Point-of-Sale Marketing in Recreational Marijuana Dispensaries Around California Schools.

PurposeAfter marijuana commercialization, the presence of recreational marijuana dispensaries (RMDs) was rapidly increasing. The point-of-sale marketing poses concerns about children's exposure. This study examined advertising and promotions that potentially appeal to children and access restrictions in RMDs around California schools.MethodsThis was a cross-sectional and observational study conducted from June to September 2018. Trained fieldworkers audited retail environments in 163 RMDs in closest proximity to 333 randomly sampled public schools in California.ResultsAbout 44% of schools had RMDs located within 3 miles. Regarding interior marketing, 74% of RMDs had at least one instance of child-appealing products, packages, paraphernalia, or advertisements. RMDs closer to a school had a higher proportion with interior child-appealing marketing. More than three fourths of RMDs had generic promotional activities; particularly, 28% violated the free-sample ban. Regarding exterior marketing, only 2% of RMDs had those appealing to children. More than 60% of RMDs had exterior signs indicative of marijuana. Approximately, one-third had generic advertisements, and 13% had advertisements bigger than 1,600 square inches. Regarding access restrictions, almost all RMDs complied with age verification, but 84% had no age limit signs, and only 40% had security personnel.ConclusionsDespite minimal point-of-sale marketing practices appealing to children on the exterior of RMDs around California schools, such practices were abundant on the interior. Marketing practices not specifically appealing to children were also common on both the interior and exterior of RMDs. Dispensaries' violation of age verification law, lack of security personnel, and presence of child-appealing marketing should be continuously monitored and prevented

FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation

Solving variational image segmentation problems with hidden physics is often expensive and requires different algorithms and manually tunes model parameter. The deep learning methods based on the U-Net structure have obtained outstanding performances in many different medical image segmentation tasks, but designing such networks requires a lot of parameters and training data, not always available for practical problems. In this paper, inspired by traditional multi-phase convexity Mumford-Shah variational model and full approximation scheme (FAS) solving the nonlinear systems, we propose a novel variational-model-informed network (denoted as FAS-Unet) that exploits the model and algorithm priors to extract the multi-scale features. The proposed model-informed network integrates image data and mathematical models, and implements them through learning a few convolution kernels. Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features. Secondly, we further design a convolution block to fuse the extracted features from the previous stage, resulting in the final segmentation possibility. Experimental results on three different medical image segmentation tasks show that the proposed FAS-Unet is very competitive with other state-of-the-art methods in qualitative, quantitative and model complexity evaluations. Moreover, it may also be possible to train specialized network architectures that automatically satisfy some of the mathematical and physical laws in other image problems for better accuracy, faster training and improved generalization.The code is available at \url{https://github.com/zhuhui100/FASUNet}.Comment: 18 page

Adversarial Image Generation and Training for Deep Neural Networks

Deep neural networks (DNNs) have achieved great success in image classification, but they may be very vulnerable to adversarial attacks with small perturbations to images. Moreover, the adversarial training based on adversarial image samples has been shown to improve the robustness and generalization of DNNs. The aim of this paper is to develop a novel framework based on information-geometry sensitivity analysis and the particle swarm optimization to improve two aspects of adversarial image generation and training for DNNs. The first one is customized generation of adversarial examples. It can design adversarial attacks from options of the number of perturbed pixels, the misclassification probability, and the targeted incorrect class, and hence it is more flexible and effective to locate vulnerable pixels and also enjoys certain adversarial universality. The other is targeted adversarial training. DNN models can be improved in training with the adversarial information using a manifold-based influence measure effective in vulnerable image/pixel detection as well as allowing for targeted attacks, thereby exhibiting an enhanced adversarial defense in testing

Optical loss compensation in a bulk left-handed metamaterial by the gain in quantum dots

A bulk left-handed metamaterial with fishnet structure is investigated to show the optical loss compensation via surface plasmon amplification, with the assistance of a Gaussian gain in PbS quantum dots. The optical resonance enhancement around 200 THz is confirmed by the retrieval method. By exploring the dependence of propagation loss on the gain coefficient and metamaterial thickness, we verify numerically that the left-handed response can endure a large propagation thickness with ultralow and stable loss under a certain gain coefficient.Comment: 6 pages with 4 figure

Phenylboronic acid-diol crosslinked 6-<i>O</i>-vinylazeloyl-d-galactose nanocarriers for insulin delivery

A new block polymer named poly 3-acrylamidophenylboronic acid-b-6-O–vinylazeloyl-d-galactose (p(AAPBA-b-OVZG)) was prepared using 3-acrylamidophenylboronic acid (AAPBA) and 6-O-vinylazeloyl-D-galactose (OVZG) via a two-step procedure involving S-1-dodecyl-S-(α', α'-dimethyl-α″-acetic acid) trithiocarbonate (DDATC) as chain transfer agent, 2,2-azobisisobutyronitrile (AIBN) as initiator and dimethyl formamide (DMF) as solvent. The structures of the polymer were examined by Fourier transform infrared spectroscopy (FT-IR) and 1H NMR and the thermal stability was determined by thermal gravimetric analysis (TG/DTG). Transmission electron microscopy (TEM) and dynamic light scattering (DLS) were utilized to evaluate the morphology and properties of the p(AAPBA-b-OVZG) nanoparticles. The cell toxicity, animal toxicity and therapeutic efficacy were also investigated. The results indicate the p(AAPBA-b-OVZG) was successfully synthesized and had excellent thermal stability. Moreover, the p(AAPBA-b-OVZG) nanoparticles were submicron in size and glucose-sensitive in phosphate-buffered saline (PBS). In addition, insulin as a model drug had a high encapsulation efficiency and loading capacity and the release of insulin was increased at higher glucose levels. Furthermore, the nanoparticles showed a low-toxicity in cell and animal studies and they were effective at decreasing blood glucose levels of mice over 96 h. These p(AAPBA-b-OVZG) nanoparticles show promise for applications in diabetes treatment using insulin or other hypoglycemic proteins

Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.

In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols