Mutagenesis in the C-terminal region of human interleukin 5 reveals a central patch for receptor alpha chain recognition.

Cassette mutagenesis was used to identify side chains in human interleukin 5 (hIL-5) that mediate binding to hIL-5 receptor alpha chain (hIL-5R alpha). A series of single alanine substitutions was introduced into a stretch of residues in the C-terminal region, including helix D, which previously had been implicated in receptor alpha chain recognition and which is aligned on the IL-5 surface so as to allow the topography of receptor binding residues to be examined. hIL-5 and single site mutants were expressed in COS cells, their interactions with hIL-5R alpha were measured by a sandwich surface plasmon resonance biosensor method, and their biological activities were measured by an IL-5-dependent cell proliferation assay. A pattern of mutagenesis effects was observed, with greatest impact near the interface between the two four-helix bundles of IL-5, in particular at residues Glu-110 and Trp-111, and least at the distal ends of the D helices. This pattern suggests the possibility that residues near the interface of the two four-helix bundles in hIL-5 comprise a central patch or hot spot, which constitutes an energetically important alpha chain recognition site. This hypothesis suggests a structural explanation for the 1:1 stoichiometry observed for the complex of hIL-5 with hIL-5R alpha.

Stimulation-dependent I kappa B alpha phosphorylation marks the NF-kappa B inhibitor for degradation via the ubiquitin-proteasome pathway.

The nuclear translocation of NF-kappa B follows the degradation of its inhibitor, I kappa B alpha, an event coupled with stimulation-dependent inhibitor phosphorylation. Prevention of the stimulation-dependent phosphorylation of I kappa B alpha, either by treating cells with various reagents or by mutagenesis of certain putative I kappa B alpha phosphorylation sites, abolishes the inducible degradation of I kappa B alpha. Yet, the mechanism coupling the stimulation-induced phosphorylation with the degradation has not been resolved. Recent reports suggest a role for the proteasome in I kappa B alpha degradation, but the mode of substrate recognition and the involvement of ubiquitin conjugation as a targeting signal have not been addressed. We show that of the two forms of I kappa B alpha recovered from stimulated cells in a complex with RelA and p50, only the newly phosphorylated form, pI kappa B alpha, is a substrate for an in vitro reconstituted ubiquitin-proteasome system. Proteolysis requires ATP, ubiquitin, a specific ubiquitin-conjugating enzyme, and other ubiquitin-proteasome components. In vivo, inducible I kappa B alpha degradation requires a functional ubiquitin-activating enzyme and is associated with the appearance of high molecular weight adducts of I kappa B alpha. Ubiquitin-mediated protein degradation may, therefore, constitute an integral step of a signal transduction process.

State of the art in continuous speech recognition.

In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.

Training and search methods for speech recognition.

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives. This paper is not concerned with the first process. Estimation of the probability of an index string involves a model of index production by any given utterance segment (e.g., a word). Hidden Markov models (HMMs) are used for this purpose [Makhoul, J. & Schwartz, R. (1995) Proc. Natl. Acad. Sci. USA 92, 9956-9963]. Their parameters are state transition probabilities and output probability distributions associated with the transitions. The Baum algorithm that obtains the values of these parameters from speech data via their successive reestimation will be described in this paper. The recognizer wishes to find the most probable utterance that could have caused the observed acoustic index string. That probability is the product of two factors: the probability that the utterance will produce the string and the probability that the speaker will wish to produce the utterance (the language model probability). Even if the vocabulary size is moderate, it is impossible to search for the utterance exhaustively. One practical algorithm is described [Viterbi, A. J. (1967) IEEE Trans. Inf. Theory IT-13, 260-267] that, given the index string, has a high likelihood of finding the most probable utterance.

Integration host factor suppresses promiscuous activation of the sigma 54-dependent promoter Pu of Pseudomonas putida.

