【生物信息】生物信息中High-Order高阶关系研究论文集锦（2）

生物信息中High-Order高阶关系研究论文集锦（2）

• 31. Hidden geometries in networks arising from cooperative self-assembly
• 32. Higher-Order Cellular Information Processing with Synthetic RNA Devices
• 33. Higher-order interactions capture unexplained complexity in diverse communities
• 34. Higher-order interactions stabilize dynamics in competitive network models
• 35. Higher-order organization of complex networks
• 36. High-Order Affinity Extension of Normalized Cut and Its Applications
• 37. High-order species interactions shape ecosystem diversity
• 38. How Structured Is the Entangled Bank? The Surprisingly Simple Organization of Multiplex Ecological Networks Leads to Increased Persistence and Resilience
• 39. Indirect effects drive coevolution in mutualistic networks
• 40. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections
• 41. Iterative random forests to discover predictive and stable high-order interactions
• 42. Linear Association in Compositional Data Analysis
• 43. Lotka-Volterra pairwise modeling fails to capture diverse pairwise microbial interactions
• 44. Making a microbiome: the many determinants of host-associated microbial community composition
• 45. Mapping the ecological networks of microbial communities
• 46. Metabolic dependencies drive species co-occurrence in diverse microbial communities
• 47. Metabolic division of labor in microbial systems
• 48. Metabolic interactions in microbial communities: untangling the Gordian knot
• 49. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules
• 50. Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation
• 51. Microbial community structure predicted by the stable marriage problem
• 52. Microbiome, metabolites and host immunity
• 53. Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes
• 54. Network structure embracing mutualism–antagonism continuums increases community robustness
• 55. Non-random coextinctions in phylogenetically structured mutualistic networks
• 56. Overcoming catastrophic forgetting in neural networks
• 57. Oral Biofilms: Pathogens, Matrix, and Polymicrobial Interactions in Microenvironments
• 58. Progress in and promise of bacterial quorum sensing research
• 59. Reconstruction of noise-driven nonlinear networks from node outputs by using high-order correlations
• 60. Representing higher-order dependencies in networks

31. Hidden geometries in networks arising from cooperative self-assembly

31. 协同自组装网络中隐藏的几何形状
    Abstract：Multilevel self-assembly involving small structured groups of nano-particles provides new routes to development of functional materials with a sophisticated architecture. Apart from the inter-particle forces, the geometrical shapes and compatibility of the building blocks are decisive factors in each phase of growth. Therefore, a comprehensive understanding of these processes is essential for the design of large assemblies of desired properties. Here, we introduce a computational model for cooperative self-assembly with simultaneous attachment of structured groups of particles, which can be described by simplexes (connected pairs, triangles, tetrahedrons and higher order cliques) to a growing network, starting from a small seed. The model incorporates geometric rules that provide suitable nesting spaces for the new group and the chemical affinity ν of the system to accepting an excess number of particles. For varying chemical affinity, we grow different classes of assemblies by binding the cliques of distributed sizes. Furthermore, to characterise the emergent large-scale structures, we use the metrics of graph theory and algebraic topology of graphs, and 4-point test for the intrinsic hyperbolicity of the networks. Our results show that higher Q-connectedness of the appearing simplicial complexes can arise due to only geometrical factors, i.e., for ν=0, and that it can be effectively modulated by changing the chemical potential and the polydispersity of the size of binding simplexes. For certain parameters in the model we obtain networks of mono-dispersed clicks, triangles and tetrahedrons, which represent the geometrical descriptors that are relevant in quantum physics and frequently occurring chemical clusters.
    摘要：涉及纳米颗粒的小型结构化组的多级自组装提供了具有复杂结构的功能材料的开发的新途径。除了粒子间作用力之外，构件的几何形状和相容性也是每个增长阶段的决定性因素。因此，对这些过程的全面了解对于设计具有所需特性的大型组件至关重要。在这里，我们引入了一个计算模型，用于协同自组装并同时附加粒子的结构化组，从单一种子（连接的对，三角形，四面体和更高阶的簇）可以描述到一个不断增长的网络，从一个小种子开始。该模型结合了几何规则，为新组提供合适的嵌套空间以及系统接受过量粒子的化学亲和力v。对于不同的化学亲和力，我们通过绑定分布式大小的团体来生成不同类型的程序集。此外，为了表征新兴的大规模结构，我们使用图论的理论和图的代数拓扑，并对网络的固有双曲性进行4点检验。我们的结果表明，由于仅有几何因子，即对于v = 0，可能会出现出现的单纯复合物的更高的Q连通性，并且可以通过改变化学势和结合单体大小的多分散性来有效地调节它。对于模型中的某些参数，我们获得了单分散点击，三角形和四面体网络，这些网络表示与量子物理学和经常发生的化学团簇相关的几何描述符。

Based on synthetic RNA devices, the system achieves higher-order cellular information processing.

