Non-native species in marine protected areas: Global distribution patterns

Marine protected areas(MPAs)across various countries have contributed to safeguarding coastal and marine environments.Despite these efforts,marine non-native species(NNS)continue to threaten biodiversity and ecosystems,even within MPAs.Currently,there is a lack of comprehensive studies on the inventories,distribution patterns,and effect factors of NNS within MPAs.Here we show a database containing over 15,000 occurrence records of 2714 marine NNS across 16,401 national or regional MPAs worldwide.To identify the primary mechanisms driving the occurrence of NNS,we utilize model selection with proxies representing colonization pressure,environmental variables,and MPA characteristics.Among the environmental predictors analyzed,sea surface temperature emerged as the sole factor strongly associated with NNS richness.Higher sea surface temperatures are linked to increased NNS richness,aligning with global marine biodiversity trends.Furthermore,human activities help species overcome geographical barriers and migration constraints.Consequently,this influences the distribution patterns of marine introduced species and associated environmental factors.As global climate change continues to alter sea temperatures,it is crucial to protect marine regions that are increasingly vulnerable to intense human activities and biological invasions.

Seabed deposition and erosion change and influence factors in the Yangshan Deepwater Port over the years

The seabed scouring and silting are very important to the construction of port and waterway engineering. Seabed deposition and erosion change is complicated due to the influence of sediment supply, human activities and other factors. The Yangshan Deepwater Port is the new deep water harbor, which is an important part of the Shanghai International Shipping Service Center. Its construction has received much attention. At present, the water depth from the 1 st to the 3 rd harbor district is currently suitable under regular dredging and tidal current action. The fourth harbor district will be built in the world’s largest fully-automated deep water wharf. In the study, bathymetry change of the entire sea area of the Yangshan Deepwater Port and the 4 th harbor district(i.e.,Phase IV project) waters were analyzed quantitatively using multiyear bathymetric, hydrological and sediment data. The results show that from 1998 to 2010, seabed changes are characterized by large volumes of erosion and sedimentation, which the southern part was deposited and the northern part was eroded in the inner harbor waters, but the seabed of the Kezhushan inlet was eroded. Seabed changes of Phase IV project waters generally show a scour tendency in recent few years with the annual scour rate about 0.7 m. Among the many factors, the existence of Kezhushan inlet and its influence of the western water flow play an important positive role in water depth changes under the ebb tide action.

Enhancing low-resource cross-lingual summarization from noisy data with fine-grained reinforcement learning

Cross-lingual summarization(CLS)is the task of generating a summary in a target language from a document in a source language.Recently,end-to-end CLS models have achieved impressive results using large-scale,high-quality datasets typically constructed by translating monolingual summary corpora into CLS corpora.However,due to the limited performance of low-resource language translation models,translation noise can seriously degrade the performance of these models.In this paper,we propose a fine-grained reinforcement learning approach to address low-resource CLS based on noisy data.We introduce the source language summary as a gold signal to alleviate the impact of the translated noisy target summary.Specifically,we design a reinforcement reward by calculating the word correlation and word missing degree between the source language summary and the generated target language summary,and combine it with cross-entropy loss to optimize the CLS model.To validate the performance of our proposed model,we construct Chinese-Vietnamese and Vietnamese-Chinese CLS datasets.Experimental results show that our proposed model outperforms the baselines in terms of both the ROUGE score and BERTScore.

6-Phosphogluconate dehydrogenase 2 bridges the OPP and shikimate pathways to enhance aromatic amino acid production in plants

The oxidative pentose phosphate(OPP)pathway provides metabolic intermediates for the shikimate pathway and directs carbon flow to the biosynthesis of aromatic amino acids(AAAs),which serve as basic protein building blocks and precursors of numerous metabolites essential for plant growth.However,genetic evidence linking the two pathways is largely unclear.In this study,we identified 6-phosphogluconate dehydrogenase 2(PGD2),the rate-limiting enzyme of the cytosolic OPP pathway,through suppressor screening of arogenate dehydrogenase 2(adh2)in Arabidopsis.Our data indicated that a single amino acid substitution at position 63(glutamic acid to lysine)of PGD2 enhanced its enzyme activity by facilitating the dissociation of products from the active site of PGD2,thus increasing the accumulation of AAAs and partially restoring the defective phenotype of adh2.Phylogenetic analysis indicated that the point mutation occurred in a well-conserved amino acid residue.Plants with different amino acids at this conserved site of PGDs confer diverse catalytic activities,thus exhibiting distinct AAAs producing capability.These findings uncover the genetic link between the OPP pathway and AAAs biosynthesis through PGD2.The gain-of-function point mutation of PGD2 identified here could be considered as a potential engineering target to alter the metabolic flux for the production of AAAs and downstream compounds.

