Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI.

BACKGROUND: Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). METHODS: Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. RESULTS: 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). CONCLUSION: Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

Slow feature-based feature fusion methodology for machinery similarity-based prognostics.

Similarity-based prediction methods utilize degradation trend analysis based on degradation indicators (DIs). These methods are gaining prominence in industrial predictive maintenance because they effectively address prognostics for machines with unknown failure mechanisms. However, current studies often neglect the discrepancies in degradation trends when constructing DIs from multi-sensor data and lack automatic normalization of operating regimes during feature fusion. In this study, a feature fusion methodology based on a signal-to-noise ratio metric that leverages slow feature analysis (SFA) is proposed. This customized metric utilizes SFA to quantify degradation trend discrepancies of constructed DIs, while automatically filtering out the effects of multiple operating regimes during feature fusion. The effectiveness and superiority of the proposed method are demonstrated using publicly available aero-engine and rolling bearing datasets.

Discriminative feature analysis of dairy products based on machine learning algorithms and Raman spectroscopy.

Discriminant analysis of similar food samples is an important aspect of achieving food quality control. The effective combination of Raman spectroscopy and machine learning algorithms has become an extremely attractive approach to develop intelligent discrimination techniques. Feature spectral analysis can help researchers gain a deeper understanding of the data patterns in food quality discrimination. Herein, this work takes the discrimination of three brands of dairy products as an example to investigate the Raman spectral feature based on the support vector machines (SVM), extreme learning machines (ELM) and convolutional neural network (CNN) algorithms. The results show that there are certain differences in the optimal spectral feature interval corresponding to different machine learning algorithms. Selecting the appropriate spectral feature interval can maintain high recognition accuracy and improve the computational efficiency of the algorithm. For example, the SVM algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 200 s. The ELM algorithm also has a recognition accuracy of 100% in the 890-980 cm-1, 1410-1500 cm-1 fusion spectral range, which takes less than 0.3 s. The CNN algorithm has a recognition accuracy of 100% in the 890-980 cm-1, 1050-1180 cm-1, 1410-1500 cm-1 fusion spectral range, which takes about 80 s. In addition, by analyzing the distribution of spectral feature intervals based on Euclidean distance, the distribution of experimental samples based on feature spectra is visually displayed. Through the spectral feature analysis process of similar samples, a set of analysis strategies is provided to deeply reveal the data foundation of classification algorithms, which can provide reference for the analysis of relevant discriminative research patterns.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments.

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

Understanding and comparing risk factors and subtypes in South Korean adult and adolescent women's suicidal ideation or suicide attempt using survey and social media data.

Objective: This study aimed to investigate the similarities and differences in risk factors for suicide among adult and adolescent women in South Korea and identify subtypes of suicidal ideation or suicide attempt in each group. Methods: Multifaceted data were collected and analyzed by linking survey and social media data. Interpretable machine learning models were constructed to predict suicide risk and major risk factors were extracted by investigating their feature importance. Additionally, subtypes of suicidal adult and adolescent women were identified and explained using risk factors. Results: The risk factors for adult women were primarily related to mental disorders, while those for adolescent women were primarily related to interpersonal experiences and needs. Two subtypes of suicidal adult women were one with high psychiatric symptoms and mental disorders of them and/or their families and the other with excessive social media use and high online victimization. Two subtypes of suicidal adolescent women were one with high psychiatric symptoms, high ACEs, and high social connectedness, and the other with frequent social media use, high online sexual victimization, and high social assurance. Conclusions: These findings enable a stratified and targeted understanding of suicide in women and help develop customized suicide prevention plans in South Korea.

Automatic and interpretable prediction of the site of origin in outflow tract ventricular arrhythmias: machine learning integrating electrocardiograms and clinical data.

The treatment of outflow tract ventricular arrhythmias (OTVA) through radiofrequency ablation requires the precise identification of the site of origin (SOO). Pinpointing the SOO enhances the likelihood of a successful procedure, reducing intervention times and recurrence rates. Current clinical methods to identify the SOO are based on qualitative analysis of pre-operative electrocardiograms (ECG), heavily relying on physician's expertise. Although computational models and machine learning (ML) approaches have been proposed to assist OTVA procedures, they either consume substantial time, lack interpretability or do not use clinical information. Here, we propose an alternative strategy for automatically predicting the ventricular origin of OTVA patients using ML. Our objective was to classify ventricular (left/right) origin in the outflow tracts (LVOT and RVOT, respectively), integrating ECG and clinical data from each patient. Extending beyond differentiating ventricle origin, we explored specific SOO characterization. Utilizing four databases, we also trained supervised learning models on the QRS complexes of the ECGs, clinical data, and their combinations. The best model achieved an accuracy of 89%, highlighting the significance of precordial leads V1-V4, especially in the R/S transition and initiation of the QRS complex in V2. Unsupervised analysis revealed that some origins tended to group closer than others, e.g., right coronary cusp (RCC) with a less sparse group than the aortic cusp origins, suggesting identifiable patterns for specific SOOs.

