Assessment of the Aryl Hydrocarbon Receptor-Mediated Effects of Aromatic Sensitizers in Paper Recycling Effluent Employing Zebrafish Embryos and in Silico Docking.

Aromatic sensitizers and related substances (SRCs), which are crucial in the paper industry for facilitating color-forming and color-developing chemical reactions, inadvertently contaminate effluents during paper recycling. Owing to their structural resemblance to endocrine-disrupting aromatic organic compounds, concerns have arisen about potential adverse effects on aquatic organisms. We focused on SRC effects via the aryl hydrocarbon receptor (AHR), employing molecular docking simulations and zebrafish (Danio rerio) embryo exposure assessments. Molecular docking revealed heightened binding affinities between certain SRCs in the paper recycling effluents and zebrafish Ahr2 and human AHR, which are pivotal components in the SRC toxicity mechanism. Fertilized zebrafish eggs were exposed to SRCs for up to 96 h post fertilization; among these substances, benzyl 2-naphthyl ether (BNE) caused morphological abnormalities, such as pericardial edema and shortened body length, at relatively low concentrations (1 µM) during embryogenesis. Gene expression of cytochrome P450 1A (cyp1a) and ahr2 was also significantly increased by BNE. Co-exposure to the AHR antagonist CH-223191 only partially mitigated BNE's phenotypic effects, despite the effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin being relatively well restored by CH-223191, indicating BNE's AHR-independent toxic mechanisms. Furthermore, some SRCs, including BNE, exhibited in silico binding affinity to the estrogen receptor and upregulation of cyp19a1b gene expression. Therefore, additional insights into the toxicity of SRCs and their mechanisms are essential. The present results provide important information on SRCs and other papermaking chemicals that could help minimize the environmental impact of the paper industry. Environ Toxicol Chem 2024;00:1-13. © 2024 SETAC.

Computational simulation-assisted template selection of magnetic MOFs molecularly imprinted materials applying the adsorption and detection of multiple fluoroquinolones.

This study utilized computational simulation and surface molecular imprinting technology to develop a magnetic metal-organic framework molecularly imprinted polymer (Fe3O4@ZIF-8@SMIP) capable of selectively recognizing and detecting multiple fluoroquinolones (FQs). The Fe3O4@ZIF-8@SMIP material was synthesized using the "common" template-ofloxacin, identified by computational simulation, demonstrating notable adsorption capacity (88.61-212.93 mg g-1) and rapid mass-transfer features (equilibration time: 2-3 min) for all tested FQs, consistent with Langmuir adsorption model. Subsequently, this material was employed as a magnetic solid-phase-extraction adsorbent for adsorption and detection of multiple FQs by combining with high performance liquid chromatography. The developed method exhibited good linearity for various FQs within the concentration range of 0.1-500 µg L-1, with low limit of detection (0.0605-0.1529 µg L-1) and limit of quantitation (0.2017-0.5097 µg L-1). Satisfactory recoveries (88.38-103.44%) were obtained when applied to spiked food samples, demonstrating the substantial potential of this Fe3O4@ZIF-8@SMIP material for rapid enrichment and identification for multiple FQs residues.

Design and comparison of computationally efficient uniaxial stress-strain models of the lung parenchyma for real-time applications.

Real-time clinical applications such as robotic lung surgery, tumor localization, atelectasis diagnosis, tumor motion prediction for radiation therapy of lung cancer, or surgery training are in need of biomechanical models of lungs, not necessarily highly accurate, but with good computational properties. These properties can include one or several of the following: low computation time, low memory resource requirement, a low number of parameters, and ease of parameter identification in real-time. Among the numerous existing models of lung parenchyma, some may be well suited for real-time applications; however, they should be extensively assessed against both accuracy and computational efficiency criteria to make an informed choice depending on the requirements of the application. After demonstrating how to derive a real-time compliant force-indentation model from a unixial stress-strain model with rational expression, the core purpose of this paper is to propose such an evaluation of selected models in fitting human lung parenchyma experimental and synthetic data of uniaxial tension. Furthermore, new uniaxial stress-strain models are developed based on an empirical observation of the volumetric behavior of the lungs along with an emphasis on computational performance. These new proposed models are competitive with existing one in terms of computational efficiency and compliance with experimental and synthetic data. One of them reduces the prediction error by 2 compared to other investigated models while maintaining an excellent adjusted coefficient of determination between 0.999 and 1 across various datasets. It exhibits excellent real-time capabilities with an explicit rational expression, only 3 parameters and linear numerator and denominator in the parameters. It is computed with only 20 floating point operations (flops) while another proposed model even requires as few as 2 flops.

