Consistency and Variation in Kernel Neural Ranking Model

This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry. We find that K-NRM has low variance on relevance-based metrics across experimental trials. In spite of this low variance in overall performance, different trials produce different document rankings for individual queries. The main source of variance in our experiments was found to be different latent matching patterns captured by K-NRM. In the IR-customized word embeddings learned by K-NRM, the query-document word pairs follow two different matching patterns that are equally effective, but align word pairs differently in the embedding space. The different latent matching patterns enable a simple yet effective approach to construct ensemble rankers, which improve K-NRM's effectiveness and generalization abilities.Comment: 4 pages, 4 figures, 2 table

Targeted in vitro gene silencing of E2 and nsP1 genes of chikungunya virus by biocompatible zeolitic imidazolate framework

Chikungunya fever caused by the mosquito-transmitted chikungunya virus (CHIKV) is a major public health concern in tropical, sub-tropical and temperate climatic regions. The lack of any licensed vaccine or antiviral agents against CHIKV warrants the development of effective antiviral therapies. Small interfering RNA (siRNA) mediated gene silencing of CHIKV structural and non-structural genes serves as a potential antiviral strategy. The therapeutic efficiency of siRNA can be improved by using an efficient delivery system. Metal-organic framework biocomposits have demonstrated an exceptional capability in protecting and efficiently delivering nucleic acids into cells. In the present study, carbonated ZIF called ZIF-C has been utilized to deliver siRNAs targeted against E2 and nsP1 genes of CHIKV to achieve a reduction in viral replication and infectivity. Cellular transfection studies of E2 and nsP1 genes targeting free siRNAs and ZIF-C encapsulated siRNAs in CHIKV infected Vero CCL-81 cells were performed. Our results reveal a significant reduction of infectious virus titre, viral RNA levels and percent of infected cells in cultures transfected with ZIF-C encapsulated siRNA compared to cells transfected with free siRNA. The results suggest that delivery of siRNA through ZIF-C enhances the antiviral activity of CHIKV E2 and nsP1 genes directed siRNAs

Selective Weak Supervision for Neural Information Retrieval

This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence). In our experiments on three TREC benchmarks, neural rankers trained by ReInfoSelect, with only publicly available anchor data, significantly outperform feature-based learning to rank methods and match the effectiveness of neural rankers trained with private commercial search logs. Our analyses show that ReInfoSelect effectively selects weak supervision signals based on the stage of the neural ranker training, and intuitively picks anchor-document pairs similar to query-document pairs.Comment: Accepted by WWW 202

Amino Acid-Coated Zeolitic Imidazolate Framework for Delivery of Genetic Material in Prostate Cancer Cell

Metal–organic frameworks (MOFs) are currently under progressive development as a tool for non-viral biomolecule delivery. Biomolecules such as proteins, lipids, carbohydrates, and nucleic acids can be encapsulated in MOFs for therapeutic purposes. The favorable physicochemical properties of MOFs make them an attractive choice for delivering a wide range of biomolecules including nucleic acids. Herein, a green fluorescence protein (GFP)-expressing plasmid DNA (pDNA) is used as a representative of a biomolecule to encapsulate within a Zn-based metal–organic framework (MOF) called a zeolitic imidazolate framework (ZIF). The synthesized biocomposites are coated with positively charged amino acids (AA) to understand the effect of surface functionalization on the delivery of pDNA to prostate cancer (PC-3) cells. FTIR and zeta potential confirm the successful preparation of positively charged amino acid-functionalized derivatives of pDNA@ZIF (i.e., pDNA@ZIFAA). Moreover, XRD and SEM data show that the functionalized derivates retain the pristine crystallinity and morphology of pDNA@ZIF. The coated biocomposites provide enhanced uptake of genetic material by PC-3 human prostate cancer cells. The AA-modulated fine-tuning of the surface charge of biocomposites results in better interaction with the cell membrane and enhances cellular uptake. These results suggest that pDNA@ZIFAA can be a promising alternative tool for non-viral gene delivery

From Tables to Frames

Pivk A, Cimiano P, Sure Y. From Tables to Frames. Web Semantics: Science, Services and Agents on the World Wide Web. 2005;3(2-3):132-146