MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Analysis of the ASMT Gene Family in Pepper (Capsicum annuum L.): Identification, Phylogeny, and Expression Profiles

Acetylserotonin methyltransferase (ASMT) in plant species, one of the most important enzymes in melatonin biosynthesis, plays a rate-limiting role in the melatonin production. In this study, based on the whole genome sequence, we performed a systematic analysis for the ASMT gene family in pepper (Capsicum annuum L.) and analyzed their expression profiles during growth and development, as well as abiotic stresses. The results showed that at least 16 CaASMT genes were identified in the pepper genome. Phylogenetic analyses of all the CaASMTs were divided into three groups (group I, group II, and group III) with a high bootstrap value. Through the online MEME tool, six distinct motifs (motif 1 to motif 6) were identified. Chromosome location found that most CaASMT genes were mapped in the distal ends of the pepper chromosomes. In addition, RNA-seq analysis revealed that, during the vegetative and reproductive development, the difference in abundance and distinct expression patterns of these CaASMT genes suggests different functions. The qRT-PCR analysis showed that high abundance of CaASMT03, CaASMT04, and CaASMT06 occurred in mature green fruit and mature red fruit. Finally, using RNA-seq and qRT-PCR technology, we also found that several CaASMT genes were induced under abiotic stress conditions. The results will not only contribute to elucidate the evolutionary relationship of ASMT genes but also ascertain the biological function in pepper plant response to abiotic stresses

Development of Polymorphic Genic SSR Markers by Transcriptome Sequencing in the Welsh Onion (Allium fistulosum L.)

Transcriptome analysis is an efficient way to explore molecular markers in plant species, for which genome sequences have not been published. To address the limited number of markers published for the Welsh onion, this study found 6486 loci of genic simple sequence repeats (SSR), which consisted of 1–5 bp repeat motifs, based on next-generation sequencing (NGS) technology and the RNA-Seq approach. The most abundant motif was mononucleotide (52.33%), followed by trinucleotide (31.96%), and dinucleotide (14.57%). A total of 2525 primer pairs were successfully designed, and 91 out of 311 tested primers were polymorphisms. Overall, 38 genic SSR markers were randomly selected to further validate the degree of genetic diversity, and 22 genic SSR markers (57.89%) showed high levels of polymorphism. The average polymorphism information content (PIC) value and the number of alleles (Na) were 0.63 and 5.27, respectively, and the unweighted pair-group method with arithmetic average (UPGMA) cluster analysis grouped the 22 Allium accessions into three groups with Nei’s similarity coefficients ranging from 0.37 to 0.99. This result suggested that these genic SSR markers could be used to develop a higher resolution genetic map and/or to analyze the phylogenetic relationships among Allium plants in the near future

Coupling of polyhydroxybutyrate and zero-valent iron for enhanced treatment of nitrate pollution within the Permeable Reactive Barrier and its downgradient aquifer

Permeable Reactive Barriers (PRBs) have been utilized for mitigating nitrate pollution in groundwater systems through the use of solid carbon and iron fillers that release diverse nutrients to enhance denitrification efficiency. We conduct laboratory column tests to evaluate the effectiveness of PRBs in remediating nitrate pollution both within the PRB and in the downgradient aquifer. We use an iron-carbon hydrogel (ICH) as PRB filler, which has different weight ratios of polyhydroxybutyrate (PHB) and microscale zero-valent iron (mZVI). Results reveal that denitrification in the downgradient aquifer accounts for at least 19.5 % to 32.5 % of the total nitrate removal. In the ICH, a higher ratio of PHB to mZVI leads to higher contribution of the downgradient aquifer to nitrate removal, while a lower ratio results in smaller contribution. Microbial community analysis further reveals that heterotrophic and mixotrophic bacteria dominate in the downgradient aquifer of the PRB, and their relative abundance increases with a higher ratio of PHB to mZVI in the ICH. Within the PRB, autotrophic and iron-reducing bacteria are more prevalent, and their abundance increases as the ratio of PHB to mZVI in the ICH decreases. These findings emphasize the downgradient aquifer's substantial role in nitrate removal, particularly driven by dissolved organic carbon provided by PHB. This research holds significant implications for nutrient waste management, including the prevention of secondary pollution, and the development of cost-effective PRBs.24 month embargo: first published 26 December 2023This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Optimizing the Ueff value for DFT+U calculation of paramagnetic solid-state NMR shifts by double Fermi-contact-shift verification

