Image processing via level set curvature flow.

We present a controlled image smoothing and enhancement method based on a curvature flow interpretation of the geometric heat equation. Compared to existing techniques, the model has several distinct advantages. (i) It contains just one enhancement parameter. (ii) The scheme naturally inherits a stopping criterion from the image; continued application of the scheme produces no further change. (iii) The method is one of the fastest possible schemes based on a curvature-controlled approach.

Recognition of Yeast mRNAs as “Nonsense Containing” Leads to Both Inhibition of mRNA Translation and mRNA Degradation: Implications for the Control of mRNA Decapping

A critical step in the degradation of many eukaryotic mRNAs is a decapping reaction that exposes the transcript to 5′ to 3′ exonucleolytic degradation. The dual role of the cap structure as a target of mRNA degradation and as the site of assembly of translation initiation factors has led to the hypothesis that the rate of decapping would be specified by the status of the cap binding complex. This model makes the prediction that signals that promote mRNA decapping should also alter translation. To test this hypothesis, we examined the decapping triggered by premature termination codons to determine whether there is a down-regulation of translation when mRNAs were recognized as “nonsense containing.” We constructed an mRNA containing a premature stop codon in which we could measure the levels of both the mRNA and the polypeptide encoded upstream of the premature stop codon. Using this system, we analyzed the effects of premature stop codons on the levels of protein being produced per mRNA. In addition, by using alterations either in cis or in trans that inactivate different steps in the recognition and degradation of nonsense-containing mRNAs, we demonstrated that the recognition of a nonsense codon led to a decrease in the translational efficiency of the mRNA. These observations argue that the signal from a premature termination codon impinges on the translation machinery and suggest that decapping is a consequence of the change in translational status of the mRNA.

Monoclonal antibodies against oxidized low-density lipoprotein bind to apoptotic cells and inhibit their phagocytosis by elicited macrophages: Evidence that oxidation-specific epitopes mediate macrophage recognition

Apoptosis is recognized as important for normal cellular homeostasis in multicellular organisms. Although there have been great advances in our knowledge of the molecular events regulating apoptosis, much less is known about the receptors on phagocytes responsible for apoptotic cell recognition and phagocytosis or the ligands on apoptotic cells mediating such recognition. The observations that apoptotic cells are under increased oxidative stress and that oxidized low-density lipoprotein (OxLDL) competes with apoptotic cells for macrophage binding suggested the hypothesis that both OxLDL and apoptotic cells share oxidatively modified moieties on their surfaces that serve as ligands for macrophage recognition. To test this hypothesis, we used murine monoclonal autoantibodies that bind to oxidation-specific epitopes on OxLDL. In particular, antibodies EO6 and EO3 recognize oxidized phospholipids, including 1-palmitoyl 2-(5-oxovaleroyl) phosphatidylcholine (POVPC), and antibodies EO12 and EO14 recognize malondialdehyde-lysine, as in malondialdehyde-LDL. Using FACS analysis, we demonstrated that each of these EO antibodies bound to apoptotic cells but not to normal cells, whereas control IgM antibodies did not. Confocal microscopy demonstrated cell-surface expression of the oxidation-specific epitopes on apoptotic cells. Furthermore, each of these antibodies inhibited the phagocytosis of apoptotic cells by elicited peritoneal macrophages, as did OxLDL. In addition, an adduct of POVPC with BSA also effectively prevented phagocytosis. These data demonstrate that apoptotic cells express oxidation-specific epitopes—including oxidized phospholipids—on their cell surface, and that these serve as ligands for recognition and phagocytosis by elicited macrophages.

Intervention of carbohydrate recognition by proteins and nucleic acids.

Carbohydrates in biological systems are often associated with specific recognition and signaling processes leading to important biological functions and diseases. Considerable efforts have been directed toward understanding and mimicking the recognition processes and developing effective agents to control the processes. The pace of discovery research in glycobiology and development of carbohydrate-based therapeutics, however, has been relatively slow due to the lack of appropriate strategies and methods available for carbohydrate-related research. This review summarizes some of the most recent developments in the field, with particular emphasis on work from our laboratories regarding the use of chemoenzymatic strategies to tackle the carbohydrate recognition problem. Highlights include the study of selectin-carbohydrate and aminoglycoside-RNA interactions and development of agents for the intervention of these recognition processes.

