CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning

In real-world applications, dynamic scenarios require the models to possess the capability to learn new tasks continuously without forgetting the old knowledge. Experience-Replay methods store a subset of the old images for joint training. In the scenario of more strict privacy protection, storing the old images becomes infeasible, which leads to a more severe plasticity-stability dilemma and classifier bias. To meet the above challenges, we propose a new architecture, named continual expansion and absorption transformer~(CEAT). The model can learn the novel knowledge by extending the expanded-fusion layers in parallel with the frozen previous parameters. After the task ends, we losslessly absorb the extended parameters into the backbone to ensure that the number of parameters remains constant. To improve the learning ability of the model, we designed a novel prototype contrastive loss to reduce the overlap between old and new classes in the feature space. Besides, to address the classifier bias towards the new classes, we propose a novel approach to generate the pseudo-features to correct the classifier. We experiment with our methods on three standard Non-Exemplar Class-Incremental Learning~(NECIL) benchmarks. Extensive experiments demonstrate that our model gets a significant improvement compared with the previous works and achieves 5.38%, 5.20%, and 4.92% improvement on CIFAR-100, TinyImageNet, and ImageNet-Subset

Tilting and twisting in a novel perovzalate, K3NaMn(C2O4)3

We acknowledge support from the National Natural Science Foundation of China (22005329, 52061160484, 51822210, 51972329), Newton International Fellowships Alumni 2018/2019 (AL\180020, AL\191011), Key Area Research and Development Program of Guangdong Province (2019B090914003), Shenzhen Science and Technology Planning Project (No. JCYJ20180507182512042, JCYJ20190807172001755, JCYJ20200109115624923), and Science and Technology Planning Project of Guangdong Province (2019A1515110975, 2019A1515011902, 2019TX05L389).In the title compound, the oxalate ligand simultaneously bridges both Mn-centred and Na-centred octahedra to produce a unique ‘doubly-interpenetrated’ perovskite-like lattice with an unconventional octahedral tilt system. In turn, the coordination requirements of the oxalate ligand lead to a rare ‘twisted’ conformation.PostprintPeer reviewe

Genetic code expansion in \u3ci\u3ePseudomonas putida\u3c/i\u3e KT2440

Pseudomonas putida KT2440 is an emerging microbial chassis for bio-based chemical production from renewable feedstocks and environmental bioremediation. However, tools for studying, engineering, and modulating protein complexes and biosynthetic enzymes in this organism are largely underdeveloped. Genetic code expansion for the incorporation of unnatural amino acids (unAAs) into proteins can advance such efforts and, furthermore, enable additional controls of biological processes of the strain. In this work, we established the orthogonality of two widely used archaeal tRNA synthetase and tRNA pairs in KT2440. Following the optimization of decoding systems, four unAAs were incorporated into proteins in response to a UAG stop codon at 34.6-78% efficiency. In addition, we demonstrated the utility of genetic code expansion through the incorporation of a photocrosslinking amino acid, p-benzoyl-L-phenylalanine (pBpa), into glutathione S-transferase (GstA) and a chemosensory response regulator (CheY) for protein-protein interaction studies in KT2440. This work reported the successful genetic code expansion in KT2440 for the first time. Given the diverse structure and functions of unAAs that have been added to protein syntheses using the archaeal systems, our research lays down a solid foundation for future work to study and enhance the biological functions of KT2440

Diff-Font: Diffusion Model for Robust One-Shot Font Generation

Font generation is a difficult and time-consuming task, especially in those languages using ideograms that have complicated structures with a large number of characters, such as Chinese. To solve this problem, few-shot font generation and even one-shot font generation have attracted a lot of attention. However, most existing font generation methods may still suffer from (i) large cross-font gap challenge; (ii) subtle cross-font variation problem; and (iii) incorrect generation of complicated characters. In this paper, we propose a novel one-shot font generation method based on a diffusion model, named Diff-Font, which can be stably trained on large datasets. The proposed model aims to generate the entire font library by giving only one sample as the reference. Specifically, a large stroke-wise dataset is constructed, and a stroke-wise diffusion model is proposed to preserve the structure and the completion of each generated character. To our best knowledge, the proposed Diff-Font is the first work that developed diffusion models to handle the font generation task. The well-trained Diff-Font is not only robust to font gap and font variation, but also achieved promising performance on difficult character generation. Compared to previous font generation methods, our model reaches state-of-the-art performance both qualitatively and quantitatively

Gut microbiome-derived hydrolases—an underrated target of natural product metabolism

In recent years, there has been increasing interest in studying gut microbiome-derived hydrolases in relation to oral drug metabolism, particularly focusing on natural product drugs. Despite the significance of natural product drugs in the field of oral medications, there is a lack of research on the regulatory interplay between gut microbiome-derived hydrolases and these drugs. This review delves into the interaction between intestinal microbiome-derived hydrolases and natural product drugs metabolism from three key perspectives. Firstly, it examines the impact of glycoside hydrolases, amide hydrolases, carboxylesterase, bile salt hydrolases, and epoxide hydrolase on the structure of natural products. Secondly, it explores how natural product drugs influence microbiome-derived hydrolases. Lastly, it analyzes the impact of interactions between hydrolases and natural products on disease development and the challenges in developing microbial-derived enzymes. The overarching goal of this review is to lay a solid theoretical foundation for the advancement of research and development in new natural product drugs and personalized treatment

The operation mechanism of poly(9,9-dioctylfluorenyl-2,7-diyl) dots in high efficiency polymer solar cells

The highly efficient polymer solar cells were realized by doping poly(9,9-dioctylfluorenyl-2,7-diyl) (PFO) dots into active layer. The dependence of doping amount on devices performance was investigated and a high efficiency of 7.15% was obtained at an optimal concentration, accounting for a 22.4% enhancement. The incorporation of PFO dots (Pdots) is conducted to the improvement of Jsc and fill factor mainly due to the enhancement of light absorption and charge transport property. Pdots blended in active layer provides an interface for charge transfer and enables the formation of percolation pathways for electron transport. The introduction of Pdots was proven an effective way to improve optical and electrical properties of solar cells

Noncanonical amino acid mutagenesis in response to recoding signal-enhanced quadruplet codons

While amber suppression is the most common approach to introduce noncanonical amino acids into proteins in live cells, quadruplet codon decoding has potential to enable a greatly expanded genetic code with up to 256 new codons for protein biosynthesis. Since triplet codons are the predominant form of genetic code in nature, quadruplet codon decoding often displays limited efficiency. In this work, we exploited a new approach to significantly improve quadruplet UAGN and AGGN (N = A, U, G, C) codon decoding efficiency by using recoding signals imbedded in mRNA. With representative recoding signals, the expression level of mutant proteins containing UAGN and AGGN codons reached 48% and 98% of that of the wild-type protein, respectively. Furthermore, this strategy mitigates a common concern of reading-through endogenous stop codons with amber suppression-based system. Since synthetic recoding signals are rarely found near the endogenous UAGN and AGGN sequences, a low level of undesirable suppression is expected. Our strategy will greatly enhance the utility of noncanonical amino acid mutagenesis in live-cell studies

Erratum: “The operation mechanism of poly (9,9-dioctylfluorenyl-2,7-diyl) dots in high efficiency polymer solar cells” [Appl. Phys. Lett. 106, 193904 (2015)]

We have noticed an error in Fig. 7 of the original article. Figs. 7(a) and 7(b) should be exchanged and the revised figure is shown below. We apologize for this error.