Production of a base population and its responses to F-1 selection in the bay scallop, Argopecten irradians irradians Lamarck (1819)

A base population of the bay scallop, Argopecten irradians irradians Lamarck, was produced by crossing two cultured bay scallop populations. After 1 year of rearing, the top 10% truncation selection of the top 10% (i=1.755) was carried out in the base population of about 1300 adults. A control parental group with a an identical number to the select parental group was randomly selected from the entire population before isolation of the select parental group. The result showed that, at the larval stage, the growth rate of larvae in the selected line was significantly higher than that of the control (P < 0.05), and that the genetic gain was 6.78%. Owing to the lower density of control at the spat stage, the mean shell length of the control line was larger than that of the select line at day 100. When the same density was adjusted between two lines in the grow-out stage (from day 100 to 160), the daily growth rate of the selected line was significantly higher than that of the control line (P < 0.05). Survival of the select line was significantly larger than that of the control line in the grow-out stage. In conclusion, the results obtained from this experiment indicate that selective breeding from a base population with a high genetic diversity established by mass spawning between different populations appears to be a promising method of genetic improvement in bay scallop, A. irradians irradians Lamarck.

Different responses to selection in two stocks of the bay scallop, Argopecten irradians irradians Lamarck (1819)

Two different stocks (A and B) of the bay scallop Argopecten irradialls irradians (Lamarck, 1819) were used to test mass selection on growth. Stock A was a descending stock from the initial introduction from U.S.A. in 1982, which had been cultured in China for about 20 years. Stock B was the third generation from a recent introduction from U.S.A. in 1999. Truncation selection was conducted by selecting the largest 11% scallops in shell length from Stock A and the largest 12.7% scallops from Stock B as parents for the respective selected groups. Before the removal of parents for truncation selection, equal numbers of scallops were randomly chosen from Stock A and B to serve as parents for the control groups. Offspring from the four groups were reared under the same hatchery, nursery, and grow-out conditions. Values of response to selection and realized heritability at larvae, spat and grow-out stages for Stock B were all significantly (P < 0.001) higher than its counterpart for Stock A. For Stock A, no significant response to selection was observed (P > 0.05) at any stage, and the realized heritability for shell length was 0.015 +/- 0.024 for larvae, 0.040 +/- 0.027 for spat, and 0.080 +/- 0.009 for grow-out, respectively. For Stock B, however, significant (P < 0.05) response to selection was observed, and the realized heritability for shell length was 0.511 +/- 0.010 for larvae, 0.341 +/- 0.022 for spat, and 0.338 +/- 0.015 for grow-out. On average, responses to selection at the three stages for Stock B was 30 x, 7.1 x, and 3 x higher than its counterpart for Stock A, respectively. Accordingly, realized heritability at above stages for Stock B was 33 X, 7.5 x, and 3.2 X higher than its counterpart for Stock A, respectively. Differences in response to selection and realized heritability between the two stocks are presumably due to differences in genetic variability. As the 20th generation from the initial introduction consisted of only 26 scallops, Stock A is known to be highly inbred, while inbreeding in Stock B is negligible. (C) 2004 Elsevier B.V. All rights reserved.

Sustained response to selection in an introduced population of the hermaphroditic bay scallop Argopecten irradians irradians Lamarck (1819)

Introduced species often start with limited genetic variability, which is problematic for selective breeding. The problem of inbreeding can be exasperated by hermaphroditism. The bay scallop Argopecten irradians irradians is a hermaphroditic species that has been introduced to and now supports a major aquaculture industry in China. Positive response to selection for fast growth was observed in one of the less inbred stocks in a previous study. In this study, we evaluated selection for the second generation to determine if response to selection can be sustained in this introduced population of a hermaphroditic species. Response to selection, realized heritability for the second generation, cumulative (over two generations), current (for the second generation) and residual (from the first generation) genetic gains were estimated by comparing three different types of lines: SS (selected for two generation), SC (selected for the first generation only) and CC (unselected for two generations). The SS line grew significantly faster (P < 0.05) than the other two lines, indicating that the second generation selection for faster growth is still effective. Response to selection and realized heritability for the second generation were 0.612 +/- 0.101 and 0.349 +/- 0.057, respectively, which are similar to those observed for the first generation. The cumulative, current, and residual gains were 17.56 +/- 5.30%, 10.63 +/- 2.46%, and 6.25 +/- 3.13%, respectively. The sustained response to selection for the second generation observed here suggests that considerable genetic variability exists in this population and that future efforts on selective breeding are likely to be fruitful. (c) 2005 Elsevier B.V. All rights reserved.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

(Semi-)Predictive Discretization During Model Selection

In this paper, we present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of predictive accuracy, which inherently ensures the optimal trade-off between goodness of fit and model complexity (including the number of discretization levels). Using the so-called finest grid implied by the data, our scoring function depends only on the number of data points in the various discretization levels. Not only can it be computed efficiently, but it is also independent of the metric used in the continuous space. Our experiments with gene expression data show that discretization plays a crucial role regarding the resulting network structure.

