Effect of land use on soil nematode community composition and co-occurrence network relationship

Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

Review of Research on English Translation of Chinese Running Sentences

In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Sentence及其结构研究——以古典主义时期作品为例

文章以sentence为研究对象,一方面对国内外曲式学教材及相关文献展开概念比较、辨析与理论梳理,阐明其基本概念与内部结构;另一方面则以古典主义时期作品为分析实例,讨论这种特定主题(句法)的“结构范型”、并进一步对其“结构变形”的情况进行归类。

Inversion tillage with straw incorporation affects the patterns of soil microbial co-occurrence and multi-nutrient cycling in a Hapli-Udic Cambisol

Inversion tillage with straw amendment is widely applied in northeastern China, and it can substantially increase the storage of carbon and improve multiple subsoil functions. Soil microorganisms are believed to be the key to this process,but research into their role in subsoil amelioration is limited. Therefore, a field experiment was conducted in 2018 in a region in northeastern China with Hapli-Udic Cambisol using four treatments: conventional tillage(CT, tillage to a depth of 15 cm with no straw incorporation), straw incorporation with conventional tillage(SCT, tillage to a depth of 15 cm),inversion tillage(IT, tillage to a depth of 35 cm) and straw incorporation with inversion tillage(SIT, tillage to a depth of 35 cm). The soils were managed by inversion to a depth of 15 or 35 cm every year after harvest. The results indicated that SIT improved soil multi-nutrient cycling variables and increased the availability of key nutrients such as soil organic carbon, total nitrogen, available nitrogen, available phosphorus and available potassium in both the topsoil and subsoil.In contrast to CT and SCT, SIT created a looser microbial network structure but with highly centralized clusters by reducing the topological properties of average connectivity and node number, and by increasing the average path length and the modularity. A Random Forest analysis found that the average path length and the clustering coefficient were the main determinants of soil multi-nutrient cycling. These findings suggested that SIT can be an effective option for improving soil multi-nutrient cycling and the structure of microbial networks, and they provide crucial information about the microbial strategies that drive the decomposition of straw in Hapli-Udic Cambisol.

Temporal characteristics of algae-denitrifying bacteria co-occurrence patterns and denitrifier assembly in epiphytic biofilms on submerged macrophytes in Caohai Lake,SW China

Denitrifying bacteria in epiphytic biofilms play a crucial role in nitrogen cycle in aquatic habitats.However,little is known about the connection between algae and denitrifying bacteria and their assembly processes in epiphytic biofilms.Epiphytic biofilms were collected from submerged macrophytes(Patamogeton lucens and Najas marina L.)in the Caohai Lake,Guizhou,SW China,from July to November 2020 to:(1)investigate the impact of abiotic and biotic variables on denitrifying bacterial communities;(2)investigate the temporal variation of the algae-denitrifying bacteria co-occurrence networks;and(3)determine the contribution of deterministic and stochastic processes to the formation of denitrifying bacterial communities.Abiotic and biotic factors influenced the variation in the denitrifying bacterial community,as shown in the Mantel test.The co-occurrence network analysis unveiled intricate interactions among algae to denitrifying bacteria.Denitrifying bacterial community co-occurrence network complexity(larger average degrees representing stronger network complexity)increased continuously from July to September and decreased in October before increasing in November.The co-occurrence network complexity of the algae and nirS-encoding denitrifying bacteria tended to increase from July to November.The co-occurrence network complexity of the algal and denitrifying bacterial communities was modified by ammonia nitrogen(NH_(4)^(+)-N)and total phosphorus(TP),pH,and water temperature(WT),according to the ordinary least-squares(OLS)model.The modified stochasticity ratio(MST)results reveal that deterministic selection dominated the assembly of denitrifying bacterial communities.The influence of environmental variables to denitrifying bacterial communities,as well as characteristics of algal-bacterial co-occurrence networks and the assembly process of denitrifying bacterial communities,were discovered in epiphytic biofilms in this study.The findings could aid in the appropriate understanding and use of epiphytic biofilms denitrification function,as well as the enhancement of water quality.

