Intergeneric somatic hybridization and its application to crop genetic improvement

Related or distant species of cultivated cs are a large pool of many desirable genes. Gene transfer from these species through conventional breeding is difficult owing to post- and pre-zygotic sexual incompatibilities. Somatic hybridization via protoplast fusion is a possible alternative for gene transfer from these species to cultivated crops. Since the early days of somatic hybridization many intergeneric somatic hybrids have been developed through symmetric fusion, asymmetric fusion and microfusion. Somatic hybrids are mainly selected by using markers such as specific media or fusion parents with special features, biochemical mutants, antibiotic resistance and complementation strategy. The hybridity of the regenerants is determined based on morphological, cytological and molecular analysis. The inheritance patterns of nuclear and cytoplasmic genomes in the somatic hybrids are diverse. Nuclear DNA from both fusion parents co-exists congruously in some hybrids with translocation and rearrangement of chromosomes, but spontaneous elimination of chromosomes from either or both fusion parents has been observed very often. In asymmetric fusion, chromosome elimination is an important issue that is a complicated process influenced by many factors, such as irradiation dose, phylogenetic relatedness, ploidy level of fusion parent and regenerants. As for chloroplast genome, uniparental segregation is mainly detected, though co-existence is also reported in some cases. The mitochondrial genome, in contrast to chloroplast, undergoes recombination and very frequent rearrangements. Somatic cell fusion has potential applications for crop genetic improvement by overcoming sexual incompatibility or reproductive barriers, and by realizing novel combinations of nuclear and/or cytoplasmic genomes.     

Non-linear cancer classification using a modified radial basis function classification algorithm

Summary This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.     

Nonparametric estimation of ROC curves in the absence of a gold standard

In the evaluation of diagnostic accuracy of tests, a gold standard on the disease status is required. However, in many complex diseases, it is impossible or unethical to obtain such a gold standard. If an imperfect standard is used, the estimated accuracy of the tests would be biased. This type of bias is called imperfect gold standard bias. In this article we develop a nonparametric maximum likelihood method for estimating ROC curves and their areas of ordinal-scale tests in the absence of a gold standard. Our simulation study shows that the proposed estimators for the ROC curve areas have good finite-sample properties in terms of bias and mean squared error. Further simulation studies show that our nonparametric approach is comparable to the binormal parametric method, and is easier to implement. Finally, we illustrate the application of the proposed method in a real clinical study on assessing the accuracy of seven specific pathologists in detecting carcinoma in situ of the uterine cervix.     

A general model for detecting genetic determinants underlying longitudinal traits with unequally spaced measurements and nonstationary covariance structure

A mixture model for determining quantitative trait loci (QTL) affecting growth trajectories has been proposed in the literature. In this article, we extend this model to a more general situation in which longitudinal traits for each subject are measured at unequally spaced time intervals, different subjects have different measurement patterns, and the residual correlation within subjects is nonstationary. We derive an EM-simplex hybrid algorithm to estimate the allele frequencies, Hardy-Weinberg disequilibrium, and linkage disequilibrium between QTL in the original population and parameters contained in the growth equation and in the covariance structure. A worked example of head circumference growth in 145 children is used to validate our extended model. A simulation study is performed to examine the statistical properties of the parameter estimation obtained from this example. Finally, we discuss the implications and extensions of our model for detecting QTL that affect growth trajectories.     

Nonlinear PI controllers for continuous bioreactors using population balance models

Continuous bioreactors are critical unit operations in many biological systems, but the unique modeling is very complicated due to the underlying biochemical reactions and the distributed properties of cell population. The scope of this paper considers a popular modeling method for microbial cell cultures by population balance equation models, and the control objective aims to attenuate undesired oscillations appeared in the nonlinear distributed parameter system. In view of pursuing the popular/practical control configuration and the lack of on-line sensors, an approximate technique by exploiting the “pseudo-steady-state” approach constructs a simple nonlinear control model. Through an off-line estimation mechanism for the system having self-oscillating behavior, two kinds of nonlinear PI configurations are developed. Closed-loop simulation results have confirmed that the regulatory and tracking performances of the control system proposed are good.     

