Climate drives rhizosphere microbiome variation and divergent selection between geographically distant Arabidopsis populations

Disentangling the contribution of climatic and edaphic factors to microbiome variation and local adaptation in plants requires an experimental approach to uncouple their effects and test for causality. We used microbial inocula, soil matrices and plant genotypes derived from two natural Arabidopsis thaliana populations in northern and southern Europe in an experiment conducted in climatic chambers mimicking seasonal changes in temperature, day length and light intensity of the home sites of the two genotypes. The southern A. thaliana genotype outperformed the northern genotype in the southern climate chamber, whereas the opposite was true in the northern climate chamber. Recipient soil matrix, but not microbial composition, affected plant fitness, and effects did not differ between genotypes. Differences between chambers significantly affected rhizosphere microbiome assembly, although these effects were small in comparison with the shifts induced by physicochemical differences between soil matrices. The results suggest that differences in seasonal changes in temperature, day length and light intensity between northern and southern Europe have strongly influenced adaptive differentiation between the two A. thaliana populations, whereas effects of differences in soil factors have been weak. By contrast, below-ground differences in soil characteristics were more important than differences in climate for rhizosphere microbiome differentiation

Application and comparative performance of network modularity algorithms to ecological communities classification

Network modularity is a well-studied large-scale connectivity pattern in networks. The detection of modules in real networks constitutes a crucial step towards a description of the network building blocks and their evolutionary dynamics. The performance of modularity detection algorithms is commonly quantified using simulated networks data. However, a comparison of the modularity algorithms utility for real biological data is scarce. Here we investigate the utility of network modularity algorithms for the classification of ecological plant communities. Plant community classification by the traditional approaches requires prior knowledge about the characteristic and differential species, which are derived from a manual inspection of vegetation tables. Using the raw species abundance data we constructed six different networks that vary in their edge definitions. Four network modularity algorithms were examined for their ability to detect the traditionally recognized plant communities. The use of more restrictive edge definitions significantly increased the accuracy of community detection, that is, the correspondence between network-based and traditional community classification. Random-walk based modularity methods yielded slightly better results than approaches based on the modularity function. For the whole network, the average agreement between the manual classification and the network-based modules is 76% with varying congruence levels for different communities ranging between 11% and 100%. The network-based approach recovered the known ecological gradient from riverside – sand and gravel bank vegetation – to dryer habitats like semidry grassland on dykes. Our results show that networks modularity algorithms offer new avenues of pursuit for the computational analysis of species communities

Activation Addition: Steering Language Models Without Optimization

Reliably controlling the behavior of large language models (LLMs) is a pressing open problem. Existing methods include supervised finetuning, reinforcement learning from human feedback (RLHF), prompt engineering and guided decoding. We instead investigate activation engineering: modifying activations at inference time to predictably alter model behavior. In particular, we bias the forward pass with an added 'steering vector' implicitly specified through natural language. Unlike past work which learned these steering vectors (Subramani, Suresh, and Peters 2022; Hernandez, Li, and Andreas 2023), our Activation Addition (ActAdd) method computes them by taking the activation differences that result from pairs of prompts. We demonstrate ActAdd on GPT-2 on OpenWebText and ConceptNet. Our inference-time approach yields control over high-level properties of output and preserves off-target model performance. It involves far less compute and implementation effort compared to finetuning or RLHF, allows users to provide natural language specifications, and its overhead scales naturally with model size

Tryptophan metabolism and bacterial commensals prevent fungal dysbiosis in Arabidopsis roots

In nature, roots of healthy plants are colonized by multikingdom microbial communities that include bacteria, fungi, and oomycetes. A key question is how plants control the assembly of these diverse microbes in roots to maintain host–microbe homeostasis and health. Using microbiota reconstitution experiments with a set of immunocompromised Arabidopsis thaliana mutants and a multikingdom synthetic microbial community (SynCom) representative of the natural A. thaliana root microbiota, we observed that microbiota-mediated plant growth promotion was abolished in most of the tested immunocompromised mutants. Notably, more than 40% of between-genotype variation in these microbiota-induced growth differences was explained by fungal but not bacterial or oomycete load in roots. Extensive fungal overgrowth in roots and altered plant growth was evident at both vegetative and reproductive stages for a mutant impaired in the production of tryptophan-derived, specialized metabolites (cyp79b2/b3). Microbiota manipulation experiments with single- and multikingdom microbial SynComs further demonstrated that 1) the presence of fungi in the multikingdom SynCom was the direct cause of the dysbiotic phenotype in the cyp79b2/b3 mutant and 2) bacterial commensals and host tryptophan metabolism are both necessary to control fungal load, thereby promoting A. thaliana growth and survival. Our results indicate that protective activities of bacterial root commensals are as critical as the host tryptophan metabolic pathway in preventing fungal dysbiosis in the A. thaliana root endosphere

