An unbroken network of interactions connecting flagellin domains is required for motility in viscous environments.

In its simplest form, bacterial flagellar filaments are composed of flagellin proteins with just two helical inner domains, which together comprise the filament core. Although this minimal filament is sufficient to provide motility in many flagellated bacteria, most bacteria produce flagella composed of flagellin proteins with one or more outer domains arranged in a variety of supramolecular architectures radiating from the inner core. Flagellin outer domains are known to be involved in adhesion, proteolysis and immune evasion but have not been thought to be required for motility. Here we show that in the Pseudomonas aeruginosa PAO1 strain, a bacterium that forms a ridged filament with a dimerization of its flagellin outer domains, motility is categorically dependent on these flagellin outer domains. Moreover, a comprehensive network of intermolecular interactions connecting the inner domains to the outer domains, the outer domains to one another, and the outer domains back to the inner domain filament core, is required for motility. This inter-domain connectivity confers PAO1 flagella with increased stability, essential for its motility in viscous environments. Additionally, we find that such ridged flagellar filaments are not unique to Pseudomonas but are, instead, present throughout diverse bacterial phyla.

Robust effects of working memory demand during naturalistic language comprehension in language-selective cortex.

To understand language, we must infer structured meanings from real-time auditory or visual signals. Researchers have long focused on word-by-word structure building in working memory as a mechanism that might enable this feat. However, some have argued that language processing does not typically involve rich word-by-word structure building, and/or that apparent working memory effects are underlyingly driven by surprisal (how predictable a word is in context). Consistent with this alternative, some recent behavioral studies of naturalistic language processing that control for surprisal have not shown clear working memory effects. In this fMRI study, we investigate a range of theory-driven predictors of word-by-word working memory demand during naturalistic language comprehension in humans of both sexes under rigorous surprisal controls. In addition, we address a related debate about whether the working memory mechanisms involved in language comprehension are language-specialized or domain-general. To do so, in each participant, we functionally localize (a) the language-selective network and (b) the 'multiple demand' network, which supports working memory across domains. Results show robust surprisal-independent effects of memory demand in the language network and no effect of memory demand in the multiple demand network. Our findings thus support the view that language comprehension involves computationally demanding word-by-word structure building operations in working memory, in addition to any prediction-related mechanisms. Further, these memory operations appear to be primarily carried out by the same neural resources that store linguistic knowledge, with no evidence of involvement of brain regions known to support working memory across domains.SIGNIFICANCE STATEMENT:This study uses fMRI to investigate signatures of working memory (WM) demand during naturalistic story listening, using a broad range of theoretically motivated estimates of WM demand. Results support a strong effect of WM demand in the brain that is distinct from effects of word predictability. Further, these WM demands register primarily in language-selective regions, rather than in 'multiple demand' regions that have previously been associated with WM in non-linguistic domains. Our findings support a core role for WM in incremental language processing, using WM resources that are specialized for language.

Artificial intelligence in virtual standardized patients: Combining natural language understanding and rule based dialogue management to improve conversational fidelity.

INTRODUCTION: Advances in natural language understanding have facilitated the development of Virtual Standardized Patients (VSPs) that may soon rival human patients in conversational ability. We describe herein the development of an artificial intelligence (AI) system for VSPs enabling students to practice their history taking skills. METHODS: Our system consists of (1) Automated Speech Recognition (ASR), (2) hybrid AI for question identification, (3) classifier to choose between the two systems, and (4) automated speech generation. We analyzed the accuracy of the ASR, the two AI systems, the classifier, and student feedback with 620 first year medical students from 2018 to 2021. RESULTS: System accuracy improved from ∼75% in 2018 to ∼90% in 2021 as refinements in algorithms and additional training data were utilized. Student feedback was positive, and most students felt that practicing with the VSPs was a worthwhile experience. CONCLUSION: We have developed a novel hybrid dialogue system that enables artificially intelligent VSPs to correctly answer student questions at levels comparable with human SPs. This system allows trainees to practice and refine their history-taking skills before interacting with human patients.

Using virtual standardized patients to accurately assess information gathering skills in medical students.

Introduction: Practicing a medical history using standardized patients is an essential component of medical school curricula. Recent advances in technology now allow for newer approaches for practicing and assessing communication skills. We describe herein a virtual standardized patient (VSP) system that allows students to practice their history taking skills and receive immediate feedback. Methods: Our VSPs consist of artificially intelligent, emotionally responsive 3D characters which communicate with students using natural language. The system categorizes the input questions according to specific domains and summarizes the encounter. Automated assessment by the computer was compared to manual assessment by trained raters to assess accuracy of the grading system. Results: Twenty dialogs chosen randomly from 102 total encounters were analyzed by three human and one computer rater. Overall scores calculated by the computer were not different than those provided by the human raters, and overall accuracy of the computer system was 87%, compared with 90% for human raters. Inter-rater reliability was high across 19 of 21 categories. Conclusions: We have developed a virtual standardized patient system that can understand, respond, categorize, and assess student performance in gathering information during a typical medical history, thus enabling students to practice their history-taking skills and receive immediate feedback.

