For the case all individual learners are trained with the same sample, we show sufficiency conditions that ensure the composite system to be better than the best of the individual: the hypothesis space of the consolidator (a) satisfies the isolation property of degree at least N, and (b) has Vapnik-Chervonenkis dimension less than or equal to that of every individual learner. If individual learners are trained by independently generated samples, we obtain a much weaker bound on the VC-dimension of the hypothesis space of the fuser. Second, in closed fusion the fuser does not have an access to either the training sample or the hypotheses of the individual learners. By suitable designing a linear threshold function of the outputs of individual learners, we show that the composite system can be made better than the best of the learners

Model based biotechnological potential analysis of Kluyveromyces marxianus central metabolism

The non-conventional yeast Kluyveromyces marxianus is an emerging industrial producer for many biotechnological processes. Here we show the application of a biomass-linked stoichiometric model of central metabolism that is experimentally validated, and mass and charge balanced for assessing the carbon conversion efficiency of wild type and modified K. marxianus. Pairs of substrates (lactose, glucose, inulin, xylose) and products (ethanol, acetate, lactate, glycerol, ethyl acetate, succinate, glutamate, phenylethanol and phenylalanine) are examined by various modeling and optimisation methods. Our model reveals the organism's potential for industrial application and metabolic engineering. Modeling results imply that the aeration regime can be used as a tool to optimise product yield and flux distribution in K. marxianus. Also rebalancing NADH and NADPH utilisation can be used to improve the efficiency of substrate conversion. Xylose is identified as a biotechnologically promising substrate for K. marxianus

Application of Permutation Genetic Algorithm for Sequential Model Building–Model Validation Design of Experiments

YesThe work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient Design of Experiment (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to support an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of Model Building–Model Validation (MB-MV) sequences, by generating optimal infill sets of test points as OLH DoEs, that preserve good space filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB-MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back (SHCB) function, as well as the Gasoline Direct Injection (GDI) engine steady state engine mapping problem that motivated this research. The case studies show that the algorithm is effective at delivering quasi-orthogonal space-filling DoEs with good properties even after several MB-MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, also is that significant reduction in the effort and cost of testing can be achieved.The research work presented in this paper was funded by the UK Technology Strategy Board (TSB) through the Carbon Reduction through Engine Optimization (CREO) project

Proapoptotic activity of Ukrain is based on Chelidonium majus L. alkaloids and mediated via a mitochondrial death pathway

BACKGROUND: The anticancer drug Ukrain (NSC-631570) which has been specified by the manufacturer as semisynthetic derivative of the Chelidonium majus L. alkaloid chelidonine and the alkylans thiotepa was reported to exert selective cytotoxic effects on human tumour cell lines in vitro. Few clinical trials suggest beneficial effects in the treatment of human cancer. Aim of the present study was to elucidate the importance of apoptosis induction for the antineoplastic activity of Ukrain, to define the molecular mechanism of its cytotoxic effects and to identify its active constituents by mass spectrometry. METHODS: Apoptosis induction was analysed in a Jurkat T-lymphoma cell model by fluorescence microscopy (chromatin condensation and nuclear fragmentation), flow cytometry (cellular shrinkage, depolarisation of the mitochondrial membrane potential, caspase-activation) and Western blot analysis (caspase-activation). Composition of Ukrain was analysed by mass spectrometry and LC-MS coupling. RESULTS: Ukrain turned out to be a potent inducer of apoptosis. Mechanistic analyses revealed that Ukrain induced depolarisation of the mitochondrial membrane potential and activation of caspases. Lack of caspase-8, expression of cFLIP-L and resistance to death receptor ligand-induced apoptosis failed to inhibit Ukrain-induced apoptosis while lack of FADD caused a delay but not abrogation of Ukrain-induced apoptosis pointing to a death receptor independent signalling pathway. In contrast, the broad spectrum caspase-inhibitor zVAD-fmk blocked Ukrain-induced cell death. Moreover, over-expression of Bcl-2 or Bcl-x(L )and expression of dominant negative caspase-9 partially reduced Ukrain-induced apoptosis pointing to Bcl-2 controlled mitochondrial signalling events. However, mass spectrometric analysis of Ukrain failed to detect the suggested trimeric chelidonine thiophosphortriamide or putative dimeric or monomeric chelidonine thiophosphortriamide intermediates from chemical synthesis. Instead, the Chelidonium majus L. alkaloids chelidonine, sanguinarine, chelerythrine, protopine and allocryptopine were identified as major components of Ukrain. Apart from sanguinarine and chelerythrine, chelidonine turned out to be a potent inducer of apoptosis triggering cell death at concentrations of 0.001 mM, while protopine and allocryptopine were less effective. Similar to Ukrain, apoptosis signalling of chelidonine involved Bcl-2 controlled mitochondrial alterations and caspase-activation. CONCLUSION: The potent proapoptotic effects of Ukrain are not due to the suggested "Ukrain-molecule" but to the cytotoxic efficacy of Chelidonium majus L. alkaloids including chelidonine

Imitation of β-lactam binding enables broad-spectrum metallo-β-lactamase inhibitors

Carbapenems are vital antibiotics, but their efficacy is increasingly compromised by metallo-beta-lactamases (MBLs). Here we report the discovery and optimization of potent broad-spectrum MBL inhibitors. A high-throughput screen for NDM-1 inhibitors identified indole-2-carboxylates (InCs) as potential beta-lactamase stable beta-lactam mimics. Subsequent structure-activity relationship studies revealed InCs as a new class of potent MBL inhibitor, active against all MBL classes of major clinical relevance. Crystallographic studies revealed a binding mode of the InCs to MBLs that, in some regards, mimics that predicted for intact carbapenems, including with respect to maintenance of the Zn(II)-bound hydroxyl, and in other regards mimics binding observed in MBL-carbapenem product complexes. InCs restore carbapenem activity against multiple drug-resistant Gram-negative bacteria and have a low frequency of resistance. InCs also have a good in vivo safety profile, and when combined with meropenem show a strong in vivo efficacy in peritonitis and thigh mouse infection models.Peer reviewe