DATA GOVERNANCE, INTEROPERABILITY AND STANDARDIZATION: ORGANIZATIONAL ADAPTATION TO PRIVACY REGULATION

The increasing availability of data can afford dynamic competitive advantages among data-intensive corporations, but governance bottlenecks hinder data-driven value creation and increase regulatory risks. We analyze the role of two technological features of data architecture that facilitate internal data governance – Application Programmatic Interfaces (APIs) that publish interdepartmental data and standardization of identity and access management (IAM) software – in shaping large dataintensive corporations’ adaptation to privacy regulation. Using annual establishment data for the largest U.S. financial services corporations and the enforcement of the General Data Protection Regulation (GDPR) in 2018 as a natural experiment, we show that internal data APIs and standardization of IAM software significantly mitigate establishments’ revenue loss and IT budget reduction in response to GDPR enforcement. Compliance costs measured by IT hiring increased substantially after GDPR enforcement only for firms without internal data APIs. Our findings highlight the importance of interoperability and standardization as technical conditions that facilitate dynamic integrative capability, allowing large data-intensive corporations to ensure proper data governance and adapt to privacy regulation

Tetramethyl pyrazine exerts anti-apoptotic and antioxidant effects in a mouse model of MPTP-induced Parkinson's disease via regulation of the expressions of Bax, Bcl-2, Nrf2 and GCLC

Purpose: To investigate the effect of tetramethyl pyrazine (TMP) on MPTP)-mediated neuronal apoptosis and oxidative imbalance in mice, and the mechanism of action involved. Methods: Forty-five mice were assigned evenly to blank control, MPTP and TMP groups. The protein concentrations of Bax, Bcl-2, cytochrome C (Cyt c), Nrf2, GCLC and cleaved caspase-3; and levels of glutathione (GSH) and thiobarbituric acid reactive products (TBARS) were evaluated and compared amongst the groups. Results: Cyt c, Bax, and cleaved caspase-3 protein levels in TMP group were significantly lower than those in MPTP group, while Bcl-2 protein expression was higher in TMP group than in MPTP mice (p < 0.05). Furthermore, TBARS was lower in TMP group than in MPTP group, while GSH level increased, relative to MPTP mice. The levels of Nrf2 and GCLC were significantly higher in TMP group than in MPTP group (p < 0.05). Conclusion: Tetramethyl pyrazine exerts anti-apoptotic and antioxidant effects on MPTP-mediated Parkinsonism via regulation of the expressions of Bax, Bcl-2, Nrf2 and glutamate-cysteine ligase catalytic subunit. Thus, TMP has potential for use in the treatment Parkinson’s disease

Combined effects of cyclic load and temperature fluctuation on the mechanical behavior of porous sandstones

Rocks in cold regions tend to experience exacerbated degradation under the combined effects of environmental and anthropogenic factors, which may arise from, for example, temperature fluctuation, mechanical excavation, and blasting. Activities related to rock support or open-pit slope optimization in cold regions require a complete understanding of the failure mechanisms of rock under the complex conditions. This paper quantitatively documents the impact of combined cyclic mechanical load and freeze-thaw cycles (i.e., the effect of stress “history”) on the microstructural evolution and mechanical degradation of three porous sandstones with distinct porosity values (from 3.9 to 14.1%). The three sandstone samples were collected from different geological regions in China. The microstructural evolution of the tested samples was quantitatively analyzed using the low-field Nuclear Magnetic Resonance (NMR) technique. To investigate sample degradation arising from the impact of the stress “history”, the cyclic-loaded and freeze-thaw cycled samples were eventually compressed to failure, during which an acoustic emission system was used to monitor microseismic activities. The results of the study show that the porosity of all tested sandstone samples was increased after cyclic load, with a much more rapid and further increase in porosity observed for samples being subsequently treated under the freeze-thaw cycles. More interestingly, the Chuxiong sandstone with relatively small porosity values were much more sensitive to the impact of cyclic load compared with the Linyi sandstone, exhibiting a somewhat larger increase rate in porosity. However, the Linyi sandstone with larger initial porosity values exhibited a relatively large increase rate in porosity under the multiple freeze-thaw treatments. The multiple freeze-thaw treatments mainly resulted in the development of relatively large pores. The results of the uniaxial compression tests show that the strength reduction of the samples being solely treated by freeze-thaw cycles was within the range of 5–10%, whereas it was within the range of 20–40% for those samples subjected to the combined cyclic load and freeze-thaw cycles

Reduced SV2A and GABAA_A receptor levels in the brains of type 2 diabetic rats revealed by [18^{18}F]SDM-8 and [18^{18}F]flumazenil PET

PURPOSE: Type 2 diabetes mellitus (T2DM) is associated with a greater risk of Alzheimer's disease. Synaptic impairment and protein aggregates have been reported in the brains of T2DM models. Here, we assessed whether neurodegenerative changes in synaptic vesicle 2 A (SV2A), γ-aminobutyric acid type A (GABA$_A$) receptor, amyloid-β, tau and receptor for advanced glycosylation end product (RAGE) can be detected in vivo in T2DM rats. Methods: Positron emission tomography (PET) using [$^{18}$F]SDM-8 (SV2A), [$^{18}$F]flumazenil (GABA$_A$ receptor), [$^{18}$F]florbetapir (amyloid-β), [$^{18}$F]PM-PBB3 (tau), and [$^{18}$F]FPS-ZM1 (RAGE) was carried out in 12-month-old diabetic Zucker diabetic fatty (ZDF) and SpragueDawley (SD) rats. Immunofluorescence staining, Thioflavin S staining, proteomic profiling and pathway analysis were performed on the brain tissues of ZDF and SD rats. Results: Reduced cortical [$^{18}$F]SDM-8 uptake and cortical and hippocampal [$^{18}$F]flumazenil uptake were observed in 12-month-old ZDF rats compared to SD rats. The regional uptake of [$^{18}$F]florbetapir and [$^{18}$F]PM-PBB3 was comparable in the brains of 12-month-old ZDF and SD rats. Immunofluorescence staining revealed Thioflavin S-negative, phospho-tau-positive inclusions in the cortex and hypothalamus in the brains of ZDF rats and the absence of amyloid-beta deposits. The level of GABA$_A$ receptors was lower in the cortex of ZDF rats than SD rats. Proteomic analysis further demonstrated that, compared with SD rats, synaptic-related proteins and pathways were downregulated in the hippocampus of ZDF rats. Conclusion: These findings provide in vivo evidence for regional reductions in SV2A and GABA$_A$ receptor levels in the brains of aged T2DM ZDF rats

Preliminary Study:Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique

Objectives/Hypothesis: To develop a deep-learning–based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P <.001), polyps (91% vs. 86%, P <.001), leukoplakia (91% vs. 65%, P <.001), and malignancy (90% vs. 54%, P <.001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence: NA Laryngoscope, 130:E686–E693, 2020

Advances in non-invasive imaging of proteinopathies in animal models of neurodegenerative diseases

ISSN:1673-5374ISSN:1876-795

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.ISSN:2296-858