Perception of motion salience shapes the emergence of collective motions.

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.

A computational method to dissect colonization resistance of the gut microbiota against pathogens.

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

An ecological framework to understand the efficacy of fecal microbiota transplantation.

Human gut microbiota plays critical roles in physiology and disease. Our understanding of ecological principles that govern the dynamics and resilience of this highly complex ecosystem remains rudimentary. This knowledge gap becomes more problematic as new approaches to modifying this ecosystem, such as fecal microbiota transplantation (FMT), are being developed as therapeutic interventions. Here we present an ecological framework to understand the efficacy of FMT in treating conditions associated with a disrupted gut microbiota, using the recurrent Clostridioides difficile infection as a prototype disease. This framework predicts several key factors that determine the efficacy of FMT. Moreover, it offers an efficient algorithm for the rational design of personalized probiotic cocktails to decolonize pathogens. We analyze data from both preclinical mouse experiments and a clinical trial of FMT to validate our theoretical framework. The presented results significantly improve our understanding of the ecological principles of FMT and have a positive translational impact on the rational design of general microbiota-based therapeutics.

Timing for the second fecal microbiota transplantation to maintain the long-term benefit from the first treatment for Crohn's disease.

Increasing evidence has shown that fecal microbiota transplantation (FMT) could be a promising treatment option for Crohn's disease (CD). However, the frequency of FMT for CD treatment remains unclear. This study aimed to evaluate the optimal timing for administering the second course of FMT to maintain the long-term clinical effects from the first FMT for patients with CD. Sixty-nine patients with active CD who underwent FMT twice and benefited from the first FMT were enrolled in this study. Clinical response, stool microbiota, and urine metabolome of patients were assessed during the follow-up. The median time of maintaining clinical response to the first FMT in total 69 patients was 125 days (IQR, 82.5-225.5). The time of maintaining clinical response to the second FMT in 56 of 69 patients was 176.5 days (IQR, 98.5-280). The fecal microbiota composition of each patient post the first FMT was closer to that of his/her donor. Compared to that of the baseline, patients prior to the second course of FMT showed significant differences in urinary metabolic profiles characterized by increased indoxyl sulfate, 4-hydroxyphenylacetate, creatinine, dimethylamine, glycylproline, hippurate, and trimethylamine oxide (TMAO). This study demonstrated that patients with CD could be administered the second course of FMT less than 4 months after the first FMT for maintaining the clinical benefits from the first FMT. This was supported by the host-microbial metabolism changes in patients with active CD. Trial registration: ClinicalTrials.gov , NCT01793831. Registered 18 February 2013. https://clinicaltrials.gov/ct2/show/NCT01793831?term=NCT01793831&rank=1.

Horizontal gene transfer can help maintain the equilibrium of microbial communities.

Horizontal gene transfer and species coexistence are two focal points in the study of microbial communities. Yet, the evolutionary advantage of horizontal gene transfer has not been well understood and is constantly being debated. Here we propose a simple population dynamics model based on frequency-dependent genotype interactions to evaluate the influence of horizontal gene transfer on microbial communities. In particular, we examine the structural stability of coexistence (i.e., the capability of the system to maintain species coexistence in response to small changes in parameters), as well as the robustness (defined as the maximal degree of perturbation the system can sustain around a stable coexistence steady state) of microbial communities. We find that both structural stability of coexistence and robustness of the microbial community are strongly affected by the gene transfer rate and direction. An optimal gene flux can stabilize the ecosystem, helping it recover from disturbance and maintain the species coexistence.

Mapping the ecological networks of microbial communities.

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

Effects of Edge Directions on the Structural Controllability of Complex Networks.

Recent advances indicate that assigning or reversing edge direction can significantly improve the structural controllability of complex networks. For directed networks, approaching the optimal structural controllability can be achieved by detecting and reversing certain "inappropriate" edge directions. However, the existence of multiple sets of "inappropriate" edge directions suggests that different edges have different effects on optimal controllability-that is, different combinations of edges can be reversed to achieve the same structural controllability. Therefore, we classify edges into three categories based on their direction: critical, redundant and intermittent. We then investigate the effects of changing these edge directions on network controllability, and demonstrate that the existence of more critical edge directions implies not only a lower cost of modifying inappropriate edges but also better controllability. Motivated by this finding, we present a simple edge orientation method aimed at producing more critical edge directions-utilizing only local information-which achieves near optimal controllability. Furthermore, we explore the effects of edge direction on the controllability of several real networks.

Edge orientation for optimizing controllability of complex networks.

Recently, as the controllability of complex networks attracts much attention, how to design and optimize the controllability of networks has become a common and urgent problem in the field of controlling complex networks. Previous work focused on the structural perturbation and neglected the role of edge direction to optimize the network controllability. In a recent work [Phys. Rev. Lett. 103, 228702 (2009)], the authors proposed a simple method to enhance the synchronizability of networks by assignment of link direction while keeping network topology unchanged. However, the controllability is fundamentally different from synchronization. In this work, we systematically propose the definition of assigning direction to optimize controllability, which is called the edge orientation for optimal controllability problem (EOOC). To solve the EOOC problem, we construct a switching network and transfer the EOOC problem to find the maximum independent set of the switching network. We prove that the principle of our optimization method meets the sense of unambiguity and optimum simultaneously. Furthermore, the relationship between the degree-degree correlations and EOOC are investigated by experiments. The results show that the disassortativity pattern could weaken the orientation for optimal controllability, while the assortativity pattern has no correlation with EOOC. All the experimental results of this work verify that the network structure determines the network controllability and the optimization effects.