Comprehensive Framework Research Articles

Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers.

Early detection and accurate diagnosis of brain morphological abnormalities are essential for the effective management and treatment of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural magnetic resonance imaging (MRI) is a powerful support tool to aid in disease diagnosis and prediction. In this research study, we present an innovative approach to predict Alzheimer's disease (AD) and mild cognitive impairment (MCI) using MRI data, which integrates regional interest (ROI)-based methodology and deep learning within a comprehensible framework. The proposed method involves dividing the brain into 138 predetermined sections based on anatomical information. Next, we apply three-dimensional vision transformers (3D-ViTs) to each ROI individually, harnessing the power of deep learning. To improve prediction accuracy, we employ a deep belief network (DBN) as an ensemble learning model. Evaluating our approach on the baseline structural MRI dataset obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, and comparing it against five other competing models, we demonstrate its performance across four binary classification tasks and a three-class classification test (AD vs MCI vs CN (Cognitively Normal)). The proposed system outperforms existing models and provides interpretable insights into the brain regions that significantly contribute to solving each classification problem. Our findings align with the existing body of literature and hold promise for guiding future research directions in this domain.

Reconstructing representations using diffusion models for multimodal sentiment analysis through reading comprehension

The primary challenge in multimodal sentiment analysis (MSA), which utilizes textual, audio, and visual information to analyze speakers' emotions, lies in constructing representation vectors that incorporate both unimodal semantic and multimodal interaction information. While existing research has extensively focused on multimodal fusion strategies, there remains insufficient exploration of the intrinsic potential within concurrently enhancing unimodal and multimodal representations. To address this gap, we introduce two additional steps to traditional three-step MSA: text modality enhancement through the machine reading comprehension (MRC) framework and multimodal representation reconstruction via proposing the diverse diffusion denoising autoencoder (D3AE) module. The MRC queries are integrated to locate sentiment-related prior knowledge, thereby deepening textual semantic understanding from a pretrained language model. Meanwhile, D3AE employs a single-step denoising strategy along with diffusion models across multiple time intervals, enabling efficient reconstruction and enhancement of multimodal representations. Extensive experiments conducted on two benchmark datasets, CMU-MOSI and CMU-MOSEI, validate that our model, MRC-D3AE, achieves state-of-the-art performance. The superiority of our model over existing baselines is primarily attributed to integrating MRC for enhancing text modality and D3AE for reconstructing multimodal representations.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no retraining, which significantly reduces the computation costs. Furthermore, we design a cosparsifying strategy to comprehensively trim all the three core elements of GNNs: graph structures, node features, and model parameters. Next, to refine the pruning operation, we introduce a regrowth process into our CGP framework, to reestablish the pruned but important connections. The proposed CGP is evaluated over a node classification task across six GNN architectures, including shallow models [graph convolutional network (GCN) and graph attention network (GAT)], shallow-but-deep-propagation models [simple graph convolution (SGC) and approximate personalized propagation of neural predictions (APPNP)], and deep models [GCN via initial residual and identity mapping (GCNII) and residual GCN (ResGCN)], on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark (OGB). Experiments reveal that the proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of the existing methods.

Intra species dissection of phytophthora capsici resistance in black pepper

IntroductionBlack pepper, a financially significant tropical crop, assumes a pivotal role in global agriculture for the major source of specie flavor. Nonetheless, the growth and productivity of black pepper face severe impediments due to the destructive pathogen Phytophthora capsici, ultimately resulting in black pepper blight. The dissecting for the genetic source of pathogen resistance for black pepper is beneficial for its global production. The genetic sources include the variations on gene coding sequences, transcription capabilities and epigenetic modifications, which exerts hierarchy of influences on plant defense against pathogen. However, the understanding of genetic source of disease resistance in black pepper remains limited. MethodsThe wild species Piper flaviflorum (P. flaviflorum, Pf) is known for blight resistance, while the cultivated species P. nigrum is susceptible. To dissecting the genetic sources of pathogen resistance for black pepper, the chromatin modification on H3K4me3 and transcriptome of black pepper species were profiled for genome wide comparative studies, applied with CUT&Tag and RNA sequencing technologies. ResultsThe intraspecies difference between P. flaviflorum and P. nigrum on gene body region led to coding variations on 5137 genes, including 359 gene with biotic stress responses and regulation. P. flaviflorum exhibited a more comprehensive resistance response to Phytophthora capsica in terms of transcriptome features. The pathogen responsive transcribing was significant associated with histone modification mark of H3K4me3 in black pepper. The collective data on variations of sequence, transcription activity and chromatin structure lead to an exclusive jasmonic acid-responsive pathway for disease resistance in P. flaviflorum was revealed. This research provides a comprehensive frame work to identify the fine genetic source for pathogen resistance from wild species of black pepper.