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Model Selection for Solving Kinematics Problems

There has been much interest in the area of model-based reasoning within the Artificial Intelligence community, particularly in its application to diagnosis and troubleshooting. The core issue in this thesis, simply put, is, model-based reasoning is fine, but whence the model? Where do the models come from? How do we know we have the right models? What does the right model mean anyway? Our work has three major components. The first component deals with how we determine whether a piece of information is relevant to solving a problem. We have three ways of determining relevance: derivational, situational and an order-of-magnitude reasoning process. The second component deals with the defining and building of models for solving problems. We identify these models, determine what we need to know about them, and importantly, determine when they are appropriate. Currently, the system has a collection of four basic models and two hybrid models. This collection of models has been successfully tested on a set of fifteen simple kinematics problems. The third major component of our work deals with how the models are selected.

Hierarchical Shape Description of Objects by Selection and Modification of Prototypes

An approach towards shape description, based on prototype modification and generalized cylinders, has been developed and applied to the object domains pottery and polyhedra: (1) A program describes and identifies pottery from vase outlines entered as lists of points. The descriptions have been modeled after descriptions by archeologists, with the result that identifications made by the program are remarkably consisten with those of the archeologists. It has been possible to quantify their shape descriptors, which are everyday terms in our language applied to many sorts of objects besides pottery, so that the resulting descriptions seem very natural. (2) New parsing strategies for polyhedra overcome some limitations of previous work. A special feature is that the processes of parsing and identification are carried out simultaneously.

Identification, Selection, and Enrichment of Cardiomyocyte Precursors

The large-scale production of cardiomyocytes is a key step in the development of cell therapy and tissue engineering to treat cardiovascular diseases, particularly those caused by ischemia. the main objective of this study was to establish a procedure for the efficient production of cardiomyocytes by reprogramming mesenchymal stem cells from adipose tissue. First, lentiviral vectors expressing neoR and GFP under the control of promoters expressed specifically during cardiomyogenesis were constructed to monitor cell reprogramming into precardiomyocytes and to select cells for amplification and characterization. Cellular reprogramming was performed using 5'-azacytidine followed by electroporation with plasmid pOKS2a, which expressed Oct4, Sox2, and Klf4. Under these conditions, GFP expression began only after transfection with pOKS2a, and less than 0.015% of cells were GFP(+). These GFP(+) cells were selected for G418 resistance to find molecular markers of cardiomyocytes by RT-PCR and immunocytochemistry. Both genetic and protein markers of cardiomyocytes were present in the selected cells, with some variations among them. Cell doubling time did not change after selection. Together, these results indicate that enrichment with vectors expressing GFP and neoR under cardiomyocyte-specific promoters can produce large numbers of cardiomyocyte precursors (CMPs), which can then be differentiated terminally for cell therapy and tissue engineering.

On Clonal Selection.

Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selections algorithms for learning from a theoertical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically andbiologically. We suggest a more capable abstraction of the clonal selection principle grounded in probabilistic estimation and approximation and demonstrate how it addresses some of the shortcomings in existing algorithms. We further show that by recasting black-box optimisation as a learning problem, the same abstraction may be re-employed; thereby taking steps toward unifying the clonal selection principle and distinguishing it from natural selection.

Using Social Media Content for Screening in Recruitment and Selection: Pros and Cons.

The article considers the arguments that have been made in defence of social media screening as well as issues that arise and may effectively erode the reliability and utility of such data for employers. First, the authors consider existing legal frameworks and guidelines that exist in the UK and the USA, as well as the subsequent ethical concerns that arise when employers access and use social networking content for employment purposes. Second, several arguments in favour of the use of social networking content are made, each of which is considered from several angles, including concerns about impression management, bias and discrimination, data protection and security. Ultimately, the current state of knowledge does not provide a definite answer as to whether information from social networks is helpful in recruitment and selection.

Model Selection Methodology in Supervised Learning with Evolutionary Computation

Rowland, J.J. (2003) Model Selection Methodology in Supervised Learning with Evolutionary Computation. BioSystems 72, 1-2, pp 187-196, Nov

Generalisation and Model Selection in Supervised Learning with Evolutionary Computation

Rowland, J. J. (2003) Generalisation and Model Selection in Supervised Learning with Evolutionary Computation. European Workshop on Evolutionary Computation in Bioinformatics: EvoBio 2003. Lecture Notes in Computer Science (Springer), Vol 2611, pp 119-130

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.