Bridging communities of practice: Emerging technologies for content-centered linking

The project fosters convergence between two communities by addressing complementary aspects of a shared opportunity. Digital humanists are at the forefront of developing ways to render cultural heritage metadata increasingly interoperable as linked open data in tandem with information professionals working in libraries, archives, and museums. Computer scientists are developing automated techniques for extracting linkable data from the content itself. Bringing these communities together offers transformational potential for the application of a critical infrastructure in humanities scholarship. Two workshops will be organized to seize this unique opportunity. The first will bring together humanities scholars and computer scientists to explore applications of new content linking technologies to dispersed and disparate material. In the second, a larger group of humanities scholars will identify specific content to which techniques described in the previous workshop will be applied

Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model

We present a neural architecture for cross-lingual mate sentence retrieval which encodes sentences in a joint multilingual space and learns to distinguish true translation pairs from semantically related sentences across languages. The proposed model combines a recurrent sequence encoder with a bidirectional attention layer and an intra-sentence attention mechanism. This way the final fixed-size sentence representations in each training sentence pair depend on the selection of contextualized token representations from the other sentence. The representations of both sentences are then combined using the bilinear product function to predict the relevance score. We show that, coupled with a shared multilingual word embedding space, the proposed model strongly outperforms unsupervised cross-lingual ranking functions, and that further boosts can be achieved by combining the two approaches. Most importantly, we demonstrate the model's effectiveness in zero-shot language transfer settings: our multilingual framework boosts cross-lingual sentence retrieval performance for unseen language pairs without any training examples. This enables robust cross-lingual sentence retrieval also for pairs of resource-lean languages, without any parallel data

Molecular Dynamics Simulation of Phosphorylated KID Post-Translational Modification

BACKGROUND:Kinase-inducible domain (KID) as transcriptional activator can stimulate target gene expression in signal transduction by associating with KID interacting domain (KIX). NMR spectra suggest that apo-KID is an unstructured protein. After post-translational modification by phosphorylation, KID undergoes a transition from disordered to well folded protein upon binding to KIX. However, the mechanism of folding coupled to binding is poorly understood. METHODOLOGY:To get an insight into the mechanism, we have performed ten trajectories of explicit-solvent molecular dynamics (MD) for both bound and apo phosphorylated KID (pKID). Ten MD simulations are sufficient to capture the average properties in the protein folding and unfolding. CONCLUSIONS:Room-temperature MD simulations suggest that pKID becomes more rigid and stable upon the KIX-binding. Kinetic analysis of high-temperature MD simulations shows that bound pKID and apo-pKID unfold via a three-state and a two-state process, respectively. Both kinetics and free energy landscape analyses indicate that bound pKID folds in the order of KIX access, initiation of pKID tertiary folding, folding of helix alpha(B), folding of helix alpha(A), completion of pKID tertiary folding, and finalization of pKID-KIX binding. Our data show that the folding pathways of apo-pKID are different from the bound state: the foldings of helices alpha(A) and alpha(B) are swapped. Here we also show that Asn139, Asp140 and Leu141 with large Phi-values are key residues in the folding of bound pKID. Our results are in good agreement with NMR experimental observations and provide significant insight into the general mechanisms of binding induced protein folding and other conformational adjustment in post-translational modification

Cross-lingual C*ST*RD: English access to Hindi information

We present C*ST*RD, a cross-language information delivery system that supports cross-language information retrieval, information space visualization and navigation, machine translation, and text summarization of single documents and clusters of documents. C*ST*RD was assembled and trained within 1 month, in the context of DARPA’s Surprise Language Exercise, that selected as source a heretofore unstudied language, Hindi. Given the brief time, we could not create deep Hindi capabilities for all the modules, but instead experimented with combining shallow Hindi capabilities, or even English-only modules, into one integrated system. Various possible configurations, with different tradeoffs in processing speed and ease of use, enable the rapid deployment of C*ST*RD to new languages under various conditions

Expression of the antimicrobial peptides in plants to control phytopathogenic bacteria and fungi

Three antimicrobial peptides exhibiting in vitro antifungal activity were expressed in Arabidopsis to compare their in planta activity. β-Purothionin, cecropin B, and phor21 were expressed under an endogenous promoter with moderate-level activity and excreted extracellularly. Expression of β-purothionin rendered the greatest antibacterial and antifungal resistance while cecropin B enhanced only antibacterial activity and phor21 did not improve antimicrobial resistance. The transgenic β-purothionin arrested fungal growth on leaf surfaces and infection of stomata. Leaf extracts from plants producing β-purothionin and cecropin B displayed membrane permeabilizing activity. The in planta antimicrobial activity of the tested peptides was consistent with previously reported in vitro experiments. The expression strategy allowed enhanced antifungal resistance without high-level transgene expression. © Springer-Verlag 2005

Maryland at FIRE 2011: Retrieval of OCR’d Bengali

In this year's Forum for Information Retrieval Evaluation (FIRE), the University of Maryland participated in the Retrieval of Indic Script OCRed Text (RISOT) task to experiment with the retrieval of Bengali script OCR’d documents. The experiments focused on evaluating a retrieval strategy motivated by recent work on Cross-Language Information Retrieval (CLIR), but which makes use of OCR error modeling rather than parallel text alignment. The approach obtains a probability distribution over substitutions for the actual query terms that possibly correspond to terms in the document representation. The results reported indicate that this is a promising way of using OCR error modeling to improve CLIR. 1