First order transition in Pb10−x_{10-x}Cux_x(PO4_4)6_6O (0.9<x<1.10.9<x<1.1) containing Cu2_2S

Lee et al. reported that the compound LK99, with a chemical formula of Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O ($0.9<x<1.1$), exhibits room-temperature superconductivity under ambient pressure. In this study, we investigated the transport and magnetic properties of pure Cu$_2$S and LK-99 containing Cu$_2$S. We observed a sharp superconducting-like transition and a thermal hysteresis behavior in the resistivity and magnetic susceptibility. However, we did not observe zero-resistivity below the transition temperature. We argue that the so-called superconducting behavior in LK-99 is most likely due to a reduction in resistivity caused by the first order structural phase transition of Cu$_2$S at around 385 K, from the $\beta$ phase at high temperature to the $\gamma$ phase at low temperature

Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202

Comprehensive transcriptome analysis reveals novel genes involved in cardiac glycoside biosynthesis and mlncRNAs associated with secondary metabolism and stress response in Digitalis purpurea

<p>Abstract</p> <p>Background</p> <p><it>Digitalis purpurea </it>is an important ornamental and medicinal plant. There is considerable interest in exploring its transcriptome.</p> <p>Results</p> <p>Through high-throughput 454 sequencing and subsequent assembly, we obtained 23532 genes, of which 15626 encode conserved proteins. We determined 140 unigenes to be candidates involved in cardiac glycoside biosynthesis. It could be grouped into 30 families, of which 29 were identified for the first time in <it>D. purpurea</it>. We identified 2660 mRNA-like npcRNA (mlncRNA) candidates, an emerging class of regulators, using a computational mlncRNA identification pipeline and 13 microRNA-producing unigenes based on sequence conservation and hairpin structure-forming capability. Twenty five protein-coding unigenes were predicted to be targets of these microRNAs. Among the mlncRNA candidates, only 320 could be grouped into 140 families with at least two members in a family. The majority of <it>D. purpurea </it>mlncRNAs were species-specific and many of them showed tissue-specific expression and responded to cold and dehydration stresses. We identified 417 protein-coding genes with regions significantly homologous or complementary to 375 mlncRNAs. It includes five genes involved in secondary metabolism. A positive correlation was found in gene expression between protein-coding genes and the homologous mlncRNAs in response to cold and dehydration stresses, while the correlation was negative when protein-coding genes and mlncRNAs were complementary to each other.</p> <p>Conclusions</p> <p>Through comprehensive transcriptome analysis, we not only identified 29 novel gene families potentially involved in the biosynthesis of cardiac glycosides but also characterized a large number of mlncRNAs. Our results suggest the importance of mlncRNAs in secondary metabolism and stress response in <it>D. purpurea</it>.</p

De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis

<p>Abstract</p> <p>Background</p> <p>American ginseng (<it>Panax quinquefolius </it>L.) is one of the most widely used herbal remedies in the world. Its major bioactive constituents are the triterpene saponins known as ginsenosides. However, little is known about ginsenoside biosynthesis in American ginseng, especially the late steps of the pathway.</p> <p>Results</p> <p>In this study, a one-quarter 454 sequencing run produced 209,747 high-quality reads with an average sequence length of 427 bases. <it>De novo </it>assembly generated 31,088 unique sequences containing 16,592 contigs and 14,496 singletons. About 93.1% of the high-quality reads were assembled into contigs with an average 8-fold coverage. A total of 21,684 (69.8%) unique sequences were annotated by a BLAST similarity search against four public sequence databases, and 4,097 of the unique sequences were assigned to specific metabolic pathways by the Kyoto Encyclopedia of Genes and Genomes. Based on the bioinformatic analysis described above, we found all of the known enzymes involved in ginsenoside backbone synthesis, starting from acetyl-CoA via the isoprenoid pathway. Additionally, a total of 150 cytochrome P450 (CYP450) and 235 glycosyltransferase unique sequences were found in the 454 cDNA library, some of which encode enzymes responsible for the conversion of the ginsenoside backbone into the various ginsenosides. Finally, one CYP450 and four UDP-glycosyltransferases were selected as the candidates most likely to be involved in ginsenoside biosynthesis through a methyl jasmonate (MeJA) inducibility experiment and tissue-specific expression pattern analysis based on a real-time PCR assay.</p> <p>Conclusions</p> <p>We demonstrated, with the assistance of the MeJA inducibility experiment and tissue-specific expression pattern analysis, that transcriptome analysis based on 454 pyrosequencing is a powerful tool for determining the genes encoding enzymes responsible for the biosynthesis of secondary metabolites in non-model plants. Additionally, the expressed sequence tags (ESTs) and unique sequences from this study provide an important resource for the scientific community that is interested in the molecular genetics and functional genomics of American ginseng.</p