Algorithm of face anti-spoofing based on pseudo-negative features generation.

Introduction: Despite advancements in face anti-spoofing technology, attackers continue to pose challenges with their evolving deceptive methods. This is primarily due to the increased complexity of their attacks, coupled with a diversity in presentation modes, acquisition devices, and prosthetic materials. Furthermore, the scarcity of negative sample data exacerbates the situation by causing domain shift issues and impeding robust generalization. Hence, there is a pressing need for more effective cross-domain approaches to bolster the model's capability to generalize across different scenarios. Methods: This method improves the effectiveness of face anti-spoofing systems by analyzing pseudo-negative sample features, expanding the training dataset, and boosting cross-domain generalization. By generating pseudo-negative features with a new algorithm and aligning these features with the use of KL divergence loss, we enrich the negative sample dataset, aiding the training of a more robust feature classifier and broadening the range of attacks that the system can defend against. Results: Through experiments on four public datasets (MSU-MFSD, OULU-NPU, Replay-Attack, and CASIA-FASD), we assess the model's performance within and across datasets by controlling variables. Our method delivers positive results in multiple experiments, including those conducted on smaller datasets. Discussion: Through controlled experiments, we demonstrate the effectiveness of our method. Furthermore, our approach consistently yields favorable results in both intra-dataset and cross-dataset evaluations, thereby highlighting its excellent generalization capabilities. The superior performance on small datasets further underscores our method's remarkable ability to handle unseen data beyond the training set.

Leveraging machine learning for prediction of antibiotic resistance genes post thermal hydrolysis-anaerobic digestion in dairy waste.

Anaerobic digestion holds promise as a method for removing antibiotic resistance genes (ARGs) from dairy waste. However, accurately predicting the efficiency of ARG removal remains a challenge. This study introduces a novel appproach utilizing machine learning to forecast changes in ARG abundances following thermal hydrolysis-anaerobic digestion (TH-AD) treatment. Through network analysis and redundancy analyses, key determinants of affect ARG fluctuations were identified, facilitating the development of machine learning models capable of accurately predicting ARG changes during TH-AD processes. The decision tree model demonstrated impressive predictive power, achieving an impessive R2 value of 87% against validation data. Feature analysis revealed that the genes intI2 and intI1 had a critical impact on the absolute abundance of ARGs. The predictive model developed in this study offers valuable insights for improving operational and managerial practices in dairy waste treatment facilities, with the ultimate goal of mitigating the spread of antibiotic resistance.

Classification of Sand-Binder Mixtures from the Foundry Industry Using Electrical Impedance Spectroscopy and Support Vector Machines.

Molding sand mixtures used in the foundry industry consist of various sands (quartz sands, chromite sands, etc.) and additives such as bentonite. The optimum control of the processes involved in using the mixtures and in their regeneration after the casting requires an efficient in-line monitoring method that is not available today. We are investigating whether such a method can be based on electrical impedance spectroscopy (EIS). To establish a database, we have characterized various sand mixtures by EIS in the frequency range from 0.5 kHz to 1 MHz under laboratory conditions. Attempts at classifying the different molding sand mixtures by support vector machines (SVM) show encouraging results. Already high assignment accuracies (above 90%) could even be improved with suitable feature selection (sequential feature selection). At the same time, the standard uncertainty of the SVM results is low, i.e., data assigned to a class by the presented SVMs have a high probability of being assigned correctly. The application of EIS with subsequent evaluation by machine learning (machine-learning-enhanced EIS, MLEIS) in the field of bulk material monitoring in the foundry industry appears possible.

A spatial feature analysis of primary health care utilization in a large city in China and its implications for family doctor contract service policy.

BACKGROUND: Family doctor contract policy is now run by the State Council as an important move to promote the hierarchical medical system. Whether the family doctor contract policy achieves the initial government's goal should be measured further from the perspective of patient visits between hospitals and community health centers, which are regarded as grass medical agencies. METHODS: The spatial feature measurement method is applied with ArcGIS 10.2 software to analyze the spatial aggregation effect of patient visits to hospitals or community health centers among 20 districts of one large city in China and analyze the family doctor contract policy published in those areas to compare the influence of visit tendencies. RESULTS: From year 2016-2020, visits to hospitals were in the high-high cluster, and the density was spatially overflow, while there was no such tendency in visits to community health centers. The analysis of different family doctor contract policy implementation times in 20 districts reflects that the family doctor contract policy has a very limited effect on the promotion of the hierarchical medical system, and the innovation of the family doctor contract policy needs to be considered. CONCLUSIONS: A brief summary and potential implications. A multi-integrated medical system along with family doctor contract policy needs to be established, especially integrated in leadership and governance, financing, workforce, and service delivery between hospitals and community health centers, to promote the hierarchical medical system.

