4 research outputs found
Effects of coconut oil and palm kernel oil treatments on ripening process in banana fruits
No full textNo Abstract.Cameroon Journal of Experimental Biology Vol.2(1) 2006: pp. 16-2
The Plant Short-Chain Dehydrogenase (SDR) superfamily:genome-wide inventory and diversification patterns
No full textBackground Short-chain dehydrogenases/reductases (SDRs) form one of the largest and oldest NAD(P)(H) dependent oxidoreductase families. Despite a conserved 'Rossmann-fold' structure, members of the SDR superfamily exhibit low sequence similarities, which constituted a bottleneck in terms of identification. Recent classification methods, relying on hidden-Markov models (HMMs), improved identification and enabled the construction of a nomenclature. However, functional annotations of plant SDRs remain scarce. Results Wide-scale analyses were performed on ten plant genomes. The combination of hidden Markov model (HMM) based analyses and similarity searches led to the construction of an exhaustive inventory of plant SDR. With 68 to 315 members found in each analysed genome, the inventory confirmed the over-representation of SDRs in plants compared to animals, fungi and prokaryotes. The plant SDRs were first classified into three major types --- 'classical', 'extended' and 'divergent' --- but a minority (10 % of the predicted SDRs) could not be classified into these general types ('unknown' or 'atypical' types). In a second step, we could categorize the vast majority of land plant SDRs into a set of 49 families. Out of these 49 families, 35 appeared early during evolution since they are commonly found through all the Green Lineage. Yet, some SDR families --- tropinone reductase-like proteins (SDR65C), 'ABA2-like'-NAD dehydrogenase (SDR110C), 'salutaridine/menthone-reductase-like' proteins (SDR114C), 'dihydroflavonol 4-reductase'-like proteins (SDR108E) and 'isoflavone-reductase-like' (SDR460A) proteins --- have undergone significant functional diversification within vascular plants since they diverged from Bryophytes. Interestingly, these diversified families are either involved in the secondary metabolism routes (terpenoids, alkaloids, phenolics) or participate in developmental processes (hormone biosynthesis or catabolism, flower development), in opposition to SDR families involved in primary metabolism which are poorly diversified. Conclusion The application of HMMs to plant genomes enabled us to identify 49 families that encompass all Angiosperms ('higher plants') SDRs, each family being sufficiently conserved to enable simpler analyses based only on overall sequence similarity. The multiplicity of SDRs in plant kingdom is mainly explained by the diversification of large families involved in different secondary metabolism pathways, suggesting that the chemical diversification that accompanied the emergence of vascular plants acted as a driving force for SDR evolution
Functional characterization of SlscADH1, a fruit-ripening associated short-chain alcohol dehydrogenase of tomato
No full textA tomato short-chain dehydrogenase-reductase (SlscADH1) is preferentially expressed in fruit with a maximum expression at the breaker stage while expression in roots, stems, leaves and flowers is very weak. It represents a potential candidate for the formation of aroma volatiles by interconverting alcohols and aldehydes. The SlscADH1 recombinant protein produced in Escherichia coli exhibited dehydrogenase-reductase activity towards several volatile compounds present in tomato flavour with a strong preference for the NAD/NADH co-factors. The strongest activity was observed for the reduction of hexanal (Km = 0.175 mM) and phenylacetaldehyde (Km = 0.375 mM) in the presence of NADH. The oxidation process of hexanol and 1-phenylethanol was much less efficient (Kms of 2.9 and 23.0 mM, respectively), indicating that the enzyme preferentially acts as a reductase. However activity was observed only for hexanal, phenylacetaldehyde, (E)-2-hexenal and acetaldehyde and the corresponding alcohols. No activity could be detected for other aroma volatiles important for tomato flavour, such as methyl-butanol/methyl-butanal, 5-methyl-6-hepten-2-one/5-methyl-6-hepten-2-ol, citronellal/citronellol, neral/nerol, geraniol. In order to assess the function of the SlscADH1 gene, transgenic plants have been generated using the technique of RNA interference (RNAi). Constitutive down-regulation using the 35S promoter resulted in the generation of dwarf plants, indicating that the SlscADH1 gene, although weakly expressed in vegetative tissues, had a function in regulating plant development. Fruitspecific down-regulation using the 2A11 promoter had no morphogenetic effect and did not alter the aldehyde/alcohol balance of the volatiles compounds produced by the fruit. Nevertheless, SlscADH1-inhibited fruit unexpectedly accumulated higher concentrations of C5 and C6 volatile compounds of the lipoxygenase pathway, possibly as an indirect effect of the suppression of SlscADH1 on the catabolism of phospholipids and/or integrity of membranes
Modeling in Finance and Insurance With Levy-It\u27o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains
No full textMathematical and statistical modeling have been at the forefront of many significant advances in many disciplines in both the academic and industry sectors. From behavioral sciences to hard core quantum mechanics in physics, mathematical modeling has made a compelling argument for its usefulness and its necessity in advancing the current state of knowledge in the 21rst century. In Finance and Insurance in particular, stochastic modeling has proven to be an effective approach in accomplishing a vast array of tasks: risk management, leveraging of investments, prediction, hedging, pricing, insurance, and so on. However, the magnitude of the damage incurred in recent market crisis of 1929 (the great depression), 1937 (recession triggered by lingering fears emanating from the great depression), 1990 (one year recession following a decade of steady expansion) and 2007 (the great recession triggered by the sub-prime mortgage crisis) has suggested that there are certain aspects of financial markets not accounted for in existing modeling. Explanations have abounded as to why the market underwent such deep crisis and how to account for regime change risk. One such explanation brought forth was the existence of regimes in the financial markets. The basic idea of market regimes underscored the principle that the market was intrinsically subjected to many different states and can switch from one state to another under unknown and uncertain internal and external