Numerical study of the stability of some autowave solutions in a mathematical model of blood clotting

The stability of some types of solution for a set of equations representing a blood clotting model is examined using numerical methods of solving perturbation problems and the apparatus of adjoint equations. It is concluded that spiral waves are stable in this system, whereas traveling pulses are unstable at parameters corresponding to a chaotic regime in the punctual problem.     

Study of normal and pathological blood vessel morphogenesis in Flt1-tdsRed BAC Tg mice

Blood vessel development and network patterning are controlled by several signaling molecules, including VEGF, FGF, TGF‐ß, and Ang‐1,2. Among these, the role of VEGF‐A signaling in vessel morphogenesis is best understood. The biological activity of VEGF‐A depends on its reaction with specific receptors Flt1 and Flk1. Roles of VEGF‐A signaling in endothelial cell proliferation, migration, survival, vascular permeability, and induction of tip cell filopodia have been reported. In this study, we have generated Flt1‐tdsRed BAC transgenic (Tg) mice to monitor Flt1 gene expression during vascular development. We show that tdsRed fluorescence is observed within blood vessels of adult mice and embryos, indicative of retinal angiogenesis and tumor angiogenesis. Flt1 expression recapitulated by Flt1‐tdsRed BAC Tg mice overlapped well with Flk1, while Flt1 was expressed more abundantly in endothelial cells of large blood vessels such as dorsal aorta and presumptive stalk cells in retina, providing a unique model to study blood vessel development. genesis 50:561–571, 2012. © 2012 Wiley Periodicals, Inc.     

Structural investigation of the A domains of human blood coagulation factor V by molecular modeling.

Factor V (FV) is a large (2,196 amino acids) nonenzymatic cofactor in the coagulation cascade with a domain organization (A1-A2-B-A3-C1-C2) similar to the one of factor VIII (FVIII). FV is activated to factor Va (FVa) by thrombin, which cleaves away the B domain leaving a heterodimeric structure composed of a heavy chain (A1-A2) and a light chain (A3-C1-C2). Activated protein C (APC), together with its cofactor protein S (PS), inhibits the coagulation cascade via limited proteolysis of FVa and FVIIIa (APC cleaves FVa at residues R306, R506, and R679). The A domains of FV and FVIII share important sequence identity with the plasma copper-binding protein ceruloplasmin (CP). The X-ray structure of CP and theoretical models for FVIII have been recently reported. This information allowed us to build a theoretical model (994 residues) for the A domains of human FV/FVa (residues 1-656 and 1546-1883). Structural analysis of the FV model indicates that: (a) the three A domains are arranged in a triangular fashion as in the case of CP and the organization of these domains should remain essentially the same before and after activation; (b) a Type II copper ion is located at the A1-A3 interface; (c) residues R306 and R506 (cleavage sites for APC) are both solvent exposed; (d) residues 1667-1765 within the A3 domain, expected to interact with the membrane, are essentially buried; (e) APC does not bind to FVa residues 1865-1874. Several other features of factor V/Va, like the R506Q and A221V mutations; factor Xa (FXa) and human neutrophil elastase (HNE) cleavages; protein S, prothrombin and FXa binding, are also investigated.     

The need for stochastic replication of ecological neural networks

Artificial neural networks are becoming increasingly popular as predictive statistical tools in ecosystem ecology and as models of signal processing in behavioural and evolutionary ecology. We demonstrate here that a commonly used network in ecology, the three-layer feed-forward network, trained with the backpropagation algorithm, can be extremely sensitive to the stochastic variation in training data that results from random sampling of the same underlying statistical distribution, with networks converging to several distinct predictive states. Using a random walk procedure to sample error-weight space, and Sammon dimensional reduction of weight arrays, we demonstrate that these different predictive states are not artefactual, due to local minima, but lie at the base of major error troughs in the error-weight surface. We further demonstrate that various gross weight compositions can produce the same predictive state, suggesting the analogy of weight space as a 'patchwork' of multiple predictive states. Our results argue for increased inclusion of stochastic training replication and analysis into ecological and behavioural applications of artificial neural networks.     

Modeling transmission of directly transmitted infectious diseases using colored stochastic Petri nets

In order to improve our understanding of directly transmitted pathogens within host populations, epidemic models should take into account individual heterogeneities as well as stochastic fluctuations in individual parameters. The associated cost results in an increasing level of complexity of the mathematical models which generally lack consistent formalisms. In this paper, we demonstrate that complex epidemic models could be expressed as colored stochastic Petri nets (CSPN). CSPN is a mathematical tool developed in computer science. The concept is based on the Markov Chain theory and on a standard well codified graphical formalism. This approach presents an alternative to other computer simulation methods since it offers both a theoretical formalism and a graphical representation that facilitate the implementation, the understanding and thus the replication or modification of the model. We explain how common concepts of epidemic models--such as the incidence function--can be easily translated into an individual based point of view in the CSPN formalism. We then illustrate this approach by using the well documented susceptible-infected model with recruitment and death.     