基于合成RNA装置的高阶细胞信息处理体系设计与实现

Higher-order interaction mechanisms represent or account for unaccounted complexity features within varied social and environmental contexts.

33. 高阶相互作用捕获不同群落中未解释的复杂性
    Abstract：Natural communities are well known to be maintained by many complex processes. Despite this, the practical aspects of studying them often require some simplification, such as the widespread assumption that direct, additive competition captures the important details about how interactions between species impact community diversity. More complex non-additive ‘higherorder’ interactions are assumed to be negligible or absent. Notably, these assumptions are poorly supported and have major consequences for the accuracy with which patterns of natural diversity are modelled and explained. We present a mathematically simple framework for incorporating biologically meaningful complexity into models of diversity by including non-additive higher-order interactions. We further provide empirical evidence that such higher-order interactions strongly influence species’ performance in natural plant communities, with variation in seed production (as a proxy for per capita fitness) explained dramatically better when at least some higher-order interactions are considered. Our study lays the groundwork for a longoverdue shift in how species interactions are used to study the diversity of natural communities.
    摘要：众所周知，自然社区是由许多复杂的过程维护的。尽管如此，研究它们的实际方面往往需要一些简化，例如广泛的假设，即直接的加性竞争捕获了关于物种之间的相互作用如何影响社区多样性的重要细节。更复杂的非加性’高阶’相互作用被认为是可以忽略或不存在的。值得注意的是，这些假设得到很好的支持，并且对自然多样性模式的精确度进行建模和解释具有重大影响。我们提出了一个数学上简单的框架，通过包含非加性高阶相互作用，将具有生物意义的复杂性纳入多样性模型。我们进一步提供的经验证据表明，这种高阶相互作用强烈影响物种在自然植物群落中的表现，当至少考虑到一些高阶相互作用时，种子产量的变异（作为人均适应度的代表）将得到更好的解释。我们的研究为如何利用物种相互作用来研究自然界多样性奠定了基础。

Stabilization of dynamics within competitive networks is facilitated by higher-order interactions, which contribute to the system's stability.

在竞争网络模型中,高阶相互作用对动态稳定性的影响是一个值得深入探究的领域.生态学家一直致力于理解维持自然界中显着生物多样性所依赖的关键机制.一方面,基于简单假设的竞争者互动模型无法生成大规模生态系统的稳定持久性.另一方面,中性模型虽然通过物种迁入与物种生成维持多样性,但其预测的人口波动范围过小,并且物种丰度与其生命周期存在强烈的正相关性,这与实证结果存在显著差异.现有的理论框架尚缺乏能够解释大型生态系统中大量物种持续共存机制的竞争网络模型.在这里,我们发现由于高阶相互作用的稳定作用,具有丰富物种组成的生态系统能够实现持久性增长.具体而言,在其他条件不变的情况下,当系统处于平衡状态时,物种间的相互关系强度与其对群落稳定性的影响程度呈非线性关系:当某种特定物种的数量发生变化时,其影响不仅限于直接关联的两种物种之间,还波及至整个群落中的其他成员.我们还发现这些高阶相互作用对于维持群落结构具有决定性意义:即使是在高度动态变化的生态系统中,当系统处于平衡状态时,各物种间复杂的相互关系仍然能够维持群落的基本组成特性.此外我们还发现当引入更高层次的复杂性因素时系统的表现会更加复杂而难以预测.
摘要：生态学家一直致力于理解维持自然界显着生物多样性所依赖的关键机制。一方面基于简单假设的竞争者互动模型无法生成大规模生态系统的稳定持久性。另一方面中性模型虽然通过迁入与新物种生成维持多样性但其预测的人口波动范围过小并且物种丰度与其生命周期存在强烈的正相关这与实证结果存在显著差异。现有的理论框架尚缺乏能够解释大型生态系统中大量物种持续共存机制的竞争网络模型在这里我们发现由于高阶相互作用的作用丰富组成的生态系统能够实现持久性的增长具体而言当系统处于平衡状态时各物种间的相互关系强度与其对群落稳定性的影响程度呈非线性的关系:当某种特定物种的数量发生变化时其影响不仅限于直接关联的两种之间还波及至整个群落中的其他成员。