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Common Traits Spark the Mitophagy/Xenophagy Interplay

Selective autophagy contributes to the wellbeing of eukaryotic cells by recycling cellular components, disposing damaged organelles, and removing pathogens, amongst others. Both the quality control process of selective mitochondrial autophagy (Mitophagy) and the defensive process of intracellular pathogen-engulfment (Xenophagy) are facilitated via protein assemblies which have shared molecules, a prime example being the Tank-Binding Kinase 1 (TBK1). TBK1 plays a central role in the immunity response driven by Xenophagy and was recently shown to be an amplifying mechanism in Mitophagy, bring to attention the potential cross talk between the two processes. Here we draw parallels between Xenophagy and Mitophagy, speculating on the inhibitory mechanisms of specific proteins (e.g., the 18 kDa protein TSPO), how the preferential sequestering toward one of the two pathways may undermine the other, and in this way impair cellular response to pathogens and cellular immunity. We believe that an in depth understanding of the commonalities may present an opportunity to design novel therapeutic strategies targeted at both the autonomous and non-autonomous processes of selective autophagy

An Evolutionary Network of Genes Present in the Eukaryote Common Ancestor Polls Genomes on Eukaryotic and Mitochondrial Origin

To test the predictions of competing and mutually exclusive hypotheses for the origin of eukaryotes, we identified from a sample of 27 sequenced eukaryotic and 994 sequenced prokaryotic genomes 571 genes that were present in the eukaryote common ancestor and that have homologues among eubacterial and archaebacterial genomes. Maximum-likelihood trees identified the prokaryotic genomes that most frequently contained genes branching as the sister to the eukaryotic nuclear homologues. Among the archaebacteria, euryarchaeote genomes most frequently harbored the sister to the eukaryotic nuclear gene, whereas among eubacteria, the α-proteobacteria were most frequently represented within the sister group. Only 3 genes out of 571 gave a 3-domain tree. Homologues from α-proteobacterial genomes that branched as the sister to nuclear genes were found more frequently in genomes of facultatively anaerobic members of the rhiozobiales and rhodospirilliales than in obligate intracellular ricketttsial parasites. Following α-proteobacteria, the most frequent eubacterial sister lineages were γ-proteobacteria, δ-proteobacteria, and firmicutes, which were also the prokaryote genomes least frequently found as monophyletic groups in our trees. Although all 22 higher prokaryotic taxa sampled (crenarchaeotes, γ-proteobacteria, spirochaetes, chlamydias, etc.) harbor genes that branch as the sister to homologues present in the eukaryotic common ancestor, that is not evidence of 22 different prokaryotic cells participating at eukaryote origins because prokaryotic “lineages” have laterally acquired genes for more than 1.5 billion years since eukaryote origins. The data underscore the archaebacterial (host) nature of the eukaryotic informational genes and the eubacterial (mitochondrial) nature of eukaryotic energy metabolism. The network linking genes of the eukaryote ancestor to contemporary homologues distributed across prokaryotic genomes elucidates eukaryote gene origins in a dialect cognizant of gene transfer in nature

Audio source separation into the wild

International audienceThis review chapter is dedicated to multichannel audio source separation in real-life environment. We explore some of the major achievements in the field and discuss some of the remaining challenges. We will explore several important practical scenarios, e.g. moving sources and/or microphones, varying number of sources and sensors, high reverberation levels, spatially diffuse sources, and synchronization problems. Several applications such as smart assistants, cellular phones, hearing aids and robots, will be discussed. Our perspectives on the future of the field will be given as concluding remarks of this chapter