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This study focuses on inter-individual convergence effects in the perception and categorization of speech sounds. We ask to what extent two listeners can come to establish a shared set of categorization criteria in a phoneme identification task that they accomplish together. Several hypotheses  are  laid  out  in  the  framework  of  a  Bayesian  model  of  speech  perception  that  we  have  developed  to  account  for  how  two  listeners  may  each  infer  the  parameters  that  govern  their partner’s responses. In our experimental paradigm, participants were asked to perform a joint  phoneme  identification  task  with  a  partner  that,  unbeknownst  to  them,  was  an  artificial  agent, whose responses we manipulated along two dimensions, the location of the categorical boundary and the slope of the identification function. Convergence was found to arise for bias but not for slope. Numerical simulations suggested that lack of convergence in slope may stem from the listeners’ prior level of confidence in the variance in VOT for the two phonemic categories. This study sheds new light on perceptual convergence between listeners in the categorization of speech sounds, a phenomenon that has received little attention so far in spite of its central importance for speech communication.

Reading acquisition primarily relies on orthographic learning.Behavioral studies show that familiarity with a novel word'spronunciation facilitates learning, particularly in semanticallymeaningful contexts. Two main components of orthographiclearning are commonly described: perceptual processing ofthe visual stimulus, to infer corresponding phonological rep-resentations, and “pronunciation correction”, to correct errorsfrom perceptual processing. Currently, pronunciation correc-tion has not been featured in reading acquisition computa-tional models. This study uses BRAID-Acq, a reading ac-quisition model, to implement and compare two pronunciationcorrection mechanisms (an “online” and a “post-processing”variant). We simulated learning of words with and withoutprior phonological knowledge and explored the impact of con-text strength and size on learning. Results indicate that bothmechanisms improve decoding. However, the post-processingmechanism induced implausible lexicalization for words with-out prior phonological knowledge, while the online mecha-nism did not. Overall, our simulation results suggest that pro-nunciation correction could be construed as an online process.

Abstract The microbes, nutrients and toxins that we are exposed to can have a profound effect on the composition and function of the gut microbiome. Thousands of peer-reviewed publications link microbiome composition and function to health from the moment of birth, right through to centenarians, generating a tantalizing glimpse of what might be possible if we could intervene rationally. Nevertheless, there remain relatively few real-world examples where successful microbiome engineering leads to beneficial health effects. Here we aim to provide a framework for the progress needed to turn gut microbiome engineering from a trial-and-error approach to a rational medical intervention. The workflow starts with truly understanding and accurately diagnosing the problems that we are trying to fix, before moving on to developing technologies that can achieve the desired changes.

One of the theoretical requirements of electrochemical impedance spectroscopy measure­ments is that the studied system should not be varying with time. Unfortunately, this is rarely the case of physical systems. In the literature, quite a few methods exist to check and correct a posteriori the effect of time-variance, allowing to use conventional equivalent circuit models to fit and interpret the data. We suggest a different approach where, for a given electro­chemical mechanism and specific experimental conditions, assuming stationarity during each measurement, a time- and frequency-dependent expression of the Faradaic impe­dance is derived from the kinetic equations. The case of a potential relaxation at zero current following an anodic steady-state polarization is considered for a system where a Volmer-Heyrovský corrosion mechanism is supposed to take place.

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

To explain how perception processes are performed, understanding how continuous sensory streams are temporally segmented into discrete units is central. This is particularly the case in speech perception where temporal segmentation is key for identifying linguistic units contained between consecutive events in time. We propose an original probabilistic construct, that we call "Bayesian gates", to segment temporally continuous streams of sensory stimuli into sequences of decoders. We first define Bayesian gates mathematically and describe their properties. We then illustrate their behavior in the context of a model of word recognition in speech perception. We show that, based on an event detection module, they sequentially parse the acoustic stimulus, so that each syllable decoder only processes a segment of the sensory signal.

