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Dissociating Reading and Auditory Comprehension in Persons with Aphasia
Get PDFLanguage comprehension is often affected in individuals with post-stroke aphasia. However, deficits in auditory comprehension are not fully correlated with deficits in reading comprehension and the mechanisms underlying this dissociation remain unclear. This distinction is important for understanding language mechanisms, predicting long-term impairments and future development of treatment interventions. Using comprehensive auditory and reading measures from a large cohort of individuals with aphasia, we evaluated the relationship between aphasia type and reading comprehension impairments, the relationship between auditory versus reading comprehension deficits and the crucial neuroanatomy supporting the dissociation between post-stroke reading and auditory deficits. Scores from the Western Aphasia Battery—Revised from 70 participants with aphasia after a left-hemisphere stroke were utilized to evaluate both reading and auditory comprehension of linguistically equivalent stimuli. Repeated-measures and univariate ANOVA were used to assess the relationship between auditory comprehension and aphasia types and correlations were employed to test the relationship between reading and auditory comprehension deficits. Lesion-symptom mapping was used to determine the dissociation of crucial brain structures supporting reading comprehension deficits controlling for auditory deficits and vice versa. Participants with Broca’s or global aphasia had the worst performance on reading comprehension. Auditory comprehension explained 26% of the variance in reading comprehension for sentence completion and 44% for following sequential commands. Controlling for auditory comprehension, worse reading comprehension performance was independently associated with damage to the inferior temporal gyrus, fusiform gyrus, posterior inferior temporal gyrus, inferior occipital gyrus, lingual gyrus and posterior thalamic radiation. Auditory and reading comprehension are only partly correlated in aphasia. Reading is an integral part of daily life and directly associated with quality of life and functional outcomes. This study demonstrated that reading performance is directly related to lesioned areas in the boundaries between visual association regions and ventral stream language areas. This behavioural and neuroanatomical dissociation provides information about the neurobiology of language and mechanisms for potential future treatment interventions
Positive Epidemiology, Revisited: The Case for Centering Human Rights and Economic Justice
Get PDFIn recent years, a growing body of research in positive epidemiology has sought to expand the traditional focus of epidemiologic research beyond risk factors for disease and towards a more holistic understanding of health that includes the study of positive assets that shape well-being more broadly. While this paradigm shift holds great promise for transforming people’s lives for the better, it is also critiqued for showcasing decontextualized perspectives that could cause great harm to the public’s health if translated uncritically into population-based interventions. In this commentary, we argue for orienting positive epidemiology within a human rights and economic justice framework to mitigate this threat and discuss two examples of previously proposed health assets (religious involvement and marriage) that demonstrate the urgent need for positive epidemiologic research to center health equity. Finally, to advance the field, we provide recommendations for how future research can address shortcomings of the extant literature by moving from individual-level applications to societal-level approaches. In doing so, we believe that positive epidemiology can be transformed into a powerful force for health equity
Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach
Get PDFThe recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of underestimation of heavy rainfall that challenges most of the DL models, a pixelwise modeling approach is adopted to facilitate a stratified sampling process in generating training data points for calibrating models to predict rain rates at locations. The proposed pixelwise convolutional LSTM (CLSTM) models are applied to data on mesoscale convective systems during the warm seasons over the Korean Peninsula. Results show a significant and consistent improvement in prediction skill scores produced by the CLSTM models than a traditional rainfall nowcasting method, the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation, across all considered lead times from 10 to 60 min. Future work needs to reduce the relatively large false positive rates produced by the CLSTM models and their blurring effect in mapping spatial distributions of rain rates, in particular for longer lead times
Truth by Consensus: A Theoretical and Empirical Investigation
Get PDFTruthful reporting about publicly observed events cannot be guaranteed by a consensus process. This fact, which we establish theoretically and verify empirically, holds true even if some individuals are compelled to tell the truth, regardless of economic incentives. In an experiment, subjects routinely misreported a commonly known event when they could monetarily gain from it. Relying on majority consensus did not help uncover the truth, especially if complying with the majority granted small personal monetary gains. This highlights the difficulties in relying on shared consensus protocols to agree on specific events, and the importance of institutions with trusted, impartial observers
Ultrasoft Platelet-like Particles Stop Bleeding in Rodent and Porcine Models of Trauma
