Sensory-specific Appetition: Postingestive Detection of Glucose Rapidly Promotes Continued Consumption of a Recently Encountered Flavor

It is generally thought that macronutrients stimulate intake when sensed in the mouth (e.g., sweet taste) but as food enters the GI tract its effects become inhibitory, triggering satiation processes leading to meal termination. Here we report experiments extending recent work (see [1]) showing that under some circumstances nutrients sensed in the gut produce a positive feedback effect, immediately promoting continued intake. In one experiment, rats with intragastric (IG) catheters were accustomed to consuming novel flavors in saccharin daily while receiving water infused IG (5 ml/15 min). The very first time glucose (16% w/w) was infused IG instead of water, intake accelerated within 6 mins of infusion onset and total intake increased 29% over baseline. Experiment 2 replicated this stimulatory effect with glucose infusion but not fructose nor maltodextrin. Experiment 3 showed the immediate intake stimulation is specific to the flavor accompanying the glucose infusion. Rats were accustomed to flavored saccharin being removed and replaced with the same or a different flavor. When glucose infusion accompanied the first bottle, intake from the second bottle was stimulated only when it contained the same flavor, not when the flavor switched. Thus we confirm not only that glucose sensed postingestively can have a rapid, positive feedback effect ('appetition' as opposed to 'satiation') but that it is sensory-specific, promoting continued intake of a recently encountered flavor. This sensory specific motivation may represent an additional psychobiological influence on meal size, and further, has implications for the mechanisms of learned flavor-nutrient associations.

On a Microcomputer Implementation of an Intrusion-detection Algorithm

The main objective of this paper is to discuss various aspects of implementing a specific intrusion-detection scheme on a micro-computer system using fixed-point arithmetic. The proposed scheme is suitable for detecting intruder stimuli which are in the form of transient signals. It consists of two stages: an adaptive digital predictor and an adaptive threshold detection algorithm. Experimental results involving data acquired via field experiments are also included.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.