The action of selection on codon bias in the human genome is related to frequency, complexity, and chronology of amino acids

BACKGROUND: The question of whether synonymous codon choice is affected by cellular tRNA abundance has been positively answered in many organisms. In some recent works, concerning the human genome, this relation has been studied, but no conclusive answers have been found. In the human genome, the variation in base composition and the absence of cellular tRNA count data makes the study of the question more complicated. In this work we study the relation between codon choice and tRNA abundance in the human genome by correcting relative codon usage for background base composition and using a measure based on tRNA-gene copy numbers as a rough estimate of tRNA abundance. RESULTS: We term major codons to be those codons with a relatively large tRNA-gene copy number for their corresponding amino acid. We use two measures of expression: breadth of expression (the number of tissues in which a gene was expressed) and maximum expression level among tissues (the highest value of expression of a gene among tissues). We show that for half the amino acids in the study (8 of 16) the relative major codon usage rises with breadth of expression. We show that these amino acids are significantly more frequent, are smaller and simpler, and are more ancient than the rest of the amino acids. Similar, although weaker, results were obtained for maximum expression level. CONCLUSION: There is evidence that codon bias in the human genome is related to selection, although the selection forces acting on codon bias may not be straightforward and may be different for different amino acids. We suggest that, in the first group of amino acids, selection acts to enhance translation efficiency in highly expressed genes by preferring major codons, and acts to reduce translation rate in lowly expressed genes by preferring non-major ones. In the second group of amino acids other selection forces, such as reducing misincorporation rate of expensive amino acids, in terms of their size/complexity, may be in action. The fact that codon usage is more strongly related to breadth of expression than to maximum expression level supports the notion, presented in a recent study, that codon choice may be related to the tRNA abundance in the tissue in which a gene is expressed

Increasing stimulus duration improves attention and memory performance in elderly with cognitive impairment

Objectives: In this study, we investigated whether increasing stimulus duration could improve performance on a test of attention and short-term memory in cognitively impaired individuals. Methods: A computer-generated forward digit span test was administered to 65 patients with mild cognitive impairment or dementia (28 intervention and 37 controls). After point of failure, testing in the intervention group was continued at the same rate, but with an average 150% digit lengthening to 800 ms. Testing of controls was continued using the standard digit span test. Results: In the intervention group, 13/28 (46.4%) improved their digit span test performance, compared to 2/37 (5.4%) in the control group ( p  = 0.00005). Conclusion: Cognitively impaired elderly participants improved performance on a test of attention and short-term memory, when stimulus duration was increased in proportion to elongation of the finger tap touch-phase previously found in a similar cohort. A possible mechanism for the effect of increased stimulus duration on attention and short-term memory is discussed

Perceptual learning of time-compressed speech: more than rapid adaptation.

BACKGROUND: Time-compressed speech, a form of rapidly presented speech, is harder to comprehend than natural speech, especially for non-native speakers. Although it is possible to adapt to time-compressed speech after a brief exposure, it is not known whether additional perceptual learning occurs with further practice. Here, we ask whether multiday training on time-compressed speech yields more learning than that observed during the initial adaptation phase and whether the pattern of generalization following successful learning is different than that observed with initial adaptation only. METHODOLOGY/PRINCIPAL FINDINGS: Two groups of non-native Hebrew speakers were tested on five different conditions of time-compressed speech identification in two assessments conducted 10-14 days apart. Between those assessments, one group of listeners received five practice sessions on one of the time-compressed conditions. Between the two assessments, trained listeners improved significantly more than untrained listeners on the trained condition. Furthermore, the trained group generalized its learning to two untrained conditions in which different talkers presented the trained speech materials. In addition, when the performance of the non-native speakers was compared to that of a group of naïve native Hebrew speakers, performance of the trained group was equivalent to that of the native speakers on all conditions on which learning occurred, whereas performance of the untrained non-native listeners was substantially poorer. CONCLUSIONS/SIGNIFICANCE: Multiday training on time-compressed speech results in significantly more perceptual learning than brief adaptation. Compared to previous studies of adaptation, the training induced learning is more stimulus specific. Taken together, the perceptual learning of time-compressed speech appears to progress from an initial, rapid adaptation phase to a subsequent prolonged and more stimulus specific phase. These findings are consistent with the predictions of the Reverse Hierarchy Theory of perceptual learning and suggest constraints on the use of perceptual-learning regimens during second language acquisition