Modeling High-Order Relationships: Brain-Inspired Hypergraph-Induced Multimodal-Multitask Framework for Semantic Comprehension.

Semantic comprehension aims to reasonably reproduce people's real intentions or thoughts, e.g., sentiment, humor, sarcasm, motivation, and offensiveness, from multiple modalities. It can be instantiated as a multimodal-oriented multitask classification issue and applied to scenarios, such as online public opinion supervision and political stance analysis. Previous methods generally employ multimodal learning alone to deal with varied modalities or solely exploit multitask learning to solve various tasks, a few to unify both into an integrated framework. Moreover, multimodal-multitask cooperative learning could inevitably encounter the challenges of modeling high-order relationships, i.e., intramodal, intermodal, and intertask relationships. Related research of brain sciences proves that the human brain possesses multimodal perception and multitask cognition for semantic comprehension via decomposing, associating, and synthesizing processes. Thus, establishing a brain-inspired semantic comprehension framework to bridge the gap between multimodal and multitask learning becomes the primary motivation of this work. Motivated by the superiority of the hypergraph in modeling high-order relations, in this article, we propose a hypergraph-induced multimodal-multitask (HIMM) network for semantic comprehension. HIMM incorporates monomodal, multimodal, and multitask hypergraph networks to, respectively, mimic the decomposing, associating, and synthesizing processes to tackle the intramodal, intermodal, and intertask relationships accordingly. Furthermore, temporal and spatial hypergraph constructions are designed to model the relationships in the modality with sequential and spatial structures, respectively. Also, we elaborate a hypergraph alternative updating algorithm to ensure that vertices aggregate to update hyperedges and hyperedges converge to update their connected vertices. Experiments on the dataset with two modalities and five tasks verify the effectiveness of HIMM on semantic comprehension.

Chinese EMR Named Entity Recognition Using Fused Label Relations Based on Machine Reading Comprehension Framework.

Chinese electronic medical record (EMR) presents significant challenges for named entity recognition (NER) due to their specialized nature, unique language features, and diverse expressions. Traditionally, NER is treated as a sequence labeling task, where each token is assigned a label. Recent research has reframed NER within the machine reading comprehension (MRC) framework, extracting entities in a question-answer format, achieving state-of-the-art performance. However, these MRC-based methods have a significant limitation: they extract entities of various types independently, ignoring their interrelations. To address this, we introduce the Fusion Label Relations with MRC (FLR-MRC) model, which enhances the MRC model by implicitly capturing dependencies among entity types. FLR-MRC models interrelations between labels using graph attention networks, integrating these with textual data to identify entities. On the benchmark CMeEE and CCKS2017-CNER datasets, FLR-MRC achieves F1-scores of 0.6652 and 0.9101, respectively, outperforming existing clinical NER methods.

A Comprehensive Evaluation Framework of Software Visualizations Effectiveness.

