Turbulent eddy diffusion models in exposure assessment - Determination of the eddy diffusion coefficient.

The use of the turbulent eddy diffusion model and its variants in exposure assessment is limited due to the lack of knowledge regarding the isotropic eddy diffusion coefficient, DT. But some studies have suggested a possible relationship between DT and the air changes per hour (ACH) through a room. The main goal of this study was to accurately estimate DT for a range of ACH values by minimizing the difference between the concentrations measured and predicted by eddy diffusion model. We constructed an experimental chamber with a spatial concentration gradient away from the contaminant source, and conducted 27 3-hr long experiments using toluene and acetone under different air flow conditions (0.43-2.89 ACHs). An eddy diffusion model accounting for chamber boundary, general ventilation, and advection was developed. A mathematical expression for the slope based on the geometrical parameters of the ventilation system was also derived. There is a strong linear relationship between DT and ACH, providing a surrogate parameter for estimating DT in real-life settings. For the first time, a mathematical expression for the relationship between DT and ACH has been derived that also corrects for non-ideal conditions, and the calculated value of the slope between these two parameters is very close to the experimentally determined value. The values of DT obtained from the experiments are generally consistent with values reported in the literature. They are also independent of averaging time of measurements, allowing for comparison of values obtained from different measurement settings. These findings make the use of turbulent eddy diffusion models for exposure assessment in workplace/indoor environments more practical.

Transcranial ultrasound neuromodulation induces neuronal correlation change in the rat somatosensory cortex.

Objective.Transcranial focused ultrasound (tFUS) is a neuromodulation technique which has been the focus of increasing interest for noninvasive brain stimulation with high spatial specificity. Its ability to excite and inhibit neural circuits as well as to modulate perception and behavior has been demonstrated, however, we currently lack understanding of how tFUS modulates the ways neurons interact with each other. This understanding would help elucidate tFUS's mechanism of systemic neuromodulation and allow future development of therapies for treating neurological disorders.Approach.In this study, we investigate how tFUS modulates neural interaction and response to peripheral electrical limb stimulation through intracranial multi-electrode recordings in the rat somatosensory cortex. We deliver ultrasound in a pulsed pattern to induce frequency dependent plasticity in a manner similar to what is found following electrical stimulation.Main Results.We show that neural firing in response to peripheral electrical stimulation is increased after ultrasound stimulation at all frequencies, showing tFUS induced changes in excitability of individual neuronsin vivo. We demonstrate tFUS sonication repetition frequency dependent pairwise correlation changes between neurons, with both increases and decreases observed at different frequencies.Significance.These results extend previous research showing tFUS to be capable of inducing synaptic depression and demonstrate its ability to modulate network dynamics as a whole.

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke.

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research.

Standardizing human brain parcellations.

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Pediatric Inpatient Problem List Review and Accuracy Improvement.

BACKGROUND AND OBJECTIVES: The problem list (PL) is a meaningful use-incentivized criterion for electronic health record documentation. Inconsistent use or inaccuracy of the PL can create communication gaps among providers, potentially leading to diagnostic delays and serious safety events. The objective of the study was to increase the rate of PL review by attending physicians for inpatients discharged from hospital pediatrics and infectious disease services from a baseline of 70% to 80% by June 2018 and to sustain the rate for 6 months. The secondary aim was to improve PL accuracy by decreasing the rate of duplicate codes and red code diagnoses that should resolve before discharge from a baseline of 12% and 11%, respectively, to 5% and sustaining the rate for 6 months. METHODS: A quality improvement team used the Institute for Healthcare Improvement Model for Improvement. We tracked duplicate codes and red codes as surrogate markers of PL quality. Rates of PL review and PL quality were analyzed monthly via statistical process control charts (p-charts) with 3-σ control limits to identify special cause variation. RESULTS: PL review improved from a baseline of 70% to 90%, and the change was sustained for 1 year. PL quality improved as duplicate codes at the time of discharge decreased from 12% to 6% and as red codes decreased from a baseline of 11% to 6%. CONCLUSIONS: The PL is an important communication tool that is underused. By engaging and educating stakeholders, incentivizing compliance, standardizing PL management, leveraging electronic health record enhancements, and providing physician feedback, we improved PL meaningful use and quality.

Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's Disease and Mild Cognitive Impairment.

In recent studies, neuroanatomical volume and shape asymmetries have been seen during the course of Alzheimer's Disease (AD) and could potentially be used as preclinical imaging biomarkers for the prediction of Mild Cognitive Impairment (MCI) and AD dementia. In this study, a deep learning framework utilizing Siamese neural networks trained on paired lateral inter-hemispheric regions is used to harness the discriminative power of whole-brain volumetric asymmetry. The method uses the MRICloud pipeline to yield low-dimensional volumetric features of pre-defined atlas brain structures, and a novel non-linear kernel trick to normalize these features to reduce batch effects across datasets and populations. By working with the low-dimensional features, Siamese networks were shown to yield comparable performance to studies that utilize whole-brain MR images, with the advantage of reduced complexity and computational time, while preserving the biological information density. Experimental results also show that Siamese networks perform better in certain metrics by explicitly encoding the asymmetry in brain volumes, compared to traditional prediction methods that do not use the asymmetry, on the ADNI and BIOCARD datasets.