Environment drives the co-occurrence of bacteria and microeukaryotes in a typical subtropical bay

The co-occurrence of bacteria and microeukaryote species is a ubiquitous ecological phenomenon,but there is limited cross-domain research in aquatic environments.We conducted a network statistical analysis and visualization of microbial cross-domain co-occurrence patterns based on DNA sampling of a typical subtropical bay during four seasons,using high-throughput sequencing of both 18S rRNA and 16S rRNA genes.First,we found obvious relationships between network stability and network complexity indices.For example,increased cooperation and modularity were found to weaken the stability of cross-domain networks.Secondly,we found that bacterial operational taxonomic units(OTUs)were the most important contributors to network complexity and stability as they occupied more nodes,constituted more keystone OTUs,built more connections,more importantly,ignoring bacteria led to greater variation in network robustness.Gammaproteobacteria,Alphaproteobacteria,Bacteroidetes,and Actinobacteria were the most ecologically important groups.Finally,we found that the environmental drivers most associated with cross-domain networks varied across seasons(in detail,the network in January was primarily constrained by temperature and salinity,the network in April was primarily constrained by depth and temperature,the network in July was mainly affected by depth,temperature,and salinity,depth was the most important factor affecting the network in October)and that environmental influence was stronger on bacteria than on microeukaryotes.

Community composition,co-occurrence,and environmental drivers of bacterioplankton community in surface and 50-m water layers in the subarctic North Pacific

The Western Subarctic Gyre(WSG)is one of the two gyre-systems in the subarctic North Pacific known for high nutrient and low-chlorophyll waters.However,the bacterioplankton in marine water of this area,either in terms of the taxonomic composition or functional structure,remains relatively unexplored.A total of 22 sampling sites from two water layers(surface water,SW and 50-m layer water,FW)were collected in this area.The physiochemical parameters of waters,Synechococcus,and bacterial density,as well as the bacterioplankton community composition and distribution pattern,were analyzed.The nutrient concentrations of DIN,DIP,and DSi,Chl-a concentration,and the average abundance of heterobacteria in FW were higher than those in SW.However,temperature and the average abundance of Synechococcus and pico-eukaryotes were higher in SW.A total of 3269 OTUs were assigned,and 2123OTUs were commonly shared;moreover,similar alpha diversity patterns were observed in both SW and FW.The bacterioplankton community showed significantly obvious correlation with salinity,DIP,DIN,and Chl a in both SW and FW.Proteobacteria,Cyanobacteria,Bacteroidota,Actinobacteriota,and Firmicutes were the main phyla while Synechococcus_CC9902,Psychrobacter,and Sulfitobacter were the dominant genera in each sampling site.Most correlations that happened between the OTUs in the cooccurrence network were positive and inter-module.Higher edges and graph density were found in SW,indicating that more correlations occurred,and the community was more complex in SW.This study provided novel knowledge on the bacterioplankton community structure and the correlation characteristics in WSG.

基于Sentence-BERT的专利技术主题聚类研究——以人工智能领域为例

[研究目的]将Sentence-BERT模型应用于专利技术主题聚类,解决专利文献为突出新颖性,常使用独特技术术语造成词汇向量语义特征稀疏的问题。[研究方法]以人工智能领域2015年-2019年的22370篇专利为实验数据。首先,采用Sentence-BERT算法对专利文献摘要文本进行向量化表示;其次,对向量化矩阵进行数据降维,利用HDBSCAN方式寻找原始数据中的高密度簇;最后,识别类簇文本集合中的主题特征,并完成主题呈现。[研究结论]对比LDA主题模型、K-means、doc2vec等方法,本文的实验结果提高了主题划分的细粒度和精确度,获得了较好的主题一致性。如何采用fine-tune策略进一步提升模型的效果,是未来该方法进一步深入探索的方向。

A Study of Nominal Predicate Sentences Under the Framework of Cognitive Grammar

Cognitive grammar,as a linguistic theory that attaches importance to the relationship between language and thinking,provides us with a more comprehensive way to understand the structure,semantics and cognitive processing of noun predicate sentences.Therefore,under the framework of cognitive grammar,this paper tries to analyze the semantic connection and cognitive process in noun predicate sentences from the semantic perspective and the method of example theory,and discusses the motivation of the formation of this construction,so as to provide references for in-depth analysis of the cognitive laws behind noun predicate sentences.