Visualizations are useful in dealing with complex software systems, especially in maintenance and evolution tasks. Software visualization tools can help reduce the cognitive burden on practitioners when trying to understand these systems. However, a major challenge in designing new visualization techniques and tools is evaluating their effectiveness for specific tasks and users. If a visualization tool is not effective for practitioners, they are unlikely to adopt it. Existing evaluation frameworks for visualizations mainly focus on expressiveness, which refers to the ability of the visualization to show all necessary information. However, evaluating the effectiveness of visualizations is an open research problem, especially in terms of quantifying it. To address this problem, we propose a multi-dimensional evaluation framework that focuses on evaluating visualizations in terms of their qualitative, quantitative, and cognitive aspects. The framework includes seven main dimensions and twenty-eight features, with the effectiveness dimension being further subdivided into four sub-dimensions. We validate our framework by using it to evaluate a number of software visualization tools. This validation demonstrates that the framework can be applied to design and evaluate new software visualization techniques and tools.

Impacts of Social Media Advertising on Purchase Intention and Customer Loyalty in E-Commerce Systems

The emergence of new technologies has had a noteworthy impact on communication systems, leading to the importance of conducting research in this area due to the significant influence of social media. Marketing operators must implement the necessary infrastructure to identify and fulfill customers' expectations, considering the advantages and the increasing number of users in social networks. This paper presents a comprehensive framework for evaluating the purchase intention of electronic commerce systems, taking into account the impact of social media advertising and customer loyalty. The paper aims to enhance purchase intention in e-commerce through social media advertising and examine influential factors that improve online shopping performance, including social media advertising's effectiveness on customer purchase behavior and brand loyalty. The paper develops a theoretical framework of nine hypotheses to evaluate purchase intention in e-commerce systems, with a focus on the effect of social media advertising on brand loyalty and purchase intention. To validate the proposed model and test the research hypotheses, we make use of information obtained from an international company located in China. The results demonstrate the positive effect of pleasurable motivation on purchase intention, with a significance level of 3.776 and a path coefficient of 0.279. Moreover, an investigation of the positive effect of customer loyalty on the recommended advertisement confirms the hypotheses, with a significance of 32.815 and a path coefficient of 0.788.

Beyond competence and retention: Developing a comprehensive evaluation framework for a rural primary care nurse practitioner residency program.

With the rise in nurse practitioner (NP) residency programs, evaluations have largely focused on retention and competency completion for residents. There is a need for expanded evaluation to ensure the sustainability of NP residency programs, to ensure timely adaptations to address resident satisfaction, and to solidify a long-term pathway of NPs well prepared for rural practice. We created a family nurse practitioner (FNP) residency program with a comprehensive evaluation framework to prepare residents for practice in rural settings. The evaluation framework was developed through collaborative engagement of an external evaluation team, program leadership, and clinical site representatives. The evaluation framework of the FNP residency program combined resident assessment and holistic program evaluation, using a rapid continuous quality improvement (QI) approach. The evaluation considered three distinct perspectives: the resident, the peer coach, and the clinical site. The rapid continuous QI approach allowed program leadership to respond swiftly to programmatic challenges, improve the residency program in response to residents' reported experiences, and emphasize sustainability for continued program impact, while assessing residents' learning and performance. The program's data-driven evaluation approach has demonstrated its success in meeting the goals of the Health Resources and Services Administration funding by increasing the number of primary care providers in rural settings. The program's expansion and continued success have further validated the efficacy of this evaluation framework in assessing, improving, and ensuring the sustainability of APRN residency programs. This article calls for the adoption of similar evaluation strategies in future residency programs to promote their long-term success and impact in rural health care settings.

ChatMatch: Exploring the potential of hybrid vision–language deep learning approach for the intelligent analysis and inference of racket sports

Video understanding technology has become increasingly important in various disciplines, yet current approaches have primarily focused on lower comprehension level of video content, posing challenges for providing comprehensive and professional insights at a higher comprehension level. Video analysis plays a crucial role in athlete training and strategy development in racket sports. This study aims to demonstrate an innovative and higher-level video comprehension framework (ChatMatch), which integrates computer vision technologies with the cutting-edge large language models (LLM) to enable intelligent analysis and inference of racket sports videos. To examine the feasibility of this framework, we deployed a prototype of ChatMatch in the badminton in this study. A vision-based encoder was first proposed to extract the meta-features included the locations, actions, gestures, and action results of players in each frame of racket match videos, followed by a rule-based decoding method to transform the extracted information in both structured knowledge and unstructured knowledge. A set of LLM-based agents included namely task identifier, coach agent, statistician agent, and video manager, was developed through a prompt engineering and driven by an automated mechanism. The automatic collaborative interaction among the agents enabled the provision of a comprehensive response to professional inquiries from users. The validation findings showed that our vision models had excellent performances in meta-feature extraction, achieving a location identification accuracy of 0.991, an action recognition accuracy of 0.902, and a gesture recognition accuracy of 0.950. Additionally, a total of 100 questions were gathered from four proficient badminton players and one coach to evaluate the performance of the LLM-based agents, and the outcomes obtained from ChatMatch exhibited commendable results across general inquiries, statistical queries, and video retrieval tasks. These findings highlight the potential of using this approach that can offer valuable insights for athletes and coaches while significantly improve the efficiency of sports video analysis.

React: recognize every action everywhere all at once

In the realm of computer vision, Group Activity Recognition (GAR) plays a vital role, finding applications in sports video analysis, surveillance, and social scene understanding. This paper introduces Recognize Every Action Everywhere All At Once (REACT), a novel architecture designed to model complex contextual relationships within videos. REACT leverages advanced transformer-based models for encoding intricate contextual relationships, enhancing understanding of group dynamics. Integrated Vision-Language Encoding facilitates efficient capture of spatiotemporal interactions and multi-modal information, enabling comprehensive scene understanding. The model’s precise action localization refines joint understanding of text and video data, enabling precise bounding box retrieval and enhancing semantic links between textual descriptions and visual reality. Actor-Specific Fusion strikes a balance between actor-specific details and contextual information, improving model specificity and robustness in recognizing group activities. Experimental results demonstrate REACT’s superiority over state-of-the-art GAR approaches, achieving higher accuracy in recognizing and understanding group activities across diverse datasets. This work significantly advances group activity recognition, offering a robust framework for nuanced scene comprehension.

Defining Spatial Reasoning: A Content Analysis to Explicate Spatial Reasoning Skills for Early Childhood Educators’ Use

Spatial reasoning is critical for mathematics learning and achievement, and its comprising skills are necessary in science, technology, engineering, and mathematics careers. To support young children in learning to reason spatially, clear definitions of the construct and supports for early childhood educators to teach the skills are needed. This study defines spatial reasoning as a comprehensive, comprehensible framework of skills. Using problem-driven content analysis, 835 text units from 103 sources, plus definitions from two reputable dictionary sources, were used to adopt, adapt, and infer the definitions for 40 terms that collectively represent spatial reasoning. Findings provide both the definitions and evidence of the extent to which various spatial reasoning skills have been investigated empirically. Directions for future research are discussed, including the need to refine the framework to ensure its utility for teachers and researchers.

What's in a Name? Global Englishes—An Umbrella Term to Address Silo Mentality or a Misunderstood Paradigm Reinforcing Silos?

AbstractIn this article, I explore growing scholarship in various sub‐fields of Applied Linguistics and Second Language Acquisition (SLA) that explore the use of English in an increasingly globalized and multilingual world. With different, yet overlapping, conceptual frameworks to guide research, scholarship in various fields provide a growing evidence base on the need for change in TESOL curricula to better reflect the global use of English, and other languages, in an ever‐increasingly globalized world. I explore the introduction of Global Englishes (GE), an umbrella term and inclusive paradigm that was introduced to unite scholarship across the fields of World Englishes (WE), English as an International Language (EIL), English as a Lingua Franca (ELF), and to draw parallels with translanguaging and multilingualism. I provide clarity on the introduction of this broader paradigm, which aimed to unite such scholarship into a comprehensible framework to guide and facilitate a paradigm shift in TESOL curricula away from outdated “native” English norms. I end with a call to focus less on terminology and for scholars to work flexibly across academic boundaries to connect scholarship, particularly when it has a shared goal of achieving a more equitable English language education.

