Analyzing the Functional Interdependence of Verbal Behavior with Multiaxial Radar Charts.

The functional analysis of complex verbal behavior requires an evaluation of topographically similar responses under multiple sources of control. Traditional graphical displays of behavior were designed to show the manipulation of isolated controlling variables and may not be amenable to displaying the multidimensional properties of complex behavior. Researchers have recently demonstrated the use of multiaxial radar charts for comparing the functional performance of biological systems. Here we extend the use of multidimensional analyses to compare the relative performance distributions of verbal behavior across four potential controlling variables. First, we provide a conceptual analysis of intraverbal and extraverbal control as continua along which stimuli range from formal to thematic and explain how the intersection of these stimulus fields creates a radar chart for multidimensional analysis. Then we demonstrate how data may be gathered through a verbal operant experimental analysis. We employed repeated measures to map the conditioning history of a child with autism spectrum disorder across 2 years of early intensive behavioral intervention and analyzed the results using shape descriptors for quantitative comparisons. We also compared the polygonal language profiles of children with autism against that of a neurotypical peer. Extending a multidimensional analysis to the field of verbal behavior provides the basis for a language growth chart that researchers and clinicians can use to monitor language acquisition over time. We discuss the use of radar charts as a framework for understanding the interdependence of verbal operants and suggest their use for complex analyses of complex verbal behavior. Supplementary Information: The online version contains supplementary material available at 10.1007/s40614-024-00404-6.

Correction to: Analyzing the Functional Interdependence of Verbal Behavior with Multiaxial Radar Charts.

[This corrects the article DOI: 10.1007/s40614-024-00404-6.].

Severe housing cost burden and premature mortality from cancer.

Unaffordable housing has been associated with poor health. We investigated the relationship between severe housing cost burden and premature cancer mortality (death before 65 years of age) overall and by Medicaid expansion status. County-level severe housing cost burden was measured by the percentage of households that spend 50% or more of their income on housing. States were classified on the basis of Medicaid expansion status (expanded, late-expanded, nonexpanded). Mortality-adjusted rate ratios were estimated by cancer type across severe housing cost burden quintiles. Compared with the lowest quintile of severe housing cost burden, counties in the highest quintile had a 5% greater cancer mortality rate (mortality-adjusted rate ratio = 1.05, 95% confidence interval = 1.01 to 1.08). Within each severe housing cost burden quintile, cancer mortality rates were greater in states that did not expand Medicaid, though this association was significant only in the fourth quintile (mortality-adjusted rate ratio = 1.08, 95% confidence interval = 1.03 to 1.13). Our findings demonstrate that counties with greater severe housing cost burden had higher premature cancer death rates, and rates are potentially greater in non-Medicaid-expanded states than Medicaid-expanded states.

KRASFormer: a fully vision transformer-based framework for predictingKRASgene mutations in histopathological images of colorectal cancer.

Detecting the Kirsten Rat Sarcoma Virus (KRAS) gene mutation is significant for colorectal cancer (CRC) patients. TheKRASgene encodes a protein involved in the epidermal growth factor receptor (EGFR) signaling pathway, and mutations in this gene can negatively impact the use of monoclonal antibodies in anti-EGFR therapy and affect treatment decisions. Currently, commonly used methods like next-generation sequencing (NGS) identifyKRASmutations but are expensive, time-consuming, and may not be suitable for every cancer patient sample. To address these challenges, we have developedKRASFormer, a novel framework that predictsKRASgene mutations from Haematoxylin and Eosin (H & E) stained WSIs that are widely available for most CRC patients.KRASFormerconsists of two stages: the first stage filters out non-tumor regions and selects only tumour cells using a quality screening mechanism, and the second stage predicts theKRASgene either wildtype' or mutant' using a Vision Transformer-based XCiT method. The XCiT employs cross-covariance attention to capture clinically meaningful long-range representations of textural patterns in tumour tissue andKRASmutant cells. We evaluated the performance of the first stage using an independent CRC-5000 dataset, and the second stage included both The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) and in-house cohorts. The results of our experiments showed that the XCiT outperformed existing state-of-the-art methods, achieving AUCs for ROC curves of 0.691 and 0.653 on TCGA-CRC-DX and in-house datasets, respectively. Our findings emphasize three key consequences: the potential of using H & E-stained tissue slide images for predictingKRASgene mutations as a cost-effective and time-efficient means for guiding treatment choice with CRC patients; the increase in performance metrics of a Transformer-based model; and the value of the collaboration between pathologists and data scientists in deriving a morphologically meaningful model.

EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization.

The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as 'cluster embedding' methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiVECS, a tool which allows the user to perform cluster embedding and explore the results using interactive visualization. EpiVECS is available as a privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs .

Culturally Responsive Language Assessment Through a Verbal Operant Experimental Analysis.

Speakers from culturally and linguistically diverse backgrounds are disproportionately identified with communication deficits, a defining feature of autism spectrum disorder. The present case study demonstrates the use of a verbal operant experimental analysis as a measure of functional language. This analysis allows a bilingual participant to switch freely between responses in Spanish and English. Prior to receiving services, a pretreatment functional analysis of verbal behavior is used to identify the speaker's present level of functional language and develop an individualized treatment plan for shaping a more complex verbal repertoire. The benefits of culturally responsive early intensive behavioral intervention are demonstrated by a posttest evaluation 5 months later. Functional analyses of language are shown to be a verbal-community-centered approach to observing and measuring the verbal behavior of speakers from diverse backgrounds.

Interpretable, non-mechanistic forecasting using empirical dynamic modeling and interactive visualization.

Forecasting methods are notoriously difficult to interpret, particularly when the relationship between the data and the resulting forecasts is not obvious. Interpretability is an important property of a forecasting method because it allows the user to complement the forecasts with their own knowledge, a process which leads to more applicable results. In general, mechanistic methods are more interpretable than non-mechanistic methods, but they require explicit knowledge of the underlying dynamics. In this paper, we introduce EpiForecast, a tool which performs interpretable, non-mechanistic forecasts using interactive visualization and a simple, data-focused forecasting technique based on empirical dynamic modelling. EpiForecast's primary feature is a four-plot interactive dashboard which displays a variety of information to help the user understand how the forecasts are generated. In addition to point forecasts, the tool produces distributional forecasts using a kernel density estimation method-these are visualized using color gradients to produce a quick, intuitive visual summary of the estimated future. To ensure the work is FAIR and privacy is ensured, we have released the tool as an entirely in-browser web-application.

Correction to: Cochran's Q Test of Stimulus Overselectivity within the Verbal Repertoire of Children with Autism.

[This corrects the article DOI: 10.1007/s40614-021-00315-w.].

Cochran's Q Test of Stimulus Overselectivity within the Verbal Repertoire of Children with Autism.

Stimulus overselectivity remains an ill-defined concept within behavior analysis, because it can be difficult to distinguish truly restrictive stimulus control from random variation. Quantitative models of bias are useful, though perhaps limited in application. Over the last 50 years, research on stimulus overselectivity has developed a pattern of assessment and intervention repeatedly marred by methodological flaws. Here we argue that a molecular view of overselectivity, under which restricted stimulus control has heretofore been examined, is fundamentally insufficient for analyzing this phenomenon. Instead, we propose the use of the term "overselectivity" to define temporally extended patterns of restrictive stimulus control that have resulted in disproportionate populations of responding that cannot be attributed to chance alone, and highlight examples of overselectivity within the verbal behavior of children with autism spectrum disorder. Viewed as such, stimulus overselectivity lends itself to direct observation and measurement through the statistical analysis of single-subject data. In particular, we demonstrate the use of the Cochran Q test as a means of precisely quantifying stimulus overselectivity. We provide a tutorial on calculation, a model for interpretation, and a discussion of the implications for the use of Cochran's Q by clinicians and researchers.

Just How Effective is Direct Instruction?

