An Integrated Object Model and Method Framework for Subject-Centric e-Research Applications

A framework that integrates an object model, research methods (workflows), the capture of experimental data sets and the provenance of those data sets for subject-centric research is presented. The design of the Framework object model draws on and extends pre-existing object models in the public domain. In particular the Framework tracks the state and life cycle of a subject during an experimental method, provides for reusable subjects, primary, derived and recursive data sets of arbitrary content types, and defines a user-friendly and practical scheme for citably identifying information in a distributed environment. The Framework is currently used to manage neuroscience Magnetic Resonance and microscopy imaging data sets in both clinical and basic neuroscience research environments. The Framework facilitates multi-disciplinary and collaborative subject-based research, and extends earlier object models used in the research imaging domain. Whilst the Framework has been explicitly validated for neuroimaging research applications, it has broader application to other fields of subject-centric research

Contrastive Learning MRI Reconstruction

Purpose: We propose a novel contrastive learning latent space representation for MRI datasets with partially acquired scans. We show that this latent space can be utilized for accelerated MR image reconstruction. Theory and Methods: Our novel framework, referred to as COLADA (stands for Contrastive Learning for highly accelerated MR image reconstruction), maximizes the mutual information between differently accelerated images of an MRI scan by using self-supervised contrastive learning. In other words, it attempts to "pull" the latent representations of the same scan together and "push" the latent representations of other scans away. The generated MRI latent space is subsequently utilized for MR image reconstruction and the performance was assessed in comparison to several baseline deep learning reconstruction methods. Furthermore, the quality of the proposed latent space representation was analyzed using Alignment and Uniformity. Results: COLADA comprehensively outperformed other reconstruction methods with robustness to variations in undersampling patterns, pathological abnormalities, and noise in k-space during inference. COLADA proved the high quality of reconstruction on unseen data with minimal fine-tuning. The analysis of representation quality suggests that the contrastive features produced by COLADA are optimally distributed in latent space. Conclusion: To the best of our knowledge, this is the first attempt to utilize contrastive learning on differently accelerated images for MR image reconstruction. The proposed latent space representation has practical usage due to a large number of existing partially sampled datasets. This implies the possibility of exploring self-supervised contrastive learning further to enhance the latent space of MRI for image reconstruction

A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boundary refinement. Empirically, we show that the proposed design choices result in a computationally efficient model, with competitive and promising results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation (BraTS) Task. We further show that the representations learned by our model are robust against data corruptions. \href{https://github.com/himashi92/VT-UNet}{Our code implementation is publicly available}

Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation

As intensities of MRI volumes are inconsistent across institutes, it is essential to extract universal features of multi-modal MRIs to precisely segment brain tumors. In this concept, we propose a volumetric vision transformer that follows two windowing strategies in attention for extracting fine features and local distributional smoothness (LDS) during model training inspired by virtual adversarial training (VAT) to make the model robust. We trained and evaluated network architecture on the FeTS Challenge 2022 dataset. Our performance on the online validation dataset is as follows: Dice Similarity Score of 81.71%, 91.38% and 85.40%; Hausdorff Distance (95%) of 14.81 mm, 3.93 mm, 11.18 mm for the enhancing tumor, whole tumor, and tumor core, respectively. Overall, the experimental results verify our method's effectiveness by yielding better performance in segmentation accuracy for each tumor sub-region. Our code implementation is publicly available : https://github.com/himashi92/vizviva_fets_202

Investigation of the neural control of cough and cough suppression in humans using functional brain imaging

Excessive coughing is one of the mostcommonreasons for seeking medical advice, yet the available therapies for treating cough disorders are inadequate. Humans can voluntarily cough, choose to suppress their cough, and are acutely aware of an irritation that is present in their airways. This indicates a significant level of behavioral and conscious control over the basic cough reflex pathway. However, very little is known about the neural basis for higher brain regulation of coughing. The aim of the present study was to use functional brain imaging in healthy humans to describe the supramedullary control of cough and cough suppression. Our data show that the brain circuitry activated during coughing in response to capsaicin-evoked airways irritation is not simply a function of voluntarily initiated coughing and the perception of airways irritation. Rather, activations in several brain regions, including the posterior insula and posterior cingulate cortex, define the unique attributes of an evoked cough. Furthermore, the active suppression of irritant-evoked coughing is also associated with a unique pattern of brain activity, including an involvement of the anterior insula, anterior mid-cingulate cortex, and inferior frontal gyrus. These data demonstrate for the first time that evoked cough is not solely a brainstem-mediated reflex response to irritation of the airways, but rather requires active facilitation by cortical regions, and is further regulated by distinct higher order inhibitory processes. Copyright © 2011 the authors