An effectual recommendation model using hybrid learning models for early detection of Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of individuals worldwide, causing progressive cognitive decline. Early prediction and diagnosis the AD accurately is crucial for effective intervention and treatment. In this study, we propose a comprehensive framework for AD prediction using various techniques, including preprocessing and denoising with Multilayer Perceptron (MLP) and Ant Colony Optimization (ACO), segmentation using U-Net, and classification with Spatial Pyramid Pooling Network (SPPNet). Furthermore, we employ Convolutional Neural Network (CNN) with SPPNet for training and develop a chatbot for recommendation based on MRI data input. The preprocessing and denoising techniques play a vital role in enhancing the quality of the input data. MLP is utilized for preprocessing, where it effectively handles feature extraction and noise reduction. ACO is employed for denoising, optimizing the data to improve the signal-to-noise ratio, and enhancing the overall performance of subsequent stages. For accurate segmentation of brain regions, we employ the U-Net architecture, which has shown remarkable success in medical image segmentation tasks. U-Net effectively identifies the regions of interest, aiding in subsequent classification stages. The classification phase utilizes SPPNet, a deep learning model known for its ability to capture spatial information at multiple scales. SPPNet extracts features from segmented brain regions, enabling robust classification of AD and non-AD cases. To enhance the training process, we employ CNN with SPPNet, leveraging the power of convolutional layers to capture intricate patterns and improve predictive accuracy. The CNN-SPPNet model is trained on a large dataset of MRI scans, enabling it to learn complex representations and make accurate predictions. Hence the proposed work can be integrated with a chatbot that takes MRI data as input and provides recommendations based on the predicted AD probability. Experimental evaluation shows that the combination of preprocessing, denoising, segmentation, and classification offers a comprehensive solution for accurate and efficient AD diagnosis and management.

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modelling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

Biogeography and diversity patterns of abundant and rare bacterial communities in paddy soils along middle and lower Yangtze River.

The middle and lower reaches of the Yangtze River serve as principal rice production bases in China, yet the biodiversity and ecological processes of bacterial communities in paddy soils are not well understood. This study explores the diversity, composition, ecological function, and assembly processes of abundant and rare bacterial communities in paddy soils. A total of 129 paddy soil samples from 43 sites along the middle and lower reaches of the Yangtze River were collected and analyzed using NovaSeq sequencing. The results showed that the dominant phylum for both abundant and rare taxa was Proteobacteria, with a greater relative abundance of the abundant taxa. The diversity of the abundant community was lower than that of the rare community. Soil properties and geographic variables explained more of the variation in the abundant community than in the rare community. The rare community exhibited a significant distance-decay relationship. The assembly of the abundant community was more influenced by stochastic processes, although both the abundant and rare communities were governed by stochastic processes. It is concluded that both abundant and rare bacterial communities exhibit differing biogeographic patterns, yet they undergo similar ecological processes in the paddy soils along the middle and lower reaches of the Yangtze River. These observations offer a theoretical framework for a deeper comprehension of the function of both abundant and rare bacteria, as well as the development and preservation of soil bacterial diversity within agricultural ecosystems.

Dynamics for a Nonlinear Stochastic Cholera Epidemic Model under Lévy Noise

In this study, we develop a comprehensive mathematical model to analyze the dynamics of epidemic cholera, characterized by acute diarrhea due to pathogen overabundance in the human body. The model is first developed from a deterministic point of view, and then it is modified to include the randomness by stochastic differential equations. The study selected Lévy noise above other well-known types of noise, emphasizing its importance in epidemic modeling. Besides presenting a biological justification for the stochastic system, we demonstrate that the equivalent deterministic model exhibits possible equilibria. The introduction is followed by theoretical analysis of the model. Through rigorous analysis, we establish that the stochastic model ensures a unique global solution. Lyapunov function theory is applied to construct necessary conditions, which on average, guarantee the model’s stability for R0s>1. Our findings suggest the likelihood of eradicating the disease when Rs is below one, a significant insight supported by graphical simulations of the model. Graphical illustrations were generated from simulating the model in order to increase the analytical results’ robustness. This work provides a strong theoretical framework for a thorough comprehension of a range of such diseases. This research not only provides a deeper understanding of cholera dynamics but also offers a robust theoretical framework applicable to a range of similar diseases, alongside a novel approach for constructing Lyapunov functions for nonlinear models with random disturbances.