ABSTRACT Understanding the evolutionary dynamics of foodborne pathogens throughout our food production chain is of utmost importance. In this study, we reveal that Salmonella Typhimurium can readily and reproducibly acquire vastly increased heat shock resistance upon repeated exposure to heat shock. Counterintuitively, this boost in heat shock resistance was invariantly acquired through loss-of-function mutations in the dnaJ gene, encoding a heat shock protein that acts as a molecular co-chaperone of DnaK and enables its role in protein folding and disaggregation. As a trade-off, however, the acquisition of heat shock resistance inevitably led to attenuated growth at 37°C and higher temperatures. Interestingly, loss of DnaJ also downregulated the activity of the master virulence regulator HilD, thereby lowering the fraction of virulence-expressing cells within the population and attenuating virulence in mice. By connecting heat shock resistance evolution to attenuation of HilD activity, our results confirm the complex interplay between stress resistance and virulence in Salmonella Typhimurium.IMPORTANCEBacterial pathogens such as Salmonella Typhimurium are equipped with both stress response and virulence features in order to navigate across a variety of complex inhospitable environments that range from food-processing plants up to the gastrointestinal tract of its animal host. In this context, however, it remains obscure whether and how adaptation to one environment would obstruct fitness in another. In this study, we reveal that severe heat stress counterintuitively, but invariantly, led to the selection of S. Typhimurium mutants that are compromised in the activity of the DnaJ heat shock protein. While these mutants obtained massively increased heat resistance, their virulence became greatly attenuated. Our observations, therefore, reveal a delicate balance between optimal tuning of stress response and virulence features in bacterial pathogens.

IntroductionA substantial amount of research from the last two decades suggests that infants' attention to the eyes and mouth regions of talking faces could be a supporting mechanism by which they acquire their native(s) language(s). Importantly, attentional strategies seem to be sensitive to three types of constraints: the properties of the stimulus, the infants' attentional control skills (which improve with age and brain maturation) and their previous linguistic and non-linguistic knowledge. The goal of the present paper is to present a probabilistic model to simulate infants' visual attention control to talking faces as a function of their language learning environment (monolingual vs. bilingual), attention maturation (i.e., age) and their increasing knowledge concerning the task at stake (detecting and learning to anticipate information displayed in the eyes or the mouth region of the speaker).MethodsTo test the model, we first considered experimental eye-tracking data from monolingual and bilingual infants (aged between 12 and 18 months; in part already published) exploring a face speaking in their native language. In each of these conditions, we compared the proportion of total looking time on each of the two areas of interest (eyes vs. mouth of the speaker).ResultsIn line with previous studies, our experimental results show a strong bias for the mouth (over the eyes) region of the speaker, regardless of age. Furthermore, monolingual and bilingual infants appear to have different developmental trajectories, which is consistent with and extends previous results observed in the first year. Comparison of model simulations with experimental data shows that the model successfully captures patterns of visuo-attentional orientation through the three parameters that effectively modulate the simulated visuo-attentional behavior.DiscussionWe interpret parameter values, and find that they adequately reflect evolution of strength and speed of anticipatory learning; we further discuss their descriptive and explanatory power.

Inflammasomes can prevent systemic dissemination of enteropathogenic bacteria. As adapted pathogens including Salmonella Typhimurium (S. Tm) have evolved evasion strategies, it has remained unclear when and where inflammasomes restrict their dissemination. Bacterial population dynamics establish that the NAIP/NLRC4 inflammasome specifically restricts S. Tm migration from the gut to draining lymph nodes. This is solely attributable to NAIP/NLRC4 within intestinal epithelial cells (IECs), while S. Tm evades restriction by phagocyte NAIP/NLRC4. NLRP3 and Caspase-11 also fail to restrict S. Tm mucosa traversal, migration to lymph nodes, and systemic pathogen growth. The ability of IECs (not phagocytes) to mount a NAIP/NLRC4 defense in vivo is explained by particularly high NAIP/NLRC4 expression in IECs and the necessity for epithelium-invading S. Tm to express the NAIP1-6 ligands-flagella and type-III-secretion-system-1. Imaging reveals both ligands to be promptly downregulated following IEC-traversal. These results highlight the importance of intestinal epithelial NAIP/NLRC4 in blocking bacterial dissemination in vivo, and explain why this constitutes a uniquely evasion-proof defense against the adapted enteropathogen S. Tm.

In visual word processing modeling, few models have success-fully accounted for a large variety of tasks, and large corpora ofbehavioral observations. We consider a dataset from a megas-tudy, in which participants performed three tasks (lexical de-cision, word naming, and word recognition in a progressivedemasking situation), on the same, large set of stimuli. Wedefine the BRAID-Phon model, an extension of a previousprobabilistic model, the BRAID model, whose originality isits visuo-attentional component, in which a visuo-attentionaldistribution spatially deploys sensory processing capabilities.BRAID-Phon includes phonological representations of words,allowing simulating the naming task. We simulated the threetasks on the dataset we considered, and analyzed predicted re-action times in terms of word frequency and word length ef-fects. Simulation results show that BRAID-Phon successfullycaptures the direction and order of magnitude of the observedeffects, in all three tasks.