Get PDFUncontrolled bleeding after trauma represents a substantial clinical problem. The current standard of care to treat bleeding after trauma is transfusion of blood products including platelets; however, donated platelets have a short shelf life, are in limited supply, and carry immunogenicity and contamination risks. Consequently, there is a critical need to develop hemostatic platelet alternatives. To this end, we developed synthetic platelet-like particles (PLPs), formulated by functionalizing highly deformable microgel particles composed of ultralow cross-linked poly (N-isopropylacrylamide) with fibrin-binding ligands. The fibrin-binding ligand was designed to target to wound sites, and the cross-linking of fibrin polymers was designed to enhance clot formation. The ultralow cross-linking of the microgels allows the particles to undergo large shape changes that mimic platelet shape change after activation; when coupled to fibrin-binding ligands, this shape change facilitates clot retraction, which in turn can enhance clot stability and contribute to healing. Given these features, we hypothesized that synthetic PLPs could enhance clotting in trauma models and promote healing after clotting. We first assessed PLP activity in vitro and found that PLPs selectively bound fibrin and enhanced clot formation. In murine and porcine models of traumatic injury, PLPs reduced bleeding and facilitated healing of injured tissue in both prophylactic and immediate treatment settings. We determined through biodistribution experiments that PLPs were renally cleared, possibly enabled by ultrasoft particle properties. The performance of synthetic PLPs in the preclinical studies shown here supports future translational investigation of these hemostatic therapeutics in a trauma setting
Medical Image Analysis Based on Graph Machine Learning and Variational Methods
No full textThis study explores advanced methodologies for enhancing brain tumor segmentation, addressing the complexity and diversity of tumor sub-regions in medical imaging. We introduce a novel approach utilizing Graph Neural Networks (GNNs) that incorporate both spectral and spatial insights for segmentation. By leveraging various supervoxel creation methods such as VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb-Huttenlocher, we structured 3D MRI images into a graph format. This format enabled the implementation of Spectral and Spatial GNNs to capture comprehensive local and global tumor characteristics effectively. Our Spectral-Spatial GNN model, integrating the Laplacian matrix, demonstrated significant improvements in segmenting distinct tumor sub-regions of Necrosis, Edema, and Enhancing Tumor. Our research shows that GNNs, combining spectral and spatial aspects, provide a superior approach for accurate and precise brain tumor segmentation. The preliminary supervoxel generation phase showed VCCS outperformed other methods in homogeneity, inertia, shape uniformity, and size uniformity, setting a strong foundation for further analysis. Hyperparameter tuning maximized the performance of our Spectral- Spatial GNN model. This model demonstrates superior accuracy in both whole tumor and sub- region segmentation compared to traditional methods and individual Spectral or Spatial GNNs, with the Dice coefficient and other metrics as proof. Our Spectral-Spatial GNN model’s accuracy and precision advances have significant potential to enhance brain tumor diagnosis, treatment planning, and fundamental research. In a separate study we introduce a novel hybrid loss function that merges the edge-preservation capabilities of the Mumford-Shah (MS) functional with the precise overlap analysis of the Dice loss, aiming to overcome limitations observed in traditional brain tumor segmentation methods. This approach significantly enhances the accuracy of delineating tumor boundaries and sub-regions. This method advances not only brain tumor segmentation but also presents potential for application in other diagnostic areas where precise segmentation is essential for effective treatment planning. We have observed that finely tuning the balance between the MS and Dice components is vital for optimal performance, emphasizing the need for tailored adjustments in our hybrid loss function for specialized applications. The success of this approach stems from its dual capacity to enhance edge detection and accurately measure overlap, leading to a more detailed and reliable medical image analysis. While primarily focused on brain tumor segmentation, the principles behind our hybrid loss function hold potential for adaptation across various medical imaging tasks, suggesting a broad applicability that could lead to improved diagnostic and therapeutic outcomes
Mechanistic Study of Antimicrobial Effectiveness of Cyclic Amphipathic Peptide [R\u3csub\u3e4\u3c/sub\u3eW\u3csub\u3e4\u3c/sub\u3e] against Methicillin-Resistant \u3cem\u3eStaphylococcus aureus\u3c/em\u3e Clinical Isolates
Get PDFAntimicrobial peptides (AMPs) are being explored as a potential strategy to combat antibiotic resistance due to their ability to reduce susceptibility to antibiotics. This study explored whether the [R4W4] peptide mode of action is bacteriostatic or bactericidal using modified two-fold serial dilution and evaluating the synergism between gentamicin and [R4W4] against Escherichia coli (E. coli) and methicillin-resistant Staphylococcus aureus (MRSA) by a checkered board assay. [R4W4] exhibited bactericidal activity against bacterial isolates (MBC/MIC ≤ 4), with a synergistic effect with gentamicin against E. coli (FICI = 0.3) but not against MRSA (FICI = 0.75). Moreover, we investigated the mechanism of action of [R4W4] against MRSA by applying biophysical assays to evaluate zeta potential, cytoplasmic membrane depolarization, and lipoteichoic acid (LTA) binding affinity. [R4W4] at a 16 mg/mL concentration stabilized the zeta potential of MRSA −31 ± 0.88 mV to −8.37 mV. Also, [R4W4] at 2 × MIC and 16 × MIC revealed a membrane perturbation process associated with concentration-dependent effects. Lastly, in the presence of BODIPY-TR-cadaverine (BC) fluorescence dyes, [R4W4] exhibited binding affinity to LTA comparable with melittin, the positive control. In addition, the antibacterial activity of [R4W4] against MRSA remained unchanged in the absence and presence of LTA, with an MIC of 8 µg/mL. Therefore, the [R4W4] mechanism of action is deemed bactericidal, involving interaction with bacterial cell membranes, causing concentration-dependent membrane perturbation. Additionally, after 30 serial passages, there was a modest increment of MRSA strains resistant to [R4W4] and a change in antibacterial effectiveness MIC [R4W4] and vancomycin by 8 and 4 folds with a slight change in Levofloxacin MIC 1 to 2 µg/mL. These data suggest that [R4W4] warrants further consideration as a potential AMP