Self-Organization, Layered Structure, and Aggregation Enhance Persistence of a Synthetic Biofilm Consortium

Microbial consortia constitute a majority of the earth’s biomass, but little is known about how these cooperating communities persist despite competition among community members. Theory suggests that non-random spatial structures contribute to the persistence of mixed communities; when particular structures form, they may provide associated community members with a growth advantage over unassociated members. If true, this has implications for the rise and persistence of multi-cellular organisms. However, this theory is difficult to study because we rarely observe initial instances of non-random physical structure in natural populations. Using two engineered strains of Escherichia coli that constitute a synthetic symbiotic microbial consortium, we fortuitously observed such spatial self-organization. This consortium forms a biofilm and, after several days, adopts a defined layered structure that is associated with two unexpected, measurable growth advantages. First, the consortium cannot successfully colonize a new, downstream environment until it selforganizes in the initial environment; in other words, the structure enhances the ability of the consortium to survive environmental disruptions. Second, when the layered structure forms in downstream environments the consortium accumulates significantly more biomass than it did in the initial environment; in other words, the structure enhances the global productivity of the consortium. We also observed that the layered structure only assembles in downstream environments that are colonized by aggregates from a previous, structured community. These results demonstrate roles for self-organization and aggregation in persistence of multi-cellular communities, and also illustrate a role for the techniques of synthetic biology in elucidating fundamental biological principles

Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer

\ua9 The Author(s) 2024.Background: Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer. Methods: The implemented ‘Pareto Guided Automated Planning’ (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a ‘Protocol Based Automatic Iterative Optimisation’ planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions’ clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution. Results: PGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review. Conclusions: PGAP enabled intuitive adaptation of automated protocols to an institution’s planning aims and yielded plans more congruent with the institution’s clinical preference than the locally produced manual clinical plans

Assessing the Quality of Goal Setting in Behavioural Support for Smoking Cessation and its Association with Outcomes

BACKGROUND: Smoking cessation behavioural support can be effective but practitioners differ markedly in effectiveness, possibly due to variation in the quality of delivery of key behaviour change techniques, such as goal setting (i.e. setting a quit date). OBJECTIVES: This study aimed to (i) develop a reliable method for assessing the quality of practitioners' support in setting quit dates and (ii) assess whether quality predicts initiation of abstinence as a first step to quitting. METHODS: A scale for scoring the quality of goal setting was developed from national guidance documents and applied to 85 transcribed behavioural support sessions. Inter-rater reliability was assessed. Associations between quality scores and quit attempts were assessed. RESULTS: The 10-item scale produced had good inter-rater reliability (Kappa = 0.68). Higher quality goal setting was associated with increased self-reported quit attempts (p < .001; OR = 2.60, 95 % CI 1.54-4.40). The scale components 'set a clear quit date' (χ (2) (2, N = 85) = 22.3, p < .001) and 'within an appropriate timeframe' (χ (2) (2, N = 85) = 15.5, p < .001) were independently associated with quit attempts. CONCLUSIONS: It is possible to reliably assess the quality of goal setting in smoking cessation behavioural support. Higher quality of goal setting is associated with greater likelihood of initiating quit attempts

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Parental transfer of the antimicrobial protein LBP/BPI protects Biomphalaria glabrata eggs against oomycete infections

Copyright: © 2013 Baron et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded by ANR (ANR-07-BLAN-0214 and ANR-12-EMMA-00O7-01), CNRS and INRA. PvW was financially supported by the BBSRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Is Bacterial Persistence a Social Trait?

The ability of bacteria to evolve resistance to antibiotics has been much reported in recent years. It is less well-known that within populations of bacteria there are cells which are resistant due to a non-inherited phenotypic switch to a slow-growing state. Although such ‘persister’ cells are receiving increasing attention, the evolutionary forces involved have been relatively ignored. Persistence has a direct benefit to cells because it allows survival during catastrophes–a form of bet-hedging. However, persistence can also provide an indirect benefit to other individuals, because the reduced growth rate can reduce competition for limiting resources. This raises the possibility that persistence is a social trait, which can be influenced by kin selection. We develop a theoretical model to investigate the social consequences of persistence. We predict that selection for persistence is increased when: (a) cells are related (e.g. a single, clonal lineage); and (b) resources are scarce. Our model allows us to predict how the level of persistence should vary with time, across populations, in response to intervention strategies and the level of competition. More generally, our results clarify the links between persistence and other bet-hedging or social behaviours

Basic Biomedical Sciences and the Future of Medical Education: Implications for Internal Medicine

The academic model of medical education in the United States is facing substantial challenges. Apprenticeship experiences with clinical faculty are increasingly important in most medical schools and residency programs. This trend threatens to separate clinical education from the scientific foundations of medical practice. Paradoxically, this devaluation of biomedical science is occurring as the ability to use new discoveries to rationalize clinical decision making is rapidly expanding. Understanding the scientific foundations of medical practice and the ability to apply them in the care of patients separates the physician from other health care professionals. The de-emphasis of biomedical science in medical education poses particular dangers for the future of internal medicine as the satisfaction derived from the application of science to the solving of a clinical problem has been a central attraction of the specialty. Internists should be engaged in the ongoing discussions of medical education reform and provide a strong voice in support of rigorous scientific training for the profession

Constraints on Nucleon Decay via "Invisible" Modes from the Sudbury Neutrino Observatory

Data from the Sudbury Neutrino Observatory have been used to constrain the lifetime for nucleon decay to ``invisible'' modes, such as n -> 3 nu. The analysis was based on a search for gamma-rays from the de-excitation of the residual nucleus that would result from the disappearance of either a proton or neutron from O16. A limit of tau_inv > 2 x 10^{29} years is obtained at 90% confidence for either neutron or proton decay modes. This is about an order of magnitude more stringent than previous constraints on invisible proton decay modes and 400 times more stringent than similar neutron modes.Comment: Update includes missing efficiency factor (limits change by factor of 2) Submitted to Physical Review Letter