35. Higher-order organization of complex networks

35. 复杂网络的高阶组织
    Abstract：Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.
    摘要：网络是理解和建模物理学，生物学，神经科学，工程学和社会科学复杂系统的基本工具。众所周知，许多网络呈现丰富的低阶连接模式，可以在各个节点和边缘的层面捕获。 然而，复杂网络的高阶组织 - 在小型网络子图的层面上 - 仍然大部分是未知的。 在这里，我们基于高阶连接模式开发了一个用于聚类网络的通用框架。该框架为获得的聚类的最优性提供数学保证，并对具有数十亿边缘的网络进行缩放。该框架揭示了许多网络中的高阶组织，包括神经网络中的信息传播单元和交通网络中的枢纽结构。结果表明，网络展示了丰富的高阶组织结构，这些结构通过基于高阶连接模式的聚类而暴露出来。

36. High-Order Affinity Extension of Normalized Cut and Its Applications

36. 归一化切割的高阶亲和拓展及其应用
    Abstract：In the normalized cut (Ncut) process, it is crucial to construct an appropriate affnity matrix. The affnity matrix is generally limited to pairwise similarity relations. However, in practice, it is necessary to use high-order affnities in several computer vision applications such as motion segmentation. In this paper, by using high-order singular value decomposition techniques, we derive a high-order affnity model directly from the Ncut relaxation formula, called high-order normalized cut (HNcut). However, in practice, it cannot directly utilize the high-order affnity matrix because of the computational resources required. To address this issue, we adopt and improve various techniques to make the proposed method more practical such as sampling strategy. Finally, we analyze the upper error bound of our algorithm based on matrix perturbation theory. To demonstrate the performance of our HNcut, we compare it with some existing algorithms for the motion segmentation and face clustering problems.
    摘要：在归一化切割（Ncut）过程中，构建适当的关联矩阵至关重要。亲和度矩阵一般限于成对相似关系。然而，在实践中，有必要在几个计算机视觉应用中使用高阶亲和力，例如运动分割。本文利用高阶奇异值分解技术，直接从Ncut弛豫公式推导出一个高阶的有限性模型，称为高阶归一化切割（HNcut）。但是，实际上，由于需要计算资源，它不能直接利用高阶有限性矩阵。为了解决这个问题，我们采用并改进了各种技术，使得所提出的方法更加实用，如采样策略。最后，我们基于矩阵摄动理论分析了算法的上限误差。为了演示我们的HNcut的性能，我们将其与一些现有的运动分割算法和人脸聚类问题进行了比较。

37. High-order species interactions shape ecosystem diversity

37. 高阶物种相互作用塑造生态系统的多样性
    Abstract：Classical theories indicate that large communities tend to become unstable when species interact in pairs, thereby establishing an upper limit on ecosystem diversity. However, species interactions often occur in complex combinations, where the interaction between two species can be influenced by one or more additional species. Through simulations of community dynamics with random pairwise interactions, we observe an inverted relationship between diversity and stability for high-order interactions. Specifically, as a community grows in size, it becomes more sensitive to pairwise interactions but maintains a consistent level of sensitivity to three-way interactions. Interestingly, the sensitivity to four-way interactions diminishes as the number of species increases. Consequently, pairwise interactions contribute to sensitivity regarding species addition, while four-way interactions influence sensitivity concerning species removal. The interplay between these two types of interactions establishes both a lower and an upper bound on achievable species numbers. These findings underscore the critical role of high-order species interactions in determining the diversity of natural ecosystems.
    Abstract：Classical theories表明，在物种之间随机交互的情况下，大型社区往往会变得不稳定，并由此确定了生态系统多样性的上限。然而，在自然界中发现的物种相互作用往往发生在复杂的高级组合中：两个物种之间的相互作用受到一个或多个其他物种的影响。在这里通过模拟具有随机成对相互作用的社区动态变化我们发现，在高阶相互作用条件下物种多样性和稳定性之间存在相反的关系关系更为具体地讲随着群落中物种数量的增加群落对于成对相互作用变得更加敏感但群落对于三重相互作用的变化则始终保持不变而群落对于四重相互作用的变化则会逐渐减弱因此成对的相互作用导致对新物种引入的敏感性而四重相互作用则导致对现有物种移除的敏感性它们两者的变化共同构成了群落中能够维持的最大值与最小值这些发现突出了高阶种间关系对于自然生态系统多样性所起的关键作用。

What is the structural makeup of the entangled bank? Its surprisingly simple organizational structure, which leads to enhanced levels of resilience and endurance in complex ecological networks.