基于co-occurrence相似度的聚类集成方法

首先提出了一种基于属性值的co-occurrence相似度概念,通过对其进一步的研究,提出了3个等价性表述;然后对属性值之间的co-occurrence相似度进行引申,给出了数据对象之间co-occurrence相似度的定义,并将其成功应用到聚类集成方法中。利用co-occurrence相似度在计算某个初始聚类结果中数据对象之间的相似度时,充分考虑了其他初始聚类结果和该初始聚类结果之间的相互影响和联系。实验表明,基于co-occurrence相似度的聚类集成(CSCE)方法能有效识别数据之间的细微结构,有助于提高聚类集成的效果。

基于加权co-occurrence矩阵的聚类集成算法

聚类集成是数据挖掘研究的一个热点。它是利用同一数据集的多个聚类划分集成在一起,以提高聚类分析的性能。当前相关研究大多没有考虑进行集成的聚类成员的质量,因此较差的成员会对集成结果产生不良影响。文中提出了一种基于加权co-occurrence矩阵的聚类集成算法(WCSCE)。该方法首先计算出聚类成员基于属性值的co-occurrence矩阵,然后对聚类成员的质量进行简单评价并赋予权重,生成加权co-occurrence矩阵,进而产生集成结果。最后通过实验验证了该算法的有效性,并提高了聚类质量。

融合Sentence-BERT和LDA的评论文本主题识别

[目的/意义]为了解决评论文本主题识别时语义描述不充分以及学习到的主题语义连贯性不强等问题。本文将Sentence-BERT句子嵌入模型和LDA模型相结合,提升评论文本主题的语义性。[方法/过程]采用Sentence-BERT模型获取评论文本句子层面的向量特征,同时,采用LDA模型获取评论文本的概率主题向量,随后使用自动编码器连接两组向量,运用K-means算法对潜在空间向量进行聚类,从类簇中获取上下文主题信息。[结果/结论]通过对评论文本数据集的实验,本文方法可以较好地获得具有语义信息的主题词。Sentence-BERT模型与LDA结合,增加了模型的复杂性。通过对比,本文方法获得的主题一致性指标(Coherence)优于目前常见的评论文本主题识别方法。

CiteOpinion: Evidence-based Evaluation Tool for Academic Contributions of Research Papers Based on Citing Sentences

Purpose:To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers,and to provide an evidencebased tool for evaluating the academic value of cited papers.Design/methodology/approach:CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers;it starts with an analysis on the citing sentences,then it identifies major academic contribution points of the cited paper,positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves(problems,methods,conclusions,etc.),and sentiment analysis and topic clustering.Findings:Citing sentences in a citing paper contain substantial evidences useful for academic evaluation.They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation,beyond simple citation statistics.Practical implications:The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers,research teams,and institutions.Originality/value:No other similar practical tool is found in papers retrieved.Research limitations:There are difficulties in acquiring full text of citing papers.There is a need to refine the calculation based on the sentiment scores of citing sentences.Currently,the tool is only used for academic contribution evaluation,while its value in policy studies,technical application,and promotion of science is not yet tested.

Co-occurrence Patterns of Above-ground and Below-ground Mite Communities in Farmland of Sanjiang Plain, Northeast China

One of the fundamental questions in community ecology is whether communities are random or formed by deterministic mechanisms. Although many efforts have been made to verify non-randomness in community structure, little is known with regard to co-occurrence patterns in above-ground and below-ground communities. In this paper, we used a null model to test non-randomness in the structure of the above-ground and below-ground mite communities in farmland of the Sanjiang Plain, Northeast China. Then, we used four tests for non-randomness to recognize species pairs that would be demonstrated as significantly aggregated or segregated co-occurrences of the above-ground and below-ground mite communities. The pattern of the above-ground mite commu- nity was significantly non-random in October, suggesting species segregation and hence interspecific competition. Additionally, species co-occurrence patterns did not differ from randomness in the above-ground mite community in August or in below-ground mite com- munities in August and October. Only one significant species pair was detected in the above-ground mite community in August, while no significant species pairs were recognized in the above-ground mite community in October or in the below-ground mite communities in August and October. The results indicate that non-randomness and significant species pairs may not be the general rule in the above-ground and below-ground mite communities in farmland of the Sanjiang Plain at the fine scale.

