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  1. This paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence. 
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    Free, publicly-accessible full text available October 1, 2024
  2. Abstract Developing an eco-friendly, efficient, and highly selective gold-recovery technology is urgently needed in order to maintain sustainable environments and improve the utilization of resources. Here we report an additive-induced gold recovery paradigm based on precisely controlling the reciprocal transformation and instantaneous assembly of the second-sphere coordinated adducts formed between β-cyclodextrin and tetrabromoaurate anions. The additives initiate a rapid assembly process by co-occupying the binding cavity of β-cyclodextrin along with the tetrabromoaurate anions, leading to the formation of supramolecular polymers that precipitate from aqueous solutions as cocrystals. The efficiency of gold recovery reaches 99.8% when dibutyl carbitol is deployed as the additive. This cocrystallization is highly selective for square-planar tetrabromoaurate anions. In a laboratory-scale gold-recovery protocol, over 94% of gold in electronic waste was recovered at gold concentrations as low as 9.3 ppm. This simple protocol constitutes a promising paradigm for the sustainable recovery of gold, featuring reduced energy consumption, low cost inputs, and the avoidance of environmental pollution. 
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    Free, publicly-accessible full text available December 1, 2024
  3. Abstract Motivation

    Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery.

    Results

    In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures.

    Availability and implementation

    The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

     
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  4. Abstract Motivation

    Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods.

    Results

    We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA.

    Availability and implementation

    The source code is available at https://github.com/BioinfoMachineLearning/EnQA.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

     
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  5. Ciliates are microbial eukaryotes that undergo extensive programmed genome rearrangement, a natural genome editing process that converts long germline chromosomes into smaller gene-rich somatic chromosomes. Three well-studied ciliates include Oxytricha trifallax , Tetrahymena thermophila and Paramecium tetraurelia , but only the Oxytricha lineage has a massively scrambled genome, whose assembly during development requires hundreds of thousands of precise programmed DNA joining events, representing the most complex genome dynamics of any known organism. Here we study the emergence of such complex genomes by examining the origin and evolution of discontinuous and scrambled genes in the Oxytricha lineage. This study compares six genomes from three species, the germline and somatic genomes for Euplotes woodruffi , Tetmemena sp. , and the model ciliate Oxytricha trifallax . To complement existing data, we sequenced, assembled and annotated the germline and somatic genomes of Euplotes woodruffi , which provides an outgroup, and the germline genome of Tetmemena sp.. We find that the germline genome of Tetmemena is as massively scrambled and interrupted as Oxytricha's : 13.6% of its gene loci require programmed translocations and/or inversions, with some genes requiring hundreds of precise gene editing events during development. This study revealed that the earlier-diverged spirotrich, E. woodruffi , also has a scrambled genome, but only roughly half as many loci (7.3%) are scrambled. Furthermore, its scrambled genes are less complex, together supporting the position of Euplotes as a possible evolutionary intermediate in this lineage, in the process of accumulating complex evolutionary genome rearrangements, all of which require extensive repair to assemble functional coding regions. Comparative analysis also reveals that scrambled loci are often associated with local duplications, supporting a gradual model for the origin of complex, scrambled genomes via many small events of DNA duplication and decay. 
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    Free, publicly-accessible full text available November 24, 2023
  6. Free, publicly-accessible full text available January 1, 2024
  7. Abstract Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (Δ E S-T ) could enable small Δ E S-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state ( S  = 1) could exhibit doublet ( S  = 1/2) behavior due to different spin distributions and solid-state interchain spin-spin interactions. Besides, these polymers showed outstanding charge transport properties with high hole/electron mobilities and can be both n- and p-doped with superior conductivities. Our results demonstrate a rational approach to obtain high-mobility semiconducting polymers with different spin ground states. 
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    Free, publicly-accessible full text available December 1, 2023
  8. Free, publicly-accessible full text available December 21, 2023