"Entangled Bank" 的组织形式是什么样的？令人难以置信的是简单的多维生态网络整合却带来了显著的持续时间和快速恢复能力
摘要：物种之间通过众多积极与消极的作用相互连接着众多生物间的相互作用构成了一个调节生态社区对干扰与气候变化等外部影响的重要网络系统。过去几十年中复杂生态系统的研究取得了巨大的进展但仍然缺乏数据与工具来更加严格地理解物种间多重相互作用类型的模式（即"多重网络"）以及这些模式对群落动态行为的影响。利用基于食物网统计建模的方法我们首次展示了包含营养与多样性的综合生态系统的整体架构图谱发现该系统展现出清晰而多维度的空间结构其分类学一致性和可预测性均源自物种特征表型分析结果表明这种非随机的多样关系映射到食物网上的模式不仅能够允许更高的物种持久性和更高的总生物量还能够促进群落对消亡事件的更强健性。
摘要：物种之间通过众多积极与消极的作用相互连接着众多生物间的相互作用构成了一个调节生态社区对干扰与气候变化等外部影响的重要网络系统。过去几十年中复杂生态系统的研究取得了巨大的进展但仍然缺乏数据与工具来更加严格地理解物种间多重相互作用类型的模式（即"多重网络"）以及这些模式对群落动态行为的影响。利用基于食物网统计建模的方法我们首次展示了包含营养与多样性的综合生态系统架构图谱发现该系统展现出清晰而多维度的空间结构其分类学一致性和可预测性均源自物种特征表型分析结果表明这种非随机的多样关系映射到食物网上的模式不仅能够允许更高的物种持久性和更高的总生物量还能够促进群落对消亡事件的更强健性。

39. Indirect effects drive coevolution in mutualistic networks

共生网络通过间接影响推动共同进化

Interaction networks, ecological stability, and collective antibiotic tolerance are key factors in polymicrobial infections.

多菌感染中的相互作用机制、生态系统稳定性和集体抗药性
Abstract：多种微生物感染构成了包含几种细菌的小型生态系统。通常情况下,这些细菌会被单独研究。然而,在是否存在抗生素的情况下,菌株之间的相互作用程度以及它们之间的相互作用是否会影响细菌的生长和生态系统的抵抗力尚不明确。我们系统地测量了72株细菌的完整生态相互作用网络,发现大多数相互作用基于进化关系聚类统计分析表明,在全局范围内竞争性和合作性的互惠互动较为丰富,而在单个宿主社区中则缺乏合作互动种群动态模型显示,通过参数化测量得出的结果表明,这些菌株间的互动限制了群落的稳定性临床分离物通常会互相保护临床相关的抗生素这一发现进一步证实了上述结论总之以上结果强调了生态交互对于指导多种微生物感染群体内菌株生长与存活的重要性

41. Iteratively constructed random forests aimed at discovering predictive, stable, and high-order interaction patterns.

41. 基于随机迭代森林发现可预测和稳定的高阶相互作用
    Abstract：Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with the same order of computational cost as the RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human-derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, third-order interactions, e.g., between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF rediscovered a central role of H3K36me3 in chromatin-mediated splicing regulation and identified interesting fifth- and sixth-order interactions, indicative of multivalent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens additional avenues of inquiry into the molecular mechanisms underlying genome biology.
    摘要：基因组学已经彻底改变了生物学，使整个转录组，整个蛋白质的全基因组结合位点以及许多其他分子过程成为可能。然而，单独的基因组测定测量作为较大分子机器的组分在体内相互作用的元素。了解这些高级相互作用如何驱动基因表达会带来实质性的统计挑战。基于随机森林（RF）和随机交叉树（RIT），并通过广泛的生物启发模拟，我们开发了迭代随机森林算法（iRF）。 iRF训练一个特征加权的决策树集合，以与RF相同的计算成本顺序来检测稳定的高阶相互作用。我们展示了iRF在两个预测问题中的高阶相互作用发现的效用：早期果蝇胚胎中的增强子活性和人源细胞系中初级转录物的选择性剪接。在果蝇中，20个成对转录因子相互作用中，iRF鉴定为稳定的（在超过一半的自举复制中返回），之前有80％报道为物理相互作用。此外，例如Zelda（Zld），Giant（Gt）和Twist（Twi）之间的三阶相互作用提出了作为后续实验的候选者的高阶关系。在人类细胞中，iRF重新发现了H3K36me3在染色质介导的剪接调控中的核心作用，并确定了有趣的五阶和六阶相互作用，表明多价核小体在剪接调控中具有特定作用。通过将相互作用的顺序与鉴定的计算成本分开，iRF开辟了探索基因组生物学分子机制的额外途径。

