True Interactions Research Articles

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

Epigenetics and Rheumatoid Arthritis: Decoding the Interplay

Rheumatoid arthritis, or RA, is a type of chronic inflammatory disorder that is defined by inflammation of the joints, pain, and joint degeneration. Thus, although genetic predispositions play a role in RA development, they are insufficient to explain differences in the disease’s initiation and activity. Epigenomics, referring to changes in the phenotype of a gene or group of genes brought by modification of the DNA molecule without altering its base sequence, offers important information on RA’s multifactorial etiology. The present article aimed at discussing the various epigenetic features in RA such as DNA methylation, histone modification, and non-coding RNA. These changes include, hypomethylation of the pro-inflammatory genes and hypermethylation of the anti-inflammatory genes and this promotes inflammation in the body specifically in RA. Other epigenetic abnormalities causing distortion of disease pathology include dysregulated histone modifications and non-coding RNAs. Knowledge of these epigenetic changes have brought about the intervention strategies in the form of epigenetic therapies. Azacitidine, vorinostat and miRNA based therapies are some of the classes of drugs which demonstrate efficacy in preclinical and clinical trials. That nevertheless there are some limitations that researchers are yet to embrace regarding specificity, delivery, and the true interactions between epigenetics. It is proposed that the further research should be concentrated on the development of precise or pharmacogenomic medicine approaches, the combination of genetic and epigenetic aspects of the drug treatment. In this context, the present review of the epigenetic targets and therapies clearly brings out the possibilities of im-proving RA management and the quality of life of patients through epigenetic interventions.

ChimericFragments: computation, analysisand visualization of global RNA networks.

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seqor CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

ChARpack: The Chemistry Augmented Reality Package.

Off-loading visualization and interaction into virtual reality (VR) using head-mounted displays (HMDs) has gained considerable popularity in simulation sciences, particularly in chemical modeling. Because of its unique way of soft immersion, augmented reality (AR) HMD technology has even more potential to be integrated into the everyday workflow of computational chemists. In this work, we present our environment to explore the prospects of AR in chemistry and general molecular sciences: The chemistry in Augmented Reality package (chARpack). Besides providing an extensible framework, our software focuses on a seamless transition between a 3D stereoscopic view with true 3D interactions and the traditional desktop PC setup to provide users with the best setup for all tasks in their workflow. Using feedback from domain experts, we discuss our design requirements for this kind of hybrid working environment (AR + PC), regarding input, features, degree of immersion, and collaboration.

WeSA: a web server for improving analysis of affinity proteomics data.

Protein-protein interaction experiments still yield many false positive interactions. The socioaffinity metric can distinguish true protein-protein interactions from noise based on available data. Here, we present WeSA (Weighted SocioAffinity), which considers large datasets of interaction proteomics data (IntAct, BioGRID, the BioPlex) to score human protein interactions and, in a statistically robust way, flag those (even from a single experiment) that are likely to be false positives. ROC analysis (using CORUM-PDB positives and Negatome negatives) shows that WeSA improves over other measures of interaction confidence. WeSA shows consistently good results over all datasets (up to: AUC=0.93 and at best threshold: TPR=0.84, FPR=0.11, Precision=0.98). WeSA is freely available without login (wesa.russelllab.org). Users can submit their own data or look for organized information on human protein interactions using the web server. Users can either retrieve available information for a list of proteins of interest or calculate scores for new experiments. The server outputs either pre-computed or updated WeSA scores for the input enriched with information from databases. The summary is presented as a table and a network-based visualization allowing the user to remove those nodes/edges that the method considers spurious.

Imaging fibrinogen assembly on calcium-doped titanium by using scanning electrochemical microscopy

