Analysis of Regional Mechanics in Canine Lung Injury Using Forced Oscillations and 3D Image Registration

Acute lung injury is characterized by heterogeneity of regional mechanical properties, which is thought to be correlated with disease severity. The feasibility of using respiratory input impedance ($Z_{rs}$) and computed tomographic (CT) image registration for assessing parenchymal mechanical heterogeneity was evaluated. In six dogs, measurements of ($Z_{rs}$) before and after oleic acid injury at various distending pressures were obtained, followed by whole lung CT scans. Each ($Z_{rs}$) spectrum was fit with a model incorporating variable distributions of regional compliances. CT image pairs at different inflation pressures were matched using an image registration algorithm, from which distributions of regional compliances from the resulting anatomic deformation fields were computed. Under baseline conditions, average model compliance decreased with increasing inflation pressure, reflecting parenchymal stiffening. After lung injury, these average compliances decreased at each pressure, indicating derecruitment, alveolar flooding, or alterations in intrinsic tissue elastance. However, average compliance did not change as inflation pressure increased, consistent with simultaneous recruitment and strain stiffening. Image registration revealed peaked distributions of regional compliances, and that small portions of the lung might undergo relative compression during inflation. The authors conclude that assessments of lung function using ($Z_{rs}$) combined with the structural alterations inferred from image registration provide unique but complementary information on the mechanical derangements associated with lung injury

Comparison of Image Registration Based Measures of Regional Lung Ventilation from Dynamic Spiral CT with Xe-CT

Purpose: Regional lung volume change as a function of lung inflation serves as an index of parenchymal and airway status as well as an index of regional ventilation and can be used to detect pathologic changes over time. In this article, we propose a new regional measure of lung mechanics --- the specific air volume change by corrected Jacobian. Methods: 4DCT and Xe-CT data sets from four adult sheep are used in this study. Nonlinear, 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration. Approximately 200 annotated anatomical points are used as landmarks to evaluate registration accuracy. Three different registration-based measures of regional lung mechanics are derived and compared: the specific air volume change calculated from the Jacobian (SAJ); the specific air volume change calculated by the corrected Jacobian (SACJ); and the specific air volume change by intensity change (SAI). Results: After registration, the mean registration error is on the order of 1 mm. For cubical ROIs in cubes with size 20 mm $\times$ 20 mm $\times$ 20 mm, the SAJ and SACJ measures show significantly higher correlation (linear regression, average $r^2=0.75$ and $r^2=0.82$) with the Xe-CT based measure of specific ventilation (sV) than the SAI measure. For ROIs in slabs along the ventral-dorsal vertical direction with size of 150 mm $\times$ 8 mm $\times$ 40 mm, the SAJ, SACJ, and SAI all show high correlation (linear regression, average $r^2=0.88$, $r^2=0.92$ and $r^2=0.87$) with the Xe-CT based sV without significant differences when comparing between the three methods. Conclusion: Given a deformation field by an image registration algorithm, significant differences between the SAJ, SACJ, and SAI measures were found at a regional level compared to the Xe-CT sV in four sheep that were studied

Strong, conductive carbon nanotube fibers as efficient hole collectors

We present the photovoltaic properties of heterojunctions made from single-walled carbon nanotube (SWNT) fibers and n-type silicon wafers. The use of the opaque SWNT fiber allows photo-generated holes to transport along the axis direction of the fiber. The heterojunction solar cells show conversion efficiencies of up to 3.1% (actual) and 10.6% (nominal) at AM1.5 condition. In addition, the use of strong, environmentally benign carbon nanotube fibers provides excellent structural stability of the photovoltaic devices

Attention-driven tree-structured convolutional LSTM for high dimensional data understanding

Modeling sequential information for image sequences is a vital step of various vision tasks and convolutional long short-term memory (ConvLSTM) has demonstrated its superb performance in such spatiotemporal problems. Nevertheless, the hierarchical data structures (e.g., human body parts and vessel/airway tree in biomedical images) in various tasks cannot be properly modeled by sequential models. Thus, ConvLSTM is not suitable for analyzing tree-structured image data that has a rich relation among its elements. In order to address this limitation, we present a tree-structured ConvLSTM model for tree-structured image analysis which can be trained end-to-end. To demonstrate its effectiveness, we first evaluate the proposed tree-structured ConvLSTM model on a synthetic Tree-Moving-MNIST dataset for tree-structured modeling. Experimental results demonstrate the superiority of the tree-structured ConvLSTM model for tree-structured image analysis compared with other alternatives. Additionally, we present a tree-structured segmentation framework which consists of a tree-structured ConvLSTM layer and an attention fully convolutional network (FCN) model. The proposed framework is validated on four large-scale coronary artery datasets. The results demonstrate the effectiveness and efficiency of the proposed method, showing its potential use cases in the analysis of tree-structured image data

How Transformational Leaders Promote Employees’ Feedback-Seeking Behaviors: The Role of Intrinsic Motivation and Its Boundary Conditions

Feedback-seeking behavior is critical to employee self-change and self-improvement, which is conducive to the sustainable development of employees’ own knowledge and abilities. Drawing on self-determination theory, this study aims to examine the underlying psychological mechanism and the boundary conditions of the relationship between transformational leadership and employees’ feedback-seeking behavior. Data were collected from a large manufacturing firm in China, encompassing 415 employees, and subsequently analyzed employing SPSS 22.0 and SPSS PROCESS Macro. The results showed that employee intrinsic motivation mediated the relationship between transformational leadership and employees’ feedback-seeking behavior. In addition, the positive association between transformational leadership and intrinsic motivation was more pronounced when organizational virtuousness was high than when it was low. On the contrary, the positive link between transformational leadership and intrinsic motivation was stronger when job complexity was low than when it was high. Organizational virtuousness and job complexity further moderated the indirect effect of transformational leadership on feedback-seeking behavior through intrinsic motivation. Our findings offer a better understanding of employee feedback-seeking behavior by considering factors that may trigger and influence employees’ psychological motivation. In this way, this study contributes to the literature on self-determination theory and feedback-seeking behavior