42. Linear Association in Compositional Data Analysis

成分数据分析中探讨了变量之间的线性相关性问题

Lotka-Volterra pairwise modeling cannot adequately describe multifarious two-way microbial interactions.

43. Lotka-Volterra成对模型未能捕捉到各种成对的微生物相互作用
    Abstract: Pairwise models are commonly used to describe many-species communities. In these models, an individual receives additive fitness effects from pairwise interactions with each species in the community (‘additivity assumption’). All pairwise interactions are typically represented by a single equation where parameters reflect signs and strengths of fitness effects (‘universality assumption’). Here, we show that a single equation fails to qualitatively capture diverse pairwise microbial interactions. We build mechanistic reference models for two microbial species engaging in commonly-found chemical-mediated interactions, and attempt to derive pairwise models. Different equations are appropriate depending on whether a mediator is consumable or reusable, whether an interaction is mediated by one or more mediators, and sometimes even on quantitative details of the community (e.g. relative fitness of the two species, initial conditions). Our results, combined with potential violation of the additivity assumption in many-species communities, suggest that pairwise modeling will often fail to predict microbial dynamics.
    摘要：成对模型通常用于描述多种群落。在这些模型中，个体通过与社区中每个物种的成对相互作用（“可加性假设”）接受加性适应效应。所有的成对相互作用通常用一个方程来表示，其中参数反映适应效应的标志和强度（‘普遍性假设’）。在这里，我们显示一个单一的方程无法定性捕获不同的配对微生物相互作用。我们为参与常见化学介导的相互作用的两种微生物物种建立机械参考模型，并试图推导出成对模型。取决于介体是可消耗还是可重复使用，相互作用是由一种或多种介体介导的，有时甚至是社区的定量细节（例如，两种物种的相对适合性，初始条件），不同的方程是适当的。我们的研究结果，加上在许多物种群体中可能违反可加性假设，表明配对建模常常无法预测微生物动态。

Constructing or establishing a microbiome involves numerous factors that influence the composition of host-associated microbial communities.

44. 创建一个微生物组：由与宿主相关的微生物群落所组成的多种因素

45. Mapping the ecological networks of microbial communities

Abstract：Complex microbial ecosystems play a crucial role in the health of associated plants and animals. These ecosystems are often modeled as directed, signed, and weighted ecological networks, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is essential for understanding their assembly rules and predicting their dynamic responses to external stimuli. However, current network inference methods rely on assuming a specific population dynamics model, which is typically unknown a priori. Additionally, these methods require longitudinal abundance data, which is often unavailable or lacks sufficient variation for reliable inference. To address these limitations, we introduce a novel method for mapping microbial community networks using steady-state data alone. Our approach can qualitatively determine interaction types (positive, negative, or neutral) without assuming any population dynamics model. When coupled with the classic Generalized Lotka-Volterra model, our method can also quantify interaction strengths and intrinsic growth rates. Through systematic validation with simulated data and application to four experimental datasets, we demonstrate our method's effectiveness in inferring microbial interactions and reconstructing ecological networks. This framework represents a significant advancement in reliably modeling complex microbial communities like the human gut microbiota.
摘要：复杂的微生物生态系统在与其相关动植物的健康中发挥着关键作用。这些生态系统通常表示为定向的、有标记的和加权的生态网络，在其中节点代表微生物类群，在其中边代表生态相互作用。推断微生物群落的基本生态网络是理解其组装规则并预测其对外部刺激的动态响应的关键步骤。然而，当前网络推断方法依赖于假设特定的人口动力学模型（通常未知先验），此外这些方法还需要拟合纵向丰度数据（通常不易获得或缺乏足够的变化以实现可靠的推断）。为了克服这些限制，在这里我们提出了一种仅使用稳态数据即可绘制微生物群落生态网络的新方法。我们的方法可以在不假设任何特定的人口动力学模型的情况下定性推断物种间的相互作用类型或符号（正数、负数或中性）。此外，在假设人口动态遵循经典的广义Lotka-Volterra模型时，我们的方法还可以定量推断物种间相互作用强度及内在增长率。通过使用模拟数据系统地验证了我们的方法，并将其应用于四个微生物群落实验数据集中的每一个后我们展示了该方法的有效性以推断微生物相互作用并重建生态网络这一框架代表了可靠建模复杂现实世界微生物群落（如人类肠道微生物群落）的关键一步。