perturbations. Implementation of such a theory has been done in the simplifying case of Markov regimes. The mathematical simplicity of the Markovian regime model allows for semi-closed or closed form solutions in most financial applications while it also allows for economically interpretable parameters. However, there is a hefty price to be paid for such practical conveniences as many assumptions made on the market behavior are quite unreasonable and restrictive. One assumes for instance that each market regime has a constant propensity of switching to any other state irrespective of the age of the current state. One also assumes that there are no intermediate states as regime changes occur in a discrete manner from one of the finite states to another. There is therefore no telling how meaningful or reliable interpretation of parameters in Markov regime models are. In this thesis, we introduced a sound theoretical and analytic framework for Levy driven linear stochastic models under a semi Markov market regime switching process and derived It\\u27o formula for a general linear semi Markov switching model generated by a class of Levy It\u27o processes (1). It\u27o formula results in two important byproducts, namely semi closed form formulas for the characteristic function of log prices and a linear combination of duration times (2). Unlike Markov markets, the introduction of semi Markov markets allows a time varying propensity of regime change through the conditional intensity matrix. This is more in line with the notion that the market\u27s chances of recovery (respectively, of crisis) are affected by the recession\u27s age (respectively, recovery\u27s age). Such a change is consistent with the notion that for instance, the longer the market is mired into a recession, the more improbable a fast recovery as the the market is more likely to either worsens or undergo a slow recovery. Another interesting consequence of the time dependence of the conditional intensity matrix is the interpretation of semi Markov regimes as a pseudo-infinite market regimes models. Although semi Markov regime assume a finite number of states, we note that while in any give regime, the market does not stay the same but goes through an infinite number of changes through its propensity of switching to other regimes. Each of those separate intermediate states endows the market with a structure of pseudo-infinite regimes which is an answer to the long standing problem of modeling market regime with infinitely many regimes.
We developed a version of Girsanov theorem specific to semi Markov regime switching stochastic models, and this is a crucial contribution in relating the risk neutral parameters to the historical parameters (3). Given that Levy driven markets and regime switching markets are incomplete, there are more than one risk neutral measures that one can use for pricing derivative contracts. Although much work has been done about optimal choice of the pricing measure, two of them jump out of the current literature: the minimal martingale measure and the minimum entropy martingale measure. We first presented a general version of Girsanov theorem explicitly accounting for semi Markov regime. Then we presented Siu and Yang pricing kernel. In addition, we developed the conditional and unconditional minimum entropy martingale measure which minimized the dissimilarity between the historical and risk neutral probability measures through a version of Kulbach Leibler distance (4).
Estimation of a European option price in a semi Markov market has been attempted before in the restricted case of the Black Scholes model. The problems encountered then were twofold: First, the author employed a Markov chain Monte Carlo methods which relied much on the tractability of the likelihood function of the normal random sequences. This tractability is unavailable for most Levy processes, hence the necessity of alternative pricing methods is essential. Second, the accuracy of the parameter estimates required tens of thousands of simulations as it is often the case with Metropolis Hasting algorithms with considerable CPU time demand. Both above outlined issues are resolved by the development of a semi-closed form expression of the characteristic function of log asset prices, and it opened the door to a Fourier transform method which is derived on the heels of Carr and Madan algorithm and the Fourier time stepping algorithm (5).
A round of simulations and calibrations is performed to better capture the performance of the semi Markov model as opposed to Markov regime models. We establish through simulations that semi Markov parameters and the backward recurrence time have a substantial effect on option prices ( 6). Differences between Markov and Semi Markov market calibrations are quantified and the CPU times are reported. More importantly, interpretation of risk neutral semi Markov parameters offer more insight into the dynamic of market regimes than Markov market regime models ( 7). This has been systematically exhibited in this work as calibration results obtained from a set of European vanilla call options led to estimates of the shape and scale parameters of the Weibull distribution considered, offering a deeper view of the current market state as they determine the in-regime dynamic crucial to determining where the market is headed.
After introducing semi Markov models through linear Levy driven models, we consider semi Markov markets with nonlinear multidimensional coupled asset price processes (8). We establish that the tractability of linear semi Markov market models carries over to multidimensional nonlinear asset price models. Estimating equations and pricing formula are derived for historical parameters and risk neutral parameters respectively (9). The particular case of basket of commodities is explored and we provide calibration formula of the model parameters to observed historical commodity prices through the LLGMM method. We also study the case of Heston model in a semi Markov switching market where only one parameter is subjected to semi Markov regime changes. Heston model is one the most popular model in option pricing as it reproduces many more stylized facts than Black Scholes model while retaining tractability. However, in addition to having a faster deceasing smiles than observed, one of the most damning shortcomings of most diffusion models such as Heston model, is their inability to accurately reproduce short term options prices. An avenue for solving these issues consists in generalizing Heston to account for semi Markov market regimes. Such a solution is implemented and a semi analytic formula for options is obtained