Salmonella Typhimurium elicits gut inflammation by the costly expression of HilD-controlled virulence factors. This inflammation alleviates colonization resistance (CR) mediated by the microbiota and thereby promotes pathogen blooms. However, the inflamed gut-milieu can also select for hilD mutants, which cannot elicit or maintain inflammation, therefore causing a loss of the pathogen's virulence. This raises the question of which conditions support the maintenance of virulence in S. Typhimurium. Indeed, it remains unclear why the wild-type hilD allele is dominant among natural isolates. Here, we show that microbiota transfer from uninfected or recovered hosts leads to rapid clearance of hilD mutants that feature attenuated virulence, and thereby contributes to the preservation of the virulent S. Typhimurium genotype. Using mouse models featuring a range of microbiota compositions and antibiotic- or inflammation-inflicted microbiota disruptions, we found that irreversible disruption of the microbiota leads to the accumulation of hilD mutants. In contrast, in models with a transient microbiota disruption, selection for hilD mutants was prevented by the regrowing microbiota community dominated by Lachnospirales and Oscillospirales. Strikingly, even after an irreversible microbiota disruption, microbiota transfer from uninfected donors prevented the rise of hilD mutants. Our results establish that robust S. Typhimurium gut colonization hinges on optimizing its manipulation of the host: A transient and tempered microbiota perturbation is favorable for the pathogen to both flourish in the inflamed gut and also minimize loss of virulence. Moreover, besides conferring CR, the microbiota may have the additional consequence of maintaining costly enteropathogen virulence mechanisms.

Salmonella Typhimurium virulence is costly and can be lost by mutation during infection. Bakkeren et al. show that virulence restoration via horizontal gene transfer is only transient while transmission bottlenecks promote long-term virulence stability.

A new Bayesian model of visual word recognition is used tosimulate neighborhood frequency effects in lexical decision.These effects have been reported as being either facilitatory orinhibitory in behavioral experiments. Our model manages tosimulate the apparently contradictory findings. Indeed, study-ing the dynamic time course of information accumulation inthe model shows that effects are facilitatory early, and becomeinhibitory at later stages. The model provides new insights onthe mechanisms at play and their dynamics, leading to betterunderstand the experimental conditions that should yield a fa-cilitatory or an inhibitory neighborhood frequency effect.

Virulence gene expression can represent a substantial fitness cost to pathogenic bacteria. In the model entero-pathogen Salmonella Typhimurium (S.Tm), such cost favors emergence of attenuated variants during infections that harbor mutations in transcriptional activators of virulence genes (e.g., hilD and hilC). Therefore, understanding the cost of virulence and how it relates to virulence regulation could allow the identification and modulation of ecological factors to drive the evolution of S.Tm toward attenuation. In this study, investigations of membrane status and stress resistance demonstrate that the wild-type (WT) expression level of virulence factors embedded in the envelope increases membrane permeability and sensitizes S.Tm to membrane stress. This is independent from a previously described growth defect associated with virulence gene expression in S.Tm. Pretreating the bacteria with sublethal stress inhibited virulence expression and increased stress resistance. This trade-off between virulence and stress resistance could explain the repression of virulence expression in response to harsh environments in S.Tm. Moreover, we show that virulence-associated stress sensitivity is a burden during infection in mice, contributing to the inherent instability of S.Tm virulence. As most bacterial pathogens critically rely on deploying virulence factors in their membrane, our findings could have a broad impact toward the development of antivirulence strategies.

Many plasmids encode antibiotic resistance genes. Through conjugation, plasmids can be rapidly disseminated. Previous work identified gut luminal donor/recipient blooms and tissue-lodged plasmid-bearing persister cells of the enteric pathogen Salmonella enterica serovar Typhimurium (S.Tm) that survive antibiotic therapy in host tissues, as factors promoting plasmid dissemination among Enterobacteriaceae. However, the buildup of tissue reservoirs and their contribution to plasmid spread await experimental demonstration. Here, we asked if re-seeding-plasmid acquisition-invasion cycles by S.Tm could serve to diversify tissue-lodged plasmid reservoirs, and thereby promote plasmid spread. Starting with intraperitoneal mouse infections, we demonstrate that S.Tm cells re-seeding the gut lumen initiate clonal expansion. Extended spectrum beta-lactamase (ESBL) plasmid-encoded gut luminal antibiotic degradation by donors can foster recipient survival under beta-lactam antibiotic treatment, enhancing transconjugant formation upon re-seeding. S.Tm transconjugants can subsequently re-enter host tissues introducing the new plasmid into the tissue-lodged reservoir. Population dynamics analyses pinpoint recipient migration into the gut lumen as rate-limiting for plasmid transfer dynamics in our model. Priority effects may be a limiting factor for reservoir formation in host tissues. Overall, our proof-of-principle data indicates that luminal antibiotic degradation and shuttling between the gut lumen and tissue-resident reservoirs can promote the accumulation and spread of plasmids within a host over time.