In the presence of m-xylene, the Pu promoter of the TOL plasmid of Pseudomonas putida is activated by the prokaryotic enhancer-binding protein XylR. The intervening DNA segment between the upstream activating sequences (UASs) and those for RNA polymerase binding contains an integration host factor (IHF) attachment site that is required for full transcriptional activity. In the absence of IHF, the Pu promoter can be cross-activated by other members of the sigma 54-dependent family of regulatory proteins. Such illegitimate activation does not require the binding of the heterologous regulators to DNA and it is suppressed by bent DNA structures, either static or protein induced, between the promoter core elements (UAS and RNA polymerase recognition sequence). The role of IHF in some sigma 54 promoters is, therefore, not only a structural aid for assembling a correct promoter geometry but also that of an active suppressor (restrictor) of promiscuous activation by heterologous regulators for increased promoter specificity.

A developmental dissociation of view-dependent and view-invariant object recognition in adolescence

Spatial generalization skills in school children aged 8-16 were studied with regard to unfamiliar objects that had been previously learned in a cross-modal priming and learning paradigm. We observed a developmental dissociation with younger children recognizing objects only from previously learnt perspectives whereas older children generalized acquired object knowledge to new viewpoints as well. Haptic and - to a lesser extent - visual priming improved spatial generalization in all but the youngest children. The data supports the idea of dissociable, view-dependent and view-invariant object representations with different developmental trajectories that are subject to modulatory effects of priming. Late-developing areas in the parietal or the prefrontal cortex may account for the retarded onset of view-invariant object recognition. © 2006 Elsevier B.V. All rights reserved.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Structural basis for receptor activity-modifying protein-dependent selective peptide recognition by a G protein-coupled receptor

Association of receptor activity-modifying proteins (RAMP1-3) with the G protein-coupled receptor (GPCR) calcitonin receptor-like receptor (CLR) enables selective recognition of the peptides calcitonin gene-related peptide (CGRP) and adrenomedullin (AM) that have diverse functions in the cardiovascular and lymphatic systems. How peptides selectively bind GPCR:RAMP complexes is unknown. We report crystal structures of CGRP analog-bound CLR:RAMP1 and AM-bound CLR:RAMP2 extracellular domain heterodimers at 2.5 and 1.8 Å resolutions, respectively. The peptides similarly occupy a shared binding site on CLR with conformations characterized by a β-turn structure near their C termini rather than the α-helical structure common to peptides that bind related GPCRs. The RAMPs augment the binding site with distinct contacts to the variable C-terminal peptide residues and elicit subtly different CLR conformations. The structures and accompanying pharmacology data reveal how a class of accessory membrane proteins modulate ligand binding of a GPCR and may inform drug development targeting CLR:RAMP complexes.

Functionalized carbon nanotube nanoelectrodes for biomolecular recognition

The objective of this research is to develop nanoscale ultrasensitive transducers for detection of biological species at molecular level using carbon nanotubes as nanoelectrodes. Rapid detection of ultra low concentration or even single DNA molecules are essential for medical diagnosis and treatment, pharmaceutical applications, gene sequencing as well as forensic analysis. Here the use of functionalized single walled carbon nanotubes (SWNT) as nanoscale detection platform for rapid detection of single DNA molecules is demonstrated. The detection principle is based on obtaining electrical signal from a single amine terminated DNA molecule which is covalently bridged between two ends of an SWNT separated by a nanoscale gap. The synthesis, fabrication, chemical functionalization of nanoelectrodes and DNA attachment were optimized to perform reliable electrical characterization these molecules. Using this detection system fundamental study on charge transport in DNA molecule of both genomic and non genomic sequences is performed. We measured an electrical signal of about 30 pA through a hybridized DNA molecule of 80 base pair in length which encodes a portion of sequence of H5N1 gene of avian Influenza A virus. Due the dynamic nature of the DNA molecules the local environment such as ion concentration, pH and temperature significantly influence its physical properties. We observed a decrease in DNA conductance of about 33% in high vacuum conditions. The counterion variation was analyzed by changing the buffer from sodium acetate to tris(hydroxymethyl) aminomethane, which resulted in a two orders of magnitude increase in the conductivity of the DNA. The fabrication of large array of identical SWNT nanoelectrodes was achieved by using ultralong SWNTs. Using these nanoelectrode array we have investigated the sequence dependent charge transport in DNA. A systematic study performed on PolyG - PolyC sequence with varying number of intervening PolyA - PolyT pairs showed a decrease in electrical signal from 180 pA (PolyG - PolyC) to 30 pA with increasing number of the PolyA - PolyT pairs. This work also led to the development of ultrasensitive nanoelectrodes based on enzyme functionalized vertically aligned high density multiwalled CNTs for electrochemical detection of cholesterol. The nanoelectrodes exhibited selectively detection of cholesterol in the presence of common interferents found in human blood.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Human Airway Epithelial Cell Responses to Neisseria lactamica and Purified Porin via Toll-Like Receptor 2-Dependent Signaling