Two major proteins from locust plasma are involved in coagulation and are specifically precipitated by laminarin, a ß-1,3-glucan

Incubation of plasma of the locust Locusta migratoria, with laminarin induced the precipitation of two major proteins with molecular masses of about 260 000 (P260) and 85 000 Da (P85). This precipitation was not observed when other polysaccharides, such as curdlan, dextran, chitin, cellulose or mannan were used. P260 and P85 were purified to homogeneity by a single step on heparin-sepharose chromatography. Since all attempts to separate P260 from P85, other than the use of sodium dodecyl sulfate, were unsuccessful, it is likely that these two molecules form a complex non-covalently associated. Treatment of P260–P85 complex with N-glycosidase F showed that P260 did not appear to be glycosylated whereas 6% of P85 molecular mass was due to N-linked carbohydrates. On the other hand, no change in molecular masses of P260 or P85 was observed once the complex had been treated with lipase. SDS-PAGE and Western blots of plasma and serum stained with blue Coomassie for proteins or with highly specific polysera to P260 or P85, respectively, showed that P260 was only present in plasma and P85 remained in both samples. This indicates that P260 is likely to be one of the most abundant plasma proteins directly involved in the coagulation process in Locusta migratoria. The addition of plasma or P260–P85 complex to a hemocyte lysate supernatant prior to its activation by laminarin induced a lower protease as well as phenoloxidase activity compared with the control. This reduction of activities was not observed in the presence of serum or when P260–P85 complex was added to a fully activated proPO system.     

Statistical analysis and stochastic modeling of neuronal spike-train activity

Considerable attention has been paid in the literature to the development of biologically interpretable mathematical models of spontaneous stationary neuronal spike-train activity. However, these models work poorly in applied spike-train analysis. In order to improve matters, we need (1) to select appropriate statistical techniques, and (2) to fit the models into the semialternating renewal (SAR) framework. In the present study, biologically interpretable models, the SAR framework, and relevant statistical techniques are integrated in a form which suits the practical research worker. The theory is illustrated with reference to the analysis of a number of spike trains.     

临床标本采集量对凝血系统检测结果的影响

目的：探讨临床标本采血量的准确性对凝血酶原时间（PT）、活化部分凝血活酶时间（APTT）测定的影响。方法：用BE-RockRotor全自动血凝仪和配套试剂分别对15例临床患者不同抽血量标本进行检测,观察不同的采血量对PT、APTT检测结果的影响,并将所得检测结果采用统计方法处理。结果：与标准采血量为1．8mL相比,采血量大于或小于1．8mL时测得的PTT、APTT结果有显著性差异。结论：临床标本采集量对凝血系统检测结果的准确性有直接影响;必须严格按照标准采血量规定采集标本,才能确保凝血系统检测结果的准确,为病人疾病的诊断提供可靠的实验室依据。     

Basophilic line of the articular cartilage in normal and various pathological states

Epiphyses of long tubular bones in the man and animals of various age, as well as experimental material of the adjuvant arthritis, with special reference to the basal part of the articular cartilage have been studied by means of histological, histochemical and histometrical methods. The structural-chemical organization of the basophilic line (tidemark) of the articular cartilage ensures its barrier role and participation in regulating selective permeability. Reconstruction of the tidemark in the process of physiological ageing and in cases of the articular pathology is aimed to preserve its integrity and in this way a complete differentiation of the noncalcified and calcified structures is secured. Disturbance of the basophilic line results in changes of the articular selective permeability, in invasion of vessels and structural elements of the bone marrow, and in development of profound distrophic and destructive changes of the cartilage--in deforming artrosis. Deflations in the structural-chemical organization of the tidemark indicate certain disturbances in the state of the system articular cartilage--subchondral bone. These data can be of prognostic importance.     

Derivation of stochastic partial differential equations for size- and age-structured populations

Stochastic partial differential equations (SPDEs) for size-structured and age- and size-structured populations are derived from basic principles, i.e. from the changes that occur in a small time interval. Discrete stochastic models of size-structured and age-structured populations are constructed, carefully taking into account the inherent randomness in births, deaths, and size changes. As the time interval decreases, the discrete stochastic models lead to systems of Itô stochastic differential equations. As the size and age intervals decrease, SPDEs are derived for size-structured and age- and size-structured populations. Comparisons between numerical solutions of the SPDEs and independently formulated Monte Carlo calculations support the accuracy of the derivations.     

A Bayesian compound stochastic process for modeling nonstationary and nonhomogeneous sequence evolution

Variations of nucleotidic composition affect phylogenetic inference conducted under stationary models of evolution. In particular, they may cause unrelated taxa sharing similar base composition to be grouped together in the resulting phylogeny. To address this problem, we developed a nonstationary and nonhomogeneous model accounting for compositional biases. Unlike previous nonstationary models, which are branchwise, that is, assume that base composition only changes at the nodes of the tree, in our model, the process of compositional drift is totally uncoupled from the speciation events. In addition, the total number of events of compositional drift distributed across the tree is directly inferred from the data. We implemented the method in a Bayesian framework, relying on Markov Chain Monte Carlo algorithms, and applied it to several nucleotidic data sets. In most cases, the stationarity assumption was rejected in favor of our nonstationary model. In addition, we show that our method is able to resolve a well-known artifact. By Bayes factor evaluation, we compared our model with 2 previously developed nonstationary models. We show that the coupling between speciations and compositional shifts inherent to branchwise models may lead to an overparameterization, resulting in a lesser fit. In some cases, this leads to incorrect conclusions, concerning the nature of the compositional biases. In contrast, our compound model more flexibly adapts its effective number of parameters to the data sets under investigation. Altogether, our results show that accounting for nonstationary sequence evolution may require more elaborate and more flexible models than those currently used.