Identification of Highly Repetitive Enhancers with Long-range Regulation Potential in Barley via STARR-seq

Enhancers are DNA sequences that can strengthen transcription initiation.However,the global identification of plant enhancers is complicated due to uncertainty in the distance and orientation of enhancers,especially in species with large genomes.In this study,we performed self-transcribing active regulatory region sequencing(STARR-seq)for the first time to identify enhancers across the barley genome.A total of 7323 enhancers were successfully identified,and among 45 randomly selected enhancers,over 75%were effective as validated by a dual-luciferase reporter assay system in the lower epidermis of tobacco leaves.Interestingly,up to 53.5%of the barley enhancers were repetitive sequences,especially transposable elements(TEs),thus reinforcing the vital role of repetitive enhancers in gene expression.Both the common active mark H3K4me3 and repressive mark H3K27me3 were abundant among the barley STARR-seq enhancers.In addition,the functional range of barley STARR-seq enhancers seemed much broader than that of rice or maize and extended to±100 kb of the gene body,and this finding was consistent with the high expression levels of genes in the genome.This study specifically depicts the unique features of barley enhancers and provides available barley enhancers for further utilization.

Semantic-aware entity alignment for low resource language knowledge graph

Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for EA.Most EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language models.However,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource KGs.Recently,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often ignored.To address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity alignment.First,we generate pseudo sentences according to the relation triples and produce representations using pre-trained models.Second,our approach explores semantic information from the connected relations by a graph neural network.Our model captures expanded feature information from KGs.Experimental results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.

CeO_(2)nanoparticles-decorated CoP nanocubes for accelerating alkaline electrocatalytic oxygen evolution reaction

Constructing heterojunction interface as an active catalyst is an effective strategy to boost electrocatalytic activity of oxygen evolution reaction(OER).Herein,we report an interfacial CoP/CeO_(2)heterostructure catalyst constructed by interface engineering and selective phosphorization procedure.X-ray photoelectron spectroscopy(XPS)suggests that coupling CeO_(2)nanoparticles on the surface of CoP will generate interfacial interaction at the two-phase interface,resulting in electron transfer between CoP and CeO_(2)components at the interface.Benefitting from the interfacial interaction,large exposed interface area,and luxuriant mesopores structure,CoP/CeO_(2)shows fascinating alkaline OER performance.At the current densities of 10 and 50 mA cm^(−2),the optimal CoP/CeO_(2)heterojunction exhibits lower overpotential(257 and 298 mV)than either CoP(288 and 354 mV)or RuO_(2)(305 and 409 mV).This work provides a facile synthetic protocol for constructing heterostructure interfaces to improve OER performance.

Exploiting comments information to improve legal public opinion news abstractive summarization

Automatically generating a brief summary for legal-related public opinion news(LPO-news,which contains legal words or phrases)plays an important role in rapid and effective public opinion disposal.For LPO-news,the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments.Consequently,we investigate the task of comment-aware abstractive text summarization for LPO-news,which can generate salient summary by learning pivotal case elements from the reader comments.In this paper,we present a hierarchical comment-aware encoder(HCAE),which contains four components:1)a traditional sequenceto-sequence framework as our baseline;2)a selective denoising module to filter the noisy of comments and distinguish the case elements;3)a merge module by coupling the source article and comments to yield comment-aware context representation;4)a recoding module to capture the interaction among the source article words conditioned on the comments.Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog,and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.

Linguistic feature template integration for Chinese-Vietnamese neural machine translation

1 Introduction and main contributions Template-based approaches have achieved significant progress in low-resource neural machine translation(NMT)recently[1],such as the efficient works,NMT-GTM[2],SoftPrototype[3],etc.However,most previous works only retrieve target sentence as template to generate translation,neglecting the utilization of linguistic feature that contained in the source sentence and template.