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  • Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation

    Alraddadi, Musfer; Conejo, Antonio J.; Lima, Ricardo (Journal of Modern Power Systems and Clean Energy, Journal of Modern Power Systems and Clean Energy, 2021-09-15) [Article]
    In this paper, we address the long-term generation and transmission expansion planning for power systems of regions with very high solar irradiation. We target the power systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources. We propose a stochastic programming model with expansion alternatives including transmission lines, solar power plants (photovoltaic and concentrated solar), wind farms, energy storage, and flexible combined cycle gas turbines. The model represents the longterm uncertainty to characterize the demand growth, and the short-term uncertainty to characterize daily solar, wind, and demand patterns. We use the Saudi Arabian power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets. The results show that a strong dependence on solar power for high shares of renewable sources requires high generation capacity and storage to meet the night demand.
  • Influence of the anionic ligands on properties and reactivity of Hoveyda-Grubbs catalysts

    Albalawi, Mona O.; Falivene, Laura; Jedidi, Abdesslem; Osman, Osman I.; Elroby, Shaaban A.; Cavallo, Luigi (Molecular Catalysis, Elsevier BV, 2021-06-26) [Article]
    Ruthenium based catalysts remain among the more successful complexes used in the catalysis of metathesis processes for the synthesis of new carbon-carbon bonds. The investigation of the influence of the different system moieties on its catalytic performance has led to important improvements in the field. To this extent, density functional theory (DFT) calculations have contributed significantly providing fundamental understandings to develop new catalysts. With this aim, we presented here a detailed computational study of how the nature of the anion ligand binding to the metal affects the global properties and reactivity of the catalyst. Geometric, energetic and electronic analysis have been performed to reach the key insights necessary to build structure-performance correlations.
  • Small-molecule inhibitors targeting Polycomb repressive complex 1 RING domain

    Shukla, Shirish; Ying, Weijiang; Gray, Felicia; Yao, Yiwu; Simes, Miranda L.; Zhao, Qingjie; Miao, Hongzhi; Cho, Hyo Je; González-Alonso, Paula; Winkler, Alyssa; Lund, George; Purohit, Trupta; Kim, EunGi; Zhang, Xiaotian; Ray, Joshua M.; He, Shihan; Nikolaidis, Caroline; Ndoj, Juliano; Wang, Jingya; Jaremko, Lukasz; Jaremko, Mariusz; Ryan, Russell J. H.; Guzman, Monica L.; Grembecka, Jolanta; Cierpicki, Tomasz (Nature Chemical Biology, Springer Science and Business Media LLC, 2021-06-21) [Article]
    Polycomb repressive complex 1 (PRC1) is an essential chromatin-modifying complex that monoubiquitinates histone H2A and is involved in maintaining the repressed chromatin state. Emerging evidence suggests PRC1 activity in various cancers, rationalizing the need for small-molecule inhibitors with well-defined mechanisms of action. Here, we describe the development of compounds that directly bind to RING1B–BMI1, the heterodimeric complex constituting the E3 ligase activity of PRC1. These compounds block the association of RING1B–BMI1 with chromatin and inhibit H2A ubiquitination. Structural studies demonstrate that these inhibitors bind to RING1B by inducing the formation of a hydrophobic pocket in the RING domain. Our PRC1 inhibitor, RB-3, decreases the global level of H2A ubiquitination and induces differentiation in leukemia cell lines and primary acute myeloid leukemia (AML) samples. In summary, we demonstrate that targeting the PRC1 RING domain with small molecules is feasible, and RB-3 represents a valuable chemical tool to study PRC1 biology.
  • DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

    Zhou, Juexiao; zhang, bin; Li, Haoyang; Zhou, Longxi; Li, Zhongxiao; Long, Yongkang; Han, Wenkai; Wang, Mengran; Cui, Huanhuan; Chen, Wei; Gao, Xin (Research Square Platform LLC, 2021-06-21) [Preprint]
    Abstract The accurate annotation of transcription start sites (TSSs) and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfil this, on one hand, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. On the other hand, various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset and thus result in drastic false positive predictions when applied on the genome-scale. To address these issues, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on both DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states. Our application, pre-trained models and data are available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release.
  • DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

    Zhou, Juexiao; zhang, bin; Li, Haoyang; Zhou, Longxi; Li, Zhongxiao; Long, Yongkang; Han, Wenkai; Wang, Mengran; Cui, Huanhuan; Chen, Wei; Gao, Xin (Research Square Platform LLC, 2021-06-21) [Preprint]
    Abstract The accurate annotation of transcription start sites (TSSs) and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfil this, on one hand, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. On the other hand, various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset and thus result in drastic false positive predictions when applied on the genome-scale. To address these issues, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on both DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states. Our application, pre-trained models and data are available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release.

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