The selective hydrogenation of CO
to value-added chemicals is attractive but still challenged by the high-performance catalyst. In this work, we report that gallium nitride (GaN) catalyzes the direct ...hydrogenation of CO
to dimethyl ether (DME) with a CO-free selectivity of about 80%. The activity of GaN for the hydrogenation of CO
is much higher than that for the hydrogenation of CO although the product distribution is very similar. The steady-state and transient experimental results, spectroscopic studies, and density functional theory calculations rigorously reveal that DME is produced as the primary product via the methyl and formate intermediates, which are formed over different planes of GaN with similar activation energies. This essentially differs from the traditional DME synthesis via the methanol intermediate over a hybrid catalyst. The present work offers a different catalyst capable of the direct hydrogenation of CO
to DME and thus enriches the chemistry for CO
transformations.
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of ...pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
Low-rank modeling has many important applications in computer vision and machine learning. While the matrix rank is often approximated by the convex nuclear norm, the use of nonconvex low-rank ...regularizers has demonstrated better empirical performance. However, the resulting optimization problem is much more challenging. Recent state-of-the-art requires an expensive full SVD in each iteration. In this paper, we show that for many commonly-used nonconvex low-rank regularizers, the singular values obtained from the proximal operator can be automatically threshold. This allows the proximal operator to be efficiently approximated by the power method. We then develop a fast proximal algorithm and its accelerated variant with inexact proximal step. It can be guaranteed that the squared distance between consecutive iterates converges at a rate of O(1/T), where T is the number of iterations. Furthermore, we show the proposed algorithm can be parallelized, and the resultant algorithm achieves nearly linear speedup w.r.t. the number of threads. Extensive experiments are performed on matrix completion and robust principal component analysis. Significant speedup over the state-of-the-art is observed.
This research studies the non‐linear relationship between interest rates and exchange rates for China and the United States using the rolling‐window method. We also investigate uncovered interest ...rate parity (UIP) and the capital market theory for the whole time period and subperiods so as to reconsider various economic connections between China and the United States. The results suggest that the effect of the latter's interest rate adjustment on China/U.S. exchange rate volatility is stronger than that of China's interest rate adjustment. Moreover, changes in the China/U.S. exchange rate have a slightly stronger effect on the U.S. interest rate than on China's interest rate. Our findings reveal that the interest rate parity theory does not hold for the entire sample period but may hold in subperiods. The results provide a reference for the steady implementation of RMB internationalization.
Benefiting from the merits of low cost, nonflammability, and high operational safety, aqueous rechargeable batteries have emerged as promising candidates for large‐scale energy‐storage applications. ...Among various metal‐ion/non‐metallic charge carriers, the proton (H+) as a charge carrier possesses numerous unique properties such as fast proton diffusion dynamics, a low molar mass, and a small hydrated ion radius, which endow aqueous proton batteries (APBs) with a salient rate capability, a long‐term life span, and an excellent low‐temperature electrochemical performance. In addition, redox‐active organic molecules, with the advantages of structural diversity, rich proton‐storage sites, and abundant resources, are considered attractive electrode materials for APBs. However, the charge‐storage and transport mechanisms of organic electrodes in APBs are still in their infancy. Therefore, finding suitable electrode materials and uncovering the H+‐storage mechanisms are significant for the application of organic materials in APBs. Herein, the latest research progress on organic materials, such as small molecules and polymers for APBs, is reviewed. Furthermore, a comprehensive summary and evaluation of APBs employing organic electrodes as anode and/or cathode is provided, especially regarding their low‐temperature and high‐power performances, along with systematic discussions for guiding the rational design and the construction of APBs based on organic electrodes.