Construction of carbon chains on ruthenium and osmium carbonyl clusters

We have used the elimination of AuX(PR₃) (X = halide, R = Ph, tol) that occurs in reactions of alkynylgold(I)-phosphine complexes with M₃(μ-H)₃ (μ₃-CBr) (CO)₉ (M = Ru, Os) to prepare the complexes M₃(μ-H)₃(μ₃-CCCR)(CO)₉ [M = Ru, R = Ph 2, CCSiMe₃3, Fc 4, CCFc 6-Ru, CC[Ru(PPh₃)₂Cp] 8; M = Os, R = CCFc 6-Os, CCCCFc 7], Fc′{(μ₃-CCC)Ru₃(μ-H)₃(CO)₉}₂5, and bis-cluster-capped carbon chain complexes {M₃(μ-H)₃(CO)₉}₂{μ₃:μ₃-C(CC)_nC} (M = Ru, n = 2 9, 3 10-Ru; M = Os, n = 3 10-Os) and {(L)(OC)₈(μ-H)₃M₃}C(CC)_nC{Co₃(μ-dppm)(CO)₇} (n = 1, M = Ru, L = CO 11, PPh₃12-Ru/P; n = 2, L = CO 12-Ru, PPh₃13; M = Os, L = CO 12-Os) in good to excellent yields. X-ray structural determinations of 2-5, 6-Ru, 6-Os, 7, 9, 11, 12-Ru, 12-Os and 12-Ru/P are reported.     

Quantum algorithm for programmed cell death of Caenorhabditis elegans

During the development of Caenorhabditis elegans, through cell divisions, a total of exactly 1090 cells are generated, 131 of which undergo programmed cell death (PCD) to result in an adult organism comprising 959 cells. Of those 131, exactly 113 undergo PCD during embryogenesis, subdivided across the cell lineages in the following fashion: 98 for AB lineage; 14 for MS lineage; and 1 for C lineage. Is there a law underlying these numbers, and if there is, what could it be? Here we wish to show that the count of the cells undergoing PCD complies with the cipher laws related to the algorithms of Shor and of Grover.     

Learning rules for optimal selection in a varying environment: mate choice revisited

The quality of a chosen partner can be one of the most significantfactors affecting an animal's long-term reproductive success.We investigate optimal mate choice rules in an environment wherethere is both local variation in the quality of potential mateswithin each local mating pool and spatial (or temporal) variationin the average quality of the pools themselves. In such a situation,a robust rule that works well across a variety of environmentswill confer a significant reproductive advantage. We formulatea full Bayesian model for updating information in such a varyingenvironment and derive the form of the rule that maximizes expectedreward in a spatially varying environment. We compare the theoreticalperformance of our optimal learning rule against both fixedthreshold rules and simpler near-optimal learning rules andshow that learning is most advantageous when both the localand environmental variances are large. We consider how optimalsimple learning rules might evolve and compare their evolutionwith that of fixed threshold rules using genetic algorithmsas minimal models of the relevant genetics. Our analysis pointsup the variety of ways in which a near-optimal rule can be expressed.Finally, we describe how our results extend to the case of temporallyvarying environments.     

基于系统安全性的死锁避免算法

死锁是多用户操作系统的一个重要事件,本文着重介绍了系统的安全状态,并对操作系统的避免死锁算法——Dijkstra银行家算法进行了研究分析。     

Numerical quantification of the significance of aquatic vegetation affecting spatial distribution of Japanese medaka (Oryzias latipes) in an agricultural canal

Aquatic vegetation plays a very important role in providing food, shelter, and nursery habitat and is also regarded as hydraulic resistance in the stream environment. To achieve better ecological restoration, this trade-off should be solved both hydraulically and ecologically. This study quantifies the effect of aquatic vegetation on the spatial distribution of Japanese medaka (Oryzias latipes) to evaluate its importance to fish habitat preference. The preference for aquatic vegetation index is calculated using a fuzzy preference intensity model (FPIM) with interactions among water depth, current velocity and cover ratio in an agricultural canal. In this model, simplified fuzzy reasoning is introduced to explicitly take the essential vagueness of fish behavior into consideration, and a simple genetic algorithm is used to search for an optimum model representation. Uncertainties in measurement errors and dispersions of the physical environment are positively taken into the model using symmetric triangular fuzzy numbers. To overcome the difficulty in model construction with insufficient data observed in an agricultural canal, this model was conjugated with a model developed in a laboratory experiment. The model obtained was then assessed using the AIC (Akaikes Information Criterion) to evaluate the significance of vegetation index with a statistical approach. The results suggest the significance of vegetation index to habitat selection by Japanese medaka and that utilization of the AIC enables us to grasp the validity of an additional factor contributing to habitat prediction with a view to a definite scale