Trends and Associated Factors of Use of Opioid, Heroin, and Cannabis Among Patients for Emergency Department Visits in Nevada: 2009–2017

To examine trends and contributing factors of opioid, heroin, and cannabis-associated emergency department (ED) visits in Nevada. The 2009 to 2017 Nevada State ED database (n = 7,950,554 ED visits) were used. Use of opioid, heroin, and cannabis, respectively, was identified by the International Classification of Diseases, 9th & 10th Revisions. Three multivariable models, one for each of the 3 dependent variables, were conducted. Independent variables included year, insurance status, race/ethnicity, use of other substance, and mental health conditions. The number of individuals with opioid, heroin, cannabis-associated ED visits increased 3%, 10%, and 23% annually from 2009 to 2015, particularly among 21 to 29 age group, females, and African Americans. Use of other substance (odds ratio [OR] = 3.91; 95% confidence interval [CI] = 3.84, 3.99; reference - no use of other substance), mental health conditions (OR = 2.48; 95% CI = 2.43, 2.53; reference – without mental health conditions), Medicaid (OR = 1.41; 95% CI = 1.38, 1.44; reference – non-Medicaid), Medicare (OR = 1.44; 95% CI = 1.39, 1.49; reference – non-Medicare) and uninsured patients (OR = 1.52; 95% CI = 1.49, 1.56; reference - insured) were predictors of all three substance-associated ED visits. With a steady increase in trends of opioid, heroin, and cannabis-associated ED visits in recent years, the main contributing factors include patient sociodemographic factors, mental health conditions, and use of other substances

Improving the Expressiveness of Deep Learning Frameworks with Recursion

Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of support for recursion. In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions. Unlike iterative implementations, which can only understand the topological index of each node in recursive data structures, our recursive implementation is able to exploit the recursive relationships between nodes for efficient execution based on parallel computation. We present an implementation on TensorFlow and evaluation results with various recursive neural network models, showing that our recursive implementation not only conveys the recursive nature of recursive neural networks better than other implementations, but also uses given resources more effectively to reduce training and inference time.Comment: Appeared in EuroSys 2018. 13 pages, 11 figure

Investigation of Enhanced Polygon Wall Boundary Model in PNU-MPS Method

With regard to demonstration of fluid flow, there are two descriptions which are Eulerian description and Lagrangian description. In the field of CFD (Computational Fluid Dynamics), a number of studies relevant to grid method based on Eulerian description have been conducted generally. However, when the grid method is employed to simulate flow field, it is inevitable to give consideration to convection term which generates severe numerical diffusion and fluctuation. To obtain the accuracy of solution, a different type of method based on Lagrangian description is come to the fore. Numerical approaches following Lagrangian description have been called meshfree or particle method. Even though particle method does not accompany convection term and fully satisfies conservation of mass, its studies have not been carried out extensively because it is difficult to implement the boundary conditions correctly due to insufficient number of particles in the vicinity of boundary. It affects directly the stability of flow field and accuracy in computation. In MPS (Moving Particle Semi-implicit) method [1], fixed-type of dummy particles are placed inside wall boundary. By placing extra particles as the wall, it seems to be not easy to satisfy the boundary condition for sharp-edged or extremely thin body configuration. In this study, the enhanced polygon wall boundary model, which was suggested originally by Mitsume et al. [2], is employed to the PNU-MPS (Pusan-National-University-modified MPS) method [3] to improve and stabilize the analysis of fluid flow with arbitrary-shaped body including sharp-edged body configuration without any additional particles. The developed simulation method, called as PNU-MPS-POLY, is adopted to the Couette flow and the lid-driven cavity flow with various corner angles. The present simulation results are validated through comparison with the analytic solutions, the experiments [4], and other simulation results [5,6]

Graphitic carbon growth on crystalline and amorphous oxide substrates using molecular beam epitaxy

We report graphitic carbon growth on crystalline and amorphous oxide substrates by using carbon molecular beam epitaxy. The films are characterized by Raman spectroscopy and X-ray photoelectron spectroscopy. The formations of nanocrystalline graphite are observed on silicon dioxide and glass, while mainly sp2 amorphous carbons are formed on strontium titanate and yttria-stabilized zirconia. Interestingly, flat carbon layers with high degree of graphitization are formed even on amorphous oxides. Our results provide a progress toward direct graphene growth on oxide materials

Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment

The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer's disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice

Curcumin induces stabilization of Nrf2 protein through Keap1 cysteine modification

The present study was aimed to investigate the effects of curcumin, a representative chemopreventive phytochemical with pronounced antioxidant and anti-inflammatory properties, on activation of Nrf2 and expression of its target protein heme oxygenase-1 (HO-1) in mouse skin in vivo and in cultured murine epidermal cells. Treatment of mouse epidermal JB6 cells with curcumin resulted in the induction of HO-1 expression, and this was abrogated in cells transiently transfected with Nrf2 siRNA. While curcumin treatment increased protein expression of Nrf2, it did not alter the steady-state level of the Nrf2 mRNA transcript. Treatment of cells with curcumin stabilized Nrf2 by inhibiting ubiquitination and subsequent 26S proteasomal degradation of this transcription factor. Tetrahydrocurcumin, a non-electrophilic analogue of curcumin that lacks the alpha,beta-unsaturated carbonyl group, failed to induce HO-1 expression as well as nuclear translocation of Nrf2 and its binding to the antioxidant/electrophile response elements. Cells transfected with a mutant Keap1 protein in which cysteine 151 (Cys151) is replaced by serine exhibited marked reduction in curcumin-induced Nrf2 transactivation. Mass spectrometric analysis revealed that curcumin binds to Keap1 Cys151, supporting that this amino acid is a critical target for curcumin modification of Keap1, which facilitates the liberation of Nrf2. Thus, it is likely that the alpha,beta-unsaturated carbonyl moiety of curcumin is essential for its binding to Keap1 and stabilization of Nrf2 by hampering ubiquitination and proteasomal degradation.