Recent neurocognitive models commonly consider speech perception as a hierarchy of processes, each corresponding to specific temporal scales of collective oscillatory processes in the cortex: 30–80 Hz gamma oscillations in charge of phonetic analysis, 4–9 Hz theta oscillations in charge of syllabic segmentation, 1–2 Hz delta oscillations processing prosodic/syntactic units and the 15–20 Hz beta channel possibly involved in top-down predictions. Several recent neuro-computational models thus feature theta oscillations, driven by the speech acoustic envelope, to achieve syllabic parsing before lexical access. However, it is unlikely that such syllabic parsing, performed in a purely bottom-up manner from envelope variations, would be totally efficient in all situations, especially in adverse sensory conditions. We present a new probabilistic model of spoken word recognition, called COSMO-Onset, in which syllabic parsing relies on fusion between top-down, lexical prediction of onset events and bottom-up onset detection from the acoustic envelope. We report preliminary simulations, analyzing how the model performs syllabic parsing and phone, syllable and word recognition. We show that, while purely bottom-up onset detection is sufficient for word recognition in nominal conditions, top-down prediction of syllabic onset events allows overcoming challenging adverse conditions, such as when the acoustic envelope is degraded, leading either to spurious or missing onset events in the sensory signal. This provides a proposal for a possible computational functional role of top-down, predictive processes during speech recognition, consistent with recent models of neuronal oscillatory processes.

When performing electrochemical impedance spectroscopy (EIS) measurements on a system, we must make sure it fulfills certain conditions. One of them is that it should be stationary that is to say, steady-state and time-invariant. Commonly studied systems are time-variant, for example a corroding electrode or a battery under operation. A corroding electrode sees its polarization resistance decrease with time. A passivating electrode sees its polarization resistance increase with time. These phenomena cause a deformation of the Nyquist impedance at low frequencies. This result was first simulated and validated by experimental measurements on a corroding steel sample undergoing uniform cor­rosion. The effect of performing impedance measurements on a discharging battery was also shown. Several methods are available to check and correct time-variance. The non-stationary distortion (NSD) indicator is used to separate valid and invalid data samples and the so called “4D impedance” method can easily produce instantaneous impedance data.

Experimental studies of speech production involving compensations for auditory and somatosensory perturbations and adaptation after training suggest that both types of sensory information are considered to plan and monitor speech production. Interestingly, individual sensory preferences have been observed in this context: subjects who compensate less for somatosensory perturbations compensate more for auditory perturbations, and vice versa. We propose to integrate this sensory preference phenomenon in a model of speech motor planning using a probabilistic model in which speech units are characterized both in auditory and somatosensory terms. Sensory preference is implemented in the model according to two approaches. In the first approach, which is often used in motor control models accounting for sensory integration, sensory preference is attributed to the relative precision (i.e., inverse of the variance) of the sensory characterization of the speech motor goals associated with phonological units (which are phonemes in the context of this paper). In the second, “more original” variant, sensory preference is implemented by modulating the sensitivity of the comparison between the predicted sensory consequences of motor commands and the sensory characterizations of the phonemes. We present simulation results using these two variants, in the context of the adaptation to an auditory perturbation, implemented in a 2-dimensional biomechanical model of the tongue. Simulation results show that both variants lead to qualitatively similar results. Distinguishing them experimentally would require precise analyses of partial compensation patterns. However, the second proposed variant implements sensory preference without changing the sensory characterizations of the phonemes. This dissociates sensory preference and sensory characterizations of the phonemes, and makes the account of sensory preference more flexible. Indeed, in the second variant the sensory characterizations of the phonemes can remain stable, when sensory preference varies as a response to cognitive or attentional control. This opens new perspectives for capturing speech production variability associated with aging, disorders and speaking conditions.