The proton (H+) as a charge carrier possesses unique properties such as fast diffusion dynamics, a low molar mass, and a small hydrated ion radius, endowing aqueous proton batteries (APBs) with a salient rate capability, a long‐term life span, and an excellent low‐temperature electrochemical performance, as well as high safety. A comprehensive summary and evaluation of APBs employing organic electrodes as anode and/or cathode is presented, together with systematic discussions for guiding the rational design and the construction of APBs based on organic electrodes
Recently, in order to improve reactive fault tolerance techniques in large scale storage systems, researchers have proposed various statistical and machine learning methods based on SMART attributes. ...Most of these studies have focused on predicting failures of hard drives, i.e., labeling the status of a hard drive as "good" or not. However, in real-world storage systems, hard drives often deteriorate gradually rather than suddenly. Correspondingly, their SMART attributes change continuously towards failure. Inspired by this observation, we introduce a novel method based on Recurrent Neural Networks (RNN) to assess the health statuses of hard drives based on the gradually changing sequential SMART attributes. Compared to a simple failure prediction method, a health status assessment is more valuable in practice because it enables technicians to schedule the recovery of different hard drives according to the level of urgency. Experiments on real-world datasets for disks of different brands and scales demonstrate that our proposed method can not only achieve a reasonable accurate health status assessment, but also achieve better failure prediction performance than previous work.
The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single ...frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we first develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Summary
In eukaryotes, mechanisms such as alternative splicing (AS) and alternative translation initiation (ATI) contribute to organismal protein diversity. Specifically, splicing factors play ...crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short‐read RNA sequencing, single molecule long‐read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid (ABA) treatment. Combining the data from the two sequencing methods, approximately 83.4% of intron‐containing genes were alternatively spliced. Two AS types, which are referred to as alternative first exon (AFE) and alternative last exon (ALE), were more abundant than intron retention (IR); however, by contrast to AS events detected under normal conditions, differentially expressed AS isoforms were more likely to be translated. ABA extensively affects the AS pattern, indicated by the increasing number of non‐conventional splicing sites. This work also identified thousands of unannotated peptides and proteins by ATI based on mass spectrometry and a virtual peptide library deduced from both strands of coding regions within the Arabidopsis genome. The results enhance our understanding of AS and alternative translation mechanisms under normal conditions, and in response to ABA treatment.
Significance Statement
In this study, a customized analytical pipeline was developed to study transcriptional and translational changes during the abscisic acid response in plants. Using single molecule long‐read sequencing and short‐read RNA sequencing, we identified numerous alternative spliced (AS) transcripts in Arabidopsis and characterized two new AS types. Proteomic identification indicates differentially expressed AS events were more likely to undergo protein translation. The entire workflow is applicable for other plant species.
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and ...interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
The rhizosheath, a layer of soil grains that adheres firmly to roots, is beneficial for plant growth and adaptation to drought environments. Switchgrass is a perennial C4 grass which can form contact ...rhizosheath under drought conditions. In this study, we characterized the microbiomes of four different rhizocompartments of two switchgrass ecotypes (Alamo and Kanlow) grown under drought or well‐watered conditions via 16S ribosomal RNA amplicon sequencing. These four rhizocompartments, the bulk soil, rhizosheath soil, rhizoplane, and root endosphere, harbored both distinct and overlapping microbial communities. The root compartments (rhizoplane and root endosphere) displayed low‐complexity communities dominated by Proteobacteria and Firmicutes. Compared to bulk soil, Cyanobacteria and Bacteroidetes were selectively enriched, while Proteobacteria and Firmicutes were selectively depleted, in rhizosheath soil. Taxa from Proteobacteria or Firmicutes were specifically selected in Alamo or Kanlow rhizosheath soil. Following drought stress, Citrobacter and Acinetobacter were further enriched in rhizosheath soil, suggesting that rhizosheath microbiome assembly is driven by drought stress. Additionally, the ecotype‐specific recruitment of rhizosheath microbiome reveals their differences in drought stress responses. Collectively, these results shed light on rhizosheath microbiome recruitment in switchgrass and lay the foundation for the improvement of drought tolerance in switchgrass by regulating the rhizosheath microbiome.
The rhizosheath is beneficial for plant adaptation to drought. Four rhizocompartments of switchgrass harbored distinct and overlapping microbial communities. The rhizosheath displayed high‐complexity communities compared to the root compartments. Rhizosheath microbiome assembly is driven by drought stress and plant ecotype.
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