The isotropic chemical shifts can be calculated by hybrid functionals, which costs lots of computational resources. To save time, DFT+U could be employed to calculate the isotropic chemical shifts. However, the calculated properties are very sensitive to the Hubbard correction value U. Here the double Fermi-contact-shift verification approach with DFT+U method is proposed with much higher computational efficiency, that is, concurrently calculate the Fermi-contact shifts on two nuclei (Li and O) to predict the optimal U. The optimal U is also helpful to the quadrupolar coupling constant C, g-factor, band structure and density of states

A Learning-Free Method for Locomotion Mode Prediction by Terrain Reconstruction and Visual-Inertial Odometry

This research introduces a novel, highly precise, and learning-free approach to locomotion mode prediction, a technique with potential for broad applications in the field of lower-limb wearable robotics. This study represents the pioneering effort to amalgamate 3D reconstruction and Visual-Inertial Odometry (VIO) into a locomotion mode prediction method, which yields robust prediction performance across diverse subjects and terrains, and resilience against various factors including camera view, walking direction, step size, and disturbances from moving obstacles without the need of parameter adjustments. The proposed Depth-enhanced Visual-Inertial Odometry (D-VIO) has been meticulously designed to operate within computational constraints of wearable configurations while demonstrating resilience against unpredictable human movements and sparse features. Evidence of its effectiveness, both in terms of accuracy and operational time consumption, is substantiated through tests conducted using open-source dataset and closed-loop evaluations. Comprehensive experiments were undertaken to validate its prediction accuracy across various test conditions such as subjects, scenarios, sensor mounting positions, camera views, step sizes, walking directions, and disturbances from moving obstacles. A comprehensive prediction accuracy rate of 99.00% confirms the efficacy, generality, and robustness of the proposed method

Nanostructured Ternary Metal Tungstate-Based Photocatalysts for Environmental Purification and Solar Water Splitting: A Review

Abstract Visible-light-responsive ternary metal tungstate (MWO4) photocatalysts are being increasingly investigated for energy conversion and environmental purification applications owing to their striking features, including low cost, eco-friendliness, and high stability under acidic and oxidative conditions. However, rapid recombination of photoinduced electron–hole pairs and a narrow light response range to the solar spectrum lead to low photocatalytic activity of MWO4-based materials, thus significantly hampering their wide usage in practice. To enable their widespread practical usage, significant efforts have been devoted, by developing new concepts and innovative strategies. In this review, we aim to provide an integrated overview of the fundamentals and recent progress of MWO4-based photocatalysts. Furthermore, different strategies, including morphological control, surface modification, heteroatom doping, and heterojunction fabrication, which are employed to promote the photocatalytic activities of MWO4-based materials, are systematically summarized and discussed. Finally, existing challenges and a future perspective are also provided to shed light on the development of highly efficient MWO4-based photocatalysts

Genome-Wide Analysis of the BAHD Family in Welsh Onion and CER2-LIKEs Involved in Wax Metabolism

BAHD acyltransferases (BAHDs), especially those present in plant epidermal wax metabolism, are crucial for environmental adaptation. Epidermal waxes primarily comprise very-long-chain fatty acids (VLCFAs) and their derivatives, serving as significant components of aboveground plant organs. These waxes play an essential role in resisting biotic and abiotic stresses. In this study, we identified the BAHD family in Welsh onion (Allium fistulosum). Our analysis revealed the presence of AfBAHDs in all chromosomes, with a distinct concentration in Chr3. Furthermore, the cis-acting elements of AfBAHDs were associated with abiotic/biotic stress, hormones, and light. The motif of Welsh onion BAHDs indicated the presence of a specific BAHDs motif. We also established the phylogenetic relationships of AfBAHDs, identifying three homologous genes of CER2. Subsequently, we characterized the expression of AfCER2-LIKEs in a Welsh onion mutant deficient in wax and found that AfCER2-LIKE1 plays a critical role in leaf wax metabolism, while all AfCER2-LIKEs respond to abiotic stress. Our findings provide new insights into the BAHD family and lay a foundation for future studies on the regulation of wax metabolism in Welsh onion

Dopants fixation of Ruthenium for boosting acidic oxygen evolution stability and activity

There is an increasing interest in understanding how defect chemistry can alter material reactivity. Here, authors tune the electronic structure of RuO2 by introducing W and Er dopants that boost acidic oxygen evolution performances by limiting oxygen vacancy formation during catalysis