46. Metabolic dependencies underlie species interaction patterns in diverse microbial communities

46. 代谢相互作用主导不同微生物群落中的物种共存

47. Metabolic division of labor in microbial systems

47. 微生物系统的代谢分工
    Abstract：Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established. Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.
    摘要：代谢途径通常设计在单一微生物群体中。然而，将异源回路引入宿主会产生大量的代谢负担，从而限制了系统的整体生产力。这种限制可以通过代谢分工（DOL）来克服，因为不同的人群在代谢途径中执行不同的步骤，从而减轻每个人群将经历的负担。虽然概念上有吸引力，但DOL有利的条件尚未得到严格的确定。在这里，我们分析了可以实施DOL的24种常见的代谢途径体系结构。我们的分析揭示了定义有利于DOL的条件的一般标准，说明途径活性对宿主群体的负担或益处以及酶和中间代谢物的转运和转换。这些标准可以帮助指导代谢途径的工程化，并且对理解天然微生物群落的进化具有影响。

48. Metabolic interactions within microbial ecosystems: a strategy to unravel complex relationships

48. 微生物群落中的代谢交互作用：解开Gordian之谜
    Abstract：代谢交换无处不在于微生物群落中。然而由于其内在动态性和社区复杂性的原因，在实际操作层面进行代谢物交叉喂养的鉴定仍是难题。因此虽然详尽描绘自然系统中运作的代谢网络的任务在未来仍将持续存在而今天的竞争焦点则主要集中在小规模高复杂度微生物群体及其特定生态系统的代谢方面上。为了实现对代谢相互作用的有效鉴定必须采用一种能够综合捕捉物种特征及其依赖关系并明确表征交换物质特性的混合方法学。从宏基因组学到成像质谱技术等多种技术手段结合运用可为这一挑战提供解决方案每种方法学的选择均需根据具体研究对象量身定制。
    摘要：代谢交换无处不在于微生物群落中。然而由于其内在动态性和社区复杂性的原因，在实际操作层面进行代谢物交叉喂养的鉴定仍是难题。因此虽然详尽描绘自然系统中运作的代谢网络的任务在未来仍将持续存在而今天的竞争焦点则主要集中在小规模高复杂度微生物群体及其特定生态系统的代谢方面上。为了实现对代谢相互作用的有效鉴定必须采用一种能够综合捕捉物种特征及其依赖关系并明确表征交换物质特性的混合方法学。从宏基因组学到成像质谱技术等多种技术手段结合运用可为这一挑战提供解决方案每种方法学的选择均需根据具体研究对象量身定制。

Metabolic modeling of species interactions within the human microbiome reveals community-level assembly mechanisms.

