The distribution and succession of microbial communities along the dispersion path of hydrothermal plumes has not been well investigated. In this study, we collected several types of samples from the Longqi hydrothermal field located on the Southwest Indian Ridge, including hydrothermal plumes at different stages of formation, a suite of water column samples across the non-buoyant hydrothermal plumes above this field, and a background seawater column approximately 350 km away from the hydrothermal field. Using CH 4 concentration anomalies, three non-buoyant plume samples between 2,535 and 2,735 m were identified within the water column. Microbial community compositions within these plumes and background seawater samples were examined based on the 16S rRNA genes and revealed significant variations and successions in community composition between different portions of the hydrothermal plumes. Near the vent orifice, representing the initial stage of plume formation, microbial populations were characterized by abundant and diverse putative vent-associated communities including (hyper)thermophiles such as Aquificaceae and Hydrogenothermaceae within the phylum Aquificae, and some epsilonproteobacterial chemolithoautotrophs such as Sulfurovum, Sulfurimonas, and Caminibacter. By contrast, in the rising buoyant plumes and adjacent seawaters, most vent-associated microbial taxa were still present but made only minor contributions to community composition. Some microbial taxa that are common in seawater columns such as alphaproteobacterial Sphingomonadaceae and SAR11 clade, deltaproteobacterial SAR324 clade, and gammaproteobacterial Pseudomonas, together with Sulfurimonas and SUP05 clade, became predominant. Members within the Sulfurimonas and SUP05 clade flourished with considerable abundance in the non-buoyant plumes, although these plumes were mainly composed of alphaproteobacterial Rhodobacteraceae, gammaproteobacterial Alteromonadaceae and Saccharospirillaceae putatively derived from the surrounding ambient seawater. We also analyzed archaeal components in the initial discharge and rising buoyant plume stages, with both primarily consisting of thaumarchaeal Nitrosopumilales

Causal event extraction (CEE) aims to identify and extract cause-effect event pairs from texts, which is a fundamental task in natural language processing. Recent research treat CEE as a sequence labeling problem. However, the linguistic complexity and ambiguity of textual description results in the low accuracy of extractors. To address the above issues, considering the prior knowledge like the causal network constructed based on the causal indicators, which can represent information transition between cause and effect, may helpful for CEE. In this article, we propose causality-associated graph neural network to incorporate in-domain knowledge by taking important causal words into account. External causal knowledge is modeled as causal associated graph (CAG). Then we use graph neural networks (GNN) to capture the complex relationship of intraevent mentions and interevent causality in a sentence based on the relationship obtained from CAG. Finally, sentence sequence and prior causal knowledge of GNN embedding are fed into multiscaled convolution and bidirectional long short-term memory networks. Experimental results on two datasets show that our method outperforms the state-of-the-art baseline.

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Event causality extraction is a challenging task in natural language processing (NLP), which plays an important role in event prediction, scene generation, question answering and textual entailment. Most existing methods focus on extracting singlescale (such as phrase) event causality, while fails to extract multi-scale (such as word, phrase, sentence) event causality. To fill the gap, we propose multi-scale event causality extraction via simultaneous knowledge-attention and convolutional neural network (KA-CNN). First, knowledge-attention takes N-gram embedding as input and takes semantic features, fused with prior knowledge through causal associative link network (CALN), as output. Second, multi-scale CNN is designed with word embedding as input and semantic feature of corpus as output. Third, bidirectional long short-term memory with conditional random field (BiLSTM + CRF) is conducted after concatenation of features from knowledge-attention and multi-scale CNN. Finally, we compare our results with other baselines. The experimental results show that our proposed method shows promising result in extracting multi-scale event causality.

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