Neutral Community Theory: How Stochasticity and Dispersal-Limitation Can Explain Species Coexistence

Neutral community theory explains biodiversity, i.e. the coexistence of several species, as the result of a stochastic balance between immigration and extinction on a local level, and between speciation and extinction on a regional level. The most popular model, presented by Hubbell in 2001, has seen many analytical developments in recent years, which can be used in model analysis, model testing and model comparison. We review these developments here, and present alternative derivations and shine previously unnoticed lights on them.     

Families,experiments, and nature: Learning science through project-based learning

This study brings together the research focused on science education through project-based learning (PBL). This learning project was carried out in a rural learning community and an attempt was made to adapt to the natural resources of the area by organizing educational outings, experimental activities, and encouraging the participation of families. The overall objective is to test the effectiveness of applying the PBL teaching methodology for learning science in a rural learning community. The methodology used has been qualitative, specifically, the participating research has been used and the information has been compiled in a field notebook. The results show that the didactic proposal had good results; showing that, in conclusion, science teaching today should be inclined toward more innovative educational methodologies such as PBL.     

Positive Effect of Ecological Restoration with Fabaceous Species on Microbial Activities of Former Guyanese Mining Sites

Understanding ecological trajectories after mine site rehabilitation is essential to develop relevant protocols adapted for gold mining sites. This study describes the influence of a range of mine site rehabilitation and revegetation protocols on soil physicochemical parameters and microbial activities related to carbon, nitrogen and phosphorus cycles. We sampled soil from six rehabilitated mining sites in French Guiana with different plant cover (herbaceous, Cyperaceous, monoculture of Clitoria racemosa and Acacia mangium and association of C. racemosa and A. mangium). We measured the mineralization potential of organic matter by estimating the mineralization of carbon, nitrogen and phosphorus and the microbial catabolic diversity balance. The results showed an improvement in the quality of organic matter on revegetated sites with tree cover. On restored sites with fabaceous species, the microbial biomass is three times higher than non-restored sites, improving the rates of organic matter mineralization and restoring the catabolic diversity to the level of natural Guyanese soils. These results confirm that the establishment of fabaceous species under controlled conditions significantly improves the restoration of microbial communities in mining soils.     

Phylogenetic characterization of a mixed microbial community capable of degrading carbon tetrachloride

Two bacterial communities (NO92 and GBS) capable of degrading carbon tetrachloride (CT) were enriched from in-house CT-contaminated water. These communities are able to degrade CT in the presence of toluene. To characterize the community structure and diversity, one enrichment (NO92) was subjected to 16S ribosomal RNA (rRNA) gene-based molecular analysis. The 16S rRNA genes were amplified from the bulk genomic community DNA and cloned into plasmid vectors. Unique 16S rRNA gene clones, i.e., phylotypes, were detected by four tetrameric restriction enzymes. Together, 123 16S rRNA gene clones were obtained; thirty-one showed different restriction fragment length polymorphism (RFLP) patterns. About 73% of the clones belong to two dominant RFLP patterns. Phylogenetic analysis based on the partial 16S rRNA gene sequences of 10 major phylotypes showed that all the phylotypes that were sequenced were affiliated with the high G+C Gram-positive bacteria. Whereas seven of the phylotypes (∼80% of the clones) were closely related to Rhodococcus, the other three (∼5% of the clones) were related to Curtobacterium. These results suggest that this CT-degrading community is diverse but is predominated by closely related bacterial groups.     

高师化学微格教学二级指导模式的构建与实施

依据实践共同体和情境学习理论,构建了由研究生辅助指导的高师化学微格教学二级指导模式,并提出了相应的教学策略。研究生担任微格训练组组长,在导师指导下具体负责本科生平时的备课和教学训练。实践研究结果表明,该模式解决了普遍存在的微格训练课时少、师资不足、资源不足等问题,提高了本科生的教学设计能力和课堂教学技能。     

Postdiagenetic Changes in Kerogen Properties and Type by Bacterial Oxidation and Dehydrogenation

A significant part of organic carbon found on the earth is deposited as fossil organic matter in the lithosphere. The most important reservoir of carbon is shale rocks enriched with organic matter in the form of kerogen created during diagenesis. The purpose of this study was to analyze whether the bacterial communities currently inhabiting the shale rocks have had any impact on the properties and type of kerogen. We used the shale rock located on the Fore-Sudetic Monocline, which is characterized by oil-prone kerogen type II. We were able to show that shale rock inhabited by bacterial communities are characterized by oxidized and dehydrated kerogen type III (gas-prone) and type IV (nonproductive, residual, and hydrogen-free). Bacterial communities inhabiting shale rock were dominated by heterotrophs of the Proteobacteria, Firmicutes, and Actinobacteria phyla. Additionally, we detected a number of protein sequences in the metaproteomes of bacterial communities matched with enzymes involved in the oxidative metabolism of aliphatic and aromatic hydrocarbons, which may potentially contribute to the postdiagenetic oxidation and dehydrogenation of kerogen. The kerogen transformation contributes to the mobilization of fossil carbon in the form of extractable bitumen dominated by oxidized organic compounds.     

江西九连山常绿阔叶林的物种多样性分析

基于线路调查和样方调查的基础上,研究了江西九连山国家级自然保护区内常绿阔叶林的主要植被群落的α多样性和β多样性,α多样性测度指标主要采用Patrick丰富度指数、Shannon-wiener多样性指数、Simpson优势度指数、Pielou均匀度指数和Margalef丰富度指数等5个指标,β多样性采     

An interpretation of COVID-19 in Tokyo using a combination of SIR models

A year and a half has passed since the outbreak of the COVID-19 pandemic. Mathematical models to predict infection are expected and many studies have been conducted. In this study, a new interpretation was created that could reproduce the daily positive cases in Tokyo using only a simple SIR model. In addition, the data on the ratio of transfer to delta variants could also be simulated. It is anticipated that this interpretation will be a basis for the development of forecasting methods.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.