Fibrinogen adsorption is a critical host response to the insertions of various medical devices. Scanning electrochemical microscopy (SECM) has the power to probe the local interactions between proteins and bulk solids in liquid environments; however, our knowledge of the distribution or the assembly structures of protein adsorption is limited. Here, fibrinogen assembly behaviors on calcium-doped titanium are imaged by resolving the strength of the SECM probe’s negative feedback in two-dimensional micro-regions. By using ion implantation techniques, calcium and/or silver were doped into polished titanium surfaces. The calcium-doped titanium was capable of releasing calcium, while the clean titanium and silver-doped titanium did not release their constituents. The two-dimensional discontinuous distribution of the probe current/steady current (i/i0) values were only detectable in calcium-doped groups, and this was incubating duration dependent, showing the micro-regions preferential for fibrinogen assembly in calcium-doped groups. This was consistent with the fibrinogen adsorption morphologies on calcium-doped titanium examined by dye-assisted scanning electron microscopy. It is concluded that protein assembly behaviors on bulk solid surfaces in liquid environments can be imaged by resolving the distribution of the SECM probe currents in two-dimensional areas. The method is promising in illuminating the true surface-protein interactions and the protein adsorption structure–function relations.

Towards a more robust non-invasive assessment of functional connectivity

Abstract Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source-level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of “third party” noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference, and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

Abstract 7400: AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer

Abstract Traditional cancer research methodologies predominantly focus on 2D tumor modeling, including spatial profiling and AI-driven analysis at the 2D level. To advance beyond these limitations, we implemented Holotomography (HT), a 3D, label-free optical imaging method. This technique, leveraging refractive index contrasts, allows for enhanced visualization of tumor tissues, organoids, and cells in three dimensions. Using serial H&E sections and unstained slides, we created detailed 3D tumor models in gastric cancer, providing insights into tumor, immune, and stromal composition, as well as cell-cell interactions and extracellular matrix characteristics. AI algorithms further enhanced our 3D tumor modeling, offering novel perspectives on the initiation and progression of tumor, immune, and stromal components at the subcellular level. Analyzing gastric tumors treated with immune checkpoint inhibitors before, during, and after treatment revealed dynamic changes in spatial composition over time, offering unique insights into the evolution of the 3D tumor ecosystem. Additionally, by integrating spatial transcriptomic and in situ molecular imaging data, we developed a novel Multimodal 3D Tumor Model. This model not only uncovers subcellular-level molecular characteristics but also elucidates cellular interactions during treatment, suggesting potential biomarkers and therapeutic approaches. Lastly, by constructing 3D cellular interaction networks incorporating true 3D interactions, we provided an in-depth view of cellular communication within the tumor microenvironment. This pioneering approach, representing the first Multimodal 3D Tumor Model at the subcellular level, offers novel insights into gastric cancer's spatial evolution, potentially revolutionizing our understanding and treatment of gastric cancer. Citation Format: Yoon Ho Choi, Minji Kim, Katya Redekop, Inyeop Jang, Jean R. Clemenceau, Isabel Barnfather, Sumanth Reddy Nakkireddy, Changjin Hong, Sunho Park, Ji-Youn Sung, Jeong Hwan Park, Shitara Kohei, Aoki Yu, Sung Hak Lee, Sam Wang, Jaeho Cheong, Tae Hyun Hwang. AI-driven multimodal 3D tumor modeling with spatial molecular insights for predicting ICI response in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7400.

Abstract 2744: Exploring the role of the MSLN/MUC16 interaction in cancer progression via the in situ proximity ligation technique

