Search

Microeukaryotic plankton communities are keystone components for keeping aquatic primary productivity. Currently, variations in microeukaryotic plankton diversity have often been explained by local ecological factors but not by evolutionary constraints. We used amplicon sequencing of 100 water samples across five years to investigate the ecological preferences of the microeukaryotic plankton community in a subtropical riverine ecosystem. We found that microeukaryotic plankton diversity was less associated with bacterial abundance (16S rRNA gene copy number) than bacterial diversity. Further, environmental effects exhibited a larger influence on microeukaryotic plankton community composition than bacterial community composition, especially at fine taxonomic levels. The evolutionary constraints of microeukaryotic plankton community increased with decreasing taxonomic resolution (from 97% to 91% similarity levels), but not significant change from 85% to 70% similarity levels. However, compared with the bacterial community, the evolutionary constraints were shown to be more affected by environmental variables. This study illustrated possible controlling environmental and bacterial drivers of microeukaryotic diversity and community assembly in a subtropical river, thereby indirectly reflecting on the quality status of the water environment by providing new clues on the microeukaryotic community assembly.

Microeukaryotic plankton communities are keystone components for keeping aquatic primary productivity. Currently, variations in microeukaryotic plankton diversity have often been explained by local ecological factors but not by evolutionary constraints. We used amplicon sequencing of 100 water samples across five years to investigate the ecological preferences of the microeukaryotic plankton community in a subtropical riverine ecosystem. We found that microeukaryotic plankton diversity was less associated with bacterial abundance (16S rRNA gene copy number) than bacterial diversity. Further, environmental effects exhibited a larger influence on microeukaryotic plankton community composition than bacterial community composition, especially at fine taxonomic levels. The evolutionary constraints of microeukaryotic plankton community increased with decreasing taxonomic resolution (from 97% to 91% similarity levels), but not significant change from 85% to 70% similarity levels. However, compared with the bacterial community, the evolutionary constraints were shown to be more affected by environmental variables. This study illustrated possible controlling environmental and bacterial drivers of microeukaryotic diversity and community assembly in a subtropical river, thereby indirectly reflecting on the quality status of the water environment by providing new clues on the microeukaryotic community assembly.

Air pollution is a major environmental issue in Hong Kong, and ozone (O3) is one of the most concerned pollutants because of its notable rising trend during the past three decades. Volatile organic compounds (VOCs) are the main precursors to O3 formation, but their characteristics over coastal water in Hong Kong were not well studied due to a lack of observations. We conducted sixteen cruise campaigns and collected 109 VOC canister samples over Hong Kong coastal water between August 2020 and July 2021. 95 VOC species were analyzed from the samples by a GC-MSD/ECD/FID system to investigate their concentration distributions, reactivities, and potential emission sources. The average concentrations of alkanes, alkenes, aromatics, alkynes, halocarbons, and alkyl nitrates over Hong Kong coastal water were 8.94 ± 5.64 ppbv, 1.18 ± 1.09 ppbv, 1.90 ± 1.48 ppbv, 1.05 ± 0.63 ppbv, 3.82 ± 1.23 ppbv, and 0.09 ± 0.04 ppbv, respectively. Source apportionment results from positive matrix factorization (PMF) analysis suggest that liquefied petroleum gas (LPG) usage (24.9%), biomass burning (19.4%), gasoline evaporation (16.2%), ship emissions (15.6%), solvent usage (14.8%), and aged air mass (9.1%) were main VOC sources over Hong Kong coastal water during the study period. Toluene, ethene and m + p-xylene were found to be the top contributors to the O3 formation over Hong Kong coastal water, while isoprene had less O3 formation contributions compared with land measurements. This study provides important insights into the reactivities and sources of VOCs over Hong Kong coastal water and serves as a stepping stone to understanding the VOC characteristics and O3 formation mechanisms in a complex coastal environment.