Multi-layered computational gene networks by engineered tristate logics.

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

m5c-iDeep: 5-Methylcytosine sites identification through deep learning.

5-Methylcytosine (m5c) is a modified cytosine base which is formed as the result of addition of methyl group added at position 5 of carbon. This modification is one of the most common PTM that used to occur in almost all types of RNA. The conventional laboratory methods do not provide quick reliable identification of m5c sites. However, the sequence data readiness has made it feasible to develop computationally intelligent models that optimize the identification process for accuracy and robustness. The present research focused on the development of in-silico methods built using deep learning models. The encoded data was then fed into deep learning models, which included gated recurrent unit (GRU), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM). After that, the models were subjected to a rigorous evaluation process that included both independent set testing and 10-fold cross validation. The results revealed that LSTM-based model, m5c-iDeep, outperformed revealing 99.9 % accuracy while comparing with existing m5c predictors. In order to facilitate researchers, m5c-iDeep was also deployed on a web-based server which is accessible at https://taseersuleman-m5c-ideep-m5c-ideep.streamlit.app/.

Flow reduction due to arterial catheterization during stroke treatment - A computational study using a distributed compartment model.

The effectiveness of various stroke treatments depends on the anatomical variability of the cerebral vasculature, particularly the collateral blood vessel network. Collaterals at the level of the Circle of Willis and distal collaterals, such as the leptomeningeal arteries, serve as alternative avenues of flow when the primary pathway is obstructed during an ischemic stroke. Stroke treatment typically involves catheterization of the primary pathway, and the potential risk of further flow reduction to the affected brain area during this treatment has not been previously investigated. To address this clinical question, we derived the lumped parameters for catheterized blood vessels and implemented a corresponding distributed compartment (0D) model. This 0D model was validated against an experimental model and benchmark test cases solved using a 1D model. Additionally, we compared various off-center catheter trajectories modeled using a 3D solver to this 0D model. The differences between them were minimal, validating the simplifying assumption of the central catheter placement in the 0D model. The 0D model was then used to simulate blood flows in realistic cerebral arterial networks with different collateralization characteristics. Ischemic strokes were modeled by occlusion of the M1 segment of the middle cerebral artery in these networks. Catheters of different diameters were inserted up to the obstructed segment and flow alterations in the network were calculated. Results showed up to 45% maximum blood flow reduction in the affected brain region. These findings suggest that catheterization during stroke treatment may have a further detrimental effect for some patients with poor collateralization.

Current advances in experimental and computational approaches to enhance CAR T cell manufacturing protocols and improve clinical efficacy.

Since the FDA's approval of chimeric antigen receptor (CAR) T cells in 2017, significant improvements have been made in the design of chimeric antigen receptor constructs and in the manufacturing of CAR T cell therapies resulting in increased in vivo CAR T cell persistence and improved clinical outcome in certain hematological malignancies. Despite the remarkable clinical response seen in some patients, challenges remain in achieving durable long-term tumor-free survival, reducing therapy associated malignancies and toxicities, and expanding on the types of cancers that can be treated with this therapeutic modality. Careful analysis of the biological factors demarcating efficacious from suboptimal CAR T cell responses will be of paramount importance to address these shortcomings. With the ever-expanding toolbox of experimental approaches, single-cell technologies, and computational resources, there is renowned interest in discovering new ways to streamline the development and validation of new CAR T cell products. Better and more accurate prognostic and predictive models can be developed to help guide and inform clinical decision making by incorporating these approaches into translational and clinical workflows. In this review, we provide a brief overview of recent advancements in CAR T cell manufacturing and describe the strategies used to selectively expand specific phenotypic subsets. Additionally, we review experimental approaches to assess CAR T cell functionality and summarize current in silico methods which have the potential to improve CAR T cell manufacturing and predict clinical outcomes.

