Functional enrichment by direct plasmid recovery after Fluorescence Activated Cell Sorting

Iterative screening of expressed protein libraries using fluorescence-activated cell sorting (FACS) typically involves culturing the pooled clones after each sort. In these experiments, if cell viability is compromised by the sort conditions and/or expression of the target protein(s), rescue PCR provides an alternative to culturing but requires re-cloning and can introduce amplification bias. We haveoptimized a simple protocol using commercially available reagents to directly recover plasmid DNA from sorted cells for subsequenttransformation. We tested our protocol with 2 different screening systems in which 60% of the sorted cell population was recovered

Single-cell characterization of autotransporter mediated Escherichia coli surface display of disulfide-bond containing proteins

Autotransporters (ATs) are a family of bacterial proteins containing a C-terminal ?-barrel-forming domain that facilitates the translocation of N-terminal passenger domain whose functions range from adhesion to proteolysis. Genetic replacement of the native passenger domain with heterologous proteins is an attractive strategy not only for applications such as biocatalysis, live-cell vaccines, and protein engineering but also for gaining mechanistic insights toward understanding AT translocation. The ability of ATs to efficiently display functional recombinant proteins containing multiple disulfides has remained largely controversial. By employing high-throughput single-cell flow cytometry, we have systematically investigated the ability of the Escherichia coli AT Antigen 43 (Ag43) to display two different recombinant reporter proteins, a single-chain antibody (M18 scFv) that contains two disulfides and chymotrypsin that contains four disulfides, by varying the signal peptide and deleting the different domains of the native protein. Our results indicate that only the C-terminal ?-barrel and the threaded ?-helix are essential for efficient surface display of functional recombinant proteins containing multiple disulfides. These results imply that there are no inherent constraints for functional translocation and display of disulfide bond-containing proteins mediated by the AT system and should open new avenues for protein display and engineering

A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s

Learning and inference algorithms for dynamical system models of dextrous motion

High dimensional time series data such as video sequences, spectral trajectories of a speech signal or the kinematic measurements of skilled human activity are encountered in several engineering applications, and computational models of such data hold considerable interest, particularly models that capture the inherent stochastic variability in the signal. Of particular interest in this dissertation are kinematic measurements of manipulator and tool motion in robot-assisted minimally invasive surgery (RMIS). A set of gesture labelled RMIS data is initially assumed to be given. The primary goal is to develop statistical models for gesture recognition for new RMIS trials from kinematic data, for eventually supporting automatic skill evaluation and surgeon training. The goal of automatically discovering the structure of dextrous motion in an unsupervised manner is also addressed, when an inventory of gestures is not known, or gesture-labeled data are not provided. A number of statistical models to address these problems have been investigated, including hidden Markov models (HMM) with linear discriminant analysis, factor-analyzed hidden Markov models and linear dynamical systems with time varying parameters. Gesture recognition accuracies for three RMIS training tasks—suturing, knot-tying and needle-passing—are shown to improve significantly with increasing model complexity, justifying the concomitant increase in the computation required to estimate model parameters from gesture-labeled data or to perform recognition. Algorithms for unsupervised structure induction have been investigated for discovering gestures used in skilled dexterous motion directly from kinematic data when gesture-labeled data are not available. An improved algorithm based on successive state splitting is presented for discovering the state-topology of a hidden Markov model. The algorithm efficiently explores an enormous space of possible topologies and yields models with a high goodness-of-fit to the RMIS kinematic data. Technical contributions of this dissertations include novel, efficient algorithms for probabilistic principal component analysis, for switching linear dynamical system parameter estimation, and for hidden Markov model topology induction. Other techniques for improving gesture recognition accuracy beyond those mentioned above are also investigated by incorporating ideas such as user-adaptation of the models

Unsupervised learning of acoustic sub-word units

Accurate unsupervised learning of phonemes of a language directly from speech is demonstrated via an algorithm for joint unsupervised learning of the topology and parameters of a hidden Markov model (HMM); states and short state-sequences through this HMM correspond to the learnt sub-word units. The algorithm, originally proposed for unsupervised learning of allophonic variations within a given phoneme set, has been adapted to learn without any knowledge of the phonemes. An evaluation methodology is also proposed, whereby the state-sequence that aligns to a test utterance is transduced in an automatic manner to a phoneme-sequence and compared to its manual transcription. Over 85 % phoneme recognition accuracy is demonstrated for speaker-dependent learning from fluent, large-vocabulary speech. 1 Automatic Discovery of Phone(me)s Statistical models learnt from data are extensively used in modern automatic speech recognition (ASR) systems. Transcribed speech is used to estimate conditional models of the acoustics given a phonemesequence. The phonemic pronunciation of words and the phonemes of the language, however, are derived almost entirely from linguistic knowledge. In this paper, we investigate whether the phonemes may be learnt automatically from the speech signal. Automatic learning of phoneme-like units has significant implications for theories of language acquisition in babies, but our considerations here are somewhat more technological. We are interested in developing ASR systems for languages or dialect