Enzymatic Characterization Of The Ammonia Tunnel In Helicobacter Pylori Asp-Trnaasn/glu-Trnagln Amidotransferase

The Helicobacter pylori (H. pylori) Asp-tRNAAsn/Glu-tRNAGln amidotransferase (AdT) plays important roles in indirect aminoacylation and translational fidelity; however, its inter-domain communication and ammonia delivery mechanisms are not well understood. In the present study, we investigated the three activities of H. pylori AdT (glutaminase, kinase and transamidase) and used these reactions as probes to examine the inter-domain communication and ammonia delivery mechanisms between this enzyme\u27s two isolated active sites. We adapted and optimized an assay to kinetically characterize a series of mutations at conserved positions throughout the putative AdT ammonia tunnel. The kinase assay enabled us to identify mutations within AdT, specifically T149 and K89, for further enzymatic characterization and molecular dynamics (MD) simulations and correlation analyses to unveil a set of 59 residues that may form the interdomain communication pathway between AdT\u27s two active sites. The glutaminase and transamidase assays identified another residue, D185, in the GatA subunit. Kinetic and computational characterizations of D185 AdT mutants suggest that D185 serves as a general acid or base in ammonia delivery. These results are the first demonstration of acid/base chemistry within an ammonia tunnel. Finally, preliminary characterization of the predicted ammonia tunnel gate residues (K89 and E126 in the GatB subunit) suggest that proper positioning of the appropriate charge states in the tunnel are important for AdT catalysis. The results presented in this dissertation extend our understanding of AdT\u27s distinct ammonia transfer mechanism

PP7 virus-like particle as a functional peptide carrying platform

Virus-like particles (VLPs) have attracted attention as therapeutic platforms for the delivery of peptide-based motifs for immunology, cell targeting, and drug delivery. The functional peptide sequences of interest are covalently attached to the VLP surface by either genetic fusion or bioconjugation techniques. Here, we report our initial exploration of the Leviviridae PP7 bacteriophage capsid as a platform for the genetically-programmed display of multiple peptide sequences of therapeutic and targeting interest. These peptides include short and long sequences that bind cell-surface EGF or transferrin receptors, as well as examples of other functional (Z-domain) and antigenic (OVA) peptides. The PP7 structure is far more tolerant than the closely related Q VLP to self-assembly of C- or N-terminally extended capsid proteins. Some of these constructs were able to form stable and homogeneous particles entirely from such proteins, thereby displaying exactly 180 copies of the functional peptide on the VLP surface, a property not shared by other Leviviridae-based platforms. Preliminary exploration of the chemical reactivity of the PP7 particle also shows it to be highly tolerant toward standard bioconjugation techniques. PP7 is therefore an excellent candidate for elaboration into useful diagnostic and therapeutic agents

A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios

Embedding-aware generative model (EAGM) addresses the data insufficiency problem for zero-shot learning (ZSL) by constructing a generator between semantic and visual feature spaces. Thanks to the predefined benchmark and protocols, the number of proposed EAGMs for ZSL is increasing rapidly. We argue that it is time to take a step back and reconsider the embedding-aware generative paradigm. The main work of this paper is two-fold. First, the embedding features in benchmark datasets are somehow overlooked, which potentially limits the performance of EAGMs, while most researchers focus on how to improve EAGMs. Therefore, we conduct a systematic evaluation of ten representative EAGMs and prove that even embarrassedly simple modifications on the embedding features can improve the performance of EAGMs for ZSL remarkably. So it's time to pay more attention to the current embedding features in benchmark datasets. Second, based on five benchmark datasets, each with six any-shot learning scenarios, we systematically compare the performance of ten typical EAGMs for the first time, and we give a strong baseline for zero-shot learning (ZSL) and few-shot learning (FSL). Meanwhile, a comprehensive generative model repository, namely, generative any-shot learning (GASL) repository, is provided, which contains the models, features, parameters, and scenarios of EAGMs for ZSL and FSL. Any results in this paper can be readily reproduced with only one command line based on GASL

Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles

Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations. However, prior work only learns to estimate a single planning trajectory, while there may be multiple acceptable plans in real-world scenarios. To solve the problem, we propose an interpretable neural planner to regress a heatmap, which effectively represents multiple potential goals in the bird's-eye view of an autonomous vehicle. The planner employs an adaptive Gaussian kernel and relaxed hourglass loss to better capture the uncertainty of planning problems. We also use a negative Gaussian kernel to add supervision to the heatmap regression, enabling the model to learn collision avoidance effectively. Our systematic evaluation on the Lyft Open Dataset across a diverse range of real-world driving scenarios shows that our model achieves a safer and more flexible driving performance than prior works

Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem

Strigolactone regulation of shoot branching in chrysanthemum (Dendranthema grandiflorum).

Previous studies of highly branched mutants in pea (rms1-rms5), Arabidopsis thaliana (max1-max4), petunia (dad1-dad3), and rice (d3, d10, htd1/d17, d14, d27) identified strigolactones or their derivates (SLs), as shoot branching inhibitors. This recent discovery offers the possibility of using SLs to regulate branching commercially, for example, in chrysanthemum, an important cut flower crop. To investigate this option, SL physiology and molecular biology were studied in chrysanthemum (Dendranthema grandiflorum), focusing on the CCD8/MAX4/DAD1/RMS1/D10 gene. Our results suggest that, as has been proposed for Arabidopsis, the ability of SLs to inhibit bud activity depends on the presence of a competing auxin source. The chrysanthemum SL biosynthesis gene, CCD8 was cloned, and found to be regulated in a similar, but not identical way to known CCD8s. Expression analyses revealed that DgCCD8 is predominantly expressed in roots and stems, and is up-regulated by exogenous auxin. Exogenous SL can down-regulate DgCCD8 expression, but this effect can be overridden by apical auxin application. This study provides evidence that SLs are promising candidates to alter the shoot branching habit of chrysanthemum

iAssembler: a package for de novo assembly of Roche-454/Sanger transcriptome sequences

<p>Abstract</p> <p>Background</p> <p>Expressed Sequence Tags (ESTs) have played significant roles in gene discovery and gene functional analysis, especially for non-model organisms. For organisms with no full genome sequences available, ESTs are normally assembled into longer consensus sequences for further downstream analysis. However current <it>de novo </it>EST assembly programs often generate large number of assembly errors that will negatively affect the downstream analysis. In order to generate more accurate consensus sequences from ESTs, tools are needed to reduce or eliminate errors from <it>de novo </it>assemblies.</p> <p>Results</p> <p>We present iAssembler, a pipeline that can assemble large-scale ESTs into consensus sequences with significantly higher accuracy than current existing assemblers. iAssembler employs MIRA and CAP3 assemblers to generate initial assemblies, followed by identifying and correcting two common types of transcriptome assembly errors: 1) ESTs from different transcripts (mainly alternatively spliced transcripts or paralogs) are incorrectly assembled into same contigs; and 2) ESTs from same transcripts fail to be assembled together. iAssembler can be used to assemble ESTs generated using the traditional Sanger method and/or the Roche-454 massive parallel pyrosequencing technology.</p> <p>Conclusion</p> <p>We compared performances of iAssembler and several other <it>de novo </it>EST assembly programs using both Roche-454 and Sanger EST datasets. It demonstrated that iAssembler generated significantly more accurate consensus sequences than other assembly programs.</p

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches

Introducing new functions into (and onto) virus-like particles

Leviphage Qß and PP7 are well studied viruses that infect E. coli. They also provide highly stable and tailorable capsid protein structures that can be manipulated in a number of ways by the molecular biologist and chemist. We will describe our work with both particles, designed to give them new binding, shielding, and catalytic properties. This involves the expression of hybrid particles bearing catalytic protein domains on the inside or outside, the use of standard polymerization methods to grow organic polymers from the surface or into the interior of the particles, and the marriage of these particles with degradable hydrogel carriers Please click Additional Files below to see the full abstract