Calculating Body Mass Index From 2D Image Using Convolutional Neural Networks and Anthropometric Measurements

Human body images encapsulate valuable biometric information. encompassing factors such as pupil color, gender, and weight. Of particular significance, body weight serves as a robust indicator of overall health. In alignment with recent health. science studies, this paper explores the viability of analyzing body weight through two- dimensional (2D) frontal-view human body images, utilizing the widely accepted Body Mass Index (BMI) as measure. To address varying levels of difficulty, three feasibility prob- lems are investigated, ranging from easy to hard. A comprehen sive framework is developed for body weight analysis, proposing the computation of five anthropometric features for precise body weight characterization. The correlation between the extracted anthropometric features and BMI values is thoroughly analyzed, affirming the usability of the selected features. For this study, a visual-body-to-BMI dataset is meticulously collected and cleaned, comprising 5900 images of 2950 subjects with corresponding labels for gender, height, and weight. findings demonstrate the feasibility of analyzing body The weight from 20 body images, with the proposed method outperforming two state-of-the-art facial image-based weight analysis approaches in most cases. Index Terms : Body weight analysis, visual analysis of body mass index (BMI), anthropometric features, visual-body-to- BMI dataset

Evaluation of the Effectiveness of Technology-Based Project Management Systems for Software Development

The article "Evaluation of the Effectiveness of Technology-Based Project Management Systems for Software Development" explores the efficacy of project management systems in the context of software development projects. In the dynamic landscape of software engineering, efficient project management is crucial for successful and timely delivery of high-quality software products. The study employs a comprehensive evaluation framework to assess the performance and impact of technology-based project management systems on software development projects. The research methodology involves a combination of quantitative and qualitative analyses, incorporating key performance indicators and user satisfaction surveys. The evaluation criteria encompass aspects such as project timelines, resource allocation, collaboration efficiency, and overall project success. By studying multiple case scenarios and employing a comparative analysis, the research aims to identify patterns, strengths, and weaknesses associated with different technology-based project management systems commonly used in the software development industry. The findings of this study are expected to contribute valuable insights to both academia and industry practitioners, shedding light on the optimal selection and utilization of project management systems for software development endeavors. As organizations increasingly rely on technology-driven solutions, understanding the effectiveness of project management systems becomes paramount for achieving enhanced productivity, streamlined workflows, and successful project outcomes in the ever-evolving field of software development.

It is not what you say but how you say it: Evidence from Russian shows robust effects of the structural prior on noisy channel inferences.

Under the noisy-channel framework of language comprehension, comprehenders infer the speaker's intended meaning by integrating the perceived utterance with their knowledge of the language, the world, and the kinds of errors that can occur in communication. Previous research has shown that, when sentences are improbable under the meaning prior (implausible sentences), participants often interpret them nonliterally. The rate of nonliteral interpretation is higher when the errors that could have transformed the intended utterance into the perceived utterance are more likely. However, previous experiments on noisy channel processing mostly relied on implausible sentences, and it is unclear whether participants' nonliteral interpretations were evidence of noisy channel processing or the result of trying to conform to the experimenter's expectations in an experiment with nonsensical sentences. In the current study, we used the unique properties of Russian, an understudied language in the psycholinguistics literature, to test noisy-channel comprehension using only simple plausible sentences. The prior plausibility of sentences was tied only to their word order; subject-verb-object (SVO) sentences were more probable under the structural prior than object-verb-subject (OVS) sentences. In two experiments, we show that participants often interpret OVS sentences nonliterally, and the probability of nonliteral interpretations depended on the Levenshtein distance between the perceived sentence and the (potentially intended) SVO version of the sentence. The results show that the structural prior guides people's final interpretation, independent of the presence of semantic implausibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).