A Deep Learning Approach to Analyzing Continuous-Time Cognitive Processes.

The dynamics of the mind are complex. Mental processes unfold continuously in time and may be sensitive to a myriad of interacting variables, especially in naturalistic settings. But statistical models used to analyze data from cognitive experiments often assume simplistic dynamics. Recent advances in deep learning have yielded startling improvements to simulations of dynamical cognitive processes, including speech comprehension, visual perception, and goal-directed behavior. But due to poor interpretability, deep learning is generally not used for scientific analysis. Here, we bridge this gap by showing that deep learning can be used, not just to imitate, but to analyze complex processes, providing flexible function approximation while preserving interpretability. To do so, we define and implement a nonlinear regression model in which the probability distribution over the response variable is parameterized by convolving the history of predictors over time using an artificial neural network, thereby allowing the shape and continuous temporal extent of effects to be inferred directly from time series data. Our approach relaxes standard simplifying assumptions (e.g., linearity, stationarity, and homoscedasticity) that are implausible for many cognitive processes and may critically affect the interpretation of data. We demonstrate substantial improvements on behavioral and neuroimaging data from the language processing domain, and we show that our model enables discovery of novel patterns in exploratory analyses, controls for diverse confounds in confirmatory analyses, and opens up research questions in cognitive (neuro)science that are otherwise hard to study.

Comparison of Structural Parsers and Neural Language Models as Surprisal Estimators.

Expectation-based theories of sentence processing posit that processing difficulty is determined by predictability in context. While predictability quantified via surprisal has gained empirical support, this representation-agnostic measure leaves open the question of how to best approximate the human comprehender's latent probability model. This article first describes an incremental left-corner parser that incorporates information about common linguistic abstractions such as syntactic categories, predicate-argument structure, and morphological rules as a computational-level model of sentence processing. The article then evaluates a variety of structural parsers and deep neural language models as cognitive models of sentence processing by comparing the predictive power of their surprisal estimates on self-paced reading, eye-tracking, and fMRI data collected during real-time language processing. The results show that surprisal estimates from the proposed left-corner processing model deliver comparable and often superior fits to self-paced reading and eye-tracking data when compared to those from neural language models trained on much more data. This may suggest that the strong linguistic generalizations made by the proposed processing model may help predict humanlike processing costs that manifest in latency-based measures, even when the amount of training data is limited. Additionally, experiments using Transformer-based language models sharing the same primary architecture and training data show a surprising negative correlation between parameter count and fit to self-paced reading and eye-tracking data. These findings suggest that large-scale neural language models are making weaker generalizations based on patterns of lexical items rather than stronger, more humanlike generalizations based on linguistic structure.

Continuous-time deconvolutional regression for psycholinguistic modeling.

The influence of stimuli in psycholinguistic experiments diffuses across time because the human response to language is not instantaneous. The linear models typically used to analyze psycholinguistic data are unable to account for this phenomenon due to strong temporal independence assumptions, while existing deconvolutional methods for estimating diffuse temporal structure model time discretely and therefore cannot be directly applied to natural language stimuli where events (words) have variable duration. In light of evidence that continuous-time deconvolutional regression (CDR) can address these issues (Shain & Schuler, 2018), this article motivates the use of CDR for many experimental settings, exposits some of its mathematical properties, and empirically evaluates the influence of various experimental confounds (noise, multicollinearity, and impulse response misspecification), hyperparameter settings, and response types (behavioral and fMRI). Results show that CDR (1) yields highly consistent estimates across a variety of hyperparameter configurations, (2) faithfully recovers the data-generating model on synthetic data, even under adverse training conditions, and (3) outperforms widely-used statistical approaches when applied to naturalistic reading and fMRI data. In addition, procedures for testing scientific hypotheses using CDR are defined and demonstrated, and empirically-motivated best-practices for CDR modeling are proposed. Results support the use of CDR for analyzing psycholinguistic time series, especially in a naturalistic experimental paradigm.

fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.

Much research in cognitive neuroscience supports prediction as a canonical computation of cognition across domains. Is such predictive coding implemented by feedback from higher-order domain-general circuits, or is it locally implemented in domain-specific circuits? What information sources are used to generate these predictions? This study addresses these two questions in the context of language processing. We present fMRI evidence from a naturalistic comprehension paradigm (1) that predictive coding in the brain's response to language is domain-specific, and (2) that these predictions are sensitive both to local word co-occurrence patterns and to hierarchical structure. Using a recently developed continuous-time deconvolutional regression technique that supports data-driven hemodynamic response function discovery from continuous BOLD signal fluctuations in response to naturalistic stimuli, we found effects of prediction measures in the language network but not in the domain-general multiple-demand network, which supports executive control processes and has been previously implicated in language comprehension. Moreover, within the language network, surface-level and structural prediction effects were separable. The predictability effects in the language network were substantial, with the model capturing over 37% of explainable variance on held-out data. These findings indicate that human sentence processing mechanisms generate predictions about upcoming words using cognitive processes that are sensitive to hierarchical structure and specialized for language processing, rather than via feedback from high-level executive control mechanisms.