Immunoglobulin A is a class of antibodies produced by the adaptive immune system and secreted into the gut lumen to fight pathogenic bacteria. We recently demonstrated that the main physical effect of these antibodies is to enchain daughter bacteria, i.e. to cross-link bacteria into clusters as they divide, preventing them from interacting with epithelial cells, thus protecting the host. These links between bacteria may break over time. We study several models using analytical and numerical calculations. We obtain the resulting distribution of chain sizes, that we compare with experimental data. We study the rate of increase in the number of free bacteria as a function of the replication rate of bacteria. Our models show robustly that at higher replication rates, bacteria replicate before the link between daughter bacteria breaks, leading to growing cluster sizes. On the contrary at low growth rates two daughter bacteria have a high probability to break apart. Thus the gut could produce IgA against all the bacteria it has encountered, but the most affected bacteria would be the fast replicating ones, that are more likely to destabilize the microbiota. Linking the effect of the immune effectors (here the clustering) with a property directly relevant to the potential bacterial pathogeneicity (here the replication rate) could avoid to make complex decisions about which bacteria to produce effectors against.

The existence of a functional relationship between speech perception and production systems is now widely accepted, but the exact nature and role of this relationship remains quite unclear. The existence of idiosyncrasies in production and in perception sheds interesting light on the nature of the link. Indeed, a number of studies explore inter-individual variability in auditory and motor prototypes within a given language, and provide evidence for a link between both sets. In this paper, we attempt to simulate one study on coupled idiosyncrasies in the perception and production of French oral vowels, within COSMO, a Bayesian computational model of speech communication. First, we show that if the learning process in COSMO includes a communicative mechanism between a Learning Agent and a Master Agent, vowel production does display idiosyncrasies. Second, we implement within COSMO three models for speech perception that are, respectively, auditory, motor and perceptuo-motor. We show that no idiosyncrasy in perception can be obtained in the auditory model, since it is optimally tuned to the learning environment, which does not include the motor variability of the Learning Agent. On the contrary, motor and perceptuo-motor models provide perception idiosyncrasies correlated with idiosyncrasies in production. We draw conclusions about the role and importance of motor processes in speech perception, and propose a perceptuo-motor model in which auditory processing would enable optimal processing of learned sounds and motor processing would be helpful in unlearned adverse conditions.

Shifts in perceptual boundaries resulting from speech motor learning induced by perturbations of the auditory feedback were taken as evidence for the involvement of motor functions in auditory speech perception. Beyond this general statement, the precise mechanisms underlying this involvement are not yet fully understood. In this paper we propose a quantitative evaluation of some hypotheses concerning the motor and auditory updates that could result from motor learning, in the context of various assumptions about the roles of the auditory and somatosensory pathways in speech perception. This analysis was made possible thanks to the use of a Bayesian model that implements these hypotheses by expressing the relationships between speech production and speech perception in a joint probability distribution. The evaluation focuses on how the hypotheses can (1) predict the location of perceptual boundary shifts once the perturbation has been removed, (2) account for the magnitude of the compensation in presence of the perturbation, and (3) describe the correlation between these two behavioral characteristics. Experimental findings about changes in speech perception following adaptation to auditory feedback perturbations serve as reference. Simulations suggest that they are compatible with a framework in which motor adaptation updates both the auditory-motor internal model and the auditory characterization of the perturbed phoneme, and where perception involves both auditory and somatosensory pathways.

To understand how bacteria evolve and adapt to their environment, it can be relevant to monitor phenotypic changes that occur in a population. Single cell level analyses and sorting of mutant cells according to a particular phenotypic readout can constitute efficient strategies. However, when the phenotype of interest is expressed heterogeneously in ancestral isogenic populations of cells, single cell level sorting approaches are not optimal. Phenotypic heterogeneity can for instance make no-expression mutant cells indistinguishable from a subpopulation of wild-type cells transiently not expressing the phenotype. The analysis of clonal populations (e.g., isolated colonies), in which the average phenotype is measured, can circumvent this issue. Indeed, no-expression mutants form negative populations while wild-type clones form populations in which average expression of the phenotype yields a positive signal. We present here an optimized colony immunoblot protocol and a semi-automated image analysis pipeline (ImageJ macro) allowing for rapid detection of clones harboring mutations that affect the heterogeneous (i.e., bimodal) expression of the Type Three Secretion System-1 (TTSS-1) in Salmonella enterica serovar Typhimurium. We show that this protocol can efficiently differentiate clones expressing TTSS-1 at various levels in mixed populations. We were able to detect the emergence of hilC mutants in which the proportion of cells expressing TTSS-1 was reduced compared to the ancestor. We could also follow changes in the frequency of different mutants during long-term infections. This demonstrates that our protocol constitutes a tractable approach to assess semi-quantitatively the evolutionary dynamics of heterogeneous phenotypes, such as the expression of virulence genes, in bacterial populations.