Towards Open Vocabulary Learning: A Survey

In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective compared to weakly supervised and zero-shot settings. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by comparing it to related concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Then, we review several closely related tasks in the case of segmentation and detection, including long-tail problems, few-shot, and zero-shot settings. For the method survey, we first present the basic knowledge of detection and segmentation in close-set as the preliminary knowledge. Next, we examine various scenarios in which open vocabulary learning is used, identifying common design elements and core ideas. Then, we compare the recent detection and segmentation approaches in commonly used datasets and benchmarks. Finally, we conclude with insights, issues, and discussions regarding future research directions. To our knowledge, this is the first comprehensive literature review of open vocabulary learning. We keep tracing related works at https://github.com/jianzongwu/Awesome-Open-Vocabulary.Comment: Project page at https://github.com/jianzongwu/Awesome-Open-Vocabular

Analysis of the transcriptome of Panax notoginseng root uncovers putative triterpene saponin-biosynthetic genes and genetic markers

<p>Abstract</p> <p>Background</p> <p><it>Panax notoginseng </it>(Burk) F.H. Chen is important medicinal plant of the <it>Araliacease </it>family. Triterpene saponins are the bioactive constituents in <it>P. notoginseng</it>. However, available genomic information regarding this plant is limited. Moreover, details of triterpene saponin biosynthesis in the <it>Panax </it>species are largely unknown.</p> <p>Results</p> <p>Using the 454 pyrosequencing technology, a one-quarter GS FLX titanium run resulted in 188,185 reads with an average length of 410 bases for <it>P. notoginseng </it>root. These reads were processed and assembled by 454 GS <it>De Novo </it>Assembler software into 30,852 unique sequences. A total of 70.2% of unique sequences were annotated by Basic Local Alignment Search Tool (BLAST) similarity searches against public sequence databases. The Kyoto Encyclopedia of Genes and Genomes (KEGG) assignment discovered 41 unique sequences representing 11 genes involved in triterpene saponin backbone biosynthesis in the 454-EST dataset. In particular, the transcript encoding dammarenediol synthase (DS), which is the first committed enzyme in the biosynthetic pathway of major triterpene saponins, is highly expressed in the root of four-year-old <it>P. notoginseng</it>. It is worth emphasizing that the candidate cytochrome P450 (Pn02132 and Pn00158) and UDP-glycosyltransferase (Pn00082) gene most likely to be involved in hydroxylation or glycosylation of aglycones for triterpene saponin biosynthesis were discovered from 174 cytochrome P450s and 242 glycosyltransferases by phylogenetic analysis, respectively. Putative transcription factors were detected in 906 unique sequences, including Myb, homeobox, WRKY, basic helix-loop-helix (bHLH), and other family proteins. Additionally, a total of 2,772 simple sequence repeat (SSR) were identified from 2,361 unique sequences, of which, di-nucleotide motifs were the most abundant motif.</p> <p>Conclusion</p> <p>This study is the first to present a large-scale EST dataset for <it>P. notoginseng </it>root acquired by next-generation sequencing (NGS) technology. The candidate genes involved in triterpene saponin biosynthesis, including the putative CYP450s and UGTs, were obtained in this study. Additionally, the identification of SSRs provided plenty of genetic makers for molecular breeding and genetics applications in this species. These data will provide information on gene discovery, transcriptional regulation and marker-assisted selection for <it>P. notoginseng</it>. The dataset establishes an important foundation for the study with the purpose of ensuring adequate drug resources for this species.</p

A magnetic biocatalyst based on mussel-inspired polydopamine and its acylation of dihydromyricetin

A support made of mussel-inspired polydopamine-coated magnetic iron oxide nanoparticles (PD-MNPs) was prepared and characterized. The widely used Aspergillus niger lipase (ANL) was immobilized on the PD-MNPs (ANL@PD-MNPs) with a protein loading of 138 mg/g and an activity recovery of 83.6% under optimized conditions. For the immobilization, the pH and immobilization time were investigated. The pH and thermal and storage stability of the ANL@PD-MNPs significantly surpassed those of free ANL. The ANL@PD-MNPs had better solvent tolerance than free ANL. The secondary structure of free ANL and ANL@PD-MNPs was analyzed by infrared spectroscopy. A kinetic study demonstrated that the ANL@PD-MNPs had enhanced enzyme-substrate affinity and high catalytic efficiency. The ANL@PD-MNPs was applied as a biocatalyst for the regioselective acylation of dihydromyricetin (DMY) in DMSO and gave a conversion of 79.3%, which was higher than that of previous reports. The ANL@PD-MNPs retained over 55% of its initial activity after 10 cycles of reuse. The ANL@PD-MNPs were readily separated from the reaction system by a magnet. The PD-MNPs is an excellent support for ANL and the resulting ANL@PD-MNPs displayed good potential for the efficient synthesis of dihydromyricetin-3-acetate by enzymatic regioselective acylation