Isolation of bone marrow-derived stem cells using density-gradient separation.

OBJECTIVE: Our laboratory has established two unique methods to isolate murine hematopoietic stem cells on the basis of functional characteristics such as the ability of stem cells to home to bone marrow and aldehyde dehydrogenase (ALDH) activity. An essential component of both protocols is the separation of whole bone marrow into small-sized cells by counter-flow elutriation. We sought to provide the scientific community with an alternate approach to acquire our stem cells by replacing elutriation with the use of density-gradient centrifugation. METHODS: The elutriated fraction 25 population was characterized based on density using a discontinuous gradient. The long-term reconstituting potential of whole bone marrow cells collected at each density interface was determined by subjecting the fractions to the two-day homing protocol, transplanting them into lethally irradiated recipient mice, and assessing peripheral blood chimerism. We also investigated the ability of high-density bone marrow cells isolated in conjunction with the ALDH protocol to repopulate the hematopoietic system of myeloablated recipients. RESULTS: Bone marrow cells collected at the high-density interface of 1.081/1.087 g/mL (fraction 3) had the capacity for homing to marrow and the ability to provide long-term hematopoietic reconstitution. Fraction three lineage-depleted ALDH-bright cells could also engraft and provide long-term hematopoiesis at limiting dilutions. CONCLUSIONS: Density-gradient centrifugation can be used in conjunction with either of our stem cell isolation protocols to obtain cells with long-term reconstitution ability. We anticipate that this strategy will encourage and enable investigators to study the biology of HSCs isolated using functional characteristics.

Left-Corner Parsing With Distributed Associative Memory Produces Surprisal and Locality Effects.

This article describes a left-corner parser implemented within a cognitively and neurologically motivated distributed model of memory. This parser's approach to syntactic ambiguity points toward a tidy account both of surprisal effects and of locality effects, such as the parsing breakdowns caused by center embedding. The model provides an algorithmic-level (Marr, 1982) account of these breakdowns: The structure of the parser's memory and the nature of incremental parsing produce a smooth degradation of processing accuracy for longer center embeddings, and a steeper degradation when they are nested, in line with recall observations by Miller and Isard (1964) and speed-accuracy trade-off observations by McElree et al. (2003). Modeling results show that this effect is distinct from the effects of ambiguity and exceeds the effect of mere sentence length.

Investigating locality effects and surprisal in written English syntactic choice phenomena.

We investigate the extent to which syntactic choice in written English is influenced by processing considerations as predicted by Gibson's (2000) Dependency Locality Theory (DLT) and Surprisal Theory (Hale, 2001; Levy, 2008). A long line of previous work attests that languages display a tendency for shorter dependencies, and in a previous corpus study, Temperley (2007) provided evidence that this tendency exerts a strong influence on constituent ordering choices. However, Temperley's study included no frequency-based controls, and subsequent work on sentence comprehension with broad-coverage eye-tracking corpora found weak or negative effects of DLT-based measures when frequency effects were statistically controlled for (Demberg & Keller, 2008; van Schijndel, Nguyen, & Schuler 2013; van Schijndel & Schuler, 2013), calling into question the actual impact of dependency locality on syntactic choice phenomena. Going beyond Temperley's work, we show that DLT integration costs are indeed a significant predictor of syntactic choice in written English even in the presence of competing frequency-based and cognitively motivated control factors, including n-gram probability and PCFG surprisal as well as embedding depth (Wu, Bachrach, Cardenas, & Schuler, 2010; Yngve, 1960). Our study also shows that the predictions of dependency length and surprisal are only moderately correlated, a finding which mirrors Dember & Keller's (2008) results for sentence comprehension. Further, we demonstrate that the efficacy of dependency length in predicting the corpus choice increases with increasing head-dependent distances. At the same time, we find that the tendency towards dependency locality is not always observed, and with pre-verbal adjuncts in particular, non-locality cases are found more often than not. In contrast, surprisal is effective in these cases, and the embedding depth measures further increase prediction accuracy. We discuss the implications of our findings for theories of language comprehension and production, and conclude with a discussion of questions our work raises for future research.

Limits of rapid log P determination methods for highly lipophilic and flexible compounds.

Lipophilicity is of crucial importance in many fields including pharmaceutical, environmental, cosmetic and food industries. Whereas different experimental strategies have been developed for rapid lipophilicity determination of new chemical entities, log P determination of highly lipophilic compounds is always challenging. In this study, three published chromatographic methods have been compared on a series of phenylalkanoic acids including the pro-perfume HaloscentD (HD-C12). Different log P values were obtained depending on the chromatographic method used for log P estimation. Molecular modelling suggested that log P variations may be due to the chromatographic conditions applied (isocratic or gradient mode, ratio methanol/water in the mobile phase), responsible of specific conformations of the molecule in solution. Thus, for flexible compounds, published methods have to be used with caution and considered as a good tool to estimate a log P range, depending on the molecular conformational state.

A model of language processing as hierarchic sequential prediction.

Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model with constraints on hierarchy depth can process large newspaper corpora with the same accuracy as a state-of-the-art parser not defined in terms of sequential working memory operations.