DNA Interactions with Ruthenium(ll) Polypyridine Complexes Containing Asymmetric Ligands

In an attempt to probe nucleic acid structures, numerous Ru(II) complexes with different ligands have been synthesized and investigated. In this contribution we focus on the DNA-binding properties of ruthenium(II) complexes containing asymmetric ligands that have attracted little attention in the past decades. The influences of the shape and size of the ligand on the binding modes, affinity, enantioselectivities and photocleavage of the complexes to DNA are described

Dietary organosulfur and organoselenium compounds as HDAC inhibitors

Histone deacetylase (HDAC) inhibitors have the potential to de-repress epigenetically silenced genes in cancer cells, leading to cell cycle arrest and apoptosis. Dietary HDAC inhibitors derived from natural phytochemicals are promising anticancer agents. In this thesis, metabolites from natural organosulfur and organoselenium compounds, i.e. allyl mercaptan (AM), β-methylselenopyruvate (MSP) and α-keto-γ-methylselenobutyrate (KMSB), were discovered to serve as HDAC inhibitors and exhibit anticancer activities in human colon cancer cells. AM is a metabolite of garlic-derived organosulfur compounds, whereas MSP and KMSB are the newly discovered α-keto acid metabolites of Se-methylselenocysteine (MSC) and selenomethionine (SM) respectively. In this thesis research, all three compounds were shown to inhibit HDAC activity in a competitive manner at micromolar levels. Molecular modeling suggested they can fit into the active site of HDAC enzymes and chelate catalytic Zn²⁺ via sulfhydryl group (AM) or keto acid group (MSP and KMSB). Studies on the structural analogs indicated that the selenium atom was also important for MSP/KMSB's HDAC inhibitory effects. In human colon cancer cells, AM, MSP and KMSB decreased HDAC activities, and induced rapid histone hyperacetylation in a dose-dependent manner. All three compounds induced rapid and sustained expression of the cell cycle inhibitor p21 at both mRNA and protein levels. There was enhanced P21 promoter activity, and hyperacetylated histone H3 was associated with the gene promoter. The induction of p21 required a Sp1/Sp3 binding sites but was independent of p53 status. P21 induction may mediate cell cycle arrest in AM/MSP/KMSB-treated colon cancer cells. MSP and KMSB also induced apoptosis in colon cancer cells, as evidenced by morphological changes, Annexin V staining and increased cleaved caspase-3, -6, -7, -9 and poly(ADP-ribose)polymerase. MSP dramatically induced the expression of pro-apoptotic Bcl-2 family gene Bmf, and knocking down Bmf expression by siRNA significantly decreased caspase activation in MSP-treated colon cancer cells. As a result of cell cycle arrest and/or apoptosis induction, these compounds significantly inhibited colon cancer cell growth. Formation of MSP was directly detected in MSC-treated colon cancer cells. MSC, the parent compound also induced histone hyperacetylation, p21 and Bmf expression in the cells. Knocking down Bmf expression reduced MSC's apoptotic effects. In colon cancer cells, SM cannot be converted to KMSB, and histone acetylation remained unchanged in SM-treated colon cancer cells. Histone hyperacetylation was also observed in the tissues of the mice gavaged with AM and its parent organosulfur compounds. These results indicate that AM/MSP/KMSB could be active metaobolites of organosulfur or organoselenium compounds contributing to their chemopreventive effects

Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-view contrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, to further boost the performance of HeCo, two additional methods are designed to generate harder negative samples with high quality. Besides the invariant factors, view-specific factors complementally provide the diverse structure information between different nodes, which also should be contained into the final embeddings. Therefore, we need to further explore each view independently and propose a modified model, called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures.Comment: This paper has been accepted by TKDE as a regular paper. arXiv admin note: substantial text overlap with arXiv:2105.0911

Role of Hydrogen Bonding in Green Fluorescent Protein-like Chromophore Emission.

The fluorescence emission from green fluorescent protein (GFP) is known to be heavily influenced by hydrogen bonding between the core fluorophore and the surrounding side chains or water molecules. Yet how to utilize this feature for modulating the fluorescence of GFP chromophore or GFP-like fluorophore still remains elusive. Here we present theoretical calculations to predict how hydrogen bonding could influence the excited states of the GFP-like fluorophores. These studies provide both a new perspective for understanding the photophysical properties of GFP as well as a solid basis for the rational design of GFP-based fluorophores

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Patrones individuales de dispersión de larvas de góbidos en un estudiaro indicados por la composición elemental de los otolitos

Otolith trace elements were used as natural tags to study the dispersal patterns of goby larvae in an estuary. Ninety-six larval gobies representing 10 species were collected in the estuary of Gongshytyan Creek in northwestern Taiwan in September 1997. Fifteen trace elements in fish otoliths were analysed with solution-based ICPMS. Trace elemental composition in otoliths differed significantly among the species. Habitat use by the larvae of the 10 species can be divided into four groups, based on principal component analysis of otolith elemental composition. All 10 goby species used the estuary as a nursery area irrespective of the fish being amphidromous or non-amphidromous. A part of the population may be lost during larval dispersal, as indicated from trace elemental composition recorded in the otolith.Se utilizó la composición elemental en los otolitos de larvas de góbidos como trazadores naturales para estudiar los patrones de dispersión en un estuario. Durante septiembre de 1997 se recolectaron 96 larvas de góbidos pertenecientes a 10 especies distintas en el estuario de Gongshytyan Creek situado en el noroeste de Taiwan . Se analizaron 15 elementos traza mediante espectroscopia de masas (ICPMS). La composición de elementos traza en los otolitos difirió significativamente entre especies. En base al Análisis de Componentes Principales de la composición elemental de los otolitos, los hábitats utilizados por las 10 especies pudieron dividirse en 4 grupos. Las 10 especies de góbidos usan el estuario como área de cría, independientemente de que las especies sean anfidromas o no-anfidromas. La composición elemental determinada para los otolitos analizados, permitió comprobar que una parte de la población puede ser perdida durante la dispersión larvaria