Keyframe image processing of semantic 3D point clouds based on deep learning

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a “semantic gap” between this and people's understanding of images. To address this “semantic gap,” a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques

SAM-Net: Integrating Event-Level and Chain-Level Attentions to Predict What Happens Next

Scripts represent knowledge of event sequences that can help text understanding. Script event prediction requires to measure the relation between an existing chain and the subsequent event. The dominant approaches either focus on the effects of individual events, or the influence of the chain sequence. However, only considering individual events will lose much semantic relations within the event chain, and only considering the sequence of the chain will introduce much noise. With our observations, both the individual events and the event segments within the chain can facilitate the prediction of the subsequent event. This paper develops self attention mechanism to focus on diverse event segments within the chain and the event chain is represented as a set of event segments. We utilize the event-level attention to model the relations between subsequent events and individual events. Then, we propose the chain-level attention to model the relations between subsequent events and event segments within the chain. Finally, we integrate event-level and chain-level attentions to interact with the chain to predict what happens next. Comprehensive experiment results on the widely used New York Times corpus demonstrate that our model achieves better results than other state-of-the-art baselines by adopting the evaluation of Multi-Choice Narrative Cloze task

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA dataset

Genome mining of sulfonated lanthipeptides reveals unique cyclic peptide sulfotransferases

Although sulfonation plays crucial roles in various biological processes and is frequently utilized in medicinal chemistry to improve water solubility and chemical diversity of drug leads, it is rare and underexplored in ribosomally synthesized and post-translationally modified peptides (RiPPs). Biosynthesis of RiPPs typically entails modification of hydrophilic residues, which substantially increases their chemical stability and bioactivity, albeit at the expense of reducing water solubility. To explore sulfonated RiPPs that may have improved solubility, we conducted co-occurrence analysis of RiPP class-defining enzymes and sulfotransferase (ST), and discovered two distinctive biosynthetic gene clusters (BGCs) encoding both lanthipeptide synthetase (LanM) and ST. Upon expressing these BGCs, we characterized the structures of novel sulfonated lanthipeptides and determined the catalytic details of LanM and ST. We demonstrate that SslST-catalyzed sulfonation is leader-independent but relies on the presence of A ring formed by LanM. Both LanM and ST are promiscuous towards residues in the A ring, but ST displays strict regioselectivity toward Tyr5. The recognition of cyclic peptide by ST was further discussed. Bioactivity evaluation underscores the significance of the ST-catalyzed sulfonation. This study sets up the starting point to engineering the novel lanthipeptide STs as biocatalysts for hydrophobic lanthipeptides improvement

Divergent Biosynthesis of Bridged Polycyclic Sesquiterpenoids by a Minimal Fungal Biosynthetic Gene Cluster

The bridged polycyclic sesquiterpenoids derived from sativene, isosativene, and longifolene have unique structures, and many chemical synthesis approaches with at least 10 steps have been reported. However, their biosynthetic pathway remains undescribed. A minimal biosynthetic gene cluster (BGC), named bip, encoding a sesquiterpene cyclase (BipA) and a cytochrome P450 (BipB) is characterized to produce such complex sesquiterpenoids with multiple carbon skeletons based on enzymatic assays, heterologous expression, and precursor experiments. BipA is demonstrated as a versatile cyclase with (−)-sativene as the dominant product and (−)-isosativene and (−)-longifolene as minor ones. BipB is capable of hydroxylating different enantiomeric sesquiterpenes, such as (−)-longifolene and (+)-longifolene, at C-15 and C-14 in turn. The C-15- or both C-15- and C-14-hydroxylated products are then further oxidized by unclustered oxidases, resulting in a structurally diverse array of sesquiterpenoids. Bioinformatic analysis reveals the BipB homologues as a discrete clade of fungal sesquiterpene P450s. These findings elucidate the concise and divergent biosynthesis of such intricate bridged polycyclic sesquiterpenoids, offer valuable biocatalysts for biotransformation, and highlight the distinct biosynthetic strategy employed by nature compared to chemical synthesis

Divergent Biosynthesis of Bridged Polycyclic Sesquiterpenoids by a Minimal Fungal Biosynthetic Gene Cluster

The bridged polycyclic sesquiterpenoids derived from sativene, isosativene, and longifolene have unique structures, and many chemical synthesis approaches with at least 10 steps have been reported. However, their biosynthetic pathway remains undescribed. A minimal biosynthetic gene cluster (BGC), named bip, encoding a sesquiterpene cyclase (BipA) and a cytochrome P450 (BipB) is characterized to produce such complex sesquiterpenoids with multiple carbon skeletons based on enzymatic assays, heterologous expression, and precursor experiments. BipA is demonstrated as a versatile cyclase with (−)-sativene as the dominant product and (−)-isosativene and (−)-longifolene as minor ones. BipB is capable of hydroxylating different enantiomeric sesquiterpenes, such as (−)-longifolene and (+)-longifolene, at C-15 and C-14 in turn. The C-15- or both C-15- and C-14-hydroxylated products are then further oxidized by unclustered oxidases, resulting in a structurally diverse array of sesquiterpenoids. Bioinformatic analysis reveals the BipB homologues as a discrete clade of fungal sesquiterpene P450s. These findings elucidate the concise and divergent biosynthesis of such intricate bridged polycyclic sesquiterpenoids, offer valuable biocatalysts for biotransformation, and highlight the distinct biosynthetic strategy employed by nature compared to chemical synthesis

Divergent Biosynthesis of Bridged Polycyclic Sesquiterpenoids by a Minimal Fungal Biosynthetic Gene Cluster

The bridged polycyclic sesquiterpenoids derived from sativene, isosativene, and longifolene have unique structures, and many chemical synthesis approaches with at least 10 steps have been reported. However, their biosynthetic pathway remains undescribed. A minimal biosynthetic gene cluster (BGC), named bip, encoding a sesquiterpene cyclase (BipA) and a cytochrome P450 (BipB) is characterized to produce such complex sesquiterpenoids with multiple carbon skeletons based on enzymatic assays, heterologous expression, and precursor experiments. BipA is demonstrated as a versatile cyclase with (−)-sativene as the dominant product and (−)-isosativene and (−)-longifolene as minor ones. BipB is capable of hydroxylating different enantiomeric sesquiterpenes, such as (−)-longifolene and (+)-longifolene, at C-15 and C-14 in turn. The C-15- or both C-15- and C-14-hydroxylated products are then further oxidized by unclustered oxidases, resulting in a structurally diverse array of sesquiterpenoids. Bioinformatic analysis reveals the BipB homologues as a discrete clade of fungal sesquiterpene P450s. These findings elucidate the concise and divergent biosynthesis of such intricate bridged polycyclic sesquiterpenoids, offer valuable biocatalysts for biotransformation, and highlight the distinct biosynthetic strategy employed by nature compared to chemical synthesis