49. 人类微生物组中物种相互作用的代谢模型揭示了社区水平的组装规则

50. Metagenomic classification and their correlation with bacterial host genomes based on DNA methylation patterns

50. 使用DNA甲基化的宏基因组分档与质粒与细菌宿主基因组的关联

Microbial community structure determined by a matching model

基于受稳定婚姻问题启发的概念模型来预测微生物群落结构
Abstract：通过实验研究发现几种稳定的生态状态。尽管这些生态状态通常具有一定的抵抗力性，在接触抗生素、益生菌或不同饮食时常常引发转移到其他生态状态。我们能否预测哪些特定的扰动会导致这种转移？在此我们提出一种新的概念模型——受稳定婚姻问题启发——既能展示这些涌现现象又能作出这样的预测。我们的模型的核心成分是微生物一次只使用一种营养素同时相互竞争。我们只需使用两个有微生物营养偏好和竞争能力的排名表即可确定所有稳定状态以及驱动一个社区从一个生态状态到另一个生态状态的特定扰动。利用7种拟杆菌属物种利用9种人体肠道中常见的多糖我们预测营养偏好中的相互补充使得这些物种能够以高丰度共存。
摘要：通过实验研究发现几种稳定的生态系统形态体态虽然这些形态体态中的每一个都具有一定的弹性但接触抗生素益生菌或不同饮食往往会引发向其他形态体态的转变我们可以预测哪些特定的干扰因素会导致这种转变？在此我们提出了一个新的概念模型——受稳定配对问题启发——既展示了这些涌现现象又作出了这样的预测我们的模型的核心成分是微生物一次只使用一种营养素同时相互竞争我们只需使用两个有微生物营养偏好和竞争能力的排名表即可确定所有稳定形态体态以及驱动一个生态系统从一个形态体态到另一个形态体态的特定干扰因素利用7种拟杆菌属物种利用9种人体肠道中常见的多糖我们预测营养偏好中的相互补充使得这些物种能够以高丰度共存。

52. Microbiome, metabolites and host immunity

52. 微生物、代谢物和宿主免疫
    Abstract：肠道环境中微生物基因组数据及其代谢反应网络规模远超宿主数量级，在多方面显著影响宿主健康状态（包括新陈代谢活动）、免疫功能（包括免疫细胞发育及活性调控）以及个体行为模式等特征指标；当微生物群落失衡时会引发疾病风险增加。宿主体内菌群与宿主体间物质交换过程包含相互影响机制；其中菌群通过分泌代谢产物来调节宿主体内生理状态；同时宿主体内的免疫系统持续监测肠道微环境中的菌群代谢状态信息及定植情况；近年来研究表明菌群产生的代谢产物对调节免疫系统发挥着关键作用；综上所述，在本综述中我们总结了基于菌群调控的代谢产物如何影响免疫发育及活性变化的核心研究发现。
    摘要：在肠道环境中其基因组数据及其代谢反应网络规模远超宿主数量级并对其健康状态（包括新陈代谢活动）、免疫功能（包括免疫细胞发育及活性调控）以及个体行为模式等方面产生重要影响；这种失衡与多种疾病相关；其中菌群通过分泌代谢产物来调节宿主体内生理状态；同时宿主体内的免疫系统持续监测肠道微环境中的菌群代谢状态信息及定植情况；近年来研究表明菌群产生的代谢产物对调节免疫系统发挥着关键作用；综上所述在这篇综述中我们总结了基于菌群调控的代谢产物如何影响免疫发育及活性变化的核心研究发现。

Multilayer networks expose the spatial organization of seed dispersal processes within the Great Rift landscape's topography.

通过多层网络分析,我们揭示了大裂谷地形中种子传播相互作用的空间结构

Network architecture incorporating the interplay between mutualistic and antagonistic relationships continuum enhances the resilience of the community network.

54. 网络结构通过包含共生与对抗关系的不同层面而增强了群落抵抗物种丧失的能力

55. Non-random coexistence extinctions in evolutionarily structured mutualistic networks

The interaction networks between plants and their animal pollinators and seed dispersers have shaped much of Earth's biodiversity. Recent studies have revealed that these mutually beneficial interactions form complex networks with a well-defined structure, which may play a significant role in maintaining biodiversity. However, the processes responsible for generating these network patterns remain poorly understood. In this study, we employ phylogenetic methods to demonstrate that the phylogenetic relationships of species can predict the number of interactions they exhibit in more than one-third of the networks, as well as the specific species with which they interact in approximately half of the networks. The phylogenetic effects on interaction patterns lead to simulated extinction events triggering cascading extinctions among related species. This results in a non-random pruning of evolutionary trees and an enhanced loss of taxonomic diversity compared to scenarios without phylogenetic signals. Our findings underscore the importance of integrating phylogenetic information with network architecture to enhance our understanding of diverse ecosystems' structural dynamics and ultimate fates.

56. Overcoming catastrophic forgetting in neural networks

56. 克服神经网络中的灾难性遗忘
    Abstract：The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.
    摘要：以顺序方式学习任务的能力对人工智能的发展至关重要。通常，神经网络不具备这一能力，人们普遍认为，灾难性遗忘是连接模型的必然特征。我们表明，有可能克服这种限制，并培养能够保持长期未经历过的任务专业知识的网络。我们的方法通过选择性地减慢对于这些任务重要的权重的学习来记住旧任务。我们通过解决一系列基于MNIST手写数字数据集的分类任务和依次学习几个Atari 2600游戏来证明我们的方法具有可扩展性和有效性。

Oral biofilms are a complex community of Pathogenic microorganisms that thrive within the Extracellular matrix and exhibit dynamic interactions with their Microenvironmental niches.

