Precarious Rock Methodology for Seismic Hazard: Physical Testing, Numerical Modeling, and Coherence Studies

The precarious rock methodology used for seismic hazard assessment includes location, age dating, field measurements of the quasi-static toppling acceleration of balanced rocks, and study of their dynamic response to realistic strong motion seismograms using numerical modeling. The work scope is contained in the task description issued by the DOE to the Seismology Laboratory of the University of Nevada, Reno and is itemized in section 2.3 below. In addition, measurement of the coherence of seismic energy at high frequencies, critical to the understanding of the variability of high frequency ground motions at the repository level, will be estimated based on data collected in limited scope portable instrument deployments. Existing high-frequency geophones that remain in place from earlier geophysical experiments will be used

Attitudes and Perceptions of Healthcare Providers and Medical Students Towards Clinical Pharmacy Services in United Arab Emirates

Purpose: To explore healthcare providers' (HCPs) and medical students’ attitudes to, and perceptions of the pharmaceutical services that clinical pharmacists can provide in United Arab Emirates.Methods: A total of 535 participants (265 HCPs and 270 medical students) were asked to complete a questionnaire over a period of three months (January through March 2009). Results: Almost three quarters of the students perceived that the clinical pharmacist is an important part of the healthcare team while 82% believed that clinical pharmacists can help improve the quality of medical care in hospitals. Eighty one percent of medical students expressed confidence in the ability of clinical pharmacists to minimize medication errors. Although slightly more than half of the respondents (53%) reported that they did not have clinical pharmacy services in their institutions, there was substantial willingness among physicians and nurses to cooperate with clinical pharmacists. The majority of physicians (92%) and nurses (87%) expressed the view that the clinical pharmacist is an important integral part of the healthcare team. Conclusion: The HCPs and medical students in the study setting valued the role of clinical pharmacists in healthcare delivery. However, new developments in pharmacy services in the UAE hospital setting is recommended for adoption in hospitals.Key words: Clinical pharmacy services, Pharmaceutical care, Perception, Healthcare providers

Development and evaluation of ibuprofen transdermal gel formulations

Purpose: To develop an ibuprofen transdermal gel with a capability for both topical and systemic drug delivery. Methods: Ibuprofen gel formulations, incorporating various permeation enhancers, were prepared using chitosan as a gelling agent. The formulations were examined for their in vitro characteristics including viscosity, pH and drug release as well as in vivo pharmacological activities. Carrageenan-induced rat paw oedema model was used for the evaluation of their analgesic and anti-inflammatory activities. A commercial ibuprofen gel product (Ibutop®) was used as a reference. Results: The formulations containing 5 % of either menthol or glycerol as permeation enhancers gave drug release patterns comparable to that of the reference product. Propanol increased the apparent viscosity of the test gels to the same extent as that of the reference. Drug release from the formulationsfitted best to the Higuchi model. A significant in vivo analgesic effect was produced by the test formulations containing 5 % menthol and 20 % propylene glycol and the effect was superior to that obtained with the reference product. However, no significant anti-inflammatory activity was exerted by any of the test gel formulations (p > 0.05).Conclusion: Ibuprofen gel preparations containing 5 % menthol and 20 % propylene glycol, respectively, exhibited pronounced analgesic activity and could be further developed for topical and systemic delivery of ibuprofen.Keywords:  Transdermal gel, Chitosan, Ibuprofen, Menthol, Propylene glycol, Penetration enhance

Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation

Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to extract local structural as well as global contextual information. We performed segmentation of glioma sub-regions, including tumor core, enhancing tumor, and whole tumor using Brain Tumor Segmentation (BraTS) 2018 dataset. Our proposed model performed significantly better than the state-of-the-art U-Net-based model (p\u3c0.05) for tumor core and whole tumor segmentation

Inception Modules Enhance Brain Tumor Segmentation.

Magnetic resonance images of brain tumors are routinely used in neuro-oncology clinics for diagnosis, treatment planning, and post-treatment tumor surveillance. Currently, physicians spend considerable time manually delineating different structures of the brain. Spatial and structural variations, as well as intensity inhomogeneity across images, make the problem of computer-assisted segmentation very challenging. We propose a new image segmentation framework for tumor delineation that benefits from two state-of-the-art machine learning architectures in computer vision, i.e., Inception modules and U-Net image segmentation architecture. Furthermore, our framework includes two learning regimes, i.e., learning to segment intra-tumoral structures (necrotic and non-enhancing tumor core, peritumoral edema, and enhancing tumor) or learning to segment glioma sub-regions (whole tumor, tumor core, and enhancing tumor). These learning regimes are incorporated into a newly proposed loss function which is based on the Dice similarity coefficient (DSC). In our experiments, we quantified the impact of introducing the Inception modules in the U-Net architecture, as well as, changing the objective function for the learning algorithm from segmenting the intra-tumoral structures to glioma sub-regions. We found that incorporating Inception modules significantly improved the segmentation performance (p \u3c 0.001) for all glioma sub-regions. Moreover, in architectures with Inception modules, the models trained with the learning objective of segmenting the intra-tumoral structures outperformed the models trained with the objective of segmenting the glioma sub-regions for the whole tumor (p \u3c 0.001). The improved performance is linked to multiscale features extracted by newly introduced Inception module and the modified loss function based on the DSC

Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability.Comment: 23 pages, 4 figure

Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can be evaluated for their robustness and the role that adversarial robustness plays in having meaningful explanations. We also discuss the limitations of gradient-based methods. Finally, we present the best practices and attributes that should be examined before choosing an explainability method. We conclude with the future directions for research in the area at the convergence of robustness and explainability

Targeted Background Removal Creates Interpretable Feature Visualizations

Feature visualization is used to visualize learned features for black box machine learning models. Our approach explores an altered training process to improve interpretability of the visualizations. We argue that by using background removal techniques as a form of robust training, a network is forced to learn more human recognizable features, namely, by focusing on the main object of interest without any distractions from the background. Four different training methods were used to verify this hypothesis. The first used unmodified pictures. The second used a black background. The third utilized Gaussian noise as the background. The fourth approach employed a mix of background removed images and unmodified images. The feature visualization results show that the background removed images reveal a significant improvement over the baseline model. These new results displayed easily recognizable features from their respective classes, unlike the model trained on unmodified data

Theoretical rationalisation for the mechanism of N-heterocyclic carbene-halide reductive elimination at CuIII, AgIII and AuIII

Reductive elimination of imidazolium salts from CuIII is extremely sensitive to the anionic ligand (X or Y) type on Cu (e.g. ΔG‡ ranges from 4.7 kcal mol-1 to 31.8 kcal mol-1, from chloride to benzyl). Weakly σ-donating ligands dramatically accelerate reductive elimination. Comparison with Ag/Au shows that the HOMO energy, strength of M-NHC and M-Y bonds and inherent stability of MIII with respect to MI are critical to governing reaction feasibility

Cellular demands of secreted protein products – systems and synthetic biology improve quality and titer

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