ISPTM: an Iterative Search Algorithm for Systematic Identification of Post-translational Modifications from Complex Proteome Mixtures

Identifying protein post-translational modifications (PTMs) from tandem mass spectrometry data of complex proteome mixtures is a highly challenging task. Here we present a new strategy, named iterative search for identifying PTMs (ISPTM), for tackling this challenge. The ISPTM approach consists of a basic search with no variable modification, followed by iterative searches of many PTMs using a small number of them (usually two) in each search. The performance of the ISPTM approach was evaluated on mixtures of 70 synthetic peptides with known modifications, on an 18-protein standard mixture with unknown modifications and on real, complex biological samples of mouse nuclear matrix proteins with unknown modifications. ISPTM revealed that many chemical PTMs were introduced by urea and iodoacetamide during sample preparation and many biological PTMs, including dimethylation of arginine and lysine, were significantly activated by Adriamycin treatment in NM associated proteins. ISPTM increased the MS/MS spectral identification rate substantially, displayed significantly better sensitivity for systematic PTM identification than the conventional all-in-one search approach and offered PTM identification results that were complementary to InsPecT and MODa, both of which are established PTM identification algorithms. In summary, ISPTM is a new and powerful tool for unbiased identification of many different PTMs with high confidence from complex proteome mixtures

The Development of Speech Research Tools on MIT\u27s Lisp Machine-based Workstations

In recent years, a number of useful speech- and language-related research tools have been under development at MIT. These tools are aids for efficiently analyzing the acoustic characteristics of speech and the phonological properties of a language. They are playing a valuable role in our own research, as well as in research conducted elsewhere. This paper describes several of the systems being developed for use on our Lisp Machine workstations

Mood and anxiety disorders and their association with non-medical prescription opioid use and prescription opioid-use disorder

Background: Non-medical use of prescription opioids represents a national public health concern of growing importance. Mood and anxiety disorders are highly associated with non-medical prescription opioid use. The authors examined longitudinal associations between non-medical prescription opioid use and opioid disorder due to non-medical opioid use and mood/anxiety disorders in a national sample, examining evidence for precipitation, self-medication and general shared vulnerability as pathways between disorders. Method: Data were drawn from face-to-face surveys of 34 653 adult participants in waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. Logistic regression models explored the temporal sequence and evidence for the hypothesized pathways. Results: Baseline lifetime non-medical prescription opioid use was associated with incidence of any mood disorder, major depressive disorder (MDD), bipolar disorder, any anxiety disorder and generalized anxiety disorder (GAD in wave 2, adjusted for baseline demographics, other substance use, and co-morbid mood/anxiety disorders). Lifetime opioid disorder was not associated with any incident mood/anxiety disorders. All baseline lifetime mood disorders and GAD were associated with incident non-medical prescription opioid use at follow-up, adjusted for demographics, co-morbid mood/anxiety disorders, and other substance use. Baseline lifetime mood disorders, MDD, dysthymia and panic disorder were associated with incident opioid disorder due to non-medical prescription opioid use at follow-up, adjusted for the same covariates. Conclusions: These results suggest that precipitation, self-medication as well as shared vulnerability are all viable pathways between non-medical prescription opioid use and opioid disorder due to non-medical opioid use and mood/anxiety disorders

Sensory Communication

Contains table of contents for Section 2, an introduction, reports on ten research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Grant 5 P01 DC00361National Institutes of Health Grant 2 R01 DC00100National Institutes of Health Grant 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-145

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NS07040)National Institutes of Health (Grant 5 R01 NS04332)National Science Foundation (Grant 1ST 80-17599)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0290

Sensory Communication

Contains table of contents on Section 2, an introduction, reports on eleven research projects and a list of publications.National Institutes of Health Grant 5 R01 DC00117National Institutes of Health Grant 5 R01 DC00270National Institutes of Health Contract 2 P01 DC00361National Institutes of Health Grant 5 R01 DC00100National Institutes of Health Contract 7 R29 DC00428National Institutes of Health Grant 2 R01 DC00126U.S. Air Force - Office of Scientific Research Grant AFOSR 90-0200U.S. Navy - Office of Naval Research Grant N00014-90-J-1935National Institutes of Health Grant 5 R29 DC00625U.S. Navy - Office of Naval Research Grant N00014-91-J-1454U.S. Navy - Office of Naval Research Grant N00014-92-J-181

Speech Communication

Contains table of contents for Part IV, table of contents for Section 1 and reports on five research projects.Apple Computer, Inc.C.J. Lebel FellowshipNational Institutes of Health (Grant T32-NS07040)National Institutes of Health (Grant R01-NS04332)National Institutes of Health (Grant R01-NS21183)National Institutes of Health (Grant P01-NS23734)U.S. Navy / Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Office of Naval Research (Contract N00014-82-K-0727

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NSO7040)National Institutes of Health (Grant 5 R01 NS04332)National Institutes of Health (Grant 5 R01 NS21183)National Institutes of Health (Grant 5 P01 NS13126)National Institutes of Health (Grant 1 PO1-NS23734)National Science Foundation (Grant BNS 8418733)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command (Contract N00039-85-C-0290)National Institutes of Health (Grant RO1-NS21183), subcontract with Boston UniversityNational Institutes of Health (Grant 1 PO1-NS23734), subcontract with the Massachusetts Eye and Ear Infirmar

Signal representation, attribute extraction and, the use of distinctive features for phonetic classification

The study reported in this paper addresses three issues related to phonetic classification: 1) whether it is important to choose an appropriate signal representation, 2) whether there are any ad-vantages in extracting acoustic attributes over directly using the spectral information, and 3) whether it is advantageous to intro-duce an intermediate set of linguistic units, i.e. distinctive fea-tures. To restrict the scope of our study, we focused on 16 vowels in American English, and investigated classification performance using an artificial neural network with nearly 22,000 vowels tokens from 550 speakers excised from the TIMIT corpus. Our results indicate that 1) the combined outputs of Seneff's auditory model outperforms five other representations with both undegraded and noisy speech, 2) acoustic attributes give similar performance to raw spectral information, but at potentially considerable com-putational savings, and 3) the distinctive feature representation gives similar performance to direct vowel classification, but po-tentially offers a more flexible mechanism for describing context dependency

ISPTM: an Iterative Search Algorithm for Systematic Identification of Post-translational Modifications from Complex Proteome Mixtures

Identifying protein post-translational modifications (PTMs) from tandem mass spectrometry data of complex proteome mixtures is a highly challenging task. Here we present a new strategy, named iterative search for identifying PTMs (ISPTM), for tackling this challenge. The ISPTM approach consists of a basic search with no variable modification, followed by iterative searches of many PTMs using a small number of them (usually two) in each search. The performance of the ISPTM approach was evaluated on mixtures of 70 synthetic peptides with known modifications, on an 18-protein standard mixture with unknown modifications and on real, complex biological samples of mouse nuclear matrix proteins with unknown modifications. ISPTM revealed that many chemical PTMs were introduced by urea and iodoacetamide during sample preparation and many biological PTMs, including dimethylation of arginine and lysine, were significantly activated by Adriamycin treatment in NM associated proteins. ISPTM increased the MS/MS spectral identification rate substantially, displayed significantly better sensitivity for systematic PTM identification than the conventional all-in-one search approach and offered PTM identification results that were complementary to InsPecT and MODa, both of which are established PTM identification algorithms. In summary, ISPTM is a new and powerful tool for unbiased identification of many different PTMs with high confidence from complex proteome mixtures