Manual on application of molecular tools in aquaculture and inland fisheries management. Part I : laboratory protocols, data management and analysis

The aim of this manual is to provide a comprehensive practical tool for the generation and analysis of genetic data for subsequent application in aquatic resources management in relation to genetic stock identification in inland fisheries and aquaculture. The material only covers general background on genetics in relation to aquaculture and fisheries resource management, the techniques and relevant methods of data analysis that are commonly used to address questions relating to genetic resource characterisation and population genetic analyses. No attempt is made to include applications of genetic improvement techniques e.g. selective breeding or producing genetically modified organisms (GMOs). The manual includes two ‘stand-alone’ parts, of which this is the second volume: Part 1 – Conceptual basis of population genetic approaches: will provide a basic foundation on genetics in general, and concepts of population genetics. Issues on the choices of molecular markers and project design are also discussed. Part 2 – Laboratory protocols, data management and analysis: will provide step-by-step protocols of the most commonly used molecular genetic techniques utilised in population genetics and systematic studies. In addition, a brief discussion and explanation of how these data are managed and analysed is also included. This manual is expected to enable NACA member country personnel to be trained to undertake molecular genetic studies in their own institutions, and as such is aimed at middle and higher level technical grades. The manual can also provide useful teaching material for specialised advanced level university courses in the region and postgraduate students. The manual has gone through two development/improvement stages. The initial material was tested at a regional workshop and at the second stage feedback from participants was used to improve the contents.

The role of labeling processes in elderly consumers' responses to age segmentation cues

This investigation explores labeling processes underlying age segmentation cue effects on discount usage intentions. Depth interviews regarding participants' experiences using senior-citizen-type discounts reveal three levels of responsiveness to consumer offerings promoted with age segmentation cues: rejecting senior citizen discounts to avoid self-devaluation, rejecting senior citizen discounts to avoid stigmatization, and assigning positive meanings to the status that promotes senior citizen discount usage. An experimental investigation, undertaken to assess the sequential ordering of these levels of responsiveness, reveals that self-devaluation and perceived stigma mediate age segmentation cue effects on discount usage intention only for younger-aged elderly. Results lend support for a stage model of consumers' progression through phases of responsiveness to "senior citizen" labeling.

Automatic labeling of sports video using umpire gesture recognition

We present results on an extension to our approach for automatic sports video annotation. Sports video is augmented with accelerometer data from wrist bands worn by umpires in the game. We solve the problem of automatic segmentation and robust gesture classification using a hierarchical hidden Markov model in conjunction with a filler model. The hierarchical model allows us to consider gestures at different levels of abstraction and the filler model allows us to handle extraneous umpire movements. Results are presented for labeling video for a game of Cricket.

Horror film genre typing and scene labeling via audio analysis

We examine localised sound energy patterns, or events, that we associate with high level affect experienced with films. The study of sound energy events in conjunction with their intended affect enable the analysis of film at a higher conceptual level, such as genre. The various affect/emotional responses we investigate in this paper are brought about by well established patterns of sound energy dynamics employed in audio tracks of horror films. This allows the examination of the thematic content of the films in relation to horror elements. We analyse the frequency of sound energy and affect events at a film level as well as at a scene level, and propose measures indicative of the film genre and scene content. Using 4 horror, and 2 non-horror movies as experimental data we establish a correlation between the sound energy event types and horrific thematic content within film, thus enabling an automated mechanism for genre typing and scene content labeling in film.

Labeling of multiple HIV-1 proteins with the biarsenical-tetracysteine system

Due to its small size and versatility, the biarsenical-tetracysteine system is an attractive way to label viral proteins for live cell imaging. This study describes the genetic labeling of the human immunodeficiency virus type 1 (HIV-1) structural proteins (matrix, capsid and nucleocapsid), enzymes (protease, reverse transcriptase, RNAse H and integrase) and envelope glycoprotein 120 with a tetracysteine tag in the context of a full-length virus. We measure the impact of these modifications on the natural virus infection and, most importantly, present the first infectious HIV-1 construct containing a fluorescently-labeled nucleocapsid protein. Furthermore, due to the high background levels normally associated with the labeling of tetracysteine-tagged proteins we have also optimized a metabolic labeling system that produces infectious virus containing the natural envelope glycoproteins and specifically labeled tetracysteine-tagged proteins that can easily be detected after virus infection of T-lymphocytes. This approach can be adapted to other viral systems for the visualization of the interplay between virus and host cell during infection.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.