Abstract Introduction: Mesothelin (MSLN) is a glycoprotein present on the surface of mesothelial cells, sometimes used as a tumor marker. Its overexpression in various cancers, including mesothelioma, ovarian, pancreatic, and lung, is linked to increased tumor aggressiveness, evasion of apoptosis, metastasis, and resistance to treatments. Mucin16 (MUC16) belongs to a family of proteins that play a crucial role in cell adhesion, signaling, and immune responses. In cancer, alterations in mucin expression and structure may contribute to tumor progression and metastasis by enhancing cell survival and immune evasion. Mesothelin is known to interact with mucins, influencing tumor cell behavior and metastatic potential. More specifically, the MSLN/MUC16 interaction facilitates tumor cell invasion and peritoneal spread in ovarian cancer. However, reliable molecular tools to study protein interactions have been scarce. Here, we relied on the Naveni® in situ proximity ligation technology to identify MSLN/MUC16 in various cancer samples. The method detects proteins located within interaction range (<40 nm) by means of antibodies conjugated to oligonucleotides that create amplified fluorescent signal. Since the technique does not compromise the structural integrity of tissues, communication between proteins can be studied in their native environment. Materials & Methods: Human FFPE tissue microarrays (TMA) with ovarian primary carcinoma and matched metastatic cancer (80 cores) and ovarian cancer tissue with normal tissue (100 cores) were deparaffinized and subjected to standard heat-induced antigen retrieval and blocking. Staining against the MSLN/MUC16 interaction was performed with the NaveniFlex Tissue MR Atto647N kit (Navinci Diagnostics). Co-staining antibodies against pan-Cytokeratin and Ki67 were included in the detection mix. Immunofluorescence (IF), using the same MSLN and MUC1 primary antibodies, was completed on an additional slide of each TMA, and then secondary antibodies conjugated to Alexa Fluor™ 488 and 594 were used for detection. Imaging was performed on an Olympus scanner VS200. Results & Discussion: In this study, we showed that the interaction is abundant in ovarian cancer, with a distinguishable difference in the signal presence between cancerous and normal adjacent tissue. Furthermore, the additional tumor marker co-stainings give insights into the tumor invasion and growth. When comparing the Naveni® signal of MSLN/MUC16 against IF staining of each, the Naveni® signal shows true interactions, as opposed to expression patterns. Understanding the roles of mesothelin and mucin in cancer, both individually and in interaction, is crucial for developing targeted therapies and diagnostic tools. Further research on their interaction and specific molecular pathways triggered by it is essential for devising effective strategies to combat cancer progression. Citation Format: Sara Bodbin, Doroteya Raykova, Agata Zieba-Wicher. Exploring the role of the MSLN/MUC16 interaction in cancer progression via the in situ proximity ligation technique [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2744.

An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods.

The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

Bayesian Inference of Chemical Mixtures in Risk Assessment Incorporating the Hierarchical Principle

Analyzing health effects associated with exposure to environmental chemical mixtures is a challenging problem in epidemiology, toxicology, and exposure science. In particular, when there are a large number of chemicals under consideration it is difficult to estimate the interactive effects without incorporating reasonable prior information. Based on substantive considerations, researchers believe that true interactions between chemicals need to incorporate their corresponding main effects. In this paper, we use this prior knowledge through a shrinkage prior that a priori assumes an interaction term can only occur when the corresponding main effects exist. Our initial development is for logistic regression with linear chemical effects. We extend this formulation to include non-linear exposure effects and to account for exposure subject to detection limit. We develop an MCMC algorithm using a shrinkage prior that shrinks the interaction terms closer to zero as the main effects get closer to zero. We examine the performance of our methodology through simulation studies and illustrate an analysis of chemical interactions in a case-control study in cancer.

Defining Bacterial RNA-RNA Interactomes Using CLASH.

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen accounting for high mortality rates among infected patients. Transcriptomic regulation by small RNAs (sRNAs) has been shown to regulate networks promoting antibiotic resistance and virulence in S. aureus. Yet, the biological role of most sRNAs during MRSA host infection remains unknown. To fill this gap, in collaboration with the lab of Jai Tree, we performed comprehensive RNA-RNA interactome analyses in MRSA using CLASH under conditions that mimic the host environment. Here we present a detailed version of this optimized CLASH (cross-linking, ligation, and sequencing of hybrids) protocol we recently developed, which has been tailored to explore the RNA interactome in S. aureus as well as other Gram-positive bacteria. Alongside, we introduce a compilation of helpful Python functions for analyzing folding energies of putative RNA-RNA interactions and streamlining sRNA and mRNA seed discovery in CLASH data. In the accompanying computational demonstration, we aim to establish a standardized strategy to evaluate the likelihood that observed chimeras arise from true RNA-RNA interactions.

Feasibility of Gene Regulatory Network Inference Under Different Noise Conditions

One of the most difficult and pressing problems in computational cell biology is the inference of gene regulatory network structure from transcriptomic data. Although this topic has been widely studied for more than a decade, it is still unclear how well these methods work and how their performance differs under different conditions. We analyze the feasibility of network inference from mRNA abundance data using simulations of gene regulatory interactions, considering both intrinsic and extrinsic noise in the process of gene expression. We find that under conditions of only intrinsic noise in gene expression, the correlation between mRNA levels of genes in an activation relationship is quite low, suggesting that the task of network inference from transcriptomic data is very difficult under these conditions. By contrast, extrinsic noise affecting the process of gene expression, which could come from an upstream regulator, external stimulus, or change in the cell state, results in higher correlation between mRNA levels of these genes, potentially making the task of network inference from mRNA data more feasible. Lastly, we analyze the problem of false positives between genes that have no direct interaction but share a common upstream regulator, and explore a strategy for distinguishing between these false positives and true interactions based on noise profiles of mRNA expression levels.

