Nitrogen stunning of rainbow trout

This study examined the use of nitrogen gas for stunning of Rainbow Trout. It was found that nitrogen could be used as an effective stunning method and that the strong aversive reaction reported for carbon dioxide stunning was not observed. Measurement of ATP, its metabolites and pH were carried out to evaluate the effects of nitrogen stunning on post mortem muscle biochemistry. This treatment was compared to percussive stunning and air asphyxiation. Post mortem ATP levels in nitrogen stunned fish (1.96 ± 0.36 μmol g^?1) were significantly higher than for asphyxiated fish (0.2 ± 0.11 μmol g^?1) but lower than values for percussively stunned fish (5.9 ± 0.89 μmol g^?1). This work indicates that the use of gasses other than carbon dioxide for stunning of fish deserves further study.     

The aseptic meningitis syndrome: a complication of posterior fossa surgery

The syndrome of aseptic meningitis is characterized by spiking fever and meningismus. CSF analysis generally shows increased pleocytosis, hypoglycorrhachia, elevated protein and negative cultures. In an earlier series, 70% of children with posterior fossa operations developed the syndrome. In a new review the incidence was slightly more than 30%. The incidence of aseptic meningitis following operation for structural lesions was 44%, which was higher than the tumor group, where the meningitic syndrome was seen in 25% of the children. It is the purpose of this paper to reexamine the impact that steroids have made on the prevalence of the aseptic meningitis syndrome, and to review recent studies that have attempted to distinguish between aseptic and bacterial meningitis.     

Preference-Based Constrained Optimization with CP-Nets

Many artificial intelligence (AI) tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CP-network—a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it admits an algorithm for finding the most preferred feasible (Pareto-optimal) outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is Pareto optimal.     

Development of interpersonal understanding: A longitudinal and comparative study of normal and disturbed youths.

Using an interview procedure, levels of interpersonal understanding were assessed for 17 behaviorally and emotionally disturbed boys and were compared longitudinally, across 3 assessments at 2- and 4-yr intervals, with those of 17 better-adjusted male peers matched for age, race, SES, and WISC-R IQ. Ss were in Grades 1–6 at Time 1, 3–8 at Time 2, and 7–22 at Time 3. Results suggest that the sequence of progression through levels of interpersonal understanding was the same for both disturbed and normal samples but that the developmental trend of trouble Ss lagged behind that of the higher-functioning group. However, longitudinal follow-up results suggest that the developmental lag of the disturbed Ss was diminishing with time. An exploratory study conducted during Time 3 suggested that relatively low levels of interpersonal understanding were related to the self-reported use of physical and avoidant strategies to resolve conflicts. Results are considered in the context of theoretical relations between thought and action. (23 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Robust linear dimensionality reduction

We present a novel family of data-driven linear transformations, aimed at finding low-dimensional embeddings of multivariate data, in a way that optimally preserves the structure of the data. The well-studied PCA and Fisher's LDA are shown to be special members in this family of transformations, and we demonstrate how to generalize these two methods such as to enhance their performance. Furthermore, our technique is the only one, to the best of our knowledge, that reflects in the resulting embedding both the data coordinates and pairwise relationships between the data elements. Even more so, when information on the clustering (labeling) decomposition of the data is known, this information can also be integrated in the linear transformation, resulting in embeddings that clearly show the separation between the clusters, as well as their internal structure. All of this makes our technique very flexible and powerful, and lets us cope with kinds of data that other techniques fail to describe properly.     

Efficient and non-parametric reasoning over user preferences

We consider the problem of modeling and reasoning about statements of ordinal preferences expressed by a user, such as monadic statement like “X is good,” dyadic statements like “X is better than Y,” etc. Such qualitative statements may be explicitly expressed by the user, or may be inferred from observable user behavior. This paper presents a novel technique for efficient reasoning about sets of such preference statements in a semantically rigorous manner. Specifically, we propose a novel approach for generating an ordinal utility function from a set of qualitative preference statements, drawing upon techniques from knowledge representation and machine learning. We provide theoretical evidence that the new method provides an efficient and expressive tool for reasoning about ordinal user preferences. Empirical results further confirm that the new method is effective on real-world data, making it promising for a wide spectrum of applications that require modeling and reasoning about user preferences.     

Preferences in AI: An overview

This editorial of the special issue “Representing, Processing, and Learning Preferences: Theoretical and Practical Challenges” surveys past and ongoing research on preferences in AI, including references and pointers to the literature. It covers approaches to representation, reasoning and learning of preferences. Methods in AI are contrasted with those in related areas, such as operations research and databases. Finally, we also give a brief introduction to the contents of the special issue.     

International quotidian dialysis registry: Annual report 2009

The International Quotidian Dialysis Registry (IQDR) is a global initiative designed to study practices and outcomes associated with the use of hemodialysis regimens of increased frequency and/or duration. Several small studies suggest that compared with conventional hemodialysis (HD), short-daily, nocturnal, and long conventional HD regimens may improve surrogate endpoints and quality of life. However, methodologically robust comparisons on hard outcomes are sorely lacking. The IQDR represents the first-ever attempt to aggregate long-term follow-up data from centers utilizing alternative HD regimens worldwide, and will have adequate statistical power to examine the effects of these regimens on multiple clinical endpoints, including mortality. To date, the IQDR has enrolled patients from Canada, the United States, Australia, and New Zealand, with plans in place to begin linking with additional commercial databases and national registries. This fifth annual report of the IQDR describes (1) a proposed governance structure that will facilitate international collaboration, stakeholder input and funding; (2) data sources and participating registries; (3) recruitment to date and patient baseline characteristics; and (4) an agenda for future research.