Optimization by orthogonal array design and humoral immunity of the bivalent vaccine against Aeromonas hydrophila and Vibrio fluvialis infection in crucian carp (Carassius auratus L.)

Aeromonas hydrophila and Vibrio fluvialis are the causative agents of a serious haemorrhagic septicaemia that affects a wide range of freshwater fish in China. In order to develop a bivalent anti-A. hydrophila and anti-V. fluvialis formalin-killed vaccine to prevent this disease, an orthogonal array design (OAD) method was used to optimize the production conditions, using three factors, each having three levels. The effects of these factors and levels on the relative per cent survival for crucian carp were quantitatively evaluated by analysis of variance. The final optimized formulation was established. The data showed that inactivation temperature had a significant effect on the potency of vaccine, but formalin concentration did not. The bivalent vaccine could elicit a strong humoral response in crucian carp (Carassius auratus L.) against both A. hydrophila and V. fluvialis simultaneously, which peaked at 3 or 5 weeks respectively. Antibody titres remained high until week 12, the end of the experiment, after a single intraperitoneal injection. The verification experiment confirmed that an optimized preparation could provide protection for fish at least against A. hydrophila infection, and did perform better than the non-optimized vaccine judged by the antibody levels and protection rate, suggesting that OAD is of value in the development of improved vaccine formulations.

When Does a Polynomial Over a Finite Field Permute the Elements of the Field?

We present a class of indecomposable polynomials of non prime-power degree over the finite field of two elements which are permutation polynomials on infinitely many finite extensions of the field. The associated geometric monodromy groups are the simple ...

on quadratic bent functions in polynomial forms

In this correspondence, we construct some new quadratic bent functions in polynomial forms by using the theory of quadratic forms over finite fields. The results improve some previous work. Moreover, we solve a problem left by Yu and Gong in 2006.

Analysis of orthogonal memory patterns

It is well known that the storage capacity may be large if all memory patterns are orthogonal to each other. In this paper, a clear description is given about the relation between the dimension N and the maximal number of orthogonal vectors with components +/-1, and also the conception of attractive index is proposed to estimate the basin of attraction. Theoretic analysis and computer simulation show that each memory pattern's basin of attraction contains at least one Hamming ball when the storage capacity is less than 0.33N which is better than usual 0.15 N.

Discriminative Orthogonal Neighborhood-Preserving Projections for Classification

Orthogonal neighborhood-preserving projection (ONPP) is a recently developed orthogonal linear algorithm for overcoming the out-of-sample problem existing in the well-known manifold learning algorithm, i.e., locally linear embedding. It has been shown that ONPP is a strong analyzer of high-dimensional data. However, when applied to classification problems in a supervised setting, ONPP only focuses on the intraclass geometrical information while ignores the interaction of samples from different classes. To enhance the performance of ONPP in classification, a new algorithm termed discriminative ONPP (DONPP) is proposed in this paper. DONPP 1) takes into account both intraclass and interclass geometries; 2) considers the neighborhood information of interclass relationships; and 3) follows the orthogonality property of ONPP. Furthermore, DONPP is extended to the semisupervised case, i.e., semisupervised DONPP (SDONPP). This uses unlabeled samples to improve the classification accuracy of the original DONPP. Empirical studies demonstrate the effectiveness of both DONPP and SDONPP.

Selection for variables in quantitative structure-activity/property relationship studies - Comparison between methods of orthogonal descriptors and leaps-and-bounds regression analysis

In this paper, the comparison of orthogonal descriptors and Leaps-and-Bounds regression analysis is performed. The results obtained by using orthogonal descriptors are better than that obtained by using Leaps-and-Bounds regression for the data set of nitrobenzenes used in this study. Leaps-and-Bounds regression can be used effectively for selection of variables in quantitative structure-activity/property relationship(QSAR/QSPR) studies. Consequently, orthogonalisation of descriptors is also a good method for variable selection for studies on QSAR/QSPR.

Variable selection by orthogonal descriptors and prediction of R-f value of phenol and aniline derivatives

Orthogonal descriptors is a viable method for variable selection, but this method strongly depend on the orthogonalisation ordering of the descriptors. In this paper, we compared the different methods used for order the descriptors. It showed that better results could be achieved with the use of backward elimination ordering. We predicted R-f value of phenol and aniline derivatives by this method, and compared it with classical algorithms such as forward selection, backward elimination, and stepwise procedure. Some interesting hints were obtained.

LEAST-SQUARES POLYNOMIAL SMOOTHING FOR INDUCTIVELY COUPLED PLASMA ATOMIC EMISSION-SPECTRA

The use of least-squres polynomial smoothing in ICP-AES is discussed and a method of points insertion into spectral scanning intervals is proposed in the present paper. Optimal FWHM/SR ratio can be obtained, and distortion of smoothed spectra can be avoided by use of the recommended method.

Studies on the hemolytic activity of tentacle extracts of jellyfish Rhopilema esculentum Kishinouye: Application of orthogonal test

The present work is first reporting the hemolytic activity of venom from jellyfish Rhopilema esculentum Kishinouye extracted by different phosphate buffer solutions and incubated at different temperature according to the orthogonal test L6(1) x 3(6). Of the seven controllable independent variables, incubated temperature and phenylmethylsulfonyl fluoride (PMSF) had strongest effect on the hemolytic activity. (c) 2006 Elsevier B.V. All rights reserved.

On the Performance of Polynomial-time CLIQUE Approximation Algorithms on Very Large Graphs

The performance of a randomized version of the subgraph-exclusion algorithm (called Ramsey) for CLIQUE by Boppana and Halldorsson is studied on very large graphs. We compare the performance of this algorithm with the performance of two common heuristic algorithms, the greedy heuristic and a version of simulated annealing. These algorithms are tested on graphs with up to 10,000 vertices on a workstation and graphs as large as 70,000 vertices on a Connection Machine. Our implementations establish the ability to run clique approximation algorithms on very large graphs. We test our implementations on a variety of different graphs. Our conclusions indicate that on randomly generated graphs minor changes to the distribution can cause dramatic changes in the performance of the heuristic algorithms. The Ramsey algorithm, while not as good as the others for the most common distributions, seems more robust and provides a more even overall performance. In general, and especially on deterministically generated graphs, a combination of simulated annealing with either the Ramsey algorithm or the greedy heuristic seems to perform best. This combined algorithm works particularly well on large Keller and Hamming graphs and has a competitive overall performance on the DIMACS benchmark graphs.