A new era of synthetic biology-microbial community design.

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities. Graphical Abstract.

Exploring essential oil-based bio-composites: molecular docking and in vitro analysis for oral bacterial biofilm inhibition.

Oral bacterial biofilms are the main reason for the progression of resistance to antimicrobial agents that may lead to severe conditions, including periodontitis and gingivitis. Essential oil-based nanocomposites can be a promising treatment option. We investigated cardamom, cinnamon, and clove essential oils for their potential in the treatment of oral bacterial infections using in vitro and computational tools. A detailed analysis of the drug-likeness and physicochemical properties of all constituents was performed. Molecular docking studies revealed that the binding free energy of a Carbopol 940 and eugenol complex was -2.0 kcal/mol, of a Carbopol 940-anisaldehyde complex was -1.9 kcal/mol, and a Carbapol 940-eugenol-anisaldehyde complex was -3.4 kcal/mol. Molecular docking was performed against transcriptional regulator genes 2XCT, 1JIJ, 2Q0P, 4M81, and 3QPI. Eugenol cinnamaldehyde and cineol presented strong interaction with targets. The essential oils were analyzed against Staphylococcus aureus and Staphylococcus epidermidis isolated from the oral cavity of diabetic patients. The cinnamon and clove essential oil combination presented significant minimum inhibitory concentrations (MICs) (0.0625/0.0312 mg/mL) against S. epidermidis and S. aureus (0.0156/0.0078 mg/mL). In the anti-quorum sensing activity, the cinnamon and clove oil combination presented moderate inhibition (8 mm) against Chromobacterium voilaceum with substantial violacein inhibition (58% ± 1.2%). Likewise, a significant biofilm inhibition was recorded in the case of S. aureus (82.1% ± 0.21%) and S. epidermidis (84.2% ± 1.3%) in combination. It was concluded that a clove and cinnamon essential oil-based formulation could be employed to prepare a stable nanocomposite, and Carbapol 940 could be used as a compatible biopolymer.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Morphology of Copper Nanofoams for Radiation Hydrodynamics and Fusion Applications Investigated by 3D Ptychotomography.

The performance of metal and polymer foams used in inertial confinement fusion (ICF), inertial fusion energy (IFE), and high-energy-density (HED) experiments is currently limited by our understanding of their nanostructure and its variation in bulk material. We utilized an X-ray-free electron laser (XFEL) together with lensless X-ray imaging techniques to probe the 3D morphology of copper foams at nanoscale resolution (28 nm). The observed morphology of the thin shells is more varied than expected from previous characterizations, with a large number of them distorted, merged, or open, and a targeted mass density 14% less than calculated. This nanoscale information can be used to directly inform and improve foam modeling and fabrication methods to create a tailored material response for HED experiments.

Impact of cardiac patch alignment on restoring post-infarct ventricular function.

Acute myocardial infarction (MI) leads to a loss of cardiac function which, following adverse ventricular remodeling (AVR), can ultimately result in heart failure. Tissue-engineered contractile patches placed over the infarct offer potential for restoring cardiac function and reducing AVR. In this computational study, we investigate how improvement of pump function depends on the orientation of the cardiac patch and the fibers therein relative to the left ventricle (LV). Additionally, we examine how model outcome depends on the choice of material properties for healthy and infarct tissue. In a finite element model of LV mechanics, an infarction was induced by eliminating active stress generation and increasing passive tissue stiffness in a region comprising 15% of the LV wall volume. The cardiac patch was modeled as a rectangular piece of healthy myocardium with a volume of 25% of the infarcted tissue. The orientation of the patch was varied from 0 to 150 ∘ relative to the circumferential plane. The infarct reduced stroke work by 34% compared to the healthy heart. Optimal patch support was achieved when the patch was oriented parallel to the subepicardial fiber direction, restoring 9% of lost functionality. Typically, about one-third of the total recovery was attributed to the patch, while the remainder resulted from restored functionality in native myocardium adjacent to the infarct. The patch contributes to cardiac function through two mechanisms. A contribution of tissue in the patch and an increased contribution of native tissue, due to favorable changes in mechanical boundary conditions.

Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models.

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

NMDtxDB: Data-driven identification and annotation of human NMD target transcripts.

The nonsense-mediated RNA decay (NMD) pathway is a crucial mechanism of mRNA quality control. Current annotations of NMD substrate RNAs are rarely data-driven, but use general established rules. We present a dataset with 4 cell lines and combinations for SMG5, SMG6 and SMG7 knockdowns or SMG7 knockout. Based on this dataset, we implemented a workflow that combines Nanopore and Illumina sequencing to assemble a transcriptome, which is enriched for NMD target transcripts. Moreover, we use coding sequence information from Ensembl, Gencode consensus RiboSeq ORFs and OpenProt to enhance the CDS annotation of novel transcript isoforms. In summary, 302,889 transcripts were obtained from the transcriptome assembly process, out of which, 24% are absent from Ensembl database annotations, 48,213 contain a premature stop codon and 6,433 are significantly upregulated in three or more comparisons of NMD active vs deficient cell lines. We present an in-depth view on these results through the NMDtxDB database, which is available at https://shiny.dieterichlab.org/app/NMDtxDB, and supports the study of NMD-sensitive transcripts. We open sourced our implementation of the respective web-application and analysis workflow at https://github.com/dieterich-lab/NMDtxDB and https://github.com/dieterich-lab/nmd-wf.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Utilizing deep learning model for assessing melanocytic density in resection margins of lentigo maligna.

BACKGROUND: Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We developed a deep learning model for detection of melanocytes in resection margins of LM. METHODS: In total, 353 whole slide images (WSIs) were included. 295 WSIs were used for training and 58 for validation and testing. The algorithm was trained with 3,973 manual pixel-wise annotations. The AI analyses were compared to those of three blinded dermatopathologists and two pathology residents, who performed their evaluations without AI and AI-assisted. Immunohistochemistry (SOX10) served as the reference standard. We used a dichotomized cutoff for low and high risk of recurrence (≤ 25 melanocytes in an area of 0.5 mm for low risk and > 25 for high risk). RESULTS: The AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 in discriminating margins with low and high recurrence risk. In comparison, the AUC for dermatopathologists ranged from 0.72 to 0.90 and for the residents in pathology, 0.68 to 0.80. Additionally, with aid of the AI model the performance of two pathologists significantly improved. CONCLUSIONS: The deep learning showed notable accuracy in detecting resection margins of LM with a high versus low risk of recurrence. Furthermore, the use of AI improved the performance of 2/5 pathologists. This automated tool could aid pathologists in the assessment or pre-screening of LM margins.

Modelling individual differences in reading using an optimised MikeNet simulator: the impact of reading instruction.

Reading is vital for acquiring knowledge and studies have demonstrated that phonology-focused interventions generally yield greater improvements than meaning-focused interventions in English among children with reading disabilities. However, the effectiveness of reading instruction can vary among individuals. Among the various factors that impact reading skills like reading exposure and oral language skills, reading instruction is critical in facilitating children's development into skilled readers; it can significantly influence reading strategies, and contribute to individual differences in reading. To investigate this assumption, we developed a computational model of reading with an optimised MikeNet simulator. In keeping with educational practices, the model underwent training with three different instructional methods: phonology-focused training, meaning-focused training, and phonology-meaning balanced training. We used semantic reliance (SR), a measure of the relative reliance on print-to-sound and print-to-meaning mappings under the different training conditions in the model, as an indicator of individual differences in reading. The simulation results demonstrated a direct link between SR levels and the type of reading instruction. Additionally, the SR scores were able to predict model performance in reading-aloud tasks: higher SR scores were correlated with increased phonological errors and reduced phonological activation. These findings are consistent with data from both behavioral and neuroimaging studies and offer insights into the impact of instructional methods on reading behaviors, while revealing individual differences in reading and the importance of integrating OP and OS instruction approaches for beginning readers.