Markedness and UG in Chinese Children's Acquisition of One-word and Negative Sentences

Thepresentstudyisaninvestigationandanalysisoftherelationshipbetweenmarkednessandfirstlanguageacquisitionsequence,asshowninthecasesofone-wordandnegativesentences.Hereourobjectivesaretoargueforthepriorityofunmarkednessovermarkednessintheacquisitionsequ...

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model

AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

The Contrasts of English Sentences and Chinese Sentences and Translation Skills

Since the opening-up policy was carried out in 1979, every facility of social modernized construction has developed at high speed; meanwhile, the need of English is increased year by year. The occasion and scope of using it are expanded with the communications among countries. Therefore, English has become the generally international language in our country; in particular, translation plays an important and irreplaceable part in English to convey information. This paper aims to introduce the contrasts of English sentences and Chinese sentences and discuss some skills of translating each other. Though it is not complete and authoritative, yet it may help some people to understand the differences between two languages and to grasp some practical skills.

Combined effects of habitat and interspec ificinteraction define co-occurrence patterns of sympatric Galliformes

Background:Disentangling the relative importance of environmental variables and interspecific interaction in modulating co-occurrence patterns of sympatric species is essential for understanding the mechanisms of community assembly and biodiversity. For the two sympatric Galliformes, Silver Pheasants (Lophura nycthemera) and Whitenecklaced Partridges (Arborophila gingica), we know little about the role of habitat use and interspecific interactions in modulating their coexistence. Methods:We adopted a probabilistic approach incorporating habitat preference and interspecific interaction using occupancy model to account for imperfect detection,and used daily activity pattern analysis to investigate the cooccurrence pattern of these two sympatric Galliformes in wet and dry seasons. Results: We found that the detection probability of Silver Pheasant and White-necklaced Partridge were related to habitat variables and interspecific interaction. The presence of Silver Pheasant increases the detection probability of White-necklaced Partridge in both the wet and dry season. However, the presence of White-necklaced Partridges increases the detection probability of Silver Pheasants in the wet season, but decreases the probability in the dry season. Further, Silver Pheasants were detected frequently in the sites of high values of enhanced vegetable index (EVI) in both the wet and dry season, and in sites away from human residential settlement in the wet season. Whitenecklaced partridges were mainly detected in low EVI sites. The site use probabilities of two Galliformes were best explained by habitat variables, Silver Pheasants and White-necklaced Partridges preferred steeper areas during the wet and dry season. Both species mainly occurred in low EVI areas during the wet season and occupied sites away from the resident settlement during the dry season. Moreover, the site use probabilities of two species had opposite relationships with forest canopy coverage. Silver Pheasants preferred areas with high forest canopy coverage whereas White-necklaced Partridges preferred low forest canopy coverage in the dry season, and vice versa in the wet season. Species interaction factor (SIF)corroborated weak evidence of the dependence of the site use of one species on that of the other in the either dry or wet season.Temporally, high overlapping of daily activity pattern indicated no significantly temporal niche differentiation between sympatric Galliformes in both wet and dry seasons. Conclusions:Our results demonstrated that the presence of two species influenced the detection probability interactively and there was no temporal partitioning in activity time between Silver Pheasants and White-necklaced Partridges in the wet and dry seasons.The site use probability of two Galliformes was best explained by habitat variables, especially the forest canopy coverage.Therefore, environmental variables and interspecific interaction are the leading drivers regulating the detection and site use probability and promoting co-occurrence of Silver Pheasants and White-necklaced Partridges.