Voice Morphing Using 3D Waveform Interpolation Surfaces and Lossless Tube Area Functions

<p/> <p>Voice morphing is the process of producing intermediate or hybrid voices between the utterances of two speakers. It can also be defined as the process of gradually transforming the voice of one speaker to that of another. The ability to change the speaker's individual characteristics and to produce high-quality voices can be used in many applications. Examples include multimedia and video entertainment, as well as enrichment of speech databases in text-to-speech systems. In this study we present a new technique which enables production of a given number of intermediate voices or of utterances which gradually change from one voice to another. This technique is based on two components: (1) creation of a 3D prototype waveform interpolation (PWI) surface from the LPC residual signal, to produce an intermediate excitation signal; (2) a representation of the vocal tract by a lossless tube area function, and an interpolation of the parameters of the two speakers. The resulting synthesized signal sounds like a natural voice lying between the two original voices.</p

Gene Prediction by Spectral Rotation Measure: A New Method for Identifying Protein-Coding Regions

A new measure for gene prediction in eukaryotes is presented. The measure is based on the Discrete Fourier Transform (DFT) phase at a frequency of 1/3, computed for the four binary sequences for A, T, C, and G. Analysis of all the experimental genes of S. cerevisiae revealed distribution of the phase in a bell-like curve around a central value, in all four nucleotides, whereas the distribution of the phase in the noncoding regions was found to be close to uniform. Similar findings were obtained for other organisms. Several measures based on the phase property are proposed. The measures are computed by clockwise rotation of the vectors, obtained by DFT for each analysis frame, by an angle equal to the corresponding central value. In protein coding regions, this rotation is assumed to closely align all vectors in the complex plane, thereby amplifying the magnitude of the vector sum. In noncoding regions, this operation does not significantly change this magnitude. Computing the measures with one chromosome and applying them on sequences of others reveals improved performance compared with other algorithms that use the 1/3 frequency feature, especially in short exons. The phase property is also used to find the reading frame of the sequence

A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation

<p/> <p>We present an efficient algorithm for segmentation of audio signals into speech or music. The central motivation to our study is consumer audio applications, where various real-time enhancements are often applied. The algorithm consists of a learning phase and a classification phase. In the learning phase, predefined training data is used for computing various time-domain and frequency-domain features, for speech and music signals separately, and estimating the optimal speech/music thresholds, based on the probability density functions of the features. An automatic procedure is employed to select the best features for separation. In the test phase, initial classification is performed for each segment of the audio signal, using a three-stage sieve-like approach, applying both Bayesian and rule-based methods. To avoid erroneous rapid alternations in the classification, a smoothing technique is applied, averaging the decision on each segment with past segment decisions. Extensive evaluation of the algorithm, on a database of more than 12 hours of speech and more than 22 hours of music showed correct identification rates of 99.4% and 97.8%, respectively, and quick adjustment to alternating speech/music sections. In addition to its accuracy and robustness, the algorithm can be easily adapted to different audio types, and is suitable for real-time operation.</p

A Decision-Tree-Based Algorithm for Speech/Music Classification and Segmentation

We present an efficient algorithm for segmentation of audio signals into speech or music. The central motivation to our study is consumer audio applications, where various real-time enhancements are often applied. The algorithm consists of a learning phase and a classification phase. In the learning phase, predefined training data is used for computing various time-domain and frequency-domain features, for speech and music signals separately, and estimating the optimal speech/music thresholds, based on the probability density functions of the features. An automatic procedure is employed to select the best features for separation. In the test phase, initial classification is performed for each segment of the audio signal, using a three-stage sieve-like approach, applying both Bayesian and rule-based methods. To avoid erroneous rapid alternations in the classification, a smoothing technique is applied, averaging the decision on each segment with past segment decisions. Extensive evaluation of the algorithm, on a database of more than 12 hours of speech and more than 22 hours of music showed correct identification rates of 99.4&#37; and 97.8&#37;, respectively, and quick adjustment to alternating speech/music sections. In addition to its accuracy and robustness, the algorithm can be easily adapted to different audio types, and is suitable for real-time operation

Pre-test performance – planned comparisons between the trained and the control groups.

<p>Pre-test performance – planned comparisons between the trained and the control groups.</p