Detecting Shape-Based Interactions Among Environmental Chemicals Using an Ensemble of Exposure-Mixture Regression and Interpretable Machine Learning Tools

There is growing interest in discovering interactions between multiple environmental chemicals associated with increased adverse health effects. However, most existing approaches (1) either use a projection or product of multiple chemical exposures, which are difficult to interpret and (2) cannot simultaneously handle multi-ordered interactions. Therefore, we develop and validate a method to discover shape-based interactions that mimic usual toxicological interactions. We developed the Multi-ordered explanatory interaction (Moxie) algorithm by merging the efficacy of Extreme Gradient Boosting with the inferential power of Weighted Quantile Sum regression to extract synergistic interactions associated with the outcome/odds of disease in an adverse direction. We evaluated the algorithm’s performance through simulations and compared it with the currently available gold standard, the signed-iterative random forest algorithm. We used the 2017–18 US-NHANES dataset (n = 447 adults) to evaluate interactions among nine per- and poly-fluoroalkyl substances and five metals measured in whole blood in association with serum low-density lipoprotein cholesterol. In simulations, the Moxie algorithm was highly specific and sensitive and had very low false discovery rates in detecting true synergistic interactions of 2nd, 3rd, and 4th order through moderate (n = 250) to large (n = 1000) sample sizes. In NHANES data, we found a two-order synergistic interaction between cadmium and lead detected in people with whole-blood cadmium concentrations and lead above 0.605 ug/dL and 1.485 ug/dL, respectively. Our findings demonstrate a novel validated approach in environmental epidemiology for detecting shape-based toxicologically mimicking interactions by integrating exposure-mixture regression and machine learning methods.

Reconstructing multi-strain pathogen interactions from cross-sectional survey data via statistical network inference.

Infectious diseases often involve multiple pathogen species or multiple strains of the same pathogen. As such, knowledge of how different pathogens interact is key to understand and predict the outcome of interventions targeting only a subset of species or strains involved in disease. Population-level data may be useful to infer pathogen strain interactions, but most previously used inference methods only consider uniform interactions between all strains or focus on marginal pairwise interactions. As such, these methods are prone to bias induced by indirect interactions through other strains. Here, we evaluated statistical network inference for reconstructing heterogeneous interactions from cross-sectional surveys detecting joint presence/absence patterns of pathogen strains within hosts. We applied various network models to simulated survey data, representing endemic infection states of multiple pathogen strains with potential interactions in acquisition or clearance of infection. Satisfactory performance was demonstrated by the estimators converging to the true interactions. Accurate reconstruction of interaction networks was achieved by regularization or penalization for sample size. Although performance deteriorated in the presence of host heterogeneity, this was overcome by correcting for individual-level risk factors. Our work demonstrates how statistical network inference could prove useful for detecting multi-strain pathogen interactions and may have applications beyond epidemiology.

Simultaneous photon counting and charge integrating for pulse pile-up correction in paralyzable photon counting detectors

Objective. In photon counting detectors (PCDs), electric pulses induced by two or more x-ray photons can pile up and result in count losses when their temporal separation is less than the detector dead time. The correction of pulse pile-up-induced count loss is particularly difficult for paralyzable PCDs since a given value of recorded counts can correspond to two different values of true photon interactions. In contrast, charge (energy) integrating detectors work by integrating collected electric charge induced by x-rays over time and do not suffer from pile-up losses. This work introduces an inexpensive readout circuit element to the circuits of PCDs to simultaneously collect time-integrated charge to correct pile-up-induced count losses. Approach. Prototype electronics were constructed to collect time-integrated charges simultaneously with photon counts. A splitter was used to feed the electric signal in parallel to both a digital counter and a charge integrator. After recording PCD counts and integrating collected charge, a lookup table can be generated to map raw counts in the total- and high-energy bins and total charge to estimate pile-up-free true counts. Proof-of-concept imaging experiments were performed with a CdTe-based PCD array to test this method. Main results. The proposed electronics successfully recorded photon counts and time-integrated charge simultaneously, and whereas photon counts exhibited paralyzable pulse pile-up, time-integrated charge using the same electric signal as the counts measurement was linear with x-ray flux. With the proposed correction, paralyzable PCD counts became linear with input flux for both total- and high-energy bins. At high flux levels, uncorrected post-log measurements of PMMA objects severely overestimated radiological path lengths for both energy bins. After the proposed correction, the non-monotonic measurements again became linear with flux and accurately represented the true radiological path lengths. No impact on the spatial resolution was observed after the proposed correction in images of a line-pair test pattern. Significance. Time-integrated charge can be used to correct for pulse pile-up in paralyzable PCDs where analytical solutions may be difficult to use, and integrated charge can be collected simultaneously with counts using inexpensive electronics.