The human airway epithelium is constantly exposed to microbial products from colonizing organisms. Regulation of Toll-like receptor (TLR) expression and specific interactions with bacterial ligands is thought to mitigate exacerbation of inflammatory processes induced by the commensal flora in these cells. The genus Neisseria comprises pathogenic and commensal organisms that colonize the human nasopharynx. Neisseria lactamica is not associated with disease, but N. meningitidis occasionally invades the host, causing meningococcal disease and septicemia. Upon colonization of the airway epithelium, specific host cell receptors interact with numerous Neisseria components, including the PorB porin, at the immediate bacterial-host cell interface. This major outer membrane protein is expressed by all Neisseria strains, regardless of pathogenicity, but its amino acid sequence varies among strains, particularly in the surface-exposed regions. The interaction of Neisseria PorB with TLR2 is essential for driving TLR2/TLR1-dependent cellular responses and is thought to occur via the porin`s surface-exposed loop regions. Our studies show that N. lactamica PorB is a TLR2 ligand but its binding specificity for TLR2 is different from that of meningococcal PorB. Furthermore, N. lactamica PorB is a poor inducer of proinflammatory mediators and of TLR2 expression in human airway epithelial cells. These effects are reproduced by whole N. lactamica organisms. Since the responsiveness of human airway epithelial cells to colonizing bacteria is in part regulated via TLR2 expression and signaling, commensal organisms such as N. lactamica would benefit from expressing a product that induces low TLR2-dependent local inflammation, likely delaying or avoiding clearance by the host.

The pattern recognition receptors Nod1 and Nod2 account for neutrophil recruitment to the lungs of mice infected with Legionella pneumophila

The intracellular bacterium Legionella pneumophila induces a severe form of pneumonia called Legionnaires diseases, which is characterized by a strong neutrophil (NE) infiltrate to the lungs of infected individuals. Although the participation of pattern recognition receptors, such as Toll-like receptors, was recently demonstrated, there is no information on the role of nod-like receptors (NLRs) for bacterial recognition in vivo and for NE recruitment to the lungs. Here, we employed a murine model of Legionnaires disease to evaluate host and bacterial factors involved in NE recruitment to the mice lungs. We found that L. pneumophila type four secretion system, known as Dot/Icm, was required for NE recruitment as dot/icm mutants fail to trigger NE recruitment in a process independent of bacterial multiplication. By using mice deficient for Nod1, Nod2, and Rip2, we found that these receptors accounted for NE recruitment to the lungs of infected mice. In addition, Rip2-dependent responses were important for cytokine production and bacterial clearance. Collectively, these studies show that Nod1, Nod2, and Rip2 account for generation of innate immune responses in vivo, which are important for NE recruitment and bacterial clearance in a murine model of Legionnaires diseases. (C) 2010 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Cutting Edge: Nucleotide-Binding Oligomerization Domain 1-Dependent Responses Account for Murine Resistance against Trypanosoma cruzi Infection

An effective innate immune recognition of the intracellular protozoan parasite Trypanosoma cruzi is critical for host resistance against Chagas disease, a severe and chronic illness that affects millions of people in Latin America. In this study, we evaluated the participation of nucleotide-binding oligomerization domain (Nod)like receptor proteins in host response to T cruzi infection and found that Nod1-dependent, but not Nod2-dependent, responses are required for host resistance against infection. Bone marrow-derived macrophages from Nod1(-/-) mice showed an impaired induction of NF-kappa B-dependent products in response to infection and failed to restrict T cruzi infection in presence of IFN-gamma. Despite normal cytokine production in the sera, Nod1(-/-) mice were highly susceptible to T cruzi infection, in a similar manner to MyD88(-/-) and NO synthase 2(-/-) mice. These studies indicate that Nod1-dependent responses account for host resistance against T cruzi infection by mechanisms independent of cytokine production. The Journal of Immunology, 2010, 184: 1148-1152.