Combining executive function training and anomia therapy in chronic post-stroke aphasia: A preliminary study of multidimensional effects.

PURPOSE: The influential relationship between executive functioning and aphasia rehabilitation outcomes has been addressed in a number of studies, but few have studied the effect of adding executive function training to linguistic therapies. The present study aimed to measure the effects of combining, within therapy sessions, executive function training and anomia therapy on naming and discourse abilities in people with chronic aphasia. METHOD: A single-case experimental design with multiple baselines across participants was used. Four persons with chronic post-stroke aphasia received 12 sessions of a tailored treatment combining executive function training and semantic feature analysis (SFA) therapy. Naming accuracy of treated items was examined over the course of the treatment while control naming scores of untreated items and discourse measures were collected pre-treatment, immediately post-treatment, and 4 weeks post-treatment, in order to investigate the multidimensional effects of the treatment and their maintenance. RESULT: Naming skills improved in all participants for treated and untreated items, were maintained over time, and were accompanied by improved discourse abilities. Visual and statistical analyses showed a significant treatment effect for naming skills in three out of the four participants. CONCLUSION: A combination of executive function training and SFA treatment in people with chronic aphasia may improve both naming skills and discourse efficiency. Further studies are needed to substantiate these promising preliminary results.

High-Frequency Language Therapy with Semantic Feature Analysis (SFA) and Transcranial Direct Current Stimulation (tDCS): A Longitudinal Single-Case Report of Semantic Variant of Primary Progressive Aphasia (svPPA).

BACKGROUND: The goal of this study was to investigate whether the combination of semantic feature analysis (SFA) and transcranial direct current stimulation (tDCS) is effective in treating word retrieval in the semantic variant of primary progressive aphasia (svPPA) and how long the potential effects last. METHODS: A 56-year-old woman diagnosed with frontotemporal dementia (FTD) and svPPA participated in this longitudinal single-subject design. A total of four 2-week stimulation phases were conducted over a 14-month period, each of which was started depending on the participant's language performance. Follow-up testing was conducted shortly after the stimulation period, approximately 2 weeks, and approximately 4 weeks thereafter. RESULTS: Significant improvement in word retrieval occurred after SFA and tDCS therapy. Two weeks after the end of each stimulation phase, approx. 80% of the trained words could be named correctly. For the untrained words, also significantly more words were correctly named at follow-ups compared to the baseline. Furthermore, the Boston Naming Test (BNT) demonstrated a significant increase in naming performance and showed that phonological cues facilitated word retrieval compared to semantic cues. CONCLUSION: The combination of SFA and tDCS was able to counteract the expected language deterioration of a participant with svPPA. This effect increased until approximately 2 weeks after each intervention. In addition, a generalization of the effect to untrained words was shown.

Unveiling protein corona composition: predicting with resampling embedding and machine learning.

Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (R2) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an R2 of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO2), silicon dioxide (SiO2) and silver (Ag), and we achieved a prediction performance with an R2 value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.

Towards label-free non-invasive autofluorescence multispectral imaging for melanoma diagnosis.

This study focuses on the use of cellular autofluorescence which visualizes the cell metabolism by monitoring endogenous fluorophores including NAD(P)H and flavins. It explores the potential of multispectral imaging of native fluorophores in melanoma diagnostics using excitation wavelengths ranging from 340 nm to 510 nm and emission wavelengths above 391 nm. Cultured immortalized cells are utilized to compare the autofluorescent signatures of two melanoma cell lines to one fibroblast cell line. Feature analysis identifies the most significant and least correlated features for differentiating the cells. The investigation successfully applies this analysis to pre-processed, noise-removed images and original background-corrupted data. Furthermore, the applicability of distinguishing melanomas and healthy fibroblasts based on their autofluorescent characteristics is validated using the same evaluation technique on patient cells. Additionally, the study tentatively maps the detected features to underlying biological processes. This research demonstrates the potential of cellular autofluorescence as a promising tool for melanoma diagnostics.

Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection.

Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder which affects a significant proportion of the population, with estimates suggesting that about 1 in 100 children worldwide are affected by ASD. This study introduces a new Deep Neural Network for identifying ASD in children through gait analysis, using features extracted from frames composing video recordings of their walking patterns. The innovative method presented herein is based on imagery and combines gait analysis and deep learning, offering a noninvasive and objective assessment of neurodevelopmental disorders while delivering high accuracy in ASD detection. Our model proposes a bimodal approach based on the concatenation of two distinct Convolutional Neural Networks processing two feature sets extracted from the same videos. The features obtained from the convolutions of both networks are subsequently flattened and merged into a single vector, serving as input for the fully connected layers in the binary classification process. This approach demonstrates the potential for effective ASD detection in children through the combination of gait analysis and deep learning techniques.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Detection and Quantification of Extraterrestrial Platinum Group Element Alloy Micronuggets from Archean Impactite Deposits by Low-Voltage Scanning Electron Microscopy/Energy-Dispersive X-ray Spectrometry.

Rare, heterogeneously composed platinum group element alloy micronuggets (PGNs) occur in primitive meteorites, micrometeorites, and terrestrial impactite deposits. To gain insight into the nature of these phases, we developed a workflow for the characterization of PGNs using modern scanning electron microscopy (SEM) and energy-dispersive X-ray spectrometry at a low accelerating voltage of 6 kV. Automated feature analysis-a combination of morphological image analysis and elemental analysis with stage control-allowed us to detect PGNs down to 200 nm over a relatively large analysis area of 53 mm2 with a conventional silicon drift detector (SDD). Hyperspectral imaging with a high-sensitivity, annular SDD can be performed at low beam current (∼100 pA) which improves the SEM image resolution and minimizes hydrocarbon contamination. The severe overlapping peaks of the platinum group element L and M line families at 2-3 keV and the Fe and Ni L line families at <1 keV can be resolved by peak deconvolution. Quantitative elemental analysis can be performed at a spatial resolution of <80 nm; however, the results are affected by background subtraction errors for the Fe L line family. Furthermore, the inaccuracy of the matrix correction coefficients may influence standards-based quantification with pure element reference samples.

Genome-wide identification of glutamate receptor-like gene family in soybean.

Glutamate receptor-like genes (GLRs) are essential in the growth and development of plants and many physiological and biochemical processes; however, related information in soybean is lacking. In this study, 105 GLRs, including 67 Glycine soja and 38 Glycine max GLRs, were identified and divided into two clades (Clades II and III) according to their phylogenetic relationships. GLR members in the same branch had a relatively conservative motif composition and genetic structure. Furthermore, the soybean GLR family mainly experienced purification selection during evolution. Cis-acting element analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomic annotations indicated the complexity of the gene regulation and functional diversity of the soybean GLR. Moreover, transcriptome data analysis showed that these GLRs had different expression profiles in different tissues, and Clade III members had higher and more common expression patterns. Additionally, the expression profiles under jasmonic acid treatment and salt stress indicate that the GLR participated in the jasmonic acid signaling pathway and plays a role in salt treatment. This study provides information for a comprehensive understanding of the soybean GLR family and a reference for further functional research and genetic improvement.

Deep learning precipitation prediction models combined with feature analysis.

Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.

Using a combined working memory - Semantic feature analysis approach to treat anomia in aphasia: A Pilot Study.

INTRODUCTION: The purpose of the study was to pilot a working memory (WM) - and modified Semantic Feature Analysis (SFA) approach to treat word finding deficits in a group of people with aphasia (PwA). Two research questions were posed: 1. Will the group of PwA be able to complete the WM tasks used in the approach? 2. Will the approach improve naming performance in PwA? METHOD: Three individuals with mild - moderate aphasia participated in this singlesubject multiple baseline treatment design. Pre-treatment assessments of language, and pre- to post-treatment assessments of WM abilities were carried out. The treatment protocol incorporated WM and linguistic tasks in order to improve naming accuracy across two treatment lists. Probes were carried out prior to treatment on each list, and at one-month following completion of treatment. Two outcome measures were obtained: Percent accuracy in completing the WM steps, and treatment effect sizes (Beeson & Robey, 2006). Additionally, modified t-tests (Crawford & Garthwaite, 2012; Crawford & Howell, 1998), were calculated in which post-treatment WM measures were compared against neurotypical control groups to detect any improvements in WM functions. RESULTS: All three participants completed the WM steps with a high degree of accuracy. A range of small to large ESs were obtained for all three participants across the two treated lists, while no meaningful ESs were obtained for the control (untreated) list. All three participants demonstrated improved scores across most of the WM measures with significant improvements noted on certain WM assessments. CONCLUSIONS: The findings revealed that the WM - SFA approach can be used successfully in individuals with mild - moderate aphasia. The proposed approach holds promise as feasible intervention designed to remediate anomia in PwA.