A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time‐series data

Abstract Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions non‐parametrically from time‐series data. NODEs, however, are slow to fit, and inferred interactions usually are not compared with the ground truth. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We compare these results against three existing approaches for estimating ecological interactions, standard NODEs, ODE models and convergent cross‐mapping (CCM). We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate and rotifer population, in the hare and lynx system, and the Maizuru Bay community featuring 11 species. Our BNGM approach allows us to reduce the fitting time of NODE systems to only a few seconds and provides accurate estimates of ecological interactions in the artificial system, as true ecological interactions are recovered with minimal error. Our benchmark analysis reveals that our approach is both faster and more accurate than standard NODEs and parametric ODEs, while CCM was found to be faster but less accurate. The analysis of the replicated time series reveals that only the strongest interactions are consistent across replicates, while the analysis of the Maizuru community shows the strong negative impact of the chameleon goby on most species of the community, and a potential indirect negative effect of temperature by favouring goby population growth. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting to larger systems, cross‐validation and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.

What constitutes a community? A co-occurrence exploration of the Costa Rican avifauna

ABSTRACT The concept of a “community” as a form of organization for natural biological systems is both widespread and widely accepted within the ecological and biological sciences. Communities have been defined as groups of organisms that interact in ways that denote interdependence between individuals and taxa (e.g. as defined by “food webs”) but they have also been defined as groups of co-occurring organisms that are assumed to interact by virtue of their shared spatiotemporal existence. The latter definition has been debated and challenged in the literature, with mounting evidence for co-occurrence being more indicative of coincident ecological niches in space and time rather than being evidence of ecological interaction or dependency. Using a dataset of 460 Costa Rican bird species divided into breeding and non-breeding season datasets, we empirically demonstrate the ways in which co-occurrence can create illusory communities based on similar occupied ecological niches and similar patterns of co-occurrence at different times of year. We discuss the importance of discerning coincidental co-occurrence from true ecological interactions that would manifest a true community, and further address the importance of differentiating communities of co-occurrence from communities of demonstrable ecological interaction. While co-occurrence is a necessary aspect of interspecific interactions, we discuss and demonstrate here that such co-occurrence does not make a community, nor should explicit patterns of co-occurrence be seen as evidence for evolutionarily important ecological interactions.

Examining acoustics and intelligibility of people with dysarthria in communicative interactions

A comprehensive understanding of the speech capabilities of speakers with dysarthria requires an examination of speech produced in true interactions. Intelligibility in such interactions may reflect joint contributions of speakers and listeners (Olmstead et al., 2020). In this study, nine people with dysarthria secondary to amyotrophic lateral sclerosis (ALS) participated in an interactive word-matching task with nine typical individuals. In experiment one, we examined speech produced by the people with dysarthria in the interactive task and compared it to speech produced in two non-interactive tasks (baseline and clear speech). Specifically, we measured F1, F2, and vowel duration of the vowels /æ, ɛ, ɪ, and i/. We found task-related differences such that acoustic contrast was greater in the interactive task than baseline. Notably, speech in the interactive task was similar to clear speech in its spectral but not temporal patterns. In experiment two, we obtained perceptual judgements of these productions from 54 naïve listeners. We found a concordant pattern: the vowels from the matching task were easier for listeners to identify than baseline. Overall, results of these studies indicate that speakers with dysarthria can make speech adjustments in communicative tasks that are beneficial for listeners.