口腔生物膜是由微环境中病原体、基质以及多种微生物共同作用形成的生态系统，在其中各种微生物之间的相互作用构成了其独特的功能网络。

58. Progress in and promise of bacterial quorum sensing research

58

59. Reconstructing noise-driven nonlinear networks based on node output data using higher-order correlation statistics

59. 通过使用高阶相关性从节点输出重构噪声驱动的非线性网络
    Abstract：Many practical systems can be described by dynamic networks, for which modern technique can measure their outputs, and accumulate extremely rich data. Nevertheless, the network structures producing these data are often deeply hidden in the data. The problem of inferring network structures by analyzing the available data, turns to be of great significance. On one hand, networks are often driven by various unknown facts, such as noises. On the other hand, network structures of practical systems are commonly nonlinear, and different nonlinearities can provide rich dynamic features and meaningful functions of realistic networks. Although many works have considered each fact in studying network reconstructions, much less papers have been found to systematically treat both difficulties together. Here we propose to use high-order correlation computations (HOCC) to treat nonlinear dynamics; use two-time correlations to decorrelate effects of network dynamics and noise driving; and use suitable basis and correlator vectors to unifiedly infer all dynamic nonlinearities, topological interaction links and noise statistical structures. All the above theoretical frameworks are constructed in a closed form and numerical simulations fully verify the validity of theoretical predictions.
    摘要：许多实际系统可以用动态网络来描述，现代技术可以测量它们的输出，并积累极其丰富的数据。尽管如此，产生这些数据的网络结构往往隐藏在数据中。通过分析可用数据来推断网络结构的问题变得具有重要意义。一方面，网络往往是由各种未知的事实驱动的，比如噪音。另一方面，实际系统的网络结构通常是非线性的，不同的非线性可以提供丰富的动态特性和实际网络的有意义的功能。尽管许多着作在研究网络重建时都考虑过每一个事实，但发现系统地将这两种困难一起对待的论文数量少得多。这里我们建议使用高阶相关计算（HOCC）来处理非线性动力学;使用两次相关解除网络动态和噪音驱动的影响;并使用合适的基和相关矢量统一推断所有动态非线性，拓扑相互作用环节和噪声统计结构。所有上述理论框架均以封闭形式构建，数值模拟充分验证了理论预测的有效性。

60. Representing higher-order dependencies in networks

60. 网络中高阶依赖关系的表示
    Abstract：To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend on several previous steps. However, we show that data derived from many complex systems can show up to fifth-order dependencies. In these cases, the oversimplifying assumption of the first-order network representation can lead to inaccurate network analysis results. To address this problem, we propose the higher-order network (HON) representation that can discover and embed variable orders of dependencies in a network representation. Through a comprehensive empirical evaluation and analysis, we establish several desirable characteristics of HON, including accuracy, scalability, and direct compatibility with the existing suite of network analysis methods. We illustrate how HON can be applied to a broad variety of tasks, such as random walking, clustering, and ranking, and we demonstrate that, by using it as input, HON yields more accurate results without any modification to these tasks.
    摘要：为确保网络分析方法的正确性，网络（作为输入）必须足够精确地表示底层数据。但是，如果将来自复杂系统的连续数据（如全球运输流量或Web点击流量流量）表示为网络，那么隐式承担马尔可夫属性（一阶依赖性）的传统网络表示形式可能很快就会受到限制。这个假设认为，当运动在网络上被模拟时，下一个运动只取决于当前节点，折中运动可能取决于前几个步骤的事实。但是，我们表明从许多复杂系统派生的数据可以显示高达五阶的依赖关系。在这些情况下，一阶网络表示的过度简化假设会导致网络分析结果不准确。为了解决这个问题，我们提出了可以在网络表示中发现和嵌入依赖关系的可变顺序的高阶网络（HON）表示。通过全面的实证评估和分析，我们确立了HON的一些理想特性，包括准确性，可扩展性以及与现有网络分析方法的直接兼容性。我们说明了HON如何应用于各种任务，如随机行走，聚类和排名，并且我们证明，通过将HON用作输入，HON可以产生更准确的结果，而不需要对这些任务进行任何修改。