Computational-Based Polyphenol Therapy for Nonsmall Cell Lung Cancer: Naringin Coamorphous Systems for Solubility and Bioavailability Enhancement.

In this research, we utilized molecular simulations to create co-amorphous materials (CAMs) of ceritinib (CRT) with the objective of improving its solubility and bioavailability. We identified naringin (NRG) as a suitable co-former for CRT CAMs based on binding energy and intermolecular interactions through computational modeling. We used the solvent evaporation method to produce CAMs of CRT and NRG, expecting to enhance both solubility and bioavailability simultaneously. The solid-state characterization using techniques like differential scanning calorimeter, X-ray powder diffraction, and Fourier-transform infrared spectroscopy affirmed the formation of a single amorphous phase and the presence of intermolecular interactions between CRT and NRG in the CAMs. These materials remained physically stable for up to six months under dry conditions at 40 °C. Moreover, the CAMs demonstrated significant improvements in the solubility and dissolution of CRT (specifically in the ratio CRT:NRG 1:2). This, in turn, led to an increase in cytotoxicity, apoptotic cells, and G0/G1 phase inhibition in A549 cells compared to CRT alone. Furthermore, CRT permeability is also improved twofold, as estimated by the everted gut sac method. The enhanced solubility of CAMs also positively affected the pharmacokinetic parameters. When compared to the physical mixture, the CAMs of CRT:NRG 2:1 exhibited a 2.1-fold increase in CRT exposure (AUC0-t) and a 2.4-fold increase in plasma concentration (Cmax).

Purkinje cell models: past, present and future.

The investigation of the dynamics of Purkinje cell (PC) activity is crucial to unravel the role of the cerebellum in motor control, learning and cognitive processes. Within the cerebellar cortex (CC), these neurons receive all the incoming sensory and motor information, transform it and generate the entire cerebellar output. The relatively homogenous and repetitive structure of the CC, common to all vertebrate species, suggests a single computation mechanism shared across all PCs. While PC models have been developed since the 70's, a comprehensive review of contemporary models is currently lacking. Here, we provide an overview of PC models, ranging from the ones focused on single cell intracellular PC dynamics, through complex models which include synaptic and extrasynaptic inputs. We review how PC models can reproduce physiological activity of the neuron, including firing patterns, current and multistable dynamics, plateau potentials, calcium signaling, intrinsic and synaptic plasticity and input/output computations. We consider models focusing both on somatic and on dendritic computations. Our review provides a critical performance analysis of PC models with respect to known physiological data. We expect our synthesis to be useful in guiding future development of computational models that capture real-life PC dynamics in the context of cerebellar computations.

Modelling constructivist language acquisition through syntactico-semantic pattern finding.

The constructivist acquisition of language by children has been elaborately documented by researchers in psycholinguistics and cognitive science. However, despite the centrality of human-like communication in the field of artificial intelligence, no faithful computational operationalizations of the mechanisms through which children learn language exist to date. In this article, we fill part of this void by introducing a mechanistic model of the constructivist acquisition of language through syntactico-semantic pattern finding. Concretely, we present a methodology for learning grammars based on similarities and differences in the form and meaning of linguistic observations alone. The resulting grammars consist of form-meaning mappings of variable extent and degree of abstraction, called constructions, which facilitate both language comprehension and production. Applying our methodology to the CLEVR benchmark dataset, we provide a proof of concept that demonstrates the online, incremental, data-efficient, transparent and effective learning of item-based construction grammars from utterance-meaning pairs.