Interactions between tRNA identity nucleotides and their recognition sites in glutaminyl-tRNA synthetase determine the cognate amino acid affinity of the enzyme.

Sequence-specific interactions between aminoacyl-tRNA synthetases and their cognate tRNAs both ensure accurate RNA recognition and prevent the binding of noncognate substrates. Here we show for Escherichia coli glutaminyl-tRNA synthetase (GlnRS; EC 6.1.1.18) that the accuracy of tRNA recognition also determines the efficiency of cognate amino acid recognition. Steady-state kinetics revealed that interactions between tRNA identity nucleotides and their recognition sites in the enzyme modulate the amino acid affinity of GlnRS. Perturbation of any of the protein-RNA interactions through mutation of either component led to considerable changes in glutamine affinity with the most marked effects seen at the discriminator base, the 10:25 base pair, and the anticodon. Reexamination of the identity set of tRNA(Gln) in the light of these results indicates that its constituents can be differentiated based upon biochemical function and their contribution to the apparent Gibbs' free energy of tRNA binding. Interactions with the acceptor stem act as strong determinants of tRNA specificity, with the discriminator base positioning the 3' end. The 10:25 base pair and U35 are apparently the major binding sites to GlnRS, with G36 contributing both to binding and recognition. Furthermore, we show that E. coli tryptophanyl-tRNA synthetase also displays tRNA-dependent changes in tryptophan affinity when charging a noncognate tRNA. The ability of tRNA to optimize amino acid recognition reveals a novel mechanism for maintaining translational fidelity and also provides a strong basis for the coevolution of tRNAs and their cognate synthetases.

Voice-processing technologies--their application in telecommunications.

As the telecommunications industry evolves over the next decade to provide the products and services that people will desire, several key technologies will become commonplace. Two of these, automatic speech recognition and text-to-speech synthesis, will provide users with more freedom on when, where, and how they access information. While these technologies are currently in their infancy, their capabilities are rapidly increasing and their deployment in today's telephone network is expanding. The economic impact of just one application, the automation of operator services, is well over $100 million per year. Yet there still are many technical challenges that must be resolved before these technologies can be deployed ubiquitously in products and services throughout the worldwide telephone network. These challenges include: (i) High level of accuracy. The technology must be perceived by the user as highly accurate, robust, and reliable. (ii) Easy to use. Speech is only one of several possible input/output modalities for conveying information between a human and a machine, much like a computer terminal or Touch-Tone pad on a telephone. It is not the final product. Therefore, speech technologies must be hidden from the user. That is, the burden of using the technology must be on the technology itself. (iii) Quick prototyping and development of new products and services. The technology must support the creation of new products and services based on speech in an efficient and timely fashion. In this paper I present a vision of the voice-processing industry with a focus on the areas with the broadest base of user penetration: speech recognition, text-to-speech synthesis, natural language processing, and speaker recognition technologies. The current and future applications of these technologies in the telecommunications industry will be examined in terms of their strengths, limitations, and the degree to which user needs have been or have yet to be met. Although noteworthy gains have been made in areas with potentially small user bases and in the more mature speech-coding technologies, these subjects are outside the scope of this paper.

Military and government applications of human-machine communication by voice.

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs.

What does voice-processing technology support today?

This paper describes the state of the art in applications of voice-processing technologies. In the first part, technologies concerning the implementation of speech recognition and synthesis algorithms are described. Hardware technologies such as microprocessors and DSPs (digital signal processors) are discussed. Software development environment, which is a key technology in developing applications software, ranging from DSP software to support software also is described. In the second part, the state of the art of algorithms from the standpoint of applications is discussed. Several issues concerning evaluation of speech recognition/synthesis algorithms are covered, as well as issues concerning the robustness of algorithms in adverse conditions.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.