Despite overwhelming evidence in support of Direct Instruction, this research-validated curriculum has not been widely embraced by teachers or school administrators. The Direct Instruction model, developed and refined by Engelmann and colleagues over the past 50 years, has been the focus of numerous research studies, systematic reviews, and meta-analyses. Although its efficacy cannot be doubted, the significance of Direct Instruction's impact may be misunderstood. We attempt to clarify the importance of Direct Instruction with help from the binomial effect-size display. Binomial effect-size displays allow for intuitive and informative data-based decision making by clearly conveying the real-world importance of treatment outcomes through a juxtaposition of the relative proportions of success. The limitations of analyzing effect sizes in absolute terms are discussed. Using the binomial effect-size display as a framework, we present a series of dichotomies in an attempt to answer the question: Just how effective is Direct Instruction?

Referent-Based Instruction to Strengthen the Verbal Behavior of Early Learners with Autism and Related Language Disorders.

The current study evaluated the use of precision teaching to address the verbal behavior deficits of children with autism and other language disorders. From 2013 to 2018, a high-research-activity doctoral university in the south-central United States operated a free clinic that provided applied behavior anlaysis services to early learners in the local community. Participants received referent-based verbal behavior instruction to strengthen their functional language skills by systematically transferring stimulus control across 4 primary verbal operants: mands, echoics, tacts, and sequelics. Referent-based instruction is premised on the notion that proportionate levels of strength among these 4 operants provide the relational flexibility of naturalistic speaking observed in typical language development. This article details the language gains made by 49 participants who received 13 weeks of intervention for 90 min a day, 4 days a week. Relative strengths and weaknesses were identified in the verbal repertoire of each participant, and individualized fluency aims were subsequently developed. Results of pretest and posttest comparisons show that there was a large effect size within the verbal behavior gains of participants who received precision teaching. Implications for implementing referent-based instruction, as well as future areas of research, are discussed.

Rethinking Essential Services in the Wake of the COVID-19 Health Crisis.

During the COVID-19 global pandemic, Cook Children's Health Care System needed a way to ensure that all employees had a reliable childcare option. This advocacy note details how Child Study Center, an applied behavior analysis facility in North Texas, transitioned into Camp Cook, a free-of-charge childcare facility that operates weekdays from 6:00 a.m. to 8:00 p.m. Closing the doors on our billable services allowed us to open them to the essential health care workers who were fighting on the front lines against COVID-19. Here we describe the redeployment of employees across 3 departments in an effort to ease the burden of childcare within our local community.

Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE.

Verbal operant analyses are the extension of functional analysis technology to the field of verbal behavior, of particular relevance to autism spectrum disorders and associated developmental disabilities. Similarly, a verbal operant analysis carefully controls specific environmental factors that influence language, and measures strength of responding across four verbal operant classes: tact, mand, echoic, and sequelic. The frequency of each independent verbal operant is then measured against one another using the stimulus control ratio equation (SCoRE) to summarize the relative strength of the speaker's repertoire. The verbal behavior SCoRE yields a statistic that can be compared against itself (e.g., for the purposes of pre/post testing) or against others (e.g., for the purposes of randomized controlled trials). The results of this evaluation provide an individualized hierarchy for diverging stimulus control across the verbal operants, from which a treatment plan for errorless language learning may be prescribed.

Referent-Based Verbal Behavior Instruction for Children with Autism.

Skinner's (1957) analysis of verbal behavior deconstructed language according to stimulus control. Although the functional independence of these verbal operants has been empirically demonstrated, more commonly, a speaker's verbal behavior is induced by a convergence of controlling stimuli. However, circumscribed stimulus control may inhibit the development of complex verbal repertoires for some individuals, including those with autism spectrum disorders. For this reason, in the current paper, we propose a behavior analytic intervention with the overarching goal of establishing multiple control over verbal behavior through the conditioning of referent stimuli.Referent-based instruction emphasizes teaching the operant class over specific targetsMultiple control is established by converging verbal behavior around the referentProgress is measured in terms of a stimulus control ratioEliminates arbitrary decision making.