Effective Connectivity of Functionally Anticorrelated Networks Under Lysergic Acid Diethylamide

Background: Classic psychedelic-induced ego dissolution involves a shift in the sense of self and a blurring of the boundary between the self and the world. A similar phenomenon is identified in psychopathology and is associated with the balance of anticorrelated activity between the default mode network, which directs attention inward, and the salience network, which recruits the dorsal attention network to direct attention outward. Methods: To test whether changes in anticorrelated networks underlie the peak effects of lysergic acid diethylamide (LSD), we applied dynamic causal modeling to infer effective connectivity of resting-state functional magnetic resonance imaging scans from a study of 25 healthy adults who were administered 100 μg of LSD or placebo. Results: We found that inhibitory effective connectivity from the salience network to the default mode network became excitatory, and inhibitory effective connectivity from the default mode network to the dorsal attention network decreased under the peak effect of LSD. Conclusions: The effective connectivity changes we identified may reflect diminution of the functional anticorrelation between resting-state networks that may be a key neural mechanism of LSD and underlie ego dissolution. Our findings suggest that changes to the sense of self and subject-object boundaries across different states of consciousness may depend upon the organized balance of effective connectivity of resting-state networks

The role of ILâ 5 in bleomycinâ induced pulmonary fibrosis

Eosinophils are known to express cytokines capable of promoting fibrosis. Interleukinâ 5 (ILâ 5) is important in regulating eosinophilopoiesis, eosinophil recruitment and activation. Lung ILâ 5 expression is elevated in pulmonary fibrosis, wherein the eosinophil is a primary source of fibrogenic cytokines. To determine the role of ILâ 5 in pulmonary fibrosis, the effects of antiâ ILâ 5 antibody were investigated in a model of bleomycinâ induced pulmonary fibrosis. Fibrosis was induced in mice by endotracheal bleomycin treatment. Animals were also treated with either antiâ ILâ 5 antibody or control IgG. Lungs were then analyzed for fibrosis, eosinophil influx, chemotactic activity, and cytokine expression. The results show that a primary chemotactic activity at the height of eosinophil recruitment is ILâ 5. Furthermore, antiâ ILâ 5 antibody caused significant reduction in lung eosinophilia, cytokine expression, and fibrosis. These findings taken together suggest an important role for ILâ 5 in pulmonary fibrosis via its ability to regulate eosinophilic inflammation, and thus eosinophilâ dependent fibrogenic cytokine production. J. Leukoc. Biol. 64: 657â 666; 1998.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141191/1/jlb0657.pd

Chapter sixteen: Rodents and other vertebrate invaders in the United States

Contents 16.1 Introduction 381 16.2 Assessing impacts of rodents and other vertebrate invaders 385 16.3 Accounts of some important vertebrate invaders 38616.3.1 Norway rant (Rattus norvegicus) 38616.3.2 Roof Rat (Rattus rattus) 38716.3.3 Polynesian rat (Rattus exulans) 38816.3.4 House mouse (Mus Musculus) 38816.3.5 Nutria (Myocastor coypus) 38916.3.6 Gambian giant pouched rat (Cricetomys gambianus) 39016.3.7 Feral swine (Sus scofa) 39016.3.8 Small Indian Mongoose (Herpestes javanicus) 39116.3.9 Rock pigeon (Columba livia) 39216.3.10 House sparrow (Passer domesticus) 39316.3.11 European starling (Sturnus vulgaris) 39316.3.12 Monk parakeet (Myiopsitta monachus) 39416.3.13 Brown tree snake (Boiga irregularis) 39516.3.14 Burmese python (Python molurus bivittatus) 39616.3.15 Coqui frog (Eleutherodactylus coqui) 39716.3.16 Sea lamprey (Petromyzon marinus) 39716.3.17 European and Asian carp (Cyprinidae) 398 16.4 Offshore Threats 399 16.5 Discussion 400 Acknowledgements 401 References 40