The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompts and (2) how to select proper demonstrations. Existing methods, however, fail to address these challenges appropriately. On the one hand, they usually recast RTE task to text-to-text prompting formats, which is unnatural and results in a mismatch between the output format at the pre-training time and the inference time for large language models (LLMs). On the other hand, they only utilize surface natural language features and lack consideration of triple semantics in sample selection. These issues are blocking improved performance in ICL for RTE, thus we aim to tackle prompt designing and sample selection challenges simultaneously. To this end, we devise a tabular prompting for RTE (\textsc{TableIE}) which frames RTE task into a table generation task to incorporate explicit structured information into ICL, facilitating conversion of outputs to RTE structures. Then we propose instructive in-context learning (I$^2$CL) which only selects and annotates a few samples considering internal triple semantics in massive unlabeled samples., Comment: LREC-COLING 2024

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://github.com/Mzzzhu/CMNEE., Comment: 13 pages, 7 figures, accepted to LREC-COLING 2024

Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun frequency and lower information density in dialogues. However, existing DRE methods still suffer from two serious issues: (1) hard to capture long and sparse multi-turn information, and (2) struggle to extract golden relations based on partial dialogues, which motivates us to discover more effective methods that can alleviate the above issues. We notice that the rise of large language models (LLMs) has sparked considerable interest in evaluating their performance across diverse tasks. To this end, we initially investigate the capabilities of different LLMs in DRE, considering both proprietary models and open-source models. Interestingly, we discover that LLMs significantly alleviate two issues in existing DRE methods. Generally, we have following findings: (1) scaling up model size substantially boosts the overall DRE performance and achieves exceptional results, tackling the difficulty of capturing long and sparse multi-turn information; (2) LLMs encounter with much smaller performance drop from entire dialogue setting to partial dialogue setting compared to existing methods; (3) LLMs deliver competitive or superior performances under both full-shot and few-shot settings compared to current state-of-the-art; (4) LLMs show modest performances on inverse relations but much stronger improvements on general relations, and they can handle dialogues of various lengths especially for longer sequences., Comment: IJCAI 2024

Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-context learning (ICL) capability for general zero and few-shot learning, recent studies indicate that current LLMs still struggle with zero and few-shot RE. Previous studies are mainly dedicated to design prompt formats and select good examples for improving ICL-based RE. Although both factors are vital for ICL, if one can fundamentally boost the ICL capability of LLMs in RE, the zero and few-shot RE performance via ICL would be significantly improved. To this end, we introduce \textsc{Micre} (\textbf{M}eta \textbf{I}n-\textbf{C}ontext learning of LLMs for \textbf{R}elation \textbf{E}xtraction), a new meta-training framework for zero and few-shot RE where an LLM is tuned to do ICL on a diverse collection of RE datasets (i.e., learning to learn in context for RE). Through meta-training, the model becomes more effectively to learn a new RE task in context by conditioning on a few training examples with no parameter updates or task-specific templates at inference time, enabling better zero and few-shot task generalization. We experiment \textsc{Micre} on various LLMs with different model scales and 12 public RE datasets, and then evaluate it on unseen RE benchmarks under zero and few-shot settings. \textsc{Micre} delivers comparable or superior performance compared to a range of baselines including supervised fine-tuning and typical in-context learning methods. We find that the gains are particular significant for larger model scales, and using a diverse set of the meta-training RE datasets is key to improvements. Empirically, we show that \textsc{Micre} can transfer the relation semantic knowledge via relation label name during inference on target RE datasets., Comment: IJCAI 2024

Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL) abilities in various tasks, they still suffer from poor performances compared to most supervised fine-tuned RE methods. Utilizing ICL for RE with LLMs encounters two challenges: (1) retrieving good demonstrations from training examples, and (2) enabling LLMs exhibit strong ICL abilities in RE. On the one hand, retrieving good demonstrations is a non-trivial process in RE, which easily results in low relevance regarding entities and relations. On the other hand, ICL with an LLM achieves poor performance in RE while RE is different from language modeling in nature or the LLM is not large enough. In this work, we propose a novel recall-retrieve-reason RE framework that synergizes LLMs with retrieval corpora (training examples) to enable relevant retrieving and reliable in-context reasoning. Specifically, we distill the consistently ontological knowledge from training datasets to let LLMs generate relevant entity pairs grounded by retrieval corpora as valid queries. These entity pairs are then used to retrieve relevant training examples from the retrieval corpora as demonstrations for LLMs to conduct better ICL via instruction tuning. Extensive experiments on different LLMs and RE datasets demonstrate that our method generates relevant and valid entity pairs and boosts ICL abilities of LLMs, achieving competitive or new state-of-the-art performance on sentence-level RE compared to previous supervised fine-tuning methods and ICL-based methods., Comment: IJCAI 2024

The mode I fracture toughness is a critical parameter which defines the rock's resistance to crack propagation, especially in hydraulic fracturing. Recently, Supercritical carbon dioxide (Sc-CO2) has been proposed as a fracturing fluid candidate in hydraulic fracturing stimulations of shale reservoirs. However, its effects on fracture toughness transition and crack propagation behaviors have not been understood well. In this study, we performed a series of semicircular bend speci-mens (SCB) before the Longmaxi shale specimens' saturation. Three-point bending tests along divider orientation showed that after Sc-CO2 saturation, mode I fracture toughness (KIC), elastic modulus (E) and absorbed energy (U-e) of shale were decreased by 22.1%, 24.5% and 44.3%, respectively. High speed camera images indicated that after Sc-CO2 saturation, mode I crack directly propagated straight along artificial pre-crack direction, decreasing the degree of crack deviation as in KIC. The results of Cronos high-precision 3D scanning system and scanning elec-tron microscopy (SEM) revealed complicated fracture mechanisms (transgranular, intergranular and mutual coupling crack mechanisms) of shale after Sc-CO2 saturation, which reduced the roughness and area of fracture crack surface. The generation of pore and cracks was the main reason for the decrease of shale resistance to fracture. Furthermore, Sc-CO2 saturated shale specimens only needed to absorb less energy to more rapidly cause the initiation and propagation of mode I cracks with the main fracture mode being transgranular cracks.

This work examines the self-collimation effect of silk materials on fluorescence emission/detection. A macroscopic regulation strategy, coupled with meso-reconstruction and meso-functionalization, is adopted to amplify the fluorescence emission of organic fluorescent dyes (i.e., Rhodamine 6G (R6G)) using silk photonic crystal (PC) films. The fluorescence emission can be linearly enhanced or inhibited by a PC as a result of the photonic bandgap coupling with the excitation light and/or emission light. Depending on the design of the silk fluorescence collimator, the emission can reach 49.37 times higher than the control. The silk fluorescence collimator can be applied to achieve significant benefits: for instance, as a humidity sensor, it provides good reproducibility and a sensitivity of 28.50 a.u./% relative humidity, which is 80.78 times higher than the sensitivity of the control, and as a novel curtain, it raises the energy conversion efficiency of the semitransparent dye-sensitized solar cells (DSSCs) by 16%.

This work examines the self-collimation effect of silk materials on fluorescence emission/detection. A macroscopic regulation strategy, coupled with meso-reconstruction and meso-functionalization, is adopted to amplify the fluorescence emission of organic fluorescent dyes (i.e., Rhodamine 6G (R6G)) using silk photonic crystal (PC) films. The fluorescence emission can be linearly enhanced or inhibited by a PC as a result of the photonic bandgap coupling with the excitation light and/or emission light. Depending on the design of the silk fluorescence collimator, the emission can reach 49.37 times higher than the control. The silk fluorescence collimator can be applied to achieve significant benefits: for instance, as a humidity sensor, it provides good reproducibility and a sensitivity of 28.50 a.u./% relative humidity, which is 80.78 times higher than the sensitivity of the control, and as a novel curtain, it raises the energy conversion efficiency of the semitransparent dye-sensitized solar cells (DSSCs) by 16%.